Google Search of the Day: Player Efficiency Rating is Useless

From the “almost too good to be true” department:

Hat tip to whoever the guy was that used that search to find my blog yesterday. See for yourself here.

Note the irony that I’m actually saying the opposite in the quoted snippet.

UPDATE: As of right now, Skeptical Sports Analysis is the #1 result for these searches as well (no quotation marks, and all have actually been used to find the site):

why was dennis rodman so good at rebounding – Leads to Rodman series, Part 1/4(b). Makes sense, although strictly speaking I’ve never addressed this question.
skynet does not know the rules of football – It’s a direct quote, so obv it leads to Skynet.
nfl predictions using regression analysis – Oddly enough, this also leads to Skynet. I guess technically my neural network is a regression.
how often are sports analysts actually – Leads to main page. Actually what? I don’t know.

Graph of the Day: NBA Player Stats v. Team Differentials (Follow-Up)

In this post from my Rodman series, I speculated that “individual TRB% probably has a more causative effect on team TRB% than individual PPG does on team PPG.” Now, using player/team differential statistics (first deployed in my last Rodman post), I think I can finally test this hypothesis:

^{Note: As before, this dataset includes all regular season NBA games from 1986-2010. For each player who both played and missed at least 20 games in the same season (and averaged at least 20 minutes per game played), differentials are calculated for each team stat with the player in and out of the lineup, weighted by the smaller of games played or games missed that season. The filtered data includes 1341 seasons and a total of 39,162 weighted games.}

This graph compares individual player statistics to his in/out differential for each corresponding team statistic. For example, a player’s points per game is correlated to his team’s points per game with him in the lineup minus their points per game with him out of the lineup. Unlike direct correlations to team statistics, this technique tells us how much a player’s performance for a given metric actually causes his team to be better at the thing that metric measures.

Lower values on this scale can potentially indicate a number of things, particularly two of my favorites: duplicability (stat reflects player “contributions” that could have happened anyway—likely what’s going on with Defensive Rebounding %), and/or entanglement (stat is caused by team performance more than it contributes to team performance—likely what’s going on with Assist %).

In any case, the data definitely appears to support my hypothesis: Player TRB% does seem to have a stronger causative effect on team TRB% than player PPG does on team PPG.

The Case for Dennis Rodman, Part 2/4 (a)(ii)—Player Valuation and Unconventional Wisdom

In my last post in this series, I outlined and criticized the dominance of gross points (specifically, points per game) in the conventional wisdom about player value. Of course, serious observers have recognized this issue for ages, responding in a number of ways—the most widespread still being ad hoc (case by case) analysis. Not satisfied with this approach, many basketball statisticians have developed advanced “All in One” player valuation metrics that can be applied broadly.

In general, Dennis Rodman has not benefitted much from the wave of advanced “One Size Fits All” basketball statistics. Perhaps the most notorious example of this type of metric—easily the most widely disseminated advanced player valuation stat out there—is John Hollinger’s Player Efficiency Rating:

In addition to ranking Rodman as the 7th best player on the 1995-96 Bulls championship team, PER is weighted to make the league average exactly 15—meaning that, according to this stat, Rodman (career PER: 14.6) was actually a below average player. While Rodman does significantly better in a few predictive stats (such as David Berri’s Wages of Wins) that value offensive rebounding very highly, I think that, generally, those who subscribe to the Unconventional Wisdom typically accept one or both of the following: 1) that despite Rodman’s incredible rebounding prowess, he was still just a very good a role-player, and likely provided less utility than those who were more well-rounded, or 2) that, even if Rodman was valuable, a large part of his contribution must have come from qualities that are not typically measurable with available data, such as defensive ability.

My next two posts in this series will put the lie to both of those propositions. In section (b) of Part 2, I will demonstrate Rodman’s overall per-game contributions—not only their extent and where he fits in the NBA’s historical hierarchy, but exactly where they come from. Specifically, contrary to both conventional and unconventional wisdom, I will show that his value doesn’t stem from quasi-mystical unmeasurables, but from exactly where we would expect: extra possessions stemming from extra rebounds. In part 3, I will demonstrate (and put into perspective) the empirical value of those contributions to the bottom line: winning. These two posts are at the heart of The Case for Dennis Rodman, qua “case for Dennis Rodman.”

But first, in line with my broader agenda, I would like to examine where and why so many advanced statistics get this case wrong, particularly Hollinger’s Player Efficiency Rating. I will show how, rather than being a simple outlier, the Rodman data point is emblematic of major errors that are common in conventional unconventional sports analysis – both as a product of designs that disguise rather than replace the problems they were meant to address, and as a product of uncritically defending and promoting an approach that desperately needs reworking.

Player Efficiency Ratings

John Hollinger deserves much respect for bringing advanced basketball analysis to the masses. His Player Efficiency Ratings are available on ESPN.com under Hollinger Player Statistics, where he uses them as the basis for his Value Added (VA) and Expected Wins Added (EWA) stats, and regularly features them in his writing (such as in this article projecting the Miami Heat’s 2010-11 record), as do other ESPN analysts. Basketball Reference includes PER in their “Advanced” statistical tables (present on every player and team page), and also use it to compute player Value Above Average and Value Above Replacement (definitions here).

