PREDICTING THE FLOW OF TENNIS PLAYER’S CARIER USING DATA MINING
DOI:
https://doi.org/10.24867/21BE21StojadinovKeywords:
tennis, data mining, clustering, regessionAbstract
This paper describes the process of clustering using of tennis players based on selected physical characteristics and playing styles. A regression curve was created for each cluster, which indicates the dependence of the players' success through the seasons. The entire process was validated using the leave-one-out method. All steps of the system are described and visualized in detail.
References
[1] Association of Tennis Professionals (ATP), Github
https://github.com/serve-and-volley/atp-world-tour-tennis- data.git.
[2] Association of Tennis Professionals (ATP), ATP World tour www.atpworldtour.com
[4] Miha Mlakar, Tea Tušar , “Analyzing and Predicting Peak Performance Age of Professional Tennis Players” , Jožef Stefan Institute Jamova cesta 39
SI-1000 Ljubljana, Slovenia, pp 1-4, 2017.
[3] “Peak Performance and Age Among Superathletes: Track and Field, Swimming, Baseball, Tennis, and Golf”, Richard Schulz, Christine Curnow, Jurnal of Gerontology: PSYCHOLOGICAL SCIENCES, Vol. 43, No. 5, P113 – 120, 1988.
https://github.com/serve-and-volley/atp-world-tour-tennis- data.git.
[2] Association of Tennis Professionals (ATP), ATP World tour www.atpworldtour.com
[4] Miha Mlakar, Tea Tušar , “Analyzing and Predicting Peak Performance Age of Professional Tennis Players” , Jožef Stefan Institute Jamova cesta 39
SI-1000 Ljubljana, Slovenia, pp 1-4, 2017.
[3] “Peak Performance and Age Among Superathletes: Track and Field, Swimming, Baseball, Tennis, and Golf”, Richard Schulz, Christine Curnow, Jurnal of Gerontology: PSYCHOLOGICAL SCIENCES, Vol. 43, No. 5, P113 – 120, 1988.
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Published
2023-01-08
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Section
Electrotechnical and Computer Engineering