ELIO LIBERATORE

I build Monte Carlo simulators that compute season outcomes instead of scraping them off a web page. Standings and fixtures come from public feeds, the model replays every remaining game thousands of times, and you get fair probabilities as JSON, CSV or an API call.

What I maintain

• MLB Playoff Odds API  - division, wild card, postseason and top-seed probabilities for all 30 baseball teams, with projected records, market edge and quarter-Kelly bet sizing.
• Soccer Predictions API  - title, top-four, playoff and relegation odds across 16 leagues including the Premier League, LaLiga, Serie A, Bundesliga, Ligue 1, Liga MX, Brasileirao and MLS.

How the models work

Team strength blends current form with a multi-season historical baseline, regressed towards average by how much season is left. Every simulated season redraws each team's true talent, so the output stays honest about how little a partial season really tells you. Competition formats are read from the data, so playoffs, split tables and conference standings map to the right rewards.

What people use them for

Comparing fair probabilities against bookmaker prices. Feeding a daily dashboard on a schedule. Backtesting a past season before trusting a model on the current one. Giving an AI agent a tool that returns computed probabilities instead of invented ones.

Neither Actor needs an API key of your own. Every task on the Tasks tab is a ready-made configuration you can run in one click.

Bugs and feature requests are welcome in the Issues tab of either Actor.