APBRmetrics

[mtamada]

A sportswriter had mentioned this in his NBA column yesterday, but I forgot who/where he was. But this article has now gone out over AP today.

http://tinyurl.com/mbtzw2
or
http://www.chicagotribune.com/business/sns-ap-bkn-nba-finals-cameras,0,6018197.story


Major League Baseball's Pitch F/X technology has revolutionized sabrmetrics. The NBA is about to do the same, using six high-def cameras, not for the purpose of creating video per se, but for generating high-granularity data: players' locations on the floor, shot locations, shot trajectories (e.g. one non-statistical purpose is to see if a shot was goaltended or a legit block), evaluating players' defensive positioning, etc.

The possibilities are endless and breathaking.

Unfortunately, I suspect that one will have to be an NBA insider to get one's hands on these data. STATS LLC is operating the system, they're not as bad as Elias in terms of monopolizing data but I'm not expecting to see them post the data on a public website (the articles don't say).

Can our members who are on the inside of the NBA make any comments about this -- prototype studies, possible uses, etc.?

[Ryan J. Parker]

[mtamada]

I guess the other thing that I'm wondering is where the push for this is coming from ... the technology is ripe for utilization, but I'm sure this is still an expensive proposition. I'd be surprised if the NBA's statistical analysts had enough pull to convince the league to spend what I presume is millions of dollars just so they can get more stats. But maybe coaching staffs, who are already spending many man-hours and dollars on video were pushing for this (I think there's at least one third-party company which already does business with NBA teams basically by watching video for them and tabulating the results)? Or Mark Cuban, perhaps with a few other analytically minded owners or GMs? Or will the networks be given access to the video and stats, to use in their broadcasts? (Doesn't sound like it, from the article.)

[Ryan J. Parker]

I think this sort of thing is a natural evolution. It could be coming from the needs of more than one source, although our interest is clearly in data collection.

Unless we here something publicly about it, I think it's safe to assume this sort of technology can be utilized by a variety of groups in the NBA. Thus it's the sort of an endeavor taken on to fill multiple needs.
_________________
I am a basketball geek.

[John Hollinger]

I got a demo of this thing before Game 4, and it is pretty darn cool. They're still working out the kinks and obviously they need to develop all the back end stuff to track all the information that this thing is capable of collecting, but in the next 5-10 years I think this might be a total game-changer.

[flyerfanatic]

this sounds cool. are they going to allow people to see what data it collects?

[royce.toyfu]

[Mountain]

(I want to say something fairly brief on this so I will.)


I have thought and speculated about the value of this kind of data before (and talked about a calculus-based global model of basketball at different levels of analysis).

It would help move even further from looking at discrete players (or say particles) and beyond just lineups (particle sets with some unity / some independence) to lineups running plays or systems and interacting with the opponent's. Mass / energies in motion with characteristics that guide / reveal "what they want to do" and determine how they interact with others.

Which NBA team hires the first physicist to analyze the data?

Economists, environmental and mechanical engineers and clinical psychologists and poker geniuses and others surely have models. tools, awarenesses that would be useful.

But I'd see if a physicist (probably at the particle level) could use his tool set to model and analyze this information. Seems apt to try to me.

The GMs probably won't. Well maybe one guy might, if he is reading and still has budget authority left and isn't satiated yet.

If any of the data becomes publicly available or if public clones eventually reveal it somebody should mess around with it in this way. Maybe Free Darko could find somebody and then move the analysis of the data into terms that the insiders or the fans can relate to. Or at least a few.


This could revolutionize basketball analytics but I assume David Stern will eventually think whether he wants to go further down this road. There are so many fans that react negatively to this direction. Could this not only revolutionize basketball analytics but also change "the game" too far? Which is viewed to a large extent by the league and owners as an entertainment product instead of as a "pure" or epic sport (the view of say a Jerry West) or physical/intellectual challenge / puzzle (the view of many here?) It might. Or maybe it can be accommodated, as the world in so many areas gets more high-tech, high-thought but mostly behind the scenes and little known or understood by most.


Or maybe this ends up just being or mostly about building a better video game.

[gabefarkas]

[Mountain]

Alright Gabe, since you asked for more.

(Though I've said much of it before.)

I've talked about possibly taking adjusted +/- down from player level or offensive / defensive splits to the 4 Factors of each a few times over the last couple years and even about splitting them into the direct part (the adjusted input credit for just where the player had the lead role in the play action) and the apparent team level adjusted affects (for all the other plays when he isn't). Splitting adjusted into 16 parts and using that to represent the full range of a player's impacts.


I've mentioned that you could try to write equations that go from attributes (age/experience, standing reach, wingspan, weight, hops, pure speed, pure agility, dribble speed and agility, basketball intelligence, quality of previous instruction, shot chart preferences, etc.) to factor performance (factor performance as perhaps the calculus derivative of 16 unique formulas based on player attributes for each partial Factor- found by some form of regression or using some other method of hierarchical / multilevel modeling or aided by other ways to get at covariances) though that would probably be difficult and may end up with limited success as a global model of all players but you might find some useful tidbits along the way.

Recently I've been on the lineups as the unit of production train of thinking. But I'd modify that and try to fill the gap between what the sum of all the 16 parts of the player adjusted data suggests and what the adjusted lineup data suggests by proposing that lineup performance is the sum of the individual adjusted factor data points + any interactive effects of pairs (I think there are 10 pairs on the court at any time) or perhaps production subunits- perimeters / interiors (that would be simpler and maybe more meaningful) or maybe use both. Maybe the interactive effects could be modeled or derived with the aid of calculus too.

