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I am a scientist, and as such, I generalize my observations into models of “How things work”. In short, people like me try to place our results – hopefully, they are technically sound – into the context of existing models and our colleagues’ findings, thereby adding to our knowledge.

Nothing high-handed; this is the “industry” of academia. I do not mean this as a good or bad thing; it is what it is. Businesses make profit; they are judged by that. No amount of integration into a community or charity giving matters if the company can’t make payroll or pay rent.

Rightly or wrongly, the academic model has a shorthand for “knowledge” or scholarship – scientific articles.  While our accomplishments are not as clear cut as “profit”,  under the publish or perish model, it does provide a convenient metric – quantification – as to our productivity. Anyone with an entrepreneurial spirit will seek to respond to this incentive in the best way she knows how.

So what does all this have to do with a recently released translation of a turn of the 20th-century, piece of Turkish literature?

Well, let’s get this out of the way: the novel is fantastic. What makes Ahmet Hamdi Tanpinar’s The Time Regulation Institute so appealing is how recognizable the characters and situations are. The hallmark of a classic is that we can see ourselves in the characters, find parallels in their circumstances, and resonance.

This novel is rife with those happy contrasts.

The first thought I had, and this is to bring it back to my reference to my day job, is that, science works by taking a fair number of observations and distilling from it a generalizable fact. In fiction, it works not just backwards but also flipped around.

The author’s work is singular. It has a specific plot, lively characters, with perhaps a dash of history. Despite its uniqueness, good works tend to sell – think Girl with the Dragon Tattoo. Its heroine is memorable, and I – along with a great many others – couldn’t get enough of her. But by no means is that book a classic.

What separates classic literature from a run-of-the-mill good book is resonance. Despite the peculiar circumstances, I find myself drawing parallels between events and people in the great books and my own life. The Time Regulation Institute is such a book.

The joy in encountering a great book is that I so enjoy thinking about it, even as I interrupt my reading so I can get things done. For the record, I am male, was born in Hong Kong, immigrated to the USA with my family when I was 7. I consider myself essentially a Westerner. Let’s just say that this point was brought home to me when my parents and I had chats, sometime after college, about how I am simply too individualistic for their tastes. Imagine my surprise (well, I just hadn’t thought about it) when I finally realized that, although I probably rated as a “good” American boy, I was found wanting in many departments if looked through lens of Chinese culture.

So I am aware of tension between a First World/developed world view and a more traditionalist one. All this is to say that, of course I understand I might be talking out of my ass when I talk about literature and art, let alone of non USA provenance. With that said, my essays on literature has always been opinion.  To me, great literature has resonance because, once we go beyond cultural details, social mores, and vernacular, deep down they speak to the great similarities between the author and reader. The only thing I can hope for when I write these essays on novel appreciation is that such play, or re-mixing, or interpretation does not mangle or warp the author’s intent too much.

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There are multiple layers to Tanpinar’s work. It is an absurdist comedy. It is commentary on the clash of the modern and the traditional (and dare I say a clash of civilizations?) It is a satire on bureaucracy. It is a trenchant observation on cultural inertia. It decries the inhumanity of the modern. It is a comedy. It is a drama. (For a proper critical take, please check out Martin Riker’s review in the NYT Book Review.)

I think it is fair to say that the book is recognizable to a modern audience, and that the themes have relevance.

The book is structured in a way that reminds me of the very tension it seeks to highlight. Our hero, Hayri Irdal, is flotsam, barely able to make ends meet. He and his family suffers indignities because they rely on the good graces of others. For a bit more than half the book, we have Hayri’s reminiscence of his life before heading the Time Regulation Institute. We might as well be sitting in a coffeehouse and listening to Hayri, because he takes his time. The pace is indicative of life where there really is not much else going on, and the only thing that matters is to converse and build relationships through trading stories. The rise of the Time Regulation and its dismantling occurs rapidly in the denouement.

