NBA

Biased Stats in the NBA

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One of my favorite NBA-related articles is Tommy Craggs’ “The Confessions Of An NBA Scorekeeper”, which recounts of the experiences of a scorekeeper named Alex in the 1990s. The article highlights the common occurrence of “stat-padding,” or the practice of inflating the stats (e.g., assists, steals, blocks, and rebounds) of players of the home team. As Craggs writes:

Alex quickly found that a scorekeeper is given broad discretion over two categories: assists and blocks (steals and rebounds are also open to some interpretation, though not a lot). “In the NBA, an assist is a pass leading directly to a basket,” he says. “That’s inherently subjective. What does that really mean in practice? The definition is massively variable according to who you talk to. The Jazz guys were pretty open about their liberalities. … John Stockton averaged 10 assists. Is that legit? It’s legit because they entered it. If he’s another guy, would he get 10? Probably not.”

“The Confessions Of An NBA Scorekeeper”

Alex’s comment on Stockton caught my attention. While I was pretty certain stat-padding existed 20 years ago and and still does to this day, I was curious as to what degree the NBA’s all-time career leaders benefited from this bias.

Methodology

I pulled the top 25 all-time career leaders for each of the following categories from Basketball Reference: points, assists, steals, blocks, and rebounds. This yielded a total of 78 unique players, as some players were ranked on the all-time list in multiple categories. I then pulled the stats for each player, split by home vs. road games.

Note that steals and blocks and were not officially recorded in the NBA until the 1973–74 season. Furthermore, not all statistics were broken out by home vs. road splits until more recently, which means the analysis of bias could not be completed for many of the older stars, including Bill Russell, Kareem Abdul-Jabbar, Magic Johnson, Moses Malone, Oscar Robertson, and Wilt Chamberlain.

Setting the Benchmark (Points)

It’s fairly well-established that teams play better at home than on the road. To confirm this, I measured each player’s points per home game and compared it to his points per road game. On average, players scored 2.8% more points per game at home than on the road, with a standard deviation of 5.1%.

Blue = Average; Green = +/- 1 SD; Red = +/- 2 SD

Strong positive outliers included Tree Rollins, Shawn Bradley, and Mookie Blaylock1Two of these players also belong on the all-time greatest names list. I’ll let you guess which., who were all more than two standard deviations higher than the mean. Jermaine O’Neal was the only negative outlier more than two SDs lower than the mean. Notably, the top six career point leaders were all below average.

I then compared each player’s home vs. road performance for assists, steals, blocks, and rebounds relative to his home vs. road scoring performance. For example, if a player scored, on average, 5% more points per game at home than on the road and grabbed 10% more rebounds per game at home than on the road, then the relative home bias of his rebounding performance would be \frac{1.10}{1.05}-1=4.76\%.

The underlying assumption here is that in the absence of any stat-padding, there should not be significant relative home bias in any of the statistical categories. However, given Alex’s scorekeeping experiences, we would expect to see some degree of bias in all four categories, especially assists and blocks.

My analysis revealed the following results:

Assists

Blue = Average; Green = +/- 1 SD; Red = +/- 2 SD

Relative to the baseline (i.e. points), assists showed a relative home bias of 6.4%, with a standard deviation of 9.6%.

Almost everyone fell within two SDs of the mean, although Theo Ratliff was an extreme positive outlier, albeit on small volume. Note that John Stockton, the all-time assists leader by a long shot, had a relative home bias of only 3.6%, indicating a very low likelihood of stat-padding. On the other hand, Jason Kidd, the second all-time assists leader, had a relative home bias of 16.5%.

Of course, a high relative home bias doesn’t necessarily mean that there was stat-padding going on. Kidd also had an average home vs. road point performance of negative 4.5%. One explanation is that he played more as a facilitator at home while having to shoulder more of the scoring burden while on the road.

Steals

Blue = Average; Green = +/- 1 SD; Red = +/- 2 SD

Relative to the baseline (i.e. points), steals showed a relative home bias of 3.2% (half that of assists), with a standard deviation of 9.3% (roughly the same as that of assists).

Again, almost everyone fell within two SDs of the mean, although Manute Bol was an extreme positive outlier on small volume. Alvin Robertson was also more than two SDs higher than the mean, on much higher volume. Remarkably, John Stockton, also the all-time steals leader by a decent margin, had a relative home bias of only 2.0%, indicating once again that he was the real deal.

