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

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

Standard

The analysis and forecasting of tennis matches by using a high dimensional dynamic model. / Gorgi, P.; Koopman, S. J.; Lit, R.

In: Journal of the Royal Statistical Society. Series A: Statistics in Society, Vol. 182, No. 4, 2019, p. 1393-1409.

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

Harvard

Gorgi, P, Koopman, SJ & Lit, R 2019, 'The analysis and forecasting of tennis matches by using a high dimensional dynamic model', Journal of the Royal Statistical Society. Series A: Statistics in Society, vol. 182, no. 4, pp. 1393-1409. https://doi.org/10.1111/rssa.12464

APA

Gorgi, P., Koopman, S. J., & Lit, R. (2019). The analysis and forecasting of tennis matches by using a high dimensional dynamic model. Journal of the Royal Statistical Society. Series A: Statistics in Society, 182(4), 1393-1409. https://doi.org/10.1111/rssa.12464

CBE

Gorgi P, Koopman SJ, Lit R. 2019. The analysis and forecasting of tennis matches by using a high dimensional dynamic model. Journal of the Royal Statistical Society. Series A: Statistics in Society. 182(4):1393-1409. https://doi.org/10.1111/rssa.12464

MLA

Gorgi, P., S. J. Koopman and R. Lit. "The analysis and forecasting of tennis matches by using a high dimensional dynamic model". Journal of the Royal Statistical Society. Series A: Statistics in Society. 2019, 182(4). 1393-1409. https://doi.org/10.1111/rssa.12464

Vancouver

Gorgi P, Koopman SJ, Lit R. The analysis and forecasting of tennis matches by using a high dimensional dynamic model. Journal of the Royal Statistical Society. Series A: Statistics in Society. 2019;182(4):1393-1409. https://doi.org/10.1111/rssa.12464

Author

Gorgi, P. ; Koopman, S. J. ; Lit, R. / The analysis and forecasting of tennis matches by using a high dimensional dynamic model. In: Journal of the Royal Statistical Society. Series A: Statistics in Society. 2019 ; Vol. 182, No. 4. pp. 1393-1409.

Bibtex

@article{0400bccf8a034808a52d339fad86fa39,
title = "The analysis and forecasting of tennis matches by using a high dimensional dynamic model",
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.",
keywords = "Association of Tennis Professionals, Bradley–Terry model, Logistic regression, Maximum likelihood, Out-of-sample analysis, Player rankings, Score-driven model, Time varying parameter",
author = "P. Gorgi and Koopman, {S. J.} and R. Lit",
year = "2019",
doi = "10.1111/rssa.12464",
language = "English",
volume = "182",
pages = "1393--1409",
journal = "Journal of the Royal Statistical Society, Series A (Statistics in Society)",
issn = "0964-1998",
publisher = "John Wiley & Sons, Inc.",
number = "4",

}

RIS

TY - JOUR

T1 - The analysis and forecasting of tennis matches by using a high dimensional dynamic model

AU - Gorgi, P.

AU - Koopman, S. J.

AU - Lit, R.

PY - 2019

Y1 - 2019

N2 - 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.

AB - 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.

KW - Association of Tennis Professionals

KW - Bradley–Terry model

KW - Logistic regression

KW - Maximum likelihood

KW - Out-of-sample analysis

KW - Player rankings

KW - Score-driven model

KW - Time varying parameter

UR - http://www.scopus.com/inward/record.url?scp=85065201409&partnerID=8YFLogxK

U2 - 10.1111/rssa.12464

DO - 10.1111/rssa.12464

M3 - Journal article

AN - SCOPUS:85065201409

VL - 182

SP - 1393

EP - 1409

JO - Journal of the Royal Statistical Society, Series A (Statistics in Society)

JF - Journal of the Royal Statistical Society, Series A (Statistics in Society)

SN - 0964-1998

IS - 4

ER -