In Defense of Rebounding Part 2

The rebounding experiment went like this: 10 basketball players, 10 coaches and 10 sportswriters, plus a group of complete basketball novices, watched video clips of a player attempting a free throw. (You can watch the videos here.) Not surprisingly, the professional athletes were far better at predicting whether or not the shot would go in. While they got it right more than two-thirds of the time, the non-playing experts (i.e., the coaches and writers) only got it right about 40 percent of the time. The athletes were also far quicker with their guesses, and were able to make accurate predictions about where the ball would end up before it was even airborne. (This suggests that the players were tracking the body movements of the shooter, and not simply making judgments based on the arc of the ball.) The coaches and writers, meanwhile, could only predict a make or miss after the shot, which required an additional 300 milliseconds.

What allowed the players to make such speedy judgments? By monitoring the brains and bodies of subjects as they watched free throws, the scientists were able to reveal something interesting about the best rebounders. It turned out that elite athletes, but not coaches and journalists, showed a sharp increase in activity in the motor cortex and their hand muscles in the crucial milliseconds before the ball was released. The scientists argue that this extra activity was due to a “covert simulation of the action,” as the athletes made a complicated series of calculations about the trajectory of the ball based on the form of the shooter. (Every NBA player, apparently, excels at unconscious trigonometry.) But here’s where things get fascinating: This increase in activity only occurred for missed shots. If the shot was going in, then their brains failed to get excited. Of course, this makes perfect sense: Why try to anticipate the bounce of a ball that can’t be rebounded? That’s a waste of mental energy.

The larger point is that even a simple skill like rebounding reflects an astonishing amount of cognitive labor. The reason we don’t notice this labor is because it happens so fast, in the fraction of a fraction of a second before the ball is released. And so we assume that rebounding is an uninteresting task, a physical act in a physical game. But it’s not, which is why the best rebounders aren’t just taller or more physical or better at boxing out – they’re also faster thinkers. This is what separates the Kevin Loves and Kevin Garnetts from everyone else on the court: They know where the ball will end up first.

This is an quote from wired.

Here is the study just in case you suspect the controls of the study.

http://brainvitge.org/papers/Aglioti%20et%20al%20%20%20Nature2008.pdf

Pay attention to the Figure 1, Figure 2, and Figure 3.

They haven’t given me permission to use their charts but in general, it took the expert players a much shorter span of time to ascertain whether or not a shot was going to miss than even expert watchers such as coaches and analysts. Expert novices were only slightly better at it than ‘pure’ novices! ***

It does not fully disprove the thesis that the rebounding stat belongs in the category of defense which is a team attribute—but it does highly indicate that rebounding is an individual skill. Well worth north of .5 win score multiplier on DREBS and 1.0 WS multiplier on OREB accreditation to the rebounder. As for how much, I think that’s for the future when AI equipped cameras replace refereeing.

Yes I am stalwartly defending win score and by extension wins produced.

But what I am truly defending are the robust principles behind wins score and wins produced, namely running the regression from wins—scratch that. Working backwards from wins to box scores to derive a sensible equation is currently our BEST method and model for predicting wins. Wins Produced and Win Score are both tested against real world data and critiqued by the community—so far I haven’t seen anything that dissuades me from its efficacy.

Data isn’t always going to agree with common sense. Useful data always disagrees with commons sense. That’s what makes it useful. Heh.

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I’d like to thank Unnamed for most of the impetus behind this work

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To Read Part One of “In Defense of Rebounding” Click Here

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