Statistical certainty

I read Bill Simmons’s The Book of Basketball. I enjoyed his book, as it is a fun survey of NBA history. The book isn’t just a numbers game or just breaking down plays. It includes enough human interest elements that it should appeal to a casual fan or diffident parties (like me; I can count the number of basketball games I’ve seen – TV or live – on both hands.) Simmons does a fantastic job of conveying his love of basketball. For me, he really brought different basketball eras to life, inserting comments from players, coaches, and sportswriters. He also seems fairly astute in breaking down plays and describing the flow of the game.

Yes, I bought the book because I think Bill Simmons’s writing. If you enjoy his blog, you will find that same breezy conversation style here. The man has a gift for dropping pop culture references and making it germane to his arguments. But what I like most is that he is earnest in trying to understand and to make his readers appreciate the people who play a game for a living.

His segment on Elgin Baylor was moving, in showing how racism affected this one man; in some ways, it was probably more effective than if he just talked in general terms about the 1960’s. His whole book works because it stays at the personal level. Even in his discussion of teams and individual players, he takes pains to discuss how this person was and is regarded by his peers and teammates.

In this way,  I think Simmons did a fantastic job of making a case that basketball can contain as much historical perspective as baseball. This is something that should not have to be argued. Baseball has a lock on “the generational game by which history can be measured” status. What seems important is that there are human elements that make it accessible between generations: things like fathers taking their sons to the games, talking about the games and players, the excitement of watching breathtaking physical acts that expand how one views the human condition, and the joy and agony of championship wins and losses. While baseball’s slow pace lends itself to the way history moves one (periods where nothing seems to happen punctuated by drama), it doesn’t mean other things happen in a vacuum. Style of play, the way the players are treated, and the composition of the player demographic all reflect the times. These games can be a reflection of society, and one can see the influence of racial injustice in something as mundane as box scores as integration occurred.

Simmons blend basketball performance, its history, and its social environment of basketball effectively, some examples could be found in his discussion of Dr. J, Russell, Baylor, Kareem, and Jordan. In discussing why there probably won’t be another Michael Jordan (or Hakeem, or Kevin McHale), he takes inventive routes. Most of his points relate to societal/basketball environment pressures. Players are drafted sooner, the high pay scale for draft picks lower motivation to prove their worth, and perhaps society itself would actively discourage players from behaving as competitively as Jordan did. I suppose it’s interesting, but I’m not sure if that matters so much if the player is perceived to be an excellent player. Regardless, it seems to me that Simmons has been thinking about these things for some time. And I found it fun to read his take on basketball.

And I liked this book because it gives the lie to the weird view that someone who hasn’t done something cannot make reasonable, intelligent statements about it. Simmons wasn’t a professional basketball player, but he certainly uses every resource available to absorb the history and characters populating the game. He read a fair bit, he watched and rewatched games, he talked to players, he talked to people who covered basketball and he watched some more.  And he isn’t afraid to raise issues that occur to readers; you’ll see what I mean when you read his footnotes.

The book (and his podcast) confirms my opinion of Simmons as the smart friend who’d be a blast to have (one who bleeds Celtics green, watches sports for a living, and must keep up with Hollywood gossip, gambles, and pop culture because it gives him ammunition for columns).

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There are some issues with the book, mainly in how statistical analysis of basketball is portrayed. I should be upfront and say that these issues did not detract from his arguments (for reasons that will be clear later), but I wish he would reconcile eyeball and statistical information.  And because I’ve decided one focus of this blog should be how non-scientists deal with science (and scientists), I thought I should offer some thoughts on some of these issues.

I am somewhat undecided about how Simmons (and I suppose I am using him as a proxy for all “non-scientist”) actually feels about statistics. He claims that team sports like basketball and football are fundamentally different from baseball; the team component of the former increase the number of additive and subtractive interactions while the latter game is composed of individual units of performance.  Thus the increase in complexity makes it difficult to model. So he discards so called simple measures of NBA player performance like WP48, PER, and adjusted plus-minus.

