I was excited to see the topic of fouls receive some attention at last week’s Sloan Conference in Boston. Although I’m not sure how I feel about the methodology (confusing) and conclusions (potentially confounded) of that paper. Nonetheless, fouls are a small part of the game that are often overlooked in analysis.

Turns out that drawing fouls is a really good thing. And committing fouls — specifically, shooting fouls — is really bad. Nothing revolutionary there.

On offense, drawing a foul has two effects:

  1. Brings a team closer to the penalty
  2. Causes foul trouble for opposing starters

When a player is in foul trouble, he loses minutes he would otherwise be on the floor (unless he plays for Don Nelson, apparently). Usually, this is on order of 5-10 minutes, as a player sits for a period before he is no longer in “foul trouble.” Occasionally, extreme cases render a player inactive for longer, like Dwight Howard in last year’s first round against Charlotte. Howard averaged around 26 minutes a game when he otherwise would have been playing closer to 40. When starters sit, they are replaced by bench players, who (theoretically) represent a downgrade.

The penalty represents a larger advantage for teams. Every foul before the penalty is 25% of the way to the automatic bonus for a team. Once in the penalty, any foul on the court produces two free throws for a team, which is the most efficient form of offense: The value of an average possession in the NBA is about 1.07 points. The value of two free throws is about 1.52 points.

On the team level, the correlation between fouls drawn per 100 possessions and ORtg is quite strong: 0.56 for last year’s playoffs. For this metric, a “foul drawn” (FD) is only counted when a player is fouled on offense. Setting screens and intentional fouls are excluded.

Here are the leaders from the 2010 playoffs in fouls drawn per 100 possessions, with free throw attempts/100 included as reference:

Clearly, there is a strong correlation between free throw attempts and fouls drawn. This allows a fairly accurate estimate of FD using FTA. However, as is the case with Opportunities Created and assists, it is the outliers who are often the most interesting. Someone like Dwight Howard shoots far less free throws than expected based on the number of fouls he draws because he’s constantly being banged around before the act of shooting, to prevent lobs or on offensive rebounding situations. Here is the full list of players ranked by FD from the 2010 playoffs who played at least 150 possessions.


On defense, committing personal fouls isn’t terribly detrimental to the team. For one, there’s a limit of six per game, and as discussed above, players will simply head to the bench if they foul too much. There is almost no correlation between personal fouls and team defensive rating.

However, there is a correlation between Shooting Free Throws (SF) and defensive rating (0.44 after 104 team games of tracking this year.) This information can be extracted from the play-by-play for comprehensive analysis by noting how many free throws a player gave to the other team by fouling. (eg 3 SF result from fouling on a 3-pointer.)

Again, free throws are the most efficient mode of scoring, so sending a player to the line is spiking the opponent’s offensive efficiency as described above. In short, shooting fouls are bad.* Using the 150 possession qualifier, here is the complete list of players from last year’s playoffs who caused the most free throws for the opposition (SF per 100 possessions).

*The obvious exception is “intentional” fouls to prevent layups or easy attempts around the goal from horribly inefficient free throw shooters.

82games just updated its numbers for the 2011 season, and of particular interest is Miami’s performance in clutch situations (5 point game or closer in the final 5 minutes). As far as I know, no one publishes team stats for these situations.

In lieu of that, we can ballpark a team’s clutch performance by looking at the team leaders in clutch minutes. Included is the percentage of clutch minutes that player has played for his team, and the player’s overall plus-minus for the season for comparison.

From 82games.com through 3/05/11

So it’s not like Miami is crumbling or lost down the stretch of these games. They are actually about 15 points better than opponents over the course of a game using this criteria. More surprisingly, Miami’s offense with James on the court (95% of its clutch minutes) boasts an Offensive Rating of over 120. By comparison, the Lakers ORtg with Bryant is just under 109. Boston’s with Pierce is 108.5. Chicago’s with Rose 108.

Hmm. Maybe Miami’s clutch problem is against elite teams only? The Heat have played 12 competitive games against the eight best teams this year (with a 2-10 record in those games). In the final two minutes of those games, Miami’s average point differential is -0.2. Basically dead even.

In the final five minutes of these games their average point differential is -2.4. That’s -29 over 60 minutes of play; Finally some evidence of close-game failures. But even 79% of that difference comes from two games against Orlando in which the Magic bombarded Miami down the stretch (in the November 24 and February 3 games). Here’s the Heat’s complete breakdown against the top-8 by section of the game:

It’s fair to say that Miami’s struggles down the stretch are overblown. With the exception of one incredibly specific, small-sampled criteria: The final 10 seconds of games when trailing by three or less. According to an ESPN graphic posted after the game, Miami is just 1-18 shooting in such scenarios.

Is it plausible that the Heat will continue to shoot 6% in these situations for the remainder of the season and the playoffs? Unlikely. Right now, they’re on the (extreme) wrong side of variance in a small sample size (18 shots).

That doesn’t mean there aren’t legitimate problems in South Beach. Only, they have a lot less to do with close games and a lot more to do with size and depth. Which, of course, were the original problems in the first place when they cleaned house in the offseason.

The Heatles aren’t losing these games in the final seconds. They are losing them in the 3rd quarter (and into parts of the 4th). And there’s no reason to believe that isn’t a direct result of playing three on five most of the time.

Miami was thin enough heading into the season before Udonis Haslem’s injury. It has now logged over 1000 minutes at center from Juwan Howard and Erick Dampier. Combined age: 73. (Yes, they still play basketball.) Mike Miller has played 500 disappointing minutes returning from injury.

Miami’s biggest problem heading into the playoffs this year isn’t the end of close games – that issue has been greatly exaggerated, and it will improve with experience and, statistically, by default. The Heat’s biggest problem is the same one they’ve had all season: size and depth.

