Predicting Esports Match Outcomes with Machine Learning: A Data Analysis Study Based on 7,033 CS2 Professional Matches

This article introduces a research project that uses machine learning algorithms such as logistic regression, random forests, and gradient boosting to predict match outcomes, based on 7,033 Counter-Strike 2 (CS2) professional match data from HLTV.org. It verifies the predictive power of features like team ratings, head-to-head records, and map win rates on match results, providing valuable references for the field of esports data analysis.

Esports has developed into a mainstream industry with hundreds of millions of viewers, but esports prediction research is relatively lagging behind. Reasons include poor data accessibility (scattered and no unified standards), game complexity (influenced by multi-dimensional factors), and data quality issues (inconsistent formats, many missing values). This study chose CS2 because HLTV.org provides relatively complete and structured professional match data.

Dataset: CS2 HLTV professional match statistics dataset from Kaggle (source: HLTV.org), spanning from May 2024 to October 2025, covering 7,033 matches across 648 events. Core fields include match_outcome, mean_hltv_rating, mean_kpr, head_to_head_win_rate, and map_win_rate.

Research Hypotheses: 1. Teams with higher HLTV Rating/KPR are more likely to win; 2. Better head-to-head records increase the probability of winning; 3. Teams with higher map win rates are more likely to win on that map.

Methodology: Three machine learning models are selected for comparison: logistic regression (baseline, strong interpretability), random forest (captures non-linear interactions), gradient boosting (excellent performance on structured data).

Data Engineering: Includes data auditing (quality check), feature engineering (conversion to model-usable features), data cleaning (handling issues like name variations), and training/test split (in chronological order to avoid future information leakage).

Findings: It is expected that head-to-head records and map win rates may have stronger predictive power than individual ratings; gradient boosting may lead in accuracy, while logistic regression coefficients are more valuable for interpretation.

Ethical Considerations: Data is only used for academic research and shall not be used for commercial gambling, etc.; data source HLTV.org is credited; it is emphasized that prediction results are for reference only and do not constitute decision-making advice.

Insights: Importance of data infrastructure, robustness of traditional machine learning methods on small and medium datasets, value of interdisciplinary integration, reproducible research template.

Future Directions: Increase feature dimensions (e.g., economic management, key round performance), introduce time series methods to capture team strength dynamics, develop map-specific models, and implement a real-time prediction system.