A surprising amount does happen around Hayri. I am not sure how much he is supposed to represent the group of Turkish men who grew up at the transition of Turkey into the modern world, but one can’t help but see Hayri as part of a class of men who are poor, without prospects and about to be tossed aside because they cannot meet the ever accelerating and wild pace of modern life. In the end, I do not think Tanpanir is saying there is much different between the modern and traditional man. The same pressures of finding work, keeping a home, and getting enough to eat are eternal.

What does change is his fit into the social order.

I would say that Hayri’s problems and stasis are caused mainly by the people surrounding him. While everyone knows each other’s business, no one helps. There’s a bit of the malicious  that marks dramas about the provincial. Although Hayri did not not excel at his studies or in his apprenticeship to a watch repairman, he seemed to be in the same circumstance as a fair number of males. It does not help that strong personalities around Hayri have dominated and bullied him about.

Absurd events happen to him: a rich aunt dies and his father inherits her legacy… only to lose it all when she wakes up as she’s being buried. Hayri despairs and is powerless to change his fate. Of course he wants more, but he has no prospects to reinvent himself. At one point, we see his pride rise after being accused of hiding riches and stealing from his father-in-law. Hayri hadn’t, of course, but a series of misunderstandings and malice led his brothers-in-law to assume that there was a large inheritance. His outburst against an accuser gets him sent to  see a psychoanalyst.

Hayri, for all his lack of ambition and poverty, he seems free with his time. He is fully embedded into society, and while his spirit is relentless crushed by those around him (everyone is a frenemy, and his second wife is more concerned with her own life). Mostly, he wanders, sits in coffeehouses, and generally lounges about with other men who seem complain a lot about having no jobs. Nevermind that his wife is stuck at home with their daughter. Ermine, Hayri’s first wife, seems nothing more than a device to show that Hayri has something good in his life – and a psychic refuge against the world.

There are a number of details of Turkish life – both in its Ottoman and modern incarnation – that Tanpinar takes time to spoof. Psychoanalysis is seen as black comedy, where craziness is defined by someone who looks to interpreting dreams for diagnosis and treatment. Bureaucracy grinds excruciatingly slowly. Justice works on a personal level, such as the more relatives you have in the courts, the better off you’ll be. Being embedded into society means that there is little privacy – gossip is the main business of family and friends. Again, it’s not too much different from what one might find in a drama about small-town America.

And the modern Turk, represented by Hilat Ayarci, is a whirlwind of activity, where it isn’t clear if, once he has stopped, he was any better off than before. Where Hayri has a fatalistic worldview, which may have hindered his advancement through life, Hilat represents the proclivity of modern man to simply redefine problems and reinvent himself. In fact, there are no problems, only opportunities. None is more clear than a simple conversation between Hilat and Hayri, where Hilat promises to actualize Hayri’s sister-in-law’s singing career. To be frank, the girl has no talent, but Hilat simply spins is around so that she occupies a unique niche in the world of Turkish songstresses.

Needless to say, she is a success.

And these schemes only become more outlandish, like a whole housing development built on air. The Time Regulation Institute is only the epitome of Hilat’s ability to instantiate thoughts. The whole premise is absurd. The Institute exists to force Turks to keep time – to recover “lost seconds”. Hayri also publishes a biography of a non-existent patron of time – Ahmet the Timely. In reality, it seems the institute exists to keep Hayri and his extended family and friends (even he was surprised at how many relatives he had) in jobs. In the end, the dream crumbles. About the last service Hilat does for Hayri is to build a department precisely to wind-down the Time Regulation Institute.

Modernity, it seems, is running in place on a treadmill.

Probably the more interesting themes in the novel do deal with time. Hayri’s day expands to fill the time; the Time Regulation Institute seeks to divide time into allotments. Even if time cannot be controlled, it can be neatened up, so to speak. The concern with productivity and how time can be lost is a huge contrast between the modern and traditional. Hayri mentions that, despite being raised in poverty, he was happy because his time was his own. In some ways, that is all he would have had. The modern need to account for every second amounts to a judgment: you have a defect in personality if you waste that second.