The formula for PER is extremely complicated, but its core idea is simple: combine everything in a player’s stat-line by rewarding everything good (points, rebounds, assists, blocks, and steals), and punishing everything bad (missed shots, turnovers). The value of particular items are weighted by various league averages—as well as by Hollinger’s intuitions—then the overall result is calculated on a per-minute basis, adjusted for league and team pace, and normalized on a scale averaging 15.

Undoubtedly, PER is deeply flawed. But sometimes apparent “flaws” aren’t really “flaws,” but merely design limitations. For example: PER doesn’t account for defense or “intangibles,” it is calculated without resort to play-by-play data that didn’t exist prior to the last few seasons, and it compares players equally, regardless of position or role. For the most part, I will refrain from criticizing these constraints, instead focusing on a few important ways that it fails or even undermines its own objectives.

Predictivity (and: Introducing Win Differential Analysis)

Though Hollinger uses PER in his “wins added” analysis, its complete lack of any empirical component suggests that it should not be taken seriously as a predictive measure. And indeed, empirical investigation reveals that it is simply not very good at predicting a player’s actual impact:

This bubble-graph is a product of a broader study I’ve been working on that correlates various player statistics to the difference in their team’s per-game performance with them in and out of the line-up. The study’s dataset includes all NBA games back to 1986, and this particular graph is based on the 1300ish seasons in which a player who averaged 20+ minutes per game both missed and played at least 20 games. Win% differential is the difference in the player’s team’s winning percentage with and without him (for the correlation, each data-point is weighted by the smaller of games missed or played. I will have much more to write about nitty-gritty of this technique in separate posts).

So PER appears to do poorly, but how does it compare to other valuation metrics?

SecFor (or “Secret Formula”) is the current iteration of an empirically-based “All in One” metric that I’m developing—but there is no shame in a speculative purely a priori metric losing (even badly) as a predictor to the empirical cutting-edge.

However, as I admitted in the introduction to this series, my statistical interest in Dennis Rodman goes way back. One of the first spreadsheets I ever created was in the early 1990’s, when Rodman still played for San Antonio. I knew Rodman was a sick rebounder, but rarely scored—so naturally I thought: “If only there were a formula that combined all of a player’s statistics into one number that would reflect his total contribution.” So I came up with this crude, speculative, purely a priori equation:

Points + Rebounds + 2*Assists + 1.5*Blocks + 2*Steals – 2*Turnovers.

Unfortunately, this metric (which I called “PRABS”) failed to shed much light on the Rodman problem, so I shelved it. PER shares the same intention and core technique, albeit with many additional layers of complexity. For all of this refinement, however, Hollinger has somehow managed to make a bad metric even worse, getting beaten by my OG PRABS by nearly as much as he is able to beat points per game—the Flat Earth of basketball valuation metrics. So how did this happen?

Minutes

The trend in much of basketball analysis is to rate players by their per-minute or per-possession contributions. This approach does produce interesting and useful information, and they may be especially useful to a coach who is deciding who to give more minutes to, or to a GM who is trying to evaluate which bench player to sign in free agency.

But a player’s contribution to winning is necessarily going to be a function of how much extra “win” he is able to get you per minute and the number of minutes you are able to get from him. Let’s turn again to win differential:

For this graph, I set up a regression using each of the major rate stats, plus minutes played (TS%=true shooting percentage, or one half of average points per shot, including free throws and 3 pointers). If you don’t know what a “normalized coefficient” is, just think of it as a stat for comparing the relative importance of regression elements that come in different shapes and sizes. The sample is the same as above: it only includes players who average 20+ minutes per game.

Unsurprisingly, “minutes per game” is more predictive than any individual rate statistic, including true shooting. Simply multiplying PER by minutes played significantly improves its predictive power, managing to pull it into a dead-heat with PRABS (which obviously wasn’t minute-adjusted to begin with).

I’m hesitant to be too critical of the “per minute” design decision, since it is clearly an intentional element that allows PER to be used for bench or rotational player valuation, but ultimately I think this comes down to telos: So long as PER pretends to be an arbiter of player value—which Hollinger himself relies on for making actual predictions about team performance—then minutes are simply too important to ignore. If you want a way to evaluate part-time players and how they might contribute IF they could take on larger roles, then it is easy enough to create a second metric tailored to that end.

Here’s a similar example from baseball that confounds me: Rate stats are fine for evaluating position players, because nearly all of them are able to get you an entire game if you want—but when it comes to pitching, how often someone can play and the number of innings they can give you is of paramount importance. E.g., at least for starting pitchers, it seems to me that ERA is backwards: rather than calculate runs allowed per inning, why don’t they focus on runs denied per game? Using a benchmark of 4.5, it would be extremely easy to calculate: Innings Pitched/2 – Earned Runs. So, if a pitcher gets you 7 innings and allows 2 runs, their “Earned Runs Denied” (ERD) for the game would be 1.5. I have no pretensions of being a sabermetrician, and I’m sure this kind of stat (and much better) is common in that community, but I see no reason why this kind of statistic isn’t mainstream.

More broadly, I think this minutes SNAFU is reflective of an otherwise reasonable trend in the sports analytical community—to evaluate everything in terms of rates and quality instead of quantity—that is often taken too far. In reality, both may be useful, and the optimal balance in a particular situation is an empirical question that deserves investigation in its own right.

