Machine learning, classification and prediction models have applications
across a range of fields. Sport analytics is an increasingly popular
application, but most existing work is focused on automated refereeing in
mainstream sports and injury prevention. Research on other sports, such as
table tennis, has only recently started gaining more traction. This paper
proposes the use of machine learning to predict the outcome of table tennis
single matches. We use player and match statistics as features and evaluate
their relative importance in an ablation study. In terms of models, a number of
popular models were explored. We found that 5-fold cross-validation and
hyperparameter tuning was crucial to improve model performance. We investigated
different feature aggregation strategies in our ablation study to demonstrate
the robustness of the models. Different models performed comparably, with the
accuracy of the results (61-70%) matching state-of-the-art models in comparable
sports, such as tennis. The results can serve as a baseline for future table
tennis prediction models, and can feed back to prediction research in similar
ball sports.Comment: 9 pages, 8 figure