Sports Betting

---
name: sports--betting
description: >-
  |
origin: "github.com/machina-sports/sports-skills (skill: betting)"
license: MIT
version: "1.0.0"
compatibility: "yana-ai >= 0.14.0"
---

# Betting Analysis

Before writing queries, consult `references/api-reference.md` for odds formats, command parameters, and key concepts.

## Quick Start

```bash
sports-skills betting convert_odds --odds=-150 --from_format=american
sports-skills betting devig --odds=-150,+130 --format=american
sports-skills betting find_edge --fair_prob=0.58 --market_prob=0.52
sports-skills betting evaluate_bet --book_odds=-150,+130 --market_prob=0.52
sports-skills betting find_arbitrage --market_probs=0.48,0.49
sports-skills betting parlay_analysis --legs=0.58,0.62,0.55 --parlay_odds=600
sports-skills betting line_movement --open_odds=-140 --close_odds=-160
```

Python SDK:
```python
from sports_skills import betting

betting.convert_odds(odds=-150, from_format="american")
betting.devig(odds="-150,+130", format="american")
betting.find_edge(fair_prob=0.58, market_prob=0.52)
betting.find_arbitrage(market_probs="0.48,0.49")
betting.parlay_analysis(legs="0.58,0.62,0.55", parlay_odds=600)
betting.line_movement(open_odds=-140, close_odds=-160)
```

## CRITICAL: Before Any Analysis

CRITICAL: Before calling any analysis command, verify:
- Odds format is correctly identified (american, decimal, or probability).
- ESPN odds are de-vigged with `devig` before computing edge vs prediction market prices.
- This module computes — it does not fetch. Obtain odds from sport-specific skills or polymarket/kalshi first.

## Workflows

### Compare ESPN vs Polymarket/Kalshi

1. Get ESPN moneyline odds (e.g., from `nba get_scoreboard`): Home: `-150`, Away: `+130`
2. Get Polymarket/Kalshi price for the same outcome (e.g., home at `0.52`)
3. De-vig: `devig --odds=-150,+130 --format=american` → Fair: Home 57.9%, Away 42.1%
4. Compare: `find_edge --fair_prob=0.579 --market_prob=0.52` → Edge: 5.9%, EV: 11.3%
5. Or all in one step: `evaluate_bet --book_odds=-150,+130 --market_prob=0.52`

### Arbitrage Detection

1. Get best price per outcome from different sources (Polymarket home at 0.48, Kalshi away at 0.49)
2. `find_arbitrage --market_probs=0.48,0.49 --labels=home,away`
3. Total implied 0.97 (< 1.0) → arbitrage found, guaranteed ROI: 3.09%

### Parlay Evaluation

1. De-vig each leg: Leg 1 → 0.58, Leg 2 → 0.55, Leg 3 → 0.50
2. `parlay_analysis --legs=0.58,0.55,0.50 --parlay_odds=600`
3. Returns combined fair probability, edge, and Kelly fraction

### Line Movement Analysis

1. Get ESPN open and close lines: Open -140, Close -160
2. `line_movement --open_odds=-140 --close_odds=-160`
3. Returns probability shift, direction, and classification (sharp_action, steam_move, etc.)

## Examples

Example 1: Edge check using ESPN and Polymarket prices
User says: "Is there edge on the Lakers game? ESPN has them at -150 and Polymarket has them at 52 cents"
Actions:
1. Call `devig(odds="-150,+130", format="american")` → fair home probability ~58%
2. Call `find_edge(fair_prob=0.58, market_prob=0.52)` → edge ~6%, positive EV
3. Call `kelly_criterion(fair_prob=0.58, market_prob=0.52)` → optimal bet fraction
Result: Present edge percentage, EV per dollar, and recommended bet size as % of bankroll

Example 2: Arbitrage opportunity detection
User says: "Can I arb this? Polymarket has home at 48 cents and Kalshi has away at 49 cents"
Actions:
1. Call `find_arbitrage(market_probs="0.48,0.49", labels="home,away")`
2. Check `arbitrage_found` in result
Result: If arbitrage: present allocation percentages and guaranteed ROI. If not: present overround and explain no guaranteed profit

Example 3: Parlay evaluation
User says: "Is this 3-leg parlay at +600 worth it?"
Actions:
1. De-vig each leg to get fair probabilities (e.g., 0.58, 0.62, 0.55)
2. Call `parlay_analysis(legs="0.58,0.62,0.55", parlay_odds=600)`
Result: Present combined fair probability, edge, EV, +EV or -EV verdict, and Kelly fraction

Example 4: Line movement interpretation
User says: "The line moved from -140 to -160, what does that mean?"
Actions:
1. Call `line_movement(open_odds=-140, close_odds=-160)`
Result: Present probability shift, direction, magnitude, and classification (sharp action, steam move, etc.)

Example 5: De-vig a standard spread
User says: "What are the true odds for this spread? Both sides are -110"
Actions:
1. Call `devig(odds="-110,-110", format="american")`
Result: Present each side as 50% fair probability, vig is ~4.5%

Example 6: Odds format conversion
User says: "Convert -200 to implied probability"
Actions:
1. Call `convert_odds(odds=-200, from_format="american")`
Result: Present 66.7% implied probability and 1.50 decimal odds

## Commands that DO NOT exist — never call these

- ~~`get_odds`~~ — does not exist. This module analyzes odds; it does not fetch them. Use nba-data/nfl-data/etc. for ESPN odds, or polymarket/kalshi for prediction market prices.
- ~~`calculate_ev`~~ — does not exist. Use `find_edge` or `evaluate_bet` instead.
- ~~`compare_markets`~~ — does not exist. Use the `markets` skill for cross-platform comparison.

If a command is not listed in `references/api-reference.md`, it does not exist.

## Troubleshooting

Error: `ValueError: unknown format` when calling `convert_odds`
Cause: The `from_format` parameter is not one of `american`, `decimal`, or `probability`
Solution: Use exactly `american`, `decimal`, or `probability` as the format string

Error: `find_edge` returns negative EV when a positive edge is expected
Cause: Fair probability and market probability may be reversed, or de-vigging was skipped
Solution: Run `devig` on sportsbook odds first, then pass the de-vigged `fair_prob` to `find_edge`

Error: `find_arbitrage` shows no arbitrage even when prices seem low
Cause: Prices may sum to more than 1.0 when all outcomes are correctly included
Solution: Verify you are using the correct probabilities for all outcomes; check `total_implied` in the result

Error: Kelly fraction is very high (greater than 0.5)
Cause: Edge estimate is very large — often from a miscalculated fair probability
Solution: Use half-Kelly or quarter-Kelly for conservative sizing. Re-verify fair probability via `devig`