I read a recent Joe Posnanski essay that emphasizes the Old World and New World differences in time perception and usage. Americans, it seems, have a hard time with leaving quantities alone. As soon as a metric is devised, we immediately rank and judge, having found some way to validate our opinions. As Posnanski describes, this concern with time and faux precision has more to do with an American’s inability to deal with the sense of a thing. A hard number – or a firm line in the sand – allows for even more subjective contortions so that our actions fall on the correct side of the line.

Posnanski’s example as an American football game. The details aren’t important, but it involves time running out on the last play of the game. The success of that play will determine who wins the contest. The play failed, but wait – there were penalties. The type of penalty is important, because it will either grant an advantage to a subsequent play or force the players to redo the play. Since the clock read zero, it would have been better for the failed team be awarded a second try; there can be no subsequent play, as there is no more time. Obviously, that essay wouldn’t have been written if the play were redone. To compound this drama of semantics – the wrong penalty was called.

In contrast, with soccer, time seems to flow more freely, embodied by the concept of injury time. Time continually runs down, but the firm end of the game will not happen until some remediation has occurred for the dead time during the game. And generally, the game will not stop during a play with potential for a team to score. That last American football play would probably have been allowed to continue and develop properly, had it been a soccer game.

In the case of soccer, players and the game are the master and time is subservient. Posnanski had a beautiful line about how soccer is generally officiated in a “literary” way, where the enjoyment, flow, beautify and “sense” of the game is more important. Of course, soccer also has ties, which says something else about the sensibilities of sports fans in the USA and outside of it.

But I think there is a difference in how time is viewed. Either people worry immensely about time, efficiency, and its usage or they don’t. It probably falls across a developed/undeveloped country divide – basically places without factories.

Another telling metaphor is in Hayri’s work at a watch repairman’s shop. Hayri did have skill in diagnosing broken watches, but alas he does not have the steady hands need to correct defects. There is an obvious parallel in how Hayri can only fix time, not create it.

I think a fair number of Tanpinar’s points, although they sound like tropes to modern ears, were forward thinking in his time (the novel was first published in 1962.) I think his most indicting criticism of modernity is that it is without substance, or at the least, considers only progression without attending to the emotional needs of the people who, by definition, are then traditional and backwards looking.

In this sense, I think perhaps this novel is more tragedy than farce or comedy. In the end, I can’t say that there is a difference to the type of life that Hayri would lead in Ottoman or in modern Turkey. In either world, he would remain destitute, surrounded by men like himself, and without prospects. The only difference is that the modern world is inscrutable to him. However well-intentioned and whimsical Hilat is, there is the sense that he is all words. Through force of personality, perhaps everyone will ignore the underlying reality. In the traditional world, Hayri at least recognizes the people and setting around him. It is as apparent to him as the innards of a watch, with every gear and spring in its place.

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The thing that hath been, it is that which shall be; and that which is done is that which shall be done: and there is no new thing under the sun.

Ecclesiastes (1:9), King James Bible

Joe Posnanski has a beautiful piece that captures how long baseball stat-geeks have been playing around with numbers. One thing that bothers me, and I know I am not the first to experience this, is that in most modern discussions about art, literature, movies, and even scientific findings, we do focus on the recent. In the case of scientific articles, this discrepancy is glaring, as we have actual evidence for this observation (we need to provide background and provenance for our ideas, and thus all scientific papers come citations.) Posnanski’s presented articles written by F.C. Lane, where he showed some fairly “modern” lines of reasoning and statistical analysis that can fit in Moneyball or Bill James’s Baseball Prospectus. Except Lane made his analysis in 1917.

 

Joe Posnanski has written another thoughtful piece on the divide between writers of a statistical bent and those who prefer the evidence of their eyes.  I highly recommend it; Posnanski distills the arguments into one about stories. Do statistics ruin them? His answer is no. Obviously, one should use statistics to tell other stories, if not necessarily better ones. He approached this by examining how one statistic, “Win Probability Added”, helped him look at certain games with fresh eyes.

My only comment here is that, I’ve noticed on his and other sites (such as Dave Berri’s Wages of Wins Journal) that one difficulty in getting non-statisticians to look at numbers is that they tend to desire certainty. What they usually get from statisticians, economists, and scientists are reams of ambiguity. The problem comes not when someone is able to label Michael Jordan as the greatest player of all time*; the problem comes when one is left trying to place merely great players against each other.

