stathead 0.2.1

Python client for the StatHead fantasy football model — rookie career predictions, historical ADP, and flattened feature matrices.

stathead

Python client for the StatHead fantasy football model. Returns pandas DataFrames of rookie career predictions, historical ADP, and the flattened feature matrix used to train the models.

Install

pip install stathead

Optional extras:

pip install "stathead[polars]"   # for .to_polars() helpers
pip install "stathead[duckdb]"   # for local SQL querying

Quick start

import stathead as sh

# 2026 rookie class predictions (77 players × ~80 columns)
rookies = sh.load_career_predictions_2026()
rookies.nlargest(10, "percentile")[["name", "position", "predictedCareerPPG", "modelTier"]]

# Historical backtest — predicted vs actual for every drafted rookie 2010-2025
backtest = sh.load_career_backtest()
wr = backtest[backtest.position == "WR"]
wr.groupby("modelTier")[["actualPPG", "predictedPPG"]].mean()

# Historical ADP, every season fully populated
adp = sh.load_adp_historical()
adp[(adp.season == 2023) & (adp.adp <= 24)]

Pinning to a specific version

Loaders resolve against the upstream GitHub repo. Pin to a commit SHA, tag, or branch for reproducibility:

sh.pin_version("a6720e5")   # or a tagged release

Clear the local cache if you want to re-fetch:

sh.clear_cache()

Data freshness

Data files are cached under ~/.cache/stathead/<ref>/ after the first download. Subsequent runs read from disk — no network roundtrip. Delete the cache directory or call clear_cache() to force a refresh.

Available loaders

Function	Returns	Shape
load_career_predictions_2026()	2026 rookie predictions	~77 × ~80 cols
load_career_backtest()	Historical rookies with pred + actual PPG	~1087 × ~100 cols
load_adp_historical()	Model-training ADP 2010-2025	4507 × 10
load_adp_ffc(season=None)	FFC PPR raw ADP (per season as fetched) — data via Fantasy Football Calculator	variable
load_prospect_grades(year=2026)	Scouting-report grades	~200 × 7
load_feature_matrix()	Raw feature-matrix.json (dict)	—
load_manual_overrides()	Manual CFBD usage overrides (dict)	—

Feature columns

Career-prediction and backtest rows include flattened model features under names like collegeDominatorRating, relativeAthleticScore, recruitRating, nflDraftPick, plus two source-agnostic families aggregated from the project's scouting-report pipeline:

• scout* — single-scout grade signals (e.g. scoutGradeDraft, scoutTierOrdinal, scoutBreadthDraft, scoutNComps).
• guide* — multi-source draft-guide aggregations (guideRankMean, guideRankSpread, guideNStrengths, guideNWeaknesses, guideSentimentNet, …).

Both families are derived numeric features (counts, means, ordinals) — no verbatim scouting-report text is shipped. hasScoutGrade / hasGuideData flag missing-data so models can distinguish "no scout coverage" from "low score".

Licensing & attribution

Package code is MIT-licensed. The data this package retrieves is derived from the StatHead project's own modeling pipeline; upstream sources (nflverse, FFC, CFBD, etc.) retain their own terms — see each source's license before redistributing. Sources whose terms do not permit third-party redistribution (e.g. KeepTradeCut dynasty values, verbatim prose from paid scouting reports) are intentionally not exposed by this client.

ADP data exposed by load_adp_ffc is courtesy of Fantasy Football Calculator — please preserve attribution when redistributing.

If you're building on these predictions, a link back to the StatHead repo is appreciated but not required.

Contributing

The package is small and focused — see python/src/stathead/ for the loader modules. Issues and PRs welcome at the main repo.