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Archive for February, 2011

The last NBA trade deadline of the current Collective Bargaining Agreement passed with a bang this week, as roughly 10% of the league changed teams (48 players in total).  Carmelo Anthony and Deron Williams were the two biggest names, but perhaps no trade meant more to the landscape of the 2011 postseason than Oklahoma City sending Jeff Green and Nenad Kristic to Boston for Kendrick Perkins and Nate Robinson.

I’ve spent the last year or so having the following arguments with people:

  1. Kendrick Perkins is more important to Boston than people realize; he’s one of the best defensive centers in the league.
  2. Jeff Green’s inability to be a legit third cog is the reason the Thunder aren’t elite. He’s undersized at power forward, doesn’t rebound particularly well and doesn’t shoot well.

And the numbers agree.

After 1250 possessions tracked from last year’s playoffs and this season, Perkins grades out as one of the best defenders in the league. Opponents are shooting just 29.3% when he guards them, one of the best figures in the league. He makes a defensive error about half as often as the average player. Offensively, he is as advertised: a negative with little range and a borderline liability down the stretch.

My player rating — to be discussed in a future post — thinks this a huge win for Oklahoma City and a step back for Boston. Such an estimation is based on the following minutes distribution for each team:

BOSTON:

  • Rondo 38
  • Allen 36
  • Pierce 33
  • Garnett 33
  • Davis 29
  • Green 27
  • Kristic 22
  • West 18
  • Wafer 4

OKLAHOMA CITY:

  • Durant 39
  • Westbrook 35
  • Ibaka 30
  • Perkins 27
  • Collison 26
  • Harden 26
  • Sefolosha 25
  • Maynor 13
  • Robinson 12
  • Cook 7

Specifically, as long as the O’Neal’s remain out of the lineup, the metric predicts a point differential drop of just over three points per game (Deltone West’s return not included). Ouch. Conversely, it predicts an even greater improvement for Oklahoma City, although still leaves them slightly short of Boston as a team. Obviously, each team’s SRS will be something to follow closely over the final quarter of the season.

The Celtics have more question marks now besides the O’Neals. West is being reintegrated into the lineup, so it’s possible he might pick up some offensive slack. More germane, though, is that Jeff Green is expected to log healthy minutes at small forward, a more natural fit for him.

Green started five games at the 3 back in 2009, averaging 14.6 points, 5.0 rebounds 2.2 assists on 50.3% True Shooting in such games. According to 82games, he’s played 10% of his minutes this year at small forward, with terrible offensive results and excellent defensive ones. Green showed similar defensive strength and offensive ineptitude at small forward last year.

Most of our remaining information on Green is at power forward. And there, he hasn’t looked good.

His opponents have shot 43.6% from the field when Green is guarding them — slightly higher than league average. His other defensive figures are marginal, at best. Offensively, Green’s outside shooting has fallen off, down to 30% from 3-point land this season. He rarely earns trips to the free throw line and rarely creates opportunities for teammates.

Ostensibly, Danny Ainge claims the Celtics need to score more to win. It’s possible that green will be a good fit for Green, and that Rajon Rondo, Paul Pierce and Doc Rivers can help his offense improve.  Only, nothing about Green’s history suggests that he’s going to help too much on the offensive end.

A tip of the cap to Sam Presti for landing a physical defensive presence in exchange for a feeble one.*

*Perkins will be a free agent at season’s end, so the thinking is Boston wanted something in return instead of losing him to free agency. He also has an injury history that may make the move look good down the road.

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The traditional stat for offensive usage looks at how often a player either shoots or turns the ball over. Which means passing and creation isn’t factored into any a player’s “usage.” Using Opportunities Created, a measure of offensive creation, it’s possible to estimate a player’s contribution, or “offensive load:” the percentage of possessions a player is directly or indirectly involved in a true shooting attempt, or commits a turnover.