His rationale is that these indicators ought to back up existing observations about NBA players. So Kobe Bryant needs to be ranked as a top-20 player of all time (WP48 ranks Bryant as a superior player – like Paul Pierce – and not a step or two behind Michael Jordan.) It seems like he wants statistics to tell him what he wants to hear, when in fact statistics helps you see things you don’t see.

But then that leads to my second point about Simmons: why does he need the model to back up his mental model of player performance? Put differently, why is it that he cannot accept differences in rankings calculated by some turn-the-crank-spit-out-value model? I think Simmons lacks a nuanced view of how these numbers ought to be interpreted, and that he refuses to see that a simple model can capture a great many things about a complex system. Sure, once you’ve set up your criteria (like some level of significance you are willing to accept), you align everything by it, but there is room for some judgement as to where that line is drawn.

Another way of describing a complex system is to say that there are many things going on at once, and they are all interacting in some way. There are 10 players on a basketball court. One player, with the ball, has options to pass, to shoot, or to move the ball. Within each of these options, he has a set of suboptions: which one of the other four guys do I pass to? Who’s open? Which open player has a good shot from where he is? Am I in my optimal position to shoot? Do I need to drive to the basket or kick the ball out to the perimenter? There are many more possibilities than these.

***

At one level, Simmons is right; it is useful to break things down into “hyperintelligent” stats – identifying the tendency of players (whether he likes breaking to his left or right when he’s starts driving from the top of the key, whether he is equally good in shooting from his left or right hand, how often he does a turnaround, fadeaway, or drives to the hoop), trying to figure out how many forced errors a defender creates, how often a unforced turnovers happen (like someone dribbling off his foot), how many blocks get slapped out of bounds vs being tipped to get possession, and so on.

But isn’t it just as intelligent to find an easy way of collapsing the complex game into a simple “x + y” formula? On several occasions, Simmons uses a short quote (and praises the person who said it) that captures everything he wanted to say in 15 pages. A simple model is analogous to that short quote.

More importantly, what if we didn’t need all these hyperintelligent stats to capture the essence of the game?

I just switched the problem from one of identifying player performance and productivity to one that captures the game a broad strokes. The two ideas are of course related but still distinct and should not be confused to mean the same thing.

This gets back to the original motives of the person who does the modeling.

If it’s a scientist or economist, I’ll tell you now that he is interested in getting the most impact with the least amount of work. He probably has to teach, run a lab/research program, and write grants and publications. He doesn’t have time to break game film down. And he certainly does not have the money to hire someone to look at game film (although I am sure he’ll have no lack of applicants for the job.) He spends his money finding people to do research and teach. If his research program is into finding ways to measure worker productivity, he will probably start with existing resources. So fine; he now has a database of NBA player box scores.

He’ll want to link these simple measures of player output to wins and losses. But players score points, not wins, and thankfully the difference in points scored and points given up correlate extremely well with wins and losses.

From there, it is relatively simple to do a linear regression for all players for all teams, finding how each of the box score stats relate to the overall points scored for each team. And as noted, some metrics have a higher correlation to the point difference (I will not use the term differential to mean difference; differential belongs to diff EQ’s.) Regardless, it seems an affliction for males that they rank things; so the researchers have these numbers, and it’s trivial to list players from high to low.

Now, here’s another consideration. In this, and in other branches of science, the data are not “clean”. That is, we scientists (generally) assume that the phenomenon we are observing conforms to a “normal” distribution – that is, there is some true state for the thing we observe (found by taking the average of our observations) and the individual pieces of observation hover around this true state (or average). So there is variation around the mean.

In my research, for example, I can measure neural responses in the olfactory bulb. I use optical indicators of neural activity; essentially, the olfactory bulb lights up with odor stimulation. The more the neurons respond, the brighter things get. The olfactory bulb is separated into these circular structures called glomeruli. Each glomerulus receives connections from the sensory neurons situated in the nose and the output neurons of the olfactory bulb (some other cells are also present, but they aren’t important for this story.)

When a smell is detected by humans (or animals and insects), what we mean is that some chemical from the odor source has been carried, through the air, into the nose and neurons become active (they fire “action potential spikes”). And the pattern of this activity, at the olfactory bulb, is quite similar – but not exactly the same – from animal to animal.

