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Prognostic Validity of Statistical Prediction Methods Used for Talent Identification in Youth Tennis Players Based on Motor Abilities

DOI zum Zitieren der Version auf EPub Bayreuth: https://doi.org/10.15495/EPub_UBT_00006530
URN to cite this document: urn:nbn:de:bvb:703-epub-6530-6

Title data

Siener, Maximilian ; Faber, Irene ; Hohmann, Andreas:
Prognostic Validity of Statistical Prediction Methods Used for Talent Identification in Youth Tennis Players Based on Motor Abilities.
In: Applied Sciences. Vol. 11 (30 July 2021) Issue 15 . - No. 7051.
ISSN 2076-3417
DOI der Verlagsversion: https://doi.org/10.3390/app11157051

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Abstract

Background: The search for talented young athletes is an important element of top-class sport. While performance profiles and suitable test tasks for talent identification have already been extensively investigated, there are few studies on statistical prediction methods for talent identification. Therefore, this long-term study examined the prognostic validity of four talent prediction methods. Methods: Tennis players (N = 174; n♀ = 62 and n♂ = 112) at the age of eight years (U9) were examined using five physical fitness tests and four motor competence tests. Based on the test results, four predictions regarding the individual future performance were made for each participant using a linear recommendation score, a logistic regression, a discriminant analysis, and a neural network. These forecasts were then compared with the athletes’ achieved performance success at least four years later (U13‒U18). Results: All four prediction methods showed a medium-to-high prognostic validity with respect to their forecasts. Their values of relative improvement over chance ranged from 0.447 (logistic regression) to 0.654 (tennis recommendation score). Conclusions: However, the best results are only obtained by combining the non-linear method (neural network) with one of the linear methods. Nevertheless, 18.75% of later high-performance tennis players could not be predicted using any of the methods.

Further data

Item Type: Article in a journal
Keywords: prognostic validity; RIOC; talent; talent identification; sport; neural network
DDC Subjects: 700 Arts and recreation > 790 Sports, games, entertainment
Institutions of the University: Faculties
Faculties > Faculty of Cultural Studies > Department of Sport Science > Chair Sport Science I - Training Science and Kinesiology
Faculties > Faculty of Cultural Studies
Faculties > Faculty of Cultural Studies > Department of Sport Science
Language: English
Originates at UBT: Yes
URN: urn:nbn:de:bvb:703-epub-6530-6
Date Deposited: 21 Jul 2022 09:34
Last Modified: 21 Jul 2022 09:34
URI: https://epub.uni-bayreuth.de/id/eprint/6530

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