Computational Model Library

Our mission is to help computational modelers develop, document, and share their computational models in accordance with community standards and good open science and software engineering practices. Model authors can publish their model source code in the Computational Model Library with narrative documentation as well as metadata that supports open science and emerging norms that facilitate software citation, computational reproducibility / frictionless reuse, and interoperability. Model authors can also request private peer review of their computational models. Models that pass peer review receive a DOI once published.

All users of models published in the library must cite model authors when they use and benefit from their code.

Please check out our model publishing tutorial and feel free to contact us if you have any questions or concerns about publishing your model(s) in the Computational Model Library.

Displaying 1 of 1 results forecast calibration clear search

An agent-based model of a sports league, a public Elo forecaster and a bookmaker. Teams have hidden true strengths that drift between seasons; the forecaster updates Elo ratings after every game; the bookmaker prices each game from the Elo forecast plus a margin (overround), spread either proportionally or loaded onto longshot prices. The model asks where the favourite-longshot pattern in betting prices comes from. With a forecaster whose ratings lag true strength, longshots win more often than their de-vigged price implies; a bookmaker that loads its margin onto longshots cancels that bias, so favourite and longshot bets both lose roughly the margin. Outputs: a calibration table (de-vigged price band vs. observed win rate) and the average return of flat bets on favourites and longshots. Python 3 standard library only; all parameters are command-line arguments; the README documents the model with a short ODD protocol and an example run. Companion empirical study and open data (CC BY 4.0): Bet Better market calibration study. Teaching model, not a betting system.

This website uses cookies and Google Analytics to help us track user engagement and improve our site. If you'd like to know more information about what data we collect and why, please see our data privacy policy. If you continue to use this site, you consent to our use of cookies.
Accept