## 4 Discussion
Depending on the combination method (union set or intersection set), a combination of methods gives the highest F\({}_{1}\) score (0.5 for intersection of TRS, MLP, and DA) or the highest RIOC value (0.741 for the union set of TRS and MLP) among all analysis options. The example of the TRS also shows that the limit value chosen (e.g., \(z_{\text{TRS}}\) = 1.0 versus \(z_{\text{TRS}}\) = 1.3) to separate top and low performers makes a large difference in sensitivity and specificity. The summation of both values (overall benefit) varies between 100% (_[PERSON]'s_ J = 0) and 157.9% (_[PERSON]'s_ J = 0.579). The same applies to the separation parameters of the other three methods. Further studies are required to be able to make a more precise statement about this. Additionally, in the analyses, the selection rate fluctuates between 7.2% and 36.1%. Although this difference has no mathematical relevance for prognostic validity due to the use of the RIOC value, an approximation of the selection rate could provide more information about prediction calculations, and differences could then be shown on a classification map (Figure 3). This study has certain limitations. In order to improve the learning phase and test phase of the calculation methods in a meaningful way (e.g., artificial neural network), more data collection is needed. However, such large samples are difficult to obtain, especially in the talent area. On the one hand, there are not many athletes at the highest level of performance, and on the other hand, physical fitness and laboratory tests are sometimes very time consuming. Thus, there is usually a lack of time and participants for large sample numbers. Nevertheless, studies by [PERSON] et al. [48] and [PERSON] et al. [51] show that, even with smaller sample sizes (\(N<150\)), valid results can be obtained even with neural networks. However, the size of the dataset must always be considered when interpreting analyses. Studies using neural networks with only a comparatively small number of participants, such as the study presented here, cannot be generalized and could turn out differently with a different sample. This problem can be reduced by averaging multiple computational runs with different hold-out partitions, but it can never be eliminated. The results of the MLP shown here must therefore be interpreted with caution. Additionally, with small datasets, overfitting can often occur with neural networks. This can be seen, for example, in the fact that the recommendation accuracy of the hold-out drops sharply as compared with the accuracy of the training and test datasets. In the analyses here, this was not the case, which may argue against overfitting. For future analyses, it also seems useful to look beyond the classical classification methods to other promising analysis approaches. [PERSON] et al. [58], for example, have already had good experiences with random forests. In their studies, F\({}_{1}\) values of up to 0.729 were found. A comparison between random forests and classical methods or other neural network solutions (e.g., radial basis functions) would be interesting for future studies. Although the test batteries used thus far generally allow for a comprehensive performance survey, there is still room for improvement in the sport-specific case of tennis. For example, missing test items on agility in combination with decision-making as well as maximum (isometric) arm strength could be added to improve the prognostics [59; 60]. In the future, this may lead to a better explanation of the variance between performance groups. In addition to motor test extensions, psychological test items could also provide further indications of potential future top performers [31; 56; 61; 62; 63]. [PERSON] et al. [31] have made the first promising attempts to integrate psychological tests into a talent identification campaign in the Swiss Soccer Federation. Nevertheless, despite optimized test tasks and the latest analysis methods, all talented athletes cannot always be found.