• KIPS Transactions on Software and Data Engineering
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• v.9 no.5
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• pp.161-168
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• 2020

Predicting outcome of the sports enables teams to establish their strategy by analyzing variables that affect overall game flow and wins and losses. Many studies have been conducted on the prediction of the outcome of sports events through statistical techniques and machine learning techniques. Predictive performance is the most important in a game prediction model. However, statistical and machine learning models show different optimal performance depending on the characteristics of the data used for learning. In this paper, we propose a new ensemble model to predict English Premier League soccer games using statistical models and the machine learning models which showed good performance in predicting the results of the soccer games and this model is possible to select a model that performs best when predicting the data even if the data are different. The proposed ensemble model predicts game results by learning the final prediction model with the game prediction results of each single model and the actual game results. Experimental results for the proposed model show higher performance than the single models.

• Journal of the Korea Society of Computer and Information
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• v.28 no.12
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• pp.23-32
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• 2023

E-sports, a burgeoning facet of modern sports culture, has achieved global prominence. Particularly, Aeon of Strife (AOS) games, emblematic of E-sports, blend individual player prowess with team dynamics to significantly influence outcomes. This study aggregates and analyzes real user gameplay data using statistical techniques. Furthermore, it develops and tests win-loss prediction models through machine learning, leveraging a substantial dataset of 1,149,950 individual data points and 230,234 team data points. These models, employing five machine learning algorithms, demonstrate an average accuracy of 80% for individual and 95% for team predictions. The findings not only provide insights beneficial to game developers for enhancing game operations but also offer strategic guidance to general users. Notably, the team-based model outperformed the individual-based model, suggesting its superior predictive capability.

• Korean Journal of Artificial Intelligence
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• v.7 no.2
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• pp.13-17
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• 2019

Currently, the sports market continues to grow every year, and among them, professional baseball's entry income is larger than the rest of the professional league. In sports, strategies are used differently in different situations, and the analysis is based on data to decide which direction to implement. There is a part that a person misses in an analysis, and there is a possibility of a false analysis by subjective judgment. So, if this data analysis is done through artificial intelligence, the objective analysis is possible, and the strategy can be more rationalized, which helps to win the game. The most popular baseball to be applied to artificial intelligence to analyze athletes' strengths and weaknesses and then efficiently establish strategies to ease the competition. The data applied to the experiment were provided on the KBO official website, and the algorithms for forecasting applied linear regression. The results showed that the accuracy was 87%, and the standard error was ±5. Although the results of the experiment were not enough data, it would be possible to effectively use baseball strategies and predict the results of the game if the amount of data and regular data can be applied in the future.

• KIPS Transactions on Software and Data Engineering
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• v.9 no.2
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• pp.61-68
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• 2020

E-sports has grown steadily in recent years and has become a popular sport in the world. In this paper, we propose a win-loss prediction model of League of Legends at the start of the game. In League of Legends, the combination of a champion statistics of the team that is made through each player's selection affects the win-loss of the game. The proposed model is a deep learning model based on Bidirectional LSTM embedding which considers a combination of champion statistics for each team without any domain knowledge. Compared with other prediction models, the highest prediction accuracy of 58.07% was evaluated in the proposed model considering a combination of champion statistics for each team.

• Journal of the Korea Society of Computer and Information
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• v.26 no.4
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• pp.213-221
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• 2021

In this paper, we suggest the Deep Neural Network Model System for predicting results of the match of 'League of Legends (LOL).' The model utilized approximately 26,000 matches of the LOL game and Keras of Tensorflow. It performed an accuracy of 93.75% without overfitting disadvantage in predicting the '2020 League of Legends Worlds Championship' utilizing the real data in the middle of the game. It employed functions of Sigmoid, Relu and Logcosh, for better performance. The experiments found that the four variables largely affected the accuracy of predicting the match --- 'Dragon Gap', 'Level Gap', 'Blue Rift Heralds', and 'Tower Kills Gap,' and ordinary users can also use the model to help develop game strategies by focusing on four elements. Furthermore, the model can be applied to predicting the match of E-sports professional leagues around the world and to the useful training indicators for professional teams, contributing to vitalization of E-sports.