PER Rewards Shooting (and Punishes Not Shooting)

As described by David Berri, PER is well-known to reward inefficient shooting:

“Hollinger argues that each two point field goal made is worth about 1.65 points. A three point field goal made is worth 2.65 points. A missed field goal, though, costs a team 0.72 points. Given these values, with a bit of math we can show that a player will break even on his two point field goal attempts if he hits on 30.4% of these shots. On three pointers the break-even point is 21.4%. If a player exceeds these thresholds, and virtually every NBA player does so with respect to two-point shots, the more he shoots the higher his value in PERs. So a player can be an inefficient scorer and simply inflate his value by taking a large number of shots.”

The consequences of this should be properly understood: Since this feature of PER applies to every shot taken, it is not only the inefficient players who inflate their stats. PER gives a boost to everyone for every shot: Bad players who take bad shots can look merely mediocre, mediocre players who take mediocre shots can look like good players, and good players who take good shots can look like stars. For Dennis Rodman’s case—as someone who took very few shots, good or bad— the necessary converse of this is even more significant: since PER is a comparative statistic (even directly adjusted by league averages), players who don’t take a lot of shots are punished.
Structurally, PER favors shooting—but to what extent? To get a sense of it, let’s plot PER against usage rate:

^{Note: Data includes all player seasons since 1986. Usage % is the percentage of team possessions that end with a shot, free throw attempt, or turnover by the player in question. For most practical purposes, it measures how frequently the player shoots the ball.}

That R-squared value corresponds to a correlation of .628, which might seem high for a component that should be in the denominator. Of course, correlations are tricky, and there are a number of reasons why this relationship could be so strong. For example, the most efficient shooters might take the most shots. Let’s see:

Actually, that trend-line doesn’t quite do it justice: that R-squared value corresponds to a correlation of .11 (even weaker than I would have guessed).

I should note one caveat: The mostly flat relationship between usage and shooting may be skewed, in part, by the fact that better shooters are often required to take worse shots, not just more shots—particularly if they are the shooter of last resort. A player that manages to make a mediocre shot out of a bad situation can increase his team’s chances of winning, just as a player that takes a marginally good shot when a slam dunk is available may be hurting his team’s chances. Presently, no well-known shooting metrics account for this (though I am working on it), but to be perfectly clear for the purposes of this post: neither does PER. The strong correlation between usage rate and PER is unrelated. There is nothing in its structure to suggest this is an intended factor, and there is nothing in its (poor) empirical performance that would suggest it is even unintentionally addressed. In other words, it doesn’t account for complex shooting dynamics either in theory or in practice.

Duplicability and Linearity

PER strongly rewards broad mediocrity, and thus punishes lack of the same. In reality, not every point that a player scores means their team will score one more point, just as not every rebound grabbed means that their team will get one more possession. Conversely—and especially pertinent to Dennis Rodman—not every point that a player doesn’t score actually costs his team a point. What a player gets credit for in his stat line doesn’t necessarily correspond with his actual contribution, because there is always a chance that the good things he played a part in would have happened anyway. This leads to a whole set of issues that I typically file under the term “duplicability.”

A related (but sometimes confused) effect that has been studied extensively by very good basketball analysts is the problem of “diminishing returns” – which can be easily illustrated like this: if you put a team together with 5 players that normally score 25 points each, it doesn’t mean that your team will suddenly start scoring 125 points a game. Conversely—and again pertinent to Rodman—say your team has 5 players that normally score 20 points each, and you replace one of them with somebody that normally only scores 10, that does not mean that your team will suddenly start scoring only 90. Only one player can take a shot at a time, and what matters is whether the player’s lack of scoring hurts his team’s offense or not. The extent of this effect can be measured individually for different basketball statistics, and, indeed, studies have showed wide disparities.

As I will discuss at length in Part 2(c), despite hardly ever scoring, differential stats show that Rodman didn’t hurt his teams offenses at all: even after accounting for extra possessions that Rodman’s teams gained from offensive rebounds, his effect on offensive efficiency was statistically insignificant. In this case (as with Randy Moss), we are fortunate that Rodman had such a tumultuous career: as a result, he missed a significant number of games in a season several times with several different teams—this makes for good indirect data. But, for this post’s purposes, the burning question is: Is there any direct way to tell how likely a player’s statistical contributions were to have actually converted into team results?

This is an extremely difficult and intricate problem (though I am working on it), but it is easy enough to prove at least one way that a metric like PER gets it wrong: it treats all of the different components of player contribution linearly. In other words, one more point is worth one more point, whether it is the 15th point that a player scores or the 25th, and one more rebound is worth one more rebound, whether it is the 8th or the 18th. While this equivalency makes designing an all-in one equation much easier (at least for now, my Secret Formula metric is also linear), it is ultimately just another empirically testable assumption.

I have theorized that one reason Rodman’s PER stats are so low compared to his differential stats is that PER punishes his lack of mediocre scoring, while failing to reward the extremeness of his rebounding. This is based on the hypothesis that certain extreme statistics would be less “duplicable” than mediocre ones. As a result, the difference between a player getting 18 rebounds per game vs. getting 16 per game could be much greater than the difference between them getting 8 vs. getting 6. Or, in other words, the marginal value of rebounds would (hypothetically) be increasing.

