Clustering Professional Baseball Players with SOM and Deciding Team Reinforcement Strategy with AHP

  • Kazuhiro Kohara
  • Shota Enomoto
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10933)


In this paper, we propose an integration method that uses self-organizing maps (SOM) and the analytic hierarchy process (AHP) to cluster professional baseball players and to make decision on team reinforcement strategy. We used data of pitchers in the Japanese professional baseball teams. First, we collected data of 302 pitchers and clustered these pitchers using the following fourteen features: number of games pitched, number of wins, number of loses, number of save, number of hold, number of innings pitched, rate of strikeout, ERA (earned run average), percentage of hits a pitcher allows, WHIP (walks plus hits per inning pitched), K/BB (strikeout to walk ratio), FIP (fielding independent pitching), LOB% (left on base percentage), RSAA (runs saved above average). Second, we created pitcher maps of all teams and each team with SOM. Third, we examined main features of each cluster. Fourth, we considered team reinforcement strategies by using the pitcher maps. Finally, we used AHP to determine the team reinforcement strategy.


Clustering Visualization Data mining Business intelligence Sport industry Baseball Decision making Self-organizing maps AHP 


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Chiba Institute of TechnologyNarashinoJapan

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