英雄联盟：实时结果预测
League of Legends: Real-Time Result Prediction
AI总结：
本文研究利用机器学习技术预测《英雄联盟》对局结果，通过不同变量和阶段分析，发现LightGBM模型在中期预测准确率达81.62%，Logistic回归和梯度提升在早期阶段表现更佳，为游戏实时预测提供新见解。
摘要：
本文提出利用机器学习技术预测《英雄联盟》对局结果的研究。旨在探索实时预测能力，考虑不同变量和阶段，强调未发布数据在过程中的基础作用。随着《英雄联盟》的流行和赛事的兴起，与该游戏相关的投注也逐渐增多，使该领域的研究更加相关。评估了多种模型，结果令人鼓舞。基于LightGBM的模型在对局时间百分比介于60%至80%的中期阶段实现了81.62%的平均准确率。另一方面，Logistic回归和梯度提升模型在游戏早期阶段表现更佳，结果令人鼓舞。本研究为将机器学习应用于电子游戏领域做出了贡献，为《英雄联盟》中的实时预测提供了有价值的见解。获得的结果可能对寻求改进策略的玩家和与该游戏相关的投注行业都有参考价值。
英文摘要：
This paper presents a study on the prediction of outcomes in matches of the electronic game League of Legends (LoL) using machine learning techniques. With the aim of exploring the ability to predict real-time results, considering different variables and stages of the match, we highlight the use of unpublished data as a fundamental part of this process. With the increasing popularity of LoL and the emergence of tournaments, betting related to the game has also emerged, making the investigation in this area even more relevant. A variety of models were evaluated and the results were encouraging. A model based on LightGBM showed the best performance, achieving an average accuracy of 81.62\% in intermediate stages of the match when the percentage of elapsed time was between 60\% and 80\%. On the other hand, the Logistic Regression and Gradient Boosting models proved to be more effective in early stages of the game, with promising results. This study contributes to the field of machine learning applied to electronic games, providing valuable insights into real-time prediction in League of Legends. The results obtained may be relevant for both players seeking to improve their strategies and the betting industry related to the game.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