By now, you may have noticed that Dikembe Mutombo was more than two SDs below the mean for both assists and steals. It doesn’t really make sense for stat-padding to go the other way, so the likely explanation for negative bias is simply underperformance. The reason why the numbers look so extreme in this case is due to small sample size. Mutombo was an all-time rebounding great who averaged 10.7 boards at home and 10.0 on the road. However, he also only scored 10.3 points at home and 9.4 on the road (benchmark of 9.8%). He had so few assists (1.0 home vs. 1.1 road) and steals (0.4 vs. 0.5) that very small absolute differences in home and road performance led to large percentage biases (-15.4% and -25.4%) relative to his baseline.

Blocks

Blue = Average; Green = +/- 1 SD; Red = +/- 2 SD

Relative to the baseline (i.e. points), blocks showed a relative home bias of 12.3% (nearly double that of assists), with a standard deviation of 19.7% (also nearly double that of assists). Blocks were by far the most biased statistic, as well as the most variable.

There were a handful of players that fell more than two SDs below the mean, while Fat Lever2Another all-time great name., Alvin Robertson, and John Stockton were all more than two SDs above the mean (on low volume). Both Robertson and Stockton had a relative home bias of nearly 80%, or almost 3.5 SDs above the average! So while the Utah Jazz scorekeepers may not have been padding Stockton’s assists and steals, they almost certainly were boosting his blocks…3Take that, Stockton! I finally got you 🙂

Interestingly, David Robinson and Tim Duncan, who both played for the San Antonio Spurs for the entirety of their careers, were between one to two SDs above the mean on relatively high volumes!4Alvin Robertson also played for five season for the Spurs at the beginning of his career.

Rebounds

Blue = Average; Green = +/- 1 SD; Red = +/- 2 SD

Relative to the baseline (i.e. points), rebounds showed a relative home bias of 1.4% (one-fifth of that of assists), with a standard deviation of 4.9% (half that of assists). In contrast to blocks, rebounds were by far the least biased statistic, as well as the least variable.

Given the lower variability, it’s not too surprising that almost all players fell within two SDs of the mean, with no positive outliers and only three negative outliers (on low volume).

Closing Thoughts

In short, the results confirmed our initial expectations. Blocks (12.3% average relative home bias, 19.7% standard deviation) and assists (6.4% Avg, 9.6% SD) showed the most evidence of bias, whereas steals (3.2% Avg, 9.3% SD) and rebounds (1.4% Avg, 4.9% SD) showed the least.

At first, I was surprised that blocks showed significantly more bias than assists. Conceptually, assists felt like a much more subjective stat to record, but the data seemed to suggest the opposite. However, I soon realized this was because of the “Mutombo problem” of small sample size. Simply put, assists occur with a lot more frequency than blocks in the NBA. While many great players average more than five assists a game over the course of their careers (the truly elite average over eight!), very few ever manage to block more than three shots a game.

It’s not uncommon for point guards like Stockton and Kidd to average fewer than 0.5 blocks per game, and in certain cases, significantly fewer than that (e.g., Steve Nash and Tony Parker averaged fewer than 0.1 blocks per game). Therefore, even if there were the same amount of absolute stat-padding for assists and blocks, the relative impact would be much greater for blocks. That is to say, a scorekeeper giving a player an “extra” assist or two every home game when the player is averaging eight or ten assists is going to have a much smaller impact than gifting an “extra” block every few home games if that player is averaging a measly 0.1 blocks a game.

As always, you can find my work here.

This is Post #21 of the “Fun with Excel” series. For more content like this, please click here.

Fun with Excel #12 – A Quick Look at Clutchness in the NBA

Jeff, , , , , , , , , , Leave a comment

With the NBA in off-season and the Euros concluded, it felt like the perfect time to churn out a couple of long-overdue posts. I’ll start with basketball first, and then soccer (which will likely be a two-part post). See, I’m trying to make up for all those months I didn’t write anything!

So What’s This About?

