Economics is Not a Science, Nor is Basketball Statistical Analysis, Part III
Gideon Rachman, a columnist for the
Financial Times,
declares "The vanity of economists needs to be challenged. Above all, their claim to scientific rigour--buttressed by models and equations--must be treated much more sceptically." Rachman explains, "When things were going well for the global economy, the prestige of economists rose steadily. They were the gurus of the age of globalisation. Governments, consultancies and investment banks rushed to hire economists, who were thought to possess vital skills and information...There has been some self-examination and soul-searching within the economics profession since the onset of the financial crisis." Rachman then quotes Nobel Prize-winning economist Professor Joseph Stiglitz: "If science is defined by its ability to forecast the future, the failure of much of the economics profession to see the crisis coming should be a cause of great concern." However, Rachman rejects Professor Stiglitz' tepid suggestion that economists should search for new "paradigms" (i.e., simply create more effective mathematical models); Rachman asserts that it is "misconceived" to categorize economics as a science.
What does this have to do with basketball? Many "stat gurus" received their formal academic training as economists and/or have been heavily influenced by the type of statistical modeling that economists do. While some "stat gurus"--most notably Dean Oliver, Roland Beech and Dan Rosenbaum--are candid about the limitations of basketball statistical analysis, others (particularly Dave Berri) act as if they have discovered the mathematical Holy Grail that unlocks all of the secrets to understanding basketball at an individual and team level. The problem with adapting economic mathematical modeling for use in analyzing basketball is that economic modeling is not even effective at what it was designed to do--namely, explain how economic markets work and predict how they will perform in the future--so it is quite a stretch to believe that something that failed in its primary function will somehow magically explain the inner workings of a sport as complicated and nuanced as professional basketball.
It is certainly a worthy goal to
try to create an accurate mathematical/statistical method for player/team evaluations but that process is still in its infancy regarding pro basketball and it is counterproductive for anyone to assert otherwise; instead of media members and fan bloggers sycophantically praising the likes of Berri there is a need for strong, authoritative questioning of the methodologies used by "stat gurus." For instance, there is good reason to believe that box score numbers--the raw data used by "stat gurus"--
are not accurate. What, if anything, are "stat gurus" doing to improve the quality of the box score data and/or how are they accounting for the unreliability of that data when they create their "advanced" formulas?
Even in cases where the box score data is accurate it still does not tell the complete story: if Pau Gasol sets a screen for Kobe Bryant, two defenders trap Bryant, Bryant passes to Lamar Odom at the top of the key and Odom feeds Gasol for a dunk the box score will show two points and a made field goal attempt for Gasol and an assist for Odom and nothing for Bryant--but if Bryant had not drawn two defenders then the entire play would not have been possible (or, at the very least, Odom's pass would have been contested to a greater degree, as would have Gasol's shot). I am not suggesting that a new stat should be created to describe the impact that Bryant had on that play but rather I am just citing one example of how difficult it is to accurately quantify what happens even on a relatively simple basketball play. This is why I trust "advanced" five on five data more than I trust individual player data: if a given five man unit has played a large enough sample of minutes against a representative sample of teams (two big "ifs") then their collective plus/minus number gives some indication of their effectiveness as a five man unit--but you cannot parse that data to figure out why that group was effective; to determine that you still have to watch video with an educated eye and break down what exactly happened on the court.
The "hard" sciences--like physics and chemistry--make progress because strong-minded, intelligent people question previous theories in order to either strengthen or topple those thoeries; even though Isaac Newton's Theory of Gravity provided a useful model for many centuries Albert Einstein questioned it and ultimately replaced it with a more complete theory (Special Relativity). Can any intelligent, open minded person really believe that within the past five to 10 years the "stat gurus" have "solved" basketball to the extent that their mantras should never be questioned and that all one needs to do to understand the sport is to stop watching games and start devouring spreadsheets? "Stat guru" advocates like Henry Abbott tend to refer to my articles about this issue as "cranky" and they suggest not too subtly that I am some kind of anti-stat Luddite who is stubbornly clinging to outdated notions but the reality is that the field of "advanced basketball statistics" would benefit more in the long run if its practitioners critically examined their methodologies instead of basking in Abbott's praise while snobbishly dismissing valid critiques.
