Author Archives: TheNBAGeek

Should D’Antoni Get Credit for Lin’s Success?

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I ran into a post on Marginal Revolution about Jeremy Lin, titled “Is Jeremy Lin a Fluke?”:

Nate Silver says no.  I say that in Mike D’Antoni’s offensive schemes a lot of point guards reap more than the statistics they would pick up on other teams and from other offenses, and since the D’Antoni scheme is not very generalizable, or capable of winning a championship, the “other team” metrics are more or less the correct ones.

Here at the Wages of Wins we have our own thoughts about Lin (more on that in tomorrow’s post), but for now I am much more interested in Tyler Cowen’s thoughts on Mike D’Antoni above.

First, I might ask, If it’s all about D’Antoni’s schemes, why is Steve Nash still one of the best point guards in the league? D’Antoni has been gone for several years, and to my knowledge his successors have changed the system a lot (and, in any case, isn’t Cowen making the argument that they couldn’t? I’m not sure what “hard to generalize” really means). Why didn’t Nash’s performance drop off with D’Antoni’s departure?

Careers per-48 Stats
SEA Min WP48 Wins PTS DRB ORB REB AST TO BLK STL PF
06-07 PHO 2682 .307 17.1 25.3 4.3 0.5 4.8 15.8 5.1 0.1 1.0 2.1
07-08 PHO 2780 .251 14.6 23.7 4.4 0.5 4.9 15.5 5.1 0.1 0.9 2.0
08-09 PHO 2484 .194 10.0 22.4 3.9 0.4 4.3 13.9 4.8 0.2 1.1 2.1
09-10 PHO 2660 .250 13.8 24.1 4.2 0.6 4.8 16.1 5.3 0.2 0.8 1.9
10-11 PHO 2497 .244 12.7 21.3 4.2 0.8 5.0 16.4 5.1 0.1 0.9 1.7
11-12 PHO 567 .231 2.7 21.8 3.0 0.5 3.6 15.2 5.3 0.0 0.8 0.8
Career Avg 2465 .241 12.4 23.4 3.9 0.8 4.7 13.9 4.6 0.1 1.1 2.3
Average PG 1624 .099 3.3 18.8 3.9 0.9 4.8 8.3 3.2 0.3 1.8 3.6

 

Career Shooting Efficiency
FG% 2FG% 3FG% FT% eFG% TS% FGA 3FGA PPS FTA
Nash 05-06 51.2% 54.8% 43.9% 92.1% 58.3% 63.2% 18.1 5.9 1.41 4.8
Nash 06-07 53.2% 57.5% 45.5% 89.9% 61.3% 65.4% 17.4 6.1 1.45 4.4
Nash 07-08 50.4% 52.7% 47.0% 90.6% 59.7% 64.1% 16.6 6.6 1.43 4.2
Nash 08-09 50.3% 52.9% 43.9% 93.3% 56.6% 61.5% 16.4 4.8 1.36 4.1
Nash 09-10 50.7% 54.0% 42.6% 93.8% 57.0% 61.5% 17.8 5.3 1.35 4.1
Nash 10-11 49.2% 52.5% 39.5% 91.2% 54.2% 60.1% 15.6 3.9 1.36 4.8
Nash 11-12 52.8% 57.7% 39.2% 85.0% 58.0% 61.3% 16.3 4.3 1.34 3.4
Nash (career) 49.7% 52.4% 43.3% 90.8% 56.2% 61.2% 17.1 5.1 1.37 4.7
Average PG 43.0% 45.7% 35.6% 80.5% 47.8% 52.5% 16.0 4.3 1.18 4.4

Although D’Antoni left the Suns after the 07-08 season, there hasn’t been an appreciable drop off in Nash’s performance. In fact, Nash is a bit of medical curiosity, since a player his age should be declining rabidly.

Furthermore, if D’Antoni’s system is so generous to point guards, why have Toney Douglas, Iman Shumpert, and Mike Bibby all been so terrible?

Raw Stats
Min WP48 Wins PTS DRB ORB REB AST TO BLK STL PF
Shumpert 751 .010 0.2 16.4 4.5 0.9 5.4 5.3 4.0 0.4 3.3 5.0
Bibby 272 -.003 -0.0 10.4 4.2 0.5 4.8 4.9 2.1 0.2 1.6 4.1
Douglas 533 -.141 -1.6 17.7 4.0 0.5 4.5 5.2 4.1 0.1 2.1 4.1
Average SG 570 .099 1.2 20.1 4.3 1.0 5.4 4.2 2.8 0.4 1.6 3.3
Average PG 617 .099 1.3 19.6 3.9 0.9 4.9 8.2 3.6 0.4 1.9 3.3

 