I’ve always been intrigued by the idea of clutchness in sports, this notion that an athlete could elevate his or her performance to a higher level in moments when it really mattered — when a game, series, or championship was on the line. And yet, despite all the statistics we have available in the modern era of the NBA, there is a certain intangible aspect of clutchness that seems driven more by subjectivity than by the numbers. It’s the reason why Kobe is widely seen a clutch player despite him being a far less efficient shooter in crunch time than Michael Jordan and LeBron James. For instance, LeBron has now taken 12 potential go-ahead shots in the final five seconds of the fourth quarter or overtime in the playoffs and made five of them (41.7%), according to Basketball-Reference. Jordan was 5-of-11 (45.5%) in such situations during his career. And Kobe? A mere 1 for 11 (9.1%) (the famous OT win over Phoenix in 2006). Now, this is obviously a very specific measurement of clutchness, and there are countless ways to tweak the criteria in order to support one narrative or another. That being said, most of the existing literature seems to draw on statistics solely to compare athletes to one another, when in reality, one of the core attributes of clutchness is the ability to outperforming oneself.

The Key Metric

To start, I needed to come up with a simple “all-in-one” statistical metric to measure a player’s performance over any given set of games. While PER comes to mind and is readily accessible for the regular season and playoffs from Basketball-Reference, it is rather cumbersome to calculate for any particular set of games. Ideally, I wanted a metric that could be calculated for a player’s regular season performance, overall playoffs performance, and NBA Finals performance (if applicable). Ranking a playoffs performance against others is a relatively straightforward task, but what I was really interested in was the difference between a player’s playoffs performance and his regular season performance, as well as his performance in the finals relative to the overall playoffs. I wanted to gauge how players performed as the stakes increased (i.e. Regular Season –> Playoffs –> Finals). I drew some inspiration from an article written during the 2015 NBA Finals, in which the author came up with a “bare-bones performance metric” that simply added a player’s points, rebounds, and assists on a per game basis. Using this as a starting point, I took things a few steps further by also incorporating a player’s steals, blocks, and turnovers, with turnovers being subtracted from the metric. Moreover, I looked at everything on a Per 36 Minutes basis in order to normalize for differences in playing time. Lastly, I multiplied my metric by a shooting efficiency factor, such that players who scored more efficiently than average were rewarded (factor > 1) while those who were less efficient were punished (factor < 1). The final all-in-one metric looks like this: Adjusted Total Score = (Points* + Assists* + Rebounds* + Steals* + Blocks* – Turnovers*) x (True Shooting %)/(League Average True Shooting %), where the * denotes stats measured on a Per 36 Minutes basis.

The Data Set

In an ideal world, I would want to look at the performance of every single player, but given time and sanity constraints, I started with the 1979-80 season (when the 3-point line was first implemented), and looked at the performance for each of the 5 players who made the All-NBA First Team that season. While All-NBA honors is not a perfect measurement of a player’s value by any means, I felt that it was a decent enough proxy to capture a snapshot of the league’s most elite players. I repeated this process for every season through 2015-16, giving me a total of 37*5=185 seasonal performances. Furthermore, I went back and added the seasonal performance for every Finals MVP (FMVP), if he wasn’t already included (i.e. wasn’t in the All-NBA First Team) for that season. This added an additional 15 seasonal performances, for a total of 200 performances (among 63 unique players). The 15 players who won FMVP but did not make the All-NBA First Team, in case you’re curious, are: 79-80 Magic Johnson, 80-81 Cedric Maxwell, 81-82 Magic Johnson, 84-85 Kareem Abdul-Jabbar, 87-88 James Worthy, 88-89 Joe Dumars, 89-90 Isiah Thomas, 94-95 Hakeem Olajuwan, 03-04 Chauncey Billups, 05-06 Dwyane Wade, 06-07 Tony Parker, 07-08 Paul Pierce, 10-11 Dirk Nowitzki, 13-14 Kawhi Leonard, and 14-15 Andre Iguodala. Another thing to note is that including the five players from each season’s All-NBA First Team necessarily captures that season’s Regular Season MVP as well.

