• Journal of the Korean Data and Information Science Society
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• v.27 no.6
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• pp.1585-1592
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• 2016

Estimation of winning percentage in baseball has always been particularly interesting to many baseball fans. We have fitted models including linear regression and Pythagorean formula to the Korean baseball data of seasons from 1982 to 2015. Using RMSE criterion for both the linear formula and the Pythagorean formula, we compared two models in predicting the actual winning percentage. Pythagorean expectation is superior to linear formula when there is either high or low winning percentage. Two methods yield very similar efficiencies when the actual winning percentage is about 50%. To understand and use for estimating winning percentage, it is easier linear formula as estimated equations.

• The Journal of the Korea Contents Association
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• v.16 no.9
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• pp.127-135
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• 2016

We identified a competition network which has never been studied before and investigated the relationship between centrality of participants in singing competition and their winning percentage within the competition network. We collected competition data from 'Immortal Songs: Singing the Legend', which is a Korean television music competition program, and constructed a competition network. We calculated centrality and winning percentage and analyzed their relationship using correlation analysis, regression analysis, and visualization. There are four main findings in this research. First, a competition network is a scale-free network whose degree distribution follows a power law. Second, there is a logarithmic relationship between the count of competition and closeness. Third, winning percentage converges to approximately 60% for players who have participated in more than 20 competitions. Lastly, a strength of opponents affects approximately 23% of winning percentage for players with less than 20 competitions. The academic significance of this study is that we pioneered the definition of the competition network and applied social network analysis method. Another significant contribution of this paper is that we found explicit patterns between the centrality and winning percentage, suggesting ways to improve social relationship in competition network and to increase winning percentage.

• Journal of the Korean Data and Information Science Society
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• v.28 no.2
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• pp.309-316
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• 2017

In baseball, estimation of winning percentage is critical and many studies for this topic have been actively performed. Pairwise winning percentage estimation using Pythagorean winning percentages of individual teams against other individual teams has the property that the sum of estimated winning percentage totals must be a constant. In this paper, we consider two types of pairwise estimation including linear formula and Pythagorean formula to the Korean baseball data of seasons from 2013 to 2016 under the criterions of RMSE and MAD. In conclusion, pairwise Pythagorean methods have the smaller RMSE and MAD than traditional Pythagorean methods. We suggest the optimal pairwise Pythagorean formula with a fixed exponent. Also we show that there are very little differences of RMSE and MAD between variation in exponent values.

• Journal of the Korean Data and Information Science Society
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• v.27 no.6
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• pp.1477-1485
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• 2016

The Pythagorean theorem for baseball based on the number of runs they scored and allowed has been noted that in many baseball leagues a good predictor of a team's end of season won-loss percentage. We study the convergence characteristics of the Pythagorean expectation formula during the baseball game season. The three way ANOVA based on main effects for year, rank, and baseball processing rate is conducted on the basis of using the historical data of Korean professional baseball clubs from season 2005 to 2014. We perform a regression analysis in order to predict the difference in winning percentage between teams. In conclusion, a difference in winning percentage is mainly associated with the ranking of teams and baseball processing rate.

• Journal of the Korean Data and Information Science Society
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• v.26 no.3
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• pp.653-659
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• 2015

The Pythagorean formula for baseball postulated by James (1982) indicates the winning percentage as a function of runs scored and runs allowed. However sometimes, the Pythagorean formula gives a less accurate estimate of winning percentage. We use the records of team vs team historic win loss records of Korean professional baseball clubs season from 2005 and 2014. Using assumption that the difference between winning percentage and pythagorean expectation are affected by unusual distribution of runs scored and allowed, we suppose that difference depends on mean, standard deviation, and coefficient of variation of runs scored per game and runs allowed per game, respectively. In conclusion, the discrepancy is mainly related to the coefficient of variation and standard deviation for run allowed per game regardless of run scored per game.

• 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.

• 한국체육학회지인문사회과학편
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• v.57 no.4
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• pp.387-394
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• 2018

The purpose of this study is to provide information regarding the strategies by identifying the main variables that determines the winning team based on the records of all games of the 2017 IIHF World Championship Top league. 64 matches were analyzed for the study. 6 variables were analyzed which included ratio of saves, shots on goal, penalties in minutes, time for power play, power play goals, and face off wins. Logistic regression analysis (LRA), multiple regression analysis (MRA), and principal component analysis (PCA) were implemented to examine the relationship between win and loss. In case of LRA, shots on goal (p<.001), face-off wins (p<.001) had significantly positive relation to winning of game whereas, penalties in minutes (p<.01) and time on power play (p<.01) had significantly negative. Using MRA, win percentage was calculated which had significant positive correlation to ratio of saves (p<.01) and face-off wins (p<.001) whereas, a significant negative with penalties in minutes (p<.001). For PCA, the winning team consisted of penalty, attack, and defense factors whereas, losing teams consisted only the attack and defense factors.

• Journal of the Korea Society of Computer and Information
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• v.24 no.2
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• pp.17-24
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• 2019

In this paper, we propose a new model for predicting effective Win/Loss in professional baseball game in Korea using machine learning technique. we used basic baseball data and Sabermetrics data, which are highly correlated with score to predict and we used the deep learning technique to learn based on supervised learning. The Drop-Out algorithm and the ReLu activation function In the trained neural network, the expected odds was calculated using the predictions of the team's expected scores and expected loss. The team with the higher expected rate of victory was predicted as the winning team. In order to verify the effectiveness of the proposed model, we compared the actual percentage of win, pythagorean expectation, and win percentage of the proposed model.

• Journal of Korean Institute of Industrial Engineers
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• v.40 no.1
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• pp.8-17
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• 2014

In this research, we employed various data mining techniques to build predictive models for win-loss prediction in Korean professional baseball games. The historical data containing information about players and teams was obtained from the official materials that are provided by the KBO website. Using the collected raw data, we additionally prepared two more types of dataset, which are in ratio and binary format respectively. Dividing away-team's records by the records of the corresponding home-team generated the ratio dataset, while the binary dataset was obtained by comparing the record values. We applied seven classification techniques to three (raw, ratio, and binary) datasets. The employed data mining techniques are decision tree, random forest, logistic regression, neural network, support vector machine, linear discriminant analysis, and quadratic discriminant analysis. Among 21(= 3 datasets${\times}$7 techniques) prediction scenarios, the most accurate model was obtained from the random forest technique based on the binary dataset, which prediction accuracy was 84.14%. It was also observed that using the ratio and the binary dataset helped to build better prediction models than using the raw data. From the capability of variable selection in decision tree, random forest, and stepwise logistic regression, we found that annual salary, earned run, strikeout, pitcher's winning percentage, and four balls are important winning factors of a game. This research is distinct from existing studies in that we used three different types of data and various data mining techniques for win-loss prediction in Korean professional baseball games.

• Journal of the Korean Data and Information Science Society
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• v.25 no.3
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• pp.493-499
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• 2014

The Pythagorean won-loss formula postulated by James (1980) indicates the percentage of games as a function of runs scored and runs allowed. Several hundred articles have explored variations which improve RMSE by original formula and their fit to empirical data. This paper considers a variation on the formula which allows for variation of the Pythagorean exponent. We provide the most suitable optimal exponent in the Pythagorean method. We compare it with other methods, such as the Pythagenport by Davenport and Woolner, and the Pythagenpat by Smyth and Patriot. Finally, our results suggest that proposed method is superior to other tractable alternatives under criterion of RMSE.