---
title: "sports-analytic-skills"
description: "Standalone agent skills for rigorous sports analytics and modeling, with an optional sports_ds data-toolkit bridge."
canonical: https://agentpluginsdirectory.com/plugins/sports-analytic-skills
last-updated: 2026-10-08
---

# sports-analytic-skills
Standalone agent skills for rigorous sports analytics and modeling, with an optional sports_ds data-toolkit bridge.
- Slug: sports-analytic-skills
- Publisher: Adam Wickwire
- Repository: https://github.com/WalrusQuant/sports-analytic-skills
- Manifest: plugin.json
- Version: 0.12.0
- License: MIT
- Category (editorial): databases
- Skills: 24 (anti-slop-analytics, baseline-models, calibration-check, data-sources, eda-sports, environment-setup, experiment-log, feature-rules, leakage-audit, model-card, model-interpretation, nflreadpy, predictive-modeling, pybaseball, ratings-strength-models, results-reporting, simulation-sports, sports-ds-bridge, sports-modeling-doctrine, sports-visualization, sportsdataverse-py, statistical-modeling, time-series-sports, validation-design)
- MCP servers: 0
- Stars: 50
- Repository created: 2026-08-24
- Repository last pushed: 2026-09-09
- Publisher type: User
- Listing: https://agentpluginsdirectory.com/plugins/sports-analytic-skills
- Schema: https://agent-plugins.org/schemas/1.0.0/plugin.schema.json

## What sports-analytic-skills does, in the publisher's words

Standalone agent skills for sports analytics and modeling.

Install the skills, point your agent at your data, and use focused guidance for EDA, time-safe features, baselines, statistical and predictive models, walk-forward validation, calibration, simulation, interpretation, and honest reporting. The skills work without this repository's Python package or pipelines.

The repository also contains an optional sports_ds toolkit for acquiring and normalizing public NFL, NBA, and MLB data. The sports-ds-bridge skill connects that toolkit to the portable artifacts consumed by the standalone skills.

- explore a team-game or player-game dataset before modeling;
- audit candidate features for decision-time leakage;
- establish constant and domain baselines;
- build a time-ordered validation design;
- assess probability calibration;
- interpret the largest misses and error slices;

From the project README, punctuation lightly normalized. Full text: https://raw.githubusercontent.com/WalrusQuant/sports-analytic-skills/HEAD/README.md

## Skills

- 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.
- 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.
- calibration-check: Evaluate whether sports-model probabilities match observed frequencies. Use for Brier score, log loss, reliability bins, ECE, segment checks, and recalibration decisions.
- 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.
- 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.
- environment-setup: Create and verify a portable Python environment for sports analysis. Use for machine setup, onboarding, dependency diagnosis, or reproducibility checks.
- 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.
- 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.
- leakage-audit: Audit sports modeling tables and workflows for target, temporal, join, preprocessing, and split leakage. Use before trusting backtests or reported predictive performance.
- 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.
- 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.
- 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.
- predictive-modeling
- pybaseball
- ratings-strength-models
- results-reporting
- simulation-sports
- sports-ds-bridge
- sports-modeling-doctrine
- sports-visualization
- sportsdataverse-py
- statistical-modeling
- time-series-sports
- validation-design

Descriptions come from the frontmatter of each SKILL.md, punctuation lightly normalized.