Analytics Are Good, But Psychometrics Can Make Them Great by Bret Levine August 20, 2015 This is not about a relief pitcher resting horizontally on a comfy couch as he spills his deepest darkest secrets to a furrowed, bearded psychologist, nor is this about prescribing medication to a team’s severely depressed kicker who just missed the game-winner. We’re talking about sports psychology, but not the kind of stereotypical psychology you’re used to. Instead, we’re talking about psychometrics – how to measure the ways that a player’s psyche (thoughts, feelings, opinions) relates to the most important thing imaginable for sport teams: performance. Seeing is believing Counting the yards that a running back gains after contact or the runs prevented by pitching independent of defense are advanced numerical methods of breaking down a player’s performance. Most of the traditional analytics work the same way; a player’s previous performance is charted, observed, and dissected to make a projection about how that player will perform in the future. A team’s forecasted performance is usually the sum of the individual players’ projected performances. This is (generally) the state of analytics in a nutshell. Not only have analytics shown that previous performance predicts some level of future performance, it also just makes sense. Watching a player hit a 3-point shot, scoring pad-side against the goalie, and hitting a home run are visible to everyone; it’s what makes sports, sports. You know that Mike Trout is a good baseball player because you can see his performance. You can see him make ridiculous plays in the outfield and then watch him hit a home run into a fishing net in the center-field bleachers. You can check the box score the next day and you can see the numbers immediately reflect his awesomeness. You can visit FanGraphs and read about a sabermetric stat that further corroborates Trout’s awesomeness, and then you can use that same stat to find out about another obscure player’s performance and realize he’s kind of awesome as well. Analytics makes sense because most of it is overtly visible – above the surface, leaving everything else that can’t be seen as “intangible”. What lies beneath Even if analysts were to measure more “intangible” characteristics, like a player’s leadership, grit, or mental toughness, they don’t seem to amount to the same numerical accessibility as traditional performance metrics, nor do they seem to be relatable to future performance. However, with carefully designed tools, psychometrics can not only measure these “intangible” characteristics, but can help predict future performance in the same way as traditional analytics. Ideally, psychometrics from players and teams can help complement performance analytics that are now readily being used. In fact, measurement of the human mind and behavior isn’t anything new – over 100 years of psychological research has shown that the human psyche is quantifiable in the same way that previous performance is quantifiable. Psychologists have measured and quantified aggression across different cultures, charismatic leadership in managers, intrinsic motivation in children, and team cohesion within collegiate and recreational sports teams. What’s more, these numbers can even fit nicely into the same models, projections, and predictions that have been used with traditional analytics. Yet despite the depth and breadth of this research, professional sports teams have been slow to tap into this area of study, pooh-poohed by pundits as “intangibles,” unseen and unrecognized by professional sport team brass. You won’t know unless you try If the results of these measurements help to win more games, what do teams have to lose? Teams should not fear the minuscule amount of time that their players would spend filling out a carefully designed survey if it means understanding more about them – and, ultimately, understanding more about their team. Teams should not fear the analysis of dugout, sideline, team bus, or hotel conversations between players, all of which include rich amounts of data that can help to explain the relationships between players. Teams should not fear the measurement of a player’s comments, quotes, tweets, or posts, their spoken or written words might reveal hidden emotions or intentions. The analytics movement is far from over, and if teams are looking for more numerical insights, look no further than psychometrics.  Ramirez, J.M., Fujihara, T., & Van Goozen, S. (2001). Cultural and Gender Differences in Anger and Aggression: A comparison between Japanese, Dutch, and Spanish students. Journal of Social Psychology. 141, 119-121.  Conger, J.A., Kanugo, R.N., & Menon, S.T. (2000). Charismatic leadership and follower effects. Journal of Organizational Behavior. 21, 747 – 767.  Marinak, B.A. & Gambrell, L.B. (2008). Intrinsic motivation and rewards: What sustains young children’s engagement with text? Literacy Research and Instruction, 47(1), 9 – 26.  Carron, A.V., Colman, M.M., Wheeler, J., & Stevens, D. (2002). Cohesion and performance in sport: A meta analysis. Journal of Sport and Exercise Pscyhology. 24, 168 – 188.