XGBoost Learning of Dynamic Wager Placement for In-Play Betting on an
  Agent-Based Model of a Sports Betting Exchange
Chawin Terawong Dave Cliff
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.