In other words, the higher the offensive load, the greater the role in the offense. It’s a good way to see who really is “carrying” an offense, so to speak. Here’s an example breakdown of a team’s offensive load, using last year’s Los Angeles Lakers in the playoffs:

The mathematically inclined might be asking: “doesn’t this mean a team’s total load can exceed 100%? Absolutely – which is a reflection of what is being measured. (The league average per team is about 130 per 100 possessions.) Basketball is a team game, and this method is allotting credit not only to the shooter but also to the creator.

Here are the breakdowns by position from last year’s playoffs:

As we might expect, guards carry the greatest load (they have the ball the most). Centers the smallest share. Not all too different from the traditional usage metric. Below are last year’s leaders in the playoffs compared to their usage rate:

It seems that the most active offensive players make something happen on about half of the possessions they play. “Something,” in this case, being a shot attempt, creating a shot attempt or turning the ball over.

Here is the complete list of load leaders from the 2010 Playoffs of players who logged over 150 possessions.

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When I was growing up, a “closer” was a term reserved for baseball pitchers. Specialists with strange facial hair who were only used when their teams protected narrow leads and needed three outs to finish the game.* Then a closer became someone with enough machismo to finish real estate deals. Kyra Sedgwick turned out to be The Closer. And finally, it devolved into a basketball term.

*I never understood the decision not to use closers when teams were behind by a run. Why opt for a lesser pitcher simply because a save opportunity wasn’t available?

In the NBA, a “closer” refers to star players who play well down the stretch of close games. Give them the ball, and they will guide a team to victory. Simplify the game and ride the best player to victory.

In other words, the best closers are the best offensive anchors at the end of tight games. So naturally, unless there is a drastic difference between normal performance and late-game performance, the best closers will be the best overall offensive players in the game.

Some people believe that clutch performance varies wildly in professional sports. That pro athletes are wired differently, some live for big moments and others shrink in them. And there is quality reasoning behind that thinking. So, when something like this starts rolling, it’s hard to stop its momentum:

It didn’t stop there. Mark Jackson kept calling Kobe Bryant the best closer during game coverage. Skip Bayless has echoed it. This informal 2009 poll of players agreed that Kobe was the King of Clutch.

Kobe’s shortcomings in such situations have been extensively documented. The meme floating around that he’s the de facto best closer/clutch player in the league is actually less erroneous than its evil twin, the Un-Clutch meme. That has been slapped on undeserving players like Karl Malone and Kevin Garnett before, and now it follows LeBron James.

Only LeBron James is plenty clutch. Actually, he’s the best closer in basketball. And it’s not even really close.

In the first batch of clutch numbers I crunched from 82 games, looking at the final 5 minutes of 5-point games or closer, LeBron practically lapped the field. In 477 minutes of closer duty from 2008 to 2010, LeBron’s Cavs were +27.2 per 36 minutes. That is mind-boggling, given that the best NBA teams in history are about +9 per 36 minutes. It’s even more superhuman when one considers how they’ve completely crumbled as a team without James.

He managed to score, rebound and distribute down the stretch of close games while shooting 10% better than league average in eFG%. Holy Superman, Batman! Frankly, he looks like the best player in NBA history based on his closer line.

The next set of numbers looked at playoff performances in such situations. Again, James showed the same pattern: his scoring, shooting and assist numbers spiked. Of the players examined in that post, only one other (Carmelo Anthony) improved his playoff shooting in the clutch, and only Steve Nash averaged more assists. Of course, LeBron scored at nearly double Nash’s rate.

It’s almost as if most of LeBron’s value is disproportionately unleashed at the end of close games. He is, in many ways, the ultimate closer.

Yet the indestructible meme following LeBron is that he’s not a closer.

Some argue that he’s too unselfish at the end of games. But he actually shot the ball more frequently than anyone from 2008-2010, including Kobe Bryant. He has attempted 69 attempts in the final 24 seconds of close games according to this ESPN study, which is about 10 per year. Again, more than anyone on the list.

Even his free throw shooting is refined when he’s closing. 81% on 187 free throw attempts from 2008-2010, up 6.3% from all other situations. He made 20 consecutive late-game free throws this year before missing one two weeks ago. The last time 82games ran “super clutch” numbers (final 2 minutes of a 3-point game), LeBron was in video-game land.

