Okhp3 Outcome Modeling Sports

okhp3-outcome-modeling-sports

OverKill Hill P³ · overkillhill.com · github.com/OKHP3

Apply the outcome-modeling core to sports without confusing raw wins with underlying performance. Sports evidence is organized as time-indexed team or player states, opponent-adjusted interactions, and structural events such as injuries, coaching changes, and roster moves.

Scope

Required workflow

1. Load okhp3-outcome-modeling-core and define the outcome, horizon, scoring rule, and as-of date.
2. Identify the sports entities, event grain, matchup relationship, and season structure.
3. Build time-indexed team or player states from only available prior events.
4. Adjust for opponent, venue, rest, schedule strength, role, and sample size where relevant.
5. Treat injuries, trades, coaching changes, and role changes as structural events or regime changes.
6. Separate descriptive performance from predictive features and avoid double-counting correlated metrics.
7. Compare simple baselines such as record, point or scoring differential, Elo-style strength, and replacement value against richer models.
8. Validate chronologically and report calibration, uncertainty, and failure modes.

Sports state model

Use a state vector appropriate to the sport. Preserve raw values and transformed values, with feature metadata. For a matchup, model the relevant difference or interaction between the two states rather than treating each team as an isolated row.

team_state(team, date)
matchup_delta(A, B, date)
outcome(matchup, date)

Do not assume that the best team wins every individual game. Aggregation can reveal persistent strength while realized games retain randomness.

Computational payload

Read references/computational-model.md for the rating update, matchup probability, and worked calculation. Read references/glossary.md before using abbreviations. Reproduce the synthetic example with scripts/calculate-sports-model.py examples/sports-example.json. The helper is local, deterministic, and read-only.

Output contract

Provide:

• target, horizon, and as-of boundary;
• source and freshness status;
• team or player state definition;
• opponent and schedule adjustments;
• major structural events;
• baseline versus candidate model;
• probability or ranking with uncertainty;
• decision interpretation and unresolved data gaps.

For betting or fantasy decisions, hand off to okhp3-outcome-modeling-markets or okhp3-nfl-fantasy-picks as appropriate.

References

• references/computational-model.md -- sports equations and example.
• references/glossary.md -- sports terms and abbreviations.
• examples/sports-example.json -- synthetic matchup fixture.
• scripts/calculate-sports-model.py -- dependency-free rating helper.
• ../okhp3-outcome-modeling-core/SKILL.md -- shared outcome-modeling contract.
• ../okhp3-nfl-fantasy-picks/SKILL.md -- NFL fantasy-specific adapter.

About

Built by Jamie Hill · OverKill Hill P³ Published at github.com/OKHP3 Part of the OKHP3/skillz Agent Skill library. MIT License -- free to use, fork, and adapt. A nod to the source is appreciated.