Sittakon Phommee and Juggapong Natwichai
Published in Data Science and Engineering (DSE) Record 2026 Vol. 7 No. 1 pp. 53-62
Abstract
This research aims to compare the performance of machine learning algorithms for classifying match outcomes in Teamfight Tactics (Set 13) using a dataset of 78,412 samples collected from the Riot API. Four additional engineered features are implemented and compared three encoding techniques Label Encoding, One-Hot Encoding, and Bag-of-Words, in combination with four classification algorithms: k-Nearest Neighbors (k-NN), Support Vector Machine (SVM), Random Forest, and XGBClassifier. The experimental results indicate that the Bag-of-Words technique achieved the highest performance across all algorithms and effectively reduced the impact of data sequence variance. Among the algorithms, XGBClassifier delivered the most accurate predictions, with an Accuracy of 85.25% and an F1-Score of 0.85. Furthermore, feature importance analysis revealed that the newly engineered feature, Total Cost, is the most significant factor influencing match outcomes.