As Rachman mentioned, before the global economy tanked economists were in high demand and the field garnered a lot of prestige--but the stiff wind of economic mayhem has trashed the thin facade of that Potemkin Village and the glorification of economists will likely be viewed by future generations as nothing more than strange reverence for a cult; many of the practitioners of "advanced basketball statistics" will have to become more rigorous in their methodologies and more conservative about their assertions or they will ultimately suffer the same fate, regardless of how many writers ride the short term gravy train by becoming "converts" and praising every utterance of the "high priests."
Labels: basketball statistical analysis
posted by David Friedman @ 1:40 AM
Economics is Not a Science, Nor is Basketball Statistical Analysis, Part II
Russ Roberts' recent Wall Street Journal article titled Is the Dismal Science Really a Science? makes some of the same points that I have stated forcefully for several years. Roberts--who is an economics professor at George Mason University--declares that economics is not a science because "most sciences make progress. Nobody in medicine wants to bring back lead goblets. Sir Isaac Newton understood a lot about gravity. But Albert Einstein taught us more." Roberts adds, "Facts and evidence still matter" but that instead of proving or disproving theories it seems like economists are primarily just "confirming our biases." Roberts concludes, "The economy is a complex system, our data are imperfect and our models inevitably fail to account for all the interactions. The bottom line is that we should expect less of economists. Economics is a powerful tool, a lens for organizing one's thinking about the complexity of the world around us. That should be enough. We should be honest about what we know, what we don't know and what we may never know. Admitting that publicly is the first step toward respectability."
The basketball "stat gurus" who received their training as economists should take heed of Roberts' words and likewise be "honest" about the limitations of their methodologies--but, of course, that will likely never happen, because how can Dave Berri and others sell books if they admit that all they are doing is shuffling numbers on spreadsheets to confirm their biases and that evaluating basketball players and teams is a far more complex enterprise than they care to admit?'
As I wrote in October 2008, Economics is Not a Science, Nor is Basketball Statistical Analysis. The data being used by "stat gurus" is incomplete and often inaccurate. Statistics can be a "powerful tool" (in Roberts' words) to help to understand basketball but they do not provide definitive answers: the player rankings produce by Berri or John Hollinger do not represent some absolute, objective reality but merely reflect the biases and limitations inherent in the formulas that Berri and Hollinger invented.
In The Difference Between Measuring Defense in Basketball and Baseball, I made the important point that basketball statistical analysis is pseudoscience because its practitioners do not base their research on the scientific method:
- Ask a Question
- Do Background Research
- Construct a Hypothesis
- Test Your Hypothesis by Doing an Experiment
- Analyze Your Data and Draw a Conclusion
- Communicate Your Results
If you are still wasting your time reading the proclamations of "stat gurus," then the next time one of these oracles speaks take note if he mentions a margin of error for his numbers, see if he explains the sample size from which he derives his calculations and monitor how he explains cause/effect relationships--i.e., it is one thing to note that a certain player's turnovers are down or his field goal percentage is up but quite another matter to explain, in the context of nine other players on the court at any given time, why that player's performance has fluctuated in a particular manner.
Labels: basketball statistical analysis, Dave Berri, John Hollinger, Russ Roberts
posted by David Friedman @ 6:39 PM
The Difference Between Measuring Defense in Basketball and Baseball
The April 6, 2009 issue of
Sports Illustrated contains an article by Albert Chen titled
Baseball's Next Top Models; Chen describes how baseball teams are using advanced statistics to ascertain which players are the best fielders at each position. The Tampa Bay Rays won the 2008 American League championship largely because they tremendously improved their defense by using advanced statistics as the basis for various personnel moves and for deciding how to most effectively deploy the players on their roster to maximize their defensive skills (for instance, they moved Akinori Iwamura from third base to second base not only because his defensive statistics are better at the latter position but also to make room for Evan Longoria to be called up as the new third baseman). Baseball statisticians have access to data that pinpoints where every single batted ball went and whether or not the fielder converted that play into an out. Although there are at least 10 players on a baseball field at any given time (one pitcher, eight fielders, one batter--assuming that there are no men on base), virtually everything that happens when the ball is in play can be broken down into a series of discrete, one on one actions: the pitcher throws the ball, the batter swings and, if he makes contact, a fielder attempts to catch the ball. Therefore, if one gathers together a large enough sample size of data, it is possible to create reliable models regarding pitchers, batters and fielders.
