<![CDATA[The Most Valuable Player (MVP) award recognizes the player who makes the most significant contribution during a football tournament. MVP candidates are typically selected based on subjective assessments that consider various aspects of player performance. Therefore, this study aims to apply the Support Vector Machine (SVM) algorithm to classify Most Valuable Player (MVP) candidates for the 2026 ASEAN Championship football tournament based on player performance statistics. The study utilizes a dataset comprising 20 players from 10 participating nations, incorporating six variables: Goals, Assists, Key Passes, Successful Dribbles, Tackles, and Interceptions. The research process involves data collection, data preprocessing, normalization using the Min-Max method, model construction using the Support Vector Machine (SVM) algorithm, and model evaluation using a Confusion Matrix, Accuracy, Precision, Recall, and F1-Score. The results demonstrate that the Support Vector Machine (SVM) algorithm can classify players into three categories: Class A (High Potential to be MVP), Class B (Potential to be MVP), and Class C (Low Potential to be MVP). Model evaluation results show an Accuracy of 90.00%, Precision of 89.47%, Recall of 90.00%, and F1-Score of 89.73%, indicating strong model performance in classifying MVP candidates based on performance statistics. This study demonstrates that the Support Vector Machine (SVM) algorithm serves as an effective machine learning-based approach to support the objective, data-driven identification of Most Valuable Player (MVP) candidates.]]>
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