Move from player capabilities to expressed production (derivation) and then put them together along with the interactive elements (integration).

But you probably need to add coach and system on their own (on average). And the coach's choices in giving out roles on plays and calling plays.


And use splits by opponent type as well. And maybe add the ref affects.

And you could go from average adjusted player factors to marginal player factors under various conditions. Again I think calculus could play a role if you had a function for what was contributing to what was going on that was accurate.

And then review the coach's actual pattern vs. optimal use of all this information too in giving out roles and calling plays. The coach's marginal impacts. Apparently we are going to hearing soon that coaching doesn't matter much? I think the Finals and a lot of the rest of the data I see suggests it matters pretty heavily.



Some inside and on blogs seem to largely think of it as a sum of discrete individuals but the variance of lineup data appears to suggest otherwise on adjusted +/- (though I haven't done enough to prove this and the mostly small samples make it near impossible to do to any meaningful degree of confidence).

Even at the player pair level there are many things that could be real vs noise- players who use different zones and open other areas to others, players who prefer the exact same zones, players with ability to score from certain zones but not given a role to do so in the play or the system, players who can and will make a certain type pass to a team vs not, where players rebound best, the impact of a guy who over-pressures on the perimeter alongside an interior guy who is quick to respond vs not, etc. But with the compiled video and the data from it you can get an indication from one tool and then try to map to the another.

And you could look at the data by position, types, type sequences and more combinations of elements and levels of study. I shared some of these type research variations / extensions with a certain GM but whether it was read or read closely with an open (perhaps in spite of writing style) or read and rejected or read and considered already done and surpassed I probably won't find out. Oh well, his choice from a position where he can make that call better than I can second-guess it, probably.



Here are some of the earlier posts along these lines:


http://tinyurl.com/nro2t9


http://tinyurl.com/mauwnt


And I'll mentioned this fairly recent one again since it gets at the quality of the adjusted data and ways to re-run it and maybe learn more.

http://tinyurl.com/m2bdoa

In case anyone else wants to take a stab (or another one) at understanding it. Admittedly it is not that well written and I guess hard to follow. But I still think the underlying ideas are worth pursuing, just to see what it finds then evaluate the findings.

And by the way if anybody on the inside is really paying attention you could use this method to get at least a first level estimate of adjusted on plays for a specific team (using a method similar to Eli W/'s first lineup adjusted post) if you mapped the data to the video or at least the start & end conditions of plays (using that or perhaps a method similar to what Ryan Parker did)


Shot charts should play a pretty large role in trying to advance the understanding of why some lineups work better than others. Ryan Parker already has pretty exact floor locations of shots, an aptitude for working with databases and interest in the connection between start and end events and has already started looking at how players being on the court affects team level shot charts. Maybe it would be worthwhile to look at whether a made inside shot (or say 3) has a recognizable and pretty immediate impact on the effectiveness of the outside game or vice versa with a made 3 pointer (or a trio of them). Maybe all the running around before a shot is launched matters a good deal or maybe you can learn enough from just the shot data- at least til you have access to more data to analyze the running around to see what matters, what creates the most good looks in the right places.




On another issue:
Jon Nichols and Eric Weiss are digging further into the college to NBA transition projection field (following John Hollinger and Hoopsanalyst). Maybe someone will check whether PER or WinScore or some other metric at the college level is better at projecting NBA performance or look at the continuum of WS divided by PER (by position) and see whether the folks in the middle are the most stable or if one end outperforms the other or if it varies between interior and perimeter players. With the big differences being how shooting efficiency and defensive rebounding are scored. Maybe PER does better with perimeter players and WinScore with bigs? I'd guess that but it could end up being the reverse and surprising.

[mtamada]

[gabefarkas]

[Mountain]

I meant what I said but I'll try to explain one more time in a bit expanded fashion.

I would ideally taken adjusted +/- down to the 4 factors. To do so you'd run the regression multiple times (instead of just one pass based on points) using shots made, rebounds, turnovers as the input and the adjusted factor for FT/FG could be found by taking the compete adjusted and subtracting the values found for the other parts.

But to separate the player's direct impact from his team level influence for any factor you'd run the regression twice. For example giving credit one time just when he scored a hoop directly. Then you'd do it again from when anyone else did on the team. And I'd do that for the other factors, offense and defense. That would give you 16 partial Factors.

This would allow to see estimates of a player's direct Factor impacts and as you say team level impacts (on other teammates) for all the Factors. It is both, not just one.

But this is just theoretical at this time. It is doable and might be worth doing if you were an insider with time and the required zeal to know as much as possible.

If you had this level of information and adjusted 4 factors for lineups and player pairs and the video information reference in the original post I think you could figure a lot of what is actually happening positive and negative and how to further optimize and be way better off than just receiving the composite adjusted +/- or even the offensive / defensive splits and wondering went exactly pushed it there.

Was it his shooting, or his passing to other shooters or his direct turnovers or his decision-making that lead to turnovers elsewhere or his trips to the line or his ability to hit teammates and get them to the line or his rebounding directly or maybe impact on rebounding by boxing out or occupying multiple defenders? Splitting adjusted into 8 partials for offense and 8 for defense would given you some leads.

I am not sure if the errors would stay the same as for the full adjusted or get worse. But that is an issue for the future.

[gabefarkas]

[Ryan J. Parker]

So multiplicity seems interesting to me. Here's something to read later: http://www.jstor.org/pss/2531814

This is the best my library could do. Point me to this.
_________________
I am a basketball geek.