Last week, Kevin McHale opined on NBATV that Miami should have LeBron be a distrbutor down the stretch and let Wade be the team’s closer. Skip Bayless loves to slam his ESPN desk and note how Wade is a great closer and LBJ isn’t.*

I don’t know what it will take to kill those ideas. I suspect the way to destroy the Un-Clutch meme is to win a championship. Hopefully, In the meantime, this is a start.

*If pressed, here are my late-game offensive player rankings since 2003. Note Wade’s absence:

  1. LeBron James
  2. Steve Nash
  3. Kobe Bryant
  4. Manu Ginobili
  5. Chris Paul

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In a prior post, I introduced the concept of Opportunities Created (OC) as a measure of playmaking.

After 104 team games tracked in the 2011 regular season (that’s 52 full games, or about 9,000 possessions of basketball), the correlation coefficient of OC to team Offensive Rating is 0.41. Which makes it fairly relevant. (By comparison, here is a table of correlation coefficients of some other stats from some long-term data.) . As we would expect, creating unguarded shots for teammates is good!

Here are the players from the 2010 playoffs who averaged more than 5 OC per 100 possessions:

Steve Nash, as many might expect, is constantly drawing extra defensive attention and then making passes that lead to open shots. (Spoiler: we see the same trend this year from Nash.) Deron Williams also caused constant havoc, even in defeat, and those two lead the pack by a significant margin. Also note that the best perimeter stars in the game create opportunities at a frequent, and comparable, rate.

Leaders by Round (min 150 possessions)

Nash actually started slowly last year, and wasn’t the leader after one round. He then absolutely dissected the San Antonio defense, and Deron Williams did the same to Los Angeles in defeat. The top-10 in OC per 100 in the first round:

  1. Williams 14.0
  2. Ginobili 12.4
  3. Nash 11.9
  4. Westbrook 11.7
  5. James 11.4
  6. Wade 10.8
  7. Nelson 10.8
  8. Bryant 10.1
  9. Rose 9.1
  10. Parker 8.7

And in the second round:

  1. Nash 18.5
  2. Williams 17.3
  3. Ginobili 12.3
  4. Bryant 11.1
  5. Parker 10.0
  6. Nelson 9.0
  7. James 8.2
  8. Carter 6.8
  9. Johnson 6.7
  10. Rondo 6.0

In the Conference Finals:

  1. Nash 16.6
  2. Dragic 11.8
  3. Nelson 10.2
  4. Bryant 10.0
  5. Pierce 7.8

And the NBA Finals:

  1. Pierce 7.7
  2. Bryant 7.5
  3. R. Allen 5.2
  4. Farmar 4.5
  5. Rondo 4.2

Here is the full list of OC per 100 for players logging over 150 possessions in the 2010 playoffs.

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I like Malcolm Gladwell. Malcolm Gladwell likes sports. My friend Harsh doesn’t like Malcolm Gladwell.

No, that’s not a test of the transitive property, although Harsh doesn’t like spectator sports. Harsh says Gladwell is a pseudo-scientist, that his work is sloppy and his conclusions often flawed. So when Jeremy Britton defended a preseason prediction by Wins Produced (WP) that Golden State would win 57 games this year, my thoughts turned to Harsh and Gladwell.

In 2006, Gladwell wrote a New Yorker article and this subsequent blog post advocating Wins Produced. As I mentioned in my overview of popular advanced basketball statistics, Wins Produced has serious problems as an individual player valuator. Yet despite its massive shortcomings, Gladwell endorsed it. He wrote:

Here’s what I think the real value of the Wages of Wins system is, though. It gives us a tool to see those instances where our intuitive ratings of players may be particularly inaccurate. In my New Yorker piece, I focused on how the algorithm tells us that Allen Iverson isn’t nearly the player we think he is.

