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Prediction of the successfulness of tennis players with machine learning methods

Andrej Panjan ; Laboratory for Advanced Measurement Technologies, Wise Technologies, Ljubljana, Slovenia
Nejc Šarabon ; Laboratory for Advanced Measurement Technologies, Wise Technologies, Ljubljana, Slovenia
Aleš Filipčič ; Faculty of Sport, University of Ljubljana, Ljubljana, Slovenia

Puni tekst: hrvatski, pdf (410 KB) str. 98-106 preuzimanja: 2.255* citiraj
APA 6th Edition
Panjan, A., Šarabon, N. i Filipčič, A. (2010). Predviđanje natjecateljske uspješnosti tenisača korištenjem metoda strojnog učenja. Kinesiology, 42. (1.), 98-106. Preuzeto s https://hrcak.srce.hr/54247
MLA 8th Edition
Panjan, Andrej, et al. "Predviđanje natjecateljske uspješnosti tenisača korištenjem metoda strojnog učenja." Kinesiology, vol. 42., br. 1., 2010, str. 98-106. https://hrcak.srce.hr/54247. Citirano 17.10.2019.
Chicago 17th Edition
Panjan, Andrej, Nejc Šarabon i Aleš Filipčič. "Predviđanje natjecateljske uspješnosti tenisača korištenjem metoda strojnog učenja." Kinesiology 42., br. 1. (2010): 98-106. https://hrcak.srce.hr/54247
Harvard
Panjan, A., Šarabon, N., i Filipčič, A. (2010). 'Predviđanje natjecateljske uspješnosti tenisača korištenjem metoda strojnog učenja', Kinesiology, 42.(1.), str. 98-106. Preuzeto s: https://hrcak.srce.hr/54247 (Datum pristupa: 17.10.2019.)
Vancouver
Panjan A, Šarabon N, Filipčič A. Predviđanje natjecateljske uspješnosti tenisača korištenjem metoda strojnog učenja. Kinesiology [Internet]. 2010 [pristupljeno 17.10.2019.];42.(1.):98-106. Dostupno na: https://hrcak.srce.hr/54247
IEEE
A. Panjan, N. Šarabon i A. Filipčič, "Predviđanje natjecateljske uspješnosti tenisača korištenjem metoda strojnog učenja", Kinesiology, vol.42., br. 1., str. 98-106, 2010. [Online]. Dostupno na: https://hrcak.srce.hr/54247. [Citirano: 17.10.2019.]

Sažetak
The aim of the study was to examine the predictability of the competitive performance of Slovene tennis players by using the most promising morphological measures and motor tests selected by automatic computer methods and by experienced tennis coaches by means of machine learning methods. The analysis included altogether 1,002 male and female tennis players who had undergone regular testing by the National Tennis Association and were positioned on the ranking list of the Slovene Tennis Association between the years 1993 and 2008. Selections of the most promising variables by means of the two automatic methods yielded similar results, whereas the selection performed on the basis of estimates made by coaches differed considerably. With regard to the analysis by means of classification methods, an accurate predictability of competitive performance for the age category younger than 16 years was observed, while the results of predictions for the age category older than 16 were poor. Among the regression methods, as opposed to the linear regression, which has yielded satisfactory results, regression trees served no useful purpose in practice. Automatic methods for identifying the most promising variables proved to be more successful than those of the coaches, which was most clearly noticeable with regard to the female tennis players and when linear regression was used.

Ključne riječi
tennis; identification; selection; predictability; competitive performance; machine learning

Hrčak ID: 54247

URI
https://hrcak.srce.hr/54247

[hrvatski]

Posjeta: 2.934 *