Obviously, basketball is a much more fluid and complex sport than baseball, at least in terms of constructing meaningful statistical models: even during an "isolation" play ostensbily involving only one ballhandler and one defender the other eight players on the court all have the potential to affect what will happen--the other four defenders may end up trapping and rotating, while the other four offensive players (depending on their size and skill sets) may be called upon to set a screen, cut to the hoop, spot up for an open jump shot or grab an offensive rebound. The play may result in an offensive rebound tip dunk or a made three pointer that never would have happened if the original ballhandler had not been talented enough to attract extra defensive attention but in the box score that original ballhandler may either receive credit for nothing (if he passes the ball and the recipient then swings it to a player who ultimately makes a three pointer) or he may even record a negative statistic (a missed field goal attempt) despite the fact that his actions directly led to the opening that created the putback opportunity.
Clearly, it is difficult for basketball statistics to fully capture what happens offensively; progress has been made in this regard but it is far from an exact science--and it is even more challenging to accurately measure basketball defense, particularly on an individual level. A perfect example of why individual basketball defense is tough to quantify took place in the first quarter of Boston's 106-104 game five overtime victory versus Chicago: Kendrick Perkins caught the ball on the left block versus Tyrus Thomas, spun baseline and scored a layup. TNT's Doug Collins noted that Thomas had positioned himself by Perkins' left shoulder (i.e., overplaying Perkins to force him to go to the baseline) because Perkins' best move from that spot is to go to the middle and shoot a jump hook; Thomas was supposed to receive help on the baseline--on an earlier play, help defender Derrick Rose stole the ball so easily from Perkins it looked like Rose was receiving a football handoff--but this time the help never arrived. How would a basketball "stat guru" evaluate that play in terms of Thomas' individual defense? Thomas' defensive rating would indicate that he allowed Perkins to score against him. Plus/minus data would award Perkins a +2 and Thomas a -2 and would also "indict" the other Bulls' defenders who were on the court at that time but would not reveal who was really at fault. A knowledgeable basketball observer would understand--as Collins immediately explained to the viewers--that Thomas did what he was supposed to do but that the help defender never arrived. Multiply this type of scenario over thousands of plays during the course of a season and it is easy to see why someone who watches basketball with understanding may come to a completely different conclusion about a player's value/skill set than someone who relies on nothing but numbers.
What about the success that Houston's General Manager Daryl Morey has had using advanced basketball statistics, as detailed in a
New York Times article that I discussed
here? If basketball statistical analysis is truly science and not pseudoscience, then it has to be based on the principles of
the scientific method:
- Ask a Question
- Do Background Research
- Construct a Hypothesis
- Test Your Hypothesis by Doing an Experiment
- Analyze Your Data and Draw a Conclusion
- Communicate Your Results
One "hypothesis" mentioned in the
New York Times article is that Daryl Morey and his staff of numbers crunchers can use advanced basketball statistics to devise a game plan to slow down Kobe Bryant, the 2008 MVP and a two-time scoring champion. If we consider the four games that Bryant's Lakers played against Morey's Rockets this season to be the "experiment," then the "data" show not only that Bryant's Lakers won all four contests (with a convincing 13.0 ppg differential) but that Bryant averaged 28.3 ppg versus Houston while shooting .530 from the field and .533 from three point range, exceeding his overall regular season averages in all three categories; oddly, Bryant's free throw percentage versus Houston was only .680 (well below his .856 regular season average) but I doubt that even the most ardent advocates of basketball statistical analysis will claim that this is a result of Houston's "free throw defense." So, the results of this "experiment" show that advanced basketball statistics have yet to enable Houston to defend Bryant more successfully than other NBA teams--and this is despite the fact that the Rockets have two of the best one on one perimeter defenders in the NBA (Ron Artest and Shane Battier) plus a 7-6 shotblocking center (Yao Ming).
Don't think that I am picking on Morey or Houston; as I wrote in my PBN article cited above, I appreciate that Morey is fully aware of the current limitations of basketball statistical analysis:
It cannot be emphasized strongly enough that Morey is not merely looking at spreadsheets and randomly assigning arcane values to certain combinations of numbers; statistics give him an indication of what to look for when he watches game film but he still has to watch game film to determine why players are putting up the numbers they do and to figure out what exactly those numbers mean.