That would be interesting and true…if Wins Produced were a good tool. It’s not. It doesn’t actually tell us when our intuitive ratings of players are off. If it did, it would be valuable as a predictive tool. Only, when big elite WP performers change teams, nothing much out of the ordinary happens.

Sometimes, someone like Michael Smith leaves his team and nothing happens at all. At 28, Smith led all full-time players in WP per 48 minutes in 2001, and never made another NBA team again. It wasn’t a case of WP telling us something new and informative, it was just another case of WP generating yet another ridiculous result.

Ah, but how does it produce such outliers? (The full calculation is explained here in detail.) In my previous assessment of the metric, I provided an example of how problematic the marginal values assigned by Wins Produced can be. (eg, how much value it assigns to a rebound or a missed shot.) Those values are generated by a regression on NBA team data, in an attempt to figure out what statistics correlate with winning. It then distributes credit based on those correlations, and does a bit of hand-waving to fit the final result as closely as possible to team wins.

But there are problems with the results because there are problems with the method. Namely:

  1. It’s correlative
  2. It’s limited by the box score

As anyone who has taken College Class 101 knows, correlation does not equal causation. It turns out, team attendance is highly correlated with winning percentage:

Who knew that all an NBA team had to do in order to win was fill the seats!

Then there’s the problem that the NBA box score doesn’t explain everything in the game, mostly ignoring defense. So Dave Berri — one of the founders of the model — crunched some numbers and determined defensive rebounding is related to winning. Only he, nor any WP proponents, apparently, stopped to think about why.

Good defensive teams win basketball games. Good defensive teams force misses. As we know, after a miss there is a rebound. The miss causes the rebound. The rebound doesn’t cause the miss. Unless you think the large crowd causes a team to win, that is.

The correlation coefficient between eFG% against and team defensive rating last year was a whopping 0.89…only there is no traditional individual metric for missed shots against. So the fruits of that labor show up in the rebounding numbers.

Here are some 2010 NBA Correlation Coefficients. As eFG% against declines, win% increases. It turns out opponents missing shots is good!

I wonder, though, if WP proponents think opponents miss shots because players are grabbing rebounds.

What’s surprising about Gladwell’s endorsement of Wins Produced isn’t that it takes a basketball expert to either (a) notice the problems with the results or (b) notice the problems with the method, it’s that anyone taking a small amount of time to read about the statistical model can see its gaping holes.

Gladwell, for my money, is a fantastic story teller. He’s a macroscopic thinker and his ideas are thought-provoking, even when raw or unsubstantiated. But it seems he gets into trouble when he probes too deeply into esoteric fields. After all, he can’t be an expert in everything, so how can he judge the quality of his expert sources?

If Gladwell dug deeper, he would have found that Wins Produced isn’t exactly popular in basketball analytics circles like APBR. He would have found that Berri blocks dissenting opinion from his blog — I was blacklisted in one day after this bizarre exchange with him. When the 2001 NBA MVP is the ninth best player on his own team, either all the coaches and writers are missing something, or Wins Produced is.

Sadly, Malcolm chose the former explanation.

(Note: A toy metric derived in the same manner as WP and able to make the same claims was made in about 10 minutes by this basketball blogger. It’s worth a good laugh.)

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Besides scoring, the major contribution to a basketball offense is playmaking. Or, more specifically, the ability to draw extra defenders away from their assignments. As a measure of how well a player does this, assists leave something to be desired; they are only tallied when a pass is made to a player who scores, regardless of how helpful the pass was. Which is why we need a way to detect who creates open shots for teammates by drawing extra defensive pressure. For that, I use something I call “Opportunities Created.”

The Method

One way to do this is to track any time a second defender leaves his man in order to help defend an offensive player. This can be voluntary defensive strategy or it can be the result of the first defender being beaten off the dribble.

Such events are actually fairly easy to keep track of. Here’s a quick example from a game:

(Yes, I believe all NBA replays should be watched in Italian.) Note the first play of the video, when the ball is fed into Tim Duncan in the post. At about 0:21, help comes to double-team Duncan. This leaves Michael Finley open for a 3. The unguarded shot — regardless of the result — was created by Duncan drawing defensive help away from Finley. That is an opportunity created (OC) by Tim Duncan.

