XGBoost Learning of Dynamic Wager Placement for In-Play Betting on an Agent-Based Model of a Sports Betting Exchange

Chawin Terawong*, Dave Cliff

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

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Abstract

We present first results from the use of XGBoost, a highly effective machine learning (ML) method, within the Bristol Betting Exchange (BBE), an open-source agent-based model (ABM) designed to simulate a contemporary sports-betting exchange with in-play betting during track-racing events such as horse races. We use the BBE ABM and its array of minimally-simple bettor-agents as a synthetic data generator which feeds into our XGBoost ML system, with the intention that XGBoost discovers profitable dynamic betting strategies by learning from the more profitable bets made by the BBE bettor-agents. After this XGBoost training, which results in one or more decision trees, a bettor-agent with a betting strategy determined by the XGBoost-learned decision tree(s) is added to the BBE ABM and made to bet on a sequence of races under various conditions and betting-market scenarios, with profitability serving as the primary metric of comparison and evaluation. Our initial findings presented here show that XGBoost trained in this way can indeed learn profitable betting strategies, and can generalise to learn strategies that outperform each of the set of strategies used for creation of the training data. To foster further research and enhancements, the complete version of our extended BBE, including the XGBoost integration, has been made freely available as an open-source release on GitHub.
Original languageEnglish
Title of host publicationProceedings of the 16th International Conference on Agents and Artificial Intelligence (ICAART2024)
Editors Ana Paula Rocha, Luc Steels , Jaap van den Herik
PublisherSciTePress
Pages159-171
Number of pages13
Volume1
ISBN (Print)978-989-758-680-4
DOIs
Publication statusAccepted/In press - 27 Dec 2023
EventICAART2024 : 16th International Conference on Agents and Artificial Intelligence - Italy, Rome, Italy
Duration: 24 Feb 202426 Feb 2024
Conference number: 16
https://icaart.scitevents.org/Home.aspx
https://portal.insticc.org/SubmissionDeadlines/63e42b755652b110e22e62a4
https://icaart.scitevents.org/?y=2024

Conference

ConferenceICAART2024
Abbreviated titleICAART2024
Country/TerritoryItaly
CityRome
Period24/02/2426/02/24
Internet address

Keywords

  • Agent-Based Models
  • Sports Betting Exchanges
  • In-Play Betting
  • Dynamic Wager Placement
  • Machine Learning
  • XGBoost

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