## 3 Results
### Classification Results  
ML models could not achieve reasonable results in classifying the initial tests of different training methods since there was no training to change players' tests. In differentiating STM1 and DTM, DT produced the lowest results, followed by RF and GradBoost models. Even though the RF and GradBoost models obtained higher results than DT, they could not \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Experiment** & **Type** & **Test** & **STM1** & **STM2** & **DTM** \\ \hline
1 & Binary & Initial & 19 & - & 21 \\ \hline
2 & Binary & Initial & - & 20 & 21 \\ \hline
3 & Binary & Initial & 19 & 20 & - \\ \hline
4 & Binary & Final & 19 & - & 21 \\ \hline
5 & Binary & Final & - & 20 & 21 \\ \hline
6 & Binary & Final & 19 & 20 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Details of Experiments. outperform XGBoost. The superior results for each metric were achieved by the XGBoost model with 0.8 accuracy. Similar results were obtained for classifying STM2 and DTM; however, RF and GradBoost produced the same classification results by outperforming DT. The XGBoost model achieved superior results. In differentiating STM1 and STM2, even though GradBoost achieved higher specificity results, RF obtained higher sensitivity, making it achieve higher general recognition ability than GradBoost. DT obtained the lowest results, and XGBoost obtained the highest recognition rates, similar to the previous experiments. Table 4 presents the obtained results in detail for all experiments of the initial tests. When the final tests of the players were used to classify the training methods, it was observed that the distinction between DTM and STM1 and STM2 had significantly improved; however, the classification between STM1 and STM2 did not improve significantly. This shows that DTM substantially influenced the players' performance. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{5}{c}{**Experiment 1 (STM1 vs. DTM)**} \\ \hline
**Model** & **Accuracy** & **Sensitivity** & **Specificity** & **F1-Score** \\ \hline
**Decision tree** & 0.683 (0.53–0.82) & 0.714 (0.52–0.91) & 0.666 (0.47–0.87) & 0.612 \\ \hline
**Random forest** & 0.733 (0.59–0.86) & 0.761 (0.56–0.95) & 0.717 (0.52–0.91) & 0.666 \\ \hline
**GradBoost** & 0.750 (0.62–0.88) & 0.761 (0.56–0.95) & 0.743 (0.54–0.93) & 0.680 \\ \hline
**XGBoost** & **0.800** & **0.809** & **0.789** (0.59–0.97)** & **0.809** \\ \hline \multicolumn{5}{c}{**Experiment 2 (STM2 vs. DTM)**} \\ \hline
**Model** & **Accuracy** & **Sensitivity** & **Specificity** & **F1-Score** \\ \hline
**Decision tree** & 0.634 (0.49–0.78) & 0.619 (0.41–0.83) & 0.650 (0.44–0.86) & 0.634 \\ \hline
**Random forest** & 0.675 (0.54–0.83) & 0.644–0.86) & 0.700 (0.50–0.90) & 0.666 \\ \hline
**GradBoost** & 0.641 (0.49–0.79) & 0.631 (0.41–0.85) & 0.700 (0.44–0.86) & 0.666 \\ \hline
**Random forest** & 0.666 (0.52–0.81) & 0.684 (0.48–0.89) & 0.650 (0.44–0.86) & 0.666 \\ \hline
**GradBoost** & 0.666 (0.52–0.81) & 0.631 (0.41–0.85) & 0.700 (0.50–0.90) & 0.648 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Classification results for initial test data. All of the models' performance increased when the results were compared to the initial test experiments. DT produced the lowest classification results for the STM1 vs. DTM and STM1 vs. STM2 experiments. However, it achieved the same results with RF in classifying STM2 and DTM. Even though RF and GradBoost obtained higher scores than DT, they could not outperform the XGBoost model. The XGBoost model achieved the highest results with 100%, 100%, and 89.70% accuracy for STM1 vs. DTM, STM2 vs. DTM, and STM1 vs. STM2 experiments, respectively. Table 5 presents the results obtained in detail for all experiments in the final tests. Figure 2 presents the confusion matrices for the initial and final test experiments of each training method.