Archive for December, 2010

Using a simple method of estimating pace, we can estimate Offensive and Defensive Ratings for teams before 1974. Let’s start with Bill Russell’s famed Boston Celtic defense. Included is the year before he joined the team and the year after he left. The league rank also includes the number of teams (eg 1/8 is first of 8 teams).

*Russell played from 1957-1969.

(1) The Celtics led the league in defense in 12 of Russells’ 13 years.
(2) From 1958-1966 they dominated the league defensively like no team I can find for a 9 year period.
(3) From 1961-1965 the ran off 5 consecutive historically dominant seasons. Look at those numbers.
(4) Before Russell they were a bottom defensive team and immediately jumped 6.3 relative points and 8.0 raw points to the top.
(5) After Russell they dropped to the middle of the pack, losing 6.2 relative points and 10.1 raw points.

According to Neil’s method at B-R, who is slightly underestimating Boston’s pace relative to the simple method (because he’s assuming fewer turnovers are in play), those uber-dominant Celtics teams are the 3rd, 5th, 6th, 8th and 13th best defensive teams of all time, relative to competition. And there’s nothing remotely comparable in NBA history for such sustained defensive dominance.

Although this is just a cursory glance, deeper investigation supports the notion of Russell’s defensive prowess, both at USF, in Boston when he was injured, and his impact on the 1956 Olympic team.


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We can use a simple method to estimate pace before turnovers were tracked, which is using the numbers of filed goals and free throws attempted by a team and assuming turnovers are a constant based on the assumed league average. That gives us a pretty accurate estimate of pace – at least in the immediate years before 1974 – and from there we can estimate offensive and defensive ratings back through the 60s and early 70s.

Neil Paine over at Basketball-Reference.com used a regression method to estimate pace, but that maps onto the 37 years of turnovers we have. The only potential issue with that is that turnovers were decreasing through the 70s and early 80s — perhaps as a result of tracking them? — and basically stabilized in the last 25 years. Neil’s data assumes that teams from the early 70s and 60s turned the ball over closer to recent rates, despite a trend toward more turnovers at the time we lose track of that data. Nonetheless, his estimations as far back as the early 60s are still within ~2 possessions of using my simple method, so regardless of method the margins of error should be fairly small. It would take an extreme shift in the league turnover average, or an historically outlying team (30 TO/game or 10 TO/game) for the method to be off by any significant margin.

In other words, FGAs and FTAs give a pretty accurate picture of how fast a team plays. In this case, here’s the formula for the simple method of pace estimation:

( Team FGA’s + Team FTA’s * 0.44 ) / Games Played / Turnover Constant

I’m using 0.974 for the turnover constant. If we had reason to believe that turnovers increased more and more as we went farther back in time, then the constant should grow smaller and smaller. There’s no evidence to show this, so I use 0.974 as the constant before 1974.

NB: that this means the further back into the 60s (and 50s we go) the wider the confidence intervals will be for the estimations.

Neil’s Regression vs. The Simple Method

If we run Neil’s regression method for 1974, the first year turnovers were recorded, it estimates four NBA teams within one possessions of their actual number, with a mean absolute value of differences* of 2.28 and a standard deviation of 2.57.

*This is the mean of all the individual differences between the actual pace and the estimated pace, taken as a positive value.

Running the simple method estimates seven teams within 1 possession of their actual number, with a mean absolute value of differences of 1.40 and standard deviation of 1.56.


An even more accurate method, available from 1971-1973 only, is to average the simple method using opponent’s data as well. For example, in 1975 this produces an average error of 1.10 and a standard deviation of 1.28. But again, that’s only useful for three seasons.

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There are a number of great debates in NBA history. West-Oscar. Magic-Bird. Garnett-Duncan. Kobe-Wade is another. In the spirit of this year’s ridiculously hyped matchup between Miami and Los Angeles, here’s my take on the two best shooting guards since MJ…

First, Kobe does and should always win a career comparison though, as he crushes Wade in longevity. Let’s get that out of the way. After that, it’s a lot more interesting. Who was better at their peak? Who was better during prime years?

