ELIO LIBERATORE

I build Monte Carlo simulators that compute outcomes instead of scraping them off a web page. Public feeds go in, thousands of replays come out as fair probabilities - JSON, CSV or an API call.

What I maintain

• Soccer Predictions API - title, top-four, playoff and relegation odds across 16 leagues, Premier League to Liga MX and MLS.
• Soccer Dixon-Coles Match Predictor - one probability grid per fixture: 1X2, over/under 2.5, BTTS and likeliest scorelines, fitted from each team's goal history.
• MLB Playoff Odds API - division, wild card, postseason and top-seed odds for all 30 teams, with market edge and quarter-Kelly sizing.
• NFL Playoff Odds API - the same for all 32 teams, No. 1 seed included.
• NHL Playoff Odds API - playoff, division and Presidents' Trophy odds for all 32 teams.
• NBA Playoff Odds API - playoff, play-in, division, conference and championship odds for all 30 teams.
• Tennis Match Winner Monte Carlo - live ATP/WTA win probability from ranking points, re-projected set by set from the live score, priced against Kalshi.
• Sports Probabilities MCP - the models above as tools, so Claude, ChatGPT or Cursor return a computed probability, not an invented one.

How the models work

Season models blend current form with a multi-season baseline, regressed towards average by how much season is left, and redraw each team's true talent every simulated season. Match models fit per-team ratings or invert a match probability into a per-set/per-fixture one from public data alone.

What people use them for

Comparing fair probabilities against market prices, feeding a dashboard on a schedule, backtesting a past season, and giving an AI agent a tool that computes instead of guessing.

No Actor needs your own API key. Every task on the Tasks tab is a ready-made configuration you can run in one click. Bugs and feature requests welcome in the Issues tab of any Actor.