• 한국체육학회지인문사회과학편
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• v.56 no.4
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• pp.397-413
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• 2017

The purpose of this study is to introduce the case and to provide related information for the physical education major to handle and utilize the social big data through the exploratory study for the application of sports industry in the fourth industrial revolution. For this study, data was collected from the article database, which covers the keyword such as 'Social Big Data', 'Sports' and so on. The analyzed articles were 86 articles. As a results, The research on social big data applied to sports industry are as follows: 1) Analysis of major issues related to sports fans' interests and sports events, 2) A study on media sports engagement, 3) The prediction analysis of sports game based on the sentiment analysis, 4) Development of salary estimation model for professional player in sports, 5) Research trend analysis and so on. In conclusion, the social big data analysis technology in the sports industry and management can be utilized variously. Therefore, the specialists of the sports industry and management field need to learn the techniques, to acquire the know-how for the research project, to convert the convergence thinking.

• KIISE Transactions on Computing Practices
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• v.23 no.1
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• pp.37-44
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• 2017

Games of the AOS genre are classified as an e-sport rather than a recreational computer game. The involved statistical analyses such as game playing patterns and the season's characters gain importance due to the expertise-requiring nature of sports. In this study, the strategic analysis of computer games was conducted by using data mining techniques on League of Legend, a representative AOS game. We designed and tested a winning forecast model using winning percentage prediction techniques such as logistic regression analysis, discriminant analysis, and artificial neural networks. The game data analysis results were represented by a probabilistic graph and used in the visualization tool for game play. Experimental results of the winning forecast model showed a high classification rate of 95% on average with potential for use in establishing various strategies for game play with the visualization tool.

• Journal of Broadcast Engineering
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• v.25 no.2
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• pp.143-150
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• 2020

Huge amounts of contents are being uploaded every day on various streaming platforms. Among those videos, game and sports videos account for a great portion. The broadcasting companies sometimes create and provide highlight videos. However, these tasks are time-consuming and costly. In this paper, we propose models that automatically predict highlights in games and sports matches. While most previous approaches use visual information exclusively, our models use both audio and visual information, and present a way to understand short term and long term flows of videos. We also describe models that combine GAN to find better highlight features. The proposed models are evaluated on e-sports and baseball videos.

• Journal of the Korean Data and Information Science Society
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• v.26 no.1
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• pp.187-196
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• 2015

The team's performances were analyzed by evaluating the scores gained by their offense and the scores allowed by their defense. To evaluate the team's attacking and defending abilities, we also considered the factors that contributed the team's gained points or the opposing team's gained points? In order to analyze the outcome of the games, three prediction models were used such as decision trees, logistic regression, and discriminant analysis. As a result, the factors associated with the defense showed a decisive influence in determining the game results. We analyzed the offense and defense by using the response variable. This showed that the major factors predicting the offense were non-stop pass and attack speed and the major factor predicting the defense were the distance between right and left players and the distance between front line attackers and rearmost defenders during the game.

• The Journal of the Convergence on Culture Technology
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• v.10 no.3
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• pp.131-135
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• 2024

Baseball attracts the largest audience among professional sports in Korea. In particular, attendance is the primary source of income in baseball. Previous studies have limitations in reflecting the characteristics of individual stadium. For instance, the KIA Tigers exhibit the highest away game revenue among domestic teams, but they show lower home game earnings. Therefore, we aim to predict the daily attendance at the Gwangju-KIA Champions Field of the KIA Tigers using deep learning. We collected and preprocessed daily attendance, dates, weather, and team-related variables for Gwangju-KIA Champions Field from 2018 to 2023. We propose a deep learning-based linear regression model to predict the daily attendance. We expect that the proposed deep learning model will be used as basic information to increase the club's revenue.