Shooting Efficiency
FG% 2FG% 3FG% FT% eFG% TS% FGA 3FGA PPS FTA
Shumpert 38.5% 41.7% 28.8% 84.6% 42.1% 45.4% 16.9 4.2 0.97 2.5
Bibby 28.6% 31.6% 27.5% 83.3% 38.6% 40.6% 12.4 9.0 0.84 1.1
Douglas 32.1% 38.1% 23.8% 85.7% 37.1% 39.3% 21.6 9.1 0.82 1.9
Average SG 42.5% 45.8% 35.7% 80.3% 48.3% 52.6% 17.2 5.6 1.17 4.3
Average PG 42.9% 46.1% 34.7% 80.9% 47.8% 52.4% 16.8 4.7 1.17 4.4

Regarding the second part of Cowen’s analysis, I am curious as to how exactly he determined that “the D’Antoni scheme is not very generalizable, or capable of winning a championship”? What huge sample of championship runs is Tyler using for his observations here? Even if we made the rather generous assumption that every Suns roster he coached was championship-caliber (as an aside, I hope he isn’t going to argue that the Knicks rosters of the past few years were), we’re talking about a sample of a few seasons, correct?

I’m fairly certain if a student presented a paper that came to economic conclusions based on evidence like this in one of Tyler Cowen’s classes, the grade would not be favorable.

Thinking Fast and Slow about basketball

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The following is another cross-post from me (@nbageek) on our sister site, The NBA Geek. Since my last post on the Illusion of Validity caused so much discussion in the comments, Dave asked me to re-post my latest article on it. Today, I talk about how the illusion of validity allows us to lend too much credence to statistics that confirm our bias, even when we know that said statistics are not significant (one could argue that some of this is the well-known “Confirmation Bias” instead). You can read the original post on The NBA Geek here.

I recently wrote a post over on the Wages of Wins Journal about how I believe the book Thinking, Fast and Slow is chalk-full of descriptions of cognitive illusions that all basketball analysts (whether they are paid to do it or not) fall prey to. One of Kahneman’s more famous illusions is the illusion of validity; the fact that people have a huge amount of confidence in their own judgment, even in the face of clear evidence that their judgment is wrong:

The confidence we experience as we make a judgment is not a reasoned evaluation of the probability that it is right. Confidence is a feeling, one determined mostly by the coherence of the story and by the ease with which it comes to mind, even when the evidence for the story is sparse and unreliable. The bias toward coherence favors overconfidence. An individual who expresses high confidence probably has a good story, which may or may not be true.

I coined the term “illusion of validity” because the confidence we had in judgments about individual soldiers was not affected by a statistical fact we knew to be true — that our predictions were unrelated to the truth. This is not an isolated observation. When a compelling impression of a particular event clashes with general knowledge, the impression commonly prevails. And this goes for you, too. The confidence you will experience in your future judgments will not be diminished by what you just read, even if you believe every word.

I encourage you to read the whole thing, it is full of examples of the illusion in action.

In basketball, one statistic that I believe illustrates the Illusion of Validity is the very popular plus/minus statistic. I’m convinced that +/- is precisely so popular because everyone kind of intuitively knows that it is meaningless. Whenever a person is convinced that a performance was great (terrible), a +/- number is dragged out and offered as evidence. “Look, this +/- number re-inforces my belief!” When the +/- number doesn’t conform to the story, it is (conveniently) dismissed as a statistic that is meaningless, or which “requires large samples” (quick, name a stat that doesn’t require large samples to converge!).

Here’s the dirty truth about +/-: it really is meaningless. Why is it meaningless? Because it is horribly inconsistent over time. Even its proponents seem to agree:

Returning to Davis, this is a crucial part of the discussion. To me, the most obvious explanation for Davis’ relatively poor net defensive plus-minus is the small sample size. At the time SI.com’s Luke Winn wrote about this statistic, Davis had been off the court for 202 possessions–less than three games’ worth. That’s just not nearly enough time to make meaningful declarations. Even the entire NBA regular season, nearly three times a long, is not sufficient for the noisiness in plus-minus to filter out. There are plenty of examples of players’ net plus-minus ratings bouncing wildly from year to year.

This doesn’t invalidate plus-minus statistics. It merely means they must be used with more caution than individual numbers. To be clear, Winn didn’t use plus-minus to say anything negative about Davis. He merely made note of the plus-minus numbers in the process of pointing out how effective Eloy Vargas has been defensively as Davis’ backup.

It surprises me that Mr. Pelton can look at a number that “bounces wildly from year to year” and, a few sentences later, use it evaluate a player:

In terms of individual statistics, Collison doesn’t impress. Because he uses so few possessions on offense and rarely blocks shots, Collison rates worse than replacement in WARP and little better in PER. Basketball-Reference.com’s Win Shares provide a superior estimate of Collison’s value but still put him barely better than average.

Meanwhile, Collison’s net plus-minus of +11.1 last season ranked eighth in the league, per BasketballValue.com. Every player ahead of him was an All-Star. The year before, the Thunder was 9.4 points better per 100 possessions with Collison on the floor.