For each of the 200 performances, I calculated the Adjusted Total Score (ATS) for the Regular Season, the Playoffs (if applicable), and NBA Finals (if applicable). Out of the 200 player performances, only 6 failed to make the Playoffs (84-85 Bernard King, 87-88 Charles Barkley, 91-92 David Robinson, 99-00 Tim Duncan, 11-12 Dwight Howard, and 12-13 Kobe Bryant), and out of the remaining 194 performances, 76 made it to the Finals. While it was easy to calculate the ATS for Regular Season and overall Playoffs, I had to manually go through the game logs of each of the 76 performance on Basketball-Reference to come up with an ATS for just the games in the Finals. Because detailed per game statistics were not available from the 79-80 and 80-81 seasons (with data on steals and turnovers missing from the 81-82 season), I could not calculate the ATS for 5 out of the 76 Finals performances. You can take a look at my organized data here.

Takeaways

I examined the performances both on an absolute ATS basis (e.g. ranking all 200 Regular Season performances by ATS) and on a differential ATS basis (e.g. what is the difference between a player’s Playoffs ATS and his Regular Season ATS, and how does that compare relative to other players?). Here are 10 takeaways I found, in no particular order:

1. MJ dominates the Regular Season, but Stephen Curry’s 2015-16 performance was one for the ages

1

Not too many surprises here, but this chart really highlights how much of a monster young Jordan was, capturing 4 out of the top 5 Regular Season ATS performances, all in a 4 year span between 1987-91. Charles Barkley deserves an honorable mention for putting up 3 out of the top 15 performances. Although he scored fewer points than some of the other players on the list, he consistently dominated the boards and shot extremely efficiently (despite averaging 26.6% from behind the arc over his career and taking an average of 125 threes per season). Lastly, Stephen Curry’s 2015-16 Regular Season was not just the best in the modern era; it blew away the competition, coming in 3.8 points higher than the second highest ATS. That’s the same difference between the second highest ATS posted by 88-89 Jordan and the 19th highest ATS by 79-80 Kareem Abdul-Jabbar (not shown).

2. Regular Season dominance does not necessarily translate into Playoffs success

2A

This is an extended version of the chart in #1 that captures the top 50 Regular Season performances. In addition, it shows each player’s performance in the playoffs, and whether he won the Championship that year. One thing that is striking about the data is the difference between the Playoffs ATS and the Regular Season ATS is negative for almost all of the performances.

2B

As the chart above illustrates, only 4 out of 50 seasons had a Playoffs ATS that exceeded the Regular Season ATS, with LeBron pulling off the feat twice (although he didn’t win the Championship in either season). In other words, the vast majority of the very best Regular Season performers (44/50 = 88%) ended up doing worse in Playoffs. Moreover, only 11 out of the 50 ended up being Champions (8 were Runners-Up, and 31 didn’t even make the Finals). Does that mean most people are decidedly unclutch? Well, not quite.

To address the first issue, I believe there are two main factors that may cause a player who just submitted an all-time Regular Season performance to experience a decline in productivity in the Playoffs. The first factor is that the player has to face more difficult opponents in the Playoffs relative to the Regular Season. This is an obvious yet easily overlooked fact. Indeed, the player likely has to play against much tighter defenses in the Playoffs (e.g. being guarded by the opposing team’s best defender, being double-teamed), when every team’s season on the line. This in turn will make it more difficult for the player to score, and especially difficult for the player to score as efficiently as he did in the Regular Season (note the negative change in True Shooting % from the Regular Season to the Playoffs in the first chart in #2). The second factor is mean regression. Simply put, we’re looking at the 50 best individual Regular Season performances over the last 37 years. Putting up elite numbers over the course of 82 games in a Regular Season is no easy task, and even the best athletes will regress toward their career averages given enough time. In this case, the Playoffs represent exactly that: an extension of the Regular Season.

Onto the second, slightly more troubling issue: how come most of these players who submitted all-time Regular Season performances didn’t even make it to the Finals? To preface my response, I must clarify that I don’t believe that my definition of clutchness (over-performance in the Playoffs/Finals relative to the Regular Season) necessarily correlates with Championships, Playoffs success, or even victory in general. At the end of the day, basketball is played 5 on 5, and one person alone can never consistently achieve victory simply by playing out of his mind. This ties back to why a phenomenal individual Regular Season leads to failure in the Playoffs more often than not. Simply put, the fact that a player is able to achieve an all-time Regular Season performance is perhaps indicative of a fundamental shortcoming of his team: that they are overly reliant on his performance in order to achieve victory.

