betting

name	betting
description	Betting analysis — odds conversion, de-vigging, edge detection, Kelly criterion, arbitrage detection, parlay analysis, and line movement. Pure computation, no API calls. Works with odds from any source: ESPN (American odds), Polymarket (decimal probabilities), Kalshi (integer probabilities). Use when: user asks about bet sizing, expected value, edge analysis, Kelly criterion, arbitrage, hedging, profit locking, weighted outcome coverage, parlays, line movement, odds conversion, or comparing odds across sources. Also use when you have odds from ESPN and a prediction market price and want to evaluate whether a bet has positive expected value. Don't use when: user asks for live odds or market data — use polymarket, kalshi, or the sport-specific skill to fetch odds first, then use this skill to analyze them.
license	MIT
metadata	{"author":"machina-sports","version":"0.2.0"}

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%

Hedge an Existing Ticket Portfolio

Use a state matrix when existing tickets pay only in specific outcomes:

1. Define exhaustive, mutually exclusive result states. Correct-score work must include listed scores plus the bookmaker's other score state.

2. Record each existing ticket's total return in every state where it wins.

3. Expand every proposed combination market into the states where it pays.

4. For state s, calculate:

   net_s = existingReturn_s + sum(stake_j * decimalOdds_j for winning j) - existingStakes - sum(stake_j)

5. A strict lock exists only when min(net_s) > 0. If any state is negative, label the plan weighted coverage or risk reduction and report that state's maximum loss.

For a disjoint branch with target return R, stake R / decimalOdds. Compare a bundle with its missing exact scores using cost per unit return:

• bundle cost: 1 / bundleOdds
• separate-score cost: sum(1 / scoreOdds)

Use the cheaper complete cover after removing states already paid by existing tickets. Do not call a portfolio profitable merely because every broad win/draw/loss category contains a winning ticket; all old and new stakes count. Cash out is guaranteed return only when an actual offer is visible and accepted. A later-match parlay adds unresolved states and therefore cannot lock the current match by itself.

When the user permits losing states, keep the matrix exhaustive, assign larger target returns to favored states, and explicitly list the sacrificed states. Weighting changes the return distribution; it does not convert partial coverage into a strict hedge.

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