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# Dumpy's Statistical Analysis -NJ @ CLE + Rebounding

New Jersey at Cleveland, February 2, 2006
Score: Cleveland 91, New Jersey 85

 Player Min. Eff. Plus-Minus Jason Collins 29.6 8 -1 Richard Jefferson 45.5 14 -4 Nenad Krstic 35.9 15 -5 Jason Kidd 36.6 14 -6 Vince Carter 42.8 29 -9 Jacque Vaughn 16.9 0 +2 Scott Padgett 11.4 8 +1 Antoine Wright 2.5 -3 -2 Clifford Robinson 18.8 -1 -6

For those that are joining us for the first time, a player’s individual plus-minus rating is a way to measure the player’s combined offensive and defensive total contribution to the team. For example, in the game above, Richard Jefferson earned a -4 rating. What that means is that, when Jefferson was on the floor, the Nets were outscored the Cavs by a total of 4 points-hence the -4 rating. In Jacque Vaughns’ case, his +2 rating means that in the time he was on the court, the Nets outscored Cleveland by 2 points. You can also measure plus-minus based on two-man combos, three-man combos, 5-man combos, etc., which can help identify the players that contribute the least or the most to team success. 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. We could use this type of analysis to determine which players play well together and which ones don’t. To continue this example, 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 "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)). For a matter of scale, the highest efficiency rating attained in a single game this season was +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. For the season, Carter, Kidd, and Jefferson have 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 in 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 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).

As usual, the chart above lists the starters above the reserves, and each of these two groups of players is listed in descending order of plus-minus rating. Incidentally, the raw stats come from www.popcornmachine.net, and may include minor errors.

Once again, Jason Collins appears at the top of the plus-minus list. Although Collins is a solid defender, we have to point out that he is helped by the fact that most of his minutes come alongside the "Big Three." That’s one reason why looking at the plus-minus ratings in terms on multiple-player combinations is helpful.

We also see that, for the second consecutive game, Coach Frank used a four-man bench consisting of Robinson, Vaughn, Padgett, and Wright. It really looks like Antoine Wright is getting a legitimate opportunity to contribute.

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.

As we did in our last installment, let’s look how the starters performed as a unit:

 Unit Stint Min. Plus-Minus Starting Five 1 (1st Quarter) 7.5 -1 2 (3rd Quarter) 8.6 +1 3 (4th Quarter) 1.7 +0 4 (4th Quarter) 5.9 -2 TOTAL 23.7 -2

Once again, I broke out the starting unit’s performance by each instance they played together. The starting unit’s performance was pretty consistent each time it entered the court, but it could not gain an advantage.

Jacque Vaughn answered the challenge I posed last time and posted a positive plus-minus rating, in fact, the highest on the team. As we did previusly, 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 5.4 +0 Kidd but not Collins 0.0 -- Neither Kidd nor Collins 11.5 +2

In the last blog entry, we looked at Vaughn’s season-to-date numbers in these three situations. Because Antoine Wright has started to get some playing time alongside Kidd, today let’s take a look at his numbers. Over the past three games, Wright has gotten an opportunity to play with Jason Kidd and Jason Collins (the previous—and possibly first—time this happened was during the first Boston game about two weeks ago). Over these three games, Wright played with Kidd and Collins for a total of 7.7 minutes, during which the Nets went a combined –10. Still have a long way to go, but hopefully Coach Frank won’t give up on the kid after just a few performances.

* * * * *
I’m going to start a new topic today and discuss rebounding. Now we’ve all read the newspaper articles and the message board threads asserting that the Nets need to add a proficient rebounder to the rotation. Is this really true? How much would a strong rebounder help the team? Should we consider the Nets’ rebounding to be "weak" when compared to the other teams in the league? Let’s take a look.

Examining the box score from the previous game, all that certainly seems to be true. We see that the Nets were out-rebounded 51-33. We’ll point out, also, that the Nets were credited with 10 rebounds as a "team," while the Cavaliers "team" was given credit for just five—so, all in all, the Nets were out-rebounded by a 56-43 margin. That makes things a little better, although the overall results still look horrible on their face.

Here’s the thing: Those numbers are mostly irrelevant when assessing rebounding skills. Let me explain. To me, the only number that really matters is offensive rebounds, not total rebounds. The reason is simple: If each team gets every defensive rebound, then the entire difference in rebounds by each team can solely be attributed to the number of possessions that result in a shot, which is impacted by things like turnovers, fouls, and blocked shots. In other words, one team grabbing 50 rebounds is no different than the opposition getting 25, if neither team gets a single offensive rebound. The key operating statistic is that both teams would have pulled down 0% of their potential offensive rebounds (and thus, by definition, 100% of their potential defensive rebounds). I hope that makes sense. In the game against Cleveland, we see that the Cavaliers pulled down six offensive rebounds to the Nets’ three. Looking through the published game log, we also can determine that, of Cleveland’s five "team" rebounds, four were on the offensive end; of the Nets’ ten "team" rebounds, one was on the offensive end. That puts the final total at 9-4 in favor of Cleveland.

