Friday, November 22, 2013

Monta Python and the Holy Fail

Monta Ellis has been playing phenomenally. Granted: we're only 12 games into the season. Whether or not Montaball continues to be a noun that is associated with positive feelings remains to be seen. Given my description of Monta's play so far, this begs the question: if Monta is playing so well, then why the title "Monta Python and the Holy Fail"? Because: 1) I thought it was clever; and 2) Monta Ellis represents one of the current failures of basketball analytics: the ability to separate player from context.



This is a difficult problem. Basketball is not baseball. In both sports, a player's performance is affected by external variables. But in baseball, those external variables are easily quantifiable. Not so much in basketball. In basketball, we have to rely on imperfect proxies. An interesting example of this is shown in the below video in which Ben Alamar, a former analytics consultant for the Thunder, describes how he estimated Russel Westbrook's passing abilities. Westbrook's passing abilities were influenced by external factors such as: UCLA's shooting ability, UCLA's offensive scheme (Westbrook mainly played the 2), opposing team defensive schemes, etc. To control for this, Alamar simply looked at the probability of Westbrook's teammates making a shot. If this probability is significantly higher after receiving a pass from Westbrook, then this would suggest point guard-esque passing skills.


But back to Monta. Maybe the Dallas Mavericks' front office had advanced statistics that suggested that Monta is a more efficient player than his statistics say. However, those of us not in the front office did not have access to such numbers. All we could do was look at his stats and say that this is a guy who shoots four three-pointers a game even though he only shoots the three at a 28.7% clip. Of course, what was missing was the context. Bluntly put: Monta was expected to carry the offense of a team that didn't complement his skills well.

Which brings us to the two questions that this post is trying to answer: where is Monta's increased production coming from and is it sustainable? To answer these questions we will look at three Montas: 2007-08's Good Monta, 2012-13's Bad Monta, and 2013-14's So-Far-So-Good-But-Let's-Hope-He-Can-Keep-It-Going Monta. Emphasis will be placed on the latter two.

Where are Monta's points coming from? Here is a chart that breaks down his points per game by shot distance for the 2012-13 and 2013-14 seasons:


And a chart showing the difference in PPG by shot distance between the two seasons:


What these charts tell us is that of the seven distance categories, Ellis has increased his scoring output in five of them. These five categories account for a 6.03 PPG increase in scoring (partially offset by a 1.86 PPG decrease in scoring in the the other two categories). 62% of the 6.03 PPG increase comes from two areas: the 20-24 ft. shot and free throws. Of course, the increase from free throws is a good indicator of sustainability while the increase from the 20-24 ft. shot is a bad indicator of sustainability. However, don't overreact too much. If Ellis kept his PPG differential in all categories except for the 20-24 ft. shot, while his 20-24 ft. shot regressed back to last year's form, Ellis would still average 20.6 PPG versus his current 23.3 PPG. Which leads us back to the second question that we asked: is his increased production sustainable? To answer that, we're going to have to look at his field goal percentages.

Here is a chart that breaks down his field goal percentage by shot distance for the 2012-13 and 2013-14 seasons:


And a chart showing the difference in field goal percentage between the two seasons (note: difference is percentage point difference):


Monta's field goal percentage this year is 7.96 percentage points higher than last last year (49.5% vs. 41.6%). From 5-9 ft. Ellis is shooting 18.97 percentage points higher. From 10-14 ft. Ellis is shooting 9.54 percentage points higher. From 15-19 ft. Ellis is shooting 24.71 percentage points higher. From 20-24 ft. Ellis is shooting 11.4 percentage points higher. These four areas are likely candidates for regression and account for 3.78 PPG (over 60%) of the 6.03 PPG increase (once again, this 6.03 PPG increase is partially offset by a PPG decrease in two of the areas).

So has Monta illegally taken up camp at the tail-end of the normal distribution and should expect to be evicted by the sheriff soon? Or has the extra space created by Dirk really been all that was standing in-between Monta Ellis and having it all? Unfortunately, this brings us back to the failure of basketball analytics. We just don't have the ability (yet, hopefully) to quantify the difference that Dirk makes on his teammates' shooting percentages. We can, however, create proxies by looking at two natural experiments in the history of the Dallas Mavericks: the trading of Devin Harris to New Jersey and the signing of Jason Terry. I submit that the difference in field goal percentage between Dallas Mavericks Devin Harris and New Jersey Nets Devin Harris as well as the difference between Dallas Mavericks Jason Terry and Atlanta Hawks Jason Terry can give us an (very) imperfect approximation for how much production we should expect Monta Ellis to be able to sustain.

Here is a chart that shows Devin Harris's field goal percentage by shot distance:


Here is a chart that shows Jason Terry's field goal percentage by shot distance:


And a chart that shows Jason Terry's and Devin Harris's combined field goal percentage, weighted by shot attempts:


By using the difference in field goal percentage between Harris's and Terry's time in Dallas and Harris's and Terry's time in New Jersey and Atlanta, we can approximate how much we can expect a similar guard (e.g. Monta Ellis) to improve upon playing in a Mavericks' uniform next to Dirk. Unfortunately, it looks like Ellis is significantly overachieving. Here is a chart showing the increase in field goal percentages of Harris/Terry (weighted) and Ellis (note: this is percent difference and not percentage point difference):


Monta's increased efficiency in the 10-14 ft. range and the 20-14 ft. range is what you would expect. However, his increased efficiency in the 5-9 ft. range and 15-19 ft. range is off the charts (not literally, though). Even though the increase is much greater than expected, I think that this is mostly attributable to Ellis's terrible 2012-2013 season. Here is a chart comparing Ellis's 2012-2013 FG% with Harris's and Terry's pre/post-Maverick weighted FG%:


The percentages for 10-14 ft. and 20-24 ft. are similar. However, Ellis under-performs substantially in the 5-9 ft. range and 15-19 ft. range.

Where we are at right now is that we can argue that we should not expect Ellis to regress significantly in the 10-14 ft. range or the 20-24 ft. range. This gives us 2.4 PPG of the 3.78 PPG gross increase that we are trying to explain. So then what about the 5-9 ft. and 15-19 ft. range? This is where the 2007-08 season comes in - Monta's most efficient season. I'm sad to say: it doesn't look good. During the 2007-08 season, Monta shot 43.1% from the 5-9 ft. range and 42.4% from the 15-19 ft. range. If we expect Ellis to regress to those levels while maintaining his current efficiency everywhere else, then we can expect Ellis to score 22.1 PPG vs his current 23.3 PPG. Not bad.

But we can do better. If we assume that Monta's "true" non-Dallas field goal percentages are 43.1% and 42.4% from 5-9 feet and 15-19 feet (which is close to the Harris/Terry weighted non-Dallas 44.5% and 44.5% [not a typo - it's the same percent]) then we can assume that Monta can improve his FG% by the same magnitude as Harris/Terry. That is, we can expect Monta's FG% from 5-9 feet and 15-19 feet to regress from 50% and 60.6% to 44.6% and 47.7%. All else equal, this gives Ellis 22.44 PPG.

In summary: this is all imperfect and mostly bullshit. There are a ton of assumptions built into this estimate. It was a lot of work to get to the answer that most of our guts already gave us: expect Ellis to regress, but not by much. At least we can say that with a little more confidence, though.

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