The statistics*

*All statistics in this post are per 75 possessions played

Kobe is averaging 26.9 pts 5.6 reb 5.0 ast 1.6 steals 0.5 blocks and 3.1 TOV on +2.8 TS% (relative to league average) for his career.

Wade is averaging 26.9 pts 5.3 reb 6.9 ast 1.9 steals 1.1 blocks and 3.9 TOV on +2.9 TS% (relative to league average) for his career.

Those numbers are eerily similar; Their scoring rates differ by 0.002% for their career. Wade edges Bryant in Win Shares/48 .190 to .188. Bryant edges Wade in career ORtg 112 to 111. So the career averages are telling us it’s really close, and I agree.

But what about their prime years? For the sake of this comparison, let’s do this 2 ways. First, the overall prime of each player (01-10 for Bryant, 05-10 for Wade):

Again, these numbers are quite similar. Kobe played 31% of his games alongside Shaq (01-03), 9% of his games in the 2004 quartet of future Hall of Famers, 37% of his games as the lone assassin (05-08) and another 24% of his games with Pau Gasol.

Wade played 42% of his games with Shaq (05-07) and 58% of his games as the lone assassin (07-10). More shots could be expected without the presence of an all-star big, but in the case of Kobe (and to a lesser degree Wade) it didn’t matter too much. Both players did see an increase in TS% playing alongside O’Neal.

Unfortunately, this comparison includes play that we don’t really want to compare. Kobe was injured in 2005. Wade in 2008. Whether they averaged 20 pts/75 or 4 shouldn’t change the overall quality of these players when healthy, and that’s what we’re interested in. Instead, let’s compare them since 2005 — with the new perimeter rules in place for both players — and remove the injury periods for each player (2005 and calendar 2010 for Kobe, 2008 and post All-Star game 2007 for Wade):

So finally we see some discrepancies. Note that Wade played with Shaq in 2006 but was essentially on his own from then on. When healthy in 2007, he put up a preposterous normalized line of

30.5/5.1/8.3 with 2.2 steals and 1.2 blocks (4.4 TOV) on +5.1 TS%

That’s one of the best lines I’ve ever seen, and given what he did in the 2006 postseason, I’m not sure Dwyane Wade wasn’t the best player in the world for about 12 months between the Spring of 2006 and his shoulder dislocation in 2007.

Bryant’s overall numbers are quite impressive. His 2010 run to start the year was some of his best basketball, before injuries ran him down:

30.6/5.8/4.5 with 2.1 steals and 0.3 blocks (3.1 TOV) on +2.5 TS%

He turns it over less than Wade, but most of that is because of role, as Wade handles the ball more and Kobe simply shoots the ball more. This is also part of the reason Wade’s assists numbers trump Bryant’s; he’s acting as the primary creator with the ball more. And he drives to the basket more – more on this in a moment. Then again, I happen to think Kobe protects the ball better than Wade and Flash creates more than Bryant, so the stats are telling us something that jibes with observation.

Playoff Statistics

Next, there’s the playoff performances. Here’s where the comparison grows extremely interesting for me. Career playoff numbers*:

*Relative TS% here is calculated by taking actual TS% and subtracting it from the weighted average of opponent’s TS% against in the regular season.

Eek. Here we go again. Note Wade’s large increase in relative TS% in the playoffs. He’s now outscoring Kobe and outshooting him. But again, this includes data from pre-prime years. Let’s use the 2005-2010 range again, removing Wade’s injured playoff games in 07:

Here’s where the statistics begin to reflect the differences I see in the players. Kobe and Wade are extremely similar in many regards. But Wade’s increased performance in the playoffs thus far has been fairly astounding. An output of 28 pts/75 on +5.8% relative TS% is really just staggeringly good. Meanwhile, Bryant’s scoring declines while his relative efficiency stays the same. Big edge to Wade here, and we’ve seen displays from him that have generated this edge: 2006 v Detroit, v Dallas, 2010 v Boston, to name a few.

