## 4 Discussion
Although an excessive number of athletes are predicted as future talent, the percentage of correctly predicted low performers is also the highest (97%). This means that this method has detected the most top performers (12 out of 16). Considering the results of the neural network in the classification map (Figure 3), similar results can be seen. In terms of both predictive values (positive predictive value 0.334 and negative predictive value 0.969), the neural network represents a good intermediate solution for talent identification. Therefore, this method seems to be well-suited to campaigns with average funding opportunities or average capital. The four analysis methods used to calculate the performance prognosis served as examples of the currently predominant strategies in general talent identification campaigns. All four methods showed a high accuracy in their classification results (Table 7). The four calculated RIOC values were in the range between 0.447 and 0.681. Comparable studies on the prognostic validity by [PERSON] and [PERSON] [53] showed RIOC values in the range of 0.55. This value was exceeded by three of the four methods. Additionally, in studies by [PERSON] t al. [26] and [PERSON] and [PERSON] [45] on the prognostic validity of talent predictions in soccer and table tennis, only RIOC values of 0.52 could be proven. The Youden Index of both studies reached values of J = 0.336 and J = 0.204, respectively. In studies by [PERSON] et al. (2016) on the classification of drop-out rates among young gymnasts, RIOC values of 0.278 (MLP) to 0.537 (weighting) could be achieved via similar calculations (with MLP, discriminant analysis, and weighting). With these analysis methods, untalented athletes could be identified at an early stage, and thus funding costs could be reduced by about 33%. In a prognostic validity study of 117 soccer players (U14), significantly higher RIOC values were obtained by means of a logistic regression. A study by [PERSON] et al. [56] showed RIOC values of 0.866 (Holistic Pattern Model) and 0.910 (Coach Assessment/Coaches' eye model). _[PERSON]'s_ J was in the range of 61% to 77%. However, in addition to motor abilities, a number of psychological components, in-game performance, and familial support were also included in the calculation. When only motor abilities are considered in their study, the logistic regression has values of \(x_{RIOC}\) = 0.544 and J = 0.432, which are comparable with the results achieved here. Nevertheless, with the addition of the in-game performance (odds ratio = 12.5*) and familial support (odds ratio = 5.2*) survey parameters, a significantly higher prognostic validity is revealed for the predictions. Thus, in the study described [56], RIOC values could be significantly increased by about 20-35%, and _[PERSON]'s_ J could be increased by about 20-30%. Comparing the four prediction methods, differences can be found despite similar RIOC values. A specific recommendation for sports practice can therefore not be derived. All values (sensitivity, specificity, and positive and negative predictive value) of the discriminant analysis method turned out to be weaker than those of the TRS. From a statistical point of view, therefore, discriminant analysis was rather negligible in terms of its worth in this study. However, the other three methods have particular advantages, which may be more or less important depending on the situation. None of the four analysis methods were able to identify all future top performers. Even when all four methods were combined, three high-performance junior tennis players could not be identified. This shows that there are limits to any talent prognosis method [56] and that the future of an athlete cannot always be predicted perfectly. Nevertheless, a combination of prediction methods still seems to be the best solution. For this purpose, the combination of a linear (e.g., TRS) and a non-linear method (neural network) seems to be suitable.