9 research outputs found
Supervised Learning for Table Tennis Match Prediction
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
ΠΠ°ΡΡΠ½Π°Ρ ΠΈΠ½ΠΈΡΠΈΠ°ΡΠΈΠ²Π° ΠΈΠ½ΠΎΡΡΡΠ°Π½Π½ΡΡ ΡΡΡΠ΄Π΅Π½ΡΠΎΠ² ΠΈ Π°ΡΠΏΠΈΡΠ°Π½ΡΠΎΠ² : ΡΠ±ΠΎΡΠ½ΠΈΠΊ Π΄ΠΎΠΊΠ»Π°Π΄ΠΎΠ² II ΠΠ΅ΠΆΠ΄ΡΠ½Π°ΡΠΎΠ΄Π½ΠΎΠΉ Π½Π°ΡΡΠ½ΠΎ-ΠΏΡΠ°ΠΊΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΊΠΎΠ½ΡΠ΅ΡΠ΅Π½ΡΠΈΠΈ, Π’ΠΎΠΌΡΠΊ, 26-28 Π°ΠΏΡΠ΅Π»Ρ 2022 Π³.
Π‘Π±ΠΎΡΠ½ΠΈΠΊ ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»ΡΠ΅Ρ ΠΈΠ½ΡΠ΅ΡΠ΅Ρ Π΄Π»Ρ ΡΠΏΠ΅ΡΠΈΠ°Π»ΠΈΡΡΠΎΠ² ΠΈ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°ΡΠ΅Π»Π΅ΠΉ Π² ΠΎΠ±Π»Π°ΡΡΠΈ ΠΌΠ°ΡΠ΅ΠΌΠ°ΡΠΈΠΊΠΈ, ΠΌΠ΅Ρ
Π°Π½ΠΈΠΊΠΈ, ΡΠ»Π΅ΠΊΡΡΠΎΡΠ΅Ρ
Π½ΠΈΠΊΠΈ, ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΊΠΈ ΠΈ Π²ΡΡΠΈΡΠ»ΠΈΡΠ΅Π»ΡΠ½ΡΡ
ΡΠΈΡΡΠ΅ΠΌ, ΡΠΈΠ·ΠΈΠΊΠΈ, Ρ
ΠΈΠΌΠΈΠΈ, Π³Π΅ΠΎΠ»ΠΎΠ³ΠΈΠΈ, Π³ΡΠΌΠ°Π½ΠΈΡΠ°ΡΠ½ΡΡ
Π½Π°ΡΠΊ ΠΈ ΡΠΊΠΎΠ½ΠΎΠΌΠΈΠΊΠΈ