What Could A Coach Do With A Brain Activity Map?

Imagine an NCAA basketball coach trying to create a game plan for their first March Madness game with absolutely no video footage of their upcoming opponent.  Sure, he has their roster with player names, height/weight and positions.  He also has a set of specific stats that show the performance of each player and the team during the season.  Yet, there is no opportunity to see the team play as a unit, how they move the ball, or their communication.  The resulting game strategy would be full of educated guesses and assumptions based on just the macro picture of the roster and the micro world of data and statistics.

Welcome to the world of today’s neuroscientists. To study the brain, they have the 30,000 foot view from tools like functional MRI scans and the microscopic world of neurons and biochemistry.  Everything in the middle, the constant communications between 100 billion neurons, is unable to be observed, leading to theories and best guesses at how we make decisions, free throws and no-look passes.
Much like a library of game video or, better yet, a live stream of the action, researchers need a way to observe and measure our brain’s massive amount of electrical activity and connectivity.  "We don't actually understand (how circuits of neurons) generate all these interesting behaviors we have, like speech and language and thoughts and memory," said John Donoghue, neuroscientist at Brown University, in a recent CNN interview.

Enter the Brain Activity Map (BAM) project.  While there are many ongoing brain mapping research projects currently underway, President Obama alluded to a much more ambitious initiative in his State of the Union address last month.  Since then, details have begun to emerge for a 10-year, $3 billion project to do for brain research what the Human Genome Project did for biology and genetics.  An article published last week in Science hints at the “big rock” goals for BAM as defined by a cross functional team of 11 scientists, including not only neuroscientists but also experts in genetics, nanotechnology, and bioengineering.

Here's a quick (and energetic) intro to BAM:

“We need something large scale to try to build tools for the future,” Rafael Yuste, a neurobiologist at Columbia University, told MIT Technology Review. “We view ourselves as tool builders. I think we could provide to the scientific community the methods that could be used for the next stage in neuroscience.”

To be sure, a project of this size and cost is not being done to help a point guard know when to pass or shoot.  Trying to solve brain disorders like Alzheimer’s or schizophrenia are much higher on the priority list.

Then again, think of the possibilities in just basketball:

-  What is happening in a player’s head when he struggles at the foul line?  We have theories of “choking” but to actually know the electrical patterns of skill versus stress could suggest new ways to deal with it.
-  How is “court vision” represented in the brain and how can we identify and/or train it?
-  Practice and repetition seem to teach a new play or skills to a team, but how can we accelerate the rate of learning?

Time will tell if this latest research initiative provides any of the benefits it promises.  It certainly could fill in the gaps of how we understand athletes as living, thinking people. It might even help us fill out our March Madness brackets.

Be sure to check out Axon’s Athletic Brain Trainer apps for iPad.

Is There Bias In Selection Of March Madness Teams?

By examining historical data, statisticians in the College of Science at Virginia Tech have quantified biases that play a role in granting Division I at-large basketball teams inclusion in the NCAA March Madness Tournament.

Assistant professors Leanna House and Scotland Leman found that in addition to the standard Ratings Percentage Index (RPI) used by the 10-member selection committee, biases such as the team's marquee and the strength of its schedule also increase the entry odds for college basketball's tournament.

"We wanted to quantify how much bias there is for bubble teams," Leman said. So-named "bubble teams" are those that do not have an automatic bid but are still considered potential teams to be invited to the tournament. Usually bout 30 teams fall into this category.

One bias for bubble teams, House and Leman found, was consideration of the marquee (or pedigree) of the team. For instance, a team that historically has an outstanding record and is usually included in the tournament has that fact in its favor.

"Having a rich history of a spot in the tournament will 'break the tie,'" House said.  She and Leman found that inclusion probabilities were much higher for marquee teams. For example, in the 2009-10 season, the bias of not being a marquee team lowered Virginia Tech's chances of receiving an at-large bid from 0.83 to 0.31. During the 1999-2000 season, the marquee bias increased the University of North Carolina's chances from 0.32 to 0.85.

"UNC's marquee status during that season had a substantial influence on the committee's decision." Leman said. "Of that, I'm sure."

The statisticians also explored the influence a team's schedule has on its RPI in addition to its record. By using a hypothetical model, Leman and House determined that the more powerhouse teams a bubble team plays in a season, regardless of whether they win or lose, will help them win a bid in the tournament.
"Of course scheduling is a complex process and involves a lot of negotiation," Leman said. "But in cases where a coach is able to select to play a powerful team or a smaller, less powerful team, it is better to pick the power team. The rule of thumb is: the more powerhouse teams, the better."