Advanced statistics

Finally, there are the advanced statistics. I’m not a PER or Wins Produced guy. So let’s quickly look at Wins Shares, on/off and raw +/- numbers from when they are available.

EDIT: I’ve found the Adjusted Plus-Minus average from Joe Ilardi, 2003-2009:

  • Wade +7.99 (+0.99 on defense)
  • Bryant +7.07 (-0.55 on defense)

Top 5 seasons by Win Shares/48:

  1. Wade 06 0.239
  2. Wade 09 0.232
  3. Kobe 06 0.224
  4. Wade 10 0.224
  5. Wade 07 0.219

82games.com has tracked on/off since 2003. Kobe’s 03-10 on/off is +8.97. Wade’s (04-10) is +8.51. Here are the top 5 seasons by on/off:

  1. Wade 06 15.5
  2. Wade 10 14.1
  3. Wade 09 14.0
  4. Kobe 06 12.4
  5. Kobe 10 12.3

In the playoffs, a lot is determined by team success. We’re also dealing with small samples. Nonetheless, here are the top PS performances by these two based on on/off, with offensive net difference in parentheses:

  1. Wade 06 +22.0 (+22.0)
  2. Kobe 03 +16.5 (+4.5)
  3. Wade 05 +16.8 (+14.5)
  4. Wade 09 +16.8 (+10.9)
  5. Kobe 09 +11.9 (+14.5)

Interestingly, both of their +/- numbers decline in the postseason. I’m not wild about including the small postseason sample sizes, but just for posterity, the raw +/- from the playoffs, 04-10:

  • Kobe +3.3
  • Wade +3.8

Beyond the Numbers

Now that I’m dizzy from that potpourri of numbers, let’s discuss some actual basketball. The most germane element here, from what I gather, is that people don’t understand that Kobe takes harder shots than Dwyane Wade. And that matters. It’s also one of the very reasons Kobe’s so revered.

But I want a player who can generate easier shots — that’s Jordan’s biggest advantage over Bryant despite the stylistic similarities in their games. It doesn’t matter if Earl Boykins can make shots from all over the court in all sorts of compromising positions and all that Shaq can do is dunk…if Shaq can make enough of his attempts dunks. Consider the numbers:

For their careers, they’ve each averaged close to 22 “attempts” (FGA’s + 0.44 coefficient for FTA’s) per game. If Bryant takes five incredibly difficult shots per game, by his own choosing, and he makes two, we might ooh and ahh at the two he makes. It doesn’t change that it’s a low-percentage shot.

Now let’s say Wade takes one difficult per game and never makes it. All of their other attempts are converted at exactly the same rate, let’s say slightly higher than Wade’s career average, or 59% TS%. That would give us the following results:

  • Wade: 24.78 ppg on 56.3 TS%
  • Kobe: 24.06 ppg on 54.7 TS%

That’s not an insignificant difference, and that’s exactly what happens with Kobe Bryant. This is essentially the difference between Wade and Bryant as offensive players. Wade gets to the rim more and takes higher-quality shots.

Incidentally, it’s also why someone like LeBron James scores so efficiently; He draws fouls and scores at the rim abundantly. (Last year, 601 attempts at 71.2% at the rim, according to NBA Hot Spots. In 2006, Bryant took 502 attempts at the rim at 58.4%)

Finally, there’s an intangible difference between these two players. Kobe has been more durable. But he’s also been quite disruptive on more than one occasion. Like Wilt Chamberlain, his attempts to “prove” things at times, like not shooting in Sacramento, is less than desirable to me. Feuding with players and coaches is less than desirable. I don’t mind demanding a trade, but I do mind someone throwing his center under the bus and not shooting in the second half of a game 7 (with accounts of Bryant yelling at his own bench during that half and standing around idly on 27 possessions, by my count).

