Abstract
Predictive modeling has long been the goal of many individuals and organizations. This science has many techniques, with simulation and machine learning at its heart. Simulations such as basketball’s BBall can model an entire season and can deduce optimal substitution patterns and scoring potential of players. Should unforeseen events occur such as an unexpected trade or long-term injury, additional simulations can be performed to assess new forms of action. Aside from the potential of simulations, machine learning techniques can uncover hidden data trends. Greyhound racing is one such area that has been explored with many different machine learners. While the choice of algorithms used in each study may differ, they all had one common similarity, they beat the choices human track experts made and were able to use the data to create arbitrage opportunities.
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Schumaker, R.P., Solieman, O.K., Chen, H. (2010). Predictive Modeling for Sports and Gaming. In: Sports Data Mining. Integrated Series in Information Systems, vol 26. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-6730-5_6
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