* Interestingly enough, it turns out the post I linked to was one where Prof. Dave Berri was defending himself against a misperception. It seems writers such as Matthew Yglesias and King Kaufman had mistook Prof. Berri’s argument using his Wins Produced and WP48 statistics, thinking  that Prof. Berri wrote other players were “more productive” than Jordan. To which Prof. Berri replied, “Did not”, but also gave some nuanced approaches in how one might look at statistics. In summary, Prof. Berri focused on the difference in performance of Jordan above that of his contemporary peers. 

The article I linked to about Michael Jordan shows that, when one compares numbers directly, care should be taken to place them into context. For example, Prof. Berri writes that, in the book Wages of Wins, he devoted a chapter to “The Jordan Legend.” at one point, though, he writes that

 in 1995-96 … Jordan produced nearly 25 wins. This lofty total was eclipsed by David Robinson, a center for the San Antonio Spurs who produced 28 victories.

When we examine how many standard deviations each player is above the average at his position, we have evidence that Jordan had the better season. Robinson’s WP48 of 0.449 was 2.6 standard deviations above the average center. Jordan posted a WP48 of 0.386, but given that shooting guards have a relatively small variation in performance, MJ was actually 3.2 standard deviations better than the average player at his position. When we take into account the realities of NBA production, Jordan’s performance at guard is all the more incredible.

If one simply looked at the numbers, it does seem like a conclusive argument that Robinson, having produced more “wins” than Jordan, should be the better player. The nuance comes when Prof. Berri places that into context. Centers, working closer to the basket, ought to have more, high-percentage shooting opportunities, rebounds, and blocks. His metric of choice, WP48, takes these into consideration. When one then looks at how well Robinson performed above his proper comparison group (i.e. other centers), we see that Robinson’s exceptional performance is something one should expect when comparing against other positions but is not beyond the pale when compared to other centers. However, Jordan’s performance, when compared to other guards, shows him to be in a league of his own.

That argument was accomplished by taking absolute numbers (generated for all NBA players, for all positions) and placing them into context (comparing to a specific set of averages, such as by position.)

This is where logic, math, and intuition can get you. I don’t think most people would have trouble understanding how Prof. Berri constructed his arguments. He tells you where his numbers came from, why there might be issues and going against “conventional wisdom”, and in this case, the way he structured his analysis resolved this difference (it isn’t always the case he’ll confirm conventional wisdom – see his discussions on Kobe Bryant.)

However, I would like to focus on the fact that Prof. Berri’s difficulties came when his statistics generated larger numbers for players not named Michael Jordan. (I will refer people to a recent post listing a top-50 of NBA players on Wages of Win Journal.*)

* May increase blood pressure.

In most people’s minds, that clearly leads to a contradiction: how can this guy, with smaller numbers, be better than the other guy? Another way of putting this is: differences in numbers always matter, and they matter in the way “intuition” tells us.

In this context, it is understandable why people give such significance to 0.300 over 0.298. One is larger than the other, and it’s a round number to boot. Over 500 at-bats, the difference between a 300-hitter and a .298-hitter  translates to 1 hit. For most people who work with numbers, such a difference is non-existent. However, if one were to perform “rare-event” screening, such as for cells in the blood stream that were marked with a probe that “lights” up for cancer cells, then a difference of 1 or 2 might matter. In this case, the context is that, over a million cells, one might expect to see, by chance, 5 or so false-positives in a person without cancer. However, in a person with cancer, that number may jump to 8 or 10.

