Valorant_Match_Analysis.ipynb

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        "<a href=\"https://colab.research.google.com/github/redswimmer/valorant-match-analysis/blob/main/Valorant_Match_Analysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nJG_CKnFxXRG"
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      "source": [
        "# Valorant Player Performance Analysis"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jxuHuRGpMMTN"
      },
      "source": [
        "I noticed there was a large amount of JSON returned from each Valorant match on Tracker.gg and was curious what player analytics I could glean from the data.  I break down the various data types returned and specifically focus on player performance including:\n",
        "\n",
        "- Round-by-round player performance\n",
        "- How loadouts affect player performance\n",
        "- Analyze Side Preference by looking at performance metrics for players when they are attackers or defenders\n",
        "- Round win predictions using random forest\n",
        "- Selecting the \"best features\" for round predictions\n",
        "\n",
        "Match data is based on a single match from [FNATIC Boaster Tracker Profile](https://tracker.gg/valorant/profile/riot/FNATIC%20Boaster%231010/overview)\n",
        "\n",
        "**NOTE: This is for educational purposes only and is not in any way affiliated with Riot Games, Tracker.gg or FNATIC.**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KDGCIPox66Yh"
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      "source": [
        "## Imports"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 672,
      "metadata": {
        "id": "w10prVCP6yEo"
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      "source": [
        "import json\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from pandas import json_normalize\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.preprocessing import StandardScaler"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ykwEMWso6u4b"
      },
      "source": [
        "## Load Match Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wtC0arT53-i6"
      },
      "source": [
        "Load single match data from the JSON file."
      ]
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    {
      "cell_type": "code",
      "execution_count": 673,
      "metadata": {
        "id": "xzZT9B3e6-na"
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      "source": [
        "# Specify the path to JSON file\n",
        "file_path = 'match.json'\n",
        "\n",
        "# Open and read the JSON file\n",
        "with open(file_path, 'r') as file:\n",
        "    match_data = json.load(file)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aOdDTU_a74W-"
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      "source": [
        "### Examine record types"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "awnmh2VT3z_a"
      },
      "source": [
        "Check the different record types returned in the JSON."
      ]
    },
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      "execution_count": 674,
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        "id": "S8QnswtS764t",
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          "output_type": "execute_result",
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            "text/plain": [
              "{'player-loadout',\n",
              " 'player-round',\n",
              " 'player-round-damage',\n",
              " 'player-round-kills',\n",
              " 'player-summary',\n",
              " 'round-summary',\n",
              " 'team-summary'}"
            ]
          },
          "metadata": {},
          "execution_count": 674
        }
      ],
      "source": [
        "# Extract the 'type' field from each segment to find unique types\n",
        "unique_segment_types = set(segment['type'] for segment in match_data['data']['segments'])\n",
        "\n",
        "unique_segment_types"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7Pia3z-t7JQK"
      },
      "source": [
        "## Parsing Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 675,
      "metadata": {
        "id": "_YaPspVQJRd9"
      },
      "outputs": [],
      "source": [
        "# Collection of dataframes for easy access later\n",
        "dataframe_collection = {}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pFG2Oil2whPg"
      },
      "source": [
        "### Match Summary"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4hCePdIk4HaN"
      },
      "source": [
        "Examine the data returned on the match summary."
      ]
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              "                               Match_ID    Mode_Name  Duration  \\\n",
              "0  f1b1095d-7fc4-4e17-8fa2-5637650374fa  Competitive   1893354   \n",
              "\n",
              "                    Date_Started  Rounds  \\\n",
              "0  2023-10-03T13:56:53.093+00:00      20   \n",
              "\n",
              "                              Season_Id                                Map_Id  \\\n",
              "0  03dfd004-45d4-ebfd-ab0a-948ce780dac4  2fe4ed3a-450a-948b-6d6b-e89a78e680a9   \n",
              "\n",
              "  Map_Name  \n",
              "0    Lotus  "
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      ],
      "source": [
        "# Extract the required fields from the JSON data\n",
        "match_id = match_data['data']['attributes']['id']\n",
        "mode_name = match_data['data']['metadata']['modeName']\n",
        "duration = match_data['data']['metadata']['duration']\n",
        "date_started = match_data['data']['metadata']['dateStarted']\n",
        "rounds = match_data['data']['metadata']['rounds']\n",
        "season_id = match_data['data']['metadata']['seasonId']\n",
        "map = match_data['data']['metadata']['map']\n",
        "map_name = match_data['data']['metadata']['mapName']\n",
        "\n",
        "# Create a DataFrame to hold the match summary\n",
        "match_summary_df = pd.DataFrame({\n",
        "    'Match_ID': [match_id],\n",
        "    'Mode_Name': [mode_name],\n",
        "    'Duration': [duration],\n",
        "    'Date_Started': [date_started],\n",
        "    'Rounds': [rounds],\n",
        "    'Season_Id': [season_id],\n",
        "    'Map_Id': [map],\n",
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        "\n",
        "dataframe_collection['match_summary'] = match_summary_df\n",
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      "source": [
        "### Team Summary"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FjceJd314MOG"
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      "source": [
        "Examine the data returned on each team summary."
      ]
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            "text/plain": [
              "  teamId  name  hasWon  roundsWon  roundsLost  score  kills  deaths  assists  \\\n",
              "0    Red   Red    True         13           7  21931     81      66       33   \n",
              "1   Blue  Blue   False          7          13  18904     66      81       17   \n",
              "\n",
              "   damage  \n",
              "0   14584  \n",
              "1   12365  "
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              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
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              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
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              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-eef821cb-ab5b-4d2f-9702-ec78292f9a63 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 677
        }
      ],
      "source": [
        "# Filter out the segments where type is \"team-summary\"\n",
        "team_summary_segments = [segment for segment in match_data['data']['segments'] if segment['type'] == 'team-summary']\n",
        "\n",
        "# Extract the required fields from 'attributes', 'metadata', and 'stats'\n",
        "attributes_data_team = pd.json_normalize([segment['attributes'] for segment in team_summary_segments])[['teamId']]\n",
        "metadata_data_team = pd.json_normalize([segment['metadata'] for segment in team_summary_segments])[['name', 'hasWon']]\n",
        "\n",
        "# Extract fields ending in '.value' from 'stats'\n",
        "stats_data_team = pd.json_normalize([segment['stats'] for segment in team_summary_segments])\n",
        "stats_data_team = stats_data_team[[col for col in stats_data_team.columns if col.endswith('.value')]]\n",
        "\n",
        "# Rename columns to remove the '.value' suffix\n",
        "stats_data_team_renamed = stats_data_team.rename(columns={col: col.replace('.value', '') for col in stats_data_team.columns})\n",
        "\n",
        "# Concatenate the extracted data to form the streamlined team summary DataFrame\n",
        "team_summary_df = pd.concat([attributes_data_team, metadata_data_team, stats_data_team_renamed], axis=1)\n",
        "\n",
        "dataframe_collection['team_summary'] = team_summary_df\n",
        "team_summary_df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NulBc04B19o6"
      },
      "source": [
        "### Player Summary"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SXPhTJ4q4TEV"
      },
      "source": [
        "Examine the data returned on each player summary."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 678,
      "metadata": {
        "id": "G0bLX-dGHYZt",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 516
        },
        "outputId": "62169bd7-1347-435a-df92-5c39fda853de"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  platformUserIdentifier teamId                              agentKey  \\\n",
              "0        Z10 kaajak#4747   Blue  f94c3b30-42be-e959-889c-5aa313dba261   \n",
              "1       Z10 kenobi#77777   Blue  6f2a04ca-43e0-be17-7f36-b3908627744d   \n",
              "2        KPI pupsik#gang    Red  1e58de9c-4950-5125-93e9-a0aee9f98746   \n",
              "3    FNATIC Boaster#1010    Red  f94c3b30-42be-e959-889c-5aa313dba261   \n",
              "4     Liquid Mistic#SUMN    Red  9f0d8ba9-4140-b941-57d3-a7ad57c6b417   \n",
              "\n",
              "   agentName        rank    currRank  score  scorePerRound  killsPerRound  \\\n",
              "0       Raze     Radiant         244   5937         296.85           1.00   \n",
              "1       Skye     Radiant         423   3311         165.55           0.55   \n",
              "2    Killjoy  Immortal 3         358   4720         236.00           0.75   \n",
              "3       Raze     Radiant     Radiant   4880         244.00           0.90   \n",
              "4  Brimstone  Immortal 3  Immortal 2   4229         211.45           0.75   \n",
              "\n",
              "   kills  ...         esr  firstKillsPerRound  firstDeathsPerRound  \\\n",
              "0     20  ...  100.000000                0.15                 0.00   \n",
              "1     11  ...   55.555556                0.25                 0.20   \n",
              "2     15  ...   40.000000                0.10                 0.15   \n",
              "3     18  ...   50.000000                0.10                 0.10   \n",
              "4     15  ...   25.000000                0.05                 0.15   \n",
              "\n",
              "   econRating  hsAccuracy  kast  clutches  roundsWinPct  trnPerformanceScore  \\\n",
              "0          67   25.000000  70.0         1          35.0                  635   \n",
              "1          38   50.000000  65.0         0          35.0                   52   \n",
              "2          82   31.034483  85.0         1          65.0                  928   \n",
              "3          51    9.375000  70.0         0          65.0                  720   \n",
              "4          51   50.000000  90.0         0          65.0                  704   \n",
              "\n",
              "                                 trnPerformanceStats  \n",
              "0  roundsWinPct,kAST,scorePerRound,damageDeltaPer...  \n",
              "1  roundsWinPct,kAST,scorePerRound,damageDeltaPer...  \n",
              "2  roundsWinPct,kAST,scorePerRound,damageDeltaPer...  \n",
              "3  roundsWinPct,kAST,scorePerRound,damageDeltaPer...  \n",
              "4  roundsWinPct,kAST,scorePerRound,damageDeltaPer...  \n",
              "\n",
              "[5 rows x 44 columns]"
            ],
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              "  <div id=\"df-b106e6de-01b4-4714-b969-a29c4a4af0b5\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>platformUserIdentifier</th>\n",
              "      <th>teamId</th>\n",
              "      <th>agentKey</th>\n",
              "      <th>agentName</th>\n",
              "      <th>rank</th>\n",
              "      <th>currRank</th>\n",
              "      <th>score</th>\n",
              "      <th>scorePerRound</th>\n",
              "      <th>killsPerRound</th>\n",
              "      <th>kills</th>\n",
              "      <th>...</th>\n",
              "      <th>esr</th>\n",
              "      <th>firstKillsPerRound</th>\n",
              "      <th>firstDeathsPerRound</th>\n",
              "      <th>econRating</th>\n",
              "      <th>hsAccuracy</th>\n",
              "      <th>kast</th>\n",
              "      <th>clutches</th>\n",
              "      <th>roundsWinPct</th>\n",
              "      <th>trnPerformanceScore</th>\n",
              "      <th>trnPerformanceStats</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Z10 kaajak#4747</td>\n",
              "      <td>Blue</td>\n",
              "      <td>f94c3b30-42be-e959-889c-5aa313dba261</td>\n",
              "      <td>Raze</td>\n",
              "      <td>Radiant</td>\n",
              "      <td>244</td>\n",
              "      <td>5937</td>\n",
              "      <td>296.85</td>\n",
              "      <td>1.00</td>\n",
              "      <td>20</td>\n",
              "      <td>...</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>0.15</td>\n",
              "      <td>0.00</td>\n",
              "      <td>67</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>70.0</td>\n",
              "      <td>1</td>\n",
              "      <td>35.0</td>\n",
              "      <td>635</td>\n",
              "      <td>roundsWinPct,kAST,scorePerRound,damageDeltaPer...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Z10 kenobi#77777</td>\n",
              "      <td>Blue</td>\n",
              "      <td>6f2a04ca-43e0-be17-7f36-b3908627744d</td>\n",
              "      <td>Skye</td>\n",
              "      <td>Radiant</td>\n",
              "      <td>423</td>\n",
              "      <td>3311</td>\n",
              "      <td>165.55</td>\n",
              "      <td>0.55</td>\n",
              "      <td>11</td>\n",
              "      <td>...</td>\n",
              "      <td>55.555556</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.20</td>\n",
              "      <td>38</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>65.0</td>\n",
              "      <td>0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>52</td>\n",
              "      <td>roundsWinPct,kAST,scorePerRound,damageDeltaPer...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>KPI pupsik#gang</td>\n",
              "      <td>Red</td>\n",
              "      <td>1e58de9c-4950-5125-93e9-a0aee9f98746</td>\n",
              "      <td>Killjoy</td>\n",
              "      <td>Immortal 3</td>\n",
              "      <td>358</td>\n",
              "      <td>4720</td>\n",
              "      <td>236.00</td>\n",
              "      <td>0.75</td>\n",
              "      <td>15</td>\n",
              "      <td>...</td>\n",
              "      <td>40.000000</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.15</td>\n",
              "      <td>82</td>\n",
              "      <td>31.034483</td>\n",
              "      <td>85.0</td>\n",
              "      <td>1</td>\n",
              "      <td>65.0</td>\n",
              "      <td>928</td>\n",
              "      <td>roundsWinPct,kAST,scorePerRound,damageDeltaPer...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>FNATIC Boaster#1010</td>\n",
              "      <td>Red</td>\n",
              "      <td>f94c3b30-42be-e959-889c-5aa313dba261</td>\n",
              "      <td>Raze</td>\n",
              "      <td>Radiant</td>\n",
              "      <td>Radiant</td>\n",
              "      <td>4880</td>\n",
              "      <td>244.00</td>\n",
              "      <td>0.90</td>\n",
              "      <td>18</td>\n",
              "      <td>...</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.10</td>\n",
              "      <td>51</td>\n",
              "      <td>9.375000</td>\n",
              "      <td>70.0</td>\n",
              "      <td>0</td>\n",
              "      <td>65.0</td>\n",
              "      <td>720</td>\n",
              "      <td>roundsWinPct,kAST,scorePerRound,damageDeltaPer...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Liquid Mistic#SUMN</td>\n",
              "      <td>Red</td>\n",
              "      <td>9f0d8ba9-4140-b941-57d3-a7ad57c6b417</td>\n",
              "      <td>Brimstone</td>\n",
              "      <td>Immortal 3</td>\n",
              "      <td>Immortal 2</td>\n",
              "      <td>4229</td>\n",
              "      <td>211.45</td>\n",
              "      <td>0.75</td>\n",
              "      <td>15</td>\n",
              "      <td>...</td>\n",
              "      <td>25.000000</td>\n",
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              "      <td>51</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>90.0</td>\n",
              "      <td>0</td>\n",
              "      <td>65.0</td>\n",
              "      <td>704</td>\n",
              "      <td>roundsWinPct,kAST,scorePerRound,damageDeltaPer...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 44 columns</p>\n",
              "</div>\n",
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              "                                                    [key], {});\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-e0c9750a-e0fc-4e8f-bc1a-50032589903e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 678
        }
      ],
      "source": [
        "# Filter out the segments where type is \"player-summary\"\n",
        "player_summary_segments = [segment for segment in match_data['data']['segments'] if segment['type'] == 'player-summary']\n",
        "\n",
        "# Extract the required fields from 'attributes' and 'metadata'\n",
        "attributes_data = pd.json_normalize([segment['attributes'] for segment in player_summary_segments])[['platformUserIdentifier']]\n",
        "metadata_data = pd.json_normalize([segment['metadata'] for segment in player_summary_segments])[['teamId', 'agentKey', 'agentName']]\n",
        "\n",
        "# Extract fields ending in '.value' from 'stats'\n",
        "stats_data = pd.json_normalize([segment['stats'] for segment in player_summary_segments])\n",
        "stats_data = stats_data[[col for col in stats_data.columns if col.endswith('.value')]]\n",
        "\n",
        "# Convert list of stats to a comma-separated string\n",
        "trn_performance_score_stats_str = [\",\".join(segment['stats'].get('trnPerformanceScore', {}).get('metadata', {}).get('stats', [])) for segment in player_summary_segments]\n",
