## 3 Results
### Classification Results
Figure 2 clearly indicates that the training of ML models using initial tests could not achieve reasonable results by misclassifying 8, 11, and 11 samples of STM1 vs. DTM, STM2 vs. DTM, and STM1 vs. STM2. After the training procedures were completed, the XGBoost model could determine the player groups of DTM and STM1/STM2 without any misclassification. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{5}{c}{**Experiment 4 (STM1 vs. DTM)**} \\ \hline
**Model** & **Accuracy** & **Sensitivity** & **Specificity** & **F1-Score** \\ \hline
**Decision tree** & 0.900 (0.81–0.99) & 0.904 (0.78–1.00) & 0.894 (0.76–1.00) & 0.904 \\ \hline
**Random forest** & 0.925 (0.84–1.00) & 0.904 (0.78–1.00) & 0.947 (0.85–1.00) & 0.926 \\ \hline
**GradBoost** & 0.950 (0.88–1.00) & 0.952 (0.86–1.00) & 0.947 (0.85–1.00) & 0.952 \\ \hline
**XGBoost** & **1.000** **1.000** **1.000** **1.000** **1.000** **1.000** **1.000** **1.000** \\ \hline \multicolumn{5}{c}{**Experiment 5 (STM2 vs. DTM)**} \\ \hline
**Model** & **Accuracy** & **Sensitivity** & **Specificity** & **F1-Score** \\ \hline
**Decision tree** & 0.926 (0.85–1.00) & 0.904 (0.78–1.00) & 0.950 (0.85–1.00) & 0.926 \\ \hline
**Random forest** & 0.926 (0.85–1.00) & 0.904 (0.78–1.00) & 0.950 (0.85–1.00) & 0.926 \\ \hline
**GradBoost** & 0.951 (0.89–1.00) & 0.952 (0.86–1.00) & 0.950 (0.85–1.00) & 0.952 \\ \hline
**XGBoost** & **1.000** **1.000** **1.000** **1.000** **1.00–1.00** **1.00** **1.000** **1.000** **1.000** \\ \hline \multicolumn{5}{c}{**Experiment 6 (STM1 vs. STM2)**} \\ \hline
**Model** & **Accuracy** & **Sensitivity** & **Specificity** & **F1-Score** \\ \hline
**Decision tree** & 0.769 (0.65–0.90) & 0.789 (0.61–0.97) & 0.750 (0.56–0.94) & 0.769 \\ \hline
**Random forest** & 0.794 (0.68–0.92) & 0.789 (0.61–0.97) & 0.800 (0.62–0.98) & 0.789 \\ \hline
**GradBoost** & 0.846 (0.74–0.96) & 0.842 (0.68–1.00) & 0.850 (0.69–1.00) & 0.842 \\ \hline
**XGBoost** & **0.897** **0.894** **0.900** **0.78–1.00** **0.77–1.00** **0.894** \\ \hline \hline \end{tabular} Bold values indicate the highest scores. Values in parentheses show the 95% confidence intervals.  
\end{table} 
Table 5: Classification results for final test data. In order to provide a clearer context for interpreting model performance, we included two baseline classifiers, a random classifier, and a majority class classifier, for each experiment. A random classifier predicts each class with equal probability (50%), and a majority class classifier always predicts the class with the higher sample count. These baselines allowed us to determine whether the models were truly learning meaningful patterns or simply benefiting from class imbalance. Table 6 shows that both baselines performed substantially worse than the proposed models, especially in terms of F1-score and sensitivity. In particular, the majority classifier achieved moderate accuracy (51-53%) due to class imbalance but completely failed to identify the minority class (0% sensitivity and F1-score), highlighting the limitations of naive strategies. In contrast, all tree-based models significantly outperformed the baselines across all metrics, demonstrating reliable and generalizable classification capabilities. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Experiment** & **Baseline** & **Accuracy** & **Sensitivity** & **Specificity** & **F1-Score** \\ \hline Exp 1. & Random & 0.500 & 0.500 & 0.500 & 0.500 \\ \hline Exp 1. & Majority & 0.525 & 0.000 & 1.000 & 0.000 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Baseline performance metrics for all experiments. The random classifier predicts classes uniformly, while the majority classifier always predicts the more frequent class. Metrics confirm that tree-based models offer substantial improvements over naive baselines. Figure 2: Confusion matrices of the XGBoost model for classifying STM1, STM2, and DTM. (**a,c,e**) represent the results from the initial tests, (**b,d,f**) show the corresponding results from the final tests.