sleeper-rosters

I play in a "keeper auction" fantasy football league that we run on the Sleeper App (great app!) and, for our league, we need to track each player's "salary," the highest price used for the player in the draft or during the season on a waiver claim.

I am new to Python so thought this would be an interesting way to learn Python & get data from the Sleeper APIs, then map players to users, rosters and transactions and do the calculations I need.

This needs cleanup but does what I wanted.

What it does:

• From Sleeper APIs, read:
  • Owner info
  • draft info
  • rosters
  • transactions for all players
• Summarize all transactions for all players, finding each player's highest-priced transaction

What it doesn't do yet (what I'll add):

• Unit testing,
• Read league info from a config file (so I don't have to hard code every year)
• Have one master python file that runs them all.

I'm putting it here anyway to save my current, working versions and in case anyone stumbles on this and would like to use any of it.

I built all of these using Python 3.9.
I ran them in the sequence noted below and then imported to Excel, which I sent to my league mates. I also attached what the final JSON looks like when imported to Excel.

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Here's my to-do notes I created for myself while building it:

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AFTER DRAFT: https://api.sleeper.app/v1/draft/<draft_id> All drafts for lg: https://api.sleeper.app/v1/league/650130288072040448/drafts --> 2021 is 650130288072040449? that draft https://api.sleeper.app/v1/draft/650130288072040449/picks

1. Save draft data to json file

2. Run parse_draft.py -o to extract, prepare usable data Note: You'll refer to this draft data later so note the file location, add to all.py

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BEFORE SEASON

1. Run get_users.py -o to get owners -> Save to ...data/2022/users.json Ex: "97088838635503616": {"display_name": "Tim_Terry", "team_name": "Vegas Vipers"},

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AFTER DRAFT

1. Download draft data from url, save as json - to data/2022/draft-2022.json Ex: https://api.sleeper.app/v1/draft/650130288072040449/picks Ex: {"round":1,"roster_id":1,"player_id":"6794","picked_by":"378716407027937280","pick_no":1,"metadata":{"years_exp":"2","team":"MIN","status":"Active","sport":"nfl","slot":"1","position":"WR","player_id":"6794","number":"18","news_updated":"1646327726661","last_name":"Jefferson","injury_status":"","first_name":"Justin","amount":"10"},"is_keeper":false,"draft_slot":1,"draft_id":"787796366440124417"}

2. Run parse_draft.py -o data/2022/parsed_draft.json to create a consumable json Ex: {'6794': {'type': 'draft', 'roster_id': 1, 'price': '10', 'date': 'Sep 1, 2022'}, '6803':

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TO UPDATE ALL IN JUST ONE PYTHON SCRIPT EXECUTION https://api.sleeper.app/v1/players/nfl -- I saved to players-from-site.json in my data dir

2. Run all.py -o <full players file from step 1> -- I defined output file as "all-out-.json" and then opened that in Excel

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TO UPDATE ONE SET OF DATA AT A TIME FOR DEBUGGING

1. Download player pool from URL, save as json (huge file) https://api.sleeper.app/v1/players/nfl -- I saved to players-from-site.json in my data dir

2. Run parse_players.py -o <full players file from step 1> to reduce pool to usable json file -> Save to data/2022/parsed_players.json Ex: {"5870": {"positions": ["QB"], "team": "NYG", "full_name": "Daniel Jones"}, ...

3. Run get_rosters.py -o to get current roster (looks ok 2022-11-02) Gets data direct from URL output is dict w key = owner_id, value = player id list

   • write to data/2022/rosters-.json Ex: [{"owner_id": "378716407027937280", "roster_id": 1, "players": ["1466", "1476", "1825", "4035", "4098", "421", "4881", "5955", "6083", "6786", "6794", "6803", "6945", "6955", "7525", "7571", "7611", "8121", "8137", "8148", "NE"]},

4. Run get_transactions.py -o (gets entire year) - gets waiver claims & prices, merge w draft, sort in dollar order desc Key = player_id, list of transaction dicts sorted by price hi to low, each transaction dict has entries for date, price, roster_id, type (draft/waiver) - merges transactions and drafts, putting results per player in reverse order of price - write to data/2022/transactions.xlsx Ex: {"1067": [{"date": "Sep 1, 2022", "price": 1, "roster_id": 5, "type": "draft"}], "1166":

5. Run final.py -o to merge teams, rosters and transactions transactions have key=player_id value = list of transaction dicts sorted in reverse price order value transaction ids have roster_id to get rosters, players & curr salaries