        "\n",
        "# Create a DataFrame with the stats string\n",
        "trn_performance_score_stats_df_str = pd.DataFrame({'trnPerformanceStats': trn_performance_score_stats_str})\n",
        "\n",
        "# Rename columns to remove the '.value' suffix\n",
        "stats_data_renamed = stats_data.rename(columns={col: col.replace('.value', '') for col in stats_data.columns})\n",
        "\n",
        "# Concatenate the extracted data to form the updated player summary DataFrame\n",
        "player_summary_df = pd.concat([attributes_data, metadata_data, stats_data_renamed, trn_performance_score_stats_df_str], axis=1)\n",
        "\n",
        "dataframe_collection['player_summary'] = player_summary_df\n",
        "player_summary_df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FFYJ_TuvF2rP"
      },
      "source": [
        "### Player Loadouts"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s3zfy20t4XZg"
      },
      "source": [
        "Examine the data returned on each player loadout summary."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 679,
      "metadata": {
        "id": "PTQg7tcwF4eM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "outputId": "2bdcfd71-f836-466e-a0a9-bab69f989596"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  loadout platformUserIdentifier  kills  deaths   kDRatio  assists  \\\n",
              "0  pistol        Z10 kaajak#4747      1       2  0.500000        1   \n",
              "1     eco        Z10 kaajak#4747      3       1  3.000000        0   \n",
              "2    semi        Z10 kaajak#4747      8       7  1.142857        3   \n",
              "3    full        Z10 kaajak#4747      8       7  1.142857        1   \n",
              "4  pistol       Z10 kenobi#77777      1       2  0.500000        1   \n",
              "\n",
              "   roundsPlayed  roundsWon  roundsLost  roundsWinPct  ...  survived  \\\n",
              "0             2          0           2           0.0  ...         0   \n",
              "1             2          1           1          50.0  ...         1   \n",
              "2             8          3           5          37.5  ...         1   \n",
              "3             8          3           5          37.5  ...         1   \n",
              "4             2          0           2           0.0  ...         0   \n",
              "\n",
              "   firstBloods  firstDeaths    esr   kAST  kasted  damagePerRound  \\\n",
              "0            0            0    0.0  100.0       2          95.000   \n",
              "1            0            0    0.0   50.0       1         233.500   \n",
              "2            3            0  100.0   75.0       6         202.500   \n",
              "3            0            0    0.0   62.5       5         199.375   \n",
              "4            0            0    0.0  100.0       2          57.500   \n",
              "\n",
              "   scorePerRound  damageDelta  damageDeltaPerRound  \n",
              "0        128.500         -107              -53.500  \n",
              "1        401.000          346              173.000  \n",
              "2        319.625          289               36.125  \n",
              "3        290.125          262               32.750  \n",
              "4        125.000         -160              -80.000  \n",
              "\n",
              "[5 rows x 26 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3ffe6b71-62ac-4526-ad6f-3e7310cb6fd9\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
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              "        vertical-align: middle;\n",
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              "    .dataframe tbody tr th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>loadout</th>\n",
              "      <th>platformUserIdentifier</th>\n",
              "      <th>kills</th>\n",
              "      <th>deaths</th>\n",
              "      <th>kDRatio</th>\n",
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              "      <th>roundsLost</th>\n",
              "      <th>roundsWinPct</th>\n",
              "      <th>...</th>\n",
              "      <th>survived</th>\n",
              "      <th>firstBloods</th>\n",
              "      <th>firstDeaths</th>\n",
              "      <th>esr</th>\n",
              "      <th>kAST</th>\n",
              "      <th>kasted</th>\n",
              "      <th>damagePerRound</th>\n",
              "      <th>scorePerRound</th>\n",
              "      <th>damageDelta</th>\n",
              "      <th>damageDeltaPerRound</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>pistol</td>\n",
              "      <td>Z10 kaajak#4747</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>2</td>\n",
              "      <td>95.000</td>\n",
              "      <td>128.500</td>\n",
              "      <td>-107</td>\n",
              "      <td>-53.500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>eco</td>\n",
              "      <td>Z10 kaajak#4747</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>50.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>1</td>\n",
              "      <td>233.500</td>\n",
              "      <td>401.000</td>\n",
              "      <td>346</td>\n",
              "      <td>173.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>semi</td>\n",
              "      <td>Z10 kaajak#4747</td>\n",
              "      <td>8</td>\n",
              "      <td>7</td>\n",
              "      <td>1.142857</td>\n",
              "      <td>3</td>\n",
              "      <td>8</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>37.5</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>75.0</td>\n",
              "      <td>6</td>\n",
              "      <td>202.500</td>\n",
              "      <td>319.625</td>\n",
              "      <td>289</td>\n",
              "      <td>36.125</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>full</td>\n",
              "      <td>Z10 kaajak#4747</td>\n",
              "      <td>8</td>\n",
              "      <td>7</td>\n",
              "      <td>1.142857</td>\n",
              "      <td>1</td>\n",
              "      <td>8</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>37.5</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>62.5</td>\n",
              "      <td>5</td>\n",
              "      <td>199.375</td>\n",
              "      <td>290.125</td>\n",
              "      <td>262</td>\n",
              "      <td>32.750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>pistol</td>\n",
              "      <td>Z10 kenobi#77777</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>2</td>\n",
              "      <td>57.500</td>\n",
              "      <td>125.000</td>\n",
              "      <td>-160</td>\n",
              "      <td>-80.000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 26 columns</p>\n",
              "</div>\n",
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              "\n",
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              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-3ffe6b71-62ac-4526-ad6f-3e7310cb6fd9 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
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              "                                                    [key], {});\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "\n",
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              "\n",
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              "\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-0e6620a1-3889-4be2-9041-7a5f75cc79cc button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 679
        }
      ],
      "source": [
        "# Filter out the segments where type is \"player-loadout\"\n",
        "player_loadout_segments = [segment for segment in match_data['data']['segments'] if segment['type'] == 'player-loadout']\n",
        "\n",
        "# Extract the required fields from 'attributes'\n",
        "attributes_data_player_loadout = pd.json_normalize([segment['attributes'] for segment in player_loadout_segments])[['loadout', 'platformUserIdentifier']]\n",
        "\n",
        "# Extract fields ending in '.value' from 'stats'\n",
        "stats_data_player_loadout = pd.json_normalize([segment['stats'] for segment in player_loadout_segments])\n",
        "stats_data_player_loadout = stats_data_player_loadout[[col for col in stats_data_player_loadout.columns if col.endswith('.value')]]\n",
        "\n",
        "# Rename columns to remove the '.value' suffix\n",
        "stats_data_player_loadout_renamed = stats_data_player_loadout.rename(columns={col: col.replace('.value', '') for col in stats_data_player_loadout.columns})\n",
        "\n",
        "# Concatenate the extracted data to form the player-loadout DataFrame\n",
        "player_loadout_df = pd.concat([attributes_data_player_loadout, stats_data_player_loadout_renamed], axis=1)\n",
        "\n",
        "dataframe_collection['player_loadout'] = player_loadout_df\n",
        "player_loadout_df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P2j3SQnTC8Pi"
      },
      "source": [
        "### Round Summary"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B_yMx5QD4edD"
      },
      "source": [
        "Examine the data returned on each round summary."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 680,
      "metadata": {
        "id": "IHeyPTFkSAU0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "outputId": "18aed06b-5aad-4f5b-e3e2-28c66ed2d5fb"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   round  defuse exploded plant.platformUserIdentifier  plant.location.x  \\\n",
              "0      1     NaN     None                     Dawn#92i            7519.0   \n",
              "1      2     NaN     None                          NaN               NaN   \n",
              "2      3     NaN     None                     Dawn#92i            7554.0   \n",
              "3      4     NaN     None                     Dawn#92i            7731.0   \n",
              "4      5     NaN     None           Liquid Mistic#SUMN            7693.0   \n",
              "\n",
              "   plant.location.y  plant.roundTime plant.site  plant  \\\n",
              "0            5236.0          21344.0          A    NaN   \n",
              "1               NaN              NaN        NaN    NaN   \n",
              "2            5888.0          25958.0          A    NaN   \n",
              "3            5992.0          67762.0          A    NaN   \n",
              "4            5992.0          58582.0          A    NaN   \n",
              "\n",
              "  defuse.platformUserIdentifier  ...  defuse.player3.platformUserIdentifier  \\\n",
              "0                           NaN  ...                                    NaN   \n",
              "1                           NaN  ...                                    NaN   \n",
              "2                           NaN  ...                                    NaN   \n",
              "3                     Eung#meow  ...                                    NaN   \n",
              "4                 Danillize#EUW  ...                              Eung#meow   \n",
              "\n",
              "   defuse.player3.location_x  defuse.player3.location_y  \\\n",
              "0                        NaN                        NaN   \n",
              "1                        NaN                        NaN   \n",
              "2                        NaN                        NaN   \n",
              "3                        NaN                        NaN   \n",
              "4                     7334.0                     5871.0   \n",
              "\n",
              "  defuse.player3.viewRadians defuse.player4.platformUserIdentifier  \\\n",
              "0                        NaN                                   NaN   \n",
              "1                        NaN                                   NaN   \n",
              "2                        NaN                                   NaN   \n",
              "3                        NaN                                   NaN   \n",
              "4                   0.615318                         Danillize#EUW   \n",
              "\n",
              "   defuse.player4.location_x  defuse.player4.location_y  \\\n",
              "0                        NaN                        NaN   \n",
              "1                        NaN                        NaN   \n",
              "2                        NaN                        NaN   \n",
              "3                        NaN                        NaN   \n",
              "4                     7563.0                     5973.0   \n",
              "\n",
              "   defuse.player4.viewRadians  roundResult  winningTeam  \n",
              "0                         NaN  Elimination          Red  \n",
              "1                         NaN  Elimination         Blue  \n",
              "2                         NaN  Elimination          Red  \n",
              "3                         NaN       Defuse         Blue  \n",
              "4                    4.051819       Defuse         Blue  \n",
              "\n",
              "[5 rows x 72 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-60c0acd5-ed4c-42e9-ad72-3849ab888d64\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>round</th>\n",
              "      <th>defuse</th>\n",
              "      <th>exploded</th>\n",
              "      <th>plant.platformUserIdentifier</th>\n",
              "      <th>plant.location.x</th>\n",
              "      <th>plant.location.y</th>\n",
              "      <th>plant.roundTime</th>\n",
              "      <th>plant.site</th>\n",
              "      <th>plant</th>\n",
              "      <th>defuse.platformUserIdentifier</th>\n",
              "      <th>...</th>\n",
              "      <th>defuse.player3.platformUserIdentifier</th>\n",
              "      <th>defuse.player3.location_x</th>\n",
              "      <th>defuse.player3.location_y</th>\n",
              "      <th>defuse.player3.viewRadians</th>\n",
              "      <th>defuse.player4.platformUserIdentifier</th>\n",
              "      <th>defuse.player4.location_x</th>\n",
              "      <th>defuse.player4.location_y</th>\n",
              "      <th>defuse.player4.viewRadians</th>\n",
              "      <th>roundResult</th>\n",
              "      <th>winningTeam</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>None</td>\n",
              "      <td>Dawn#92i</td>\n",
              "      <td>7519.0</td>\n",
              "      <td>5236.0</td>\n",
              "      <td>21344.0</td>\n",
              "      <td>A</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Elimination</td>\n",
              "      <td>Red</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>None</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Elimination</td>\n",
              "      <td>Blue</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>NaN</td>\n",
              "      <td>None</td>\n",
              "      <td>Dawn#92i</td>\n",
              "      <td>7554.0</td>\n",
              "      <td>5888.0</td>\n",
              "      <td>25958.0</td>\n",
              "      <td>A</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Elimination</td>\n",
              "      <td>Red</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>NaN</td>\n",
              "      <td>None</td>\n",
              "      <td>Dawn#92i</td>\n",
              "      <td>7731.0</td>\n",
              "      <td>5992.0</td>\n",
              "      <td>67762.0</td>\n",
              "      <td>A</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Eung#meow</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Defuse</td>\n",
              "      <td>Blue</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>None</td>\n",
              "      <td>Liquid Mistic#SUMN</td>\n",
              "      <td>7693.0</td>\n",
              "      <td>5992.0</td>\n",
              "      <td>58582.0</td>\n",
              "      <td>A</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Danillize#EUW</td>\n",
              "      <td>...</td>\n",
              "      <td>Eung#meow</td>\n",
              "      <td>7334.0</td>\n",
              "      <td>5871.0</td>\n",
              "      <td>0.615318</td>\n",
              "      <td>Danillize#EUW</td>\n",
              "      <td>7563.0</td>\n",
              "      <td>5973.0</td>\n",
              "      <td>4.051819</td>\n",
              "      <td>Defuse</td>\n",
              "      <td>Blue</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 72 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-60c0acd5-ed4c-42e9-ad72-3849ab888d64')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-60c0acd5-ed4c-42e9-ad72-3849ab888d64 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-60c0acd5-ed4c-42e9-ad72-3849ab888d64');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-5217335a-a33e-41a6-83e3-8960116f91c5\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-5217335a-a33e-41a6-83e3-8960116f91c5')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-5217335a-a33e-41a6-83e3-8960116f91c5 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 680
        }
      ],
      "source": [
        "# Filter out segments where the type is \"round-summary\"\n",
        "round_summary_segments = [segment for segment in match_data['data']['segments'] if segment['type'] == 'round-summary']\n",
        "\n",
        "# Extract all fields from 'attributes'\n",
        "attributes_data_round = pd.json_normalize([segment['attributes'] for segment in round_summary_segments])\n",
        "\n",
        "# Flatten the metadata, including plant and defuse details\n",
        "metadata_data_round_flattened = pd.json_normalize([segment['metadata'] for segment in round_summary_segments])\n",
        "\n",
        "# Function to flatten specific sections like 'plant' or 'defuse' into a single row with multiple columns for each player\n",
        "def flatten_section(section_data, section_name):\n",
        "    flattened = {}\n",
        "    if isinstance(section_data, list):\n",
        "        for i, loc in enumerate(section_data):\n",
        "            if loc:\n",
        "                for key, value in loc.items():\n",
        "                    if isinstance(value, dict):\n",
        "                        for sub_key, sub_value in value.items():\n",
        "                            flattened[f'{section_name}.player{i+1}.{key}_{sub_key}'] = sub_value\n",
        "                    else:\n",
        "                        flattened[f'{section_name}.player{i+1}.{key}'] = value\n",
        "    return flattened\n",
        "\n",
        "# Apply the function to flatten 'plant.playerLocations' and 'defuse.playerLocations' into individual columns\n",
        "plant_locations_flattened = pd.DataFrame(metadata_data_round_flattened['plant.playerLocations'].apply(lambda x: flatten_section(x, 'plant') if x else {}).tolist())\n",
        "defuse_locations_flattened = pd.DataFrame(metadata_data_round_flattened['defuse.playerLocations'].apply(lambda x: flatten_section(x, 'defuse') if x else {}).tolist())\n",
        "\n",