In other words, Morey appears to understand the limits of a purely mathematical approach to the game and thus uses numbers to confirm what his eyes tell him -- and vice versa. This is a completely different approach from the one taken by far too many stat gurus who are so enamored with their formulas that they dismiss the importance of actually watching games -- perhaps because they are in fact not truly capable of watching basketball games with any real understanding of what is happening on the court.
It is a laudable goal for basketball statisticians to strive to analyze the sport as effectively as baseball statisticians evaluate baseball but when "stat gurus" and their buddies in the writing business act as if basketball has already been "solved" from an analytical/statistical standpoint they are actually hurting their cause more than helping it, because intelligent observers can plainly see that such claims are false. As Cleveland General Manager Danny Ferry
recently told me about basketball statistical analysis, "to just make decisions off of statistics would be a mistake but it can be an important part of the equation in basketball." It would be foolish for an NBA GM to not look at statistical data but it would be even more foolish for him to rely solely or even primarily on such data at this juncture; in the Perkins/Thomas example, it is much more useful for a GM or coach to know that Thomas did what he was assigned to do--and to find out which player missed the help assignment--than to get a spreadsheet filled with numbers detailing how many times Perkins scored in the post with Thomas as the primary defender, because without the proper context that data could be dangerously misleading if it influenced the GM or coach to make a negative evaluation of Thomas' defense.
Labels: basketball statistical analysis, Boston Celtics, Daryl Morey, Houston Rockets, Kendrick Perkins, Kobe Bryant, Shane Battier, Tyrus Thomas
posted by David Friedman @ 4:48 AM
"The Formula That Killed Wall Street": A Cautionary Tale for Anyone Who Places Too Much Value on Basketball Statistical Analysis
I have written several articles that are critical of the methods used in basketball statistical analysis, including a post titled
Economics is Not a Science, Nor is Basketball Statistical Analysis. Some of the most prominent proponents of basketball statistical analysis are economists by trade and they apply the methodologies and techniques used in that field to try to quantify what happens on the basketball court. Let me make it quite clear that I am entirely in favor of
trying to better quantify and measure the effectiveness of basketball players and teams; what I object to is the haughty contentions by some people in this field that they have already succeeded in
accurately making such measurements. Basketball statistical analysis provides some interesting tools that can assist anyone who is trying to compare players and teams but there are limitations to what the numbers alone can accurately depict.
Basketball statistical analysis and new video technology have already made for a good marriage in terms of helping teams to more easily produce accurate scouting reports depicting the tendencies, strengths and weaknesses of players and teams. It is now possible to quickly break down game film (or, to be precise, game DVD) and catalog what happened on every pick and roll play, every out of bounds play, every postup and so forth. Most if not all teams are already applying some form of statistical analysis to the tendencies that emerge from game footage and this obviously represents a quantum leap forward in terms of game planning. Cavaliers assistant coach Hank Egan called this "corporate knowledge" when I interviewed him
more than three years ago and he said that technological improvements have helped to increase the sophistication of defensive play in the NBA.
The problem is when some people invent certain formulas in which they add up some numbers, multiply other numbers by certain factors, subtract some other numbers and then produce a final number that supposedly "rates" a player's overall performance. It should be obvious that this "rating" is limited by several factors: the accuracy of the original boxscore data, whether or not the additions, multiplications and subtractions correctly value what a player does and, perhaps most importantly of all, the fact that not everything that a player does on the court is captured numerically. I have yet to see any of these stat gurus say that Player X is rated 33.4 with a margin of error of +/- 2.5 points; the stat gurus don't even mention a margin of error because they could not begin to calculate one: they are not performing scientific measurements like a biologist or an astrophysicist--they are massaging basketball statistics in a way that they find appealing and that they
believe to be correct (or that will produce conclusions that fit in with their own preconceptions and will be easy to market to book publishers or in other forms of media).
