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Dumpy's Statistical Analysis - Miami & More Rebounding

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Miami at New Jersey, February 4, 2006
Score: New Jersey 105, Miami 92

Player Min. Eff. Plus-Minus
Vince Carter 40.0 14 +21
Jason Collins 32.4 11 +19
Jason Kidd 41.3 29 +17
Richard Jefferson 43.6 26 +16
Clifford Robinson 37.6 21 +16
Zoran Planinic 0.5 0 +1
Linton Johnson 0.5 0 +1
Antoine Wright 5.3 2 -4
Marc Jackson 8.5 -1 -4
Scott Padgett 15.9 6 -8
Jacque Vaughn 14.4 6 -10

For those that are joining us for the first time, here are the obligatory explanations of the data contained in the above table.

Plus-Minus Rating. The plus-minus rating is a way to measure each player's combined offensive and defensive total contribution to the team. Two simple examples will show how this works. In the game above, Vince Carter earned a +21 rating. What that means is that, during the 40.0 minutes when Carter was on the floor, the Nets outscored Miami by a total of 21 points—hence the +21 rating. In Jacque Vaughns' case, his -10 rating indicates that in the time he was on the court, the Nets were outscored by the Heat by 10 points. We can also measure plus-minus based on two-man combos, three-man combos, 5-man combos, etc., which can enable us to identify groups of players that play exceptionally well or poorly together. Data on 5-man combinations can help us identify the players that contribute the least or the most to team success by comparing different combinations that differ by one player. For instance, if we know that, over the course of the season, a unit consisting of [Kidd, Carter, RJ, Collins and Krstic] has outscored its opponent by a greater margin than a unit consisting of [Kidd, Carter, RJ, Jackson and Krstic] in the same number of minutes, then we can generally conclude that Collins adds more to the team success than does Jackson. However, we have to be careful not to make a sweeping generalization that Collins is better than Jackson; there may very well be other four-man combinations that Jackson plays better with than Collins does—it is just up to us to identify them. The data relied upon here comes from the web site www.popcornmachine.net, and may include minor errors.

Efficuency Rating. The "efficiency" stat purports to calculate how much a player contributes to the team, based solely on traditional box-score statistics. Specifically, the "efficiency" measurement is defined as ((Points + Rebounds + Assists + Steals + Blocks) - ((Field Goals Att. - Field Goals Made) + (Free Throws Att. - Free Throws Made) + Turnovers)). You can think of this stat as basically [good stuff] minus [bad stuff]. For a matter of scale, the highest efficiency rating attained in a single game this season so far has been +70, by Kobe Bryant in his 81-point game. In second place is Kobe's 62-point game, which earned him a +55 efficiency rating. On a per-game basis, the leader for the season is currently LeBron James, at just a shade under 30.0. At of today, only 32 players have a per-game efficiency rating above 20.0. As of last week, for the season, Carter, Kidd, and Jefferson had efficiency ratings of 21.7, 21.7, and 20.4, respectively, which puts them 17th, 19th, and 24th among players that qualify for the league leaders. The top Nets reserve in this category is Scott Padgett, who has earned a 22.6 efficiency rating/48 (it makes sense to look at reserves on a per-48-minute scale), which places him 105th overall and about the same as Wally Szczerbiak, Joe Johnson, and Zach Randolph. Former Net Brian Scalabrine is 401st (out of 424 players) with a rating/48 of 8.6. Five Nets have an efficiency rating/48 between 350th and 400th overall: Jacque Vaughn (13.1); Linton Johnson (12.3); Zoran Planinic (11.2); Jason Collins (10.6); and Antoine Wright (9.3) (also through last week).

As usual, the chart above lists the starters above the reserves, and each of these two groups of players is listed in descending order by plus-minus rating.