        "# Extract fields ending in '.value' from 'stats'\n",
        "stats_data_round = pd.json_normalize([segment['stats'] for segment in round_summary_segments])\n",
        "stats_data_round_values = stats_data_round[[col for col in stats_data_round.columns if col.endswith('.value')]]\n",
        "\n",
        "# Rename columns to remove the '.value' suffix\n",
        "stats_data_round_values_renamed = stats_data_round_values.rename(columns={col: col.replace('.value', '') for col in stats_data_round_values.columns})\n",
        "\n",
        "# Concatenate all the DataFrames to create the final round_summary DataFrame\n",
        "round_summary_df = pd.concat([\n",
        "    attributes_data_round,\n",
        "    metadata_data_round_flattened.drop(['plant.playerLocations', 'defuse.playerLocations'], axis=1),\n",
        "    plant_locations_flattened,\n",
        "    defuse_locations_flattened,\n",
        "    stats_data_round_values_renamed\n",
        "], axis=1)\n",
        "\n",
        "dataframe_collection['round_summary'] = round_summary_df\n",
        "round_summary_df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1aR9OnR6Ed4r"
      },
      "source": [
        "### Player Round By Round"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X55Celwx4gmy"
      },
      "source": [
        "Examine the data returned on each player's round summary.  This is the bulk of the data we'll be focusing on."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 681,
      "metadata": {
        "id": "yBaU0k4xEgIl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "d714cebf-d4b7-4964-89ac-ae38090be6a7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   round platformUserIdentifier teamId  teamSide  \\\n",
              "0      1               Dawn#92i    Red  attacker   \n",
              "1      1     Liquid Mistic#SUMN    Red  attacker   \n",
              "2      1        Z10 kaajak#4747   Blue  defender   \n",
              "3      1       Z10 kenobi#77777   Blue  defender   \n",
              "4      1    FNATIC Boaster#1010    Red  attacker   \n",
              "\n",
              "                               agentKey  hasWon  score  kills  deaths  \\\n",
              "0  6f2a04ca-43e0-be17-7f36-b3908627744d    True    140      1       0   \n",
              "1  9f0d8ba9-4140-b941-57d3-a7ad57c6b417    True    585      2       1   \n",
              "2  f94c3b30-42be-e959-889c-5aa313dba261   False    202      1       1   \n",
              "3  6f2a04ca-43e0-be17-7f36-b3908627744d   False    195      1       1   \n",
              "4  f94c3b30-42be-e959-889c-5aa313dba261    True    180      1       1   \n",
              "\n",
              "   kdRatio  assists  damage  loadoutValue  remainingCredits  spentCredits  \\\n",
              "0      1.0        0     105          1000                50           750   \n",
              "1      2.0        0     270           900                 0           800   \n",
              "2      1.0        0     135           700               100           700   \n",
              "3      1.0        1      60          1000                50           750   \n",
              "4      1.0        0      86           800                 0           800   \n",
              "\n",
              "   agentName  \n",
              "0       Skye  \n",
              "1  Brimstone  \n",
              "2       Raze  \n",
              "3       Skye  \n",
              "4       Raze  "
            ],
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              "      <th>damage</th>\n",
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              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Dawn#92i</td>\n",
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              "      <td>attacker</td>\n",
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              "      <th>1</th>\n",
              "      <td>1</td>\n",
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              "      <td>Red</td>\n",
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              "      <td>270</td>\n",
              "      <td>900</td>\n",
              "      <td>0</td>\n",
              "      <td>800</td>\n",
              "      <td>Brimstone</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>Z10 kaajak#4747</td>\n",
              "      <td>Blue</td>\n",
              "      <td>defender</td>\n",
              "      <td>f94c3b30-42be-e959-889c-5aa313dba261</td>\n",
              "      <td>False</td>\n",
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              "      <td>135</td>\n",
              "      <td>700</td>\n",
              "      <td>100</td>\n",
              "      <td>700</td>\n",
              "      <td>Raze</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>Z10 kenobi#77777</td>\n",
              "      <td>Blue</td>\n",
              "      <td>defender</td>\n",
              "      <td>6f2a04ca-43e0-be17-7f36-b3908627744d</td>\n",
              "      <td>False</td>\n",
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              "      <td>60</td>\n",
              "      <td>1000</td>\n",
              "      <td>50</td>\n",
              "      <td>750</td>\n",
              "      <td>Skye</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>FNATIC Boaster#1010</td>\n",
              "      <td>Red</td>\n",
              "      <td>attacker</td>\n",
              "      <td>f94c3b30-42be-e959-889c-5aa313dba261</td>\n",
              "      <td>True</td>\n",
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          },
          "metadata": {},
          "execution_count": 681
        }
      ],
      "source": [
        "# Filter out the segments where type is \"player-round\"\n",
        "player_round_segments = [segment for segment in match_data['data']['segments'] if segment['type'] == 'player-round']\n",
        "\n",
        "# Extract the required fields from 'attributes'\n",
        "attributes_data_player_round = pd.json_normalize([segment['attributes'] for segment in player_round_segments])[['round', 'platformUserIdentifier']]\n",
        "\n",
        "# Extract the required fields from 'metadata'\n",
        "metadata_data_player_round = pd.json_normalize([segment['metadata'] for segment in player_round_segments])[['teamId', 'teamSide', 'agentKey', 'hasWon']]\n",
        "\n",
        "# Extract fields ending in '.value' from 'stats'\n",
        "stats_data_player_round = pd.json_normalize([segment['stats'] for segment in player_round_segments])\n",
        "stats_data_player_round = stats_data_player_round[[col for col in stats_data_player_round.columns if col.endswith('.value')]]\n",
        "\n",
        "# Rename columns to remove the '.value' suffix\n",
        "stats_data_player_round_renamed = stats_data_player_round.rename(columns={col: col.replace('.value', '') for col in stats_data_player_round.columns})\n",
        "\n",
        "# Concatenate the extracted data to form the player-round DataFrame\n",
        "player_round_df = pd.concat([attributes_data_player_round, metadata_data_player_round, stats_data_player_round_renamed], axis=1)\n",
        "\n",
        "# Add Agent Name column\n",
        "unique_agents = player_summary_df[['platformUserIdentifier', 'agentName']].drop_duplicates()\n",
        "\n",
        "# Merge the player_round_df with the unique_agents to get the agentName\n",
        "player_round_df = player_round_df.merge(unique_agents, on='platformUserIdentifier', how='left')\n",
        "\n",
        "# Now player_round_df will have an additional column 'agentName' from player_summary_df\n",
        "dataframe_collection['player_round'] = player_round_df\n",
        "player_round_df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yhxbqzt1JFFC"
      },
      "source": [
        "### Player Round Damage"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OWVmeKVN4pxA"
      },
      "source": [
        "Examine the data returned on each player's round damage summary."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 682,
      "metadata": {
        "id": "te3wPuYRJHsC",
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          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "55b5a945-550b-4da4-da0e-4f3d6c53877c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   round platformUserIdentifier opponentPlatformUserIdentifier  damage  \\\n",
              "0      1               Dawn#92i                  Danillize#EUW     105   \n",
              "1      1     Liquid Mistic#SUMN                      Eung#meow     105   \n",
              "2      1     Liquid Mistic#SUMN                    kwert#kwert     135   \n",
              "3      1     Liquid Mistic#SUMN                Z10 kaajak#4747      30   \n",
              "4      1        Z10 kaajak#4747             Liquid Mistic#SUMN     105   \n",
              "\n",
              "   legshots  bodyshots  headshots  \n",
              "0         0          0          1  \n",
              "1         0          0          1  \n",
              "2         0          1          1  \n",
              "3         0          1          0  \n",
              "4         0          0          1  "
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          },
          "metadata": {},
          "execution_count": 682
        }
      ],
      "source": [
        "# Filter out the segments where type is \"player-round-damage\"\n",
        "player_round_damage_segments = [segment for segment in match_data['data']['segments'] if segment['type'] == 'player-round-damage']\n",
        "\n",
        "# Extract the required fields from 'attributes'\n",
        "attributes_data_player_round_damage = pd.json_normalize([segment['attributes'] for segment in player_round_damage_segments])[['round', 'platformUserIdentifier', 'opponentPlatformUserIdentifier']]\n",
        "\n",
        "# Extract fields ending in '.value' from 'stats'\n",
        "stats_data_player_round_damage = pd.json_normalize([segment['stats'] for segment in player_round_damage_segments])\n",
        "stats_data_player_round_damage = stats_data_player_round_damage[[col for col in stats_data_player_round_damage.columns if col.endswith('.value')]]\n",
        "\n",
        "# Rename columns to remove the '.value' suffix\n",
        "stats_data_player_round_damage_renamed = stats_data_player_round_damage.rename(columns={col: col.replace('.value', '') for col in stats_data_player_round_damage.columns})\n",
        "\n",
        "# Concatenate the extracted data to form the player-round-damage DataFrame\n",
        "player_round_damage_df = pd.concat([attributes_data_player_round_damage, stats_data_player_round_damage_renamed], axis=1)\n",
        "\n",
        "dataframe_collection['player_round_damage'] = player_round_damage_df\n",
        "player_round_damage_df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_Wd3Xyh9JTKY"
      },
      "source": [
        "### Player Round Kills"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lCZqCMhF4yoF"
      },
      "source": [
        "Examine the data returned on each player's round kills summary."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 683,
      "metadata": {
        "id": "Deg1oR1XlWWJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "outputId": "7de17734-8b63-4db0-d903-b674d6442ec5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   round platformUserIdentifier opponentPlatformUserIdentifier weaponName  \\\n",
              "0      1               Dawn#92i                  Danillize#EUW      Ghost   \n",
              "1      1     Liquid Mistic#SUMN                      Eung#meow      Ghost   \n",
              "2      1     Liquid Mistic#SUMN                    kwert#kwert      Ghost   \n",
              "3      1        Z10 kaajak#4747             Liquid Mistic#SUMN      Ghost   \n",
              "4      1       Z10 kenobi#77777            FNATIC Boaster#1010      Ghost   \n",
              "\n",
              "  weaponCategory  gameTime  roundTime  opponentLocation_x  opponentLocation_y  \\\n",
              "0       Sidearms    112849      57822                7544                5111   \n",
              "1       Sidearms     62318       7291                6445                 899   \n",
              "2       Sidearms     63542       8515                5742                1740   \n",
              "3       Sidearms     64084       9057                5993                -451   \n",
              "4       Sidearms     67415      12388                6345                1230   \n",
              "\n",
              "  finishingDamage_damageType  ... kills_player7.viewRadians  \\\n",
              "0                     Weapon  ...                       NaN   \n",
              "1                     Weapon  ...                  1.192866   \n",
              "2                     Weapon  ...                  1.132655   \n",
              "3                     Weapon  ...                  0.677922   \n",
              "4                     Weapon  ...                       NaN   \n",
              "\n",
              "   kills_player8.platformUserIdentifier kills_player8.location_x  \\\n",
              "0                                   NaN                      NaN   \n",
              "1                       KPI pupsik#gang                   3374.0   \n",
              "2                       KPI pupsik#gang                   3549.0   \n",
              "3                                   NaN                      NaN   \n",
              "4                                   NaN                      NaN   \n",
              "\n",
              "   kills_player8.location_y  kills_player8.viewRadians  \\\n",
              "0                       NaN                        NaN   \n",
              "1                    2589.0                   1.051868   \n",
              "2                    2815.0                   0.677923   \n",
              "3                       NaN                        NaN   \n",
              "4                       NaN                        NaN   \n",
              "\n",
              "   kills_player9.platformUserIdentifier kills_player9.location_x  \\\n",
              "0                                   NaN                      NaN   \n",
              "1                           kwert#kwert                   5764.0   \n",
              "2                                   NaN                      NaN   \n",
              "3                                   NaN                      NaN   \n",
              "4                                   NaN                      NaN   \n",
              "\n",
              "   kills_player9.location_y  kills_player9.viewRadians  damage  \n",
              "0                       NaN                        NaN     135  \n",
              "1                    1864.0                   5.203362     105  \n",
              "2                       NaN                        NaN     135  \n",
              "3                       NaN                        NaN     183  \n",
              "4                       NaN                        NaN     145  \n",
              "\n",
              "[5 rows x 49 columns]"
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>round</th>\n",
              "      <th>platformUserIdentifier</th>\n",
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              "      <th>weaponName</th>\n",
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              "      <th>opponentLocation_x</th>\n",
              "      <th>opponentLocation_y</th>\n",
              "      <th>finishingDamage_damageType</th>\n",
              "      <th>...</th>\n",
              "      <th>kills_player7.viewRadians</th>\n",
              "      <th>kills_player8.platformUserIdentifier</th>\n",
              "      <th>kills_player8.location_x</th>\n",
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              "      <th>kills_player9.platformUserIdentifier</th>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Dawn#92i</td>\n",
              "      <td>Danillize#EUW</td>\n",
              "      <td>Ghost</td>\n",
              "      <td>Sidearms</td>\n",
              "      <td>112849</td>\n",
              "      <td>57822</td>\n",
              "      <td>7544</td>\n",
              "      <td>5111</td>\n",
              "      <td>Weapon</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>135</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>Liquid Mistic#SUMN</td>\n",
              "      <td>Eung#meow</td>\n",
              "      <td>Ghost</td>\n",
              "      <td>Sidearms</td>\n",
              "      <td>62318</td>\n",
              "      <td>7291</td>\n",
              "      <td>6445</td>\n",
              "      <td>899</td>\n",
              "      <td>Weapon</td>\n",
              "      <td>...</td>\n",
              "      <td>1.192866</td>\n",
              "      <td>KPI pupsik#gang</td>\n",
              "      <td>3374.0</td>\n",
              "      <td>2589.0</td>\n",
              "      <td>1.051868</td>\n",
              "      <td>kwert#kwert</td>\n",
              "      <td>5764.0</td>\n",
              "      <td>1864.0</td>\n",
              "      <td>5.203362</td>\n",
              "      <td>105</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>Liquid Mistic#SUMN</td>\n",
              "      <td>kwert#kwert</td>\n",
              "      <td>Ghost</td>\n",
              "      <td>Sidearms</td>\n",
              "      <td>63542</td>\n",
              "      <td>8515</td>\n",
              "      <td>5742</td>\n",
              "      <td>1740</td>\n",
              "      <td>Weapon</td>\n",
              "      <td>...</td>\n",
              "      <td>1.132655</td>\n",
              "      <td>KPI pupsik#gang</td>\n",
              "      <td>3549.0</td>\n",
              "      <td>2815.0</td>\n",
              "      <td>0.677923</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>135</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>Z10 kaajak#4747</td>\n",
              "      <td>Liquid Mistic#SUMN</td>\n",
              "      <td>Ghost</td>\n",
              "      <td>Sidearms</td>\n",
              "      <td>64084</td>\n",
              "      <td>9057</td>\n",
              "      <td>5993</td>\n",
              "      <td>-451</td>\n",
              "      <td>Weapon</td>\n",
              "      <td>...</td>\n",
              "      <td>0.677922</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>183</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>Z10 kenobi#77777</td>\n",
              "      <td>FNATIC Boaster#1010</td>\n",
              "      <td>Ghost</td>\n",
              "      <td>Sidearms</td>\n",
              "      <td>67415</td>\n",
              "      <td>12388</td>\n",
              "      <td>6345</td>\n",
              "      <td>1230</td>\n",
              "      <td>Weapon</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>145</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 49 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-803be90e-28c0-4899-97f9-a0dc2d79bacd')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-803be90e-28c0-4899-97f9-a0dc2d79bacd button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-803be90e-28c0-4899-97f9-a0dc2d79bacd');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-83876f5b-40a5-44b7-bda9-c45967343324\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-83876f5b-40a5-44b7-bda9-c45967343324')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-top-color: var(--fill-color);\n",
