Fuzzy theory emerged in the 1960s and since the 1990s several studies have been published dealing with fuzzy theory and how it can be used in data diagnosis in sports. While the classical theory operates with sharp borders between groups and the key concept is membership (an element either belongs to the set or does not), fuzzy theory enables the minimisation of issues that arise from the strict mathematical and statistical requirements of probability theory and defines the grade of membership in the interval with the aid of the membership function. The aim of this research study is to present ways in which fuzzy logic can be used when evaluating tennis players` results using the TENDIAG1 test battery and for comparing tennis players` levels with the aid of the fuzzy approach and the probability approach. The research set consisted of 12-year-old tennis players (n = 88, age: 12.5 ± 0.3; height: 157.4 ± 7.0 cm; weight: 45.3 ± 6.3 kg). The research data were obtained using the TENDIAG1 test battery in the period 2000-2010. The test battery consisted of nine measurements and of somatic and motor preconditions tests. For individual tests, based on expert evaluations and the nature of the data the membership function sets L and G were identified. Results of the tennis players were then assigned various grades based on both the probability approach and the fuzzy approach. A comparison of the results obtained with these approaches showed their considerable mutual relationship (r = 0.92, p = 0.05). Detailed analysis of the results showed that the fuzzy evaluation provides a significant differentiation of the results of individual tennis players. Especially for those who obtained an equal score in the evaluation, the fuzzy evaluation gives a finer and more precise resolution of the overall level.
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