Win Prediction in Esports: Mixed-Rank Match Prediction in Multi-player Online Battle Arena Games

Esports has emerged as a popular genre for players as well as spectators, supporting a global entertainment industry. Esports analytics has evolved to address the requirement for data-driven feedback, and is focused on cyber-athlete evaluation, strategy and prediction. Towards the latter, previous work has used match data from a variety of player ranks from hobbyist to professional players. However, professional players have been shown to behave differently than lower ranked players. Given the comparatively limited supply of professional data, a key question is thus whether mixed-rank match datasets can be used to create data-driven models which predict winners in professional matches and provide a simple in-game statistic for viewers and broadcasters. Here we show that, although there is a slightly reduced accuracy, mixed-rank datasets can be used to predict the outcome of professional matches, with suitably optimized configurations

Video Highlight Prediction Using Audience Chat Reactions

Sports channel video portals offer an exciting domain for research on multimodal, multilingual analysis. We present methods addressing the problem of automatic video highlight prediction based on joint visual features and textual analysis of the real-world audience discourse with complex slang, in both English and traditional Chinese. We present a novel dataset based on League of Legends championships recorded from North American and Taiwanese Twitch.tv channels (will be released for further research), and demonstrate strong results on these using multimodal, character-level CNN-RNN model architectures.Comment: EMNLP 201

Esports Analytics Through Encounter Detection

Esports is computer games played in a competitive environment, and analytics in this domain is focused on player and team behavior. Multiplayer Online Battle Arena (MOBA) games are among the most played digital games in the world. In these es, teams of players fight against each other in enclosed arena environs, with a complex gameplay focused on tactical combat. Here we present a technique for segmenting matches into spatio‐temporally defined components referred to as encounters, enabling performance analysis. We apply encounter‐based analysis to match data from the popular esport game DOTA, and present win probability predictions based on encounters. Finally,metrics for evaluating team performance during match runtime are proposed

Narrative Bytes : Data-Driven Content Production in Esports

Esports - video games played competitively that are broadcast to large audiences - are a rapidly growing new form of mainstream entertainment. Esports borrow from traditional TV, but are a qualitatively different genre, due to the high flexibility of content capture and availability of detailed gameplay data. Indeed, in esports, there is access to both real-time and historical data about any action taken in the virtual world. This aspect motivates the research presented here, the question asked being: can the information buried deep in such data, unavailable to the human eye, be unlocked and used to improve the live broadcast compilations of the events? In this paper, we present a large-scale case study of a production tool called Echo, which we developed in close collaboration with leading industry stakeholders. Echo uses live and historic match data to detect extraordinary player performances in the popular esport Dota 2, and dynamically translates interesting data points into audience-facing graphics. Echo was deployed at one of the largest yearly Dota 2 tournaments, which was watched by 25 million people. An analysis of 40 hours of video, over 46,000 live chat messages, and feedback of 98 audience members showed that Echo measurably affected the range and quality of storytelling, increased audience engagement, and invoked rich emotional response among viewers

Esports Analytics on PlayerUnknown's Battlegrounds Player Placement Prediction using Machine Learning

PUBG (PlayerUnknown’s Battlegrounds) is a video game that has become popular in the past year. This paper aims to predict the placement of PUBG players during the match by detecting the influential features set that can impact the outcome of the PUBG game and build the best prediction model using a machine learning approach. In this study, the dataset is taken from Kaggle, which has 29 attributes that are categorized into one label (winPlacePerc). The training set has divided into five sets with each set has 6000 instances. The decision tree regression model was applied to find the optimum prediction. Other regression models such as Linear Regression and Support Vector Machine are also utilized to compare with the decision tree model’s result. Based on the result analysis, the walkDistance feature was deemed as the most significant factor influencing the results of a PUBG game. Furthermore, there are other common features obtained from the five datasets that represent the crucial factors which are boosts, DBNOs, killPlace, kills, rideDistance and matchDuration. From the three regression models, the Support Vector Machine model built on the significant features has the best performance in terms of RMSE value while the Decision Tree Regression model has the fastest prediction speed among these regression models

Predictive Analysis and Comparison of Various Models on Esports Competitions

eSports has emerged as a popular genre for players and viewers, promoting a global industry in entertainment. The study of eSports has grown to resolve the need for data driven feedback, which focuses on assessment, strategy, and prediction of cyber-athletes. The focus of this project is to create and compare various models to predict the likely winner for professional games based on the data recorded from various eSports tournament matches. Pro-games have the top industry and audience attention but are restricted in number. The project is dominant on Deep Learning and Machine Learning, where the predictions are made using the model that we will build. This project can play a big part in gauging which model is most suitable for predicting the results of a match

League of Legends: Real-Time Result Prediction

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.Comment: 8 page