Exploring predictors of results of Dota 2 matches - Tcypin, Gleb; Rejthar, Jan; Kavřík, Dominik; Vysoká škola ekonomická v Praze. Fakulta informatiky a statistiky (2025)
This thesis investigates the prediction of professional Dota 2 match outcomes using machine learning techniques, focusing on pre-game and early-game features limited to the first 10 minutes of gameplay. Alongside the development of predictive models, it introduces a structured data governance framework inspired by modern data engineering practices, enabling scalable data collection, transformation, and modelling. Three classification algorithms are applied: Logistic Regression, Multi-Layer Perceptron (MLP), and XGBoost. Each model is trained on a dataset adjusted to suit its specific characteristics and working principles, incorporating custom transformations such as binning and one-hot encoding. The models undergo initial evaluation and are then refined through feature pruning and hyperparameter tuning. The impact of individual variables is assessed using model coefficients, permutation importance (measured on the training data), and Shapley values. The models achieve competitive accuracy, with all reaching up to 65.7%. Although pruning and tuning yield incremental improvements, none of the individual models surpasses the baseline Logistic Regression, which also reaches 65.7%. A stacked ensemble model that combines the best-performing versions of all three base learners slightly improves performance to 65.8%. Key predictors include early-game differences in experience, kills, and net worth, as well as the presence of a team on the ELO leaderboard. The results confirm that early-game data alone can support meaningful predictions and that combining ensemble techniques with structured preprocessing pipelines enhances both performance and interpretability. Future work may involve integrating real-time match data and team-specific historical information to further improve predictive accuracy.
Vedoucí práce: Jan Rejthar
Bakalářská práce (Bc.)—Vysoká škola ekonomická v Praze. Fakulta informatiky a statistiky, 2025
Obsahuje bibliografii
Textový (vysokoškolská kvalifikační práce)
Rok obhajoby 2025
Způsob přístupu: Internet
Keywords: data analytics [obor bakal. práce]; bakalářské práce; bachelor's theses