Sometimes, we see fewer responses to the same smell. Other times, we see a few more responses. Sometimes we see a different pattern from what we expect. Sometimes, we see no responses. This might happen once every 15 animals. Not a whole lot to take away from our general, broad stroke understanding of how this part of the brain processes smell information. In most cases, some of these things might be explained technically; the animal was in poor health, or our stimulus apparatus has a leak, or the smell compound is degraded. We know this because we can improve the signal by fixing the equipment or giving the animal a drug to clear up its nose (mucus secretion – snot! – is a problem).

And as a direct analogy to this WP48 vs “hyperintelligent stats” problem, we find that a complex smell (compose of hundreds of different chemicals) may be “recreated” by using a few of these chemicals. There is good empirical evidence this is the case: prepared food manufacturers and fragrance makers can mimick smells and flavor reasonably well. This is akin to capturing the essence of the smell (or sport) with a few simple chemicals (or box scores). And generally, we don’t even need people to describe to us what they smell to figure this out (i.e. break down game film to create detailed stats). We can simply force them to make them answer a simple question: do these two things smell the same to you, yes or no? Thus “complex” brain processes and decision making can be boiled down into a forced-choice test results. Do we lose information? Yes, but everyone realizes this is a start. As we know more, and new technology becomes available, we can do more and ask more with less effort. Then we will be able to better use the information we have. As far as I know, most statheads have access to box-scores (although there is nothing to stop them from breaking down game film aside from time and money issues.)

But that’s the broad strokes view. If we get into details (that is, as if we started working with the “hyperintelligent” stat breakdowns), we find that of course there is more going on, and that the differences we see are not only technical issues. For example, the pattern of activity we see differs slightly from animal to animal, but this is because the cells that form connections with the olfactory bulb do not hit the same spot. And if we can use a single chemical to recreate a smell, the smell itself is still different enough that humans generally can tell something is missing. So the other chemicals are in fact detected and contributing some information that the brain uses to form the sensation of smell. And we know that the way neurons respond to a single chemical differs from how they respond to a mixture, confirming that there is in fact additional information being transmitted.

The important point is that the simple model captures an important part, but not all, of the complex system. One problem that can occur with increasing the complexity of models is that overfitting occurs: the model becomes applicable to one small part, rather than the whole, system. Even game film breakdown hinders  if it gives you so many options that you are back where you started. You’d probably avoid focusing on rare events and just concentrate on the things that happen often – which, again, is the point of a simple model.

The intense break down of game film to provide detailed portraits of player effectiveness could be combined with the broad strokes analysis. A metric like WP48 can tell a coach where a player is deficient. The coach can use the detailed breakdown to figure out why the player isn’t rebounding, passing, shooting well, and so on. That’s where things like defensive pressure, help defense, and positional analysis can be used for further evaluation. And I’m not sure if stat heads argued otherwise.

Deficiencies of statistical models

As in the things that models explicitly ignores.

One thing statistical models do not address is the fan’s enjoyment of a player. Actually, I suppose one might be able simply chart percent-capacity of stadiums when a particular player comes to town, but that’s something I don’t think Simmons would argue. There’s something to be said about how a player scores: Simmons pays tribute to Russell and Baylor, the first players to make basketball a vertical game. He cites Dr. J. as introducing the urban playground style  into basketball. He loves talking about the egos of players, especially when players take MVP snubs personally and then dominates the so-called MVP in a subsequent game.

Simmons also offers a rebuttal to PER, adjusted plus/minus, and “wages of win” metrics in his ranking of Allen Iverson – by saying that he doesn’t care. It’s sufficient for him that he finds Iverson a presence on the court. His emotions are acted out as basketball plays. He finds Iverson’s toughness and anger on the court fascinating to watch.

But Simmons does use metrics: the standard box scores. I would ask this: if Iverson didn’t score as much as he did, would Simmons still care? As Berri has noted, the rankings by sportswriters, the salaries given to scorers, and PER rankings all correlate highly with volume scoring (i.e. the points total, not field-goal percentage). Despite the tortured arguments writers might make, and the lip service given to building a lineup with complete players, “good” players are players who score a lot.