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              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-83876f5b-40a5-44b7-bda9-c45967343324 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 683
        }
      ],
      "source": [
        "# Function to flatten player locations into a single row with multiple columns for each player\n",
        "def flatten_player_locations(player_locations, prefix=''):\n",
        "    flattened = {}\n",
        "    if isinstance(player_locations, list):\n",
        "        for i, loc in enumerate(player_locations):\n",
        "            if loc:\n",
        "                for key, value in loc.items():\n",
        "                    if isinstance(value, dict):\n",
        "                        for sub_key, sub_value in value.items():\n",
        "                            flattened[f'{prefix}player{i+1}.{key}_{sub_key}'] = sub_value\n",
        "                    else:\n",
        "                        flattened[f'{prefix}player{i+1}.{key}'] = value\n",
        "    return flattened\n",
        "\n",
        "# Filter out the segments where type is \"player-round-kills\"\n",
        "player_round_kills_segments = [segment for segment in match_data['data']['segments'] if segment['type'] == 'player-round-kills']\n",
        "\n",
        "# Extract the required fields from 'attributes'\n",
        "attributes_data_player_round_kills = pd.json_normalize([segment['attributes'] for segment in player_round_kills_segments])[['round', 'platformUserIdentifier', 'opponentPlatformUserIdentifier']]\n",
        "\n",
        "# Extract the non-nested fields from 'metadata'\n",
        "metadata_data_player_round_kills_non_nested = pd.json_normalize([segment['metadata'] for segment in player_round_kills_segments])[['playerLocations', 'assistants', 'weaponName', 'weaponCategory', 'gameTime', 'roundTime']]\n",
        "\n",
        "# Extract the nested fields 'opponentLocation' and 'finishingDamage' from 'metadata'\n",
        "metadata_data_player_round_kills_nested = pd.json_normalize([segment['metadata'] for segment in player_round_kills_segments], sep='_')[['opponentLocation_x', 'opponentLocation_y', 'finishingDamage_damageType', 'finishingDamage_damageItem', 'finishingDamage_isSecondaryFireMode']]\n",
        "\n",
        "# Apply the function to flatten 'playerLocations' into individual columns\n",
        "player_locations_flattened_kills = pd.DataFrame([flatten_player_locations(segment['metadata'].get('playerLocations'), 'kills_') for segment in player_round_kills_segments])\n",
        "\n",
        "# Extract fields ending in '.value' from 'stats'\n",
        "stats_data_player_round_kills = pd.json_normalize([segment['stats'] for segment in player_round_kills_segments])\n",
        "stats_data_player_round_kills_values = stats_data_player_round_kills[[col for col in stats_data_player_round_kills.columns if col.endswith('.value')]]\n",
        "\n",
        "# Rename columns to remove the '.value' suffix\n",
        "stats_data_player_round_kills_renamed = stats_data_player_round_kills_values.rename(columns={col: col.replace('.value', '') for col in stats_data_player_round_kills_values.columns})\n",
        "\n",
        "# Concatenate the extracted data to form the player-round-kills DataFrame, including the flattened playerLocations and assistants\n",
        "player_round_kills_df = pd.concat([\n",
        "    attributes_data_player_round_kills,\n",
        "    metadata_data_player_round_kills_non_nested.drop(['playerLocations', 'assistants'], axis=1),\n",
        "    metadata_data_player_round_kills_nested,\n",
        "    player_locations_flattened_kills,\n",
        "    stats_data_player_round_kills_renamed\n",
        "], axis=1)\n",
        "\n",
        "dataframe_collection['player_round_kills'] = player_round_kills_df\n",
        "player_round_kills_df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uonYQNPoB4uH"
      },
      "source": [
        "### Export to CSV"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZAutaEXk41OX"
      },
      "source": [
        "Export each dataframe to a CSV in the exports folder for easier reading.  I used this to cross-check my results and interface with OpenAI for analysis."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 684,
      "metadata": {
        "id": "9xspRovlCPvW"
      },
      "outputs": [],
      "source": [
        "# for name, df in dataframe_collection.items():\n",
        "#     df.to_csv(f'exports/{name}.csv', index=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eVyd2dtENUgU"
      },
      "source": [
        "## Analysis"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r9BWNQSVZio6"
      },
      "source": [
        "### By Player"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hr22uv9gFxf_"
      },
      "source": [
        "Calculate Player Performance based on K/D ratio, assists, and damage"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 685,
      "metadata": {
        "id": "A1E_XxGhRdId",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 746
        },
        "outputId": "05b4a0f1-35d8-4d33-a79c-09b0ebaafb17"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# We'll normalize each of these metrics to have a common scale (0-1) and sum them up for an performance score\n",
        "def calculate_player_performance(df):\n",
        "    df['kdRatio_norm'] = df['kdRatio'] / df['kdRatio'].max()\n",
        "    df['assists_norm'] = df['assists'] / df['assists'].max()\n",
        "    df['damage_norm'] = df['damage'] / df['damage'].max()\n",
        "    df['performance'] = df['kdRatio_norm'] + df['assists_norm'] + df['damage_norm']\n",
        "    return df\n",
        "\n",
        "player_round_df = calculate_player_performance(player_round_df)\n",
        "\n",
        "# Plot the top 10 players based on Performance\n",
        "top_players = player_round_df.groupby('platformUserIdentifier')['performance'].mean().sort_values(ascending=False).head(10)\n",
        "plt.figure(figsize=(14, 7))\n",
        "sns.barplot(x=top_players.index, y=top_players.values, palette='viridis')\n",
        "plt.title('Top 10 Players Based on Performance')\n",
        "plt.xlabel('Player ID')\n",
        "plt.ylabel('Performance')\n",
        "plt.xticks(rotation=45)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5_5m_f3JRt_W"
      },
      "source": [
        "The bar chart above shows the top 10 players based on an 'Performance' score. This score is a simple sum of the normalized values of K/D ratio, assists, and damage dealt by each player.\n",
        "\n",
        "Here's how the performance is calculated:\n",
        "\n",
        "1. Normalize each metric (K/D ratio, assists, and damage) by dividing it by the maximum value in the dataset for that metric.\n",
        "2. Sum these normalized metrics to get an 'Performance' score for each player."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pu6dE9J6F2Zs"
      },
      "source": [
        "Analyze Economic Management by looking at how efficiently players are managing their in-game credits.  We will calculate an 'Economic Efficiency' metric based on credits spent and remaining credits"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 686,
      "metadata": {
        "id": "3ce6RpMWST1l",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 746
        },
        "outputId": "db2c38ff-2607-419c-ae76-1433f84780f0"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Calculate Economic Efficiency and Skip the rows where loadoutValue is zero before the division\n",
        "player_round_df['economic_efficiency'] = np.where(player_round_df['loadoutValue'] == 0, np.nan, (player_round_df['spentCredits'] + player_round_df['remainingCredits']) / player_round_df['loadoutValue'])\n",
        "\n",
        "# Plot the top 10 players based on Economic Efficiency\n",
        "top_economic_players = player_round_df.groupby('platformUserIdentifier')['economic_efficiency'].mean().sort_values(ascending=False).head(10)\n",
        "plt.figure(figsize=(14, 7))\n",
        "sns.barplot(x=top_economic_players.index, y=top_economic_players.values, palette='coolwarm')\n",
        "plt.title('Top 10 Players Based on Economic Efficiency')\n",
        "plt.xlabel('Player ID')\n",
        "plt.ylabel('Economic Efficiency')\n",
        "plt.xticks(rotation=45)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BMaKXOujSXgO"
      },
      "source": [
        "The bar chart above showcases the top 10 players based on an 'Economic Efficiency' score. This score is calculated as the sum of credits spent and remaining credits, divided by the loadout value. A higher value indicates better economic management, which implies that the player is either spending wisely or accumulating more credits without compromising effectiveness.\n",
        "\n",
        "This kind of insight is valuable for a coach who wants to manage the team's in-game economy better. Knowing who the economic anchors are could help in strategizing credit distribution and planning for crucial rounds."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1yV3ou-NF9Yc"
      },
      "source": [
        "Analyze Side Preference by looking at performance metrics for players when they are attackers or defenders"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 687,
      "metadata": {
        "id": "cAFH3Ov1S0zj",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 764
        },
        "outputId": "f32d9191-1e83-4a53-ac29-f82a8c0187f2"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x700 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x700 with 1 Axes>"
            ],
            "image/png": 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xxsjFxUUnT57Uli1b7CH3s88+q6ZNm2rBggXatWuXJPEPpFTu6NGjypAhg7JmzaqtW7eqVq1a+v3333X9+nVJUv369TVhwgRJnN70pB48TczPz09NmzbV1KlTFRoa6szS8Ah58+ZVpkyZ1KBBA7Vq1UrTpk1TUFBQyno/GCCR7d271+zevdsYY0xUVJS9/bnnnjMvvfSSk6pCYqlcubJp165drPbt27eb0qVLm86dO5vo6GgnVJYyxbwHDh06ZDp27Ghq1qxpOnbsaLZs2WLvM3v2bJMnTx7z3nvvmcOHDzurVCSRmNfA3bt3jTH3XgsPLtu0aZNxcXEx8+bNS/4CEcuuXbtMnjx5zKpVq+xtv/32m6lcubIpWbKkuXTpkhOrQ3ysWLHC2Gw2Y7PZTJMmTUy3bt3Mjh07zOXLl40xxixbtszkzZvXNG7c2L7O/d9Z8N9FR0ebK1eumIwZMxqbzWa++uorh995TZs2NQUKFDA3btyw90fqtWDBAlO+fHmTM2dO06hRIxMSEmLu3LljjDHm888/N+7u7mbPnj1OrvLpEfM3ljHGTJkyxZQuXdr+/or5noGUITIy0hhjzKpVq4ynp6fJlCmT2bJli709peAIISSamP/ylCpVSmXKlJEkh9OHPvzwQ+3du1fr1693Vol4Aub/J9kvvviiDhw4oIULFzosL1++vBo1aqSdO3fq9u3bzigxxTH//7+kf/31lwICAnTz5k09//zzWr16tQYMGGCfqPa1117T559/riVLlmjChAmcPvYUuf/qK71795YkFStWzL48Zs6g5557TsHBwfrpp5908+bNlPWfIwsKDQ3V1atXlSdPHntbzZo11bt3b504cUINGzbk9LEU7uLFi/Lz89Nzzz2n27dv6/z586pVq5YCAgLUpUsXXbx4UcOHD9fGjRv1xhtvSBKnXSQSm82mzJkzq1WrVkqbNq1+/vlnffPNN+rfv78kafDgwcqcObOGDBmiu3fvcppsKnH/peXvP9L/5ZdfVp8+fVSpUiX169dPPj4+cnNzkyR16tRJWbNmtV/hD0/m66+/Vrly5dS/f38dOHBAHTt2VNGiRfXuu+9KEkfopzDu7u5aunSpRowYoXnz5qly5cp66aWXtG7dujhPpXTWGTT85kOimDNnjoYNGxbnpSZdXV0lSRUqVNCdO3f0+++/S+LQ0dQgIiIiVlubNm2UPn16TZw4Ub/++qvDh9fzzz+vS5cucejq/2ez2RQSEqJ27dqpVatW+v777zVy5Eht3bpVmzdv1i+//GLvGxMKLVu2TMOGDeOS9E+BmDBo//79qlChgsaNG6dFixbF6mez2eTl5aW6detq0aJF+vvvvzmFJRnF9TwXK1ZMRYoU0fLlyx1O86xTp45KlCihkJAQNWrUSNeuXUvmavE4Fy5ckCS1bt1avXv3lq+vrzJmzKhRo0Zp586d6tu3r/bs2aPx48era9euSpcunWbMmKFPP/3UyZWnbvd/F7hz544k6f3339frr7+uihUrqk6dOvr9999Vq1YthYaG6tlnn9W2bdv0559/xlofKU/M77ODBw/qzTffVMOGDdWzZ0+tWLFCktSsWTMNHz5c5cqVk/S/z9V///1XOXPmVOHChZ1We2r24O+nzJkzS5LmzZunL7/8UoMHD9bw4cPl6empwYMHS2IewpTkwIED6tKli9q0aaPg4GAtX75cfn5+atu2rTZs2GAPhb777junTjRNIIQnEh0drb///luvv/66Pv74Yw0fPtz+RUD63weZMUaFCxdWt27dNGnSJB0+fJgPrBTu8OHDatWqlTZu3Cjp3i+Yu3fvysfHRzNnzlRYWJg+++wzffXVV5LuhUcbNmxQ9uzZlT59emeWnqLs2LFDOXLk0Ntvvy3p3vOULVs2lS9fXjdv3pT0vy/Cr732mgYOHKjt27fLy8vLaTXjyd1/NbEKFSqoVatWCg4Otn95jmvC1XfffVflypXTJ598ojt37vAZmQyio6Ptz3N4eLhu3Lgh6d6X7nLlymnRokVavHixvX9kZKQKFCigIUOGKCoqSnPmzHFK3Yjb3Llz1aRJEy1ZskSS1LZtW73wwgu6ePGievbsKWOMWrdurd9//10bN27UZ599pnbt2qlu3brq2bOnc4tP5VxcXHTmzBldu3ZN7u7ukqR8+fIpa9asOn36tJo0aaKtW7cqW7Zsmjp1qowxWrdunb7++mv7+ki5XFxcdPjwYVWuXFlhYWEqUaKEfv31Vw0cOFAffPCBJKlEiRLy8PCQ9L9QYurUqbp7965KlizptNpTs5jnMeafD82bN9egQYNUvHhxlSpVSocPH1bt2rWVLVs2rV271h6wwvn++usvzZ49W40aNVLbtm3tZ0+sWrVKzz77rNq1a6cpU6aoZ8+eateunXOPOnbCaWp4CrVp08YMGDDAeHh4mF69epmbN286LI85L3/Pnj0mY8aM5rvvvnNGmYinEydOmPz585vMmTObxo0bmz/++MO+LOa88LNnz5qWLVuaEiVKmGzZspnq1aubLFmymF27djmr7BTp3Llz5vPPP7ffj5kroUGDBmbEiBFxrnP9+vVkqQ1Ja+fOnSZDhgymX79+xhhjJk2aZNzc3MzBgwcfus5bb71lKlSoYJ9bA8lj8ODBpnr16qZkyZJm8uTJxhhjrl27Zho2bGief/550759ezNlyhQTEBBgateubSIiIkypUqVM586dnVw5YoSEhJhixYqZDBkymJdfftksWLDAvmzGjBkmMDDQvPzyy+avv/566DZS2rwOqUHM77Rbt26Z559/3qRPn95MmjTJHD9+3BhjzIULF0yBAgXMBx98YF9n7ty5pn///vY5nq5cucI8QilcVFSU6d27t3nttdfsbZcvXzYDBw6M87Pw119/Ne+8847JnDmzw5w3SLjx48ebmjVrmmnTphlj7v0t1a5dO/PLL78YY4zp37+/KVasmLHZbOaLL75wZqn4/0JDQ01gYKDJmDGjadCggb399u3b9p9feeUVU6FCBfPss886/W8nAiE8kejoaBMVFWVq165t5s6daxYvXmzc3NzMoEGDzK1bt8zgwYNjTb45atQoe6iAlOf27dvmzTffNC+//LKZOnWqCQoKMvXr148zFAoNDTV//fWXGTNmjPnxxx/NsWPHnFV2ivCwCUljvuje/4U3KCjIHhQYY8y0adPsEwrzxTj1u3LlivHx8TE9evRwaKtYsaLp3LnzQ//wDA0Ntf8hhaRz/3t17NixJmfOnGbo0KHmnXfeMS4uLvb9FhoaaoYOHWpq1KhhypYta1544QVz69YtY8y9UHfkyJHGGN6zKcVbb71lMmTIYJo1a2aCg4PN4sWL7ctmzJhhatSoYZo1a2af2J39ljhivuddu3bNtG/f3vj5+ZnKlSubZcuWGWOM2bhxoylVqpTD/jDGmMWLFz8yoEPK0rJlS1O3bl2HtqtXr5oRI0YYf39/M2bMGGOMMeHh4earr74yVatWNfv27XNGqanag59LR44cMa+99pqpVq2aCQ4ONhcvXjSdO3c2L7zwgr3PunXr+PsqBbh/3+3YscPUq1fP5MmTx8yZM8feHhERYf/57Nmz5urVq8lZYpwIhPBEYr5UDxs2zEyaNMkYY8wvv/xi3NzcTObMmU2jRo1MWFiYMcbE+pBiJvyU66effjJff/21MebelVjiCoXYf45i3guHDx82Q4cONW+99Zb5+uuvzZEjRxyWxzxvr776qvnss8+MMcb+n9L7rz6F1CdmH0dFRZno6Gizc+fOWH26detmihQpYv9cvP/LA++p5Ld//34zfPhw+x+uxtw7esHV1dV0797doe/9R+7169fPZM+e3Rw9ejTZasXDxQSsJ0+eNE2bNjUDBw409evXN3Xr1jVLliyx95sxY4apVauWqVWrljl9+rSzyn2qXL161RQqVMi888479ra5c+eaFi1aGJvNZrp162aWL19uPv74YzNgwABz8eJFgrhUImY/xfxu+uyzz0xgYGCsf/5dvHjRtG3b1tSoUcN+hkBkZKS5du1a8hb8FHjwH4sxz2dYWJj5v//7P1OxYkVTokQJM3r0aOPh4WGGDBkSaxuEQskv5r1y9epVc/PmTRMeHm6Mufcdo1atWqZ+/frmp59+svdPaUejcsIuEmznzp06ceKEpP+d8505c2YtW7ZMktSoUSM988wzCg0NVYECBZQ2bVpJsl9xIEbMZNNIOWLmNQkODlbHjh0lSQ0bNlTnzp1ljNGQIUO0adMmSfeuKnf69Gmn1ZqSmP9/NbE///xTlSpV0rFjx3TmzBnNmzdP1apV04YNG2LNjxAVFSUPDw8NHz5cY8aM0fbt2x2uPoXUJWbOoJMnT2ratGkyxqhs2bIOyyXpo48+0rVr1zRq1ChJjpM/8pmYvLZs2aJSpUpp6NChDhdEaN68uWbPnq0vvvhCffr0sc/dkDFjRh04cEAvvfSSfvjhB61cuZKJUp3s8uXLkmSfsyZDhgxycXFRtmzZNHHiRLm4uGjSpElaunSpJKldu3Z6+eWX5efnp2eeecZpdT9NbDab3n77bS1atEg9evSQdO899PXXX+vbb7/V0qVLNXr0aP3yyy9auXKlDh06xPxoKdyDE3zHfH+pV6+e9uzZo7Fjx9rnQDTGKHv27Orfv7/WrVunzZs3S7r3nvT29k7ewlO5mO8RkjRhwgS1adNGderU0RdffKFLly6pZs2a2rJli4KDg7V+/XqlS5dOX375pfbu3euwnQf/3kLSMsbIZrNp2bJlevHFFxUYGKiKFSvq559/VsmSJTV27FhFREToq6++sl9MJuZ3Vorh1DgKqc7MmTONu7u7mTt3rjHmfyn0smXLTIsWLYwxxpQpU8Z+rqunp6fp1KkTaXUKd+3aNXPt2jVz9uxZh/b7E+yff/7ZfqTQunXrzLvvvmsyZ85sbt68yX/7jDE3btwwtWvXNr1797a3bd682Xh5eZkMGTLYj0CI+e/PK6+8Ytzd3Y2np6fZvn27U2pG4ojZp3v37jWenp4mZ86c9s+8+98b0dHRJiIiwrz11lumZs2a/Pc0BZg0aZJxdXU1AwYMiHWE1vz5843NZjMTJ050aP/55585rS8FmDNnjilTpozp27evCQkJsR91t3z5cpMtWzZz9OhRs2vXLlO/fn1Tv359s3TpUvu6Me/Lh53mi4S5du2a+fLLL02WLFlMz549HZbt2bPH9O/f35QpU8bYbDZTqVIl+1GUSHli3hNHjx41AwYMMB06dDDjxo0zFy5cMMbc+/xzc3MzvXr1MleuXLGv9/fff5tSpUqZbdu2OaXup0mfPn1M1qxZTZcuXcybb75psmbNalq0aGHWrVtn77Np0ybTs2dPU716dT7HUoBffvnFpE2b1owcOdJs27bNvP7668bNzc1s3brVGGPM7t27Td26dU2VKlXMr7/+6uRqYyMQQrxNnjzZuLu7m+LFixt/f38TGhpqX3bhwgVTqVIlkzFjRlO1alVz8eJFY4wxs2bNMtWrV+cXfwq2d+9eU6FCBVO8eHFTsGBB06NHD3Pu3Dn78vv/SPr5559NgwYNTKZMmUym/9fefQZEcX1tAH+WXsSCoCh2sIGoqNjFBoiKvXfsJcauKBbsXWNXbNHYYu9dgx27YkPsXawIgtKf9wPvTljR/E1UdpHz+xKZnSVnd5iZO+fee27mzMqFTiSdA0WLFlWK/KnVq1ePFSpUoJmZmTKXPi4ujq1atWKGDBmkfkIal7xgvpmZGdu1a8dChQpxwoQJX3zP8ePHaWBgwI0bN6ZWmOnePzWYZ8yYQT09Pc6ZMyfFa3/99ZeS3JNGt+54/PgxHR0daWJiQlNTUzZt2pStW7fm+fPn+eHDB/bq1UuZxn706FHWrVuXZcqU4fHjx5XfIe2Sf099DnwuoRYWFqYkhZJ3jJBJHSbBwcFs1qwZL1++nHoBi39FfTyvXLlCKysrtmrViq6urixbtiznzp2rvL5u3Trq6+uzdevW3LJlC+/cucOhQ4fS1tZWo/0o/r3z588zX758PHHihLLt8OHDdHFxobe3N1+/fq2x/6fT+kTqi4mJYaNGjThq1CiSSfenggULslu3bhr7BQYGsn79+nz06JE2wvxHkhASX2Xx4sXU19fn1q1befToUdrZ2SkZzvj4eL5+/ZoNGjRgmzZtGBoa+tnfIY0v3fPw4UNmy5aNgwcP5p9//snFixczY8aMrFGjBo8cOaLsl7zR5+HhwcyZM/Pq1avaCFlnhYaGskyZMhpF/e7evctcuXJxy5YtrF69Ojt27KjctB88eKDUFxJpU/JkkLm5OYcPH06SbNmyJd3c3D47MlJ9HfT09KSXlxdjY2Pl2viDJb9+7dq1i2vWrOGKFSs09pk2bdoXk0Kk1GTQRStWrGCDBg3YtGlTjhkzhiNHjqSVlRUHDBigdFypR7kGBARwwIABktT7Dh48eMBp06bxzZs3JD+fFMqePTvHjx+vrRDFN7h9+zbz5MnDESNGKNvq1aun3N/UbZiAgABWrFiRuXLlor29Pe3s7LS+UtLP4MKFC7S1tVVWZlO3Dw4cOEAjIyMGBASkeI+0IbQrPDycxYoV46lTpxgeHs6cOXNqJIP8/f2VZ+Pkq4zpEkkIif/pt99+o0qlUlaHiImJYYkSJTSq25Pks2fPZLnsNGbdunUsUaKEMtSeJB89ekRHR0dWr15dY+hvfHw8fXx8aGBgkO57+D7tJVXr1asXS5UqRW9vb86ePZsZMmTgL7/8QpIcO3YsS5UqpfF+kfbdvXuXKpVKaSyTSSvqqFQqjWWvPyWr8qWO5Ofo0KFDmTNnTpYrV45ZsmRhvXr1eOXKFWWfadOm0dDQkBMnTtRWuOJ/2Lt3r0aiYdmyZfT09GTDhg354sULnjt3jtOmTWOBAgWYIUMGPnjwIMXvkOvvf6NOBPj6+rJAgQIcP368sjpO8u/05cuX9PPzY8mSJRkcHKyNUMV/FBcXx6lTp7Jjx458//69cm3s1q0bGzZsyPr167NXr15KZ9bLly9569YtXrx4UZkZIL7e51agPXfuHC0sLLhnzx6SmgkEBweHL3ZaiNQXEhKi/Ltly5b09vZm7ty52bNnT6UzIiIignXq1OGcOXOYmJios8k7SQiJ/2ns2LHKcnnqXtItW7YwZ86cPHjwIEnJTqdVS5cupZ2dnbKKgXo55UePHjF//vwpkn47duxgUFBQaoepk549e0Y/P78UdWB8fX3p7u7OypUrc9KkScr2efPmsWzZsjKs9yfw6QPlsmXLlH/Hx8czNjaWjRs3ZpMmTTSm1pJyrdSW6dOnM2fOnDx//jxJ8o8//qBKpWKNGjUYFBSkHJdRo0axcuXKcpx0UFRUFAcMGMDcuXMrKzSSSSOFXF1d2ahRIyXJ+ubNG6XOkySAvo36+1OPCCLJIUOGsHTp0hw7duxnk0JXrlyhubk59+/fn6qxim9348YNjaXix44dSyMjI44YMYJ9+vShm5sbixcvLgmgb5T8fHn37h2joqKU+07Xrl2ZKVMmjePw7t07Fi1alKtXr071WEVKjx49YunSpblkyRKS5MKFC5k3b15WqFBB49gOGzaMhQoV4v3797UU6deRhJD4T+7evUs7Ozv6+fmRlAZXWnX9+nUaGRkptRbIvwtJX7t2jUZGRlyzZo22wtM5yf/O9+zZQ3d3d2XpYn9/f419Px0t5+3tzRYtWujcUpPi31H/Ddy/f58HDhz44n4LFy5kpkyZlB5yuUZqz4sXL9izZ0/lWrZ582ZmzpyZU6dOZZ48eVi9enVevHgxRW+tJIV0z/379+nr68vChQtz8uTJyvaVK1eyWrVqbNSokTKCNTExUc67b6T+/i5evEiVSsXAwEDltYEDB6ZICqk7DZ8/f84KFSrw1KlTqR6z+Hr/6/wICwtj2bJlNWoj7t69mzY2Njx58uSPDu+nlfx7nzZtGmvWrMny5cvT09OTz58/56NHj9i0aVMaGxtz4sSJnDFjBj09PVm8eHHpVNSST9sDz549Y/PmzdmxY0cmJiYyKiqKPXv2ZIkSJVivXj2OGDGCLVu2ZJYsWZTpf7pMEkLis44dO/Y/R4JMnTqVmTNn5s2bN1MpKvE9qG9E6v+OGTOGNjY23LBhA8mki15cXBxjYmJYrlw5jhs3Tmux6qJnz55xz549jImJYatWrdigQQOWKFGCNWvWVEZYkX/fPIKCgti/f39mypRJ6i6lcepz5urVq7SxsWH9+vUZHx+v0bhL3mgoV64cW7ZsKQ+lqezThlt0dDR37drF169f8+LFi7Szs+Ps2bNJksuXL6dKpWKJEiV4+/btL/4OoV3Jz6EHDx7Qx8cnRVLojz/+YI0aNdikSZN0P635e0heI83CwoJDhgzR2E6S/fr1Y+nSpTly5EiNTpDhw4ezaNGifP78eeoGLb6a+jiGhoYyICCAhw4d0qhrqE48qNs16p9Pnz5NBwcHmQ74Hfj6+jJbtmxctmwZDx8+zOzZs9PFxYVhYWF8/fo1x4wZwxIlSrBSpUoaHYqSFEp96jbB3bt3lQR4YGAgTUxMuHDhQpJJxfMXLVrEJk2asGbNmuzZsydv3LihrZD/FUkIiRSOHTtGlUpFNzc3ZXj951y9epXFixfnzJkzSUoPuK57+PChMsQ3+bEKCQlhly5daGdnx7Vr12q8p1q1asq0J3lASvrexowZw8aNG5NMGsJraGio1AYiNUcGhYeHc/ny5SxevHia6CEQX5b84cjU1JSlSpVixowZlSTCp+dHYmIix40bR0dHRz558iTV402vkh+HTZs2KYkB9ciFOXPm0M3NTVmp5ffff2ePHj3YtGlTaWTroAcPHjAqKopkyqTQ0KFDWbhwYc6aNUvZvmbNGjo5OXHYsGGpHuvPRP1dX7p0iaampim+z+SJg6FDh7JMmTKsUaMGR44cyQ4dOjBHjhySlNNhyVcTK1y4MEuUKEFra2tWqlRJY0GR5Puq+fj4sHLlyhpLzot/7/79+yxVqpQy0njXrl3MlCkT58+fr7HfmzdvGBcXp9zbZIED7QkMDKRKpWLVqlWVFYIXLFjAPHnyaKwKp5aWnpskISRS2LNnD62srNikSRPWq1ePFy5c+OK+HTp0YM6cOVMxOvFf3Lx5kyqVira2tkql++QPP1euXGGvXr1obGzMPn36cNasWezTpw8tLCxkJaxPBAcH08HBgRMmTKC7uzsLFizI5s2b89WrV+zbty+XLFmi0YCKjIzUqL0g0p7kD0dmZmZKAemyZcuyc+fOjImJ+ez73r17R5VKxbFjx6ZarOlZ8vMuMDCQZcqUYYMGDZSkXUJCAn/99Vc6OTnxxYsXjIiIoJeXl8Z0T0kK6Y5Tp06xQIECXLZsmVLn7tOkUL9+/VimTBkeOnRI2X7gwAE5jt/BrVu3qKenp1GviSQnT57MypUra6wou3r1anp7e7NKlSrs0aNHmukVT89u377NHDlycMiQIQwNDeWhQ4fYpEkTDhgwgOTnp8j4+PgwS5YsUkvyO7hw4QJtbGxIJk3Dy5AhAxctWkQyqRDxvHnzUiTj0lKC4Wf04sULFipUiFmyZKGdnR1nzpzJ1atXs1evXhwyZAjfvn2bZo+RJIRECg8fPqSnpydXrFjB6tWrs06dOkpxxk+XGT116hQbNGggo4N02MuXL+nu