It is interesting that, until fairly recently, economists believed that they had created a formula that--as
Wired author Felix Salmon writes in
Recipe for Disaster: The Formula That Killed Wall Street--"allowed hugely complex risks to be modeled with more ease and accuracy than ever before." Salmon explains that David X. Li's "Gaussian copula function" formula "made it possible for traders to sell vast quantities of new securities, expanding financial markets to unimaginable levels. His method was adopted by everybody from bond investors and Wall Street banks to ratings agencies and regulators. And it became so deeply entrenched—and was making people so much money—that warnings about its limitations were largely ignored." Li's formula provided "a brilliant simplification of an intractable problem," enabling financial analysts to plug in some data and derive "one clean, simple, all-sufficient figure that sums up everything." The problem is that "people used the Gaussian copula model to convince themselves they didn't have any risk at all, when in fact they just didn't have any risk 99 percent of the time. The other 1 percent of the time they blew up. Those explosions may have been rare, but they could destroy all previous gains, and then some." The economic mess that we are all struggling through now is one of those "1 percent explosions."
Nicholas Nassim Taleb, who I quoted in my
Economics is Not a Science, Nor is Basketball Statistical Analysis post, told Salmons, "People got very excited about the Gaussian copula because of its mathematical elegance, but the thing never worked. Co-association between securities is not measurable using correlation. Anything that relies on correlation is charlatanism."
Salmon concludes, "In the world of finance, too many quants (quantitative analysts) see only the numbers before them and forget about the concrete reality the figures are supposed to represent. They think they can model just a few years' worth of data and come up with probabilities for things that may happen only once every 10,000 years. Then people invest on the basis of those probabilities, without stopping to wonder whether the numbers make any sense at all."
This is very analogous to the current situation with basketball statistical analysis; too many of its practitioners "see only the numbers before them and forget about the concrete reality the figures are supposed to represent." They do not have the wisdom or humility to admit that their simple, pretty formulas represent just one, limited interpretation of raw data.
A
Wired article titled
Road Map for Financial Recovery: Radical Transparency Now! contains a parable that very aptly describes the flaws in the methods used by some basketball statistical analysts:
As (Christopher) Cox sees it, that massive computational power has primarily been used by financial engineers, who create abstract models of how the market should operate and make bets based on those models. "You know Borges, the writer?" Cox asks. "He wrote those fantastical short stories. He has one called On Exactitude in Science." The parable tells of a kingdom obsessed with creating a perfect map of itself—an essentially useless quest that leads them to draw a map that is the same size as the territory it is supposed to represent. Cox sees the story as a metaphor for the modern financial industry, which is so obsessed with modeling the market that it has lost sight of the data beneath those models.Basketball statistical analysts do not yet have all of the necessary data to completely "model" the sport, nor do they fully understand how to use the data that they have. Trying to produce such a model is certainly a worthy task--but I just wish that the people who are working toward this goal would stop declaring "Mission Accomplished!" when the reality is that they are in the beginning or intermediate stages of that mission.
Labels: basketball statistical analysis, Felix Salmon, Nassim Nicholas Taleb, Wired
posted by David Friedman @ 12:07 AM
The Proper Application of Basketball Statistical Analysis
Although I wrote a post titled Economics is Not a Science, Nor is Basketball Statistical Analysis, I certainly support any and all efforts to make basketball statistical analysis more precise and scientific. As I wrote in that article, "No one should misinterpret what I am saying to mean that I am some kind of Luddite who is against using basketball statistics; what I am against is the misuse of basketball statistics, just like I am against the misuse of media platforms by people who spout hype and biased commentary as opposed to communicating information in a fact based, objective manner."
Daryl Morey, the General Manager of the Houston Rockets, uses basketball statistical analysis as one tool to evaluate players but, as Michael Lewis explained in a recent New York Times article, Morey is fully aware that numbers only tell part of the story and that to completely understand what happens in a basketball game you have to watch the sport with understanding.
Here is an examination of what Lewis' piece tells us about basketball statistical analysis, and what Lewis' piece says about why Shane Battier defends Kobe Bryant as well as any other player in the NBA:
A recent New York Times article by Michael Lewis provides an intelligent, in-depth discussion of how basketball statistical analysis can be properly applied to help executives and coaches make personnel and strategic decisions that will increase their team's chances to win. Lewis focuses on the stories of two people: Houston Rockets General Manager Daryl Morey and one of Morey's prize acquisitions, forward Shane Battier.
Battier won the 2001 NCAA Player of the Year award but he is nevertheless -- in Morey's words --"at best a marginal NBA athlete." Morey notes that virtually the only way Battier can score is by making spot-up jumpers right after receiving a pass.
Morey explains, "That's the telltale sign of someone who can't ramp up his offense. Because you can guard that shot with one player. And until you can't guard someone with one player, you really haven't created an offensive situation. Shane can't create an offensive situation. He needs to be open." Morey concludes, "He can't dribble, he's slow and hasn't got much body control."
