# BBE: Simulating the Microstructural Dynamics of an In-Play Betting Exchange via Agent-Based Modelling

arXiv: https://arxiv.org/abs/2105.08310 · HTML: https://mlsanity.com/paper/2105.08310/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 118 references that are in the map.
- First posted on arXiv 18 May 2021.

## Builds on (most cited first, 5 of 5)

- [The Application of Machine Learning Techniques for Predicting Results in Team Sport: A Review](https://mlsanity.com/paper/1912.11762/) (2019)
- [BSE: A Minimal Simulation of a Limit-Order-Book Stock Exchange](https://mlsanity.com/paper/1809.06027/) (2018)
- [Beating the market with a bad predictive model](https://mlsanity.com/paper/2010.12508/) (2020)
- [Using Stock Prices as Ground Truth in Sentiment Analysis to Generate Profitable Trading Signals](https://mlsanity.com/paper/1811.02886/) (2018)
- [Exploring Narrative Economics: An Agent-Based-Modeling Platform that Integrates Automated Traders with Opinion Dynamics](https://mlsanity.com/paper/2012.08840/) (2020)

## Built on by (most cited first, 2 of 2)

- [XGBoost Learning of Dynamic Wager Placement for In-Play Betting on an Agent-Based Model of a Sports Betting Exchange](https://mlsanity.com/paper/2401.06086/) (2024)
- [Efficient Market Dynamics: Unraveling Informational Efficiency in UK Horse Racing Betting Markets Through Betfair's Time Series Analysis](https://mlsanity.com/paper/2402.02623/) (2024)

## Abstract (arXiv)

I describe the rationale for, and design of, an agent-based simulation model of a contemporary online sports-betting exchange: such exchanges, closely related to the exchange mechanisms at the heart of major financial markets, have revolutionized the gambling industry in the past 20 years, but gathering sufficiently large quantities of rich and temporally high-resolution data from real exchanges - i.e., the sort of data that is needed in large quantities for Deep Learning - is often very expensive, and sometimes simply impossible; this creates a need for a plausibly realistic synthetic data generator, which is what this simulation now provides. The simulator, named the "Bristol Betting Exchange" (BBE), is intended as a common platform, a data-source and experimental test-bed, for researchers studying the application of AI and machine learning (ML) techniques to issues arising in betting exchanges; and, as far as I have been able to determine, BBE is the first of its kind: a free open-source agent-based simulation model consisting not only of a sports-betting exchange, but also a minimal simulation model of racetrack sporting events (e.g., horse-races or car-races) about which bets may be made, and a population of simulated bettors who each form their own private evaluation of odds and place bets on the exchange before and - crucially - during the race itself (i.e., so-called "in-play" betting) and whose betting opinions change second-by-second as each race event unfolds. BBE is offered as a proof-of-concept system that enables the generation of large high-resolution data-sets for automated discovery or improvement of profitable strategies for betting on sporting events via the application of AI/ML and advanced data analytics techniques. This paper offers an extensive survey of relevant literature and explains the motivation and design of BBE, and presents brief illustrative results.