Sleeper MCP
This server does not currently have a deployable release on Glama. Follow the repository's setup instructions to install and configure it for your MCP client, including any required credentials and local dependencies.
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.pyThe 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 modelRegister
Claude Code:
claude mcp add sleeper -s user -e SLEEPER_USERNAME=<your_sleeper_username> -- uv run --directory /path/to/sleeper-mcp python server.pyClaude 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.
This server cannot be deployed
Maintenance
Related MCP Connectors
Free fantasy sports AI: ESPN, Sleeper and Fantrax league data for Claude and ChatGPT. Read-only.
Fantasy analysis for your ESPN, Yahoo, and Sleeper leagues. Reads your leagues, never changes them.
NFL analytics tools for AI agents: stats, fantasy, injuries, schedules, and advanced analysis.
Fantasy football for any AI agent: draft board, start/sit, waivers, your Sleeper league. Free.
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