walrusquant
14 skills with substantive source documentation and an official publisher or observed installation demand. Read each skill’s original documentation through its card.
walrusquantbaseline-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.
- instalações
- 1
- GitHub Stars
- 49
- Atualizado
- 9 de set.
walrusquantcalibration-check
Evaluate whether sports-model probabilities match observed frequencies. Use for Brier score, log loss, reliability bins, ECE, segment checks, and recalibration decisions.
- instalações
- 1
- GitHub Stars
- 49
- Atualizado
- 9 de set.
walrusquanteda-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.
- instalações
- 1
- GitHub Stars
- 49
- Atualizado
- 9 de set.
walrusquantenvironment-setup
Create and verify a portable Python environment for sports analysis. Use for machine setup, onboarding, dependency diagnosis, or reproducibility checks.
- instalações
- 1
- GitHub Stars
- 49
- Atualizado
- 9 de set.
walrusquantfeature-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.
- instalações
- 1
- GitHub Stars
- 49
- Atualizado
- 9 de set.
walrusquantmodel-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.
- instalações
- 1
- GitHub Stars
- 49
- Atualizado
- 9 de set.
walrusquantnflreadpy
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.
- instalações
- 1
- GitHub Stars
- 49
- Atualizado
- 9 de set.
walrusquantpredictive-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.
- instalações
- 1
- GitHub Stars
- 49
- Atualizado
- 9 de set.
walrusquantpybaseball
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.
- instalações
- 1
- GitHub Stars
- 49
- Atualizado
- 9 de set.
walrusquantresults-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.
- instalações
- 1
- GitHub Stars
- 49
- Atualizado
- 9 de set.
walrusquantsimulation-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.
- instalações
- 1
- GitHub Stars
- 49
- Atualizado
- 9 de set.
walrusquantsports-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.
- instalações
- 1
- GitHub Stars
- 49
- Atualizado
- 9 de set.
walrusquantsports-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.
- instalações
- 1
- GitHub Stars
- 49
- Atualizado
- 9 de set.
walrusquanttime-series-sports
Engineer and compare time-safe sports form features. Use for rolling windows, EWMA, rest, schedule gaps, early-season handling, and chronological evaluation.
- instalações
- 1
- GitHub Stars
- 49
- Atualizado
- 9 de set.