## 4 Discussion  
The aim of this study was to compare four statistical prediction methods used for the calculation of individual talent prognosis as part of talent identification. To investigate the prognostic validity of the talent-detection procedure, the match between the talent forecasts and actual later tennis performance was analyzed by means of various individual variables. In addition to the attempt to achieve as many correct positive talent predictions (sensitivity, recall) as possible, it is equally important to secure as many correct negative forecasts (specificity) as possible [26]. The quality of any prognostic attempt can then be determined based on the combination of both criteria. Given a random talent prediction rate of every second child judged as a future top performer, the sensitivity (8/16) and specificity (79/158) would be 50%. In this case, in total 87 children would receive correct predictions of their future performance, resulting in 50% (87/174) correct prognosis. In another example, with a random prediction of every fourth child to be a future top performer, the sensitivity would be 25% (4/16), and the specificity would be 75% (118/158). However, the total number of correct recommendations would be 70.1% (122/174). Although both examples are purely random ratings, the overall results are different. The larger group of low performers therefore distorts the overall talent prediction quality. Thus, the prognostic validity of talent identification cannot be determined only by the sensitivity and specificity of the chosen analysis. Both parameters are also highly dependent on the number of predictions made (selection rate). For example, if we consider (binary) logistic regression, the sensitivity (37.5%) is lowest. In this method, however, only twelve predictions of future top performers were made. This means that, even if all the predictions were correct, the sensitivity would still only reach a maximum of 75%, and this would still be as high as the sensitivities of the TRS and MLP (Table 7). The same applies to specificity. As soon as an analysis produces 17 or more predictions of future top performers, 1 of the cases must necessarily be wrong (there were only 16 observed top performers). With every overly \"optimistic\" false forecast made in this way, the highest possible specificity decreases by about 0.6% (1/158). Consequently, the comparison of the four analytical methods purely on the basis of sensitivity, specificity, and thus, the total hit rate is not sufficiently valid. To overcome this problem, it makes sense to consider the Youden Index [56], F\({}_{1}\) score, and RIOC value [57]. All three variables provide a prediction of the validity of prognostic methods. However, the RIOC value has an advantage in that it measures the accuracy of the method used on the basis of the maximum possible accuracy. Thus, the selection rate has almost no influence on the calculation, and therefore, different methods with different selection rates can be better compared. It makes sense to not always consider a method only in relation to a random result (J = 0) but to also include the maximum possible result. In the example of logistic regression, the Youden Index reaches a theoretical maximum of J = 0.75. However, the TRS could reach a value of up to 85.4% with the existing selection rate. This is 10% above the maximum potential J for logistic regression. A direct comparison of the two values is therefore sometimes difficult. In practice, the positive predictive value is important because it indicates how many of the forecasts made actually predict a later top performer. Here, the logistic regression, with the calculation method and selection rate specified by SPSS, shows the largest positive predictive value. This method, therefore, may appeal to financially weak institutions because only a few particularly promising tennis players are recommended, and the available budget can thus be better calculated and channeled. For financially strong companies or clubs, the TRS may be appealing.