In my last entry, I noted:

"With a minus-one efficiency rating, Cliff Robinson continued his trend of rotating good, mediocre, and bad performances (as least as measured in this way). Over the past ten games, his efficiency ratings have looked like: 2, 12, 24, 4, 9, -3, 25, 8, 5, and now -1. Like yesterday, the game he earned a 4 was the second of a back-to-back. However, his extreme outlier –3, +24, and +25 performances each followed a day off, so there doesn't seem to be much of a pattern from a workload perspective. Look for him to break out during one of the next two games."

Guess what? Bang, zoom, here comes Uncle Cliffy! Nice job.

Jason Collins lost his spot atop the leader board, but I don't think anyone would complain after the way he shut down Shaq defensively. Over the past three games, Collins has earned an average efficiency rating of 10.3. You'd have to go all the way back to January 10 to find a single game where Collins earned an efficiency rating above 8. In addition, over his last six games, Collins has led the starters in plus-minus rating on four occasions and finished second in two more. This streak started one game after Collins returned from his one-game break on January 21 to rest his multiple injuries. Coincidence?

The last item I'll note is the continuing poor performance by Jacque Vaughn. We'll get back to that in a minute.

As we did in our last installment, let's look how the starters performed as a unit. Note that the starters for this game include Cliff Robinson in place of the ill Nenad Krstic:

Unit Stint Min. Plus-Minus
Starting Five 1 (1st Quarter) 7.4 +6
2 (3rd Quarter) 8.6 +8
3 (4th Quarter) 7.0 +7
TOTAL 23.0 +21

Once again, I broke out the starting unit's performance by each instance they played together. What a terrific performance by the starting unit.

As mentioned above, once again Jacque Vaughn finished with one of the lowest plus-minus rating on the team. As we have done previously, let's break out how Vaughn performed when playing alongside Kidd and Collins, alongside Kidd without Collins, and without either:

Vaughn Plus:

Unit Min. Plus-Minus
Kidd and Collins 7.3 -6
Kidd but not Collins 1.0 -1
Neither Kidd nor Collins 6.0 -3

For the third consecutive game—since we started examining this—Vaughn has done his best work when he is NOT on the court with Kidd. So far, there seems to be little difference in Vaughn's performance when he is on the court with Kidd with or without Collins, so we're getting close to abandoning the theory that Vaughn and Collins makes for a bad combination. Instead, it appears that the bad combination is Vaughn plus Kidd. As we've said before, the Nets need a reserve SG/SF to step up and perform productively, and take the pressure off of Vaughn to play that role. It looks as if Coach Frank is continuing to give rookie Antoine Wright an opportunity to establish himself as that player. In the game against the Heat, though, the only wing player that came off the bench to play with Kidd was Jacque Vaughn. We'll continue to follow this for the next few weeks.

Now let's return to the topic I began in my last entry, offensive rebounds. If you remember, I argued that the key rebounding statistic is not the total number of rebounds, but the number of offensive rebounds grabbed by the Nets and their opposition, expressed as a percentage of total opportunities. I called this the "rebound conversion rate," or "RCR," and broke it down into three components: "Offensive RCR," which measures each teams success rate at grabbing offensive rebounds, "Defensive RCR," which measures each team's success at stopping the opponent from getting offensive rebounds, and "Net RCR," which is the difference between the two figures. First, let me reprint the team-by-team chart from last time:

TEAM Off RCR Def RCR Net RCR
1 Utah .331 .266 .065
2 New York .329 .281 .048
3 Cleveland .290 .243 .047
4 Dallas .319 .285 .034
5 Orlando .293 .264 .029
6 Miami .270 .242 .028
7 Milwaukee .287 .259 .028
8 LA Lakers .297 .276 .021
9 LA Clippers .261 .242 .019
10 Detroit .306 .292 .014
11 Atlanta .310 .299 .011
12 Seattle .319 .309 .010
13 Houston .269 .262 .007
14 NEW JERSEY .246 .244 .002
15 Chicago .262 .260 .002
16 NO/Oklahoma City .261 .261 .000
17 Washington .292 .296 -.004
18 San Antonio .244 .248 -.004
19 Philadelphia .269 .282 -.013
20 Denver .269 .282 -.013
21 Boston .266 .287 -.021
22 Charlotte .279 .302 -.023
23 Indiana .247 .274 -.027
24 Golden State .253 .283 -.030
25 Toronto .239 .270 -.031
26 Minnesota .247 .282 -.035
27 Memphis .252 .288 -.036
28 Portland .285 .324 -.039
29 Sacramento .237 .282 -.045
30 Phoenix .218 .281 -.063