For another example: try Bill Simmons’s ranking of the top 100 basketball players in his book, The Book of Basketball. Frankly, a lot of the descriptions, justifications, arguments, and yes, statistics that Simmons cites looks similar. However, my point here is that, in his mind, Simmons’s ranking scheme matters.  The 11th best player of all time lost something by not being in the top-10, but you are still better off than the 12th best player. Again, as someone who works with numbers, I think it might make a bit more sense to just class players into cohorts. The interpretation here is that, at some level, any group of 5 (or even 10)  players ranked near one another are practically interchangeable in terms of their practicing their craft. The differences between two teams of such players is only good for people forced to make predictions, like sportswriters and bettors. With that said, if one is playing GM, it is absolutely a valid criterion to put a team of these best players together based on some aesthetic consideration. It’s just as valid to simply go down a list and pick the top-5 players as ordered by some statistic.* If two people pick their teams in a similar fashion, then it is likely a crap shoot as to which will be the better team in any one-off series. Over time (like an 82-game season), such differences may become magnified. Even then, the win difference between the two team may be 2 or 3.

* Although some statistics are better at accounting for variance than others.

How this leads back to Posnanski is as follows. In a lot of cases, he does not just simply rank numbers; partly, he’s a writer and story teller. The numbers are not the point; the numbers illustrate. Visually, there isn’t always a glaring difference between them, especially when one looks at the top performances.

Most often, the tie-breaker comes down to the story, or, rather, what Posnanski wishes to demonstrate. He’ll find other reasons to value them. In the Posnanski post I mentioned, I don’t think the piece would make a good story, even if it highlighted his argument well, had it ended differently.

My life (I am an American male) does not revolve around sports. I do follow the Boston Bruins, but they are never must-see TV for me – even when they are in the playoffs. Sorry, I prefer reading, making sure my house is in order, and spending time with family and friends.

My interest in sports run along mathematical lines; I am more interested in statistical analysis and model building than in the games (and especially for baseball.) That and drinking beer while watching games.

So it is strange that I read just about everything Joe Posnanski writes. He writes about baseball, and without exception I  read his pieces about living and long-dead ball players whom I have (mostly) never seen.

This piece is particularly good. The way I would approach describe Posnanski is that he is about nuance. Nick Hornby isn’t the first to notice that males tend to love ranking things. Bill Simmons and Chuck Klostermann have also made similar points, in their own entertaining ways. Posnanski, in addition to offering his own rankings, a number of observations that tempers the ranking. In other words, the separation between 2 players may not be as large as the gulf implied by, for example, a “first” and “second” ranking. This is interesting and somewhat in contrast to the approach of most sports columnists.

At any rate, here’s the nuance: Ryan and Suzuki are the best at what they do, but they don’t rank among the best baseball players ever. I won’t repeat Posnanski’s arguments here, but he’s not out to trash either guy. He’s simply trying to work through and present an informed opinion and analysis. The pair, Ryan and Suzuki, can be considered exceptional players along one-dimension. Ryan threw more strikeouts than anyone; Suzuki is a hit machine. But because of other inefficiencies in their game, they actually do not help their teams as much as one might think (in terms of preventing runs for Ryan and driving the offense for Suzuki.)

The greater point is this: I think Posnanski is among the best writers in explaining numbers to an audience. In all seriousness, I want that talent in describing science to non-scientists. When Posnanski gets rolling on presenting statistical arguments for baseball excellence, I applaud the effort because he is able to note all the ways in which these “binary answers” have many shades of gray. When Posnanski talks numbers, I don’t see a difference between him and a scientist who is trying to explain ideas to laymen. And of course his writing talent makes you want more. Or at least it makes me want to read more.

Although this blog is ostensibly about books, I’ve written a lot about sports, mostly dealing with how non-scientist readers perceive statistical analysis of athlete productivity. This issue fascinates me; I think how people think about sports statistics provides a microcosm in how they may respond to similar treatments in the scientific realm. Economists, mathematicians, engineers and physicists will provide a better explanation of the analysis than I can. Instead, I want to focus on the people who draw (shall we say) interesting conclusions about research.

In a recent podcast, Bill Simmons interviewed Buzz Bissinger on the BS Report (July 28, 2010). Bissinger gained some negative exposure as he had railed against the blogosphere and sports analysis. In this podcast, Bissinger was given some time to elaborate on his thoughts. He most certainly is not a raving lunatic, but he did say a few things that I find representative of how statistical analyses are often misinterpreted by non-scientists (and  even scientists.)