Using win percentage differentials, this is a testable theory. Just as we can correlate an individual player’s statistics to the win differentials of his team, we can also correlate hypothetical statistics the same way. So say we want to test a metric like rebounds, except one that has increasing marginal value built in: a simple way to approximate that effect is to make our metric increase exponentially, such as using rebounds squared. If we need even more increasing marginal value, we can try rebounds cubed, etc. And if our metric has several different components (like PER), we can do the same for the individual parts: the beauty is that, at the end of the day, we can test—empirically—which metrics work and which don’t.

For those who don’t immediately grasp the math involved, I’ll go into a little detail: A linear relationship is really just an exponential relationship with an exponent of 1. So let’s consider a toy metric, “PR,” which is calculated as follows: Points + Rebounds. This is a linear equation (exponent = 1) that could be rewritten as follows: (Points)^1 + (Rebounds)^1. However, if, as above, we thought that both points and rebounds should have increasing marginal values, we might want to try a metric (call it “PRsq”) that combined points and rebounds squared, as follows: (Points)^2 + (Rebounds)^2. And so on. Here’s an example table demonstrating the increase in marginal value:

The fact that each different metric leads to vastly different magnitudes of value is irrelevant: for predictive purposes, the total value for each component will be normalized — the relative value is what matters (just as “number of pennies” and “number of quarters” are equally predictive of how much money you have in your pocket). So applying this concept to an even wider range of exponents for several relevant individual player statistics, we can empirically examine just how “exponential” each statistic really is:

For this graph, I looked at each of the major rate metrics (plus points per game) individually. So, for each player-season in my (1986-) sample, I calculated the number of points, points squared, points^3rd. . . points^10th power, and then correlated all of these to that player’s win percentage differential. From those calculations, we can find roughly how much the marginal value for each metric increases, based on what exponent produces the best correlation: The smaller the number at the peak of the curve, the more linear the metric is—the higher the number, the more exponential (i.e., extreme values are that much more important). When I ran this computation, the relative shape of each curve fit my intuitions, but the magnitudes surprised me: That is, many of the metrics turned out to be even more exponential than I would have guessed.

As I know this may be confusing to many of my readers, I need to be absolutely clear: the shape of each curve has nothing to do with the actual importance of each metric. It only tells us how much that particular metric is sensitive to very large values. E.g., the fact that Blocks and Assists peak on the left and sharply decline doesn’t make them more or less important than any of the others, it simply means that having 1 block in your scoreline instead of 0 is relatively just as valuable as having 5 blocks instead of 4. On the other extreme, turnovers peak somewhere off the chart, suggesting that turnover rates matter most when they are extremely high.

For now, I’m not trying to draw a conclusive picture about exactly what exponents would make for an ideal all-in-one equation (polynomial regressions are very very tricky, though I may wade into those difficulties more in future blog posts). But as a minimum outcome, I think the data strongly supports my hypothesis: that many stats—especially rebounds—are exponential predictors. Thus, I mean this less as a criticism of PER than as an explanation of why it undervalues players like Dennis Rodman.

Gross, and Points

In subsection (i), I concluded that “gross points” as a metric for player valuation had two main flaws: gross, and points. Superficially, PER responds to both of these flaws directly: it attempts to correct the “gross” problem both by punishing bad shots, and by adjusting for pace and minutes. It attacks the “points” problem by adding rebounds, assists, blocks, steals, and turnovers. The problem is, these “solutions” don’t match up particularly well with the problems “gross” and “points” present.
The problem with the “grossness” of points certainly wasn’t minutes (note: for historical comparisons, pace adjustments are probably necessary, but the jury is still out on the wisdom of doing the same on a team-by-team basis within a season). The main problem with “gross” was shooting efficiency: If someone takes a bunch of shots, they will eventually score a lot of points. But scoring points is just another thing that players do that may or may not help their teams win. PER attempted to account for this by punishing missed shots, but didn’t go far enough. The original problem with “gross” persists: As discussed above, taking shots helps your rating, whether they are good shots or not.

As for “points”: in addition to any problems created by having arbitrary (non-empirical) and linear coefficients, the strong bias towards shooting causes PER to undermine its key innovation—the incorporation of non-point components. This “bias” can be represented visually:

^{Note: This data comes from a regression to PER including each of the rate stats corresponding to the various components of PER.}

This pie chart is based on a linear regression including rate stats for each of PER’s components. Strictly, what it tells us is the relative value of each factor to predicting PER if each of the other factors were known. Thus, the “usage” section of this pie represents the advantage gained by taking more shots—even if all your other rate stats were fixed. Or, in other words, pure bias (note that the number of shots a player takes is almost as predictive as his shooting ability).

For fun, let’s compare that pie to the exact same regression run on Points Per Game rather than PER:

^{Note: These would not be the best variables to select if you were actually trying to predict a player’s Points Per Game. Note also that “Usage” in these charts is NOT like “Other”—while other variables may affect PPG, and/or may affect the items in this regression, they are not represented in these charts.}

Interestingly, Points Per Game was already somewhat predictable by shooting ability, turnovers, defensive rebounding, and assists. While I hesitate to draw conclusions from the aesthetic comparison, we can guess why perhaps PER doesn’t beat PPG as significantly as we might expect: it appears to share much of the same DNA. (My more wild and ambitious thoughts suspect that these similarities reflect the strength of our broader pro-points bias: even when designing an All-in-One statistic, even Hollinger’s linear, non-empirical, a priori coefficients still mostly reflect the conventional wisdom about the importance of many of the factors, as reflected in the way that they relate directly to points per game).