I should point out that I didn't make this statistic up, although I will credit myself for giving it a really cool name. I personally got this data from ESPN's web site. I feel this is an example of data being publicly available, but few people fully grasping their import. An analog may be baseball's long-lasting reliance on batting average, until it finally became common understanding that on-base percentage was a better statistic for measuring a batter's success rate. In the meantime, due mostly to the popular metric of the day, Steve Garvey was a perennial All-Star, while today we look at his record and wonder why. But I digress.

I want to go back and spend a minute to emphasize just what this chart indicates (something I didn't do last time). Let's say that the top team on the list, Utah, faces the worst team on the list, Phoenix. Let's also assume that each team grabs 45 total rebounds (which is about the typical amount, I think). Under those conditions, I estimate that Utah should get somewhere around eight more offensive rebounds then Utah. In my last entry, I surmised that each offensive rebound is directly responsible for somewhere between one and 1.5 points. So, all in all, that's like saying that Phoenix has conceded somewhere between eight and twelve points just from their poor rebounding skills, before the game has even started. Is Phoenix good enough to make up those points in other areas? Maybe—but that's a heck of a hole to work out of.

With this in mind, let's take a look at the offensive rebounds in the game against the Heat. Individual Nets were credited with 41 rebounds, including five offensive and 36 defensive. The Nets team was also credited with nine rebounds, and by examining the game log, I've calculated that two of these were on the offensive end. On the other side, the individual Heat players were credited with 37 rebounds, including seven offensive and 30 defensive. The Miami team was also credited with eight rebounds, and by examining the game log, I've calculated that four of these were on the offensive end. Thus, the Nets' offensive RCR for the game is 7/41 = .171. Miami, on the other hand, converted 11/54 = .204 of its rebound opportunities. The Nets' net rebound conversion rate, or NRCR, would be calculated as .171-.204 = -.033. For Miami, the NRCR would be the inverse of that figure, or .033. Incidentally, this result is almost exactly in line with expectations, given the team-by-team data provided in the chart above.

Here is a list of the offensive rebounds in the game, along with the net result:

Team Quarter Time Rebounder Result
Miami 1 2:02 Mourning 2-point dunk
Miami 1 0:56 [Miami] Shooting foul (2-2)
Miami 2 3:18 Simien 2-point shot
Miami 2 2:34 Mourning 2-point shot
Miami 2 1:46 Simien Shooting foul (2-2)
NJ 3 8:37 Carter 2-point dunk
Miami 3 8:30 [Miami] Missed shot
NJ 3 5:41 Collins Missed shot
NJ 3 2:49 Jefferson 3-point shot
Miami 4 11:26 Wade 3-point shot
Miami 4 9:03 Walker Missed shot
Miami 4 9:00 [Miami] Missed shot
NJ 4 8:25 Jefferson 2-point dunk
NJ 4 6:28 Collins 2-point shot + 1 free throw
Miami 4 5:20 [Miami] Offensive foul
NJ 4 3:33 [NJ] 3-point shot
NJ 4 2:47 [NJ] 2-point shot
Miami 4 2:30 O'Neal Shooting foul (1-2)

In this game, then, the 18 offensive rebounds were directly responsible for 29 points, an average of 1.6 points per offensive rebound. Overall, the Nets narrowly outscored the Heat 15-14 in points-after-offensive-rebounds, despite being out-rebounded as noted above. In fact, the Nets scored after all but one offensive rebound, a great success ratio.

