## 4 Results
### Predicting Injuries
#### 4.3.1 Model Evaluation
Similarly, the recall value shows that half of the real injuries can be identified by the model. Based on this performance, the KNN_2 model needs regular improvement to anticipate football players' injuries, even though it is easy to use and effective. In conclusion, XGBoost (XGB_2) is the most successful model for predicting player injuries, achieving the highest accuracy and a critical balance between precision and recall. Its high recall rate is crucial for reducing the risk of overlooking actual injury cases. The AUC scores further reinforce the models' standings, with Random Forest (RFC_2) leading in terms of ROC performance, followed by Decision Tree (DTC_2) and XGBoost (XGB_2), which both offer reasonable performance in injury classification. A thorough summary of the performance measures for each model examined in this study is given in Table 5. We report the Root Mean Square Error (RMSE) for goals and assist prediction; lower numbers denote better performance. We present accuracy, precision, recall, and Area Under the Curve (AUC) ratings for injury prediction. To give a thorough overview, we have included numerous measures for injury prediction and RMSE for goals/assists prediction. Certain findings are excluded for particular reasons:  
* Injury measurements are not relevant for the Naive Model because it uses goals/assists prediction as a baseline exclusively. * Since Gradient Boosting and Ridge Regression are suitable for managing continuous outcomes, they were only applied to objectives and assistance prediction. For these models, injury measurements are therefore irrelevant. * As XGBoost and K-Nearest Neighbors are appropriate at classification tasks, they were especially used for injury prediction. Consequently, RMSE values for objectives and aids are not relevant. * Decision-Tree and Random Forest can be comprehensive models, but they may sometimes underperform in any of metrics. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model (Baseline)** & **RMSE (Goals/Assists)** & **Accuracy (Injury)** & **Precision (Injury)** & **Recall (Injury)** & **AUC (Injury)** \\ \hline Naive Model & 6.62 & NA & NA & NA & NA \\ \hline Gradient Boosting & 4.05 & NA & NA & NA & NA \\ \hline Ridge Regression & 3.90 & NA & NA & NA & NA \\ \hline Model (Injury investigations) & RMSE (Goals/Assists) & Accuracy (Injury) & Precision (Injury) & Recall (Injury) & AUC (Injury) \\ \hline XGBoost & NA & 0.72 & 0.55 & 0.60 & 0.65 \\ \hline K-Nearest Neighbors & NA & 0.63 & 0.405 & 0.50 & 0.54 \\ \hline Model (Comprehensive) & RMSE (Goals/Assists) & Accuracy (Injury) & Precision (Injury) & Recall (Injury) & AUC (Injury) \\ \hline Decision Tree & 4.41 & 0.72 & 0.58 & 0.35 & 0.66 \\ \hline Random Forest & 4.24 & 0.69 & 0.50 & 0.50 & 0.70 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Summary of Model Performance Metrics. We can immediately compare model performance inside each prediction task by presenting the data in Table 5. By using suitable methods for analysis, we can determine which models work best for a given analytical problem in football performance and injury risk assessment.