Clint Dempsey is How Important? or Language Determines Reality

Devin Pleuler, a self-described “data scientist”, wrote a recent article on measuring the importance of a player based on the esoteric stat “centrality.” Now first and foremost, I love esoteric stats. One of my favorite philosophers, up there with Schopenhauer and Spinoza, is Ludwig Wittgenstein. My boy Ludwig dropped knowledge on the intersection between life, language and reality. One of my favorite Wittgenstein quotes is, “Language is a part of our organism and no less complicated than it.” As a poet with an idiosyncratic relationship to oral communication, I’ll co-opt any big brains who makes talking funny sound smartypants.

Language is a living thing, growing and changing. I’ll argue until I’m blue in the face, or unemployed as a composition adjunct, that contemporary grammar and syntax rules are fleeting at best, that OMG will soon be a word, and certain apostrophes soon superfluous. Language does not live in the dictionary (or MS Word’s spellcheck algorithms), it only exists between people. We are communicating regardless of whether we are using words found in Webster’s or syntax applauded by Strunk and White; in fact, the usage of new words and syntax actually represents a more faithful approximation to the reality we currently inhabit, not Shakespeare’s or Wilde’s or Updike’s. We live in a world of McGriddle’s and smartphones, of twerking and LMFAO.

The statistical revolution in American sports started by Bill James and continued by Aaron Schatz, among others, is just another new language. In computer science and sports, advanced metrics are getting us closer to something we collectively share called reality. Pleuler, in his article, applies some of these stats to better understand the value of soccer players in MLS:

The more a player sees of the ball, the more important they usually are. This is common sense.

A coach obviously prefers having their stronger players handle the ball more often than their weaker players. But this is not a cut-and-dry rule. Clint Dempsey and Michael Bradley are two of the best players in MLS, but due to differences in positioning they see the ball at significantly different rates.

Dempsey, in matches he has started, has attempted 9.5 percent of Seattle’s overall passes. In contrast, Bradley has attempted a league-leading 16.3 percent of Toronto’s overall attempted passes. This percentile measurement is called “Usage Rate,” and Bradley has been a leader in this category at every stop along his career path.

However, usage rate isn’t perfectly illustrative. It treats every pass between every player exactly the same. One metric that is gaining traction in the soccer analytics community is “Centrality” and its many derivatives. The flavor we will use is “Eigenvector Centrality,” which is a “measure of the influence of a node in a network.” Using the passing networks commonly featured in this series, we can use centrality to calculate just how influential a player’s passes are to their team’s overall ball circulation on a game-by-game basis.

For example, below is the Seattle Sounders network from their 2-1 loss to Real Salt Lake this past weekend. Each player’s position is representative of their average touch location through the match. More importantly, the thickness of the lines between players are representative of the volume of passes exchanged between them.

Pleuler takes his time slowly building the pillars of his case and gets you, the reader, invested before bullrushing to his conclusion. This is my problem with the piece.

If you’re gonna have stats-driven analysis, you best earn all those stats. If we are gonna talk using new words, I better be drinking your Kool-Aid, or catching your drift, or down with OPP, see? Numbers don’t work just ‘cause they’re numbers and have some magic pre-imbued in them. Check out the rest of the article at, and tell me if you’re equally confused as to where the hell “expected centrality” comes from?

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