<![CDATA[Athlete readiness classification is critical for optimizing training load and preventing overtraining-related injuries. This study develops and compares three machine learning algorithms XGBoost, Random Forest, and MLP Neural Network to classify athlete readiness into three ordinal categories: Lower, Middle, and Upper. The dataset comprises 1153 instances with nine multidimensional features encompassing physiological parameters (training duration, intensity, interval days, points, recovery) and psychological indicators (mental score, athlete category, consistency). Data preprocessing involved label encoding for categorical variables and standard scaling for numerical features, followed by a stratified 80:20 train-test split. Model performance was evaluated using weighted precision, recall, F1-score, confusion matrix, and one-vs-rest ROC-AUC curves with 5-fold cross-validation. Results indicate that XGBoost achieved the highest predictive performance (F1-score: 0.93, AUC: 0.99), followed by Random Forest (F1-score: 0.91, AUC: 0.98) and MLP Neural Network (F1-score: 0.85, AUC: 0.94). Feature importance analysis revealed that mental score, training intensity, and consistency were the strongest predictors of readiness status. The proposed framework offers a robust, data-driven decision support tool for sports practitioners, enabling objective readiness monitoring and dynamic training adjustments. Future work will focus on real-time wearable integration and automated hyperparameter optimization]]>
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