7k4vLy+WL1+eBQoUUIZxJ+9peP36Nf/880+WLFmSFSpUYM2aNaWH7xPq1R4OHDig9BI8f/6cjRs3prOzM/PkySPDqH9SN27coJmZmUZP+YABA+jg4PDZ5ZfVD6RTpkyRv4lUkLwRNnnyZHbs2JEFCxakkZERGzdurDyghoSEMGPGjMybNy/t7Ozo5OQkdb10mJeXF4sXL84//vjjsyOFbt++TUdHR2UKYHKSFPrv4uLiOHHiRKpUKm7cuFHZPnHiRGbKlEkZ1fDpd5x8JIPQXR8/fmSPHj3Ypk0bjfNp4sSJzJs3r3KuqZ04cYLdu3dn0aJFZbTzf/C5c+L9+/d0d3fnoEGDmCFDBi5evFh57erVq/Tw8PjsqBOR+pKv9DZ37lyOHj2a06ZNY8+ePVm7dm3a29uzUqVKvHjxohaj/DaSEBIa4uPjef/+fZYsWZJv377l9u3bWbt2bdatW5eWlpYcMWKExs0jOjpaudBJ40s3nThxgh06dODBgwd5584dVqxYkXZ2dp9NCpFJI1rUBdJESrGxsaxcuTJtbW2VnjQvLy/q6+sry4xLg/jnkPw4rlmzhjNmzNDY/vLlS1pbW3PEiBFf/B0yvDt1TZs2jRYWFty/fz9v3LjBSZMm0cXFhY0bN+a1a9dIkvfu3eOkSZM4f/585fjIcdIdjx8/5qNHj5SfmzdvTgcHB65cufKzI4W8vLzYu3fvVI/zZ3fv3j0OGTKEFhYW3L17NxcuXEhLS0vu27dP26GJbxQZGcmxY8dy+fLlJP8+n86dO8fChQunWD01Li6Oe/fu5ePHj1M91rQu+bUqNjZWGVEcHh7ONm3a0NjYmL/++quyz4cPH1inTh3WrVtXOtt1wKVLl2hpacn169fz1atXfPToEStVqsQ///yTcXFx3LlzJ4sWLUqVSkUPDw9th/ufSUJIkEz5ANu4cWOePn2aJLl+/XpmypSJVlZWGkuIykNv2pF8NYhbt26xYsWKLFCgAJ89e0Yy6YaVmJiokQWX4/t5gwcPZqNGjbht2zZlJFWBAgXYsWNHtmzZUmMYvUi71A0x9TnyudcTEhI4YMCAFNMnROpTX788PT05ePBgjdeWLVtGOzs7NmnSRBkplPz6Jp0ZumPDhg2sXbs2p06dqlGQWJ0U+uOPPzTqtL19+5ZVq1blvHnztBHuT0N9PiRvA5BJybkBAwbQ3NycKpVKGR2S/JwZMmRIimllQrd8buGDkJCQFNuCg4NZqFAhvn79WtkmC8d8H+PHj6eXlxdr1KjBw4cPk0w6v8qVK8fy5cuze/funDRpEqtWraoxclWSQqlP/bf/9OlTvn79mr169aKjoyObN2/OwMBAHjt2jJkyZVJGBL169Yq+vr5pejS4HkS69vLlSwCASqUCkxKEAICoqCgEBQUBACZOnAhbW1sUK1YMCxYswOnTp5X3CN0WFxcHAKhYsaKyrWDBgli5ciVsbGxQuXJlhIaGQk9PD5MnT8aePXuU/eT4fl6nTp2wZcsWNGjQALGxsTh37hw2b96MUaNG4c2bNzh//ry2QxTfKDExEXp6erh58yZsbW3h4+OTYh89PT3o6emhQYMGCAwMRGBgoBYiTd/U1zcg6XplbGyMDBkyIDQ0VLmXAUnnrJubG/bs2YPRo0fj1q1bGtc3fX39VI1bfN6yZcvQrVs3VK5cGW5ubrCxsUFCQgIAYP369XBwcMC0adMwf/583Lp1CyEhIejYsSOio6PRvXt3LUeftqlUKrx69QoDBgxAcHCwsj1Xrlzo06cP+vXrB3Nzc4SEhAD4+5wZM2YMpk2bBldXV63ELb6Onp4e7t69i3PnzinXvkKFCgFIut+pt7179w7v3r2DSqWCSqXCyJEj4ebmhoiICI1rqvh3Zs+ejXnz5qFo0aIwNzeHh4cHFi9ejFy5cmH9+vWoWbMmrly5gtOnT6NEiRK4ePEiDA0NER8fDz09eVRPDTExMQAAklCpVDhx4gQ8PDygUqkwf/58TJkyBZaWlnB3d8ehQ4dQq1Yt+Pv74/Xr17CyssL48eNRpEgRLX+Kb6DVdJTQqosXL7JLly5KfaDkPT5z587lyJEjWbp0aVatWpVRUVHctm0bnZ2dU/S+Ct1y//59dujQQfn5S70L6uljhQoVore3N1UqVZrObn9v6h6CsLAwZXW25Dp06EBLS0t6enoqvaPjx4/ngwcPUjVO8X0lLyCdIUMGZsqUifXr12dkZOQX39OmTRtWqVJFioenosDAQM6cOTPF1FZfX1/myZMnxVz+3377jR4eHnRzc+PIkSOl11XHHDhwgFZWVtywYUOK15IXbe/VqxddXFyoUqlYqVIl1qhRQ5Zi/gbJz4Nr166xVq1aPH78OEly4MCByvn16NEjDhw4kBYWFly1ahVJcuTIkTQxMfnHhUeEdiU/vpMmTWKJEiWUZeQDAwNTXAcvXrxIW1tbxsTEcPTo0TQ1NeW5c+dSNeafwaff6+zZs7l9+3bl5zFjxlBfX18pIk2mHJUv05hTz/Llyzls2DBlOXky6Z7k7u6usV94eDh37tzJvHnz0srKikZGRsr1Mq2ThFA6FRcXx6tXr9LJyYkBAQF88uQJK1asqNz8t2zZQpVKxWrVqmlMhQgICJCGtI6LiIhgpkyZlBvN4MGDv7hSmLpQbtasWdN0MbQfZcuWLXRxcaG9vT27deumrFb09OlT9u3bl0FBQVy9ejU9PT354sULLUcrvlXypeXNzMw4efJkXrp0iXp6ehqFVT+1evVqWlhY8OzZs6kVarr322+/sWrVqiSTpjkkLypcuXJlFipUiMePH+fLly8ZHR3Nhg0b8vfff6ePjw9tbW1T1MgQ2qF+CPL19WXbtm01Xrt48SJ/++039uzZU2MluHv37vHAgQO8evWqcs7Kw9N/9+zZMy5YsIAJCQmcMmUK7e3tWaJECbq6umokXB8+fMiBAwcya9asrFmzJs3MzHj+/HktRi6+xpMnTxgZGcmHDx/S29ubS5YsYeHChTlo0CDl/FGfh3fu3GGZMmXYqVMnGhkZyfH9D5Indnbu3MlVq1bR3d2d27Zt09hv7NixNDAw4NKlS1M7RPGJrl27skSJEpwwYQLfvn1Lkvz999+VNob6mKr/++zZMw4bNozFihXjnTt3tBLz9yYJoXTo+PHjnDlzJkly3bp11NPTY7Zs2di/f3+N/Xbv3q0kgz7NXEtSSPfcvHmT8+fPJ0nu2LGDXl5ezJkzJ+vXr6/0sCY/jgkJCezRoweNjY2VYqvib2fOnKG1tTV9fX05a9YsWltb093dXekNVT+AvHv3jrVr1+aTJ0+0Ga74Tq5du0aVSkVfX19lW6tWrejp6ak0FD4VHx9Pd3d3JWEofpzTp0/zzJkzJMnq1auzUqVKtLa2Zv/+/ZURIjExMaxevTrz5s3LAgUK0MHBgXZ2diSTev0KFy7MV69eae0ziJQ6d+7M5s2bK/eqkSNHsmbNmsyZMyddXV2pUqk4evToz75X2iPfZsmSJaxataryPWbNmpWFChVSRl4lLyT85MkT9u7dmzly5JCRQWlATEwMO3fuzC5dupAkp06dysyZM7Nhw4af3f/06dNUqVQ0MzOT1cT+g+Rt7KFDh9LQ0JDOzs5UqVTs06dPijbE+PHjqVKpuGPHjtQOVVDzeA0cOJClS5fmuHHjGB0dzWXLlrF8+fIaI0+T7x8VFcX379+narw/kiSE0qF58+YxV65cJJNWGrCwsGCVKlWU1x8+fKit0MQ3OHDgAI2MjHjz5k2+efOGdnZ2dHZ2Vl7/dMrL5cuX6e7uLj1An3Hz5k0uX76cY8eOVbY9evSIdnZ2rFmzZooh1PJA8nOIi4vj2LFjOWnSJI3t/v7+zJQpkzKl8nMFOmWEwo+nHunTqlUrkuSpU6eop6fHZs2aKfskv85t2rSJ8+fP58KFC5VGXbdu3Vi5cuWfqiH3M5g7dy4zZMjA5s2bs1ixYsyfPz+nTp2qtEf8/PyYL18+Pn/+XBY8+M5iY2NZp04d1qlTh1WqVGHx4sXZrFkzBgUFsUOHDhw+fLhGsemHDx9+dhq10D2JiYlctWoVvb29eeXKFVasWJFmZmZs3bo1SXLKlCk8ePCgsv+jR4/Yrl07KR/wjc6ePUs3NzeePHmSL1684IwZM6hSqTh16tQUo1N///13aT+ksi+12fv3709nZ2dOnz6dU6ZMYdeuXXn79m3euXOH9+7d48OHD3nu3Lmf8vqnIqVKWHrUs2dPHD58GBEREShbtixMTU0xZMgQtG7dGm3btsWIESOkqHAaQhLx8fGYPHkynj17hmPHjkGlUuHFixc4deoUZs2ahYiICKxcuVKjQF14eDgyZcqkxch1S3x8PD58+IBs2bIhNjYWvXv3xpw5c5TXHz58iBo1aqBQoUIYNWoUKlSooMVoxY/w/v17WFhYAPi7uDQAlC1bFnny5MHGjRvl2qhFd+/eRbVq1dCiRQts3LgR2bJlQ40aNTB06FA0atQIvr6+qFGjBgwMDDTed/XqVSxcuBDr169HQEAAihcvrqVPIADgypUrsLCwQP78+ZVtkydPxr1795CYmIgRI0bAxsYGJiYmAICpU6diz5492L9/P4yNjbUV9k8nJiYGxsbGePv2LbJnzw5nZ2ecOHEC/fv3x4EDBwAAO3fuTNvFUgUaNWqEgIAAeHh4wNfXF1OnTkVISAjevXuHw4cPI1++fMq+UVFRMDc3116wadyCBQtw/PhxqFQqrFmzRmkvzJw5E4MGDcKUKVPQvXt3ZMyYUeN98fHxKe5b4sd5/vw5bty4AVdXVxgaGirb+/Tpg7NnzyI0NBSPHj2Cs7Mz7t69CyMjI5ibmysFp3PmzKnF6L8/KV2ezqirqHfo0AFPnjxBlSpVsGPHDhgaGqJKlSooVKgQfHx85IEnjUlISIChoSHKly+PHTt2IGfOnLh27RoGDhyIEiVKYP/+/fDz81MebtV5YEkGaYqIiEDGjBkRGBgIa2trnD17Fnfu3AGQ9J3lzZsXAQEBOH36NKZNm4bo6GgtRyy+h7CwMOXf6mQQkLQyi/pcadasGW7duoVbt24BSEoWidQVHR0NOzs7DB06FDNnzkS9evWwb98+bNmyBcWLF4eRkRHKly+folH94cMH3Lp1C9euXZNkkA54+fIlRowYgSNHjgBISvIBwNChQ7F48WIsXboU+fLlU5JB0dHROHbsGAoXLizJoO/M2NgYsbGxqFu3LmxtbeHg4AAjIyO8f/8ed+/eRdu2bSUZ9BN48eIFYmJiUKNGDZQsWRKPHz/G48ePceTIEeTLl09jlWFJBv07n7YF3r9/jy1btuD8+fNK+xEABgwYgBkzZsDX1xfTp09HVFSUxvskGfTjJR8DM3v2bIwdO1ZJBu3btw8AMGfOHLi6uiJjxoxo37491q9fj+DgYJw6dQrnzp3D8ePHf7pkECAJoXTH2NgYwcHBaNiwIYoUKaI8/Dx8+BCmpqYoVaoUjIyMtByl+LfUNxIfHx+QxKNHjwAA9+/fBwAMHDgQ9vb2yhK+kvBL6ebNm8iTJw8uXLgAZ2dn7N+/H9euXcOwYcPw4MEDqFQqkESePHlw5coVTJ06VXlgEWnX+/fv0b59eyxfvlzZ9uly5gDQrl07PHv2DCtXrgQAWQpWC0xMTHD9+nWMHz8eBQoUQJkyZZA1a1YYGhoiJiYGI0aMSNHrCgBmZmZo0qQJdu/eLckgLXv06BGyZcuGcuXKYc6cOQgMDEShQoXw559/KvuoH7Cio6MREhKCxo0b4/Hjx5g/fz4AyPLX39mCBQtgb2+PnTt3IiQkBM7Ozjhz5gzGjx+P8+fP49KlS9oOUXyD5s2bQ19fH7Nnz0ZQUBC8vLxw584dvH//HocOHQIAZZl58e8cPHgQe/fu1djm4+OD+fPn4927d1i6dKnSHgeA/v37Y+TIkfjrr79gZmaW2uGmeyqVCi9fvkRERAT8/Pzw6tUr9O/fH5UqVcKKFSuUTt6pU6eiRo0auHbtGrZs2QIjIyPY29vDysoKtra2Wv4UP4h2ZqqJ1PK5eZKTJk1SamTkz5+fFhYW9PT05JIlS+jt7c2QkJDUDlP8B5/WUVi+fDlr1KjB+Ph4tmvXjnny5KGdnR27dOnCypUraylK3ac+R169esX69euze/fuSsHZixcv0tzcnE2bNlWWk5f6FWmf+hiq62IMHjyY48aN44sXL+jl5cWTJ09q7K/+G5k2bRrt7Ox48+bN1A04nfr0/hUZGUkvLy+OHj2aW7ZsYbZs2VioUCFWqVKFvXr14siRI1PUShO649q1a3R3d1fOnypVqlBfX5+DBw9OsW9kZCSbNGnCmjVr0s3NTZaW/4HevHmj/Ltu3bo0NTXltm3bGBMTwzp16nDhwoVSJy8NSkxM5IcPH3j48GGSSQXBCxcuzLx585Ikt2/fzpIlS0px8G/g6+tLS0tLkkmrz06fPl15bc6cObS1taWvry8fPXqk8b5PV60SP15iYiJjY2NZs2ZN9u7dm2TSytkGBgZ0dXX97HsGDRrEAgUKcNq0aT/9NVASQj+x5BeaFStW8MqVKym2lylThra2tty7dy/Dw8NZpEgRLl68ONVjFf/N8uXLWa1aNZKax7V+/fq0sLDgqVOnSJJFihRhz549tRKjrlJ/X0+fPlW2/f7773RxceFff/2lbLt06RIzZ85MDw+PFDd1kXa9evWK3t7evHXrFm/cuEEHBwfmyZOHtWrV4sePH5X9kp9Xu3btYoECBZTVF8WP8WkjOSAgQFl96v79+8p2W1tbFi5cmKGhody/fz+LFCkixVB1XLVq1dihQweGhoYyV65cLFq0KE+dOsX4+Hj6+/szPDxc2XfdunVcsmSJkgSSwqvfRn1ePXnyhFevXuXjx481vu9u3boxc+bM7NixI3/99VeSSUmDe/fuaSVe8e+oj2/y+1dy7du3p6WlJc3NzXnz5k2GhYVx4MCBfP78eWqG+VM4dOiQUhy6devWrFWrFrNly8aJEydqJK1nz57NXLlyccSIESnOI0kGaceRI0eYPXt2btu2jVWrVmXWrFk5atQokkkrjQUFBWkcG19f33RxDZSE0E8o+apR8fHxvHTpErNly5biou/h4UFnZ2f27duXI0aMIElev35deuB0nPpC9eLFC5YtW5ZTpkzReL1jx47MmzcvnZ2d2a1bN5JkUFCQMsJF/O348ePKcsbqZE+LFi00VmcjyXPnztHW1laWlv+JXL58mfXr1+euXbtIJiVNLSwseOPGDZLksWPHlFEJySXvTRffn/r6pr4P/fHHH3RyctJooMXFxbFUqVIsWLAgmzVrxm3btpFMWlJZ7l+6KflxyZcvH42NjTlgwAAOHjyYHTt2ZOHChenm5saPHz9+9kFJjuu3UX+nW7ZsoYODAwsUKMAyZcqwWbNmvHXrFqOjozlnzhzevn2bR48eZdmyZXn69GktRy2+lvr47t27ly1btmSLFi24bds2JeF35swZFi5cmLdv3+bw4cPZtm1bfvz48YvJI/HPXFxcWLNmTZLk+vXraWpqyq5du5JMea2aO3cu9fX16e/vn+pxCk3qv/fp06fTyMiIXl5ePHLkCHPkyMGaNWvS0tJSmSWT3u45khD6ycyePZtWVlbKQw5JBgcH097eXuNB5s2bN1yzZg3JpN5XExMTZTQJmf5OhLTm1KlT7NSpE1u2bMmIiAiNBrSPjw9DQ0MZGhrKTJkyce3atVqMVLdt2bKFKpWKOXLkYO/evTlr1iw+e/aMZcuW5bBhwzT2Tb7srvg5LFq0iHZ2dixatCjd3NzYo0cPzp49m02aNGGdOnU0loeVId4/3sCBA1mnTh2N+8+ff/5JLy8vkn+PEDlz5gxHjx7NsLAwDh8+nBUrVtT4PXL/0j3q8yY4OJjZs2dXku6PHz+mkZERXVxctBleuhAQEEBzc3POmTOHcXFxnDlzJlUqlTIqPHkCfODAgbx79662QhX/wZEjR2hgYMCePXvS2dmZpUuXpo+Pj9L2DwsLI5nU7mnfvr3ys/j3Hj58SE9PTw4bNow5cuRg7ty5lWlIxYoV49GjRzX237hxo9yXdEi5cuWYIUMGbty4kSRZqVIlWllZacwOSG9k2fmfTGBgIPz9/XHhwgVMmjQJXl5euH37Npo2bYqTJ0/C3NwcJKGnp6expPKaNWvQokULqXKfBkRHR2PcuHFYtmwZsmbNiuvXrwMAYmNjNQqCk8SyZcvg4eGBPHnyaCtcnUISKpUK0dHRSkHocePG4dChQ6hbty4uXryIK1euoHz58rh//z6mT5+O0qVLa7xXpH1xcXHKyhK5cuWCvr4+bt26hcOHD6N3794wMTHB3r17kTdvXi1Hmn5ER0dj/vz5WLduHRwcHLB8+XIYGBhg9uzZOHLkCLZu3apxDqqvdx8+fECXLl2wevVqKfSt4x49egRXV1dUrVoVz549Q7169bB161Y8evQIdnZ2mDRpknK9Fd+Puki3j48PoqKisGDBAoSGhqJcuXLw8vJSinXLcuNp1+PHj+Hv74/s2bPj119/BQCMGjUKBw4cQJUqVeDr64ssWbIo+7958wZZs2bVVrhpmrr9MGbMGEyaNAm+vr4YPHgw8ufPD5JwcHDAjh07YGFhofGcBSStCKyvr6/F6NOf5MdAvaKiqakpypYti9WrV6N06dLYv38/KlasCC8vL3Tt2hVA+lt8R1pPP5kKFSrg119/RalSpeDj44Pdu3cjNjYW8fHxSExMhEqlUk6M5BepNm3awMDAAPHx8doKXXwlExMTdO7cGd26dcPt27fh5+cHADAyMlJWEQOSLmbe3t6SDEpGpVIhICAAbdu2xYYNGwAkrcBRsGBB5MuXD8uXL4eHhwf++usvHD16FNu2bVNWtElvN4efyYkTJzSWeDU0NERcXBxcXFxgZWUFFxcXXL16FVu3bsWDBw+QNWtWJRkkfSY/3sWLF2FiYoJevXqhW7duuH79Ory9vZGYmKjcvwDNc1Cd/DYzM8PatWuVTg6hG5IfC/W/jYyMMGbMGKxcuRLdu3dHv379oKenhyNHjqBAgQIICAiQ8+07Sf496unpQU9PD+/fv0f+/Pnx/PlzlClTBp6enpg3bx4AYNeuXdi+fbu0AdOga9euoU2bNtiwYQOyZ8+ubB8zZgxq1aqFY8eOYcqUKXjz5o3ymiSD/jtDQ0MEBwdj6dKliI+PR758+WBqaors2bMjU6ZMmDRpkrKC86edFJIM+vHU95uPHz8iISEBenp6OH36NEjCyMgIo0aNwo4dOzBixAjExMTg+PHjOH78OBo3box58+YhMjIyXbb3JSH0E1E3AEqXLo1ff/0VpUuXxrBhw7B06VLo6+ujb9++GDJkCHx8fDBw4EB07doV+/bt0/gdMkJI96iPa2hoKB49eoS3b9+iQIECGDBgAAYPHox169Zh8uTJAJJuNskb4nI8U8qZMyfevHmDhQsXok2bNsiZMyesrKywYcMGmJmZYdasWfD390evXr3QunXrdHlj+FmQxL59++Dl5aUsJ6p26tQp1KxZEydPnkS+fPnQtGlTnDlzBsePH4eZmRnmzJkDQBKBP9q4cePQr18/JCQkwNTUFG3atEGXLl1w48YN9OjRA0DSw8uaNWuwa9cuBAQEYN++fVi6dCnCwsI0fpeMENINyXtkN2zYgL179+Ldu3ewsbFBhw4dEBcXhxkzZiBnzpwoUKAAbG1tUbZsWQQGBsr59p0FBQXhzJkzAAALCwusXbsWFSpUQL169eDv76+MmN20aRNu3LghCbk0yN7eHnZ2dnjx4gUCAgIQGxsLIOneNWbMGNStWxdbt27F7NmzJWn+HyQ/JxISEhAfH482bdrA29sb+/fvh6+vL+zs7GBgYICiRYviypUryr4i9enp6eHBgwdwd3fH48ePsX79elSsWBFHjhwBAFSpUgUA0KlTJ7x48QLFihVDXFwcvL29kTdvXrx+/VqL0WtRas9RE9/fl5bCCwwMpLe3N21sbJg1a1YOGTKE9erVY7169disWTO2atVKVu3Qceq6C9u2baOTkxMdHByYO3dujhw5ko8fP+aLFy84fPhwFi5cOEVxafFlr1+/5ubNm1mqVCkWK1aMixcvprm5OUeOHKns87mCwiJtUhfUf/jwId+/f5/idW9vb6pUKg4cOJAkOWbMGPbu3ZsfPnxI1TjTowcPHij3IfUKYhEREVywYAFdXFyop6dHGxsburq6Mm/evCxQoADt7e3p5uYmNRl03ODBg5kzZ04uW7ZMY1GLsWPHskGDBtyyZQsbNmzIwMBAklKf63tITEzUKCCtUqnYsmVLkuSHDx9YpkwZWltb882bN0xISGBcXBx9fX2ZK1cupZiq0G2fO0+io6PZvXt3lixZkjNnzkxRLHrixIkaKzSKr/Ola9KrV69IkpGRkcyXLx/LlCnDqKgobtq0iSqVinfu3EnNMMUnoqOjmS9fPubPn596enr8/fffSf59PI8cOcI6derw/Pnz7NChAxs3bqzFaHWDJITSuOTJoHPnzvHEiRMaq4ydOnWKXbt2paOjo9Lo+pQ0qnXbwYMHaW5uzlmzZvH9+/ccMWIEDQwMuGnTJpLko0ePOHLkSFpbW3PmzJlajla3qC/+58+f55IlS7h06VJlFSn1a7/88gvr1q3LvHnz0srKiidPntRavOL7Ul8f4+Pjefv2bapUKs6YMYMRERHKPiNGjGCWLFk4d+5cenh4MCgoiKGhoXz69Km2wk6Xtm3bRpVKxcOHD5NMSgrNnz+f1apVY+3atZX91KvmqM/fL3WICO3y9/enjY0Nz5w5k6KNcefOHWVb06ZNlYRF8mSG+PeSf3/r16+ngYEBGzZsyBo1aijJ7cDAQObOnZuFCxdmlSpV6OXlRWtra168eFGboYuvpD6+586d46JFi7hixQoeP36cZNIKSl26dKGLiwtnzJghK4h9o+T3llWrVrFt27YarycmJrJMmTLMnz8/K1euzKtXr5JMWlVMOtu1R92Zu23bNhoZGdHW1pbBwcEp2grqYuv37t2jvb09Q0ND03V7QhJCaVjyhpOvry8dHByYPXt2VqhQgX369FFeO3nyJNu1a0cnJyeuW7fus+8XuichIYGJiYns1q0bf/nlF5LkkydPaG9vz+7du2vs+/jxY44bN056JZJR/31v3ryZOXPmZOnSpenq6korKyueOHFCY98DBw6wR48etLS0VJafFz8fHx8fmpiYcN68eYyMjCRJnj17lvfv32dYWBgbNWrEqVOnajnK9OHThldwcDBbtWrFbNmyKSt9qJNCJUqUYIcOHRgTE/OPv0NoR/JOKLWOHTuyW7duGtu+1Pkkx/HbfPrgv3btWurp6XH16tU8evQos2bNyrdv3yqvR0REcOzYsRw4cCBnz54t7YY0ZtOmTcycOTPLlCnDIkWK0MDAgBMnTiSZ9LfQqVMnVqxYkePHj5ek0H+U/Jp09OhRduzYkUZGRhwyZIiy/a+//mKPHj1Ikj179mTevHk1fockhbRnx44drFatGtetW8cSJUqwWLFiPHv27BfvNXKeSELopzB+/Hhmy5aNR48e5evXr9mvXz+qVCp6e3sr+5w+fZr16tVjmzZttBip+Brq7La68dy4cWNu2LCBUVFRzJkzJ7t166bRC6he3lJuPikdPXqUVlZWyrK6586do0qloqmpKffu3auxb0JCgizD+pOIj4//YsJ7+PDh1NfX59y5c1NMCdu7dy/v3buXGiGma8kbZYcOHVKmMty+fZvt2rWjpaWlRlJo0aJFzJ07N8eMGaONcMU/8Pb2TpH4iYmJYcWKFdm3b1+SmomguLg4njx5UknIqklS6L/p27cvp06dqlzvQkJCWKBAAS5ZsoQkee3aNebPn58vX74kKSPC07rg4GBmy5aNixYtYmxsLENDQzl//nwaGhpy8uTJJJMeblu0aMGaNWsqoyDEfzNw4ECWL1+eHTp0oKOjI7Nnz85evXopr6uvY2FhYezfv79cx3RAUFAQbWxsuHLlSpJJ96NixYrR0dGR58+fV47Rpk2bZGBEMpIQSmN27Nihkcm8du0aq1WrxgMHDpAk9+3bxwwZMrBjx460srJily5dNPaVi5XuSl5j4cCBA9y1axdJslevXnRycmKePHnYu3dvJWEUExPD5s2bc/To0ek+GfS5i/qHDx84atQopS7QkydPmCdPHnbs2JHt27ensbGxkkyTm8LP4dmzZxo/Hz9+nJMmTeL06dOVaySZNKJST09PY6SQSB2fjmzNnz8///zzT0ZFRZEkb968qSSF1NPHwsPDuXXrVnmY1UHPnj1TRm4lr1EyZMgQWltb8+7duxr73717l127dpUpSt/JggULNL7LiIgIXr9+Xfn55cuXtLa25oULF5Rtf/75J8+cOUNS7n1pTUBAAB0cHPjixQuN7XPmzKGpqSnPnj1LMqmGyqf3Q/Hv7Ny5k5aWljx16hTJv0fWOTg4KKP2yZRJVrlPaU9wcDBHjRqldEZER0eTTOpod3JyYvHixblmzRoOHTqU+vr60gGYjCSE0pB58+axVKlSGjfwxMREzp07l69fv+bRo0eZM2dO+vv7Mz4+nq1bt6ZKpWL9+vU1fo8khXTP27dv6erqys6dO3PDhg1UqVTctm0bSfL69eusWLEi8+TJoySDEhIS6Ovryzx58vD27dvaDF3r1H/PUVFRfPXqFQMCAvjkyRPGxcXx3r17PHHiBMPDw1muXDmlJ/vEiRNUqVRUqVQ8ePCgNsMX38mCBQtYq1Ytnj59miS5e/du6unp0c3Njbly5WKxYsU0agAMHz6cJiYmnDZtmpKMEKln9OjRzJ49O48ePZqi0PejR4/Ypk0bWltbc8+ePRqvSWNbdyQvvL906VI6Oztz//79JJNGqlStWpVlypThzZs3GRMTwxcvXrBu3bqsVKmStEO+0aeJtt27d3PMmDEa51JcXBxfvHih8VA7fPhwqlQqeRBKo44ePUqVSqUkAdXn0b1795g3b16l3Si+3aJFi2hnZ6fRPnj16hX79evHDBkycPDgwcp2uZ5p3+vXr1m+fHlmzpyZrVq1UrYnTwq5urqyVKlStLe3l06JT0hCKI1RN4YvXbqUorhm//792bVrV+WPf9SoUaxTpw5btmwpFysdFxERQX9/f+bNm5fGxsbKUMfExETGxsZy5cqVLFasGAsWLMiWLVuyXr16tLKySvcXNPXfdUhICNu3b88iRYrQxMSEGTNmZOvWrRkUFEQyqZBmqVKlGBwcTDJptFzz5s05ePBgpci0SNuOHz/OvHnzslmzZgwICGDDhg05f/58kknFAxcvXsyiRYuyffv2ynsGDRqUor6G+PFevHjBcuXKKde558+f88yZMxw0aBCXL1/Ojx8/8unTp/Ty8qKnpydJGcmgy169esWnT5/SycmJHh4ePHLkCMm/V3IxMTGhg4MDHR0dWbp0aY2ODfHvLVu2jKVKlVKSbyQ5a9YsqlQqTpo0ie/evSOZ9P3GxsaySJEiDAgI4Lhx42hubs5z585pK3TxL6iveUFBQTxz5gxjYmIYHh5Od3d3tmnThjdv3lT2ff/+PYsXL87169drK9yfhvq6tG/fPhYuXDjFQiPXrl2jlZUV7e3tOWDAAG2EKJJJ3jbYv38/y5Urx/z582tcH9WjWBMSEnj79m2+fv061ePUdZIQSiPUF6iEhAQeOnSIKpWK/v7+GpnrWrVqKY3nmJgYNmnShP7+/il+h9AtyVeNyJAhA3PkyKExR5lMOp5Xr15l//796e3tzbFjx/LWrVvaCFdnqP+eg4KCmCNHDvbo0YMrVqxgcHAwfXx8aGdnxyJFivD06dNK7aArV66QTFpZqk6dOjIy5Ceh/ls4c+YM7ezs2KpVK7q6uioJQTIp6bpw4UKWKFFCo6i4evlYkXqePHnCIkWKcOHChdy+fTvbtWvH8uXLs2jRonRyclIKez948EDuWzpo8+bN/OOPP0gmdUS1a9eOZNLxcnZ2ZvXq1Xns2DGSSb2zf/75JxcuXMi1a9cqnVrpfZrzt7h37x6LFy9Od3d3jamwc+fOpUql4vjx45WkEElWqFCB2bNnp7Gx8WcLgAvdo24XbtmyhTY2NpwxYwYfP35MMmk0XsWKFdm8eXOePHmSd+7c4dChQ2ljY8OHDx9qM+w06dN7jPrnO3fusGjRomzbtq3GSPwbN26wadOmHDlyJMuUKZPuO2a1RX2OREZGaoxWPXLkCMuVK8fGjRsrnROk5ohWkZIkhNKoQYMG0dTUlIsXL1aGCK9atYp58uRh9erVWa5cORYrVkxpdEnvqm5SH5e3b9/y0aNHPH36NBcvXszixYtr1H8SmpIng8zMzDhs2LAUDxjr16+ns7Mzy5Yty6CgILZo0YIqlYply5ZlhgwZePnyZW2ELn4Q9d9EYGAgCxYsSJVKpTy0qj1//pyWlpYaiXK5Nv5YX0ro9OjRg7a2tjQxMeHgwYN56NAhkmSdOnU4cODAr/odIvVFRkayX79+1NfXp5eXF83MzDSupcmTQurC4J+SaX//zeHDh5WpYg8fPmSpUqVYo0YN7tu3T9ln9uzZVKlUnDBhAl+/fs3ExES2bt2aNjY2SoeI0F3J70fqmqALFixQZgSobdy4kR4eHlSpVHR0dGS+fPkkMfGNfvvtN3bq1IkdOnRgSEgIyaTyApaWlmzatCkXL17MwMBAuru7s2PHjnz06BGNjIy4dOlSLUee/qjPkz179tDT05OVK1dm5cqVldpohw8fZoUKFdikSROlc0L8M0kI6bh/aggPHjyYhoaGXLx4MePi4vj27VuuWrWKbdu2Zd++fZUHZGl86Sb1BW3nzp2sWbOmMiw1PDycs2fPZvHixTVWb1m8eDH37NnDxMREeYhlUp0RKysrNmvWTNmWmJiokRhavHgxM2bMyMWLFzMsLIyLFi3ib7/9lu5HV/3szp8/T3t7e7q7u2uMBoqPj2f58uW5cOFCLUaXfiS/f23atInLli3jwoULlXtSUFAQr169qvGemjVrctSoUakap/h33r17x2LFilGlUinLXaunJ5FJSaFSpUrRw8NDWRxBfJvjx48zX758HDBggDIK5H8lhdTH5unTpzJyRMctWLBA4+fY2Fg2a9ZMKV4cGRnJ4OBg+vn5cc6cOYyIiCCZNLL80qVLDA0NTfWY07rk96eRI0fSysqKrVq1YpkyZWhqaqpMOTp9+jTr1KnDPHnysECBAqxQoQI/fvzIhIQEuri4cPv27dr6COnazp07aWZmxtGjR/Pw4cOsWbMmLS0tlTbFwYMHWaVKFbq7u6eY9idSkoSQDkt+sTp27BgPHDig8XBDJiWFDAwM6O/vr/EgrE4YyLBs3bZlyxZmyJCBfn5+GiuDhIeHc+7cuXRycqKbmxsHDRpElUqlMWc8vbt//z5dXFxYv359Hj9+XOO15AmzypUrs2nTpqkdnkgF6uN89+5dBgYG8vr16wwLCyOZ1LNnZ2fH6tWr09/fnydOnODQoUNpZmaW7guxp4bk52C/fv2YOXNmOjk50dLSkkWLFuW+ffuUBMK7d+8YFBTEOnXq0MnJSe5bOih5e+TNmzfs3LkzW7duzYwZM2rUvFPXMHz48CFz587NX3/9VSvx/ozGjBnDMmXKcPDgwXzw4AHJLyeF1NPH1NMvhe66f/8+CxcuzDt37ijbYmNj2bJlS3bu3JmBgYHs0aMHPTw8mDdvXpYpU4aNGzfWWHFY/HcvX77kkCFDlAUp3r17x86dO9PMzIx79+4lmdQmf/78uTJyiCSHDh3K3Llz89GjR1qJOz2Lioqih4cHx40bRzJptUs7Ozt2795dY7+dO3fSw8NDmW4pvkwSQjrq06V5c+fOTUdHRxoYGHDIkCEay7smTwolX2FCRpHotvv37zN//vycO3cuyb+P18WLFxkREcHExERu2LCBderUYY0aNWSK02fcunWLnp6erFWrlkZSKPnffrVq1di6dWtthCd+IPUx3rx5M/Pnz09bW1va29uzTJkyygiwkydP0t7eniqVijVq1GDLli016gqJH+/p06esWLEiL126xHfv3jEyMpJubm50cHBQztlNmzaxdOnSdHd3V5JEMrJVdyRPBu3atYtBQUGMjo7m69evOWDAAFpYWGhMz0xMTGR4eDjfvn0rx/EbzZ49W6NQ8Lhx4+js7PxVSaFFixZpdDQJ3aWuZ6heNp4kp0+fTkdHR5qZmbF58+Zcv349Y2NjOWbMGHp5eWkr1J/KunXrlGl3ydsGUVFR7NKlC83NzTXqdJFJo7IaNGjAHDlyyDQ9LXn79i0dHBwYHBzMN2/eMGfOnBozKlasWKEkTKVW6NeRhJCOmzhxInPkyKEMdxs7dixVKhW7deumkRQaNGgQjYyMuGbNGpKSDEoLrly5wmLFivH+/ft89eoVZ86cyWrVqtHU1JReXl4aCaDIyEgtRqrbkieFko+gS0hI4OPHj1m7dm2uWLGCpJwXP5uTJ0/S3NycCxcuZHBwMHfs2EFPT09mzZpVSQpdvHiRWbNmZc+ePTUKrYof77fffmOlSpVYr149vn//XiOxUKFCBbq6uio/BwQESMFhHZT8mjls2DDa2tpy1apVSk2TR48eccCAAcyYMSOXL1/OxMRE1qtXT2NkkCSF/pvHjx+zffv2KaY4jxkz5otJIXd3d+7YsUMb4Yp/acqUKVy3bp3yc1hYGHPkyMHy5csr2y5evMjAwECSfydmf/31VzZo0IAfPnxI3YB/Ap+W4Xj48CFbtWpFQ0NDpdaMep8PHz6wW7duVKlUGivzxcTEcPLkycqqtSL1JH/u9fT0ZJ8+fZg3b1727NlTWUnszZs3dHd35/Lly0lKu/9rSUJIhz18+JDNmzfnpk2bSCZNL8qSJQv79OlDQ0NDduvWTWOI6YABA2hiYiJD49KIt2/f0szMjG5ubsyTJw8bNWrE8ePH88CBA7SxsZFCdf/Cl0YK+fj4sESJEnJO/KRmzZrFunXramy7f/8+a9WqxSpVqigPrWfPnpVpYqkgeWP748ePnDlzJm1tbVmoUCFlu7q37tSpU7S0tEwxYkuSB7oj+bEYP348s2fPzpMnT6Z4EH379i2HDBmi9LQXLlxYVnT5TtTfdWBgIFevXq1s/1xS6NGjR8yfPz/r168vnUhpgLe3Nw0MDLht2zZl27Fjx5g/f37WqFEjxf63bt2ij48PM2bMKAXCv9HevXuV6eWPHz+ml5cXra2tlSSPOokQFRXFyZMnSyeFDrh37x6dnZ25YcMGkuTUqVOZI0cOjY4lMqnjwsHBQbkuiq8jCSEdFh4ezj///JMRERE8ffo08+TJwzlz5pAkhw8fTpVKxRYtWvDp06ckk+a9FixYkHv27NFm2OIz1DcX9RBGdSb7xo0b7NmzJ6dMmcInT54oD1Tu7u4pigyKf5Y8KXTx4kVOmTJFVhP7yY0ePZq2trYpVlNct24d7e3tNXqTROp5+fIlSfL169f09/enqakpe/XqpbHPkSNHmDdvXinwroMGDBig8fO7d+9YvXp15Z705MkTHj16lJ07d+aMGTOUgrYnTpzgihUrZKTXd6Sefte0aVM6OztrjChJnhRSF41+/Pgx7927p61wxb/Up08fmpiYcMuWLcq2U6dOMVeuXHRzc1O2nTx5kp6enixZsqS0ab7R3bt3qVKp2KVLF6XT6OnTp6xduzatra2VWp2fjiaS65l23blzh15eXuzfvz9J8sWLF2zdujWdnZ3ZoUMHTp8+ne3bt2fmzJl56dIl7QabBklCSEd8aTUxdW/q8OHD2bhxY6XXZ+LEiWzQoAGrVq2qvHfbtm3Mnz+/rCahY9QPqfv27WObNm1YqVIl+vn58cKFCyQ1e2ETEhI4bNgwZs+eXVleVny9W7du0cvLi9myZaOhoSHPnz+v7ZDED/TXX3+xePHiXLJkicaohQsXLjBfvnwpVrASP9769etpYGCgPLS8e/eOCxYsoKmpKbt06cKLFy/y8uXLrF27NitUqCBLyuuY48eP08vLS+Ph5/nz53RycuLo0aO5fv16Nm/enFWqVGHp0qXp7OxMHx+fFA9LMtLr+zp16hRbtWrFKlWqcO3atcr2MWPG0MXFhb169ZLitmlEYmKict0LDw9n+/btaWVlxZ07dyr7qJNC7u7uyraAgAA+efIk1eNN6z43ZWjPnj00MzNj9+7dNZJCderUYY4cOaTtoAPUx+3hw4fKiNPt27fTyMhImRb79OlTTp06ldWqVWPlypXZvn17Xrt2TWsxp2WSENIByRvE165d4/Hjx/ny5UvlBFAvP+nl5cWPHz8yLi6O9erVU6rfq4WEhEgySEdt27aNpqamHDp0KH19fenp6cly5copqxqQSQ9SjRs3pq2trRSq+wY3b95k/fr15abwE1E3DK5cucK//vpLOW+io6PZsmVLVqxYkYsWLWJUVBSjo6Pp4+NDR0dHvnr1Spthp0uvXr1irVq1aGtrq0wHUyeFMmbMSH19ffbu3Ztt2rRRkniSFNIdMTExyvFInngYP348c+fOzYwZM9LX15cBAQEkybZt27Jr167aCPWnpb7ehYWFMTo6WjkegYGBSjIu+bHx8fGhq6srX7x4oZV4xb+jPr6bNm1iuXLl2KhRIxoYGNDCwiLFSKH8+fOzdOnS2gr1p6T+/vfu3UsjIyN2795dqS/49OlTli1bNsVUdKEdR48epaGhIZs0aaKMRPXz86OdnR1v3LihsW9iYqJ0RHwDSQhp2acFG4sWLUoLCwu6urrSz8+PERERJJNW0lGpVKxUqRKLFi1KR0fHFNMkhG66cuUKixYtyiVLlpBMmkZhbW1NOzs7lixZkmfOnCFJnj9/ngMHDpSl5b8DqV/x89myZQszZMjAggULUqVScdiwYSSTamy0adOGTk5OzJw5M11dXWlpaSlJ1VTwaSJH/fObN29Yt25dZs+eXUkKhYWFcdGiRcydOzd79OihvEeWTtYd6oeihIQEhoSE0NLSUqOWydWrVzXqFpKkh4cHBw0alKpxpgfbt29n6dKl6erqyvbt2yvTzJMnhZJPH5Pkd9py7tw5mpqacsmSJXz8+DGvXr1Kb29vmpqacuvWrcp+R48epaOjo3T2/gebN2/mwYMHSSbNqhg+fLhyj1I/N+3Zs4eGhobs27cv37x5QzLpXJJOCt1w8+ZNZsuWjWZmZixevDgXL17M9evXs3PnzvTz82NkZKTGM7A8D/93khDSEePHj6eNjQ0PHjzI6OhotmjRgjlz5uQvv/yiFD7bsWMH+/bty1GjRinJIMmG6r6goCC2b9+eHz9+5IMHD2hvb8/u3btz3759LFCgAMuUKaOsIidzlIX4m/rm/vLlS5YuXZrLly/nrVu3uHbtWhoYGLB79+4kkxKAFy9e5KxZs7hq1SqZbpnKFi1apDyQqo/ZmzdvlOH36gKob9684cKFC5k1a1YOHjxYa/GKlJYsWUJjY2Nlql9kZCS3bt3KIkWK0MPDQ2Pfd+/e8dSpU6xbty6LFSsm963vRH3uXLx4kSYmJhw1ahT79+9PZ2dnOjo6Mjo6mmRSUqhVq1Z0cnLixo0btRmy+I82bdrE4sWLa6x8GRsby/bt2zNjxozcvXu3sl2S