Bissinger took the opportunity to trash Michael Lewis’s Moneyball, mostly by pointing out how Billy Beane isn’t so smart, and that all in the end, the statistical techniques didn’t work – only Kevin Youkilis – mentioned in the book, had proven to be a success. I think that misses the point. Yes, the book documents the tension between the scouts and the stat-heads. I think Lewis chose this approach to make the book more appealing, by taking the human interest angle, than simply writing a technical description of Beane’s “new” approach. Perhaps Lewis overstates the case in showing how entrenched baseball GMs were in relying on eyeball and qualitative skill assessments, but the point I got from the book was that: Beane worked under money constraints. He needed a competitive edge. Most baseball organizations relied on scouts. Beane thought that to be successful, he needed to do something different (but presumably had some relevance) to provide baseball success.

Beane could have used fortune tellers; I think the technique in Moneyball (i.e. statistical analysis) is besides the point. Beane found something that was different and based more of his decisions on this new evaluation method. This is a separate issue from how well the new techniques performed. the first issue is whether the new technique told him something different. As it happens (as documented in Moneyball,  Bill James’s Baseball Abstracts, and by many sports writers and analysts), it did. The result is that Beane was able to leverage that difference – in this case, he valued some abilities that others did not – and signed those players to his roster. The assumption is that if his techniques couldn’t give him anything different from previous methods of evaluation, than he would have had nothing to exploit.

The second point is whether the techniques told him something that was correct. And again, the stats did provide him with a metric that has a high correlation with winning baseball games – the on-base percentage. So one thing he was able to exploit was the perception in value of batting average (BA) versus on-base percentage (OBP). He couldn’t sign power hitters: GMs – and fans – like home runs. He avoided signing hitters with high BA and instead signed those with high OBP.

This led to a third point: Beane can only leverage OBP to find cheap players (and still win) so long as there were few GMs doing the same. Of course the cost of OBP will increase if others come onboard and have deep pockets (like the Yankees and the Red Sox.) So Beane – and other GMs – would have to become more sophisticated in how they draft and sign players. Especially if they work under financial constraints. As my undergraduate advisor said, “You have to squeeze the data.”

One valid point point Bissinger made was that the success of the Oakland A’s coincided with the Big Three pitchers. So clearly, Bissinger wrote off a significant amount of  Oakland success to the three. That’s fine, as the question can be settled by looking at data. What annoyed me is when readers do not pay attention to the argument. I just felt that Moneyball was more about how one can find success by examining what everyone else is doing, and then doing something different. The only constraint is whether  something different would bring success.

I felt that Bissinger is projecting when he assumes that using stats means the rejection of visual experience. The importance of Moneyball is in demonstrating that one can find success by simply finding out what people have overlooked. Once the herd follows, it makes sense to seek out alternative measures, or, more likely, to find out what others are ignoring. If the current trend is on high OBP and ignoring pitchers with a high win-count, then a smart GM needs to exploit what is currently undervalued. Statistics happens to be one such tool – but it isn’t the only tool.

And part of the reason I write this is, again, to highlight the fact that people usually have unvoiced assumptions about the metrics they use. The frame of reference is important. In science, we explicitly create yardsticks for every experiment we perform. We assess things as whether they differ from control. It is a powerful concept. And even if the yardstick is simply another yardstick, we can still draw conclusions based on differences (or even similarities, if one derives the same answer by independent means.)

This brings me to recent Joe Posnanski and David Berri posts. The three posts I selected all demonstrate  the internal yardsticks (hidden or otherwise) that people use when they make comparisons. I am a fan of these writers. I think Posnanski has provided a valuable service in bridging the gap between analysis and understanding, facts and knowledge. Whether one agrees or disagrees with his posts, I think Posnanski is extremely thoughtful and clear about his assumptions and conclusions, which facilicates discussion.  The post has a simple point: Posnanski wrote about “seasons for the ages.” A number of readers immediately wrote to him, complaining about how just about anyone who hits 50 home runs in a season would qualify. To which Posnanski coined a new term (kind of like a sniglet) – obviopiphany.He realized that most people simply associate home runs with a fantastic season for a hitter. That isn’t what Posnanski meant, and in the post he offers some correction.

