The analysis and forecasting of tennis matches by using a high dimensional dynamic model

We propose a high dimensional dynamic model for tennis match results with time varying player-specific abilities for different court surface types. Our statistical model can be treated in a likelihood-based analysis and can handle high dimensional data sets while the number of parameters remains small. In particular, we analyse 17 years of tennis matches for a panel of over 500 players, which leads to more than 2000 dynamic strength levels. We find that time varying player-specific abilities for different court surfaces are of key importance for analysing tennis matches. We further consider several other extensions including player-specific explanatory variables and the match configurations for Grand Slam tournaments. The estimation results can be used to construct rankings of players for different court surface types. We finally show that our proposed model produces accurate forecasts. We provide evidence that our model significantly outperforms existing models in the forecasting of tennis match results.