Combine technology, statistics, and sports to find that sweet spot where science and persuasion meet.

Head coach Keith Guy and the Muskegon Heights Tigers headed into Tuesday night’s quarterfinals of the Michigan high-school basketball playoffs armed with a secret weapon. On the coach’s iPad, there was a series of charts and diagrams that plotted almost everything that’s plottable about his team and its opponents, Cadillac High School. This included their shot locations and scoring pace, the offensive and defensive potency of every five-man unit they’d put on the floor this season and how effective their star players have been when they’ve received the ball at any spot on the court.

Moneyball hits the high school hardwood. For about $2,000 bucks cutting edge high school basketball coaches acquire the services of

Vasu Kulkarni, a 25-year-old computer whiz and basketball junkie from Bangalore, India, who developed a program that helps human analysts quickly break down game film.

Coaches send digital game film to Kulkarni who farms it out to analysts who then run it through Kulkarni’s program to spew out enough statistics to warm the heart of a second year research methods and statistics grad student. All that sabermetrician sass, just like the pros, only at your local high school.

The science part of me shouts, Show Me The Money! I have trouble believing that this approach makes much positive difference, especially at the high school level, but even at the pros. I’m not aware of any good research evidence that demonstrates a practical, positive effect for winning championships with moneyball. Not that it isn’t illuminating if only for embarrassment.

For coaches, Krossover’s results can be rather shocking. For years, Tammy Lusinger, head coach of the girls’ basketball team at Mansfield Summit High School in Arlington, Texas, had a favorite play called “Bama” in which the girls cleared out one side of the court then set up a series of screens to free a player for an open shot. “It’s a great looking little play,” Lusinger said last week. But after she started using Krossover, she was in for a surprise: The numbers showed that they were only scoring on that play 5% of the time.

Talk about a fabulous demonstration of the powers of self persuasion. Coaches and players convince themselves that they’ll beat you with Bama, then they Count the Change and come up short. See how easy it is to fool yourself in a complex game like basketball or even life. But back to my original request to view the money. Here’s the closing line on the Bama story.

Earlier this month, Lusinger and Mansfield Summit won their second Texas state championship in the last four years.

See, Kulkarni began selling this service in 2010 which means that Tammy Lusinger’s team won a state championship without it and then won a championship with it. I appreciate the lessons learned and that the service can be a valuable teaching tool, but where’s the money?

Just as people mistakenly believed Bama made them invincible, they will also mistakenly believe that high school hardwood moneyball makes them invincible. Again, don’t get me wrong. As the son of a high school football coach, I appreciate preparation and game tape (ask me how many times I’ve seen the coach’s film of the 1956 Tangerine Bowl). Information is useful. But we’re clearly in a statistical stampede and that brings us back to persuasion.

You see the powerful Normative influence going on here. Simply as the Comparison Cue (If Others Are Doing It, You Should, Too), high school moneyball is golden. And, it can function as a Warrior Cue, like those H-P calculators that mark seasoned financial veterans against their younger laptop colleagues. Just flash an image from Krossover during warm-ups and the opponents will know they are facing a technologically superior team!

All of this is made possible through the double edged sword of cheap technology. Kulkarni has made a computer program that takes raw digital images, converts motion into numbers, then sausages those numbers into millions of marvelous casings called EFG%, ORtg, PER, and even OWS. Every adoring parent has the latest digital video camera and you can see the moms and dads coordinating themselves into teams with plans for positioning and editing. And parents probably pony up the 2k in cash to buy the Krossover service.

In its own way the Moneyball Persuasion Play play fools people with randomness. Simply because technology makes Scientific Science available, people think they now know something when all they are doing is running the new version of Bama to win the state championship.

Read the Count in the NYTimes.

A map, on the other hand, turns the mathematical rigor into a simple chart. Goldsberry showed off a map that plotted every spot on the floor where Bryant had shot from, color-coded by shooting percentage, so that it was clear where the defense would do best to send him. Virtually anybody can understand a well-designed chart,” Goldsberry said.

Goldsberry is a visiting scholar at . . . can you be surprised . . . Harvard. He’s done these colorful charts for public health that have Counted Everything with Color and Shape thereby Changing the World of epidemiologists and public health. Now, he’s bringing his talents to sports.

All those Relative Ratios in Living Color!

This is the curse of Billy Beane and a misreading of Michael Lewis’s great book on Beane, Moneyball. Beane got a bean-counter to solve a specific problem: building a winning baseball team on a small budget. That’s why it is called Moneyball, not because the technique was Money in that Swingers argot of You’re So Money. It saved money. Since then everyone thinks they can Count their way to sports titles which was not the aim of BillyBall.