5v/ewoULaWRkxCNHjpAkZ8+eTZVKxYkTJyrJHvV/1QXxvb29NYqxS1Io9amvgckTd1OmTOG4ceM4atQodunShbVq1aKjoyNr1aolbb3vSBJCOuDatWusUKECd+3aRZI8ePAgzc3NWb9+fRYqVIj9+vVTbhrJE0DSCEs71PO+27ZtyzZt2ii9fbVr16alpSVdXV354cMHyW4L8Yl9+/bR19eXnTp14vv375Xtu3btoqGhIXv06CEr6mjRmzdvaGdnR3t7e6WHNfncfzs7Ozo6Oio10969e0d/f3+qVCoOHz5ca3GLv338+JGVKlWiSqVi5syZlWWuo6KiuH37dhYsWJC1atVS9t+/fz/LlSvH2rVrK6MxpXPq+7hw4QL37t3LyZMnk0z6Xs+dO8fixYuzSJEiSnLg+PHj7Nixo6ykk0atXbuWhoaGStte3Z4/c+YMVSoVVSqVUidF/DuLFi2igYGBxvQ7kly1ahX19PQ4fvx4jevV1KlTWb9+fY2arCL1qdsNR48eZdGiRXn48GG+f/+eV69eZaVKlXj48GGGh4dz5cqVtLGxoUqlYps2bbQc9c9DEkJa8OnwtsTERK5du5ZhYWE8duwYs2fPzsWLF5Mka9WqRSsrK7Zu3VrjYUjoJvWx/fDhgzLdT+3du3csWbIkZ86cSTKpVkOnTp04b948ZW6sEELTggULqFKpmCtXLj579ozk371Hu3btopmZGTt06KCMpBQ/1ueS1sHBwXRxcWGRIkWUpBCZdB2sXbs2zczMNGoyhIWFcdmyZQwJCUmVmMX/NmXKFLq7u7Ndu3Y0MjLiqVOnSGomhTw9PZX9z5w5o5yH0jn1fbx69YpFixbVmBJLJp1z58+fZ/HixVmsWDGl9paMHEm73r59SxcXF7Zu3Zpv375Vtt+5c4dt2rRhv379UtRIEf/b4sWLaWRkpDHtTr39+fPnXLJkCfX19Tl27Fg+fPiQ0dHRbNSokUZBb0kKpT51u2Lnzp0cOXIkvby86OjoyE6dOvHOnTv8888/mT17dmXq5M2bN9mrVy9ev35dm2H/VCQhlMqSN6YnTpzI6dOnk/z7xt6tWzf26tVLaWD169ePZcuW5YABA+QipeOSX9A8PDxYpEgRNmzYkKtXr1aGejdo0ICenp7ct28ffXx8aG9vz6dPn2ozbCF03ooVK6inp8exY8emqAGwZcsWWltbS1I1FSS/B7169UrjO79z5w5LlizJokWL8vXr1ySTRje0bduWly5dSnH/ktGQ2vXpeXT9+nVmzpyZc+fO5S+//EJjY+MUI4WKFCmSosCttEu+n5iYGG7evJmlS5dmqVKlNF5LTEzkhQsXmCdPHpYtW1bZJnSb+hjdvXuX169fV6bPJiQk0N/fn5UqVWKLFi0YGhrKp0+fcvjw4axcubIk+/6DgIAAqlQqjhkzRmO7l5cXy5Ytq3RW/PHHHzQwMGChQoVYoEABOjk5SU1WLdi9e7dSY1D9vTdt2pTz588nSa5cuZKtW7dmpkyZOH/+fNavX5+DBw9WBkfIqNTvSxJCqUSd+CGT5gnHxcXRxcUlxZDQRo0asVGjRsrJ0bx5c65atSrFvEqhm3bv3k0jIyP6+PhwwYIFdHNzY7ly5ejn58eEhARu376d1atXp42NDQsVKqRMoxBCaE41unTpEm/evMmoqCiS5Jw5c6inp8fJkycr+6n/K1PGUtfw4cNZpkwZZs6cmd7e3sqKR3fv3qWLiwttbGzYvXt3litXjmXKlFEabnL/0h3JRyWojRs3jl26dOGNGzfYvHlzmpiYKCv6RUVFcf369WzRooUcxx/ow4cP3LlzJwsUKEA3NzeN1xITE3np0iWpm5FGqO9PW7dupb29PYsUKUITExMOGTKEYWFhjI+P59KlS1muXDnq6emxSJEisiDCN7h16xarVKnC+vXr89y5cyTJJk2asHjx4rx37x7Jv+9BZ8+e5YIFCzh79mypyaoFoaGhzJ8/Pzt27KgxyqdKlSqcMmWK8vPr16/p7+/PrFmz0tramtbW1rx06ZIWIv75SUIoFVy+fJkqlYotWrRQtsXFxdHR0ZF79uxRtiUkJHDs2LEsW7Ysa9WqxYoVK9LBwUEa02lAQkICIyMjWbduXY2h3tHR0Rw6dChLly6tDEl98uQJQ0JCZESDEMmoG8+bN2+mo6Mj8+fPz0qVKtHNzU1ZTnnhwoXU09Pj1KlTpScvFSW/98ybN4/Zs2fnsmXLOGfOHHp5edHZ2Zlz5swhmfRA27NnTzZt2pQdOnRQaszI/Ut3rFixgiYmJpw+fbpGp9Tu3bvp6OjIp0+fMjw8nM2bN6epqamSFEo+akGO57dRX7/Onz/PJUuWcOnSpQwODiaZ9D3v3LmThQsXpru7uzbDFN9o7969zJQpExcsWKDUP1GpVOzevbsyYiUuLo7bt2/noUOHpCbUN7p16xY9PT1Zt25dVq5cmc7Ozrx//z5JzYLF58+f13ifJINS34ULF+ji4sIuXbrw6tWrJJNWrVy9ejVJzanIly9fZo8ePZg3b145R34QSQilgsTERB48eJDW1tZs3ry5st3BwYF79+4l+fcffmRkJCdMmMCOHTuya9eukrlOY6pWraoswas+ZnFxcaxcuTJbtmypzdCE0HkBAQE0Nzfn/PnzGRUVxaVLl1KlUnHevHnKPgsXLqRKpeKsWbO0GGn6dP78eQ4ZMoSrVq1Stt25c4eDBg1i6dKlGRAQoGyXBRB0U3h4ON3c3KhSqVinTh3WrFmT7u7uPHHiBBMTE9mtWze2b9+eZFLnRZs2bahSqaRWw3eUPPmdM2dOZXl5KysrHj9+nOTfSSFHR0e6uLhoM1zxH719+5Zt27bl+PHjSZL379+nvb09a9euTUNDQ3p7e8vD7Q9w69Yturm5MVOmTNywYQNJzQR2rVq1WL58eaWGq9CeixcvslSpUuzUqROvXr3KFi1a8PDhw5/dNyEhQWNVPvF9SUIolSQmJvLAgQPMmjUrGzduzMTERFaoUIEHDx5U9lGvPPXpUG5pTOue169f89KlS3zw4IHSaxoTE8N69eppFE9VPxSNGzeOlSpVUo6xEOJv6obZiBEj2KdPH5Lks2fPmDt3bvbu3VvZT12La+nSpVJwMxWp65eoV79Rz/FXu3v3LosWLaoxNTr5e4XuSExM5OXLl+nh4cE8efLw2rVr7Ny5Mz08PFiyZEl27tyZ5cuX5/Pnz0mS9+7do5+fn7RDvrOjR4/SyspKWUDk3LlzVKlUNDU1VZYc//jxIzdv3kwXFxelmKpIOyIjI7lq1So+ePCAr169YvHixdm5c2eSSVOgDQwM2L59e2UVWvH93Llzh7Vq1WLt2rV59OhRZXvt2rVZqFAhZeSq0L6LFy+yTJky7NChAzNkyKBMl3V3d2eNGjXo6urK9u3bS12tH0xFkhCpgiQOHTqEZs2aoWTJknj8+DE+fvyIfPny4f3794iPj4eBgQFq1KiB2bNnK+9RqVRajlwkd/XqVbRv3x4fPnzA+/fvMWTIEHTv3h2mpqYICgpC+fLl0atXL8yYMUN5T/v27REdHY21a9fCwMBAi9ELoTs+vb517doV1tbW6NWrF8qXL4+6deti0aJFUKlU2L59O169egVvb285h7RkzZo1aNeuHZo1a4ZZs2YhR44cymtNmzaFkZER1qxZI/esNOD69eto3bo1LCwssGfPHkRHR+P333/HkiVLEBUVhTNnziBPnjwa71G3UcTX+1wb7uPHj5g8eTJIYuzYsXj69CkqVqyImjVrIiEhAevXr8e+fftQrVo1xMTEIC4uDhkyZNDSJxBf63PHOjw8HJkyZYK/vz/WrFmDDRs2wMbGBkuWLMHixYvx6NEjXLp0CTlz5tRS1D+v27dvo0+fPtDT04Ovry9mzpyJa9eu4dq1azA0NJTrmQ65ePEivL29oaenB0dHR9SqVQvv3r3D27dvYWJignr16sHR0VHbYf7UJCGUytRJoT59+iA0NBRLliwBSbx//x56enoAgLZt28pFSkcFBQWhcuXK6NatGzp27IglS5Zg/fr1uHHjBiwtLQEAGzZsQPv27eHq6orcuXODJDZu3IhTp07ByclJy59ACO1L3nDet28fTp8+jVGjRmHixIk4fPgw7t27h1q1amHx4sUgidjYWPTu3RvW1tbw8/ODsbGxlj9B+pL8eP3+++/o3Lkzhg4dip49eyJ37tyIjIyEq6sr3NzcMHXqVC1HKz71pY6lGzduoEWLFjAwMMCRI0eQKVMm3LlzBxYWFsiePTsSExOVdon499Tf34cPH/Dhwwdcu3YNBQsWRPbs2fH48WM8e/YMTk5O8PDwQIkSJeDv74+TJ0+iSpUqAJKujR4eHlr+FOJrqM+x8+fP486dO7C1tUWpUqVgbm4Okhg6dCiOHz+OAwcOIEOGDPDx8UHx4sXRpEkTmJiYaDv8n9bt27fRv39/HDhwAAUKFMDVq1clGaSjLl++jG7duqFEiRIYPnw48uXLp+2Q0pdUHpGUbvzTMPm4uDgePHiQ2bJlU4aPfkpqBumeq1ev0szMjKNHj1a2RUVFsVy5cty6dSt37NjBW7dukSSDgoLYpk0b1q9fn23btlUKpgmRnm3ZskX5t3rIdu3atTl16lSSSVMxCxcuzKxZs/L27dskk6Zi+vr60tbWliEhIakfdDrzpUUMkt/TFi9eTJVKRWdnZ3bu3JkNGjRgiRIlZEqsjlEXgya/3Ca5fv06S5QoQQcHB4aHh//P/cXXUZ8/ISEhbN++vbLCVMaMGdm6dWtlueXAwECWKlVKKSh97do1Nm/enIMHD5ZpsWnMli1bmClTJubOnZuFCxdm27Zt+fr1a5JJBdv19PRYr1491qlThxkzZpR2YSoJDg7mr7/+qkx7lemvuuvixYt0cXFhixYt5PqXyiQh9J2Fh4czNjb2f851TEhI4IEDB2htbc3q1aunUnTiv4qOjlYKASY3atQo6unp0cnJiTY2NsycObMyX1n9wCtzlYVIKqipUqnYuHFjje01atTg3LlzlZ+Dg4NpY2PDkiVLsnTp0qxXrx6tra1lKd5UsH//fhYpUoQREREk/zkp9Mcff1ClUrFSpUpct26dsl2ud7ph6tSpdHBw0EjC/lNSyNnZmSVLlmRYWFgqRfjzUp83QUFBzJEjB3v06MEVK1YwODiYPj4+tLOzY5EiRXj69GmldtCVK1dIkiNGjGCdOnUYFRWlzY8g/oXExETGxMSwefPmXLVqFd+8ecOFCxeycuXK9PT05MuXL0mSGzZsYIMGDdihQwfleIvUJckg3Xf27FlWrVqVz54903Yo6YokhL6j1atXs1atWnR0dGSLFi2UZca/JDExkTt37mTt2rVlCVcdl5CQwBMnTtDe3p5Vq1YlSc6YMYOZM2fm1q1bGRERwWPHjtHFxYXVqlXju3fvlJ526WkVIuk82L9/P7Nnz66x2qKrqyuXLFlC8u/C+q9fv+bMmTM5aNAg+vv7886dO1qJOb1QX6OuXLnCAgUKsHTp0l+VFFIvoezn5ycPsDrm6NGjbNasGatWrcqNGzcq2/8pKZQzZ062a9cutUL8KSVPBpmZmXHYsGEpHkLXr19PZ2dnli1blkFBQWzRogVVKhXLli3LDBky8PLly9oIXfxL6nPp7du3fP/+PRs2bMhr164pr61du5aVKlXSSAp9+PBBZgAI8T9IAenUJzWEvpONGzeiXbt2mDRpEl6/fo3Hjx9jzZo1mDx5MgYNGvRVRTZlvr7uO3fuHJo2bYq4uDjExcVh8+bNcHV1VV7v0qULrl27hsDAQCmsKsQn+P811Fq1agVXV1ds2bIFVatWxS+//ILmzZtr7PvhwweYmZlpKdL0KyQkBI0bN4ahoSGOHz8OCwuLFPcmJqtJs3TpUvTq1Qt9+/bF8OHDkTlzZi1FLtQSEhKgr6+Py5cvY+bMmXjy5AkGDBgALy8vAF+uKfTgwQPkzp0b+vr6qR3yT+Xx48coVaoUqlevjg0bNgBI+s4TEhKUuiVLlizBoEGDMH36dDRr1gzr16/Hx48fUbduXRQsWFCb4Yt/Ydu2bRg2bBiyZcuGhw8f4ujRo8ibNy+ApDb9+vXr4e/vj9jYWOzatUupNSmEELpEEkLfSdu2bWFpaYk5c+YAACIjI7FixQr0798fo0ePxvDhw7Ucofhezp49i759+yI8PBw3btwAAMTFxcHQ0BDdu3fH+/fvsXz5cikUKMRnqJNCzZs3R40aNfD48WO8ffsWTk5OCAsLg5GREYyMjJA5c2asXLkSenp6klz9gQICAnDkyBHExsaicuXKqFu3LkJCQtCkSRMYGBh8VVJo3rx5GDlyJG7fvg0rKyttfRQBzY6l/fv3Y+vWrdi0aRNy586NcePG/c+kEPB3Qkn8Nw8ePEDz5s2RI0cODB48GJUrV1ZeS/69V6lSBTY2Nti4caO2QhX/gfoYXr9+HRUrVsTAgQMRFhaGXbt2wcTEBIGBgcqqcImJifjjjz+wfv16LF68GLlz59Zy9EIIkZIkhL6DuLg4VK1aFS4uLimWi/f390fPnj2xevVqtG7dWsuRiu8hMTER58+fR4sWLZAnTx789ddf0NfXx4gRIzBv3jycPHlSlkcU4h+ok0K9e/fGvXv3lJWp3r59CwMDA6hUKjRp0kTOox9s6dKl8PHxgZOTE65cuQJzc3MMHToUv/zyC27cuIGWLVtCT0/vq5JC7969k9FBWvTpsRk0aBDWr1+Pbt26ISIiAlu2bEGOHDkwcOBANGrUCMA/J4XEt1EveU0SI0aMUJJCyb/z6tWrI2fOnFizZo02QxX/walTp/Du3TtcuHABI0eOREJCAo4fP47BgwcjMTERR48e1UgKRUZGImPGjFqOWgghviBVJ6j9xMaNG8e8efPy+vXrGtvj4uI4aNAglixZUgpkpRGRkZF8/vz5/5zDevbsWebPn5+1atXiiBEjaGpqyvPnz6dSlELovq9ZbTFHjhz09vZOxagESS5dupRGRkbcvHkz4+Pjee3aNXp5edHR0ZEPHjxgYmIir169yuLFi7NkyZJKTaFPj6n6Z6mVpjuuXbvGfPnycf/+/cq2Y8eO0cvLi+XKleOePXuU7XLcfpxbt27R09OTtWrV4okTJ5TtCQkJfPz4MWvXrs0VK1aQlOOQlkRFRbFMmTJUqVRs3769sj0+Pp4BAQEsU6YMy5Urp1wzhRBC10nBmm+QkJCg/NvNzQ358+fHrFmz8ODBAwBJPUEGBgZwdXXF48ePERERoaVIxde6efMmvL290ahRI6xevRoxMTFf3NfFxQUbNmzAtWvXMGHCBJw4cQKlS5dOxWiF0C2JiYkAkkZNAknXwC8xMDBAjRo1sHLlSuzZswfu7u7Ka//0PvHtTp48iW7duuGXX35B48aNoaenB0dHR7Ro0QLPnj1DfHw8VCoVihUrhrVr10KlUqFw4cL48OFDihEl6p9lpIl2tG/fHhMmTNDYZmZmhsjISERHRyvbqlSpgsGDByM4OBijR4/GunXrAMhx+5EKFiyIOXPmQKVSYdy4cThx4gQAQE9PD/PmzcOzZ89Qs2ZNAHIc0hIzMzP8/vvvqF27No4cOYLQ0FAAgL6+PlxdXTFjxgy8efMG9evXl3uZECJNkITQf3Dt2jUASRf/+Ph4AED58uXRtGlTnD9/HlOmTMHNmzeVG3zhwoVhY2Oj0TgTuufq1auoVq0a7O3tMXr0aHTp0gXGxsb/+J7SpUtj+/btuH//PkqVKpVKkQqhe9RTVm7cuIFu3brB3d0dffr0wcGDB7/4Hj09Pbi5uWH58uW4d+8enj59CkAejn6khIQEZMyYEbVq1cL169exefNm5ft+8+YNTE1NYWRkpOzv6OiI5cuXw93d/X9eD0XqioqKgoeHB4YMGZLitRw5ciAkJAQJCQnKQ6mrqytKlSqF8PBwnDlzJrXDTZeSJ4XGjx+PS5cuYerUqZg/fz5WrlyJXLlyaTtE8R8UK1YMM2bMQLZs2VC9enW8efMGQNI9rXLlylixYgWWL18u9zIhRJogNYT+pXXr1qFt27bo1asX5s6dCwCIjY1VGtCzZs3Cpk2bkJCQgH79+iFTpkyYNWsWIiIicOLECVlFTEc9ffoUNWrUgKenp1IHCpCV34T4N0JCQlCuXDk0bdoUiYmJSqHNqVOnon///l98H0l8/PhRVhX7wVatWoWXL19i4MCBCAwMxPTp0/H69WtMmDAB0dHRqFevHlauXInmzZt/sb6MFBzWTYsXL8axY8ewevVqAMC4ceMwfvx4rFy5Eo0bN4aRkRHCw8PRrVs3eHl5oU2bNnJvS0W3b9/GgAEDcPbsWYSFhSEwMFBGFOuoz137vnQ9DAkJQdu2bREZGYmTJ0/KKmJCiDRJEkL/wtGjR9GlSxfkz58fr169QtWqVTFr1iwAmkmh3bt3Y+vWrVi7di0cHR2ROXNm7NmzB4aGhpJg0FFr1qzB/PnzsW7dOmXJUCHEvzNkyBBcv34du3fvBpBUaHjlypUYOHAg/Pz8MHLkSC1HmD7x/5e8dnFxQdeuXdGrVy8AwOnTpzF9+nTcunULN27cwIoVK9C2bVvEx8cry2ML3aROzCUmJiImJgYzZ87EmjVrULVqVSxcuBAA0L9/fyxYsABt2rRB1qxZcfbsWXz48AFnzpyBnp6etEdSWUhICIYMGYKJEydKwXwdpT4n3r59i9DQULx+/Rqurq7/+J6QkBB4e3vj/v37CA4ORpYsWVIpWiGE+D6kJfAVEhMTkZCQgCNHjqBs2bKYOnUq2rZti0OHDqFfv34AACMjI6XeTN26dbF06VLcunULBw4cwIEDB2BoaIj4+HhpfOmoM2fOICYm5rPJIHXONDY2Fu/evUvlyIRIO549e6YxyidTpkzo27cvFi5cCD8/P6xcuVKL0aVfKpUK8fHxePfuHUxNTZXt5cuXx+DBg2FnZ4dChQopq4QZGBgo9aCEblKP0nrx4gVMTU3RvXt3dO/eHSdPnkT37t0BAL/99hvmzJmD2NhYnD9/Hnny5MGpU6ckGaQlhQsXxqZNmyQZpKPU50RwcDDatGmD8ePHK50b6nbg566LhQsXxtKlS+Hg4IC3b9+masxCCPE9SGvgK4SFhUFfXx/9+/dHly5dULJkSXTu3BkdOnTQSAoZGxtrFJq2tbVFlixZoFKpkJiYKD2uOkh9kzcxMcGbN2+UOk/JB86phwn37dtXKcQphEipQoUKOHLkCIKDgwH8fe506dIFvr6+GDt2LO7du6fNENM1CwsLZM+eHcDfDzblypXDsGHD4ODggBkzZmD79u0AIMmCNGDr1q2wtbXF2bNnYWVlhbZt26Jjx44IDAxUkkLdu3fH0qVLcfjwYaxatUo6p7TM0NBQ2yGIz1Ang65evYoqVaqgTJkyGDp0KKZMmQIgqcYkACWZ+ilHR0fs378fdnZ2qRq3EEJ8D9Ii+B82b96Mhg0b4sWLF8iYMSOqV68OAMicOfNnk0JRUVGYOnUqYmJiNOYbS+NLt9y5cwcHDhxQjlGpUqUQGRmJOXPmIC4uTulRV4uNjUVUVBTMzc21FbIQOiN5wjR549jV1RXFixfHtGnTcP/+fWVflUoFLy8vREVF4fnz56keb3p1+PBhjB8/HkDSg2hUVBQsLCwAJB0X9XEsW7YsBg0aBGtra/j6+uLYsWNai1l8vZIlS6Jx48Zwc3PDuXPnkDVrVrRv3x4dO3bE6dOnlamBJiYmShtEvfqpEOJvenp6ePLkCZo0aYK2bdti3LhxKF68OABgxowZKFmypDLl+UtJIUn2CSHSKslS/IOlS5eiWbNmOHnyZIrlx0nC0tISnTp1gre3N/766y/06NED9erVw8yZM+XGoMNiY2Ph7+8PT09P7NmzBwBQp04dFCpUCLNmzcLKlSs1amjEx8dj/PjxOHv2LKpWrarN0IXQusTERKhUKo1VVdQjI52cnNCsWTMEBQUptWnUSddChQoha9as+PDhg9ZiT09iYmKwYcMGbNy4Eb/99huioqIQExODuLg4AElTjpJ3WpQvXx4DBgxAgwYNUKlSJW2FLb7g03KPJJE/f37MnDkTtWvXRtWqVVMkhbZs2YLp06drvE9WPRJCkzq5c+DAAdja2iodvAAwZ84c+Pn5oWvXrli0aJFGUkhKsAohfhbSTfQF/v7+6N27N3777TcsWbIEoaGhyJMnj9LbrVKpQBJZs2ZFly5dEBkZibFjx8LFxQWPHz+WOfo66vr167h16xY6duyIqKgotG7dGitXrkSDBg2wfft2VK5cGSNGjMCRI0fQu3dvXL58GefPn8fmzZsREBAgBadFupa8xoKHhwfq1auHBQsWQF9fXyms36NHD3z8+BEbN25Er169MGzYMGTPnh2rV69GeHg4HBwctP0x0gVjY2P4+flh6tSp2LBhA548eQIzMzMcP34cly9fRmJiorKM/MePH/HixQt06dIFEydOBCCriekadSJnwYIF8PDwgL29PUgiT548mDZtGkiiWrVqOHHiBJydndGmTRvkyJEDTZs21XLkQug2dTv95MmT+PDhA/LlywcAiIyMxIsXL3DgwAGUKFECJUqUwMiRIxEXF4fJkydLclUI8dOQVcY+w9/fH7/88gu2bNmCunXrInPmzPjjjz/QqFGjz+4fFRWF6tWrIz4+HmfPnoWBgYGs0qKDgoKC4OzsjIkTJ2Lo0KG4d+8epkyZgvXr12PFihVo2LAh3rx5gyFDhuDo0aN4/PgxcuXKhdKlS8PPz08KQQoB4MmTJ2jUqBE+fPgAQ0NDVKtW7bOrLW7evBmbNm3C+vXr4eDggOjoaGzcuBHOzs5ajD79ef78OSZMmIAjR47gxo0bsLOzg4WFBaKiopTRXiYmJsicOTP++usvuW/pmOTLXYeGhqJ+/foIDQ3FsWPHkC9fPuX1mzdvwsvLC+Hh4di1axfKlSun/A5J7gnxv3l7eyM4OBhnzpxRtsXExCiJ83fv3qFr1654+/YtDhw4IOeUEOKnIS2/T8ybNw+DBg3Cpk2bUL9+fXz48AHW1tZ4//79F98ze/ZsxMfH48yZM5IM0lHXr19HhQoVMHr0aAwdOhQAUKBAAfj4+ABIagiok0ILFixAbGws7t+/jwIFCkBPT09j5SQh0iP16KCtW7fCysoKQ4YMwYULF7BixQr069cPs2bNUlZbNDY2RpMmTdCkSROMGjUKxsbGsLCwgLW1tbY/RrqTI0cOjBgxAvr6+sicOTOqVaum1BWKjY2FgYGBMv1BpVJJ8kDHqJNBJGFjY4MFCxZg1KhRqF69Og4fPowCBQoASJqS6eTkhJMnT2Lo0KEICAhQjqkcTyH+Nw8PD2zcuBELFixQ6m8lH+WfKVMmmJmZoXDhwjL6XwjxU5ERQskEBwejXLlyWL58OZo2bao0pipUqICaNWti/Pjxn50G9v79e5iZmUFfX1+SQTroxo0bqFKlCuzt7ZWen7i4OKXOU/KRQqtWrUK9evUASK+qEMDfiaDo6GiYmJggPDwcu3fvRuvWrREREQF/f3+sXLkSbm5uykih5OeX0A3Pnz/HxIkTcebMGTRq1AjDhg0DoHmsko9GEdqVvK0xffp0XL16FYsXL4axsTEuXryIYcOG4fbt2zh69Chy586N6OhodOrUCd26dUPVqlXlOArxGcnvZx8/fkRERAQsLS1hYWGBp0+fokmTJnj37h2GDx+Odu3aKe+Li4vD6NGjsWrVKvz111+wt7fX4qcQQojvS1LcydjZ2eHq1avKnHt1rszCwgKhoaEANAsydujQAefOnYOFhQX09fVlaXkdFBQUBBcXF2TLlg0mJiaYNGkSAChL7wJ/jxRq0aIFOnXqhK1btwKAJINEuqduPN++fRuNGzfG/fv3kSlTJrRu3RoAkDFjxs+utmhoaIi1a9ciOjpai9GL5HLkyIHhw4ejfPny2LVrF0aMGAFAc2UcSSLohuTJoPPnz+PVq1dYtWoVRo0ahfj4eJQqVQqTJk1CoUKFUKRIEXTr1g1VqlTB3bt3UaVKFahUqs+ugiREepa8Bl6bNm1QpUoVlC1bFo6OjpgxYwbMzMywdOlSkMSwYcPQr18/XLhwAb///ju6deuG+fPnY/v27ZIMEkL8dGSE0D9Qj/bp27cvXr58iXXr1imv1a1bF8HBwbh165YkgXTUpUuXULp0aYwfPx59+vSBr68vzpw5g4YNGyq948lHdN2/fx8jRozAyZMncf36dZiZmckDkki31I3nK1euoGrVqggPD8fly5eVpXiBv0eUvHnzBr///jtWrFgBNzc3kMTcuXPx8OFD5M6dW4ufQnwqNDQUQ4YMgYmJCfz9/eUap8N8fHywefNm1K5dG0FBQQgMDESXLl0wd+5cGBgYIDQ0FPPmzcONGzdgbW2NefPmwdDQUBa0EOIT6nvV1atXUaVKFbRs2RKVKlUCSezduxfr169Hw4YNsXDhQrx79w6TJ0/Grl278OHDB9jY2KBEiRIYN26c1JIUQvyUJCH0FSZPnoy1a9ciKCgIJFGvXj3cuXMH165dg6GhoUwt0kHx8fHw8/NDfHw8pkyZAiDpQWjixIk4e/YsGjRo8Nmk0IMHD2BiYgIbGxutxS6EtqkfKC9fvoyKFSvC29sbJ0+exJgxY9CwYUONqUXqf799+xZLly7FsGHDkClTJhw6dAilSpXS8icRn/P27VtkzpxZo3aQ0K5Pj0NAQAAaNmyI3bt3o3LlyoiOjsaOHTvQvn17dOrUCTNnzoSJiQkAzcK3Mm1diM97/fo13N3d4ebmhmnTpmm8Nn36dAwdOhTt2rXD8uXLERUVhY8fP+L27duwt7eHmZkZMmTIoKXIhRDix5JWw1ewtLREdHQ0EhIS0LhxY41kkDS+dJOBgQFGjBgBU1NTAEn1gGxsbDB8+HBMmDAB27dvBwAMGzZMoxC4erlRIdIrdTIoKCgIFStWxIABAzB+/HjY2triyZMnADSnFqn/bWlpiZs3b8Lc3BzHjx+XnlQdZmlpCQAykkRHNG/eHL6+vihZsqSyLSoqClZWVsqIPBMTEzRv3hyRkZHo0qULsmTJgmHDhiFDhgxKMoiktEeE+IQ62Xr79m0AQOfOnZXtiYmJ0NfXx6BBg/Dx40f4+fmhV69ecHFxQYYMGWQhBCFEuiAtwa/g7OyMhIQElCpVCjdu3JBkkA67fv06ZsyYgevXryvJICCpHlBCQgKyZ8+O4cOHo2zZsti+fTumTp0KAHIchfh/enp6uHjxIqpUqYL+/fsrK1LlyZMHYWFhAP6ur5bcqlWrsGPHDhw5ckSSQWmEJIN0g5GRERwcHDS25cyZEw8fPsTp06cB/H3OlS9fHhkzZsSkSZMwePBgjTpdMtJLiL+p62ip60Xev38f9+/fh7m5OQAoK/Cp9xswYADy5MmD3bt3aydgIYTQEmkNfgUzMzPcv38f+vr6CA4OlmSQjvr48SMaNmwIPz8/LFiwAJ6enrh69Spev34NAMr0CHVSqEKFCli2bBl+++03LUcuhO54+/YtPDw80LVrV0yYMEHZbmNjg5CQEAD4bMHa+vXr48KFCzJNTIivlJCQAABYvXo1jIyMMH/+fAQEBCAuLg5OTk5o2bIlxo8fj2PHjinJnixZsqBly5bYvHkzVq5cicGDB+Pjx4/a/BhC6Bz16Mfr169j8uTJiI+Ph6WlJSIiIvDgwQNlH+DvxLi5uTkMDQ0RFRWlrbCFEEIrJCH0Fezt7TFv3jycO3dOkkE6zNTUFJ07d0aJEiXQsmVL2Nraon379ujSpQv+/PNPfPz4UWlUZ8+eHYMGDUKjRo3QqFEjLUcuhO7IkCED9u7dixkzZgD4+6E1S5Ysygih5DXTZs6ciRMnTiBTpkzImzdv6gcsRBqlPo/Uo3/mzJmDjh07Km2N7t27w9LSEr169cK8efOwdetWeHt7Izg4GI0aNcLWrVsxf/58ZcU4IYTmtGcnJyfo6+vDwMAA5cqVQ6lSpdC3b188evQIenp6iIuLA5B0nwsPD0f+/PmVTg0psSqESC+kqPS/JMkg3aSeI37+/HlMmjQJkydPRsGCBREYGIirV6+iR48eaNSoEYoVK4ZRo0YhNjYWpqamUhBciP9BfY5Mnz4dO3fuxNGjR5Xzzc/PD+PHj8fly5fh5OSk7VCFSJOSF5SuXLkynj17hrVr16J8+fI4f/481q9fj8WLFyN//vzInDkzDh48CENDQwDAoUOHkCtXLhQpUkSbH0EInaBOBt24cQNlypTBkCFDMHr0aOX1OXPmYOrUqShatCiWL1+usQqmn58fVqxYgWPHjknnhhAiXZGEkPgpJE/Uubm5wczMDDt27AAAdOrUCXv27EG7du2wY8cOhIWFoW/fvhg6dCj09PSk7oIQX2HFihUYOnQogoODkSVLFvj5+WHq1Kk4ceIESpcure3whEhzkhf1Tn4PK1++PF6+fIk1a9agQoUKAIBXr15BT08PlpaWUKlU0jklxCfU59O1a9dQvXp1WFtb48aNGwA0V+IbO3Ysli5divfv36NTp06Ijo5GZGQkdu7cKatjCiHSJZkyJtKs27dvY/DgwQCSikLHxsYCAGbNmoWwsDBcvXoV3t7e2Lt3L44cOYJp06bh8uXL8Pb2RosWLaCvry/JICG+Uvbs2UESFhYWGD9+vCSDhPgPDh8+rBRq19PTU+qYqFe7BIDTp08jW7ZsaNeuHU6ePIm4uDhYW1sja9asUKlUSExMlGSQEMkknyZWrlw5FCtWDOHh4ejbty8AwNjYGDExMQCAUaNGYcmSJWjZsiX++usvXLp0CVmyZMGpU6ckGSSESJekRSHSrKCgIMyYMQPR0dGYO3cujIyMkJCQgGzZssHc3BweHh6wsLDArl27UKRIEZCEqampsrKYEOLrOTo6IkuWLGjatCn27t2LU6dOSTJIiH8hJiYGGzZsQGBgIIyNjTF48GAlKaSnpwcDAwPExcXB0NAQp0+fRuXKleHh4YHAwEBl+XlAVocT4lN6eno4f/48KlasiOHDh2PEiBFYtmwZhg8fDgCYPXs2jI2NERsbCyMjI9SqVQu1atVCREQEMmbMKOUDhBDpmiSERJrl5eWFNWvWoHPnzkhISMCCBQugr6+PbNmyoWfPnmjRogVmzZqlPLTKaCAh/juVSoVbt27h/v37OHv2LEqUKKHtkIRIU4yNjZWplps3b0ZiYiJ8fHw0kkKGhobKv0+cOIE+ffrA0dFR26ELofM+fPiAnj17ws/PDwDQokULANBIChkZGWlMt7SwsAAgSVYhRPomNYREmpK85gIAREVFYdu2bejSpQs6duyIBQsWAABevnyJ7t27o1ixYhg7dixIyg1fiG+QkJCASZMmoWnTplLAVohvEBoaigkTJuDcuXNo1KgRfHx8APx9f3vx4gX69u2Ltm3bwsvLCwBkBIMQ/4K6UHtERAT+/PNPDB8+HK1bt8bs2bMByPkkhBDJSUJIpBm3b9/GihUrUKJECXh4eCBDhgwwMDBAYmIi1q5di65du6J9+/bw9/cHkDRPfM6cOXjw4AEyZ86s3eCF+AlII1qI7yN5Uqhhw4YYOnQoAOD58+do1qwZXr58iRs3bkitICG+UfKkULt27TBz5kxthySEEDpFWhoiTQgPD0eLFi1w+fJlAEDVqlURExODPn36oFChQmjbti2MjY3Ro0cPqFQqLFq0CIMGDcKOHTvw7t07SQgJ8R1IMkiI78PGxgbDhw/HhAkTsG3bNujr66NTp05o06YNwsLCcP36dRgYGEgSVohvlDFjRrRs2RJ6enro1q0bjI2NMWnSJG2HJYQQOkNGCIk0Y9asWdi+fTvMzc3h5uaGe/fu4dixY7h58ybq1q0LS0tLWFlZYcqUKejXrx9mzpyJqKgomJubazt0IYQQIoXQ0FBMnDgRZ8+exc2bN5EzZ04EBQXB0NBQlpYX4jsKDw/Htm3bUKFCBRQqVEjb4QghhM6QhJDQaRcvXsSOHTswevRoAMBvv/2GPXv2IHfu3Fi0aBEMDAxw5MgRHD58GAcOHMDbt29x//59AEl1hKysrLQYvRBCCPHPQkND4ePjg1evXmH79u2SDBLiB1HXFhJCCPE3SQgJnXXlyhU4Ozujf//+mD59urJ99uzZWLduHRwcHDB27FjkypVLee3ixYsICQlByZIlUbRoUW2ELYQQQvwrYWFhyJQpE/T09CQZJIQQQohUIwkhoZOCgoJQoUIF9O/fHxMmTEjx+pw5c7BhwwbY29tj0qRJyJEjhxaiFEIIIb6fT1fSFEIIIYT4kaTVIXTOnTt3UL58eQwcOBATJkyAOme5atUqBAQEAAD69OmDFi1a4M6dOxg5ciSePXumzZCFEEKIbybJICGEEEKkJml5CJ2SmJiI5cuXw8LCAlmzZgUAqFQqjB8/HgMHDkSGDBmUfX/99Ve0atUKp0+fxoQJE5CQkKCtsIUQQgghhBBCiDRFJqkLnaKnp4fevXvjw4cP+PPPP2FiYoKIiAjMmTMHK1euhIuLC4C/h9X/8ssvMDQ0hIeHhyzNK4QQQgghhBBCfCWpISR0UmhoKCZMmICDBw/i7t272L9/P2rUqIGEhAQl8SO1FoQQQgghhBBCiP9GnqaFTrKxscGIESNQq1YtODg44NKlSwAAfX19ZWqYJIOEEEIIIYQQQoj/RqaMCZ2VPXt2DBs2DImJidi4cSPi4+Ph4+MDfX19GR0khBBCCCGEEEJ8A5kyJnSeevrYpUuXULNmTYwZM0bbIQkhhBBCCCGEEGmaDLEQOs/GxgbDhw9HwYIFcerUKbx580bbIQkhhBBCCCGEEGmajBASacaLFy8AJE0lE0IIIYQQQgghxH8nCSEhhBBCCCGEEEKIdEamjAkhhBBCCCGEEEKkM5IQEkIIIYQQQgghhEhnJCEkhBBCCCGEEEIIkc5IQkgIIYQQQgghhBAinZGEkBBCCCGEEEIIIUQ6IwkhIYQQQgghhBBCiHRGEkJCCCGEEP9vxYoVyJw5s7bDEEIIIYT44SQhJIQQQoh0w9vbGyqVCiqVCkZGRrC3t8fYsWMRHx+v7dC+Sr58+TBr1iyNn9Wfx9TUFPny5UPz5s3x119/aS9IIYQQQqQJkhASQgghRLri6emJ58+f4/bt2xg4cCBGjx6NadOmaTssDXFxcV+979ixY/H8+XOEhITgjz/+QObMmeHm5oYJEyb8wAiFEEIIkdZJQkgIIYQQ6YqxsTFsbGyQN29e9OzZE25ubtixY8dn97179y4aNGiA7NmzI0OGDHBxccGhQ4eU18eOHYtixYqleF/JkiUxcuRI5eelS5eiaNGiMDExQZEiRbBgwQLltQcPHkClUmH9+vWoWrUqTExMsGbNmq/+PBYWFrCxsUGePHng6uqKxYsXY+TIkRg1ahRCQkK++vcIIYQQIn2RhJAQQggh0jVTU1PExsZ+9rXIyEjUqVMHhw8fxqVLl+Dp6Yl69erh0aNHAIBOnTohODgY586dU95z6dIlXLlyBR07dgQArFmzBqNGjcKECRMQHByMiRMnYuTIkVi5cqXG/2vo0KHo27cvgoODUatWrW/6TH379gVJbN++/Zt+jxBCCCF+XgbaDkAIIYQQQhtI4vDhw9i/fz9+/fXXz+5TokQJlChRQvl53Lhx2Lp1K3bs2IHevXsjV65cqFWrFn7//Xe4uLgAAH7//XdUrVoVBQoUAAD4+flhxowZaNy4MQAgf/78uHHjBvz9/dGhQwfld/fr10/Z51tZWloiW7ZsePDgwXf5fUIIIYT4+UhCSAghhBDpyq5du5AhQwbExcUhMTERrVu3xujRoz+7b2RkJEaPHo3du3fj+fPniI+Px8ePH5URQgDQtWtXdOrUCTNnzoSenh7Wrl2L3377DQAQFRWFu3fvonPnzujatavynvj4eGTKlEnj/1WmTJnv+jlJQqVSfdffKYQQQoifhySEhBBCCJGuVK9eHQsXLoSRkRFy5swJA4MvN4cGDRqEgwcPYvr06bC3t4epqSmaNm2qMcWsXr16MDY2xtatW2FkZIS4uDg0bdoUQFJCCQCWLFmCcuXKafxufX19jZ/Nzc2/10fEmzdv8OrVK+TPn/+7/U4hhBBC/FwkISSEEEKIdMXc3Bz29vZfte/Jkyfh7e2NRo0aAUhK8Hw6DcvAwAAdOnTA77//DiMjI7Rs2RKmpqYAgOzZsyNnzpy4d+8e2rRp810/xz+ZPXs29PT00LBhw1T7fwohhBAibZGEkBBCCCHEFxQsWBBbtmxBvXr1oFKpMHLkSCQmJqbYr0uXLihatCiApCRScmPGjEGfPn2QKVMmeHp6IiYmBufPn0dYWBgGDBjwzTG+f/8eoaGhiIuLw/3797F69WosXboUkyZN+urElxBCCCHSH0kICSGEEEJ8wcyZM9GpUydUrFgRVlZW8PHxQURERIr9ChYsiIoVK+Lt27cppoZ16dIFZmZmmDZtGgYPHgxzc3M4OTmhX79+3yXGUaNGYdSoUTAyMoKNjQ3Kly+Pw4cPo3r16t/l9wshhBDi56QiSW0HIYQQQgiRlpFEwYIF0atXr+8y6kcIIYQQ4keTEUJCCCGEEN/g1atX+PPPPxEaGoqOHTtqOxwhhBBCiK8iCSEhhBBCiG+QLVs2WFlZYfHixciSJYu2wxFCCCGE+CqSEBJCCCGE+AYy+14IIYQQaZGetgMQQgghhBBCCCGEEKlLEkJCCCGEEEIIIYQQ6YwkhIQQQgghhBBCCCHSGUkICSGEEEIIIYQQQqQzkhASQgghhBBCCCGESGckISSEEEIIIYQQQgiRzkhCSAghhBBCCCGEECKdkYSQEEIIIYQQQgghRDojCSEhhBBCCCGEEEKIdOb/AC383Ocrx6vgAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Calculate average K/D ratio for each player when they are an attacker or a defender\n",
        "side_preference_df = player_round_df.groupby(['platformUserIdentifier', 'teamSide'])['kdRatio'].mean().reset_index()\n",
        "side_preference_df = side_preference_df.pivot(index='platformUserIdentifier', columns='teamSide', values='kdRatio').fillna(0)\n",
        "\n",
        "# Plotting the top 10 players who have a significant difference in K/D ratio when they are attackers or defenders\n",
        "side_preference_df['difference'] = abs(side_preference_df['attacker'] - side_preference_df['defender'])\n",
        "top_side_preference_players = side_preference_df.sort_values('difference', ascending=False).head(10)\n",