Why is this so important? Well, we can say with some certainty that each offensive rebound has a tangible value, since they are almost always immediately followed by a high-percentage shot and/or shooting foul. Without any long-term stats available to me, I would bet that each offensive rebound is worth something around a point or a point and a half. Let’s take a look at the thirteen offensive rebounds in this game, and what resulted:

 Team Quarter Time Rebounder Result Cleveland 1st 6:13 Gooden Missed shot NJ 2nd 9:22 Carter 2-point field goal NJ 2nd 8:07 Padett Missed shot Cleveland 3rd 5:07 Pavlovik Shooting foul (1 for 2) Cleveland 3rd 3:01 [Cleveland] Missed shot Cleveland 3rd 1:51 Jackson 2-point field goal NJ 3rd 0:07 Robinson 3-point field goal Cleveland 4th 6:09 Pavlovik Missed shot Cleveland 4th 6:07 [Cleveland] Blocked shot Cleveland 4th 5:56 [Cleveland] Time out Cleveland 4th 5:20 Varejao Missed shot NJ 4th 5:07 [NJ] Shooting foul (2 for 2) Cleveland 4th 2:18 Ilgauskas 2-point field goal

In this game, then, the 13 offensive rebounds were directly responsible for 12 points, a little less than I would have expected, but in all fairness one game cannot be considered a valid statistical sample. In fact, surprisingly, the Nets actually outscored the Cavs 7-5 in points-after-offensive-rebounds, despite being out-rebounded as noted above. I’m going to keep following this for a few games and see what I discover with regard to the value of offensive rebounds.

Looking at this chart, the big thing that jumps out at me is how the frequency of offensive rebounds by Cleveland increased as the game went on. Perhaps this was related to fatigue (this being the second game in a back-to-back for the Nets).

Anyway, here’s my point: I really believe that counting total rebounds to measure a team’s rebounding success is silly. Instead, I prefer to look solely at what I will call the "rebound conversion rate," or "RCR," which I will define as the number of offensive rebounds a team collects, expressed as a percentage of opportunities.

Let’s use this game as an example of this new metric. Including those credited to the "team," Cleveland pulled down 56 rebounds, including nine offensive and 47 defensive. The Nets grabbed four offensive and 39 defensive rebounds, for a total of 43. Thus, the Nets’ RCR for the game is 4/(47+4) = .078. Cleveland, on the other hand, converted 9/(39+9) = .188 of its rebound opportunities. We can also represent this as a net result: The Nets’ net rebound conversion rate, or NRCR, would be calculated as
.078-.188 = -.110. For Cleveland, the NRCR would be the inverse of that figure, or .110.

So which teams are best at this metric? Is there a correlation between NRCR and a team’s success? Let’s take a look.

 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

We can see from this chart that the Nets are right about in the middle of the group. Let’s return now to the questions we initially asked. Are the Nets a bad rebounding team? No, not really. Could the Nets use a proficient rebounder on the club? Sure, of course. But from this chart, it doesn’t appear that this needs to be a top priority. The Nets are an average rebounding team, nothing more, nothing less. We see that the Nets’ NRCR is about the same as Detroit and San Antonio, among other teams, and no one would stand around and argue that they need to improve their rebounding skills.

Let’s look now at the individual components of the NRCR. First, let’s sort the list by defensive RCR:

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

Now, let’s sort the list by offensive RCR:

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

I should point out that these charts are derived from team-by-team data that are posted on a variety of web sites. I don’t believe that they incorporate "team" rebounds, but I tend to believe that these occur on a random basis, i.e., they are the not the result of some "skill," so this exclusion should be irrelevant with a large data sample.

These two charts really crystallize the state of the Nets’ rebounding skills for me. The Nets are one of the top teams in the league at restricting the opposition’s offensive rebounds, yet they are among the worst at getting offensive rebounds themselves. The net result is, as we’ve seen, an average rebounding team overall. This extreme "split" is actually quite similar to that of San Antonio. These are two teams that apparently make a concerted effort to limit the offensive rebounds of the opposition. In contrast to this are the Pistons, who are among the best at getting offensive rebounds, but among the worst at limiting the opposition. The net result is about the same as the Nets and Spurs, as we’ve seen.

So, yes, the Nets could surely increase their offensive rebounds, but they need to be wary of limiting their effectiveness on the defensive end at the same time. It may turn out that they acquire a bruising rebounder, only to see their defensive RCR increase by the same percentage as their offensive RCR, leaving the net result approximately zero. Are there some players on the Nets that are particularly good at limiting the opposition’s RCR? Are others particularly weak? Is this something we can even determine? We’ll take a look next time.

--Dumpy