However, I should be clear and say that Simmons’s approach does not detract from his defense of his rankings. He uses player and coach testimonies, historical relevance, visual appeal of their playing style, sports writers, and the box scores to generate a living portrait of these players as people. Outside of the box scores, there are enough grist for the mill. I would suggest that it is these arguments that make the whole argument process fun. Even in baseball, supposedly the sport with the most statistically validated models of player performance (and Berri would argue that basketball players and their contribution to team records are even more consistent), there are enough differences of opinion concerning impact, playing styles, and relvance to confound Hall of Fame/MVP arguments (see Joe Posnanski).

Because Simmons is upfront about his criteria (even if the judgement of each might be not as “objective” as a number), it is fine for him to weight non-statistical arguments for greatness. It’s how he defined the game. Just as Berri defined “player productivity” in terms of his WP48 metric. Because Berri publishes in peer-reviewed journal, he needs methods that are reproducible. Science, and in general the peer review process, is a different process than writing books or Hall-of-Fame arguments or historical rankings. The implicit understanding of peer-review is that the work is technically sound and reproducible. Berri cannot take the chance of publishing a Simmons-like set of criteria and have other sports economist “turn the crank” and come out with different rankings. But Berri can publish an algorithm, and proper implementation will yield the same results.

Does this mean that Berri is right? Or that a formula is better than Simmons’s criteria? Mostly no. The one time where it is “better” is when one is preparing the analysis for peer-review. In this case, it is nicer to have a formula, or a process, or a set of instructions, that yield the same result each and everytime the experiment is run. In other words, we try to remove our bias as much as possible. Bias here does not mean anything pernicious; it just is a catch-all term for how we think a certain way (with our own gut feelings about the validity of ideas and research direction). Being objective simply means we try to make sure that our interpretation conforms to the data, and that the work is good enough so that other researchers come to the same general conclusions.

I think Simmons actually doesn’t need to trash statistics, nor does he need to ignore it. Once he establishes ground rules, he can emphasize or deemphasize how important box scores are in his evaluation. As it is, I found his arguments compelling. His strength, again, is to make basketball history an organic thing. He does his best to eliminate the “you had to be there” barrier and tries to place the players in the context of their time.

Now, one might ask why stats can’t be used to resolve these arguments about all time greats. Leaving aside the issue of the different eras (and frankly, this can be addressed by normalizing performance scores to the standard deviation for a given time period, as Berri does here ), there is the issue of what the differences in these metrics mean. In the same article I cited, Berri reports that the standard deviation for the performance of all power forwards, defined by his WP48 metric, is about .110. His average basketball player has a WP48 of .100. Kevin Garnett, for example, has a WP48 (2002-2003) of 0.443. That translates roughly that Garnett is more than 4x as productive as an average player, but normalized to the standard deviation, he is only 3.5x as productive.

But how much different is a power forward from Kevin Garnett if the other forward has a WP48 of 0.343? One might interpret this to mean that Garnett is still nearly 1 standard deviation better than the other player, but it could also mean that their performance fall within 1 standard deviation of each other. Depending on the variation of each player’s performance for a given year, compared to his career mean, they could be statistically similar. That is, the difference might be accounted for by the “noise” in slight upticks/downticks in rebounds/assists/steals/turnovers/shooting percentages/blocks. If you prefer, how about the difference between a .300 hitter and a .330 hitter? Over 500 at-bats, the .300 has 150 hits, and the .330 hitter has 165; the difference would be 15 hits over the course of a season. Are the two hitters really that different? The answer would depend on the variability of batting average (for the compared players) and how these numbers look with a larger sample set (i.e. over a career with over 5000 at-bats, for instance.) The context for the difference must be analyzed.

Here’s another example: let’s assume that Simmons and Berri’s metric turned out similar listings, perhaps with different order (one difference is that Iverson would be nowhere near Berri’s top 96.) And further, let us assume that the career WP48 scores are essentially within 1.5 standard deviations of one another. How might Simmons break with the WP48 rankings?

