League of Legends: Real-Time Result Prediction

• URL: http://arxiv.org/abs/2309.02449v1
• Date: Sat, 2 Sep 2023 02:01:51 GMT
• Title: League of Legends: Real-Time Result Prediction
• Authors: Jailson B. S. Junior and Claudio E. C. Campelo
• Abstract summary: This paper presents a study on the prediction of outcomes in matches of the electronic game League of Legends (LoL) using machine learning techniques. A variety of models were evaluated and the results were encouraging. This study contributes to the field of machine learning applied to electronic games, providing valuable insights into real-time prediction in League of Legends.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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ability to predict real-time results, considering different variables and stages of the match, we highlight the use of unpublished data as a fundamental part of this process.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.

Related papers

• Lasso Ridge based XGBoost and Deep_LSTM Help Tennis Players Perform better [1.6016817180824583]
  We develop a sliding-window-based scoring model to assess player performance and quantify momentum effects. We propose a Derivative of the winning rate algorithm to quantify game fluctuation, employing an LSTM_Deep model to pre-dict fluctuation scores. Our findings provide valuable in-sights into momentum dynamics and game fluctuation, offering implications for sports analytics and player training strategies.
  arXiv  Detail & Related papers  (2024-05-11T15:02:08Z)
• The Evolution of Football Betting- A Machine Learning Approach to Match Outcome Forecasting and Bookmaker Odds Estimation [0.0]
  This paper explores the history of professional football and the betting industry, tracing its evolution from clandestine beginnings to a lucrative multi-million-pound enterprise. Initiated by the legalization of gambling in 1960 and complemented by advancements in football data gathering pioneered by Thorold Charles Reep, the symbiotic relationship between these sectors has propelled rapid growth and innovation. The primary aim of this study is to utilize Machine Learning (ML) algorithms to forecast premier league football match outcomes.
  arXiv  Detail & Related papers  (2024-03-24T20:08:16Z)
• Bayes-xG: Player and Position Correction on Expected Goals (xG) using Bayesian Hierarchical Approach [55.2480439325792]
  This study investigates the influence of player or positional factors in predicting a shot resulting in a goal, measured by the expected goals (xG) metric. It uses publicly available data from StatsBomb to analyse 10,000 shots from the English Premier League. The study extends its analysis to data from Spain's La Liga and Germany's Bundesliga, yielding comparable results.
  arXiv  Detail & Related papers  (2023-11-22T21:54:02Z)
• A Graph-Based Method for Soccer Action Spotting Using Unsupervised Player Classification [75.93186954061943]
  Action spotting involves understanding the dynamics of the game, the complexity of events, and the variation of video sequences. In this work, we focus on the former by (a) identifying and representing the players, referees, and goalkeepers as nodes in a graph, and by (b) modeling their temporal interactions as sequences of graphs. For the player identification task, our method obtains an overall performance of 57.83% average-mAP by combining it with other modalities.
  arXiv  Detail & Related papers  (2022-11-22T15:23:53Z)
• GCN-WP -- Semi-Supervised Graph Convolutional Networks for Win Prediction in Esports [84.55775845090542]
  We propose a semi-supervised win prediction model for esports based on graph convolutional networks. GCN-WP integrates over 30 features about the match and players and employs graph convolution to classify games based on their neighborhood. Our model achieves state-of-the-art prediction accuracy when compared to machine learning or skill rating models for LoL.
  arXiv  Detail & Related papers  (2022-07-26T21:38:07Z)
• Machine learning models for DOTA 2 outcomes prediction [8.388178167818635]
  This research paper predominantly focuses on building predictive machine and deep learning models to identify the outcome of the Dota 2 MOBA game. Three models were investigated and compared: Linear Regression (LR), Neural Networks (NN), and a type of recurrent neural network Long Short-Term Memory (LSTM)
  arXiv  Detail & Related papers  (2021-06-03T12:10:26Z)
• Models, Pixels, and Rewards: Evaluating Design Trade-offs in Visual Model-Based Reinforcement Learning [109.74041512359476]
  We study a number of design decisions for the predictive model in visual MBRL algorithms. We find that a range of design decisions that are often considered crucial, such as the use of latent spaces, have little effect on task performance. We show how this phenomenon is related to exploration and how some of the lower-scoring models on standard benchmarks will perform the same as the best-performing models when trained on the same training data.
  arXiv  Detail & Related papers  (2020-12-08T18:03:21Z)
• Combining Machine Learning and Human Experts to Predict Match Outcomes in Football: A Baseline Model [14.121432441882895]
  We present a new application-focused benchmark dataset for prediction of match outcomes for games of football (soccer) Our dataset is focuses on a representative time-period over 6 seasons of the English Premier League, and includes newspaper match previews from The Guardian. The models presented in this paper achieve an accuracy of 63.18% showing a 6.9% boost on the traditional statistical methods.
  arXiv  Detail & Related papers  (2020-12-08T11:52:14Z)
• Interpretable Real-Time Win Prediction for Honor of Kings, a Popular Mobile MOBA Esport [51.20042288437171]
  We propose a Two-Stage Spatial-Temporal Network (TSSTN) that can provide accurate real-time win predictions. Experiment results and applications in real-world live streaming scenarios showed that the proposed TSSTN model is effective both in prediction accuracy and interpretability.
  arXiv  Detail & Related papers  (2020-08-14T12:00:58Z)
• Model-Based Reinforcement Learning for Atari [89.3039240303797]
  We show how video prediction models can enable agents to solve Atari games with fewer interactions than model-free methods. Our experiments evaluate SimPLe on a range of Atari games in low data regime of 100k interactions between the agent and the environment.
  arXiv  Detail & Related papers  (2019-03-01T15:40:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.