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

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:

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