Again, this surprises me (but it shouldn’t!). If a measurement is horribly inconsistent over time, there are two possibitities:

  1. Whatever you are measuring is itself wildly inconsistent over time.
  2. You are not measuring what you think you are measuring.

Plus/Minus varies wildly over time. So we need to consider two possibilities:

  1. Basketball performances is very inconsistent and fluctuates wildly over time
  2. plus/minus is not measuring basketball performance.

I would postulate that 1) is not possible. Almost all aspects of basketball performance at both team and individual levels are pretty consistent over time. You can see the math in The Wages of Wins, but truthfully anyone with access to an Excel sheet and the internet can do the math themselves: raw box score stats in any given year correlates well with the same stat in the previous year. So, when we look at the choice above, any reasonable scientist should apply Occam’s Razor and conclude that plus/minus isn’t actually measuring basketball performance.

So, apparently it only measures something useful when you want it to. It’s the illusion of validity in action! As we’ve argued many times, the box-score contains lots of information. In other words, the box-score is fine, you’re just doin’ it wrong.

I was recently talking with the Wages of Wins author David Berri about plus/minus:

Recently I was updating a model that I had presented at a meeting.  The model was based on more than 1,000 observations, and I was adding another years worth of data.  In the process of adding the data, I miscoded a few observations (less than 10).  When I re-estimated the model — with the miscoded data — the results I had seen earlier went from statistically significant to insignificant.  Once I fixed the problem, the results became statistically significant.

This experience highlights a problem researchers often face.  Small changes in a data set can dramatically impact a result.  That is why we a) check our data b) re-estimate our models with different independent variables and across different data sets (i.e. conduct robustness checks), and c) report our findings in the following fashion: “the data suggests the following…”

In other words, in the social sciences we avoid saying we “proved” something.

When I look at the adjusted plus-minus work, I fail to see these kind of efforts. The specific model is not often reported. And we see no effort at any kind of robustness checks.  Furthermore, the nature of the model — regressing small segments of a game on essentially some dummy variables — suggests that the results are never going to be definitive. This is because all the factors that can impact outcomes in these small segments are not controlled for in the model.

All of this indicates that the results from this research are unreliable. What is interesting, though, is that even when people acknowledge the lack of reliability, they still quote the results (while noting they are unreliable).  And that leads one to wonder, how do you know when something that is unreliable can be relied upon?

That last paragraph is the Illusion of Validity in action. If it supports what I believe to be true, then it must be meaningful!

“I know what the stats say but…”: The illusion of validity in basketball fandom

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The book “Thinking, Fast and Slow” by Daniel Kahneman is fascinating reading; it’s sort of a “greatest hits” of the cognitive fallacies that he and various colleagues (the most famous of whom is Amos Tversky) documented through clever experimentation. I’ve often thought that how we as fans think about basketball falls prey to each of these fallacies and plan to write a series on various fallacies and how they apply to thinking about basketball on my website, The NBA Geek. But it occurs to me that one article won’t be enough to cover all the ways that the illusion of validity affects us (yes, all of us, even me).

It’s pretty geeky to have a “favorite scientist”, but nobel prize winner Daniel Kahneman is probably it for me. I think the greatest take-away from Kahneman’s work is that we simply cannot trust ourselves when it comes to decision-making (or judgement-making) in complex situations. And another, perhaps more important, take-away is that this knowledge that one cannot trust one’s self won’t actually protect you from making that mistake anyway. A great illustration of this is from this New York Times article. Kahneman and his colleagues in the military had designed a program to evaluate officer candidates, and predict who should succeed:

We were willing to make that admission because, as it turned out, despite our certainty about the potential of individual candidates, our forecasts were largely useless. The evidence was overwhelming. Every few months we had a feedback session in which we could compare our evaluations of future cadets with the judgments of their commanders at the officer-training school. The story was always the same: our ability to predict performance at the school was negligible. Our forecasts were better than blind guesses, but not by much.

We were downcast for a while after receiving the discouraging news. But this was the army. Useful or not, there was a routine to be followed, and there were orders to be obeyed. Another batch of candidates would arrive the next day. We took them to the obstacle field, we faced them with the wall, they lifted the log and within a few minutes we saw their true natures revealed, as clearly as ever. The dismal truth about the quality of our predictions had no effect whatsoever on how we evaluated new candidates and very little effect on the confidence we had in our judgments and predictions.

I thought that what was happening to us was remarkable. The statistical evidence of our failure should have shaken our confidence in our judgments of particular candidates, but it did not. It should also have caused us to moderate our predictions, but it did not. We knew as a general fact that our predictions were little better than random guesses, but we continued to feel and act as if each particular prediction was valid. I was reminded of visual illusions, which remain compelling even when you know that what you see is false. I was so struck by the analogy that I coined a term for our experience: the illusion of validity.

I had discovered my first cognitive fallacy.