As was the case after the previous game, the frequency of offensive rebounds increased as the game went on.

Finally, at the end of my last entry, I promised to take a look at which Nets contribute the most and least to the team's net RCR. I feel like Dora the Explorer. Where do you go if you don't know where to find certain statistics? www.82games.com! Say it with me . . . wow, I think I've had too much toddler time. OK, moving right along, let's limit this study to the Nets' five primary bigs, Jason Collins, Nenad Krstic, Cliff Robinson, Mark Jackson, and Scott Padgett. Here we go:

Player Off. Rebs Off. Reb chances Ind. Off. % Team Off. RCR Def. Rebs Def. Reb. chances Ind. Def. % Team Def. RCR Team Net RCR
Collins 46 893 5.2% 26.9% 107 867 12.3% 24.6% 2.3%
Krstic 98 1255 7.8% 26.7% 150 1196 12.5% 26.5% 0.2%
Robinson 26 946 2.7% 24.7% 119 974 12.2% 27.0% -2.3%
Padgett 40 469 8.5% 27.7% 76 442 17.2% 27.1% 0.6%
Jackson 30 372 8.1% 30.6% 57 409 13.9% 29.1% 1.5%

First off, a disclaimer: This information does not account for variances due to other players on the court at the same time. Regardless, I think there is plenty here that we can use to make some general observations.

First, the Nets are clearly a better rebounding team when Jason Collins is in the game. Although Collins is just fourth on the team (of the five bigs in the study) at grabbing offensive rebounds—and nearly last in defensive rebounding—the team as a whole does its best with him in the lineup. Here, now, we have some statistical proof of the value of some of the "little things" that Collins does—in this case, boxing out during rebounding opportunities. Second on the team is a surprise: Marc Jackson. With Jackson in the lineup, the team is at its best at offensive rebounding, but at its worst on the defensive end. As a whole, though, the team is better off with him than without him in the lineup for this purpose. The top individual rebounder on both the offensive and defensive ends is Scott Padgett, but his proficiency doesn't appear to help the team very much. These numbers suggest that Padgett takes a me-first approach to rebounding, and doesn't give his teammates a better opportunity to get loose balls. He is the anti-Collins. Krstic comes in about average, and Robinson below average.

One could argue that the Nets' are a better rebounding team with Collins in the lineup simply because he plays most of his minutes with Jason Kidd. In other words, that Collins looks good because Kidd is a superior rebounder. If that was true, however, then we should see a similar effect with regard to Krstic, who also plays most of his minutes with Kidd—yet, as we see here, Krstic is just an average rebounder by this metric. Also, who is to say that Kidd isn't a good rebounder because of Collins—not the other way around? This is almost a chicken-and-the-egg dilemma. This season, however, since Collins has missed a number of games, we have the opportunity to look at whether his presence has had an impact on the offensive rebounds that Jason Kidd and the other members of the team are able to pull down. I'll save that for another day.

Just for fun:

Player Off. Rebs Off. Reb chances Ind. Off. % Team Off. RCR Def. Rebs Def. Reb. chances Ind. Def. % Team Def. RCR Team Net RCR
Ben Wallace 187 1574 11.9% 32.7% 382 1593 24.0% 32.1% 0.6%

Ben Wallace, the ultimate stat whore. Wallace's individual numbers are through the roof, but you can see that he makes little difference in the team's overall net rebounding success rate. All in all, he makes as much difference to the Pistons' rebounding rate as Scott Padgett does to the Nets'. Again, we have to be aware of possible covariance with the impact of other players on the court; in this case, it is possible that Wallace is making an otherwise terrible rebounding team average. Still, though, it is interesting stuff. Perhaps next time we'll take a look at some of the other top rebounders in the league, and see how they compare to Collins with regard to overall effectiveness, as measured by net RCR. Until then,

--Dumpy