        "\n",
        "# Plot\n",
        "plt.figure(figsize=(14, 7))\n",
        "top_side_preference_players[['attacker', 'defender']].plot(kind='bar', figsize=(14, 7), colormap='coolwarm')\n",
        "plt.title('Top 10 Players with Side Preference based on K/D Ratio')\n",
        "plt.xlabel('Player ID')\n",
        "plt.ylabel('Average K/D Ratio')\n",
        "plt.xticks(ticks=range(len(top_side_preference_players)), labels=top_side_preference_players.index, rotation=45)\n",
        "plt.legend(title='Team Side')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EVFdrVnFS5uy"
      },
      "source": [
        "The bar chart above illustrates the top 10 players who exhibit a significant difference in their average K/D ratio when playing as attackers versus defenders. Each player's average K/D ratio for both sides is displayed, allowing us to observe which side they seem to prefer or excel at.\n",
        "\n",
        "Understanding these preferences can be instrumental for a coach. For example, if a player has a notably higher K/D ratio as an attacker, it might be beneficial to position them as such in crucial rounds."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ebyPSgF9DDmP"
      },
      "source": [
        "### By Loadout"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9wwvA4EaItuA"
      },
      "source": [
        "Analyze Loadout Efficiency by player, breaking down how different types of loadouts affect individual player performance"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 688,
      "metadata": {
        "id": "8bxM7xZJZxrE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "a6e4dfa0-4057-4ce7-8784-0e809badc97e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  platformUserIdentifier loadout  kills  deaths  assists\n",
              "1          Danillize#EUW    full    1.0     4.0      0.0\n",
              "3          Danillize#EUW    semi    5.0     6.0      0.0\n",
              "0          Danillize#EUW     eco    2.0     4.0      1.0\n",
              "2          Danillize#EUW  pistol    2.0     2.0      0.0\n",
              "5               Dawn#92i    full    8.0     1.0      0.0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e54e56d7-4409-49a2-ac8f-890ba6ed93f1\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>platformUserIdentifier</th>\n",
              "      <th>loadout</th>\n",
              "      <th>kills</th>\n",
              "      <th>deaths</th>\n",
              "      <th>assists</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Danillize#EUW</td>\n",
              "      <td>full</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Danillize#EUW</td>\n",
              "      <td>semi</td>\n",
              "      <td>5.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Danillize#EUW</td>\n",
              "      <td>eco</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Danillize#EUW</td>\n",
              "      <td>pistol</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Dawn#92i</td>\n",
              "      <td>full</td>\n",
              "      <td>8.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e54e56d7-4409-49a2-ac8f-890ba6ed93f1')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-e54e56d7-4409-49a2-ac8f-890ba6ed93f1 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-e54e56d7-4409-49a2-ac8f-890ba6ed93f1');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-6cac7cca-73ed-4235-ae2c-217c194ccf90\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6cac7cca-73ed-4235-ae2c-217c194ccf90')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-6cac7cca-73ed-4235-ae2c-217c194ccf90 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 688
        }
      ],
      "source": [
        "# Calculate average kills, deaths, and assists for each player and type of loadout\n",
        "loadout_efficiency_by_player_df = player_loadout_df.groupby(['platformUserIdentifier', 'loadout']).agg({\n",
        "    'kills': 'mean',\n",
        "    'deaths': 'mean',\n",
        "    'assists': 'mean'\n",
        "}).reset_index()\n",
        "\n",
        "# Reorder the DataFrame based on the desired loadout order: 'full', 'semi', 'eco', 'pistol'\n",
        "loadout_order = ['full', 'semi', 'eco', 'pistol']\n",
        "loadout_efficiency_by_player_df['loadout'] = pd.Categorical(loadout_efficiency_by_player_df['loadout'], categories=loadout_order, ordered=True)\n",
        "loadout_efficiency_by_player_df = loadout_efficiency_by_player_df.sort_values(['platformUserIdentifier', 'loadout'])\n",
        "\n",
        "# Plotting Loadout Efficiency by Player for the top 10 players based on average kills\n",
        "top_players = loadout_efficiency_by_player_df.groupby('platformUserIdentifier')['kills'].mean().sort_values(ascending=False).head(10).index\n",
        "top_players_loadout_efficiency = loadout_efficiency_by_player_df[loadout_efficiency_by_player_df['platformUserIdentifier'].isin(top_players)]\n",
        "\n",
        "# Show a sample of the loadout_efficiency_by_player_df DataFrame\n",
        "loadout_efficiency_by_player_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 689,
      "metadata": {
        "id": "GjU8hPaIcMqY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 824
        },
        "outputId": "c83da4bb-a27a-419e-e9a2-4aeb88707d10"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x1000 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1287.75x800 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "# Average Kills by Loadout for Top 10 Players\n",
        "plt.figure(figsize=(16, 10))\n",
        "sns.catplot(x=\"kills\", y=\"platformUserIdentifier\", hue=\"loadout\", data=top_players_loadout_efficiency, kind=\"bar\", height=8, aspect=1.5, palette=\"coolwarm\")\n",
        "plt.title('Average Kills by Loadout for Top 10 Players')\n",
        "plt.xlabel('Average Kills')\n",
        "plt.ylabel('Player ID')\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 690,
      "metadata": {
        "id": "qrKg9k4Xb9Yu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 806
        },
        "outputId": "4e6bbd11-9df9-4e5b-e391-fefb72d9f9af"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1287.75x800 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "# Average Deaths by Loadout for Top 10 Players\n",
        "sns.catplot(x=\"deaths\", y=\"platformUserIdentifier\", hue=\"loadout\", data=top_players_loadout_efficiency, kind=\"bar\", height=8, aspect=1.5, palette=\"coolwarm\")\n",
        "plt.title('Average Deaths by Loadout for Top 10 Players')\n",
        "plt.xlabel('Average Deaths')\n",
        "plt.ylabel('Player ID')\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PQgKs0a39tB9"
      },
      "source": [
        "Summarize loadout analysis for all players"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 692,
      "metadata": {
        "id": "9jbNN42H800n",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 769
        },
        "outputId": "2a716a51-09d2-4aad-85c1-985166b8a415"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x700 with 3 Axes>"
            ],
            "image/png": 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            "text/plain": [
              "  loadout  kills  deaths  assists\n",
              "0     eco    3.1     3.0      1.1\n",
              "1    full    4.4     4.0      0.6\n",
              "2  pistol    1.7     1.7      0.8\n",
              "3    semi    5.5     6.0      2.5"
            ],
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              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
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              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-b5c0cefa-bf62-4e09-bb48-f95fff690c5f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 692
        }
      ],
      "source": [
        "# Calculate average kills, deaths, and assists for each type of loadout\n",
        "loadout_efficiency_df = player_loadout_df.groupby('loadout').agg({\n",
        "    'kills': 'mean',\n",
        "    'deaths': 'mean',\n",
        "    'assists': 'mean'\n",
        "}).reset_index()\n",
        "\n",
        "# Plotting Loadout Efficiency\n",
        "fig, axes = plt.subplots(1, 3, figsize=(20, 7))\n",
        "sns.barplot(x='kills', y='loadout', data=loadout_efficiency_df.sort_values('kills', ascending=False), ax=axes[0], palette='coolwarm')\n",
        "sns.barplot(x='deaths', y='loadout', data=loadout_efficiency_df.sort_values('deaths', ascending=False), ax=axes[1], palette='coolwarm')\n",
        "sns.barplot(x='assists', y='loadout', data=loadout_efficiency_df.sort_values('assists', ascending=False), ax=axes[2], palette='coolwarm')\n",
        "\n",
        "axes[0].set_title('Average Kills by Loadout')\n",
        "axes[1].set_title('Average Deaths by Loadout')\n",
        "axes[2].set_title('Average Assists by Loadout')\n",
        "\n",
        "for ax in axes:\n",
        "    ax.set_ylabel('')\n",
        "\n",
        "axes[0].set_ylabel('Loadout Type')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n",
        "# Show the new loadout_efficiency_df DataFrame\n",
        "loadout_efficiency_df.head()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8ec-ATfzaGja"
      },
      "source": [
        "The set of bar charts above represents the average performance metrics—kills, deaths, and assists—associated with different types of loadouts:\n",
        "\n",
        "- **Average Kills by Loadout:** The \"semi\" loadout leads with an average of about 5.5 kills, followed by \"full\" with approximately 4.4 kills.\n",
        "- **Average Deaths by Loadout:** Interestingly, the \"semi\" loadout also has the highest average deaths of about 6.0, suggesting it might be a high-risk, high-reward choice.\n",
        "- **Average Assists by Loadout:** Again, the \"semi\" loadout stands out with an average of 2.5 assists."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9Y2u1IJ1-Bwb"
      },
      "source": [
        "Analyze Damage Metrics by player, breaking down how different types of loadouts affect individual player performance"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 693,
      "metadata": {
        "id": "wvCiF1gVa-lK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "e04f8291-c60c-40cd-a3ec-68953822aec5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  platformUserIdentifier loadout  damagePerRound  damageDeltaPerRound\n",
              "1          Danillize#EUW    full       53.833333           -41.333333\n",
              "3          Danillize#EUW    semi      149.500000           -64.833333\n",
              "0          Danillize#EUW     eco       91.333333           -26.166667\n",
              "2          Danillize#EUW  pistol      152.500000             9.500000\n",
              "5               Dawn#92i    full      358.500000           263.750000"
            ],
            "text/html": [
              "\n",
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              "      <th></th>\n",
              "      <th>platformUserIdentifier</th>\n",
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              "      <th>damageDeltaPerRound</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Danillize#EUW</td>\n",
              "      <td>full</td>\n",
              "      <td>53.833333</td>\n",
              "      <td>-41.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Danillize#EUW</td>\n",
              "      <td>semi</td>\n",
              "      <td>149.500000</td>\n",
              "      <td>-64.833333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Danillize#EUW</td>\n",
              "      <td>eco</td>\n",
              "      <td>91.333333</td>\n",
              "      <td>-26.166667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Danillize#EUW</td>\n",
              "      <td>pistol</td>\n",
              "      <td>152.500000</td>\n",
              "      <td>9.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Dawn#92i</td>\n",
              "      <td>full</td>\n",
              "      <td>358.500000</td>\n",
              "      <td>263.750000</td>\n",
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              "  </tbody>\n",
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              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-a87e2572-3e4c-4036-9547-af3f4cfadf59 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-a87e2572-3e4c-4036-9547-af3f4cfadf59');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e0a6009a-0a3b-41c7-bd28-c82a306d45a0')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-e0a6009a-0a3b-41c7-bd28-c82a306d45a0 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 693
        }
      ],
      "source": [
        "# Calculate average 'damagePerRound' and 'damageDeltaPerRound' for each player and type of loadout\n",
        "damage_metrics_by_player_df = player_loadout_df.groupby(['platformUserIdentifier', 'loadout']).agg({\n",
        "    'damagePerRound': 'mean',\n",
        "    'damageDeltaPerRound': 'mean'\n",
        "}).reset_index()\n",
        "\n",
        "# Reorder the DataFrame based on the desired loadout order: 'full', 'semi', 'eco', 'pistol'\n",
        "damage_metrics_by_player_df['loadout'] = pd.Categorical(damage_metrics_by_player_df['loadout'], categories=loadout_order, ordered=True)\n",
        "damage_metrics_by_player_df = damage_metrics_by_player_df.sort_values(['platformUserIdentifier', 'loadout'])\n",
        "\n",
        "# Plotting Damage Metrics by Player for the top 10 players based on average 'damagePerRound'\n",
        "top_players_damage = damage_metrics_by_player_df.groupby('platformUserIdentifier')['damagePerRound'].mean().sort_values(ascending=False).head(10).index\n",
        "top_players_damage_metrics = damage_metrics_by_player_df[damage_metrics_by_player_df['platformUserIdentifier'].isin(top_players_damage)]\n",
        "\n",
        "# Show a sample of the damage_metrics_by_player_df DataFrame\n",
        "damage_metrics_by_player_df.head()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 694,
      "metadata": {
        "id": "xPTkM7UFdMop",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "bb89fe08-60ca-4b2f-a057-e428f792cb2e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1587.75x1000 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Average Damage Per Round by Loadout for Top 10 Players\n",
        "sns.catplot(x=\"damagePerRound\", y=\"platformUserIdentifier\", hue=\"loadout\", data=top_players_damage_metrics, kind=\"bar\", height=10, aspect=1.5, palette=\"coolwarm\")\n",
        "plt.title('Average Damage Per Round by Loadout for Top 10 Players')\n",
        "plt.xlabel('Average Damage Per Round')\n",
        "plt.ylabel('Player ID')\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 695,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "vTU5cMD2dPQ5",
        "outputId": "5ca2bffe-6a9f-461a-be4d-1035cf3a9d70"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1587.75x1000 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Average Damage Delta Per Round by Loadout for Top 10 Players\n",
        "sns.catplot(x=\"damageDeltaPerRound\", y=\"platformUserIdentifier\", hue=\"loadout\", data=top_players_damage_metrics, kind=\"bar\", height=10, aspect=1.5, palette=\"coolwarm\")\n",
        "plt.title('Average Damage Delta Per Round by Loadout for Top 10 Players')\n",
        "plt.xlabel('Average Damage Delta Per Round')\n",
        "plt.ylabel('Player ID')\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bVLmKMJ6dTvr"
      },
      "source": [
        "We've broken down the Damage Metrics by individual players, focusing on the top 10 players based on their average damagePerRound. The bar charts represent:\n",
        "\n",
        "- **Average Damage Per Round by Loadout for Top 10 Players:** This chart shows how much damage each of the top 10 players inflicts per round for various loadout types. It's a good indicator of a player's offensive impact on the game.\n",
        "\n",
        "- **Average Damage Delta Per Round by Loadout for Top 10 Players:** This chart shows the difference between damage inflicted and damage received for the top 10 players across each loadout. A positive number means the player deals more damage than they receive, indicating a strong net impact."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UJqKZzpjWhQ2"
      },
      "source": [
        "## Round Win Predictions"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X47LNlPu_aEA"
      },
      "source": [
        "### Data Preprocessing"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zPTGxWcx_8nb"
      },
      "source": [
        "Replace missing values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 696,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5JaJlvPJ_iSF",
        "outputId": "924d8682-3e3e-4c40-ab9c-3e1d30706b04"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "round                     0\n",
              "platformUserIdentifier    0\n",
              "teamId                    0\n",
              "teamSide                  0\n",
              "agentKey                  0\n",
              "hasWon                    0\n",
              "score                     0\n",
              "kills                     0\n",
              "deaths                    0\n",
              "kdRatio                   0\n",
              "assists                   0\n",
              "damage                    0\n",
              "loadoutValue              0\n",
              "remainingCredits          0\n",
              "spentCredits              0\n",
              "agentName                 0\n",
              "kdRatio_norm              0\n",
              "assists_norm              0\n",
              "damage_norm               0\n",
              "performance               0\n",
              "economic_efficiency       1\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 696
        }
      ],
      "source": [
        "player_round_df.isna().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 697,
      "metadata": {
        "id": "0irhkR-X_-hC"
      },
      "outputs": [],
      "source": [
        "# Fill the missing economic_efficiency values wiht 0.  They were only NAN to avoid the case where we were dividing by 0\n",
        "player_round_df['economic_efficiency'] = player_round_df['economic_efficiency'].fillna(0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 698,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "deCjB0uAAAtE",
        "outputId": "ad081ec8-f6bc-4803-f7d6-e8a5a9b26a30"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "round                     0\n",
              "platformUserIdentifier    0\n",
              "teamId                    0\n",
              "teamSide                  0\n",
              "agentKey                  0\n",
              "hasWon                    0\n",
              "score                     0\n",
              "kills                     0\n",
              "deaths                    0\n",
              "kdRatio                   0\n",
              "assists                   0\n",
              "damage                    0\n",
              "loadoutValue              0\n",
              "remainingCredits          0\n",
              "spentCredits              0\n",
              "agentName                 0\n",
              "kdRatio_norm              0\n",
              "assists_norm              0\n",