In other words, even faced with the sure knowledge that the data they were collecting was useless, they remained absolutely stone-cold certain that what they were seeing revealed the true nature of the men they were evaluating:

I coined the term “illusion of validity” because the confidence we had in judgments about individual soldiers was not affected by a statistical fact we knew to be true — that our predictions were unrelated to the truth. This is not an isolated observation. When a compelling impression of a particular event clashes with general knowledge, the impression commonly prevails. And this goes for you, too. The confidence you will experience in your future judgments will not be diminished by what you just read, even if you believe every word.

It’s that last bit that is telling. I fall prey to this myself constantly. As my readers know, I am a pretty avid believer that WP48 tells us far more about basketball performance than the naked eye ever could. Yet I couldn’t possibly count the number of times I have seen a basketball game and thought “player X was Amazing!”, only to check the box score and discover he was terrible-to-average, committing lots of turnovers (which my mind glazed over), missing lots of shots (which weren’t as important as those three THUNDEROUS DUNKS, SURELY) or grabbing no rebounds.

As basketball fans (or basketball analysts — I like to give myself fancy titles to lend more validity to my statements), are convinced that we are experts in this field. That what we see on the basketball court has meaning, regardless of what the data says. It is why coaches are so reluctant to give up on players drafted highly — they see things in on-court performances that convince them the player is capable of so much more than what the box-score tells them. It is why fans of certain players get outraged whenever we post an article that shows how they are overrated.

We want what we see to have meaning, to fit into a narrative.  Players that look spectacular when scoring, well, they must be great players! Look at that athleticism! Everybody knows he’s a great player! Meanwhile, players that score lots of boring put-backs, or don’t really get shots off the dribble and only shoot when they are open and passed-to, well, they slip by, beneath our notice, and somehow the buckets that they score don’t count the full 2 points in our cognitive registry.

The illusion of validity is why I get deeply suspicious whenever a fan, sportswriter, coach, or GM says anything to the effect of “the numbers don’t tell the whole story”. This is, in fact, true, but what the person saying this usually means is “I don’t care what the numbers say because I am convinced that what I have seen is correct.” Which is, thanks to this illusion, almost never true. If I make an argument that the data says a player isn’t good, and someone points out “Yes, but if you watch the games you will notice that this year they are only shooting threes from the slot, and rarely from the corner, where he used to excel,” then that person is pointing out a hole in the data that’s worth investigating. If the argument is along the lines of “anyone who’s watching him can clearly see he’s much better than that,” then I’m certain the illusion of validity is doing its dirty work.

Moral of the story: you can’t trust yourself. And if you find yourself saying, “I know, but this time I’m sure!” then you really can’t trust yourself.

-Patrick

Put ‘em in coach, they’re ready to play!

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The following is a cross-post from our sister site, The NBA Geek, called “Put Me In, Coach”. In it, I take a look at some of the rookies who have so far played extremely well but don’t seem to be getting rewarded for it with more playing time from their coaches. You can read the original article here.

Believe it or not, one of the reasons I wrote the software for the NBA Geek’s stats sections is because I’m super curious about the data, and I love sorting/filtering/searching through data to find things to write about*, but there wasn’t a tool for me to do so efficiently. There are tons of sites out there, but Basketball Reference is the only one that comes remotely close to offering the kinds of sorting/filtering that I want, and of course, until my site and the one that Andres built at The Wages of Wins, most of them didn’t have wins produced, or only had it for their favorite teams. I used to get downright itchy waiting for David Berri to profile a team or player I liked. Finally I got fed up and decided the only way to always get the answers I wanted was to write a tool myself.

Today I’m going to use that tool to evaluate the rookies that, in my mind, are getting a mystifying lack of burn from their coaches. Take a look at the entire rookie class on the player page. Feel free to mess around with the sorting. Here are the top ten sorted by minutes played at the time of this writing (before games on Jan 5th):

NAME TEAM POS GP MIN PTS REB AST WP48 Wins
Fredette, Jimmer SAC PG 7 178 18.3 3.0 4.9 -.075 -.3
Rubio, Ricky MIN PG 6 166 16.2 6.7 12.1 .263 .9
Brooks, Marshon NJN SG 7 163 29.2 7.1 1.5 .168 .6
Cole, Norris MIA PG 7 159 24.2 3.6 7.2 .021 .1
Irving, Kyrie CLE PG 6 155 26.3 6.8 9.9 .123 .4
Pargo, Jeremy MEM PG 6 137 16.5 4.6 7.0 -.031 -.1
Knight, Brandon DET G 6 137 21.0 3.5 5.6 -.044 -.1
Morris, Markieff PHO PF 6 118 20.7 13.0 2.8 .260 .6
Thompson, Tristan CLE PF 6 116 21.5 12.0 0.8 .085 .2
Singleton, Chris WAS SF 6 116 9.5 5.8 0.0 .067 .2
Walker, Kemba CHA PG 6 110 24.0 6.1 8.3 -.045 -.1

And here are the top ten sorted by WP48 (I ignored players with < 50 minutes):