I could make a similar pie-chart for Win% differential, but I think it might give the wrong impression: these aren’t even close to the best set of variables to use for that purpose. Suffice it to say that it would look very, very different (for an imperfect picture of how much so, you can compare to the values in the Relative Importance chart above).

Conclusions

The deeper irony with PER is not just that it could theoretically be better, but that it adds many levels of complexity to the problem it purports to address, ultimately failing in strikingly similar ways. It has been dressed up around the edges with various adjustments for team and league pace, incorporation of league averages to weight rebounds and value of possession, etc. This is, to coin a phrase, like putting lipstick on a pig. The energy that Hollinger has spent on dressing up his model could have been better spent rethinking the core of it.

In my estimation, this pattern persists among many extremely smart people who generate innovative models and ideas: once created, they spend most of their time—entire careers even—in order: 1) defending it, 2) applying it to new situations, and 3) tweaking it. This happens in just about every field: hard and soft sciences, economics, history, philosophy, even literature. Give me an academic who creates an interesting and meaningful model, and then immediately devotes their best efforts to tearing it apart! In all my education, I have had perhaps two professors who embraced this approach, and I would rank both among my very favorites.

This post and the last were admittedly relatively light on Rodman-specific analysis, but that will change with a vengeance in the next two. Stay tuned.

Update (5/13/11): Commenter “Yariv” correctly points out that an “exponential” curve is technically one in the form y^x (such as 2^x, 3^x, etc), where the increasing marginal value I’m referring to in the “Linearity” section above is about terms in the form x^y (e.g., x^2, x^3, etc), or monomial terms with an exponent not equal to 1. I apologize for any confusion, and I’ll rewrite the section when I have time.

The Aesthetic Case Against 18 Games

By most accounts, the NFL’s plan to expand the regular season from 16 to 18 games is a done deal. Indulge me for a moment as I take off my Bill-James-Wannabe cap and put on my dusty old Aristotle-Wannabe kausia: In addition to various practical drawbacks, moving to 18 games risks disturbing the aesthetic harmony—grounded in powerful mathematics—inherent in the 16 game season.
Analytically, it is easy to appreciate the convenience of having the season break down cleanly into 8-game halves and 4-game quarters. Powers of 2 like this are useful and aesthetically attractive: after all, we are symmetrical creatures who appreciate divisibility. But we have a possibly even more powerful aesthetic attachment to certain types of asymmetrical relationships: Mozart’s piano concertos aren’t divided into equally-sized beginnings, middles and ends. Rather, they are broken into exposition, development, and recapitulation—each progressively shorter than the last.

Similarly, the 16 game season can fairly cleanly be broken into 3 or 4 progressively shorter but more important sections. Using roughly the same proportions that Mozart would, the first 10 games (“exposition”) would set the stage and reveal who we should be paying attention to; the next 3-4 games (“development”) would be where the race for playoff positioning really begins in earnest, and the final 2-3 weeks (“recapitulation”) are where hopes are realized and hearts are broken—including the final weekend when post-season fates are settled. Now, let’s represent the season as a rectangle with sides 16 (length of the season) and 10 (length of the “exposition”), broken down into consecutively smaller squares representing each section:

^{Note: The “last” game gets the leftover space, though if the season were longer we could obviously keep going.}

At this point many of you probably know where this is going: The ratio between each square to all of the smaller pieces is roughly equal, corresponding to the “divine proportion,” which is practically ubiquitous in classical music, as well as in everything from book and movie plots to art and architecture to fractal geometry to unifying theories of “all animate and inanimate systems.” Here it is again (incredibly clumsily-sketched) in the more recognizable spiral form:

The golden ratio is represented in mathematics by the irrational constant phi, which is:

1.6180339887…

Which, when divided into 1 gets you:

.6180339887…

Beautiful, right? So the roughly 10/4/1/1 breakdown above is really just 16 multiplied by 1/phi, with the remainder multiplied by 1/phi, etc—9.9, 3.8, 1.4, .9—rounded to the nearest game. Whether this corresponds to your thinking about the relative significance of each portion of the season is admittedly subjective. But this is an inescapably powerful force in aesthetics (along with symmetricality and symbols of virility and fertility), and can be found in places most people would never suspect, including in professional sports. Let’s consider some anecdotal supporting evidence:

The length of a Major League Baseball season is 162 games. Not 160, but 162. That should look familiar.
Both NBA basketball and NHL hockey have 82-game seasons, or roughly half-phi. Note 81 games would be impractical, because of need for equal number of home and road games (but bonus points if you’ve ever felt like the NBA season was exactly 1 game too long).
The “exposition” portion of a half-phi season would be 50 games. The NHL and NBA All-Star breaks both take place right around game 50, or a little later, each year.
Though still solidly in between 1/2 and 2/3 of the way through the season, MLB’s “Summer Classic” usually takes place slightly earlier, around game 90 (though I might submit that the postseason crunch doesn’t really start until after teams build a post-All Star record for people to talk about).
The NFL bye weeks typically end after week 10.
Fans and even professional sports analysts are typically inclined to value “clutch” players—i.e., those who make their bones in the “Last” quadrant above—way more than a non-aesthetic analytical approach would warrant.