The Posnanski post has a simple theme and an interesting suggestion: the outrage over steroids may be due to the fact that people assume that home run hitters are good hitters. Since steroids help power, the assumption is that steroids make hitters good – which in most cases simply means more home runs. But Posnanski – and others sabermetricians – propose that one must hit home runs in the context of getting fewer strikeouts and more walks. The liability involved in striking out more, and not walking, is too much and washes out the gains made from hitting the ball far. Thus Posnanski posts names a 5 players who are not in the Hall of Fame, and aren’t home run hitters, but who nevertheless produced at the plate – according to some advanced hitting metrics. I won’t go into this more, except to say that here, Posnanski makes his assumptions clear. He uses OBP+, wins above replacement player, and other advanced metrics to make his point. But it is telling that Posnanski had to stitch together the assumptions his readers had – that the yardstick for good hitting simply boils down to home runs.

The Berri posts describe something similar. One of them is from a guest contributor, Ben Gulker, writing about how Rajon Rondo was not going to be selected for Team USA in the world championship because he doesn’t gather enough points. The other highlights how the perception of Bob McAdoo  changed as a function of the fortunes of his team. Interestingly enough, McAdoo became a greater point getter while becoming a less efficient shooter and turning the ball over more; at the same time, his reputation was burnished by the championships his teams won.

The story has been told many times by Berri. It seems that in general, basketball writers and analysts associate good players as those who score points (in the literal sense, regardless of shooting percentage) and who played on championship teams. There are several problems here. Point getting must take place in the context of a high shooting percentage. One must not turn the ball over, one must rebound, one must not commit an above average number of fouls, and hopefully get a few steals and blocks. I don’t think anyone would disagree that such a player is a complete player and ought to be quite desirable, regardless of how many championship rings he has or if he scores only 12 points a game. Berri has examined this issue of yardsticks, and he has found that what sports writers, coaches, and GMs think of players has an extremely high correlation with, simply, how many points they get (this is shown by what the writers write and how they vote for player awards, how often coaches play someone, and how much GMs pay players.)  The verbiage writing up about the defensive prowess and the “little things” are ignored when the awards are given and fat contracts handed out. Point getters get the most accolades and the most money.

And the other point is how easily point getters reflect the luster of championships. Nevermind that no player can win alone, but this again is an example of how people end up with not only unspoken yardsticks, but also choose a frame of reference without analyzing if it is the correct one. The reference point is a championship ring. As has been documented, championships are not good indicators of good teams. The regular season is. This is simply due to sample sizes. More games are played in the regular season. Teams are more likely to arrive at their “true” performance level than in a championship tourney with a variable number of games – and frankly where streaks matter. A good team might lose four games in a row, in the regular season, but they may lose only 10 for the year. In a tournament, they would be bounced out if they lose four in a series.

In this context, the Premier League system in soccer makes sense. The best teams compete in a regular season; the team with the best record is the champion. So people who assume that a point-getter who plays on a championship is better than a player who shoots efficiently (but with fewer points) and rebounds/steals/blocks/does not turnover above average, and on a non-champion team, make two errors. They selected the wrong metric twice over.

With that said, I could only have made that point because of newer metrics that provide another frame of reference. Moreover, the new metrics tend to have improved predictive abilities over simply looking at point-getting totals. Among the new metrics, there are some that show a higher correlation with the scoring difference (and thus win/loss record) of teams. It doesn’t matter what they are, but an important point is that one can derive these conclusions about which metric is better or worse.

This is the main difference in scientific  (of which I include athlete productivity analysis) and lay discourse. In the former, the assumptions are made bare and frames discussion. A good scientific paper (and trust me, there are bad ones) makes excruciatingly detailed descriptions of controls, the points of comparisons, any algorithms/formulae, and how things are compared. In the lay discourse, this isn’t the standard one would use, because communicating scientific findings to other scientists use a stylized convention. Using such a mode of communication with friends would make one a bore and a pedant – not to mention one would become lonely real quick.

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