NAME TEAM POS GP MIN PTS REB AST WP48 Wins
Leuer, Jon MIL PF 5 66 18.9 11.6 1.5 .320 .4
Stiemsma, Greg BOS C 5 82 12.9 12.9 2.3 .280 .5
Rubio, Ricky MIN PG 6 166 16.2 6.7 12.1 .263 .9
Morris, Markieff PHO PF 6 118 20.7 13.0 2.8 .260 .6
Leonard, Kawhi SAS SF 6 99 15.0 14.1 1.5 .225 .5
Kanter, Enes UTH C 6 89 15.6 17.8 1.1 .194 .4
Burks, Alec UTH SG 5 56 28.3 5.1 6.9 .193 .2
Brooks, Marshon NJN SG 7 163 29.2 7.1 1.5 .168 .6
Williams, Derrick MIN F 6 106 19.5 11.3 1.4 .142 .3
Irving, Kyrie CLE PG 6 155 26.3 6.8 9.9 .123 .4

Notice any glaring differences? 6 of the top 10 per-minute performers aren’t in the top 10 minutes played. And half of the players getting the most minutes are playing terrible basketball. I realize we are still in the realm of small sample sizes. But here’s two things: 1)the sample sizes aren’t going to get big very fast if you are allocating minutes in nibble-size chunks instead of hearty meals, and 2) it’s not like some of these guys have all-stars in front of them getting minutes.

The one that confuses me the most is Greg Stiemsma. I mean, look at that Celtics team. This is a team that has had reporters all season (and pre-season) long saying that their center position is weak. Their options are:

  • Kevin Garnett playing out of position (and let it be noted that Rivers wants to rest Garnett a lot, so this option has limits)
  • Jermaine O’Neal, who’s old and hasn’t played good basketball for a decade. Relying on him to return to 2002 form seems like a fool’s mission to me.
  • Chris Wilcox, who’s always been average/above average, but is getting old

Then, along comes a rookie who seems to be answering all of the Celtics’ prayers:

Raw Stats
Min WP48 Wins PTS REB AST TO BLK STL PF
Stiemsma 82 .280 0.5 12.9 12.9 2.3 1.8 7.6 0.6 9.4
Average C 139 .099 0.3 17.6 13.0 2.0 2.7 2.1 1.3 5.1
Shooting Efficiency
FG% 2FG% 3FG% FT% eFG% TS% FGA PPS FTA
Stiemsma 61.5% 61.5% 0.0% 75.0% 61.5% 66.6% 7.6 1.69 4.7
Average C 49.8% 50.5% 32.0% 66.7% 50.4% 54.1% 13.9 1.27 5.4

The kid’s basically doing what he’s supposed to on a team full of veterans: rebound, block shots, don’t turn the ball over, don’t take bad shots, hit free throws. He’s just what the doctor ordered. Unless you are Doctor Rivers, who usually employs him on the bench. Look, I don’t know, maybe he’s just on a hot-shooting streak. Maybe he’ll foul out every game if you gave him 25 minutes. But again, you’ve got Jermaine O’Neal on the floor. Isn’t it worth giving him some minutes to just see what happens? How much worse than O’Neal can he possibly be?

And how about Enes Kanter and Alec Burks? These two have really got me confused and #SMH.

Raw Stats
Min WP48 Wins PTS REB AST TO BLK STL PF
Kanter 89 .194 0.4 15.6 17.8 1.1 1.1 1.6 1.1 3.8
Burks 56 .193 0.2 28.3 5.1 6.9 4.3 0.9 1.7 3.4
Average C 139 .099 0.3 17.6 13.0 2.0 2.7 2.1 1.3 5.1
Average SG 142 .099 0.3 20.3 5.5 4.4 2.9 0.5 1.6 3.6
Shooting Efficiency
FG% 2FG% 3FG% FT% eFG% TS% FGA PPS FTA
Kanter 41.7% 41.7% 0.0% 64.3% 41.7% 48.1% 12.9 1.21 7.6
Burks 47.8% 52.6% 25.0% 76.9% 50.0% 57.5% 19.7 1.43 11.1
Average C 49.8% 50.5% 32.0% 66.7% 50.4% 54.1% 13.9 1.27 5.4
Average SG 41.2% 45.3% 32.9% 78.8% 46.6% 51.0% 17.8 1.14 4.7

Here’s a shooting guard who’s shooting the ball well (that is what the shooting guard is supposed to do, right?), passing well, and getting to the line like he’s Dwyane Wade. And a center whose shooting woes are made up for by several positives: 1) he rebounds like Kevin Love, 2) he apparently has a grip of steel because he’s rarely turning the ball over, and 3) he’s not chucking lots of shots so his shooting percentage isn’t really hurting much. And this is a team that clearly is going all-out on youth anyway. They traded away Okur. They aren’t going to make the playoffs with these veterans. What is Utah worried about!? Why is Raja Bell playing even one minute instead of Burks? More importantly why is Raja Bell still in the NBA!?