              "damage_norm               0\n",
              "performance               0\n",
              "economic_efficiency       0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 698
        }
      ],
      "source": [
        "# Sanity check\n",
        "player_round_df.isna().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GNDibQsuAXPC"
      },
      "source": [
        "Encode categorical variables"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 699,
      "metadata": {
        "id": "Awyq_JIDA2x7"
      },
      "outputs": [],
      "source": [
        "# Encode categorical variables\n",
        "label_encoder = LabelEncoder()\n",
        "player_round_df['teamId_encoded'] = label_encoder.fit_transform(player_round_df['teamId'])\n",
        "player_round_df['teamSide_encoded'] = label_encoder.fit_transform(player_round_df['teamSide'])\n",
        "player_round_df['agentName_encoded'] = label_encoder.fit_transform(player_round_df['agentName'])\n",
        "player_round_df['platformUserIdentifier_encoded'] = label_encoder.fit_transform(player_round_df['platformUserIdentifier'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6YLUQTctA7Md"
      },
      "source": [
        "### Feature Selection"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FJmOClF_LMoj"
      },
      "source": [
        "Lets start with all features and see how that performs."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 700,
      "metadata": {
        "id": "uqBUvwXmLL6Z"
      },
      "outputs": [],
      "source": [
        "features = ['round', 'platformUserIdentifier_encoded', 'teamId_encoded', 'teamSide_encoded', 'agentName_encoded', 'score', 'kills', 'deaths', 'kdRatio', 'assists', 'damage', 'loadoutValue', 'remainingCredits', 'spentCredits', 'performance', 'economic_efficiency']\n",
        "target = 'hasWon'"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6uX_pZxMBA-0"
      },
      "source": [
        "### Model Training"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 701,
      "metadata": {
        "id": "LGeCxU8KW_S3"
      },
      "outputs": [],
      "source": [
        "# Splitting the dataset into training and testing sets\n",
        "X = player_round_df[features]\n",
        "y = player_round_df[target]\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
        "\n",
        "# standardize features\n",
        "stdsc = StandardScaler()\n",
        "X_train_std = stdsc.fit_transform(X_train)\n",
        "X_test_std = stdsc.transform(X_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s_I8y2hWXSV8"
      },
      "source": [
        "We've successfully prepared the dataset for machine learning. Here's a quick recap of the steps:\n",
        "\n",
        "1. Encode Categorical Variables: We encoded the teamId, teamSide, platformUserIdentifier and agentName columns into numerical values suitable for machine learning algorithms.\n",
        "2. Feature Selection: We selected a set of features that include round information, player statistics, and economic variables.\n",
        "3. Data Splitting: We've split the data into training and test sets, with 80% for training and 20% for testing.\n",
        "\n",
        "Next, we'll move on to Model Training. We'll use a Random Forest Classifier for this initial exercise. The choice is motivated by the model's robustness and ability to handle both categorical and numerical features well."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 702,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "N_7AMz0BXacc",
        "outputId": "bf82eb6c-2003-4ada-8855-6883ad3553b3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(random_state=0)"
            ],
            "text/html": [
              "<style>#sk-container-id-16 {color: black;background-color: white;}#sk-container-id-16 pre{padding: 0;}#sk-container-id-16 div.sk-toggleable {background-color: white;}#sk-container-id-16 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-16 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-16 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-16 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-16 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-16 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-16 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-16 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-16 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-16 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-16 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-16 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-16 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-16 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-16 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-16 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-16 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-16 div.sk-item {position: relative;z-index: 1;}#sk-container-id-16 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-16 div.sk-item::before, #sk-container-id-16 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-16 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-16 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-16 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-16 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-16 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-16 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-16 div.sk-label-container {text-align: center;}#sk-container-id-16 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-16 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-16\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-16\" type=\"checkbox\" checked><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(random_state=0)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 702
        }
      ],
      "source": [
        "# Initialize and train the Random Forest Classifier\n",
        "rf_classifier = RandomForestClassifier(n_estimators=100, random_state=0)\n",
        "rf_classifier.fit(X_train_std, y_train)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oXPdKsWqFgVx"
      },
      "source": [
        "### Evaluation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 703,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ukDuomRCFjlL",
        "outputId": "9d7a7afa-453a-4a83-be1f-7be14589292e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "       False       0.96      0.88      0.92        25\n",
            "        True       0.82      0.93      0.87        15\n",
            "\n",
            "    accuracy                           0.90        40\n",
            "   macro avg       0.89      0.91      0.90        40\n",
            "weighted avg       0.91      0.90      0.90        40\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Make predictions on the test set\n",
        "y_pred = rf_classifier.predict(X_test_std)\n",
        "\n",
        "# Evaluate the model\n",
        "print(classification_report(y_test, y_pred))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V_t2yAtVXkyN"
      },
      "source": [
        "We've successfully trained a Random Forest Classifier and evaluated its performance on the test set. Here are some key metrics:\n",
        "\n",
        "- Accuracy: The model achieved an accuracy of approximately 90%, which means it correctly predicted the outcome of about 90% of the rounds in the test set.\n",
        "- Classification Report:Precision, recall, and F1-score are also reasonably good, indicating that the model performs well for both classes (win and loss).\n",
        "\n",
        "Lets try and see if we can do better."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Zh0RejDjPP7g"
      },
      "source": [
        "### Identify the most important features"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wLh-sEKOPTRV"
      },
      "source": [
        "Use a Random Forest to identify the most important features."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 704,
      "metadata": {
        "id": "upbXKoDvPY_A"
      },
      "outputs": [],
      "source": [
        "# Train-test split the data\n",
        "train_df, test_df = train_test_split(player_round_df, test_size=0.3, random_state=0)\n",
        "\n",
        "# Create the training set\n",
        "X_train = train_df[features]\n",
        "y_train = train_df[target]\n",
        "\n",
        "# Create the testing set\n",
        "X_test = test_df[features]\n",
        "y_test = test_df[target]\n",
        "\n",
        "# Standardize features\n",
        "stdsc = StandardScaler()\n",
        "X_train_std = stdsc.fit_transform(X_train)\n",
        "X_test_std = stdsc.transform(X_test)\n",
        "\n",
        "# Train a Random Forest model\n",
        "rf = RandomForestClassifier(random_state=0)\n",
        "rf.fit(X_train_std, y_train);"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pQxwht2yPv8T"
      },
      "source": [
        "Display the proportional importance of each feature"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 705,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DEN0pA2wPxwy",
        "outputId": "123ac04b-17d3-4c74-cc94-325aa62f373a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.12546639, 0.03895581, 0.04240723, 0.16244184, 0.02965713,\n",
              "       0.04833908, 0.01013297, 0.16641767, 0.00856367, 0.01397795,\n",
              "       0.03788023, 0.05896944, 0.06020956, 0.05136804, 0.08467042,\n",
              "       0.06054256])"
            ]
          },
          "metadata": {},
          "execution_count": 705
        }
      ],
      "source": [
        "# feature_importances_ will always sum to 1\n",
        "rf.feature_importances_"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hfXKnighP3vb"
      },
      "source": [
        "Get the indices of the most important features sorted by importance"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 706,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MY8RtcCqP5k7",
        "outputId": "521388b7-af50-4c06-9e42-0c55e9de403a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 7,  3,  0, 14, 15, 12, 11, 13,  5,  2,  1, 10,  4,  9,  6,  8])"
            ]
          },
          "metadata": {},
          "execution_count": 706
        }
      ],
      "source": [
        "# Sort the feature importances by their index position, then reverse the order\n",
        "# so that they are displayed from largest (most important) to smallest.\n",
        "\n",
        "index_of_best_features_in_order = rf.feature_importances_.argsort()[::-1]\n",
        "index_of_best_features_in_order"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k9w4tp2yQDMC"
      },
      "source": [
        "Order the features by importance using the sorted indices above"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 707,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "74UJ5FPIQE6A",
        "outputId": "cd452dc6-69c3-4365-be06-c2f9a7bdbe46"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['round', 'platformUserIdentifier_encoded', 'teamId_encoded',\n",
              "       'teamSide_encoded', 'agentName_encoded', 'score', 'kills', 'deaths',\n",
              "       'kdRatio', 'assists', 'damage', 'loadoutValue', 'remainingCredits',\n",
              "       'spentCredits', 'performance', 'economic_efficiency'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 707
        }
      ],
      "source": [
        "X_train.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 708,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ppvwq56JQHXU",
        "outputId": "9e1a54ff-c589-4917-d5f5-72481f43d030"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The best features in order:\n",
            "['deaths' 'teamSide_encoded' 'round' 'performance' 'economic_efficiency'\n",
            " 'remainingCredits' 'loadoutValue' 'spentCredits' 'score' 'teamId_encoded'\n",
            " 'platformUserIdentifier_encoded' 'damage' 'agentName_encoded' 'assists'\n",
            " 'kills' 'kdRatio']\n"
          ]
        }
      ],
      "source": [
        "# Display the best features in order\n",
        "features = np.array(X_train.columns)\n",
        "\n",
        "list_of_best_features_in_order = features[index_of_best_features_in_order]\n",
        "\n",
        "print(\"The best features in order:\")\n",
        "print(list_of_best_features_in_order)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z5fRTNqTQNCM"
      },
      "source": [
        "Sort the importance values, largest to smallest"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 709,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wioZPQhbQOaR",
        "outputId": "99a4bc36-c49f-41d6-9d17-5d4cf14c4b46"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.16641766992449714,\n",
              " 0.16244184331883607,\n",
              " 0.12546638807178534,\n",
              " 0.08467041879825361,\n",
              " 0.060542558451795535,\n",
              " 0.06020955631741856,\n",
              " 0.05896944424973018,\n",
              " 0.051368035003490194,\n",
              " 0.0483390796712862,\n",
              " 0.042407234225343544,\n",
              " 0.038955810590319336,\n",
              " 0.03788023398024381,\n",
              " 0.029657134681257625,\n",
              " 0.013977946571671184,\n",
              " 0.010132974170167083,\n",
              " 0.008563671973904506]"
            ]
          },
          "metadata": {},
          "execution_count": 709
        }
      ],
      "source": [
        "importances_ordered = sorted(rf.feature_importances_, reverse=True)\n",
        "importances_ordered"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 710,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101
        },
        "id": "3EzBESDwQSZP",
        "outputId": "293642ee-bf29-41d2-9d7b-a71f4c413ab9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              deaths  teamSide_encoded     round  performance  \\\n",
              "importance  0.166418          0.162442  0.125466      0.08467   \n",
              "\n",
              "            economic_efficiency  remainingCredits  loadoutValue  spentCredits  \\\n",
              "importance             0.060543           0.06021      0.058969      0.051368   \n",
              "\n",
              "               score  teamId_encoded  platformUserIdentifier_encoded   damage  \\\n",
              "importance  0.048339        0.042407                        0.038956  0.03788   \n",
              "\n",
              "            agentName_encoded   assists     kills   kdRatio  \n",
              "importance           0.029657  0.013978  0.010133  0.008564  "
            ],
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>deaths</th>\n",
              "      <th>teamSide_encoded</th>\n",
              "      <th>round</th>\n",
              "      <th>performance</th>\n",
              "      <th>economic_efficiency</th>\n",
              "      <th>remainingCredits</th>\n",
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              "      <th>spentCredits</th>\n",
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              "      background-color: #3B4455;\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-6ef71b62-ff74-496a-b4b5-fdcd9c7b05de button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-6ef71b62-ff74-496a-b4b5-fdcd9c7b05de');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 710
        }
      ],
      "source": [
        "most_important_features_df = pd.DataFrame(data=[importances_ordered],\n",
        "                                          columns=list_of_best_features_in_order,\n",
        "                                         index=[\"importance\"])\n",
        "most_important_features_df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-Lu1KHYPQX8b"
      },
      "source": [
        "Grab the features with an importance of 3% or higher"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 711,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ajNdiqh_QeS9",
        "outputId": "d789ebac-6973-4fd6-dabc-0b45f904709c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['deaths',\n",
              " 'teamSide_encoded',\n",
              " 'round',\n",
              " 'performance',\n",
              " 'economic_efficiency',\n",
              " 'remainingCredits',\n",
              " 'loadoutValue',\n",
              " 'spentCredits',\n",
              " 'score',\n",
              " 'teamId_encoded',\n",
              " 'platformUserIdentifier_encoded',\n",
              " 'damage']"
            ]
          },
          "metadata": {},
          "execution_count": 711
        }
      ],
      "source": [
        "most_important_features = []\n",
        "for col in most_important_features_df.columns:\n",
        "    if (most_important_features_df.loc['importance'][col]) >= 0.03:\n",
        "      most_important_features.append(col)\n",
        "\n",
        "most_important_features"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LTFO4z3CQijw"
      },
      "source": [
        "Try again but only using the best features"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 712,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Dw_cg2TQQzMM",
        "outputId": "fb474477-007e-44fa-9bd6-2f7b58d8c294"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "       False       1.00      0.96      0.98        25\n",
            "        True       0.94      1.00      0.97        15\n",
            "\n",
            "    accuracy                           0.97        40\n",
            "   macro avg       0.97      0.98      0.97        40\n",
            "weighted avg       0.98      0.97      0.98        40\n",
            "\n"
          ]
        }
      ],
      "source": [
        "features = most_important_features;\n",
        "\n",
        "# Splitting the dataset into training and testing sets\n",
        "X = player_round_df[features]\n",
        "y = player_round_df[target]\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
        "\n",
        "# Standardize features\n",
        "stdsc = StandardScaler()\n",
        "X_train_std = stdsc.fit_transform(X_train)\n",
        "X_test_std = stdsc.transform(X_test)\n",
        "\n",
        "# Initialize and train the Random Forest Classifier\n",
        "rf_classifier = RandomForestClassifier(n_estimators=100, random_state=0)\n",
        "rf_classifier.fit(X_train_std, y_train)\n",
        "\n",
        "# Make predictions on the test set\n",
        "y_pred = rf_classifier.predict(X_test_std)\n",
        "\n",
        "# Evaluate the model\n",
        "print(classification_report(y_test, y_pred))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JIRoofx4bGVH"
      },
      "source": [
        "By using the \"best features\" we even further increased our model's performance:\n",
        "- Accuracy: The model achieved an accuracy of approximately 97%, which is a significant increase over the previous 90%.\n",
        "- Classification Report: We also saw a significant uptick in both F1 scores.\n",
        "\n",
        "While the model is relatively simple and the dataset is not very large, these are promising results for a first attempt. The model could serve as a baseline for more complex models and feature engineering in the future.\n"
      ]
    }
  ],
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      "display_name": "Python 3",
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