 The analysis and forecasting of tennis matches by using a high dimensional dynamic modelP. Gorgi, Siem Jan Koopman and R. Lit Journal of the Royal Statistical Society Series A, 2019, vol. 182, issue 4, 1393-1409 Abstract: 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. Date: 2019 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:bla:jorssa:v:182:y:2019:i:4:p:1393-1409 Ordering information: This journal article can be ordered from Access Statistics for this article Journal of the Royal Statistical Society Series A is currently edited by A. Chevalier and L. Sharples More articles in Journal of the Royal Statistical Society Series A from Royal Statistical Society Contact information at EDIRC. |
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