By Steve Sailer
03/08/2011
Here’s an article by Dave Johns in Slate about NBA statistics. A huge amount of intellectual effort has been expended in recent years to bring basketball up to speed statistically with baseball. There has been a lot of progress, but the Holy Grail goal of coming up with a foolproof system for ranking players still has no consensus.For example, how good is Kevin Love (the 22-year-old Minnesota center who is an offshoot of the extended clan of Loves and Wilsons famous for the Beach Boys)? For example, Offensive Win Shares rates him as the best offensive player in the league and fifth best overall in total Win Shares. Offensive Rating sees him as the fifth best offensive player in the league. Player Efficiency Rating says he’s the third best player in the league. Other rating systems don’t see him in the top ten.
Johns likes to disparage Love to show the problems being dealt with by the sophisticated statistics:
Rebounds also suffer from so-called "diminishing returns"-the idea that players on the same team effectively compete with one another for boards. Often a particular player-say, Minnesota Timberwolves center Kevin Love-serves as his team’s designated glass-cleaner, and he scoops up balls that his teammates might well have grabbed anyway.
Okay, but, presumably, coach Kurt Rambis tells his players to let Love grab the easy rebounds (such as missed free throws by the other team) because he has such a good outlet pass, which ought to count for something in an overall ranking, right? Moreover, Love’s offensive rebounding statistics are stellar, and there aren’t all that many easy offensive rebounds.
The [plus-minus] technique can also examine the impact of top rebounders: Kevin Love consistently rebounds in double digits, but his contribution to his team’s total boards is only about two to three per game, according to one analysis.
But the analysis Johns links to shows Love as being the best rebounder in the league by a margin of about 20% over the second best rebounder.
But, old fashioned stats can give a more well-rounded picture of Love than advanced rankings. This year, Love is on track to become the first player since Moses Malone’s last MVP season in 1983 to average over 20 points and 15 rebounds per game (He’s currently at 20.9 and 15.8). He’s making 42.7% of three pointers and 86.2% of free throws, which are outstanding percentages for a center.
On the other hand, Love is under 6'8" in his bare feet and is a white guy who can’t jump all that great: he doesn’t block shots (only 0.4 per game, which is really low for a center).
(By the way, my impression is that rebounding correlates better with being a good all-around basketball player than does shot-blocking. The all-time bjg men like Russell, Chamberlain, Kareem, and Walton tended to be great at both rebounding and shot-blocking, but lots of guys are only good at one or the other. In general, the guys who are only good at shot-blocking are more often the weird Manute Bol-type talents. For example, on the playground, I was a pretty good shot blocker but I was an all-time awful rebounder. Partly it was getting pushed around by less skinny guys, but much of my rebounding deficit was cognitive: I never had the slightest clue where the ball was going to bounce. In contrast, I had a pretty good idea when somebody was going to shoot.)
Moreover, Love and doesn’t create much offensively down low (making only 48.3% of two pointers). His team, the Minnesota Timberwolves, has a very bad won-loss record, 15-50, and gives up a lot of points.
The more I look at it, the more I come around to middlebrow sportswriter Bill Simmons’s convenient conclusion that basketball’s old-fashioned box score stats are quite useful and Holy Grail one-number ranking statistics haven’t yet gotten there. Looking at his non-advanced stats, Love’s unusual combination of bulk and touch makes him look like a guy who could be extremely useful on a good team (like, say, Bill Laimbeer on the Detroit Pistons of the late 1980s) but who (at least not yet in his quickly evolving career) can’t be expected to carry a bad team.
On the other hand, there’s a lot of learning available from advanced stats that don’t try to rank everybody, but just try to look at elements of performance, such as a player’s shooting percentage from the left or right sides of the court.
Much less intelligence has been devoted to analyzing teacher performance. Much of the recent work has been devoted to, yes, the Holy Grail of ranking teachers on Value Added so that bad teachers can be fired and good teachers rewarded.
The New York Times has an article on a hard-working NYC 7th grade English teacher with two Ivy League degrees who is much admired by all her students, many of whom qualify for Stuyvesant, yet who only ranks at the 7th percentile among all NYC teachers. The article shows the complex formula used in the calculation, which appears to baffle most teachers.
I’m always looking for sports analogies for social science statistics, since Americans think harder about sports. Being a teacher isn’t really like being a player, it’s more like being a coach. Probably the closest analogy is being a coach in a big high school with separate Freshman, Sophomore, and Varsity football teams. If you are the Sophomore team’s coach, you more or less inherit the Freshman team’s players (although the best will be sent up to the Varsity). So, if your Sophomore team consistently winds up with a worse record than the same players achieved on the Freshman team, your job will be in trouble.
Yet, there are all sorts of complications.
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