Let us tackle how Berri would have constructed his ranking: he would simply list players from highest to lowest WP48. That’s probably because he is in peer-review article mode. And frankly, if you profess to have a metric, why would you throw it out? You might if, like Simmons, you defined the argument differently. Of his Pyramid of Fame rankings, he lists a few arguments that do not encompass basketball productivity. Again, the idea of historical relevance, player/coach testimony, and the style and flair of the players enter into Simmons’s arguments. So all things being equal, and if the difference in rankings by metric is slight, there really is no reason against weighing the statistics more than any other attribute. Heck, even if the metric differences are large, it wouldn’t matter. Simmons like his other arguments more anyway.

But if you do talk about the actions on the court, then I believe you are in fact constrained. Of the metrics I had mentioned, WP48 offers high correlation with point-difference and thus with win-loss records. Further, some of the other metrics actually correlate with points-scored by players, suggesting that there is no difference between that metric and simply looking at the aggregate point total. So there are actually models that do reasonably well in predicting and “explaining” the mechanics of how teams win and lose.

In a way, I think the power of a proper metric is not in ranking similarly “productive” players, but in identifying the surprisingly bad or good players. Iverson is an example of the former; Josh Smith (of the 2009-2010 Hawks) of the latter. It might not be as powerful a separator of players with similar scores, because their means essentially fall within 1 standard deviation of one another; in essense, they are statistically the same. In this case, it  helps to have other information to aid evaluation (and this isn’t easy; as Malcolm Gladwell has written, and Steven Pinker taken issue with, some measuring sticks are less reliable than others.)

Another example where statistics is powerful is in determining, in the aggregate, if player performance varies from year to year. Berri found that it isn’t, suggesting that the impact of coaching and teammate changes may not be as high as one thinks. However, such a finding in no way precludes coaches and teammates from having an effect on teammates. It just means that these people are too few to affect the mean. Or perhaps it suggests that coachs are not using information properly to make adjustments that are meaningful to player performance. Overall, I suppose, one cause for why Simmons hates advanced stats and rankings is that he isn’t sensitive to the importance of standard deviation, and ironically enough,  he applies the mean tyrannically when there is such a concept as statistical insignificance.

But Berri has never pushed his work as a full explanation of the game of basketball. First, he doesn’t present in-game summaries: he only looks at averages over time. There’s nothing in his stat to indicate the ups and downs (i.e. standard deviation in performance) a player experiences from game to game. Even in baseball, hitting .333 does not guarantee a hit every 3 at-bats. It just means that over time, a hitter’s hit streaks and lulls add up to some number that is a third of his at-bats. Berri’s metric (and any other work that proposes to measure player performance) certainly cannot predict what a given box score would be, for a given game, for a given player.

Regardless, I do not see a problem with Simmons’s ranking his players. Simply, he values entertainment value as much as production. I would say he values the swings in performance just as much, if not more (more on this later). Yes, he says stats do not matter, but of course it does. It’s interesting that all the scoring lines he cites, in admiration, all lead with a high score or score per game. And if you can’t shoot, rebound, pass, steal, or block and coughs the ball up a lot, it wouldn’t matter how pretty you make everything look.

No-no’s

Joe Posnanski has pointed out that, whenever someone trashes stats, he tends to offer some other supplemental numbers that back up his point. In other words, the disagreement isn’t about statistics per se, but between the distinction of “obvious” stats vs. “convoluted” stats.

Even if one disagrees with basketball statistics, at least he can believe that statheads came up with a formula first and turned the crank before comparing the readout with their perceptions of players. Hence Simmons blowing up when PER or WP48 doesn’t rank his favorites highly.

Simmons approaches this from the opposite direction. He has an outcome in mind and “builds” a stat/model to fit it (like his 42-Club). But he mistakes his way of tinkering with what modelers actually do. Berri arrived at his model by performing linear regression on a particular box score and seeing whether the point-difference increased. It isn’t an arbitrary way of deriving some easy to use formulation. The regression coefficients are meaningful in that, what it says is, if you increase shooting percentage by this amount, the point-difference goes up by that amount. It so happens that points scored by a player did not increase the point-difference. And he built it by using all players; it’s strange to decide before hand what players are great, and then build a metric around that. Why even bother in the first place?

