Beating the House: Identifying Inefficiencies in Sports Betting Markets

TL;DR

This paper demonstrates that sports betting markets are inefficient by developing an algorithm that outperforms the market across multiple leagues, using a novel dataset and a non-parametric win probability model.

Contribution

It introduces a new betting algorithm and dataset, showing how to identify positive expected value opportunities in sports betting markets.

Findings

Betting algorithm achieves above-market returns in NFL, NBA, NCAAF, NCAAB, and WNBA.

Novel dataset of sports bets enables better analysis of market inefficiencies.

Non-parametric model effectively identifies positive expected value situations.

Abstract

Inefficient markets allow investors to consistently outperform the market. To demonstrate that inefficiencies exist in sports betting markets, we created a betting algorithm that generates above market returns for the NFL, NBA, NCAAF, NCAAB, and WNBA betting markets. To formulate our betting strategy, we collected and examined a novel dataset of bets, and created a non-parametric win probability model to find positive expected value situations. As the United States Supreme Court has recently repealed the federal ban on sports betting, research on sports betting markets is increasingly relevant for the growing sports betting industry.