A Database Design for Selecting a Golden Glove Winner using Sabermetrics

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1 , pp A Database Design for Selecting a Golden Glove Winner using Sabermetrics Wu-In Jang and Young-Ho Park *, Department of Multimedia Science, Sookmyung Women s University, Cheongpa-ro 47 gil 100,Yongsan-gu, Seoul, , Korea {leader0710, yhpark}@sm.ac.kr *Corresponding Author Abstract. Baseball is a sport of record. Baseball data has been used strategic and operation direction, annual salary, including foreign scout in various fields based on records. Sabermetrics has designed to evaluate player. Sabermetrics analyzes baseball data more objective, complex. In this paper, we propose the use of Sabermetrics for selecting a golden glove winner. We construct database suitable for analysis system by selecting attributes to evaluate a player. As far as we know, this is the first attempt to select golden glove winner. Keywords: Baseball Data, Sabermetrics, Golden Glove, Baseball Database 1 Introduction The source of the data baseball is records. Baseball calls sport of record products a number of records. Korea Baseball Organization official scorekeepers leave behind record on recoding paper. This record becomes useful statistics. The record and statistics of other sports only shows player s capability, but the record and statistics of baseball serve as one element that determines the win or lose through the analysis. A full-scale baseball data research accelerates by Sports Statistics. It is possible to do various researches using record and statistics. Baseball data research is plentiful and exciting baseball in accordance with reinterpretation. In Major League Baseball, MLB take note of Sabermetrics [1]. Bill James defines Sabermetrics as research to find objective knowledge of baseball [2]. Sabermetrics the way to evaluate and analyze player s capability based on a number of statistics. There is limit to a simple record for grasp player s everything. Sabermetrics is evaluated as objective and complex standard to assess the player through various statistical indicators. In the paper, we propose a database design for selecting golden glove winner using Sabermetrics. Specifically, we select attributes to assess the player through sites that provide professional baseball data. We include relief pitcher and closer pitcher in bullpen pitcher, perform operation for distinguish between the same name. As far as we know, this is the first attempt to select golden glove winner. ISSN: ASTL Copyright 2015 SERSC

2 The rest of the paper proceeds as follows. Section 2 discusses the related research and explains the current standard s problems about golden glove. Section 3 describes the way to construct database. Section 4 shows future work. 2 Related Work There have been a number of researches that focus on the baseball data. We briefly review each of these researches and explain about golden glove. 2.1 Baseball Data Analysis Methods In [3], the author makes a comparative study player s hitting capability focus on hitter s data in Major League Baseball from 1995 to This paper uses Bayesian and Stochastic frontier model in order to increase efficiency. In [4], the author analyzes effect on the productivity of scoring on base percentage and slugging average in Korean baseball league. This paper shows a correlation between batting indicators (average, on base percentage, slugging average, Isolated Power, On base percentage Plus Slugging percentage) and goal average per game from 2007 to In [5], the authors use a Markov chain model in order to analyze Korea professional baseball. This paper defines hitter s batting result and stochastic process that indicates runner status based on This paper predicts game result by organizing the Markov chain. 2.2 The current standards and problems Golden glove is the award given annually to the Korea baseball League baseball players judged to have exhibited superior individual fielding performances at each fielding position [6]. Golden glove is derived from gold glove. Gold glove is the award given the best defensive players [7]. Korea baseball league has awarded golden glove from In 1982 selected superior defensive player as gold glove, but from 1984 has evaluated with consideration for offense, defense and the popularity ratings. Golden glove winner is selected by a vote of pro-baseball press corps and baseball officials. Table 1 is golden glove candidate selection Standard. There is a change for each field to maintain the number of candidates every year. Pennant race, personal title No.1 player is automatically candidate s standard is as follows. Table 1. Golden glove candidate selection Standard. Position Pitcher Catcher Infielder Standard SP: ERA 3.20 less than, 13W more than CP: 30S more than 85G more than, AVG more than 1B: 85G more than, AVG more than Copyright 2015 SERSC 39