At 13:30 of the video, Dirk Nowitzki drives on Tim Duncan and forces Ginobili to help. The ball is swung around until Jerry Stackhouse ends up with a jumper (despite a nice close-out effort by Finley). That is also an OC by Nowitzki, who originally broke down the Spurs defense, despite it not registering as an assist.

Every OC has to end in some kind of attempt, even if it is a fouled attempt. If there is never a shot (or foul), there can never be an OC. (What exactly would the player have created, then?) OC’s can be registered when a player draws defensive pressure and the following occurs:

  • An open attempt
  • An open “hockey” attempt (extra passing, as in the Nowitzki play above)
  • A foul at the rim on a layup attempt (created by the scrambling of the defense)
  • An offensive rebound putback (created because the rebounder’s defender was forced to help)

The Results

As we would expect, guards create much more than bigs. They have the ball in their hands a lot, drive and dish a lot, and are often the defensive focus of pick and roll action. (It has become popular to “jump,” or trap the pick and roll to prevent the dribbling guard from penetrating or taking an open jump shot.) In last year’s playoffs, here is the positional breakdown of OC’s:

The breakdown by position is similar to data tracked this year as well.

So how well do assists correlate to OC’s? Overall, for the 133 players who logged at least 150 possessions in last year’s playoffs, the average error was 0.86 assists (with a standard deviation of 1.81 assists). Of those 133 qualifying players, the following had the largest discrepancy between assists and OC’s per 100 possesions (number of OC’s per 100 in parentheses):

  1. Rajon Rondo 6.8 (5.6 OC’s per 100)
  2. Ronny Price 5.8 (1.2 OC’s)
  3. Jason Kidd 4.7 (4.7 OC’s)
  4. Luke Walton 4.0 (4.0 OC’s)
  5. Jamrio Moon 3.9 (0.0 OC’s)

and the following players have the largest discrepancy between OC’s and assists (meaning they create more than assists would suggest):

  1. Brandon Roy 5.3 (8.7 OC’s)
  2. Nowitzki 4.0 (8.3 OC’s)
  3. Ginobili 3.3 (12.4 OC’s)
  4. Westbrook 2.8 (11.7 OC’s)
  5. Reddick 2.4 (6.3 OC’s)

I will post the complete leaders from last year’s playoffs in a follow up post.

The Discussion

There is a fairly strong correlation with assists (R=0.83). However, the error rate in certain players is enormous, which was the impetus for the stat in the first place; we want to know who’s creating opportunities, not simply who is passing to good players.

It might be worthwhile to simply track double-teams, but there isn’t always an attempt of some kind after a double team. Sometimes, the ball is re-entered into the post and a second or third double team can come on the same possession. If the player turns the ball over, nothing positive came from the double team.

Future consideration should be given to the defensive version of this metric: “OC’s Against.” Those occur every time a defender has a teammate help him in his assignment. If a player never needs defensive help, it means that he is never responsible for the offensive “power plays” (4 on 3 on the rest of the court) that occur because of an OC.

Another useful follow up exercise would be to look for “ball-stoppers.” Most NBA players and teams are coached well enough to take advantage of the power play provided  when two defenders commit to one offensive player. Every once in a while, a player will allow the defense to recover and lose that advantage by holding the ball or not shooting when he should shoot, allowing a scrambling defense to recover. It’s fairly easy to spot, and seems worth tracking in the future.

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On Sunday, in an 85-82 win over Miami, Paul Pierce didn’t make a field goal. Zero hits in ten tries against LeBron James and the Heat. This might have seemed like a shocking result for a nine-time All-Star.

It wasn’t.

It was merely the low point in a longtime struggle against one of the game’s best perimeter defenders. (In Pierce’s defense, he was coming off of a bout with the flu, had a sprained wrist and left foot injury.)