Instead of his performance dropping in many key situations, Wade’s improves. I saw Wade in 2005 and 2006 enter a mode of extreme concentration at critical moments, often turning games around in the 4th quarter and leading his team to wins with nearly flawless shooting. I’ve never really seen Bryant do that. I’ve seen Wade torch great defenses that befuddled the rest of the league (Detroit and Boston) and I’ve seen Bryant grow frustrated against similar defenses.

And defensively, Wade has been better over this period. The perimeter barrage that he and LeBron have unleashed in Miami this year are like a Jordan-Pippen Lite defensive tandem. At his best, when he was younger, Bryant was a better on-ball defender. But Wade’s been one of the better defensive shooting guards in the league while Bryant has coasted or been assigned to inferior offensive players to rest.

But these players are extremely close at their best. In the end, I’m siding with Wade here. I just trust him more.

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One common trend in basketball discussions is the misuse of statistics. Since most people lack any formal education in statistics, and since humans fall prey to all sorts of statistical phenomena — Gambler’s Fallacy, for example — it never hurts to reiterate that statistics only capture what they are trying to capture. Nothing more, nothing less.

So how should we interpret the box score in basketball?

The first thing to remember is that basketball is a game of possessions. Adjusting for pace and minutes played is important in normalizing statistics. We want to compare numbers on a level playing field. Without normalizing, comparing raw statistics would be like comparing the speed of two runners in kilometers/hr versus miles/hr, or the averages of an NBA player after halftime versus per game.

Playing more minutes/possessions is indeed meaningful, but not that meaningful. While it’s better to have a star on the court for 90% of the game than 80% of the game, remember that fatigue is an issue and that in some situations, 2 mpg can be explained away simply because one player sat during garbage time of more blowouts.

Furthermore, if two players are both 10 points better per 100 possessions than their backups, the 2 mpg difference will result in a 0.25 increase in team efficiency at today’s pace (~93 possessions per game). In other words, with two superstars of the same value with the same quality backup, one would need to play eight minutes more per game to raise his team’s efficiency by a single point.

As for the specific figures from the box:


Points are not the be-all end-all that some make them out to be, but more importantly they aren’t even a true measure of “scoring ability.” They are, actually, just a measure of points. A number of variables go into a player scoring a basket, primarily who his teammates are and how successful the opposing defense is in defending him.

Think of the multitude of ways to score that don’t represent the same skill set or level of difficulty:

  • Making a contested perimeter jumper off the dribble
  • Scoring in the post against a double team
  • Driving by one’s man — with or without a screen — and finishing at the rim

All of these are ways individuals create offense. They don’t really have the help of their teammates. But then again, sometimes the above actions don’t result in scores, and we see players:

  • Shooting open jumpers or 3-pointers
  • Scoring on an uncontested putback
  • Getting an open layup or dunk

These three situations were assisted by the creation of other people’s offense. The creator drives and kicks to the open shooter because the defense had to sag. The offensive rebounder is suddenly alone because his man went to double a post threat. And if someone gets to play with a great creator who draws defensive attention, sometimes just by magically running to the hoop he will have an open layup or dunk.

Knowing how people generate their shot attempts (and subsequent points) is important in understanding them. Which leads us to…

FGA’s and True Shooting%

Points can’t be properly interpreted in a vacuum. A 30 point game isn’t too sexy if a player took 40 shots to get there. (However it might be more understandable if the player’s teammates shoot poorly as well and are unable to generate any offense. Both of these apply to understanding Allen Iverson’s statistics in Philadelphia.)

It’s standard to evaluate volume (FGA’s) in conjunction with efficiency (TS%), the thinking being that generating more good shot attempts is challenging and we should expect to see a corresponding dip in efficiency. Similarly, we would not expect a player shooting six times a game at high efficiency to maintain his shooting percentage if he were suddenly asked to generate more shots outside of the easy layups his teammates creates for him.


