Clustering Player Performance in Pokémon TCG Tournaments: A K-means Approach to Identifying Performance Groups Based on Wins, Losses, and Tournament Statistics - Sembina, Gulbakyt; Naizabayeva, Lyazat (International Journal Research on Metaverse, 2025)
This study applies K-means clustering to analyze player performance in competitive Pokémon TCG tournaments, categorizing players into four distinct performance groups based on metrics such as wins, losses, and ties. Using a dataset comprising over 186,000 players, the study identifies key clusters representing varying levels of success in the game. The data was preprocessed by handling missing values and standardizing features to ensure uniform contribution across metrics. MiniBatchKMeans was employed to optimize clustering for large datasets, resulting in a model that groups players into low, moderate, and high-performance categories. The clustering results provide valuable insights into the distribution of player performance and help identify trends in competitive dynamics. A Silhouette Score of 0.4582 indicates that the clustering is moderately effective, with some overlap between clusters, suggesting that further refinement may be needed. Visualizations, including scatter plots, box plots, and heatmaps, were used to interpret the cluster characteristics, showing that top-performing players cluster into smaller groups, while a large majority of players exhibit moderate performance. The findings offer important implications for both players and tournament organizers: players can refine strategies based on their cluster profiles, while organizers can use clustering insights to design more balanced and engaging tournament formats. Future research could explore alternative clustering methods and incorporate additional performance features to further optimize player segmentation and enhance tournament design.