Semantics Analysis of Expected Goals in Soccer Data Using Machine Learning - Anuj Razdan (International Journal of Data Structure Studies, 2025)
In recent years, the increasing availability of soccer data has greatly enhanced the accuracy and depth of player performance evaluation. Soccer, being one of the most popular sports worldwide, attracts millions of fans due to its simple rules, minimal equipment requirements, and high entertainment value. However, analyzing an entire match manually can be time-consuming, leading to a growing demand for automated methods that can summarize and interpret game data efficiently. This study focuses on extracting, processing, and analyzing soccer data from a CSV file to evaluate player performance and compute expected goals (xG). The approach employs a Gradient Boosting Classifier to assess predictive performance, with the F1 score used as the primary evaluation metric. Additionally, the analysis includes player-specific performance metrics and team-based insights, offering a comprehensive understanding of individual contributions and match outcomes. The integration of machine learning and statistical modeling in this work demonstrates how data-driven techniques can provide valuable insights into soccer analytics, supporting coaches, analysts, and fans in making informed decisions.
Keywords: Machine Learning; Soccer data; data preprocessing; player; semantics analysis