walrusquant

Choose public sports data sources for a modeling question across NFL, NBA, MLB, NHL, college sports, soccer, and more. Use before ...

Create and verify a portable Python environment for sports analysis. Use for machine setup, onboarding, dependency diagnosis, or r...

Record sports-modeling experiments with the hypothesis, data cut, validation charter, metrics, leakage status, decision, commands,...

Audit sports modeling tables and workflows for target, temporal, join, preprocessing, and split leakage. Use before trusting backt...

Write a durable sports model card covering identity, intended use, target, decision time, data, features, baselines, validation, r...

Interpret sports models using held-out predictions, coefficients, error slices, largest misses, calibration context, and stability...

Load NFL schedules, play-by-play, rosters, and player or team statistics directly from nflverse with nflreadpy. Use for NFL acquis...

Load MLB Statcast, batting, pitching, standings, and player lookup data directly with pybaseball. Use for bounded pitch-level pull...

Build and evaluate sports strength models, including sequential Elo ratings, offense/defense splits, power ratings, and strict pre...

Simulate game and season outcomes from user-supplied probabilities or ratings, summarize uncertainty, and test sensitivity to assu...

One or two sentences. State WHAT the skill does and WHEN an agent should load it. Write for discovery, not marketing.

Connect the optional sports_ds Python toolkit to the standalone sports analytics skills. Use when the user explicitly mentions spo...

Define a sports analysis or prediction question, grain, decision time, baselines, primary metrics, validation, and acceptance crit...

Create honest sports-analysis figures from user-owned data, including distributions, rates, rating trajectories, calibration plots...

Engineer and compare time-safe sports form features. Use for rolling windows, EWMA, rest, schedule gaps, early-season handling, an...