Predicting League of Legends Match Results Based on Machine

Abstract: League of Legends (LoL) is a highly popular multiplayer online competitive game, featuring intricate game mechanics and team cooperation, making the prediction of match outcomes a challenging task. This study utilizes a dataset from Kaggle, comprising 9,879 ranked matches ranging from Diamond I to Master tier, to build a machine learning model predicting the ultimate winner, either the blue or red team, based on the features of the first 10 minutes of gameplay. Through steps such as data loading, preprocessing, and feature engineering, we provided effective inputs for the model. For model selection, we opted for the Logistic Regression algorithm, achieving a model accuracy of 0.7277 through data splitting and training. This accuracy robustly supports predictions of the winning side, whether blue or red. However, to further enhance model performance, we recommend exploring additional feature en#2;gineering methods, investigating alternative machine learning algorithms, and fine-tuning hyperpa#2;rameters. The introduction of deep learning models is also a promising avenue to better capture the complex relationships within the game. Through these improvements, we anticipate increasing the model’s predictive accuracy for future matches, offering valuable insights for game development and enhancement.