Rebounding % is the best statistic here, although it’s not in the box. Once we adjust for pace or calculate rebound % (an estimate of the rebounds of the total number of available misses in a game), one of the more difficult tasks is to determine impact rebounding. That is, who’s rebounding is truly helping their team more.

Taking boards from teammates instead of from the opposition isn’t terribly helpful. If all six of a guards rebounds are from misses that his teammates were in a position to grab, then his rebounding isn’t helping much. Then again, if all six of his rebounds were boards that the opponent otherwise would have grabbed, he’s contributing quite a bit to the differential. Measuring this is a difficult task.

The final note about rebounds is that positional adjustments and scheme are important to know. This matters quite a bit, as we want players under the hoop to grab more rebounds than players on the perimeter. And if teams don’t crash the glass on offense, we need to account for that in comparing offensive rebounding numbers to teams who do.


One of my least favorite stats. Ideally, we want to understand who creates more offense for teammates. Assists can often be simply passing the ball to a great shooter, a great scorer, or feeding a great post player. These passes border on rudimentary and can result in huge assist totals despite doing very little.

However, assists do a decent job of approximating creation. Some players have examples that swing one way or another. Rajon Rondo has huge assist numbers this season, but many of those are from making the “correct pass” versus creating for teammates. The creators often have many “hockey assists” from the extra passes that their double team draw, and as a result are creating more offense that their assist total would suggest.

Opposite of Rondo, we have someone like Deron Williams, creating more than the numbers would suggest. In last year’s playoffs, Williams tallied 12% more assists/75 possessions than Rondo and had a 2% higher assist rate. However based on my tracking, he created 248% more offense for his teammates than Rondo!


An important stat. Turnovers are worse than missed shots for two reasons: there is no available offensive rebounding opportunity (which results in a second chance 26% of the time) and turnovers often lead to decreased defensive efficiency because of the resulting fast breaks for opponents.

It’s important to know an individual’s role here again. We would expect more turnovers from someone who has the ball more. Turnover % attempts to capture this, and it’s a pretty good estimator from what I can gather.


Another good stat. These are almost the inverse of turnovers, because we know the defender pilfered a possession from the opposition and likely led to an odd-man break for his team. Steals are not a measure of being a good defender, as sometimes players cheat a lot or reach a lot to generate steals, and there is no current statistic for the number of times they are burned on failed steal attempts.

But steals are important and they are generally well-tracked. For instance, forcing a jump and winning it counts as a steal. Although there are times deflections for turnovers are missed.


Blocks is the final major stat in the box, but I find them somewhat deceiving. For bigs, blocks are, yes, just a measure of blocks. It’s a moderate estimate of that player’s presence at the rim (usually), but it’s not an indicator that someone is a defensive superstar. Tim Duncan has never finished higher than 3rd in blocks per game, but he certainly was the best post defender in the NBA on more than one occasion. For perimeter players, a small boost in blocks can often indicate some defensive prowess, but again, it’s not a major stat for determining great defensive play, and there isn’t too much inherent value in a block to begin with.

I will probably follow up with another post on some of the advanced statistics and how to treat +/- numbers. But the final piece of the puzzle is always glancing at the team’s stats and using them as context. How good is a team’s offensive or defensive rating? How well do they rebound? How many other scorers do they have? All of these must be factored in when understanding a player’s contributions. More on this later.

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One of the talking heads on ESPN – I can’t remember which, they all look the same at this point – noted that Michael Vick should be the MVP of the league over Tom Brady because of…wait for it…Matt Cassell.

Matt Cassell you say!?

Here is the argument: Cassell won 11 games in 2008 when Brady was injured. So how valuable can Brady be to the Patriots if they just kept on ticking with some backup quarterback?

It’s a reasonable place to start, but as I will explore in many future posts, evaluating an NFL quarterback is just about the hardest task in sports analysis. It’s a complex interaction of 11 offensive players and 11 defensive players and the two respective coordinators playing chess with those pieces. So I won’t be too dogmatic here.