In their last meeting of the 2006 season, Pierce scored 50 points against James and the Cavs. That was back when LeBron wasn’t a great defender. Since then, they’ve met 28 times in the regular season and playoffs. Pierce’s numbers per 36 minutes in those games compared with his regular season averages since 2007:

Besides the massive drop in shooting, Pierce earns fewer trips to the line and his rebounding declines as well. The result is a somewhat horrific four-year stretch against James in which Paul has averaged 16.6 points per 36 minutes on 50.5% True Shooting.

James has played on plenty of good defensive teams, so it’s not a solo effort by any stretch. Nor does he guard Pierce on every possession the Truth is in the game. But by cursory measures, Pierce struggles against LeBron more than anyone in the league. His second worst career FG% against a team, after Cleveland, is 41.7% against Minnesota.

This was never more apparent than in their 2008 series, in which Pierce was aggressive with LeBron on the bench and nearly invisible with James on the court. In the first six games, Pierce averaged 15.8 points per game on 47.7% TS. Some of his decline can be attributed to exerting so much effort guarding LeBron — something he does well — but LeBron clearly defends Pierce well too, using his size and strength to eliminate Pierce’s advantage over other wing defenders.

The Celtics finally unleashed Pierce in game 7 of that series by using a lot of screen and roll action into LeBron. Something to keep in mind with a probable playoff battle looming again this May.

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In the last post, I looked at nine of the most explosive wing scorers of the past 25 years. In a 40-point game, the ball has to go in the hole frequently, thus, TS% is quite good on average in such games. But what about removing scoring from the equation and simply looking at shooting volume?

High-Volume Shooting

Let’s use field goal attempts to examine what happens when these players shoot a lot, setting the cutoff at 30 or more FGA’s in a game. These are high-volume attempt games, in which efficiency counts more than lower volume games.

Returning to variance, here are the standard deviations for the same nine players in 30+ FGA games. “Stdev” is the standard deviation for the statistic to its left:

Again, LeBron James is a beacon of consistency, although he only shoots 30+ shots about once in every 20 games. LeBron also shoots the ball much, much, much better than anyone else when he shoots it this much. Note the ridiculous TS%.

So does that translate to team success? Actually, no. The ONLY player of these nine perimeter scoring-machines to see his team’s win% increase when he shoots the ball so much is…you guessed it, Allen Iverson. (Kudos if you actually guessed it.) Below are the results, along with frequency of 30-shot games and relative true shooting percentage (Rel TS%):*

This, despite Iverson having a break-even relative TS% (only Wilkins was worse relative to the league environment in such games). Which hits at the volume-efficiency tradeoff argument, because Iverson seems to be a player who can increase his volume — here, 95 of 561 games (16%) with over 30 attempts — and maintain similar efficiency to his normal standard. That’s not a ringing endorsement for Iverson as a team cog, but it certainly helps to justify his role and value in a system like Philadelphia’s.

On the opposite end of the spectrum is Kobe Bryant, whose teams suffer mightily when he shoots the ball a lot. And, unfortunately, he’s done this about every eight games in his career. Bryant’s relative TS% in such games is almost 3% off his normal average in the same time period, and his scoring varies greatly. (How many players have a 40-point difference between their two highest FGA games?)

This is further evidence that good players can shoot too much. All of these stars, except for Allen Iverson, see a drop in their team win% in high-volume attempt games. Some might cry chicken-and-egg; Are the star players suddenly shooting this much because the team is losing, or are they losing because of so much shooting? There is ample evidence that one player going rogue, or worse, forcing shots doesn’t help an offense in the first place. Being behind is no excuse to abandon ship and undertake a flawed strategy.

Coming full circle, as far as I know, there isn’t a single advanced metric that considers variance. Nor is there an advanced metric that takes into account team strength in matters like variance and volume. Means are beneficial, but wins are tallied after 48 minutes. It’s not like overall point differential — while a great predictor — determines playoff seedings. Perhaps we should look beyond averages and weigh consistency and team strength against those averages in individual player analysis.

*Relative TS% and win% difference are weighted by year. For eg, if half of one’s 40-point games were in a single season, that one season’s TS% and win% differential accounted for half the weight in both categories.