Not to say that Counting is not important, but you have to know what you are Changing.

And see the FauxItAll ‘Pataphysicians eating the menu. Take the Ruler of Moneyball and make it a Metaphor of Change.

The fundamental knowledge question is not what you know, but how you know what you know. Everyone knows, but even those who rely on common sense eventually realize such knowledge can be limited, wrong, and dangerous. The scientific method provides one of the more reliable paths for knowing what you know even with its obvious imperfections. As a result even the most practical people will add science to their common sense for better results. As in this specific case of the New York Giants and their new statistician.

In 2009, the site came to the attention of the Giants’ Mr. Berger, an executive of the team since the early 1980s. As director of information, Mr. Berger is the team’s official wonk, entering every play by every NFL team into a database in search of trends and tendencies that might be useful for Giants coaches . . . Doubtful about the accuracy of Mr. Hornsby’s data, Mr. Berger checked it against the NFL data set and found Pro Football Focus was nearly perfect. Impressed, Mr. Berger sent Mr. Hornsby a congratulatory note. Mr. Hornsby was so surprised he thought one of his friends had pulled a prank on him.

Thus, the unusual story of Mr. Hornsby, football statistician for the Giants; unusual because Hornsby is British, never played American football, and does all of his work on that side of the pond. But, that’s science, right? You can find in anywhere.

Yet, is this science? It is certainly mathed up and employs sophisticated statistical analysis. But, do all these numbers add up to generalizable knowledge, the great goal of knowing?

Certainly what Hornsby does is not unique. Many other sports teams use statistical science. Moneyball, the Michael Lewis pop best seller, is a tribute to it. And, of course, this kind of science is not restricted to sports although we’re certainly in a Golden Age of Sports Statistics and Science. Hedge funds run on super smart statistical analysis. Yet, where the application of the scientific method has produced many reproducible wonders in science, where’s the beef in sports?

Really? Is there any reliable and valid data that using statistical science in sports makes any championship difference?

The quick comeback is that if it does work, no one would tell you because its their Secret Sauce. Of course the comeback misses the point. If you’ve really got science going, it is as obvious as a hammer on your thumb. You simply cannot hide science once it is out of the bag (see Nuclear Weapons). If indeed sports statistics produced winners, everyone would quickly know it and ban it the way they did with steroids, corked bats, and gamblers hanging out with players.

I’m not claiming that sports statistics are not scientific at all. Think about a sabermetrician approach involving blows to the head, following symptoms, and later cognitive health problems. Instead of those transparently biased Tooth Fairy studies that so delight lawyers and Malcolm Gladwell, we might actually learn how football impact affects the brains of players. And that would require statistical science instead of the fairy tales we currently to gain headlines and out of court settlements. Statistical science in sports can generate science.

But, for now, I think we are fooled by randomness, to revive that sweet metaphor. People tend to latch onto sports statistics in a top of the head, Cue-driven fashion, then savor and save the correct calls, but fire and forget all the many losers. If sports statistics was a science, you could call a game from a computer program, just like chess. But, sports are not like chess and never will be. Even as complicated, complex, and dense as chess is held to be, it is checkers against the information and analytic demands a football game requires.

We typically associate this kind of intellectual pride with those Nobel economists who run a hedge fund into the ground and nearly take the world economy with them through their sports statistics. But realize that even Jock Talk allows for numbers in their alphabets. Heck, they even understand X’s and O’s as a kind of binary code, so they have an intuitive grasp of machine language.

But the analysis is persuasive, not scientific.

Truly honorable people—in the wake of some monumental botch—fall on their swords. Most of us, however, would prefer that someone else be chosen to take the hit. In “Scapegoat: A History of Blaming Other People,” British writer Charlie Campbell traces the habit of buck passing back to the Garden of Eden, where Eve, an apparently gullible person with far too much time on her hands, blamed a talking snake for persuading her to pick the forbidden fruit, thus unleashing our continuing pageant of sorrows.

So begins the review of Scapegoat, the new book by Charlie Campbell.  The review paints an interesting picture of the book and if you enjoy pop nonfiction, it sounds like a potential good read.  You might want to check it out.

Where’s the persuasion?

Attribution Theory!

When the kick sails wide right, why did we lose?  The Kicker, aka an External Attribution.

P.S. For more on Eve as the mother of Attribution Theory, consult this analysis.