Errors in True Shooting%

As discussed before, True Shooting percentage is an estimate of points per shot. But it’s not exact, counting a free throw attempt as 0.44 shots. Why isn’t a free throw 1/2 a shot, you ask? Because of “And One” opportunities, when someone scores and is fouled for one extra bonus free throw. In Marv Albert’s language, it’s known as “Yes, and it counts.”

These are bonus chances after a successful conversion, so to count these free throws as half an attempt would actually be penalizing players for drawing an And One and missing compared to players who never drew the foul at all. To obtain a precise measurement of points per shot (PPS), we’d have to differentiate between And One free throw attempts and the conventional trips to the line. Without doing that, the 0.44 coefficient minimizes error across the league between points per shot and True Shooting percentage.

So how much can TS% be off by measuring PPS? Mathematically speaking, we can observe the following:

  • Free Throw percentage essentially does not affect TS% accuracy.
  • The ratio of And One FTA to total FTA affects TS% accuracy. 12% is perfect accuracy. The smaller the ratio, the more TS% will overestimate PPS. The larger the ratio, the more TS% will underestimate PPS.
  • The ratio of FGA/FTA slightly compounds TS% accuracy. The more free throws taken relative to field goals, the more TS% errors are magnified (both overestimating or underestimating PPS).

The 0.44 coefficient used for FTs in the TS formula is designed to minimize these errors as much as possible. It does that well across the league, But obviously, not all players have the same frequency of 3-point play opportunities.

The way to generate a truly accurate percentage would be to comb through play-by-play data and separate And Ones from other free throw attempts. In 2005 and 2006, 82games provided some And One data we can look at for an idea of how accurate TS% is among high-volume players. PPS/2 is points per shot divided by two, which is what TS% is trying to measure:

% of And1s is the percentage of total FTA that are And1 FTA. PPS/2 is points per shot divided by 2, which is the what TS% is trying to approximate. Error is the difference between the actual player efficiency and his listed TS% using 0.44 for free throw attempts.

As we can see, TS errors are generally small. In the games I’ve tracked this year, Wade and Bryant have And One ratios of around 12% (0.1% error for both) and James is just over 8% (for an overestimation of 0.3%). It would be nice to add And Ones to box scores, or completely track them in play-by-play, but in the meantime, TS% does a great job approximating points per shot.

A poster on the realgm forums named Nonemus recently wondered how everyone’s favorite triumvirate of wings, Kobe Bryant, LeBron James and Dwyane Wade, have stacked up against elite teams in the playoffs. Some of the numbers are worth examining here, namely how these three have performed against defenses separated by quality. Are any of them bottom-feeders? Do they equally suffer against the best defensive teams? Has one played a disproportionately large amount of games against amazing defenses?

First, we need to define elite defenses. Since the rule changes in 2005, only 41 teams have posted a defensive rating of 104 or lower. Which means, on average, a 104 DRtg is about the 6th best defense in the league and roughly three points better than average. Certainly a fair cutoff point with which to work. Similarly, let’s call “solid” defensive opponents those with a DRtg between 104 and 107 (roughly better than average), and “bad” defenses having below average Defensive Ratings (lower than 107).

Using that distinction, it turns out Dwyane Wade has played the majority of his playoff games against elite defenses (68% of all games versus such teams). LeBron has played 42% against top defenses and Kobe 38%. Below are their statistics, per 36 minutes, broken down by defensive quality. (GmSc is their Game Score).

Fittingly, Bryant and James show improvement the easier the defensive foe. Wade, however, has some surprising results. His performance versus elite D and non-elite D isn’t too different. (Note, those six games against “solid” defenses are from the 2006 Finals against Dallas.) He quite clearly outperforms the other two against elite defensive teams, even ramping up his three-point % and assists.

LeBron’s history against elite defensive teams is a tale of two players. In his first 15 games against such opponents, James struggled mightily, to put it mildly. He was dreadful, posting a 45.9% TS percentage and averaging over four turnovers per 36 minutes. Hide the women and children.

Below are his splits — the first 15 games are against 2006 Detroit, 2007 San Antonio and the first four games against Boston in 2008:

So James has been a different player against top defenses since game 5 against Boston back in 08, scoring and shooting better than he has even against solid defenses and posting a monstrous Game Score that tops Wade’s or Bryant’s GmSc against even the weakest defenses. The lesson, as always, is that LeBron James has been really good for the last few years.

Here is how each player’s series looks visually, measured using Game Score. The x-axis is a team’s defensive rating and y-axis the players average GmSc for the series:

The coefficient of correlation between Game Score and Opponent Defensive rating is as follows for each player:

  1. LeBron .582
  2. Kobe .561
  3. Wade .409

Which implies that LeBron’s Game Score by series is the most heavily influenced by opposing defense and Wade’s is the least affected. That is, the more positive correlation suggests that as the defense is worse, the performance better. That bottom-feeding trend is the strongest in LeBron’s case, and can be seen above with all his data points in the upper right quadrant.

All of this begs the question: Is it better for performance to vary according to defensive strength, or better to remain consistent regardless of opponent quality? In his only two series against bad defensive teams, Wade shows no appreciable improvement. LeBron and Kobe feed off bad defenses, to a certain degree.

In the playoffs, teams can expect to encounter difficult defenses on the path to a title. Since the inception of the three-point line in 1980, only five teams with an SRS over 6 had a defensive rating over 107. And 58% of those 6+ SRS teams qualified as “elite,” with a DRtg of 104 or lower. Which means in this case, Dwyane Wade may provide a distinct advantage on the game’s biggest stage.

Here is a complete list of each players series against elite defenses in the playoffs since 2005.

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:


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


  • 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.

Offensive Load

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.

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

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.

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.)

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.