And for Berri to report differently on these aggregate data because Kobe isn’t ranked any higher, actually would become scientific fraud. But as I noted above, applying these WP48 rankings isn’t as hard and firm a process as Simmons thinks. There is some room for flexibility, depending on what one tries to accomplish.

In general, I agree that more break downs in the game would be useful, in the sense that more data is always nice. The problem, for academics, is that these stats might remain proprietary, and it becomes difficult to apply across all teams. Even if we could get all the “hyperintelligent” stat breakdowns from a single team, it is unclear if other teams would view the break down in the same way. The utility for examining general questions about worker (i.e. player) productivity for academic publication becomes less clear. The database ought to help the teams – assuming they are intellectually honest enough to verify that their stats that produce a better picture of player productivity and aren’t impressed by the gee-whiz-ness of it all. My guess is that they won’t be entirely successful, as Simmons still has a job trashing bad GM decisions.

Standard Deviations

Why I watch sports: it seems to be similar to the way Simmons does. He watches over a thousand hours of sports each year, waiting for the chance to see something he has never seen before. Something that stretches the imagination and the realm of human physical achievement.

I feel the same way; I am team and sport agnostic, and although I used to follow Boston Bruins hockey religiously, I left that behind in high school. Although I have lived in Boston from the age of 7 onwards, I had not been infected by the Red Sox or Celtics bug (even during their mid-80’s run). I did root for the Red Sox in 2003 and 2004, but that was because of the immense drama involved in the playoff games against the Yankees. And Bill Simmons’s blog for the season.

Perhaps I prove Simmons’s point about stat heads; I like to say that I am interested in sports in the abstract. I like the statistical analysis for the same reason Dave Berri had pointed out in his books. There is a wealth of data in there to be mined. I thought one good example of the type of research that can come from these data is finding evidence for racial bias in the way basketball referees call games.

However, what got me interested in watching professional sports was Simmons writing about it. Although I didn’t watch football, basketball, or baseball for a long time, I did watch the Olympics and, believe it or not, televised marathons. Partly it was because my wife and I were running, but mostly I saw the track and field type sports as a wonderful spectacle. So it wasn’t that much of a stretch to fall into a stereotypical male activity.

At any rate, I was amazed at Usain Bolt’s performance in the 2008 Summer Olympics. I was disappointed by Paula Radcliffe injuring herself during the Athens Olympics, and then relieved when she won the NYC marathon, setting a new speed record in the process. I rooted for Lance Armstrong to win his seventh Tour. I rooted for the Patriots to get their perfect season. And until the Colts laid down and the Saints loss a couple of weeks ago, I wanted the Colts and the Saints to meet in the Super Bowl, both sporting 18-0 records. I was glad that the Yankees won the World Series, and with that fantasy baseball lineup, I hope they continue to win. I want to see the best teams win, and win often. And yes, I wish the regular season records lined up with the championship winners for a given season. Then we wouldn’t have arguments about best regular season records and the championship winners.

This isn’t because I’m a bandwagon fan; I watch sports now for the same reason that Simmons does. To see the best of the best do great things. But not always because they might have a competitor who wants it more, leading to the best failing, at times. This drama is the power of sports.

And I can see why Simmons argues so passionately against stats. He likes the visceral impact of sports. I can say that Bolt ran a 9.69s 100 m. But it was nothing compared to seeing Bolt accelerate, distance himself from the other runners, and then slow down as he pulled into the finish line. He blew away the competition. My eyes were wide and my mouth hung open: he slowed down! And he was 2 strides ahead of everybody. And he set a new record. Even if Bolt didn’t set the record, he still made it look easy. On the field, on that particular day, he out-classed his competitors. It is watching the struggle of the competitors (like Phelps winning the 100m fly by 10 milliseconds), on that day, that matters. Over time, if one didn’t watch that particular heat, then the line World Record: Usain Bolt, 100 m, 9.69s doesn’t quite hit you the same way.

But then, there is this. What if instead of looking at the single race, you looked at the athlete performing in 8 or 20 or  50 events for a year? And at these events, the same set of athletes compete over and over?