But let’s look at this claim more closely. The 2008 Patriots had an offfensive SRS of 2.3 (11th in the league) against a weak schedule (-2.4). They scored 25.6 ppg and averaged 5.3 yards per play…which happened to be the league average.

This year’s Patriots have the second highest offensive SRS since the 2000 Rams Greatest Show on Turf (12.6), behind only Tom Brady’s 2007 Patriots (a record 15.9 OSRS).

Here’s how the 08 Patriots stack up against the 07 and 10 Patriots:

The New England personnel was similar from 07 to 08, but the results weren’t in the same stratosphere. And Cassell’s rushing attack in 08 netted 142 yards/game on 4.4 ypg. That’s better than Brady’s running game from the previous year, which yielded 115 yards per game on 4.1 yards per carry.

Comparing 08 to this year, Brady isn’t throwing to Randy Moss anymore, but to Deion Branch and two rookie tight ends. There is no Kevin Faulk. Two former practice players – BenJarvis Green-Ellis and Danny Woodhead – comprise the backfield. And they’ve all been quite good, but it’s pretty hard to argue that they’re better than the 07 or 08 supporting cast.

The real kicker is that Cassell isn’t just some backup. He’s emerged as one of the better QB’s in the league this year, posting a 96.2 QB rating (fifth in the NFL after week 15) with 24 TDs. He’s even fifth on Peter King’s MVP ballot this week. So, at least by these standards, Brady’s offenses are That Much Better than what an elite QB did in the same organization.

Matt Cassell isn’t evidence for Michael Vick’s MVP candidacy.* He’s evidence for Tom Brady’s.

*The case for Vick: Philadelphia’s 7.8 Offensive SRS and the Eagles are 8-2 in his full games, averaging 31.6 ppg, 414.7 yards per game and 6.5 yards per play (!). The case against: 2-2 v Sagarin’s top 10 and he missed almost a quarter of the season, which means he’d have to be 33% more valuable than Brady to exceed his contributions.

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After the previous post examining Karl Malone and Kobe Bryant’s statistics in elimination games, I thought it might be worth it to run the same analysis on Malone’s sidekick, John Stockton. Bryant’s won five championships next to Shaq and Gasol. Malone ran up against Jordan and Olajuwon (four times in five years) and he certainly never had a running mate like The Diesel or the Spaniard.

Malone’s No. 2 was John Stockton, and the more I place his career under a microscope, the more I think that Stock might be one of the most overrated players of my lifetime. First, it was the Jazz assist-inflation issue. Second, Stockton’s inability or unwillingness to take over games by using his scoring. And now, the revelation that, *gulp* John Stockton’s performance plummeted when the Jazz faced elimination.

Is it possible that Stockton’s failures created the perception that Karl Malone was a choker? Look at Stockton’s performance in 16 elimination games from 1991 to 1998, with the same criteria that was used in the last post:

Now that is a precipitous drop off. We’re talking Jean Van de Velde levels of misfiring. Stock boosts his rebounding, but otherwise he was dreadful in those 16 games. He’s known for his steals, assists and efficiency…and that’s exactly what disappears in this sample.

His best game of the lot is a 21 point 11 assist performance (7-11 shooting) against Portland in 1992. Fittingly, Utah won that game. In the eight losses, Stockton averaged 15.5 points and 8.4 assists with 2.7 turnovers (all per 75 possessions) while posting a 51.9 TS%.

And yet, somehow, this is hardly ever mentioned with Stockton and beaten to death with Malone. In his recent “Book of Basketball,” ESPN’s Bill Simmons ranked Malone 18th and Stockton 25th, all-time. He wrote:

Fatal flaw: The deer-in-the-headlights routine in big games. Time and time again, he came up short when it mattered.

Only he wrote it about Karl Malone. Maybe it’s not Malone whom history should chastise for failing to produce in big games. Maybe it’s John Stockton.