This frank assessment of Battier's physical limitations did not prevent Morey from discerning that Battier can be a valuable member of a playoff caliber NBA team. What did Morey see in Battier that many other observers -- including Houston owner Leslie Alexander, who initially was very skeptical about trading for Battier -- did not? Here is Lewis' explanation:
Battier's game is a weird combination of obvious weaknesses and nearly invisible strengths. When he is on the court, his teammates get better, often a lot better, and his opponents get worse -- often a lot worse. He may not grab huge numbers of rebounds, but he has an uncanny ability to improve his teammates' rebounding. He doesn't shoot much, but when he does, he takes only the most efficient shots. He also has a knack for getting the ball to teammates who are in a position to do the same, and he commits few turnovers. On defense, although he routinely guards the N.B.A.'s most prolific scorers, he significantly reduces their shooting percentages. At the same time he somehow improves the defensive efficiency of his teammates -- probably, Morey surmises, by helping them out in all sorts of subtle ways. "I call him Lego," Morey says. "When he's on the court, all the pieces start to fit together. And everything that leads to winning that you can get to through intellect instead of innate ability, Shane excels in. I'll bet he's in the hundredth percentile of every category."
Morey tells Lewis that in basketball there can be, as Lewis phrases it, "a tension ... between the interests of the team and the interests of the individual." This does not exist so much in baseball or football. "There is no way to selfishly get across home plate" is the analogy Morey uses; in contrast, Morey says that when he worked for the Celtics the team had a point guard who would not pass the ball to a particular player, something that would not necessarily negatively affect that point guard's statistics but definitely negatively affected the team.
A basketball player can amass good individual statistics but not really be helping the team overall; my favorite example of this phenomenon is Stephon Marbury, who has produced more 20 ppg/8 apg seasons (six) than anyone other than Oscar Robertson (10)--but Marbury has made every team he joined worse (and every team he has left has gotten better, including this year's Knicks after management banished Marbury). Morey points out that at contract time, assists are more valuable for a point guard than points scored, so point guards sometimes eschew their own easy scoring opportunities to make passes that they hope will turn into assists; although on the surface these plays may seem to be unselfish, each extra, unnecessary pass entailed a risk that slightly lowered the probability of the team scoring on that possession.
In contrast, Battier's value is that he is--as Lewis puts it--"the most abnormally unselfish basketball player" Morey has ever observed. Battier willingly sacrifices his personal statistics for the betterment of the team. Morey uses an adjusted plus/minus system as one analytical tool to evaluate players. Plus/minus simply records the point differential when a player is on the court versus when he is off the court, so the number can be "noisy" due to the impact that the other nine players have on the game but Morey uses certain methods that he will not disclose in order to get rid of a lot of the "noise." By Morey's reckoning, Battier's adjusted plus/minus ranks him among the very best players in the NBA.
Other numbers show that when players who are widely recognized as superstars are guarded by Battier they tend to have subpar games. Morey had to watch a lot of game film to figure out why this is the case and what he discovered is that Battier does a lot of subtle things to make the game more difficult for the player he is guarding. You can
read the article to get all of the details about exactly how Battier does this but the important point is that Morey did not reach his conclusions about Battier merely by crunching some numbers; the numbers gave Morey some idea of what to look for but to complete his analysis he still needed to watch game film with understanding.
It cannot be emphasized strongly enough that Morey is not merely looking at spreadsheets and randomly assigning arcane values to certain combinations of numbers; statistics give him an indication of what to look for when he watches game film but he still has to watch game film to determine why players are putting up the numbers they do and to figure out what exactly those numbers mean.
In other words, Morey appears to understand the limits of a purely mathematical approach to the game and thus uses numbers to confirm what his eyes tell him--and vice versa. This is a completely different approach from the one taken by far too many stat gurus who are so enamored with their formulas that they dismiss the importance of actually watching games--perhaps because they are in fact not truly capable of watching basketball games with any real understanding of what is happening on the court.
A significant portion of Lewis' article delves deeply into the specific reasons that Battier is able to guard 2008 NBA MVP Kobe Bryant more effectively than just about anyone else in the league. Lewis notes that although Bryant "is better at pretty much everything than everyone else...there are places on the court, and starting points for his shot, that render him less likely to help his team."