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In the last post, I examined different measures of variance in this generation’s Mt. Rushmore of wing players, LeBron, Kobe, Wade and Michael Jordan, all the while keeping in mind that it’s possible for inconsistent play to result in a few more wins on weak teams and fewer wins on good teams.

Of those four superstars, Kobe Bryant had the most games with “inefficient shooting” (under 50% True Shooting) and the fewest games with “efficient” shooting (over 60% True Shooting). However, we ignored the amount of shots he attempted when he was shooting poorly or shooting well. Turns out, all four players shoot more when they’re shooting poorly. And of the group, Dwyane Wade has the biggest increase in FGA’s per 36 minutes in his inefficient shooting games. In order of change in FGA’s per 36 from good games to bad:

  1. Wade +1.2 (17.6  in good shooting games to 18.8 in bad ones)
  2. Kobe +0.9 (19.1 to 20.1)
  3. Jordan +0.4 (21.7 to 22.1)
  4. LeBron +0.2 (18.7 to 18.9)

Before we focus on attempts any further, let’s first look at what happens when elite wings score a lot.

High Volume Scoring

There have been just nine wing players with at least 25 40-point games since 1987 (the beginning of Basketball-Reference’s game logs):

  • Michael Jordan
  • Dominique Wilkins
  • Allen Iverson
  • Vince Carter
  • Kobe Bryant
  • Tracy McGrady
  • Gilbert Arenas
  • LeBron James
  • Dwyane Wade

We have our four usual suspects and five more players who collectively amassed 35 All-Star game appearances and 26 All-NBA nods. Not too shabby. Here is the volume and frequency of 40-point games from this group during their prime scoring years:

Not surprisingly, the greatest scorer in NBA history, Michael Jordan, dropped 40 in nearly one in every five games during his prime years. Yikes. Although Jordan isn’t the most efficient of the bunch in such games. That would be Gilbert Arenas, who boasts nearly 70% True Shooting in his 40-pointers:*

As expected, all these players increase their efficiency in 40-point games. Although Kobe’s shooting numbers are surprisingly low, residing next to someone labeled as an inefficient “chucker,” Allen Iverson. So Iverson and Kobe must not be helping their teams win those big games as much as their contemporaries. Right?

Wrong.

It turns out that Iverson’s teams actually improved the most when he scored 40 or more!*

In Iverson’s 72 40-point games, Philadelphia’s win% improved by nearly 20%. That’s a startling contrast – about 16 extra wins over the course of a season. But why would Iverson’s teams improve so much when he has the lowest relative TS% of the lot?

If we buy the argument that AI’s 76er teams lacked a scorer who could create his own offense — certainly a reasonable stance — then Iverson’s scoring explosions shored up that offensive deficiency and buoyed them to victory more often than his run-of-the-mill 25 or 30-point nights, regardless of the drop in efficiency relative to his peers. (This somewhat echoes Paine’s Monte Carlo run.) Besides, AI’s shooting efficiency in such games is still significantly better than both the league average and his own career average.

There’s also further evidence here supporting the idea that weaker teams are helped more by big performances: Jordan played on the best teams in this time period (win% with MJ in the lineup of .713) and saw the smallest change in team W-L when going for 40. From 1990-1998, once Chicago ascended to elite team status, the Bulls were 68-20 when Michael went for 40 or more, for a .772 win%. Slightly worse than his team’s .779 win% (387-110) when he didn’t go for 40.

Tracy McGrady played on the second worst teams of these nine players (Arenas the worst). When McGrady was in Orlando (01-04) the Magic went 19-11 (.633) in his 40-pointers. 121-144 (.457) in his other games. Then he went to a better Houston Rocket team, and went 7-4 (.636) in 40-point games and 119-66 (.643) in other games.

The same reasoning explains why LA has faired so well despite Kobe’s lower efficiency numbers; Many of Bryant’s games were in 03, and 05-07 when his team needed volume scoring. LA was 50-24 (.676 win%) in his 40-point games in those years, while going 112-119 (.485) in Kobe’s non-40 games. (In the other seasons, a .724 win% in his 29 40-point games and a .715 win% in all other games.)