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Winning can do wonders for an individual’s reputation in a team sport. I have long contested, with great resistance, that the conflation between individual results and team results plays tricks on even the most objective minds. Kobe Bryant has a history of falling short the bigger the situation (playoffs, Finals, game 7’s, GW shot attempts), yet has garnered a reputation as one of the best clutch players ever. Conversely, Karl Malone is seen by many as not just a failure, but a true choker. People often describe their perceptions of facial expressions in these situations:

Kobe looks like he wants it. Look at his face!

Malone looks scared. Look at his face!

It’s not exactly a scientific approach. And it’s one that preys on our cognitive biases. Bryant won early in his career with a great overtime performance, in a game everyone was watching (game 4 of the 2000 Finals). Malone was labeled as a failure early on. We then sought out to affirm those opinions, and selectively remembered what supported them and ignored what didn’t. Winning creates the perception of “clutchness” and alleviates the appearance of choking, regardless of how the individual plays. Peyton Manning was labeled a choker, but then lost that moniker after winning a Super Bowl…in arguably his worst postseason performance.

But back to Malone. One thing that stood out to me when combing back through his career was just how many big games he had in the playoffs and just how little help he had. Indeed, he even tormented some teams in the West. Our lasting memory of him is disappearing in the final minutes of Game 6 in 1998 and MJ stripping him on the penultimate play. But no one remembers the absolute gem he had to force that game in the first place…

Curious, I thought it would be interesting to compare Malone’s performances in elimination games from his highest profile period to Bryant’s elimination game performances. After all, what game is bigger than the one you can’t lose?

Before I present all the data, let’s just look at the true shooting percentage numbers:

Reg Season TS% Elimination Game TS%
Player A 55.6% 50.5%
Player B 58.7% 55.3%

Stop and think about those numbers telescopically. Player A drops over 5% in TS% – a catastrophic dip – from above average to well below average. Player B drops 3.4%, but still maintains a figure comparable to Player A’s regular seasons figure, and is still well above average. Which of those players would you say is a choker? Which of those players would you say shoots poorly when his team is facing elimination?

Player A, of course, is Kobe Bryant. Player B, Malone. Below are their full statistics in elimination games, for Malone 91-98 (Basketball-Reference doesn’t have playoff logs before 91) and for Bryant 00-10 (essentially Bryant’s prime). Statistics are normalized per 75 possessions played. Regular season averages are weighted based on the percentage of elimination games from that season. (eg 4 of Malone’s 16 elimination games are from 1998, so 25% of his regular season averages come from 1998.)

Wow. Kobe’s scoring, shooting and assists drop heavily. He has a slight increase in rebounding. If that’s not surprising enough, Malone actually increases his scoring in elimination games while significantly reducing his turnovers. His rebounding goes up as well.

Bryant certainly faced tougher defenses, but the Jazz also met plenty of stingy defenses. For an idea of the difference between a 101.6 DRtg and a 103.6 DRtg, that’s about the disparity between this year’s 6th-ranked Bulls defense (101.8 DRtg) and this year’s 10th-ranked 76ers defense (104.0 DRtg). Those Bulls teams allow an opponent’s TS% of 52.0% while the Sixers is 52.4%. So the difference in defensive quality explains little of the difference in performance.

Of course, there’s also the fact that Malone was the primary focus of every defense he faced throughout those years while Bryant had Shaq by his side for six of 13 games and Pau Gasol in another five. Malone’s secondary option was feeble by comparison: John Stockton’s high-scoring playoff game from 1991-1998 was 28 points! (An interesting follow up might be Stockton’s performances in these 16 games.) Indeed only three times did a Jazz player other than the Mailman go over 30 in that 8-year playoff period: Hornacek twice in 1996 (30 each time) and Jeff Malone in 1992 (33).

Yet Malone is heavily docked in all-time comparisons because of some perceived inability to play well at big times. And Bryant seems to get a boost because of it. While statistics certainly do not tell the entire story – ironically, I’d say Malone’s defense was better than Bryant’s as well, but that’s not a statistical debate – they certainly do fly in the face of conventional wisdom in this case.

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