At the beginning of each March Madness decision-making process, the selection committee is provided documentation that contains season statistics and the RPI for each team. Other measures of team strength are excluded.

"The RPI accounts for known, quantitative biases in raw winning percentages that may impact their ratings, but it has been shown repeatedly that raw winning percentages per team are not adequate for ranking teams," Leman said. "Tournament decisions made for teams with only moderately high RPIs (bubble teams), until now, were not clear."

Leman and House say their research was motivated by a chance meeting with Virginia Tech head basketball coach Seth Greenberg in a restaurant in the spring of 2010. At that time, Virginia Tech had not won a bid for the tournament. Greenberg suggested that he would like to know how tournament decisions are made for at-large teams.

The two statisticians, along with graduate assistants John Szarka and Hayley Nelson, stepped up to the challenge and have presented their conclusions just in time for this year's March Madness to begin.
"We don't want to create, improve, or validate a ranking system," House said. "Our goal was simply to evaluate how the selection committee has chosen teams for the tournament in the past."

Source: Virginia Tech

See also: For Sports Betting, The Crowd Usually Picks The Favorite and Sports Superstitions Just Might Work

Smart Professors' Advice: Don't Pick Upsets In Your NCAA Basketball Brackets

Its Tournament time and your NCAA brackets may be a mess after the first two rounds. You knew you should have picked upsets, but which ones?  Well, it turns out it doesn't matter... the odds are still against you picking the right underdog.

New research from Indiana University and the University of Wyoming has found that strategists, regardless of their sports expertise, would be better off sticking with the numbers -- but what's the fun in that? Bettors often think picking the upsets will give them an edge, and that they know how to pick them.

"Picking the lower seed is a good strategy, but people think, 'I can't win by doing that because everyone else is doing this,'" said Ed Hirt, professor in IU Bloomington's Department of Psychological and Brain Sciences. "The upsets people pick are no better than chance. People have this idea that they know how many upsets will occur, but can they predict the ones that will occur? They pick upsets but not the right ones and end up sabotaging their efforts."

Hirt's study, co-authored by Sean M. McCrea, University of Wyoming, was published in the "Journal of Applied Social Psychology." McCrea said they were surprised by how little expertise or favoring an underdog really explained people's tournament predictions.

"Instead, it seems that people who follow basketball are aware of the possibility of upsets and fool themselves into believing that they can figure out which upsets will happen," he said. "The problem is that the tournament seedings summarize most of the useful information one could use (win-loss record, strength of schedule, etc. ) and so the upsets are much less predictable than one might think."

Wisconsin's Bo Ryan realizing he should have picked Cornell

Other studies have shown that making NCAA bracket predictions based on rankings from other experts, such as sportswriter polls or gambling bookies, are no more successful than choosing the lower seeds. Hirt and McCrea sought to examine whether bettors used probability matching to pick upsets, if this approach was more successful than picking winning teams based on seeding, and whether people use probability matching because they viewed basketball as a skilled, non-random activity that could be predicted -- essentially, thinking they just know better.

Probability matching describes a scenario where individuals predict a specific outcome based on an existing rate of occurrence. For example, in the first round of the NCAA tournament, prognosticators often expect an upset in the contests between No. 5 and No. 12 seeds, so bettors often attempt to pick which of the four games involving a 5-12 matchup will see the upset.

Hirt and McCrea examined bracket strategies as a way to study this common decision-making behavior, which frequently is seen when individuals make predictions or judgments in areas involving skill, such as hiring decisions, outcomes of races or predicting stock prices. Hirt says this behavior relies on a confidence that an individual's insight can trump variability or discern patterns in randomness.

For the study, they examined NCAA tournament results from 1985-2005 and the first-round predictions of more than 3 million entries in an ESPN Tournament Challenge. They also designed a series of studies involving varying degrees of perceived randomness.

Their study provides one of the first demonstrations that probability matching is used more frequently for predictions of social behavior than for predictions of random events.

"We want to deny the fact that there's variability, that there are bad days," Hirt said. "We want to think we can predict these things. It's human nature to think that things aren't random, serendipitous, that we should be able to predict what someone will do or outcomes of situations that we care about."

Source:Indiana University and Match Madness: Probability Matching in Prediction of the NCAA Basketball Tournament1 : MATCH MADNESS. Journal of Applied Social Psychology

See also: Inside The BCS Computer Ranking Black Box and Sports Fans Have Selective Memories