Implementing the BBE Agent-Based Model of a Sports-Betting Exchange

Dave Cliff*, James Hawkins, James Keen, Roberto Lau-Soto

*Corresponding author for this work

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

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In this paper we describe three independent implementations of a new agent-based model (ABM) that simulates a contemporary sports-betting exchange, such as those offered commercially by companies including Betfair, Smarkets, and Betdaq. The motivation for constructing this ABM, which is known as the Bristol Betting Exchange (BBE), is so that it can serve as a synthetic data generator, producing large volumes of data that can be used to develop and test new betting strategies via advanced data analytics and machine learning techniques. Betting exchanges act as online platforms on which bettors can find willing counterparties to a bet, and they do this in a way that is directly comparable to the manner in which electronic financial exchanges, such as major stock markets, act as platforms that allow traders to find willing counterparties to buy from or sell to: the platform aggregates and anonymises orders from multiple participants, showing a summary of the market that is updated in real-time. In the first instance, BBE is aimed primarily at producing synthetic data for in-play betting (also known as in-race or in-game betting) where bettors can place bets on the outcome of a track-race event, such as a horse race, after the race has started and for as long as the race is underway, with betting only ceasing when the race ends. The rationale for, and design of, BBE has been described in detail in a previous paper that we summarise here, before discussing our comparative results which contrast a single-threaded implementation in Python, a multi-threaded implementation in Python, and an implementation where Python header-code calls simulations of the track-racing events written in OpenCL that execute on a 640-core GPU -- this runs approximately 1000 times faster than the single-threaded Python. Our source-code for BBE is being made freely available on GitHub.
Original languageEnglish
Title of host publicationProceedings of the 33rd European Modeling and Simulation Symposium
EditorsM Affenzeller, A. Bruzzone, F. Longo, A. Petrillo
Number of pages11
ISBN (Print)9788885741577
Publication statusPublished - 17 Sep 2021
EventEMSS 2021: European Modeling & Simulation Symposium -
Duration: 15 Sep 202117 Sep 2021
Conference number: 33


ConferenceEMSS 2021
Internet address


  • Betting Exchange
  • Agent-Based Model
  • Synthetic Data Generation


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