Then there’s Derrick Williams. Speaking strictly as a Timberwolves fan, I’ll be the first to say that Williams has not been amazing. He’s made his share of bone-headed plays. But he should be playing more. A lot more. And the primary reason is that for all his rookie faults, he’s still having a decent season. And, far more importantly, he is so much better than Michael Beasley that…well, I have no words:

Raw Stats
Min WP48 Wins PTS REB AST TO BLK STL PF
Beasley 194 -.114 -0.5 20.0 9.2 1.7 4.2 0.5 0.7 4.5
Williams 106 .142 0.3 19.5 11.3 1.4 4.1 0.0 2.3 5.0
Average SF 146 .099 0.3 19.1 7.3 3.2 2.4 0.8 1.5 3.7
Average F 144 .099 0.3 19.4 9.4 2.8 2.4 1.0 1.5 4.2
Shooting Efficiency
FG% 2FG% 3FG% FT% eFG% TS% FGA PPS FTA
Beasley 40.2% 40.3% 40.0% 41.2% 42.5% 42.9% 21.5 0.93 4.2
Williams 51.5% 61.9% 33.3% 41.7% 57.6% 56.2% 14.9 1.30 5.4
Average SF 42.6% 45.1% 36.7% 77.6% 48.1% 52.4% 16.3 1.17 4.4
Average F 44.9% 47.7% 35.1% 73.9% 48.8% 52.7% 16.4 1.19 4.7

Note that I am comparing Beasley to SFs and Williams to a combinations of the average SF/PF; Williams WP48 would be higher if we treated him as an SF and Beasley’s would be even lower if we treated him as a PF (see Calculating Wins Produced for the section on how we adjust for position).

Basiically, if you gave all of Beasley’s minutes to Williams, the Wolves could easily have won 4-5 games by now. No, I’m not joking. Beasley is truly earth-shatteringly bad. To put in perspective what a selfish chucker Beasley is, he ranks 8th among small forwards the NBA in FGA per 48 minutes (50 minutes minimum), but fifty-first in true shooting. No, not 51st in the NBA, 51st among small forwards. That means that essentially all of the starting SFs and two-thirds of the back-up SFs in the league are shooting better than he is, but he’s shooting 22 shots per 48. And I don’t keep stats for “contested 22-footers per 48″ but having watched every Timberwolves game I’m going to guess he leads the league by a fat margin, ahead of even Kobe (I’m guessing that’s the reason that he’s 25th among small forwards at getting to the line, despite all those shots). Oh, yeah, and he turns the ball over a shitton. In short, Beasley is the very definition of a player that shoots you out of games. Every single time they choose to iso Beasley instead of just letting Ridnour or Rubio create off the dribble / pick-n-roll, a Timberwolf pup dies in the wild.

Williams is as turnover prone as Beasley, but I can chalk Williams’ TOs up to rookie mistakes and a general rookie “over-eager” attitude, which he may learn from. Most of his TOs come from ill-advised passes. Beasley, however, is a third-year player; the vast majority of his turnovers come from him trying to “create a shot” (a phrase that I guarantee I’ll be ranting about in a future article) and I don’t expect him to change anytime soon.

Last but definitely not least is Jon Leuer:

Raw Stats
Min WP48 Wins PTS REB AST TO BLK STL PF
Leuer 66 .320 0.4 18.9 11.6 1.5 0.7 2.2 2.9 5.8
Average PF 142 .099 0.3 19.8 11.6 2.4 2.4 1.2 1.4 4.7
Shooting Efficiency
FG% 2FG% 3FG% FT% eFG% TS% FGA PPS FTA
Leuer 55.0% 57.9% 0.0% 100.0% 55.0% 59.7% 14.5 1.30 2.9
Average PF 47.1% 49.5% 31.8% 71.1% 49.2% 53.0% 16.5 1.20 5.1

As I wrote on Monday in my Geeks of the Week post, Leuer has been amazing so far. He’s shooting efficiently, has a a bunch of steals and blocks and almost no turnovers. But he hasn’t played many minutes. He could just be playing well over a small stretch, but why on earth wouldn’t Scott Skiles want to play him more and find out? Does he have Kevin Love and Blake Griffin ahead of Leuer on the depth chart? What on earth is going on? I find this exceptionally puzzling since Mbah a Moute appears to have been struggling with injuries.

It’s particularly odd that teams seem so reluctant to dole out minutes to rookies because they seem equally reluctant to let go of 2nd- and 3rd-year players on their rookie contracts who are truly terrible. Minnesota’s examples include Wes Johnson and previously Jonny Flynn and Corey Brewer — there’s simply no reason to hang on to these players (or give them minutes). It’s times like these when I start to think that minute allocation by most coaches may as well be arbitrary.