Here are some possible outcomes: Phelps and Bolt lose every other match, essentially giving us a single transcendental moment. Phelps and Bolt win half their meets. Phelps and Bolt utterly dominate the field, winning 65% or more of their meets.

For first case, we would probably admit that the Phelps and Bolt phenomena was a one-off. For whatever reason, the contingencies (no sports gods or stars aligning here!) lined up such that they did highly improbable feats (but not impossible. This distinction is the point of this section.) The third case proves our point; they are not perfect, but they sure are good. The second case is a bit trickier: since they are right on the borderline, we need some analysis to help us decide. One way might be to sum up our individual observations about these two. Being .500, while giving us a single breathtaking moment might be persuasive. Or one might look at how everybody else did (Phelps and Bolt might have won 50% of the time, but if the remainder is split among their competitors, they have still dominated the field.)

But then what if Bolt and Phelps won 49% of the time, and some other competitor won 50% of the time? What then? Here, criteria are important. Most of the time, we say better meaning, well, something is better. Generally, we aren’t specific about what we mean by it.

In the book, Simmons ranks his top 96 players in a pyramid schematic. He is rather specific about what he wants in a player. And as one expects, he is specific about the types of intangibles his basketball player should have (basically, basketball sense – i.e. The Secret, if he made his teammates better, winnability, and if you choose someone based on “if your life depended on this one guy winning you a title.”) The evaluation of those intangibles, however, is not as precise as he’d like. However, the advantage here is that one might be able to answer “why” questions. In some cases, Simmons seemingly ranked two players differently while giving them the same arguments (like the consistency of Tim Duncan and John Stockton. Somehow, Stockton just rubbed Simmons the wrong way, while Duncan’s consistency makes him the seventh best player of all time.) And his emphasis on projecting Bill Russell’s game into the modern era seemed like Russell should have ranked lower. On occasion, I was left with the feeling that the arguments did not match the ranking.  From what he said about the stat inflation and how Wilt didn’t get the secret, I thought he would be ranked lower than 6.

Dave Berri has the opposite problem: he has a mathematically defined metric and when he says better or worse, it’s whether this metric is higher or lower between the players being compared. He can further break down this stat to show where a player is good or deficient (whether shooting percentage, blocks, turnovers, fouls, steals,  and assists are above or below the average). He can tell you the hows, with his model spitting out a number that combines these different performance stat into a metric of productivity. But he simply ranks players numerically, without talking about how these differences one might see between the players (and one might not be able to see it… it could be one more missed shot or one less rebound every couple of games.)

I am amazed that Simmons cannot reconcile eyeball and statistical information. Just about every time Simmons bitches out scorers, he talks about how this player didn’t get “The Secret”. It isn’t about scoring; it’s about having a complete game. It is about making the team better with the skills you have. To top it off, Simmons then says that point getters are one dimensional. You can’t shy away from rebounds. It’s great to have a few steals/blocks. Sure, not every athlete can do it all, and certainly not be as prolific as superstars, but you can’t avoid doing those things.

I’m sure Berri is nodding his head, agreeing with Simmons. Point getting isn’t the same as being a efficient shooter (at least average field goal and free throw percentages). And you certainly can’t be below average in the other areas if you want to help your team.

But Berri generally writes about the average. Simmons focuses on the standard deviations. He doesn’t just care about the scoring line; he focuses on Achilles-wreaking-havoc-on-the-Trojans type of performances. He loves the stories of Jordan’s pathological competitiveness. In other words, Simmons lives for the outlier moments.

And I think therein lies the nutshell (and to borrow a Simmons device, I could have said this 5500 words ago and shortened this review.) Simmons views the out-of-normal performance as transcendent, as examples of players who wanted something more or had something to prove. He treats the extreme as something significant; he uses a back story to it to give the event meaning. That’s fine. It’s also fine when Berri (and stat heads) are constrained in treating outliers as noise (possibly) or irrelevant to the general scope of the model, if they desire a model of what usually happens and are not concerned with doing the job of a GM and a coach for free. Because they both defined the game they wish to play in.