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, 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 Get ESPN moneyline odds (e.g., from nba get_scoreboard ): Home: -150 , Away: +130 Get Polymarket/Kalshi price for the same outcome (e.g., home at 0.52 ) De-vig: devig --odds=-150,+130 --format=american → Fair: Home 57.9%, Away 42.1% Compare: find_edge --fair_prob=0.579 --market_prob=0.52 → Edge: 5.9%, EV: 11.3% Or all in one step: evaluate_bet --book_odds=-150,+130 --market_prob=0.52 Arbitrage Detection Get best price per outcome from different sources (Polymarket home at 0.48, Kalshi away at 0.49) find_arbitrage --market_probs=0.48,0.49 --labels=home,away Total implied 0.97 (< 1.0) → arbitrage found, guaranteed ROI: 3.09% Parlay Evaluation De-vig each leg: Leg 1 → 0.58, Leg 2 → 0.55, Leg 3 → 0.50 parlay_analysis --legs=0.58,0.55,0.50 --parlay_odds=600 Returns combined fair probability, edge, and Kelly fraction Line Movement Analysis Get ESPN open and close lines: Open -140, Close -160 line_movement --open_odds=-140 --close_odds=-160 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: Call devig(odds="-150,+130", format="american") → fair home probability ~58% Call find_edge(fair_prob=0.58, market_prob=0.52) → edge ~6%, positive EV 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: Call find_arbitrage(market_probs="0.48,0.49", labels="home,away") 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: De-vig each leg to get fair probabilities (e.g., 0.58, 0.62, 0.55) 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: 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: 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: 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