HackerPapers

arXiv:1710.02824stat.AP

Beating the bookies with their own numbers - and how the online sports betting market is rigged

Lisandro Kaunitz, Shenjun Zhong, Javier Kreiner

HN submission. Found in a submission link or body. Submitters do not count as commenters in the main ranking; a submission is not an endorsement.

Abstract

The online sports gambling industry employs teams of data analysts to build forecast models that turn the odds at sports games in their favour. While several betting strategies have been proposed to beat bookmakers, from expert prediction models and arbitrage strategies to odds bias exploitation, their returns have been inconsistent and it remains to be shown that a betting strategy can outperform the online sports betting market. We designed a strategy to beat football bookmakers with their own numbers. Instead of building a forecasting model to compete with bookmakers predictions, we exploited the probability information implicit in the odds publicly available in the marketplace to find bets with mispriced odds. Our strategy proved profitable in a 10-year historical simulation using closing odds, a 6-month historical simulation using minute to minute odds, and a 5-month period during which we staked real money with the bookmakers (we made code, data and models publicly available). Our results demonstrate that the football betting market is inefficient - bookmakers can be consistently beaten across thousands of games in both simulated environments and real-life betting. We provide a detailed description of our betting experience to illustrate how the sports gambling industry compensates these market inefficiencies with discriminatory practices against successful clients.

arXiv comments
30 pages, 5 figures
Journal reference
DOI
Subjects
stat.APApplications (Statistics) cs.OHOther Computer Science stat.OTOther Statistics

On Hacker News

No comments linking this paper were found in the collected sources. Found in 4 HN submissions from Oct 2017 to Nov 2024. Open submission discussions.

Comments linking this paper, per year.

Discussed in comments0 threads

No comments linking this paper were found in the collected sources. Open submission discussions.

Submitted to Hacker News

Connected through shared HN threads, not inferred similarity or a recommendation.