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Sleeper MCP

by dhegadde

Sleeper MCP

MCP server that gives Claude live data on your Sleeper leagues plus a fantasy projection model and simulations.

Data sources

  • Sleeper API: leagues, rosters, matchups, transactions, players, trending, projections

  • FantasyCalc: trade-market values and rankings

  • nflverse (via nflreadpy): play-by-play-derived stats since 2016, expected fantasy points (ffopportunity), snap counts, injury reports, schedules with Vegas spreads and totals

Related MCP server: Yahoo Fantasy MCP

Tools

Data: get_nfl_state, get_my_leagues, get_league, get_league_format, get_rosters, get_matchups, get_transactions, search_players, get_player, get_trending, get_rankings, get_free_agents, evaluate_trade, get_projections

Model: project_lineup, simulate_matchup, playoff_odds, ros_projections, trade_impact, buy_low_sell_high, waiver_targets, model_info

Model

analytics/ builds player-week features (recent usage, xFP, target/air-yard share, snap %, Vegas implied total, opponent points allowed by position, injury reports, Sleeper's projection), then trains LightGBM heads for the mean plus 10th/50th/90th percentiles, receptions, and passing TDs (the last two rescore to any league's scoring). Simulations sample each player from a two-piece normal fit to those percentiles.

Retrain weekly (Tuesday after Monday Night Football):

uv run python train.py

The script prints the backtest against Sleeper's projections. Features refresh on their own every 6 hours; retraining only updates the model weights.

Backtest

Each test season is held out of training. Compared with Sleeper's own weekly projections on the same player-weeks:

Held-out season

Player-weeks

Start/sit calls correct (model vs. Sleeper)

RMSE (model vs. Sleeper)

MAE, median prediction (model vs. Sleeper)

80% band coverage

2024

5,226

65.0% vs. 65.1%

6.15 vs. 6.18

4.37 vs. 4.45

80.6%

2025

5,408

65.4% vs. 65.4%

5.99 vs. 6.11

4.22 vs. 4.42

79.7%

In short: about as good as Sleeper for start/sit decisions, slightly better on large misses, and its floor/ceiling ranges are calibrated, which is what the simulations rely on. Mean-prediction MAE is about equal to Sleeper's.

Setup

Requires uv and Python 3.12+.

uv sync
uv run python train.py      # first run downloads nflverse data and trains the model

Register

Claude Code:

claude mcp add sleeper -s user -e SLEEPER_USERNAME=<your_sleeper_username> -- uv run --directory /path/to/sleeper-mcp python server.py

Claude desktop app: SLEEPER_USERNAME=<your_sleeper_username> ./add-to-claude-app.sh (it waits for the app to quit, since the app overwrites its config while running).

License

MIT. Data comes from the Sleeper API, FantasyCalc, and nflverse under their own terms; none of it is redistributed here.

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