A Comparative Study of Feature Selection Technique for Predicting the Professional Tennis Matches Outcome in a Grand Slam Tournament

Abstract

Tennis is one of the world's most played sports, attracting many spectators to participate in the game. One of the most essential strokes in a tennis match is serve performance. This research is intended to determine the most critical strokes in tennis serve performance in predicting the tennis match outcome. This research focuses on the Grand Slam Tournaments of the Australian Open, French Open, Wimbledon, and US Open. The data are collected on the tennis serve performances such as Percentage First Serve In (PFSI), Percentage First Serve Won (PFSW), Percentage First Serve Return Won (PFSRW), Aces, and many more. For one tournament, it consists of 254 observations. This study applied feature selection methods available in R programming, such as Correlation Matrix, Relative Importance Metrics, Boruta, MARS, and cForest. Selecting the most essential and correlated variables with the match status can improve the model and help produce better results. This might help the practitioners to apply this method to obtain the closest result to the actual outcome when we include the most correlated variables in the model. From the result obtained, variables of first and second serve, either win on serve or return serve, are identified as the most critical attributes in the tennis match. As a future implication, we suggest that these are all the factors the players need to pay extra attention to in winning the tennis match.