| Abstract: |
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. |