3. The best performances in the overall Playoffs by ATS seem to fit our narrative

3

With only 4 Championships and 1 Runner-Up, the top 15 Playoff performances continue to support the theory that simply putting up a scintillating individual performance may do more harm than good to your chances of winning a Championship. The top 4 performances, which all had positive ATS and TS% differentials from the Regular Season, are admittedly clutch, and yet not one of them even made it to the Finals. LeBron and the Cavaliers swept the first two rounds in 2009 but were stopped in the Conference Finals by a resilient Dwight Howard and Orlando Magic team. Hakeem put up unreal numbers in 1987 but the Rockets lost in the second round to Seattle in 6 games. David Robinson had a remarkable 76.0% TS Percentage (the highest out of my sample size of 200), but the Spurs only managed to win Game 1 against the Warriors in the first round before being eliminated. Lastly, Hakeem put up even more ridiculous numbers in 1988, but the Rockets were similarly eliminated by Dallas in the first round in which they only won 1 game.

4. Extreme over-performance (super clutch) in the Playoffs won’t win you Championships, but moderate over-performance (kinda clutch) might

4

First, let’s appreciate how phenomenal Hakeem Olajuwon was in the Playoffs. Out of the 43 Playoff performances that were objectively clutch (i.e. Playoffs ATS > Regular Season ATS), Hakeem joins Tim Duncan and Magic Johnson as the only players to appear 4 times. However, his 1987 and 1988 Playoff performances were quite literally off the charts, and I highly doubt anyone ever comes close to his margin of over-performance ever again.

And yet, is too much of a good thing ultimately bad? The top 10 performance all featured a margin of over-performance of at least 5.0 in terms of ATS. These performances were decidedly “super clutch,” and yet, 9 out of 10 failed to even make the Finals. Only Isiah Thomas won a Championship (1/10 = 10%) and it’s interesting to note that his absolute Playoffs ATS of 32.6 was significantly lower than anyone else’s in the top 10.

Outside of the top 10 (anyone with an over-performance greater than 0 but less than 5.0), there seems to be a sweet spot. 17 out of the 33 in the “kinda clutch” group won Championships (17/33 = 52%), a significantly higher percentage than the top 10. But it gets better, because…

5. It pays to moderately under-perform (kinda unclutch) as well

5

The sweet spot doesn’t end there. Turns out, it pays to moderately under-perform as well. Those who were “kinda unclutch” and posted a Playoffs ATS that was lower than their Regular Season ATS by less than 2.0 actually won Championships at fairly significant clip (14/36 = 39%). In contrast, anyone who posted a Playoffs ATS that was lower than their Regular Season ATS by more than 2.0 experienced significantly less success, going 16 out of 115 with regards to Championships (16/115 = 14%).

Why might this be the case? Well, being super unclutch (Playoffs ATS – Regular Season ATS < 2.0) is obviously detrimental to your chances of winning a Championship, because it means that the team is not getting your usual productivity that it is used to. In the absence of teammates stepping up, it is likely that the team will perform worse on the whole. On the other end of the spectrum, a spectacular over-performance over an entire Playoffs may suggest that a team has too much of an “one man army” mentality. This may not be by design, but rather due to a team having few or no other options besides heavily depending on its star player to win games. In contrast, any performance lying in the sweet spot of moderate over/under-performance indicates an achievement that is within expectations and therefore less susceptible to either of the two issues plaguing the two extremes mentioned above.

6. Absolute ATS appears to be a good determinant of Championships

6

The chart above shows the top 20 NBA Finals performances, sorted by ATS. It is no surprise that the list features some of the greatest NBA players of all time, but it’s nice to see the numbers backing up the age-old claim that the best players “show up” when things matter the most. Shaq in his prime has a great case for Best Finals Player Ever, submitting 3 of the top 4 Finals performances of all time, coinciding with the Lakers’ first three-peat.

6. It’s hard to argue that LeBron isn’t clutch, but perhaps he’s just really unlucky

Out of the top 20 performances, 18 led to Championship (17 of which won Finals MVP). The remaining 2 were both put up LeBron James. Despite everything that he’s accomplished, LeBron is perhaps still one of the unluckiest players in the history of the NBA Finals. Indeed, he’s put up historic Finals performances three years in a row and has gone 1-2 in the Finals against 3 of the greatest teams in the modern era (and arguably of all time): 13-14 Spurs, 14-15 Warriors, and 15-16 Warriors. Pretty impressive if you ask me.