Etc.
So fine, say you accept this argument about how people observe sports, your next question may be: well, what’s wrong with 18 games? any number of games can be divided into phi-sized quadrants, right? Well, the answer is basically yes, it can, but it’s not pretty:

The numbers 162, 82, and 16 all share a couple of nice qualities: first they are all roughly divisible by 4, so you have nice clean quarter-seasons. Second, they each have aesthetically pleasing “exposition” periods: 100 games in MLB, 50 in the NBA and NHL, and 10 in the NFL. The “exposition” period in an 18-game season would be 11 games. Yuck! These season-lengths balance our competing aesthetic desires for the harmony of symmetry and excitement of asymmetry. We like our numbers round, but not too round. We want them dynamic, but workable.

Finally, as to why the NFL should care about vague aesthetic concerns that it takes a mathematician to identify, I can only say: I don’t think these patterns would be so pervasive in science, art, and in broader culture if they weren’t really important to us, whether we know it or not. Human beings are symmetrical down the middle, but as some guy in Italy noticed, golden rectangles are not only woven into our design, but into the design of the things we love. Please, NFL, don’t take that away from us.

Graph of the Day: Tim Duncan’s Erstwhile(?) Consistency

While San Antonio is having a great season, Tim Duncan is on the verge of posting career lows in scoring and rebounding (by wide margins). He’s getting a bit older and playing fewer minutes, for sure, but before this year he was one of the most consistent players in NBA history:

^{Note: Data excludes any seasons where player started fewer than 42 games.}

If that graph is kind of confusing, ignore the axes: more flat means more consistent. Spikes don’t necessarily represent decline, as a bad/great year can come at any time. Question mark is where Duncan projects for 2010-11.

C.R.E.A.M. (Or, “How to Win a Championship in Any Sport”)

Does cash rule everything in professional sports? Obviously it keeps the lights on, and it keeps the best athletes in fine bling, but what effect does the root of all evil have on the competitive bottom line—i.e., winning championships?

For this article, let’s consider “economically predictable” a synonym for “Cash Rules”: I will use extremely basic economic reasoning and just two variables—presence of a salary cap and presence of a salary max in a sport’s labor agreement—to establish, ex ante, which fiscal strategies we should expect to be the most successful. For each of the 3 major sports, I will then suggest (somewhat) testable hypotheses, and attempt to examine them. If the hypotheses are confirmed, then Method Man is probably right—dollar dollar bill, etc.

Conveniently, on a basic yes/no grid of these two variables, our 3 major sports in the U.S. fall into 3 different categories:

So before treating those as anything but arbitrary arrangements of 3 letters, we should consider the dynamics each of these rules creates independently. If your sport has a team salary cap, getting “bang for your buck” and ferreting out bargains is probably more important to winning than overall spending power. And if your sport has a low maximum individual salary, your ability to obtain the best possible players—in a market where everyone knows their value but must offer the same amount—will also be crucial. Considering permutations of thriftiness and non-economic acquisition ability, we end up with a simple ex ante strategy matrix that looks like this:

These one-word commandments may seem overly simple—and I will try to resolve any ambiguity looking at the individual sports below—but they are only meant to describe the most basic and obvious economic incentives that salary caps and salary maximums should be expected to create in competitive environments.

Major League Baseball: Spend

Hypothesis: With free-agency, salary arbitration, and virtually no payroll restrictions, there is no strategic downside to spending extra money. Combined with huge economic disparities between organizations, this means that teams that spend the most will win the most.

Analysis: Let’s start with the New York Yankees (shocker!), who have been dominating baseball since 1920, when they got Babe Ruth from the Red Sox for straight cash, homey. Note that I take no position on whether the Yankees filthy lucre is destroying the sport of Baseball, etc. Also, I know very little about the Yankees payroll history, prior to 1988 (the earliest the USA Today database goes). But I did come across this article from several years ago, which looks back as far as 1977. For a few reasons, I think the author understates the case. First, the Yankees low-salary period came at the tail end of a 12 year playoff drought (I don’t have the older data to manipulate, but I took the liberty to doodle on his original graph):

^{Note: Smiley-faces are Championship seasons. The question mark is for the 1994 season, which had no playoffs.}

Also, as a quirk that I’ve discussed previously, I think including the Yankees in the sample from which the standard deviation is drawn can be misleading: they have frequently been such a massive outlier that they’ve set their own curve. Comparing the Yankees to the rest of the league, from last season back to 1988, looks like this:

^{Note: Green are Championship seasons. Red are missed playoffs.}

In 2005 the rest-of-league average payroll was ~$68 million, and the Yankees’ was ~$208 million (the rest-of-league standard deviation was $23m, but including the Yankees, it would jump to $34m).

While they failed to win the World Series in some of their most expensive seasons, don’t let that distract you: money can’t guarantee a championship, but it definitely improves your chances. The Yankees have won roughly a quarter of the championships over the last 20 years (which is, astonishingly, below their average since the Ruth deal). But it’s not just them. Many teams have dramatically increased their payrolls in order to compete for a World Series title—and succeeded! Over the past 22 years, the top 3 payrolls (per season) have won a majority of titles:

As they make up only 10% of the league, this means that the most spendy teams improved their title chances, on average, by almost a factor of 6.

