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Posts Tagged ‘On/Off’

Unless something drastic happens in the final weeks of the NBA season, Derrick Rose is going to win the MVP. He’s not a horrible choice — he’s in the top-5 on my ballot — but he’s probably not the correct choice. His supporters point to Chicago’s immense improvement from a year ago in the face of injuries to Joakim Noah and Carlos Boozer, who have missed a combined 54 games due to injury.

Only Rose isn’t responsible for a lot of that improvement.

It’s been well documented that under rookie coach Tom Thibodeau, Chicago has one of the top defenses in the NBA. The Bulls have improved their offensive rating 4.3 points, from 103.5 to 107.8, and their defensive rating 5.3 points, from 105.3 to 100.0. Here’s Rose’s individual improvement from last season to this:

Stats per 36 minutes

There’s no doubt he’s improved offensively and that has driven Chicago’s offensive improvement. Of course, the Bulls defensive improvement has been even more significant, and Rose plays a relatively small role in Chicago’s defensive dominance.

In 14 Bulls games I’ve tracked this year, the Bulls are boasting a 113.2 ORtg and 100.7 DRtg. The team breakdown is as follows:

Pos: Possesions played, OC: Opportunities Created, FD: Fouls Drawn

Rose’s huge EV numbers currently ranks 3rd in my database this year (although his outperforming his season averages on offense in this sample). He’s certifiably playing like a monster. His offensive load of over 54% — tops in the league — is indicative of just how much he does for Chicago on that end. He’s 2nd in Opportunities Created and 14th in assists per game, so it’s not just a shooting festival. Let’s give Rose a lot of offensive credit, but keep in mind that he’s not quite Steve Nashing* a weak offensive team, he’s Allen Iversoning* a weak offensive team. (Yes, players can be verbs too.)

*Nashing – to quarterback an otherwise weak offense to a top offense in the league. Also a superior version of “Iversoning,” which is carrying a weak offense to respectability.

Rose is a good defender too, but he’s not largely responsible for his team’s performance on that side of the ball: Chicago’s defense with Rose on the court is 101.8. Without Rose, it’s 93.1. From the 14 games I’ve tracked, Rose has the second lowest defensive usage on the team. Not surprisingly, Chicago’s defense is powered by players like Noah, Asik, Deng and Ronnie Brewer. Just from their defense, Chicago is getting about 19 or 20 wins above .500. The offense is dead average.

(It’s also impossible to ignore the value of COY Thibs. It’s rare we can clearly point to a coach lifting a team a few SRS points, and Thibs does that with his defensive schemes. Their rotations are ridiculously tight and they are as good in that department as the historical 2008 Celtics D.)

And lost in the Bulls shuffle is the all-star level play of Luol Deng. He’s defending incredibly well, and having his best offensive season since 2007. Even Deng’s three most frequent lineups without Rose have done well.

Not surprisingly, Chicago doesn’t have a large overall point differential with and without Rose (+1.2 with him). In 826 minutes, the Bulls are a staggering +7.3 without Derrick Rose. That’s not to say he isn’t great — he is — but starting with Chicago’s impressive record and distributing credit to Rose from there is giving him equal-part credit for their team defense, and that’s just wrong.

Rose is buoying the offense from below average to average, which shouldn’t be ignored. That’s precisely the reason he is a viable MVP candidate. But for people to think Rose is the reason for a 20-win jump like we’re seeing with Chicago is a gross misapplication of credit.

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I’m a big fan of On/Off data, which compares a team’s point differential with a player on the court versus when he’s off the court. I’ve referenced it frequently in the past and think it’s one of the more telling reflections of a player’s value to his team in the advanced stat family.

The nice part about On/Off is that it represents what actually happened. The problem with On/Off is it ignores the reasons why it happened. And sometimes, it creates a fuzzy picture because of it.

For example, let’s suppose Kobe Bryant plays the first 40 minutes of the a game and injures his ankle with the score tied at 80. LA wins the game 98-90. The Lakers were dead even when he was in the game, and +8 with him out of the game – Bryant’s on/off would be -8.