P.P.S. Please let there be no scapegoats in today’s Super Bowl.

Here’s a graphic, but not vulgar, take on Bias.  Begin with Objective Processing.

Start with a simple 2 way ANOVA experimental design with WATTage manipulations on the X axis and Argument Quality in the body of the table.  The Y axis holds the attitude scores.  Dual Process Models predict that lovely fan shaped interaction between WATTage and Argument.  Under Low WATT, the Other Guys lack willingness or ability to think, so they don’t use Argument Quality to determine attitude.  But, under High WATT, Other Guys seek Args, then elaborate over them in that Long Conversation in the Head along the Central Route.  Argument Quality is decisive with Strong Args producing positive change and Weak Args producing negative change.

Now.  Let’s Bias this experiment.  Make the attitude object self-relevant, self-defining, self-loving.  What happens with our 2 way ANOVA now?

See the fan shift up with this (positive) example.  See that Weak Arguments produce more positive change.  That fan shift between the two experiments is the DNA, the hominid bone, the crucial difference that marks two breeds in the same species.  Both show the impact of High WATTage.  Both show the impact of Argument Quality.  But see how Bias, biases that Long Conversation in the Head with Weak Arguments in this example.

Now realize that this is exactly what’s going on with college football coaches who vote each week on the rankings of teams that determines BCS bowls and championships.  They see the weaker argument as the stronger under the stimulus of a biasing treatment like financial incentive.  Consider this press report on the study.

Research conducted by Yale University economist Matthew Kotchen and University of Calif.-Santa Barbara political scientist Matthew Potoski, which covers the USA Today coaches poll administered by the American Football Coaches Association from 2005 to 2010, shows that coaches rank their own teams, teams in their own conference, and teams that they’ve defeated more favorably than merited. The researchers argue those biases skew the results of the poll, which is one of the components in the system used to determine which teams get to play in major bowl games, and what two teams go to the national championship game.

The abstract of the study admirably explains itself.

This paper provides a study on conflicts of interest among college football coaches participating in the USA Today Coaches Poll of top 25 teams. The Poll provides a unique empirical setting that overcomes many of the challenges inherent in conflict of interest studies, because many agents are evaluating the same thing, private incentives to distort evaluations are clearly defined and measurable, and there exists an alternative source of computer rankings that is bias free. Using individual coach ballots between 2005 and 2010, we find that coaches distort their rankings to reflect their own team’s reputation and financial interests. On average, coaches rank teams from their own athletic conference nearly a full position more favorably and boost their own team’s ranking more than two full positions. Coaches also rank teams they defeated more favorably, thereby making their own team look better. When it comes to ranking teams contending for one of the high-profile Bowl Championship Series (BCS) games, coaches favor those teams that generate higher financial payoffs for their own team. Reflecting the structure of payoff disbursements, coaches from non-BCS conferences band together, while those from BCS conferences more narrowly favor teams in their own conference. Among all coaches an additional payoff between $3.3 and $5 million induces a more favorable ranking of one position. Moreover, for each increase in a contending team’s payoff equal to 10 percent of a coach’s football budget, coaches respond with more favorable rankings of half a position, and this effect is more than twice as large when coaches rank teams outside the top 10.

You can read the gory details in the extended paper (pdf) if you like, but that Abstract gives it up.  Financial incentives bias coaches and you see it in how they express their attitudes on Argument Quality (their ratings of college football teams).  Every way you can identify self relevance – whether My team, My conference, My opponent versus Their team, conference, or opponent – the researchers document the Biased outcome.  When it is Mine, it is Better and when it is Yours is it Worse.

In this instance we can interpret the computer rankings of football teams as the Objective Processing version of this experiment.  Given no self interest, where do the different Arguments take you?  Now.  Compare the computer Objective side of the experiment to the human Biased side.  If, indeed, there is no Biased Processing in coaches with financial incentives then there should be no differences between the attitudes for “computer voters” versus human voters.

While the careful quantitative analysis reveals the markers, it is the persuasion theory that predicts, describes, and explains the results.  Bias alters how you process Arguments, making the Weak seem Strong, when it favors you, and the Strong seem Weak, when it doesn’t.

P.S.  One of the oldest attitude experiments ever published (pdf) was entitled, They Saw A Game, and it studied the attitude differences between fans at a college football game.  Here’s a nice description of the study if you don’t want to read the pdf.  The more things change, the more they stay the same, right?

P.P.S.  Of course, since this was published in 1954 and only available as a weird looking scanned PDF, you know it’s not true.  We need an MRI replication to prove it, right?