Fuzzy Clustering of Baseball Statistics

Abstract

A previous investigation into the ability of fuzzy clustering to be a sound method for comparing Major League Baseball players' batting averages yielded promising results. Yet, the study involved a rather small sample of 90 batting averages, which were fuzzy clustered into three categories. Furthermore, the primary study focused on batting averages, a statistic that is incapable of reflecting a player's hitting ability on its own, and it certainly does not account for the defensive skill of a player. While the original work highlighted some of the inherent advantages to fuzzy clustering, the small sample size and number of groups did not allow for a complete spectrum to be generated. Without an uncondensed continuum from which to compare the relative overall skills of all players, the original results limited the practical applications of fuzzy clustering in Major League Baseball. The current research aims to greatly improve upon the first study and emphasize the potential gains from the implementation of fuzzy clustering in the practices of Major League Baseball. In an effort to provide a more comprehensive analysis of baseball statistics, this investigation includes two additional hitting statistics, on base percentage and slugging percentage, and incorporates fielding percentage. The three added statistics reflect a player's bat control, power, and defensive reliability, respectively, all of which teams use to gauge a player's skills. All three offensive statistics are averaged to generate an inclusive measure of a player's offensive capabilities, and the corresponding fielding percentage was added as a second dimension into the fuzzy clustering program. The new four-input model is a more developed and more applicable version of the one produced in the original research. Fuzzy clustering of batting averages, on base percentages, slugging percentages, and fielding percentages is an innovative way for teams to compare an individual's skills to that of all professional players simultaneously, since fuzzy clustering is ideal for establishing relationships between data that would not normally be associated. Baseball statisticians will no longer be forced to merely note the numerical difference between players' three key hitting statistics and a critical defensive measure. Instead, players can be grouped according to their relative production, providing organizations with a more comprehensive view of players' capabilities. In this investigation, 968 Major League Baseball players' selected statistics were fuzzy clustered into nine groups, in an effort to better express the range of baseball skills. The results of the research offer insight into an amount of data that cannot efficiently be processed by an individual, which would make fuzzy clustering of batting averages an invaluable tool for Major League Baseball. Motivational resources are greatly needed in such a mentally and emotionally draining sport, and fuzzy-clustered statistics would provide organizations with such a resource. Players can be greatly uplifted when shown that they rank relatively well among their peers. Successful players can also use their relative groupings to secure better contracts. Owners, however, can save money in contract negotiations and retain desired players that are currently not performing well, according to the fuzzy cluster to which they belong. An additional way for owners to conserve funds is by utilizing fuzzy clustering in the scouting process. Instead of large travel budgets, fuzzy clustering can be used to compare the skill of a prospect among his peers. Finally, the teams that are the quickest to apply fuzzy clustering of baseball data to player trades will gain a competitive edge. Teams will seek trades for players of the same relative skill level, and possibly receive two or more players for one that has slightly better nominal numbers. Major League Baseball is filled with countless amounts of data. Fuzzy clustering of statistics would alleviate some of the inefficient data processing, and the pioneering organization will accrue the most benefits from the implementation of fuzzy clustering of baseball statistics.