So these players are helping bad teams with big scoring nights and not doing much for good teams with the same outbursts. Balance, it seems, is indeed better.

Yet we haven’t completely addressed the issue of what happens when players shoot a lot. That is the topic of Part III

*Relative TS% and win% difference are weighted by year. For eg, if half of one’s 40-point games were in a single season, that one season’s TS% and win% differential accounted for half the weight in both categories.

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Last June, Neil Paine over at Basketball-Reference examined consistent vs. inconsistent performances by Kobe Bryant and LeBron James vs. the Boston Celtics. Using one catch-all metric (statistical plus-minus), James and Bryant had similar average performances over the course of their series. But their game-to-game performances varied greatly; James was high-variance — some great games and some awful ones — while Bryant was steadier throughout. If we buy Neil’s simple Monte Carlo simulation, his findings were:

  • Good teams are helped more by a consistent player
  • Average teams are helped more by a consistent player
  • Bad teams are helped more by a high-variance player

This makes sense to a certain degree; Big performances by stars can boost bad teams to wins they otherwise wouldn’t have had, and the bad performances still result in losses they probably would have incurred anyway. In theory, the inverse would hold true for good teams and really bad performances by stars.

Last year’s NBA Finals aside,  Bryant is actually more high-variance than James using measures like points, FG% and GameScore. (GameScore is a rough measure of productivity for a single game.) Below is a comparison of variance between the best wings of my lifetime, Kobe (2001-2010), LeBron (2006-2010), Dwyane Wade (2006-2010) and Michael Jordan (1987-1998):

“Stdev” is the standard deviation of the statistic to its left. If we use a summary statistic like GameScore, LeBron wins the consistency battle handily. Jordan would place second by virtue of his ridiculous 25.3 average GameScore, then Wade and Kobe by the same logic.

If we focus on consistency of shooting and scoring, LeBron wins again. (LeBron outpacing the field is becoming a theme on this blog.) Of course, one could argue LeBron played with a weaker team from 2006-2010, so higher variance would be better when compared to Kobe and Jordan. But unlike Neil’s Monte Carlo run, LeBron’s averages are significantly higher than Kobe’s and Wade’s to begin with.

Kobe, not surprisingly, is higher variance with his FG% — easily the lowest of the lot — and in particular with his scoring performances. But only looking at standard deviations overlooks the importance of the averages. A lower average means more poor shooting games.

EDIT: Bryant’s GameScore standard deviation is 9.5 (mean 22.0) from 2005-2007 on his “weak” teams.

Another way to view consistency is by frequency of games, delineated in a specific range. For instance, we can call games over 60% True Shooting (TS) “efficient” shooting games and games under 50% TS “inefficient” shooting games.

Player Efficient Games (> 60% TS) Inefficient Games (< 50% TS)
Michael Jordan 41.4% 20.5%
LeBron James 40.1% 21.6%
Dwyane Wade 37.8% 26.7%
Kobe Bryant 35.3% 29.3%

Now Bryant’s shooting inconsistency can be seen more clearly. While James and Jordan have an efficient game twice as often as an inefficient one, Kobe shoots well a little more than 1/3 of the time, and shoots poorly a little less than 1/3 of the time. And, if we come full circle to the original claim about consistency helping good teams, that doesn’t bode well for Kobe Bryant’s impact on wins relative to his averages.

For those visually curious, and for the sake of consistency, here is the distribution of TS% for all games played in the respective time frames:

The frequency of games based on TS% for elite wings. Frequency (y-axis) is the percentage of games a player shot a given TS% (x-axis) for the following years: Jordan (87-98) Kobe (01-10) LeBron (06-10) and Wade (06-10).

Of course, none of this accounts for volume — in theory, players should shoot more when they shoot well, and shoot less when they shoot poorly. And that is the topic of the next post: high-volume scoring games.

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