betting
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.
Install via CLI (Recommended)
clawhub install openclaw/skills/skills/antonelli182/sports-skills-bettingBetting 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
devigbefore 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
Metadata
Not sure this is the right skill?
Describe what you want to build — we'll match you to the best skill from 16,000+ options.
Find the right skillPaste this into your clawhub.json to enable this plugin.
{
"plugins": {
"official-antonelli182-sports-skills-betting": {
"enabled": true,
"auto_update": true
}
}
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Football (soccer) data across 13 leagues — standings, schedules, match stats, xG, transfers, player profiles. Zero config, no API keys. Covers Premier League, La Liga, Bundesliga, Serie A, Ligue 1, MLS, Champions League, World Cup, Championship, Eredivisie, Primeira Liga, Serie A Brazil, European Championship. Use when: user asks about football/soccer standings, fixtures, match stats, xG, lineups, player values, transfers, injury news, league tables, daily fixtures, or player profiles. Don't use when: user asks about American football/NFL (use nfl-data), college football (use cfb-data), NBA (use nba-data), WNBA (use wnba-data), college basketball (use cbb-data), NHL (use nhl-data), MLB (use mlb-data), tennis (use tennis-data), golf (use golf-data), Formula 1 (use fastf1), or betting odds (use polymarket or kalshi). Don't use for live/real-time scores — data updates post-match. Don't use get_season_leaders or get_missing_players for non-Premier League leagues (they return empty). Don't use get_event_xg for leagues outside the top 5 (EPL, La Liga, Bundesliga, Serie A, Ligue 1).
football-data
Football (soccer) data across 13 leagues — standings, schedules, match stats, xG, transfers, player profiles. Zero config, no API keys. Covers Premier League, La Liga, Bundesliga, Serie A, Ligue 1, MLS, Champions League, World Cup, Championship, Eredivisie, Primeira Liga, Serie A Brazil, European Championship. Use when: user asks about football/soccer standings, fixtures, match stats, xG, lineups, player values, transfers, injury news, league tables, daily fixtures, or player profiles. Don't use when: user asks about American football/NFL (use nfl-data), college football (use cfb-data), NBA (use nba-data), WNBA (use wnba-data), college basketball (use cbb-data), NHL (use nhl-data), MLB (use mlb-data), tennis (use tennis-data), golf (use golf-data), Formula 1 (use fastf1), or betting odds (use polymarket or kalshi). Don't use for live/real-time scores — data updates post-match. Don't use get_season_leaders or get_missing_players for non-Premier League leagues (they return empty). Don't use get_event_xg for leagues outside the top 5 (EPL, La Liga, Bundesliga, Serie A, Ligue 1).
kalshi
Kalshi prediction markets — events, series, markets, trades, and candlestick data. Public API, no auth required for reads. US-regulated exchange (CFTC). Covers football (EPL, UCL, La Liga), basketball, baseball, tennis, NFL, hockey event contracts. Use when: user asks about Kalshi-specific markets, event contracts, CFTC-regulated prediction markets, or candlestick/OHLC price history on sports outcomes. Don't use when: user asks about actual match results, scores, or statistics — use the sport-specific skill: football-data (soccer), nfl-data (NFL), nba-data (NBA), wnba-data (WNBA), nhl-data (NHL), mlb-data (MLB), tennis-data (tennis), golf-data (golf), cfb-data (college football), cbb-data (college basketball), or fastf1 (F1). Don't use for general "who will win" questions unless Kalshi is specifically mentioned — try polymarket first (broader sports coverage). Don't use for news — use sports-news instead.
markets
Markets orchestration — connects ESPN live schedules with Kalshi and Polymarket prediction markets. Unified dashboards, odds comparison, entity search, and bet evaluation across platforms. Use when: user wants to see prediction market odds alongside ESPN game schedules, compare odds across platforms, search for a team/player on Kalshi or Polymarket, check for arbitrage between ESPN odds and prediction markets, or evaluate a specific game's market value. Don't use when: user wants raw prediction market data without ESPN context — use polymarket or kalshi directly. For pure odds math (conversion, de-vigging, Kelly) — use betting. For live scores without market data — use the sport-specific skill.
polymarket
Polymarket sports prediction markets — live odds, prices, order books, events, series, and market search. No auth required. Covers NFL, NBA, MLB, football (EPL, UCL, La Liga), tennis, cricket, MMA, esports. Supports moneyline, spreads, totals, and player props. Use when: user asks about sports betting odds, prediction markets, win probabilities, market sentiment, or "who is favored to win" questions. Don't use when: user asks about actual match results, scores, or statistics — use the sport-specific skill: football-data (soccer), nfl-data (NFL), nba-data (NBA), wnba-data (WNBA), nhl-data (NHL), mlb-data (MLB), tennis-data (tennis), golf-data (golf), cfb-data (college football), cbb-data (college basketball), or fastf1 (F1). Don't use for historical match data. Don't use for news — use sports-news instead. Don't confuse with Kalshi — Polymarket focuses on crypto-native prediction markets with deeper sports coverage; Kalshi is a US-regulated exchange with different market structure.