sports-analytic-skills

In the author's own words, from each skill's SKILL.md.

• anti-slop-analytics

  Review sports figures, tables, notebooks, and reports for chartjunk, fake certainty, cropped axes, baseline erasure, metric laundering, and weak reproducibility. Use when asked to clean up or audit analytical presentation.

  skills/anti-slop-analytics/SKILL.md on GitHub (opens in a new tab)

• baseline-models

  Design and evaluate simple sports prediction baselines before accepting more complex models. Use for constant-rate, home-advantage, logistic, Elo-style, or market-reference comparisons.

  skills/baseline-models/SKILL.md on GitHub (opens in a new tab)

• calibration-check

  Evaluate whether sports-model probabilities match observed frequencies. Use for Brier score, log loss, reliability bins, ECE, segment checks, and recalibration decisions.

  skills/calibration-check/SKILL.md on GitHub (opens in a new tab)

• data-sources

  Choose public sports data sources for a modeling question across NFL, NBA, MLB, NHL, college sports, soccer, and more. Use before acquisition code or whenever source coverage, grain, licensing, or historical depth is unclear.

  skills/data-sources/SKILL.md on GitHub (opens in a new tab)

• eda-sports

  Exploratory data analysis for user-provided sports data: grain, key integrity, coverage, missingness, entity balance, base rates, outliers, structural breaks, and leakage red flags. Use before feature engineering or model fitting.

  skills/eda-sports/SKILL.md on GitHub (opens in a new tab)

• environment-setup

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

  skills/environment-setup/SKILL.md on GitHub (opens in a new tab)

• experiment-log

  Record sports-modeling experiments with the hypothesis, data cut, validation charter, metrics, leakage status, decision, commands, and artifacts. Use for trials, model comparisons, and reproducible research history.

  skills/experiment-log/SKILL.md on GitHub (opens in a new tab)

• feature-rules

  Define, review, and document point-in-time legal sports-model features. Use when creating rolling form, rest, matchup, rating, roster, injury, or contextual predictors.

  skills/feature-rules/SKILL.md on GitHub (opens in a new tab)

• leakage-audit

  Audit sports modeling tables and workflows for target, temporal, join, preprocessing, and split leakage. Use before trusting backtests or reported predictive performance.

  skills/leakage-audit/SKILL.md on GitHub (opens in a new tab)

• model-card

  Write a durable sports model card covering identity, intended use, target, decision time, data, features, baselines, validation, results, limits, maintenance, and kill conditions. Use when freezing or sharing a model.

  skills/model-card/SKILL.md on GitHub (opens in a new tab)

• model-interpretation

  Interpret sports models using held-out predictions, coefficients, error slices, largest misses, calibration context, and stability checks. Use after time-aware evaluation when explaining what drives a model and where it fails.

  skills/model-interpretation/SKILL.md on GitHub (opens in a new tab)

• nflreadpy

  Load NFL schedules, play-by-play, rosters, and player or team statistics directly from nflverse with nflreadpy. Use for NFL acquisition, schema review, bounded snapshots, and preparing user-owned analysis artifacts.

  skills/nflreadpy/SKILL.md on GitHub (opens in a new tab)

• predictive-modeling

  Build and evaluate predictive sports models from user-provided modeling data. Use for binary outcome models, feature/model selection, chronological backtests, probability scoring, and comparison to baselines.

  skills/predictive-modeling/SKILL.md on GitHub (opens in a new tab)

• pybaseball

  Load MLB Statcast, batting, pitching, standings, and player lookup data directly with pybaseball. Use for bounded pitch-level pulls, season tables, schema checks, and user-owned baseball data artifacts.

  skills/pybaseball/SKILL.md on GitHub (opens in a new tab)

• ratings-strength-models

  Build and evaluate sports strength models, including sequential Elo ratings, offense/defense splits, power ratings, and strict pre-event matchup features. Use for rankings, opponent adjustment, and strong prediction baselines.

  skills/ratings-strength-models/SKILL.md on GitHub (opens in a new tab)

• results-reporting

  Report sports analysis and modeling results with the question, data, methods, validation, baselines, metrics, interpretation, limits, figures, and reproduction pointers. Use for research notes, reports, and final answers.

  skills/results-reporting/SKILL.md on GitHub (opens in a new tab)

• simulation-sports

  Simulate game and season outcomes from user-supplied probabilities or ratings, summarize uncertainty, and test sensitivity to assumptions. Use for standings, win totals, matchup distributions, and scenario analysis.

  skills/simulation-sports/SKILL.md on GitHub (opens in a new tab)

• sports-ds-bridge

  Connect the optional sports_ds Python toolkit to the standalone sports analytics skills. Use when the user explicitly mentions sports_ds, wants its NFL/NBA/MLB public-data loaders or CLI, needs toolkit setup/troubleshooting, or wants to convert toolkit output into a skill's documented input artifact.

  skills/sports-ds-bridge/SKILL.md on GitHub (opens in a new tab)

• sports-modeling-doctrine

  Define a sports analysis or prediction question, grain, decision time, baselines, primary metrics, validation, and acceptance criteria before choosing algorithms. Use at the start of any sports modeling project.

  skills/sports-modeling-doctrine/SKILL.md on GitHub (opens in a new tab)

• sports-visualization

  Create honest sports-analysis figures from user-owned data, including distributions, rates, rating trajectories, calibration plots, and walk-forward metric comparisons. Use for exploration and communication.

  skills/sports-visualization/SKILL.md on GitHub (opens in a new tab)

• sportsdataverse-py

  Load public multi-sport data directly with SportsDataverse Python. Use for NBA, MLB, NHL, college sports, soccer, and other supported league sources when a user needs schedules, box scores, rosters, or event data.

  skills/sportsdataverse-py/SKILL.md on GitHub (opens in a new tab)

• statistical-modeling

  Guided statistical modeling for user-provided sports data: selecting models for binary, continuous, and count outcomes; assumption checks; effect sizes; time-aware inference; GLM diagnostics; and complete reporting.

  skills/statistical-modeling/SKILL.md on GitHub (opens in a new tab)

• time-series-sports

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

  skills/time-series-sports/SKILL.md on GitHub (opens in a new tab)

• validation-design

  Design chronological sports-model validation and a written evaluation charter. Use for walk-forward splits, grouped time folds, metric locking, tuning boundaries, and go/no-go rules.

  skills/validation-design/SKILL.md on GitHub (opens in a new tab)

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