In this case, sample size is an issue. But that becomes less of a problem over the course of an entire season. The real concern is the normal variance involved in everyone else’s game. Practically speaking, it takes little outside the norm for Kobe to have played 40 brilliant minutes while his teammates missed a few open shots, and for the opponent to miss a few open shots down the stretch while Kobe’s teammates start hitting them.

The tendency is to look at a result like that and conclude that Kobe hurt his teammates’ shooting and when he left the game it helped their shooting. He very well may have by not creating good looks for them.

Then again, players hit unguarded 3-pointers about 38% of the time. Which means if the average shooter attempts five open 3-pointers, he will miss all five about 10% of the time, simply based on the probabilistic nature of shooting. A fact that has little to do with Kobe or any of the other players on the court.

In our hypothetical situation, all it takes is an 0-5 stretch from the opponent and a 3-5 stretch from LA to produce Kobe’s ugly -8 differential. The great college basketball statistician Ken Pomeroy ran some illuminating experiments on the natural variance in such numbers. His treatise is worth the read, but the gist of it is that his average player — by definition — produced a -57 on/off after 28 games (-5.7 per game) due to standard variance in a basketball game outside of that player’s control. Think about that.

For fun, I just ran the same simulation and my average player posted a +5.6 rating of his college season:

Average Player Simulation

So in two simulations, the average player’s On/Off ranged from -5.7 to +5.6. One guy looks like an All-Star, the other like an NBDL player.

“The Team Fell Apart When Player X Was Injured”

This is a common argument for MVP candidates: Look at how the team fared when he missed a few games and conclude the difference is the actual value a player provides to his team. Only this line of thinking runs into the same problems we saw above with on/off data.

Let’s take Dirk Nowitzki and this year’s Dallas Mavericks. In 62 games with Dirk, Dallas has a +4.9 differential (7.8 standard deviation). In nine games without Dirk, a -5.9 differential (7.5 standard deviation).

Which means, with a basic calculation, we can say with 95% confidence that without Nowitzki, Dallas is somewhere between a -1.0 and -10.8 differential team. Not exactly definitive, but in all likelihood they are much worse without Dirk. OK…but we can’t definitively say how much worse they are.

In a small sample, we just can’t be extremely conclusive. In this case, nine games doesn’t tell us a whole lot. New Orleans started the season 8-0…they aren’t an 82-win team.

We can perform the same thought-experiment with Dirk’s nine games that we did with Kobe’s eight minutes to display how unstable these results are. Let’s say Dallas makes three more open 3’s against Cleveland and the Cavs miss three open 3’s. What would happen to the differential numbers?

  1. That alone would lower the point differential two points per game.
  2. Our 95% confidence interval now becomes -12.1 points to +4.4 points.

That’s from adjusting just six open shots in a nine game sample.

Jason Terry — a player who benefits from playing with Dirk Nowitzki historically — had games of 3-16, 3-15 and 3-14 shooting without Dirk. He shot 39% from the floor in the nine games. By all possible accounts, Terry is better than a 39% shooter without Nowitzki. He shot 26% from 3 in those games. Let’s use his Atlanta averages instead, from when he was younger and probably not as skilled as a shooter: How would that change the way Dallas looks sans-Dirk?

Well, suddenly Terry alone provides an extra 1.7 points per game with his (still) subpar shooting. The team differential is down to -2.2 with a 95% confidence interval of -10.4 to +6.1. Just by gingerly tweaking a variable or two, the picture grows hazier and hazier.

Making Sense of it All

So, what can we say using On/Off data? It’s likely Dallas is a good deal better with Dirk Nowitzki. But, hopefully, we knew that already.

To definitely point to a small sample and say, “well this is how Dallas actually played without Dirk, so that’s his value for this year” ignores normally fluctuating variables — like Jason Terry or an open Cleveland shooter — that have little to do with Dirk Nowitzki’s value. So while such data reinforces how valuable Dirk is, we can’t say that’s how valuable he is.

We can’t ignore randomness and basic variance as part of the story.

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