Estimating Player Market Value in Virtual Leagues: A Clustering Approach Using Player Attributes for Metaverse Applications - Turistiati, Ade Tuti; Monk, Lincoln James Faikar; Ramadhan, Hafizh Faikar Agung (International Journal Research on Metaverse, 2025)
The advent of virtual environments, particularly within the Metaverse, has revolutionized the way sports simulations and virtual leagues operate. In these environments, understanding and predicting player market value is essential for optimizing team management, player scouting, and in-game strategies. This paper presents a clustering approach using K-Means to segment players based on their performance attributes and predict their market value in virtual leagues. The dataset includes various player attributes such as age, goals scored, assists, minutes played, and performance metrics like expected goals (xG) and expected assists (xA). The K-Means clustering algorithm was applied to partition players into three distinct groups based on their performance profiles. The results indicated that high-performing players, characterized by high goals scored, assists, and other key metrics, were grouped in one cluster, while lower-performing players were segmented into another. These clusters correspond to different player market values, with higher-performance clusters being associated with higher market value. The clustering analysis reveals significant patterns that can inform virtual league operations, including player trading, recruitment, and team-building strategies. The findings suggest that virtual league developers, managers, and gamers can leverage these clusters to make more informed decisions regarding player acquisitions and team compositions. Furthermore, the clustering results can be used to dynamically adjust player values based on their performance attributes, offering a realistic simulation of real-world sports economics. Future research may explore more advanced clustering techniques, such as hierarchical clustering, and expand the dataset to include additional attributes like player psychology or external factors like fan sentiment. Overall, this paper highlights the potential of clustering algorithms to enhance player market valuation and decision-making within virtual leagues.