7. The Pistons really knew how to step up and win in the Finals 

7A

The first chart ranks Finals over-performance relative to the Regular Season.

7B

The second chart ranks Finals over-performance relative to the entire Playoffs. The two charts tell similar stories, and it’s very interesting to see 3 Pistons players (from 2 different eras) in the top 10 for both. In both eras, I think the degree of over-performance reflects the team’s focus on teamwork and de-emphasizing the importance of the superstar during the regular season. However, Joe Dumars, Isiah Thomas, and Chauncey Billups all knew how to step up on the biggest stage and it was their clutch performances in the Finals that provided the extra spark to help the team achieve the Championship. Special shout-out to Kawhi Leonard (13-14 Spurs), who I think fits the same mold.

8. Andre Iguodala’s Finals MVP in 2014-15 was well-deserved

Many people claim that LeBron was absolutely robbed of the Finals MVP award in 14-15, and while I can see where they are coming from (he put up the 20th best Finals performance in a losing effort), the two charts above show why Iguodala’s performance was deserving of the FMVP as well: while his absolute Finals ATS was not very high by any means, he did put the 5th highest over-performance relative to the Regular Season and the 12th highest over-performance relative to the Playoffs. Pretty clutch if you ask me!

9. Magic deserved to win Finals MVP in 1987-88…

At least at first glance: 12th highest absolute Finals ATS (42.9) on a Championship team, 8th highest over-performance relative to the Regular Season (+5.0) and the 6th highest over-performance relative to the Playoffs (+7.0). In comparison, James Worthy, the man who ended up taking home FMVP honors that year, had a Finals ATS of 31.2 and performance relative to the Regular Season and Playoffs of +0.8 and -0.5, respectively. However, while Magic appears more worthy (no pun intended) of the award when looking at the overall stats, Worthy was arguably more clutch at key points during the series, something I didn’t appreciate until I dug deeper into the history of the 1988 NBA Finals. The series went to 7 games between the Los Angeles Lakers, who were looking to be the first team in 19 years to repeat as Champions, and the Detroit Pistons, who were led by point guard Isiah Thomas. The Pistons stole the first game in LA, but James Worthy stepped up big in Game 2 as Magic battled the flu, leading all scorers with 26 points.  In Game 3, with Magic still sick, Worthy once again led the Lakers in scoring with 24 points as they won a critical game in Detroit and took a 2-1 lead in the series (the Finals were still played in a 2-3-2 format at this point). However, the resilient Pistons took the next 2 games at home and a 3-2 lead, setting themselves up just one game away from the franchise’s first Championship. Detroit seemed almost destined to win it all, after Isiah Thomas put up 25 points in the 3rd quarter (still an NBA finals record) despite suffering a badly sprained ankle earlier in the quarter. Thomas would finish the game with 43 points, but Worthy’s team-leading 28 points proved to be vital as the Lakers held off the Pistons to win by a single point, 103-102. In the deciding Game 7, Worthy put up an incredible triple-double (36 points, 16 rebounds, 10 assists) to propel the Lakers to a 108-105 victory and the team’s second consecutive Championship. Not only was the triple-double impressive in its own right, but it would turn out to be the only triple-double in Worthy’s entire NBA career. I don’t know about you, but that sounds like the definition of clutch to me.

Anyway, it’s these kinds of situations that make me realize how much I love doing exercises like this, because despite how far statistics have advanced in sports over the years, it will never be possible to boil athletic performances into just a matrix of values. Sometimes, real life is even more amazing than the numbers.