 

The NBA Geeks of the Week, week 1

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The following is a cross-post from one of Wages of Wins Network sites, The NBA Geek.  In it, Patrick Minton (@nbageek) picks the best win-producers of last week’s games, along with the best rookie. The original article appears here. Also, bear in mind that stats are in per-48 numbers rather than per-game numbers wherever that makes sense.

The NBA has played a whole week of basketball, and thanks to the compressed schedule we’ve had a few games more than we would have in most seasons. It’s time for me to pick what I’ll call the NBA Geeks of the Week. Sort of the all-geek team. NOTE: I wrote this article before Sunday’s games because I want to consider just the games in the first 7 days.

Player of the Week: LeBron James

Yeah, this was easy.  LeBron has been a monster so far, producing 1.7 wins in 4 games, on 66% true shooting and with insane defensive numbers (averaging more than twice as many blocks and steals as the average SF while fouling a lot less).  Even by his lofty standards (he’s always in the top 5 of all NBA players in WP48), he’s been absolutely incredible.  Fun fact: I could have drafted him in my fantasy league’s auction draft but I thought that having enough money to get 3 good players (which ended up being Rose, Bosh, Randolph) would be better than having one fantastic player. Yeah, that decision was every bit as stupid as you are thinking it was.

Rookie of the Week: Ricky Rubio

Holy cow am I happy that Rubio appears to be the real thing. I jokingly called him the rookie of the year recently but if he keeps up numbers like this…damn, he will be, actually.  Rubio produced 0.6 wins with a WP48 of .335 over 85 minutes. In fact, he was so good that he eclipsed Kevin Love (who had a rough week, by his standards) as the best Timberwolf! Jon Leuer was in fact much more spectacular than Rubio, but I gave Rubio the nod because Leuer only played 43 minutes (good coaching there, huh?) and well, you know, sample size. Here were the candidates:

 

WP48 PTS REB AST TO BLK STL PF
Morris .316 21.9 12.7 2.8 2.8 2.1 1.4 8.5
Kanter .142 15.2 18.3 0.8 0.0 1.5 1.5 5.3
Brooks .243 34.4 6.8 2.8 2.3 1.1 1.1 4.5
Leuer .559 20.1 14.5 2.2 1.1 2.2 4.5 4.5
Rubio .335 13.6 7.9 12.4 4.5 0.0 2.3 4.5
Average C .099 17.2 12.9 2.0 2.8 2.2 1.3 5.5
Average PF .099 20.0 11.8 2.6 2.5 1.2 1.5 4.7
Average PG .099 19.3 4.9 8.2 3.9 0.4 2.0 3.8
Average SG .099 20.8 5.3 4.3 2.8 0.5 1.5 3.7

Shooting comparison

FG% 2FG% 3FG% FT% eFG% TS% FGA PPS FTA
Morris 61.1% 66.7% 55.6% 80.0% 75.0% 76.7% 12.7 1.72 3.5
Kanter 33.3% 33.3% 0.0% 72.7% 33.3% 43.8% 13.7 1.11 8.4
Brooks 48.9% 52.6% 33.3% 85.7% 52.1% 57.4% 26.5 1.30 7.9
Leuer 63.6% 63.6% 0.0% 100.0% 63.6% 70.5% 12.3 1.64 4.5
Rubio 61.5% 60.0% 66.7% 75.0% 69.2% 72.6% 7.3 1.85 4.5
Average C 48.4% 48.9% 34.7% 66.0% 49.0% 52.9% 13.8 1.24 5.5
Average PF 46.5% 49.2% 30.2% 71.0% 48.6% 52.6% 16.6 1.20 5.4
Average PG 41.8% 44.9% 34.2% 80.0% 46.8% 51.8% 16.5 1.17 4.9
Average SG 42.4% 46.5% 34.3% 77.5% 48.2% 52.4% 17.8 1.17 4.7

 

As you can see, Leuer was indeed spectacular in his 43 minutes, basically shooting and rebounding the hell out of the ball, blocking shots, picking off passes, and rarely turning it over. Seems like the kind of thing that would make me want to give him a little more burn to see if it’s the real thing, right, coach? Rubio will likely thank Mr. Skiles in his award-acceptance speech.

Rubio’s main strengths are his exceptional passing (he’s 4th in the league in per48-assists. No, not rookies, the league) and rebounding (he’s 6th among point guards in rebounding), and the fact that he doesn’t take bad shots (who said this guy can’t shoot?). His turnovers are a bit high, but the good news is that this isn’t really because he makes bad decisions; you rarely catch him doing the nonsense that Timberwolves fans have grown accustomed to from their point guards, like picking up the dribble too early, driving mindlessly to the hoop without knowing what to do when the inevitable help defender comes over, trying passes from impossible angles (although his passing skill does give him wider angles than most). No, most of his turnovers have come from being double- or triple-teamed by big athletic wing men named “James” or “Wade”. In other words, he’ll probably need some time to get used to just how big and fast NBA defenders are compared to European players, after which he’ll get a better feel for what won’t work in this league, and I expect those turnovers to come down. One thing that fans need not worry about is his handle or his speed — he’s way ahead of the pack on both of those, which is why he looks spectacular in the pick-and-roll; he’s great at splitting the double-team if the help defender traps, and he’ll run circles around bigs that don’t show hard.