National Basketball Association: Recruit (Or: “Press Your Bet”)

Hypothesis: A fairly strict salary cap reigns in spending, but equally strict salary regulations mean many teams will enjoy massive surplus value by paying super-elite players “only” the max. Teams that acquire multiple such players will enjoy a major championship advantage.

Analysis: First, in case you were thinking that the 57% in the graph above might be caused by something other than fiscal policy, let’s quickly observe how the salary cap kills the “spend” strategy:

Payroll information from USA Today’s NBA and NFL Salary Databases (incidentally, this symmetry is being threatened, as the Lakers, Magic, and Mavericks have the top payrolls this season).

I will grant there is a certain apples-to-oranges comparison going on here: the NFL and NBA salary-cap rules are complex and allow for many distortions. In the NFL teams can “clump” their payroll by using pro-rated signing bonuses (essentially sacrificing future opportunities to exceed the cap in the present), and in the NBA giant contracts are frequently moved to bad teams that want to rebuild, etc. But still: 5%. Below expectation if championships were handed out randomly.
And basketball championships are NOT handed out randomly. My hypothesis predicts that championship success will be determined by who gets the most windfall value from their star player(s). Fifteen of the last 20 NBA championships have been won by Kobe Bryant, Tim Duncan, or Michael Jordan. Clearly star-power matters in the NBA, but what role does salary play in this?

Prior to 1999, the NBA had no salary maximum, though salaries were regulated and limited in a variety of ways. Teams had extreme advantages signing their own players (such as Bird rights), but lack of competition in the salary market mostly kept payrolls manageable. Michael Jordan famously signed a lengthy $25 million contract extension basically just before star player salaries exploded, leaving the Bulls with the best player in the game for a song (note: Hakeem Olajuwon’s $55 million payday came after he won 2 championships as well). By the time the Bulls were forced to pay Jordan his true value, they had already won 4 championships and built a team around him that included 2 other All-NBA caliber players (including one who also provided extreme surplus value). Perhaps not coincidentally, year 6 in the graph below is their record-setting 72-10 season:

^{Note: Michael Jordan’s salary info found here. Historical NBA salary cap found here.}

The star player salary situation caught the NBA off-guard. Here’s a story from Time magazine in 1996 that quotes league officials and executives:

“It’s a dramatic, strategic judgment by a few teams,” says N.B.A. deputy commissioner Russ Granik. .
Says one N.B.A. executive: “They’re going to end up with two players making about two-thirds of the salary cap, and another pair will make about 20%. So that means the rest of the players will be minimum-salary players that you just sign because no one else wants them.” . . .
Granik frets that the new salary structure will erode morale. “If it becomes something that was done across the league, I don’t think it would be good for the sport,” he says.

What these NBA insiders are explaining is basic economics: Surprise! Paying better players big money means less money for the other guys. Among other factors, this led to 2 lockouts and the prototype that would eventually lead to the current CBA (for more information than you could ever want about the NBA salary cap, here is an amazing FAQ).

The fact that the best players in the NBA are now being underpaid relative to their value is certain. As a back of the envelope calculation: There are 5 players each year that are All-NBA 1st team, while 30+ players each season are paid roughly the maximum. So how valuable are All-NBA 1st team players compared to the rest? Let’s start with: How likely is an NBA team to win a championship without one?

In the past 20 seasons, only the 2003-2004 Detroit Pistons won the prize without a player who was a 1st-Team All-NBAer in their championship year.
To some extent, these findings are hard to apply strategically. All but those same Pistons had at least one home-grown All-NBA (1st-3rd team) talent—to win, you basically need the good fortune to catch a superstar in the draft. If there is an actionable take-home, however, it is that most (12/20) championship teams have also included a second All-NBA talent acquired through trade or free agency: the Rockets won after adding Clyde Drexler, the second Bulls 3-peat added Dennis Rodman (All-NBA 3rd team with both the Pistons and the Spurs), the Lakers and Heat won after adding Shaq, the Celtics won with Kevin Garnett, and the Lakers won again after adding Pau Gasol.

Each of these players was/is worth more than their market value, in most cases as a result of the league’s maximum salary constraints. Also, in most of these cases, the value of the addition was well-known to the league, but the inability of teams to outbid each other meant that basketball money was not the determinant factor in the players choosing their respective teams. My “Recruit” strategy anticipated this – though it perhaps understates the relative importance of your best player being the very best. This is more a failure of the “recruit” label than of the ex ante economic intuition, the whole point of which was that cap+max –> massive importance of star players.

National Football League: Economize (Or: “WWBBD?”)

Hypothesis: The NFL’s strict salary cap and lack of contract restrictions should nullify both spending and recruiting strategies. With elite players paid closer to what they are worth, surplus value is harder to identify. We should expect the most successful franchises to demonstrate both cunning and wise fiscal policy.

Analysis: Having a cap and no max salaries is the most economically efficient fiscal design of any of the 3 major sports. Thus, we should expect that massively dominating strategies to be much harder to identify. Indeed, the dominant strategies in the other sports are seemingly ineffective in the NFL: as demonstrated above, there seems to be little or no advantage to spending the most, and the abundant variance in year-to-year team success in the NFL would seem to rule out the kind of individual dominance seen in basketball.

