빅 데이터 분석 기법을 이용한 한국 프로야구 타자 평가 지표 개발
Efficient estimation model of hitter using Big Data analysis in Korea Baseball League
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When the size of data is very big so that we cannot collect, manage and process that data with usual software program, such data is called a Big data. Baseball is very appropriate area to analyze using Big data. Because there are lots of things to research and investigate such as the direction of the ball hit by player, the movement of a fielder and the course of the ball pitched by player. From now on, departing from using usual indicators like a batting average, analyzing non-standardized data is going to be significant issue in baseball. This thesis suggests Highballpoint to estimate hitter in Korea Baseball League using Big data. This consists of 'Base score', 'Distribution score' and 'Pitch waste score'. The 'Base score' is a point of event for result of hitting calculated on 'Run value' based on Run. The 'Contribution score' is a point of context for result of hitting based on 'Win expectancy' which calculates probability of winning. The 'Pitch waste score' is a point for waiting a number of balls when pitcher pitches hitter. The Highballpoint considers all events calculated on Run and winning of team unlike the OPS and Casspoint which is another estimation of hitter. The OPS does not consider several events except single, double, triple, homerun to estimate hitter. The Casspoint highly estimates homerun score more than the others therefore hitter who hit homerun is ranked on high position. The Highballpoint calculates all events based on empirical data. It also estimates result of hitting considered team win or lose.