Mixed Membership Martial Arts: Data-Driven Analysis of Winning Martial Arts Styles

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A major analytics challenge in Mixed Martial Arts (MMA) is understanding the differences between fighters that are essential for both establishing matchups and facilitating fighter analysis. Here, we model ~18,000 fighters as mixtures of 10 data-defined prototypical martial arts styles, each with characteristic ways of winning. Fighters of interest generally have few bouts, often less than 10, on which we can base our analysis. Accordingly, our approach balances this typically modest amount of fighter-specific data with broader patterns across fighters in order to accurately predict the performance of individual fighters. While we define prototypical styles based upon how fighters win fights, we find that styles also determine how likely they are to win. We also find that the impact of style is similar to that of experience. Reliably winning MMA fights requires dynamic striking (kicks, elbows and knees) as well as the capacity to “go the distance”, i.e., win by decision. By contrast, fighters who favor submissions that sacrifice positional control (the guillotine choke and leg submissions), tend to win less overall. While previously under-appreciated, style has massively shaped the history of MMA; the most successful athletes in the sport (defending United Fighting Championships [UFC] champions) almost exclusively favor the high-impact styles that we uncovered.

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