The AllGeek Team:

WP48 PTS REB AST TO BLK STL PF
James .535 42.8 9.7 9.1 3.9 1.9 3.6 2.6
Lowry .354 19.6 9.2 17.0 5.2 0.7 3.7 4.8
Anderson .311 29.0 9.7 1.5 0.7 1.1 0.7 4.5
Harden .353 25.1 9.4 6.3 1.6 0.3 0.9 3.1
Ginobili .590 36.8 7.9 6.1 3.7 0.9 2.3 2.8
Hawes .388 17.3 18.0 5.8 3.2 2.5 1.8 3.2
Average C .099 17.2 12.9 2.0 2.8 2.2 1.3 5.5
Average PF .099 20.0 11.8 2.6 2.5 1.2 1.5 4.7
Average PG .099 19.3 4.9 8.2 3.9 0.4 2.0 3.8
Average SF .099 18.9 7.4 3.2 2.3 0.8 1.5 3.6
Average SG .099 20.8 5.3 4.3 2.8 0.5 1.5 3.7

 

Shooting comparison

FG% 2FG% 3FG% FT% eFG% TS% FGA PPS FTA
James 59.8% 60.5% 0.0% 82.9% 59.8% 66.0% 26.6 1.61 13.3
Lowry 40.5% 52.4% 25.0% 90.5% 45.9% 57.3% 13.7 1.43 7.8
Anderson 46.6% 52.4% 43.2% 88.9% 60.3% 62.9% 21.6 1.34 3.3
Harden 42.2% 56.5% 27.3% 87.8% 48.9% 63.5% 14.1 1.78 12.9
Ginobili 60.5% 68.4% 54.2% 93.3% 75.6% 79.6% 20.0 1.84 7.0
Hawes 67.6% 69.7% 0.0% 50.0% 67.6% 67.1% 12.3 1.41 1.4
Average C 48.4% 48.9% 34.7% 66.0% 49.0% 52.9% 13.8 1.24 5.5
Average PF 46.5% 49.2% 30.2% 71.0% 48.6% 52.6% 16.6 1.20 5.4
Average PG 41.8% 44.9% 34.2% 80.0% 46.8% 51.8% 16.5 1.17 4.9
Average SF 42.6% 45.3% 36.4% 80.2% 48.2% 52.9% 15.9 1.19 4.5
Average SG 42.4% 46.5% 34.3% 77.5% 48.2% 52.4% 17.8 1.17 4.7

 

Point Guard: Ty Lawson was a scoring machine shooting 66% from the field. Lawson’s play was so good that no one seems to be noticing that Galinari has been terrible.  But I gave the nod this week to Kyle Lowry. Both were great, but Lowry’s greatness was more “traditional” for point guards, racking up insane assist numbers, getting to the line a lot (more than Lawson, on far fewer shots), and putting up spectacular rebound numbers for a PG.

Shooting Guard: I suspected this would be James Harden’s break-out year, and things are looking great so far.  Harden produced 1.1 wins in 153 minutes by crushing the average 2 guard in every category but 3-point shooting. He had nearly as many free throws as field goal attempts, which is insane.

Small Forward: We’ve already covered our starter, LeBron, but my 6th man this week is Manu, who probably would have won player of the week if he’d had more minutes. Manu’s shooting efficiency was off the charts last week. There was simply no more efficient scorer in the NBA, and he managed to dish out quite a few assists along the way, all while putting up his usual above-average defensive numbers.

Power Forward: Man was I surprised that this wasn’t Kevin Love.  Ryan Andersen…well, let’s be honest, he didn’t do a ton of stuff but he just shot the hell out of the ball. 43% from downtown is pretty good efficiency from your power forward, and it’s also nice that he pretty much never turned the ball over. His other numbers were not that great though, so I don’t predict he’ll be in the Geeks of the Week very often. Honorable mentions to Love and Kris Humphries.

Center: What can one say about Dwight Howard, other than….wait, Spencer Hawes!? What the hell? Yeah, turns out Howard had sort of an off week, with a WP48 of only .340 (slacker) and Hawes…well…I think congress should be gearing up for the inevitable steroids inquisition. Hawes rebounded like Kevin Love (he can DO that? Why doesn’t he do that all the time!?), and shot like Hakeem Olajuwon…wait, he shot like the Dream probably shoots when the Dream is dreaming. 70% FG are you kidding me? How did he not get to the line more? Hack-a-Hawes should have become a tactic at some point. And, oh yeah, he was passing the rock like Larry Bird out there, and swatting shots like flies! What the hell has he been eating in the off season?

It was a fun week looking at the numbers, I look forward to doing this every week. Let me know if I missed your favorites in the comments.