Battier has an extraordinarily high basketball IQ, so the Rockets feed him scouting reports that are much more detailed than usual. Battier's genius as a defender is that during the game he is able to process all of this information and thus attempt to force Bryant to do things that he does relatively less well.
It is important to understand that neither Battier nor any other sole defender can truly shut down Bryant. As Sam Hinkie--Houston's Vice President of Basketball Operations and the head of the team's front office basketball analytics staff--says, "With most guys, Shane can kick them from their good zone to bad zone, but with Kobe you're just picking your poison. It's the epitome of, Which way do you want to die?"
Much of the latter portion of Lewis' article describes a Jan. 13 game that the Rockets lost to the Lakers, 105-100. Battier hounded Bryant into 13-32 field goal shooting but Bryant still scored 33 points and in the fourth quarter he had 13 points on 5-9 shooting, including a huge three-pointer with just 27 seconds left. The Rockets could be pleased that they forced Bryant into a below-average shooting night and that he had to make a low-percentage shot to clinch the game but the bottom line is that sometimes a player or a team can do everything by the numbers and still not be able to stop a great player. Of course, Morey believes that in the long run if the Rockets consistently play by the numbers--taking high-percentage shots and making low-risk plays while forcing opponents to do the opposite--then they will greatly increase their chances to win games, playoff series and, ultimately, a championship.
Labels: basketball statistical analysis, Kobe Bryant, Michael Lewis, Shane Battier
posted by David Friedman @ 4:36 AM
A Computer Model Reproduces Data, It Does Not Provide It
In a January 17, 2003 lecture at the California Institute of Technology (a portion of which was recently
reprinted by The Wall Street Journal), Michael Crichton declared:
I take the hard view that science involves the creation of testable hypotheses...
I regard consensus science as an extremely pernicious development that ought to be stopped cold in its tracks. Historically, the claim of consensus has been the first refuge of scoundrels; it is a way to avoid debate by claiming that the matter is already settled. Whenever you hear the consensus of scientists agrees on something or other, reach for your wallet, because you're being had.Let's be clear: The work of science has nothing whatever to do with consensus. Consensus is the business of politics. Science, on the contrary, requires only one investigator who happens to be right, which means that he or she has results that are verifiable by reference to the real world. In science consensus is irrelevant. What is relevant is reproducible results. The greatest scientists in history are great precisely because they broke with the consensus.
There is no such thing as consensus science. If it's consensus, it isn't science. If it's science, it isn't consensus. Period...I would remind you to notice where the claim of consensus is invoked. Consensus is invoked only in situations where the science is not solid enough. Nobody says the consensus of scientists agrees that E=mc2. Nobody says the consensus is that the sun is 93 million miles away. It would never occur to anyone to speak that way...
To an outsider, the most significant innovation in the global warming controversy is the overt reliance that is being placed on models. Back in the days of nuclear winter, computer models were invoked to add weight to a conclusion: "These results are derived with the help of a computer model." But now large-scale computer models are seen as generating data in themselves. No longer are models judged by how well they reproduce data from the real world--increasingly, models provide the data. As if they were themselves a reality...
This fascination with computer models is something I understand very well. Richard Feynman called it a disease. I fear he is right. Because only if you spend a lot of time looking at a computer screen can you arrive at the complex point where the global warming debate now stands.
Nobody believes a weather prediction twelve hours ahead. Now we're asked to believe a prediction that goes out 100 years into the future? And make financial investments based on that prediction? Has everybody lost their minds?What does this have to do with basketball? Some people believe that the sport can be scientifically analyzed by crunching numbers a certain way--but, as Crichton notes, there is a big difference between using a computer model to "add weight to a conclusion" (or, in basketball terms, provide some information about the performance of a player or a team) and using a computer model to "provide the data. As if they were themselves a reality." If you build a computer model of box score data that values rebounding or not turning the ball over or shooting a high percentage and then rank players accordingly, that model will provide some interesting results to consider--but those results are a model; they are not reality and it is possible to tinker with the numbers to produce a another model that will produce completely different rankings. In other words, as I have repeatedly said,
Economics is Not a Science, Nor is Basketball Statistical Analysis.Labels: basketball statistical analysis, Michael Crichton
posted by David Friedman @ 6:30 AM
