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In the ever-evolving landscape of eSports, Counter-Strike: Global Offensive (CS: GO) stands as a global giant, captivating millions of players and viewers alike. With professional tournaments and competitive play flourishing, accurately predicting match outcomes has become a fascinating challenge for analysts, players, and betting platforms. Machine learning, with its potential for uncovering hidden patterns in data, offers a promising solution to this challenge. This research explores the use of machine learning classification models to predict the winner in Counter-Strike matches. By analyzing historical data, including in-game statistics, player performance metrics, and team coordination, this study aims to develop robust predictive models that can provide accurate forecasts for match outcomes. We experiment with three popular machine learning algorithms: Logistic Regression, Decision Trees, and Random Forest. Each algorithm has its unique strengths, from the simplicity of Logistic Regression to the power of Random Forest's ensemble learning techniques. By comparing these models based on their accuracy and effectiveness, we seek to determine which approach best fits the complex, dynamic nature of CS: GO gameplay. The study uses key features such as player kill-death ratios, team economy, round wins, and strategic elements to train the models. Extensive data preprocessing, including feature engineering, normalization, and handling missing data, ensures the models receive clean and meaningful input for training. Additionally, hyperparameter tuning and cross-validation are employed to optimize model performance. The Random Forest model achieves the highest accuracy at 85%, followed by Decision Trees at 80%, and Logistic Regression at 74%. Although Random Forest outperforms the others, each model offers unique insights into predicting match outcomes. This research holds practical applications for eSports betting, team strategy optimization, and improving audience engagement. Future work could involve integrating real-time data to make live predictions and exploring more advanced algorithms like deep learning. The ultimate goal is to build models that offer real-time predictions with higher accuracy, enhancing the experience for all stakeholders in the CS: GO competitive scene.

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Counter-Strike Winner Prediction Using Machine Learning

  • 会议论文

  • 发表日期：

    2026年

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  • Udit Ranjan Kar,
  • Ritwika Chakrabarty,
  • Dhrubasish Sarkar,
  • Dipak Kumar Kole

摘要

In the ever-evolving landscape of eSports, Counter-Strike: Global Offensive (CS: GO) stands as a global giant, captivating millions of players and viewers alike. With professional tournaments and competitive play flourishing, accurately predicting match outcomes has become a fascinating challenge for analysts, players, and betting platforms. Machine learning, with its potential for uncovering hidden patterns in data, offers a promising solution to this challenge. This research explores the use of machine learning classification models to predict the winner in Counter-Strike matches. By analyzing historical data, including in-game statistics, player performance metrics, and team coordination, this study aims to develop robust predictive models that can provide accurate forecasts for match outcomes. We experiment with three popular machine learning algorithms: Logistic Regression, Decision Trees, and Random Forest. Each algorithm has its unique strengths, from the simplicity of Logistic Regression to the power of Random Forest's ensemble learning techniques. By comparing these models based on their accuracy and effectiveness, we seek to determine which approach best fits the complex, dynamic nature of CS: GO gameplay. The study uses key features such as player kill-death ratios, team economy, round wins, and strategic elements to train the models. Extensive data preprocessing, including feature engineering, normalization, and handling missing data, ensures the models receive clean and meaningful input for training. Additionally, hyperparameter tuning and cross-validation are employed to optimize model performance. The Random Forest model achieves the highest accuracy at 85%, followed by Decision Trees at 80%, and Logistic Regression at 74%. Although Random Forest outperforms the others, each model offers unique insights into predicting match outcomes. This research holds practical applications for eSports betting, team strategy optimization, and improving audience engagement. Future work could involve integrating real-time data to make live predictions and exploring more advanced algorithms like deep learning. The ultimate goal is to build models that offer real-time predictions with higher accuracy, enhancing the experience for all stakeholders in the CS: GO competitive scene.