Okhp3 Outcome Modeling Sports

---
name: okhp3-outcome-modeling-sports
description: >
  Model team, game, and player outcomes from repeated sports events, matchup
  history, schedules, injuries, and performance metrics. Use for sports
  rankings, matchup forecasts, season projections, or fantasy-oriented state
  models. Load `okhp3-outcome-modeling-core` first; this adapter does not
  provide live odds or trade execution.
license: MIT
compatibility: >
  Any Agent Skills-compatible client with access to user-supplied or approved
  sports data. Live data retrieval requires a separate approved data skill.
metadata:
  author: "Jamie Hill (OverKill Hill P³)"
  version: "1.1.0"
  category: "universal"
  origin: "okhp3/skillz"
  homepage: "https://overkillhill.com"
  author-github: "https://github.com/OKHP3"
  in_scope: "Team, game, player, matchup, and season state modeling; Opponent-adjusted metrics, schedule context, roster events, and uncertainty; Sports ranking and projection specifications"
  out_of_scope: "Fabricating current scores, rosters, injuries, odds, or transactions; Treating one game as proof of a team or player skill level; Executing bets, trades, or sportsbook actions"
  status: enhanced-computational-payload
  tags: sports forecasting, matchup model, team strength, player state, opponent adjustment, schedule
  triggers: team projection, game forecast, player projection, Elo, EPA, strength of schedule, injury impact
  inputs: dated sports events, team or player states, opponent context, venue, rest, and structural events
  outputs: adjusted state, matchup probability or ranking, uncertainty, calibration, and data gaps
  runtimes: Portable prose by default; optional Python 3.9+ standard library helper for local JSON arithmetic
---

# okhp3-outcome-modeling-sports

**OverKill Hill P³** · [overkillhill.com](https://overkillhill.com) · [github.com/OKHP3](https://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

| In scope | Out of scope |
|----------|-------------|
| Matchup, team, player, and season projections | Guaranteed picks or certainty claims |
| Opponent adjustment, schedule context, and roster events | Unverified live sports facts |
| Ranking, calibration, uncertainty, and scenario analysis | Bet placement or bankroll execution |

---

## 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.

```text
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](https://overkillhill.com) · [OverKill Hill P³](https://overkillhill.com)
Published at [github.com/OKHP3](https://github.com/OKHP3)
Part of the [OKHP3/skillz](https://github.com/OKHP3/skillz) Agent Skill library.
MIT License -- free to use, fork, and adapt. A nod to the source is appreciated.