Proceedings of the 12th International Conference on Kinanthropology. Sport and Quality of Life. 7. – 9. 11. 2019



Sports performance is influenced by a many of factors that can be characterised as its rela-tively independent – although synergetic – components. The most frequently mentioned are the fitness, somatic, tactical, mental and technical factors of sports performance. The subject of interest in sport is the process of monitoring and evaluating the level of these individual factors, i.e. the diagnostics of sports performance. When diagnosing the level of performance prerequi-site for tennis, it is recommended to use those diagnostic methods that focus on tennis-specific performance prerequisites. Analyses of modern tennis show speed (reaction, action), strength (explosive), strength endurance and specific coordination abilities to be the most important motor prerequisites. Diagnostics of the motor prerequisites of an athlete are often performed in practice employing motor tests and test batteries. Methods of evaluating the results obtained are generally based on the probability approach, though an alternative is provided by a method based on the theory of fuzzy logic. The aim of the research was to use the theory of fuzzy logic in evaluating the level of performance prerequisites and compare evaluation results by using of a classical discrete approach and a fuzzy approach. The two approaches are evaluated and compared using the results of testing of a group of 15–16-year old tennis players (n = 203, age M ± SD = 15.97 ± 0.57 years, height M ± SD = 181.9 ± 6.8 cm, weight M ± SD = 71.6 ± 8.6 kg) who took part in regular testing conducted by the Czech Tennis Association in the years 2000–2018 using the TENDIAG1 test battery. STATISTICA12 software was used for the anal-ysis of data using a probability approach. FuzzME software was used for analysis using of a fuzzy approach. The testing of research data (the Kolmogorov-Smirnov test) demonstrated the normal distribution of the frequency of the results of individual tests in the test battery. The level of agreement of the results (the Pearson correlation coeficient) obtained by the two approach-es (the discrete and the fuzzy approaches) was high both from the effect size (ES, large) and statistical significance points of view (r = 0.89, p = 0.05). The evaluation of the effect size (ES) of the differences between the mean values of the results obtained by the two approaches us-ing the Cohen’s d did not demonstrate any substantively significant difference (d = 0.16). For a more detailed analysis, two subsets were selected from the original group of tennis players. They consisted of players with an overall evaluation (probability approach) of 4–5 points and 8–9 points, respectively. The level of agreement between the results in the subgroup with the evaluation 4–5 points was low from both the effect size (ES, small) and statistical significance points of view (r = 0.15, p = 0.05), while the agreement in the subgroup with the evaluation of 8–9 points was at a medium level in terms of the effect size (ES, medium) and statistically insignificant (r = 0.47, p = 0.05). The effect size (ES) assessment of the differences between mean values of the results obtained by the two approaches did not demonstrate any effect (d = 0.12) in the group with the overall score of 4–5 points, and a large effect (d = 0.89, large) in the group with an overall score of 8–9 points. Despite the similarity of the results obtained by the probability and fuzzy methods, it was shown that the fuzzy approach enables a finer dif-ferentiation of the level of fitness prerequisites in players on the evaluation boundaries. Since that the results for individual items in the TENDIAG1 test battery indicate the level of individual performance prerequisites, the use of different weighting criteria may be considered for future evaluation using the fuzzy approach. For this approach, the use of the point method, a paired comparison method or the Saaty method can be considered for the identification and calcula-tion of individual subtests weighting.

Klíčová slova

diagnostics; fuzzy logic; FuzzME; probability approach; TENDIAG1 test battery


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