Thus, to investigate whether cunning and fiscal sense are predominant factors, we should imagine what kinds of decisions a coach or GM would make if his primary qualities were cunning and fiscal sensibility. In that spirit, I’ve come up with a short list of 5 strategies that I think are more or less sound, and that are based largely on classically “economic” considerations:

1. Beg, borrow, or steal yourself a great quarterback:
Superstar quarterbacks are probably underpaid—even with their monster contracts—thus making them a good potential source for surplus value. Compare this:

Note: WPA (wins added) stats from here.

With this:

The obvious caveat here is that the entanglement question is still empirically open: How much do good QB’s make their teams win v. How much do winning teams make their QB’s look good? But really quarterbacks only need to be responsible for a fraction of the wins reflected in their stats to be worth more than what they are being paid. (An interesting converse, however, is this: the fact that great QB’s don’t win championships with the same regularity as, say, great NBA players, suggests that a fairly large portion of the “value” reflected by their statistics is not their responsibility).

2. Plug your holes with the veteran free agents that nobody wants, not the ones that everybody wants:
If a popular free agent intends to go to the team that offers him the best salary, his market will act substantially like a “common value” auction. Thus, beware the Winner’s Curse. In simple terms: If 1) a player’s value is unknown, 2) each team offers what they think the player is worth, and 3) each team is equally likely to be right; then: 1) The player’s expected value will correlate with the average bid, and 2) the “winning” bid probably overpaid.

Moreover, even if the winner’s bid is exactly right, that just means they will have successfully gained nothing from the transaction. Assuming equivalent payrolls, the team with the most value (greatest chance of winning the championship) won’t be the one that pays the most correct amount for its players, it will—necessarily—be the one that pays the least per unit of value. To accomplish this goal, you should avoid common value auctions as much as possible! In free agency, look for the players with very small and inefficient markets (for which #3 above is least likely to be true), and then pay them as little as you can get away with.

3. Treat your beloved veterans with cold indifference.
If a player is beloved, they will expect to be paid. If they are not especially valuable, they will expect to be paid anyway, and if they are valuable, they are unlikely to settle for less than they are worth. If winning is more important to you than short-term fan approval, you should be both willing and prepared to let your most beloved players go the moment they are no longer a good bargain.

4. Stock up on mid-round draft picks.
Given the high cost of signing 1st round draft picks, 2nd round draft picks may actually be more valuable. Here is the crucial graph from the Massey-Thaler study of draft pick value (via Advanced NFL Stats):

The implications of this outcome are severe. All else being equal, if someone offers you an early 2nd round draft pick for your early 1st round draft pick, they should be demanding compensation from you (of course, marginally valuable players have diminishing marginal value, because you can only have/play so many of them at a time).

5. When the price is right: Gamble.

This rule applies to fiscal decisions, just as it does to in-game ones. NFL teams are notoriously risk-averse in a number of areas: they are afraid that someone after one down season is washed up, or that an outspoken player will ‘disrupt’ the locker room, or that a draft pick might have ‘character issues’. These sorts of questions regularly lead to lengthy draft slides and dried-up free agent markets. And teams are right to be concerned: these are valid possibilities that increase uncertainty. Of course, there are other possibilities. Your free agent target simply may not be as good as you hope they are, or your draft pick may simply bust out. Compare to late-game 4th-down decisions: Sometimes going for it on 4th down will cause you to lose immediately and face a maelstrom of criticism from fans and press, where punting or kicking may quietly lead to losing more often. Similarly, when a team takes a high-profile personnel gamble and it fails, they may face a maelstrom of criticism from fans and press, where the less controversial choice might quietly lead to more failure.

The economizing strategy here is to favor risks when they are low cost but have high upsides. In other words, don’t risk a huge chunk of your cap space on an uncertain free agent prospect, risk a tiny chunk of your cap space on an even more uncertain prospect that could work out like gangbusters.

Evaluation:

Now, if only there were a team and coach dedicated to these principles—or at least, for contrapositive’s sake, a team that seemed to embrace the opposite.

Oh wait, we have both! In the last decade, Bill Belichick and the New England Patriots have practically embodied these principles, and in the process they’ve won 3 championships, have another 16-0/18-1 season, have set the overall NFL win-streak records, and are presently the #1 overall seed in this year’s playoffs. OTOH, the Redskins have practically embodied the opposite, and they have… um… not.
Note that the Patriots’ success has come despite a league fiscal system that allows teams to “load up” on individual seasons, distributing the cost onto future years (which, again, helps explain the extreme regression effect present in the NFL). Considering the long odds of winning a Super Bowl—even with a solid contender—this seems like an unwise long-run strategy, and the most successful team of this era has cleverly taken the long view throughout.

Conclusions

The evidence in MLB and in the NBA is ironclad: Basic economic reasoning is extremely probative when predicting the underlying dynamics behind winning titles. Over the last 20 years of pro baseball, the top 3 spenders in the league each year win 57% of the championships. Over a similar period in basketball, the 5 (or fewer) teams with 1st-Team All-NBA players have won 95%.

In the NFL, the evidence is more nuance and anecdote than absolute proof. However, our ex ante musing does successfully predict that neither excessive spending nor recruiting star players at any cost (excepting possibly quarterbacks) is a dominant strategy.

On balance, I would say that the C.R.E.A.M. hypothesis is substantially more supported by the data than I would have guessed.