10. The king of unclutch is Karl Malone, but you might be surprised to see who else has joined him…

10A

The Mailman always delivers…except in the Playoffs. Malone owns the top two under-performances (Playoffs vs. Regular Season ATS), and 3 out of the top 10. In fact, he posted an under-performance in each of his 11 seasons that appeared in this data set. And yes, the two times he did make the Finals, he under-performed as well. Yikes. But let’s not forget about 15-16 Stephen Curry, who put up the 4th worst under-performance overall, as well as the worst by a Regular Season MVP and the worst by a player who made the Finals. Of course, Curry was a victim of his own success. His Regular Season performance was simply too good, and it looked even better under my calculation of ATS (given that it is on a Per 36 Minutes basis and Curry managed to put up monstrous numbers on just 34.2 minutes per game in the Regular Season). Add to that an injury in the Playoffs which forced him to miss playing time, and it’s not surprising that his performance fell back down to earth in the Playoffs and Finals.

At 28 years old, Curry will likely have more opportunities to prove himself as a clutch performer in the Playoffs. And while the Warriors’ off-season acquisition of Kevin Durant may have just made their path to the Finals (and perhaps future Championships) a lot easier, I invite both Warriors fans and haters to take a look at who fills in the #10 and #11 slots of the top 15 worst under-performances in the Playoffs of all-time :).

Fun with Excel #3 – Corruption in the NBA?

Jeff, , , , 4 Comments

My father was a big fan of the Chicago Bulls back in the ’80s and ’90s, so I had the good fortune of watching some of the best playoff basketball (i.e. Michael Jordan) that the NBA (and the world) has ever witnessed. Perhaps that is the same reason why the last decade or so of NBA basketball has seemed to pale noticeably in terms of excitement. It is generally agreed upon among basketball fans that the game as it is played today is (a lot) less physical (and perhaps less exciting) than it once was.

Officiating has also seemingly become a bigger determinant of results, and like virtually all professional team sports, the blame often lands on the referees. “If it weren’t for that call, they would have won the game,” is a phrase we hear all too often, and one that I am guilty of committing as well. However, have changes in officiating really been that significant over the last few decades, and if so, how would we measure such a phenomenon? The answer, of course, lies in the numbers.

Luckily, statistics for the NBA are readily available, but for the purposes of my project, I decided to look at playoff statistics from the 1983-84 season to the latest 2012-13 season. However, even if the data is easily accessible, oftentimes the most time-consuming aspect of a project is collecting the data and organizing it in a way that makes it easy to analyze. This was no exception. Luckily, with a little vlookup and text parsing (the latter is needlessly complex in Excel) magic, I was able to largely automate the process of converting 30 years of raw playoff data into something I could process more easily.

My first goal was to see if there were any high level trends in the NBA playoffs through time, in particular the number of games played and the point differential in each game. Moreover, I wanted to analyze these metrics by playoff round (e.g. first round (1R), conference semifinals (2R), conference finals (3R), and finals (4R)). If we were to believe that officiating actually had a measurable impact on playoff results, we may expect to find the following:

  • An overall longer playoff campaign
  • Smaller average point differentials, to convey the appearance of “closer” games

Why would the NBA want any of these things to happen? The answer is simple: profits. More games played/closer games = more tickets sold/higher TV ratings. In fact, the NBA switched from a best-of-five format to a best-of-seven format in the first round starting in the 2003 playoffs.

The Results (and the data)

I’ll make a few observations, but the data really speaks for itself here. In the first chart, we see that after adjusting for the NBA’s change in playoff format since the 2002-03 campaign, both the average numbers of games played in the playoffs and the average number of games played per round has not shown any noticeable shift through time. The average points differential chart shows the same story, and in fact both charts seem to suggest some cyclical trends through time. Lastly, the average free throw attempts and fouls chart actually displays a noticeable decrease through time on a per game adjusted basis. Perhaps this is a testament to just how physical the game was back in the 1980s and 90s, which MJ himself has suggested on many an occasion.

 

Conclusion

The data doesn’t seem to indicate any obvious playoff trends that may have been caused by officiating. However, more granular foul data (which may not be available) may help clarify the story. In particular, even if the average number of fouls per game has trended down over the last 30 years, have the types of fouls called changed in any significant way? Perhaps more calls are coming during particularly tight stretches of games, or conversely, during blow outs, to ensure that the losing team is “still in it.” Of course, all of this is pure speculation, and without hard evidence, it is difficult to move forward. As Sir Arthur Conan Doyle once said through his most famous character Sherlock Holmes, “It is a capital mistake to theorize before one has data. Insensibly one begins to twist facts to suit theories, instead of theories to suit facts.” Until such facts are found, our theories will remain theories.