## 4 Results
### Predicting Injuries
#### 4.3.1 Model Evaluation  
We compared several ML models to predict the target variable in the model comparison in Table 4. The fine-tuned Decision Tree model (DTC_2) stood out with an accuracy of 0.72, reflecting the significant impact of hyperparameter optimization. Similarly, the adjusted Random Forest model (RFC_2) saw an increase in accuracy to 0.69 after fine-tuning its parameters. XGBoost (XGB_2) performed admirably from the outset, maintaining a high accuracy of 0.72 following optimization, affirming its suitability for the predictive task at hand. The K-Nearest Neighbors model (KNN_2) also benefited from hyperparameter adjustments, achieving an improved accuracy of 0.63. However, accuracy alone is not the sole indicator of a model's effectiveness, especially when predicting injuries where the cost of false negatives can be significant. Therefore, it is crucial to shift the attention to precision and recall ratings, especially for cases involving injuries. Precision measures the rate at which models correctly predict injuries, whereas recall assesses their ability to identify all actual injuries, thus reducing missed cases. In predicting player injuries, the Random Forest model (RFC_2) achieves a precision of 0.5 and a recall of 0.5, indicating that it correctly identifies half of the injured players. The Decision Tree Model (DTC_2) has a higher precision of 0.58 but a lower recall of 0.35, indicating that it misses a large number of injured players. XGBoost (XGB_2) comes out on top with a balanced precision of 0.55 and the highest recall of 0.6, identifying 60% of the actual injuries, making it the most suitable model for this application. Moreover, the Area Under the Receiver Operating Characteristic (ROC) Curve (AUC) values provided additional insights into the models' classification abilities. Among the models, the ROC curve for Random Forest (RFC_2) shows the most promise. With an AUC value of 0.70, it clearly outperforms random guessing in its ability to discern between \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **AUC Score** \\ \hline
**Decision Tree (DTC\_2)** & **0.72** & 0.58 & 0.35 & 0.66 \\ \hline Random Forest (RFC\_2) & 0.69 & 0.50 & 0.50 & 0.70 \\ \hline
**XGBoost (XGB\_2)** & **0.72** & 0.55 & 0.60 & 0.65 \\ \hline K-Nearest Neighbors (KNN\_2) & 0.63 & 0.405 & 0.50 & 0.54 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of fine-tuned ML models for predicting injury. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Feature Importance** & **Decision Tree** & **Random Forest** & **Gradient Boosting** \\ \hline Minutes Played (Min) & 0.024 & 0.397 & 0.305 \\ \hline Matches Played (MP) & 0.022 & 0.047 & 0.054 \\ \hline Starts & 0.073 & **0.412** & 0.058 \\ \hline
90 Minutes Played (90s) & **0.874** & 0.114 & **0.552** \\ \hline Age & 0.006 & 0.030 & 0.032 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Feature importance scores of predictors across models for Goals and Assists. injured and healthy players. The Decision Tree model (DTC_2) has an AUC of 0.66, which is better than random chance but less powerful than RFC_2. In contrast, the K-Nearest Neighbors model (KNN_2) has an AUC of 0.54, which is only slightly better than random chance and suggests the model struggles with class separation in this context. XGBoost (XGB_2) has an AUC of 0.65, placing it between the Random Forest and Decision Tree models in terms of performance, indicating a moderate ability to differentiate between the classes. With an accuracy of 0.63 and an AUC score of 0.54, the K-Nearest Neighbors (KNN_2) model appears to marginally outperform random guessing. The recalculated values show that the model achieves a precision of around 0.405 and a recall of 0.5, implying that although the model has a high probability of false positives, it may detect 50% of actual injury cases. The below-average precision of the model, which yields many false positive predictions, clearly illustrates its incapacity to anticipate injuries.