3 Outfielder Designated Hitter 2B: 85G more than, AVG more than 3B: 85G more than, AVG more than SS: 85G more than, AVG more than 85G more than, AVG more than Golden glove winner is selected by consideration for offense, defense and the popularity ratings. However, this method is important to offense rather than defense and is criticized for losing fairness in vote. In addition to that, this method gets in the way of fair award in accordance with the exception of relief pitcher and foreign player. This method needs to reconsider evaluation criteria. 3 Proposed Method In this section, we describe the proposed database design. We construct database for selecting golden glove winner. We first review a number of baseball data, and then select attributes to decide golden glove winner. We collect a baseball data in 2014 that is publicly available at Korea Baseball Organization, KBO [8], BASEBALLLAB [9] and KBR [10]. We select players of the basic indicators in [8] and Sabermetrics in [9], [10]. Table 2 is attributes to store in the database in order to predict golden glove winner in hitter. The attribute screening criteria is limited in contribution to team and multiple (offense, defense, base running) assessment indicator. The attributes decide golden glove winner in hitter. Table 2. Golden glove winner prediction hitter attributes. Attribute WAR woba wraa ISO wsb FPCT CS% RC GW RBI Full Name Wins Above Replacement weighted On Base Average weighted Runs Above Average Isolated Power Weighted Stolen Base Runs Fielding Percentage Caught Stealing average Runs Created Game Winning Runs Batted In WAR is indicator that representing the team brought about a few wins on when individual record of the player is transformed to a score value. woba is indicator showing batting performance of the same type on base percentage. wraa is scoring contribution indicator based on the average player of the league. ISO is indicator representing only power capability to the exclusion of running. wsb is base running statistical indicator. FPCT is indicator representing defense capability. CS% is indicator corresponding catcher. RC is indicator that offense has been converted into a score value based on the basic indicator of the hitter. GW RBI represents decisive RBI that lead game to win. We use in terms of contribution to a team, WAR and GW 40 Copyright 2015 SERSC

4 RBI indicator. Also we use woba, wraa, ISO, RC indicator in offense, FPCT, CS% indicator in defense, wsb indicator in base running. Table 3 is the group of attributes to store in the database in order to predict golden glove winner in pitcher. The attribute screening criteria is limited in contribution to team and pitcher ability to the exclusion of luck and position player s help. The attributes decide golden glove winner in pitcher. Attribute WAR FIP LOB% FPCT PKO Table 3. Golden Glove winner prediction pitcher attributes. Full Name Wins Above Replacement Fielding Independent Pitching Left on Base Percentage Fielding Percentage PicK Off WAR and FPCT are attributes that can be applied to a pitcher. FIP is indicator that only evaluates pitcher s performance to the exclusion of defense impact. LOB% is indicator that rate not to allow the scoring among runners on base. PKO is indicator that the pitcher picks a runner off. We use in terms of contribution to a team, WAR indicator. Also we use FIP, LOB%, FPCT, PKO indicator in order to pitcher s ability to evaluate. The attributes to store in the database are name_team, position and games played. We perform operation for distinguish between the same name through the name and team s combination. In order to intuit this attribute, we convert 9 teams to number based on ranking of This attribute makes PK (Primary Key) in database later on. Pitcher position is reclassified from new standard. We include relief pitcher and closer pitcher in bullpen pitcher. We decide two winners that one is for the start pitcher, and one is for the bullpen pitcher. The division of the starting and bullpen hews to number compete in an elimination match. Minimum number of games played follows in the current standards. 4 Analysis Through a database to save Sabermetrics we strengthen purpose of the golden glove using defense indicator and include relief pitcher and closer pitcher in bullpen pitcher. This is the first attempt to include relief pitcher in candidate. Above all, there is a further significance to comprehensive assessment based on the record. This is focused on contribution to a team and player s performance to the exclusion of public opinion and popular. Foreign players apply to same standard with local players. In future work, we will describe the detail algorithm and experimental results by using database. Copyright 2015 SERSC 41

5 5 Conclusion In the paper, we have proposed a database design for selecting golden glove winner. We believe that the proposed database design has a positive impact to the development of a pro-baseball in Korea as it improves the reliability of selecting a golden glove winner. In the future, we plan to develop prediction algorithms and construct analysis system. We make a comparison between results and 2014 golden glove winners. Furthermore we prove objectivity about golden glove winner. References 1. Sabermetrics, 2. James, B.: The Bill James Baseball Abstract, New York: Ballantine Books (1982) 3. Gary, K.: Comparing the Performance of Baseball Players: A Multiple-Output Approach. 4. J.American Statistical Association, 97(459), (2002) 5. Kim, H, J.: Effects of on-base and slugging ability on run productivity in Korean professional baseball. J. Korean Data & Information Science Society, 23(6), (2012) 6. Moon, H, W., Woo, Y, T., Shin, Y, W.: Analysis of the Korean Baseball League using a Markov Chain Model. J. Applied Statistics, 26(4), (2013) 7. Golden Glove, 8. Gold Glove, 9. KBO, BASEBALLLAB, KBR, 42 Copyright 2015 SERSC