SAS,PHO,LAL : “Untangibles” Not Captured by the Box Score

Our goal here is to structure and quantify the ‘Untangible’ attributes that do not show up in the end game boxscores. We overcome this boxscore ‘low-resolution’ measurement issue by using modern Statistical techniques, a Bayesian model. For each game, we structure the relationship between the teams’ points, their boxscores, and the teams’ additional untangible (latent) component of variation.

Deep playoff teams like OKC, MIA, and SAS, appear at the top of the ‘untangibles’ chart. Surprisingly, The Toronto Raptors and the Phoenix Suns are right behind the Spurs. The Lakers and their historical rivals Celtics, appear near the bottom. Both teams were in rebuilding mode in 2013. As a proxy for a team’s average ‘untangible’ effects, we will incorporate latent variation, due to unmeasured important micro scale events.

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The championship Spurs had extremely great chemistry. Coach Popovich knows a good thing when he sees it. For nearly a decade, he has steered the Spurs ship working with Tim Duncan, Manu Ginobiflop, and Tony Parker as the core. ‘Eyeballing’ a Spurs game, the dazzling buffet of ball movement was easy on the eyes. The ball movement was like a fast paced soccer match. Further, running more plays for the riverside CA native, Kawhei Leonard, was like Vin Diesel hitting the NOS button. Shoutouts to the 909.

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Phoenix had pocket rockets. The dynamic duo point guard combo of Goran Dragic and Eric Bledsoe was a refreshing yet effective approach for the Suns. This system went against the traditional cookie cutter lineup you see across the league.

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The rehabbing Lakers were vomiting in the boxscore on a nightly basis. It was obvious the Lakers were going to be painfully bad. With the lame duck coaching situation of pringles D’Antoni and the spread of contagious injuries, fans knew to expect poor performance. Thus, the Lakers are near the bottom of the untangibles chart. The small ‘untangible’ effect tells us the variation in the Lakers point rates were already well accounted for in their disgusting boxscore measures.

As seen on @joy_behar_swagg ‘s instagram of a framed photo of a @woptype_swagg tweet

Boxscores are useful as descriptive end game summaries. Although ripe with information, you always hear the criticism, for good reason, that important ‘untangible’ attributes do not show up in the boxscore. As the name suggests, it all boils down to a purely ‘measurement’ issue; the boxscores lack the high resolution necessary for capturing dynamical point increasing game effects like: hustle, hands in the face, box outs, helping the help defender, spacing configurations, player interactions, etc.

Using box scores, we look at a season’s worth of (30 x 82 / 2) match-ups of pairwise (home team I versus away team J) combinations. To model the analysis (figure below), we make the following contextual assumptions:

1) We care about Wins, but we really care about Points (most points wins the game): For a game, each team’s Point Rate is the bivariate Poisson outcome.
2) Team I and team J’s point rates depend on boxscores: Via principal components of two way interactions of the raw boxscores (Field Goals, Rebounds, Turnovers, etc).
3) After accounting for boxscores, we structure what’s leftover: Use team level random effects.
4) Team I just ‘matches better’ against team J: The random effects are allowed to be pairwise correlated.

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We demonstrated that although boxscores are limited, they are still helpful if used with appropriate methods. An obvious alternative to study boxscore untangibles is to approach the problem with ‘high-resolution’ data, like SportVU. This directly allows you to define and measure what the boxscore untangibles are. After talking to some NBA franchises, teams are barely scratching the surface of SportVU; setting up databases, defining the measures, and doing basic visualizations. To truly harness this extra information, NBA franchises need to start shifting towards model based analysis like these guys http://arxiv.org/pdf/1405.0231v1.pdf

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