Computer System Consistently Makes Most Accurate NCAA Picks

Sports professionals and fans get pretty emotional about their picks for the NCAA basketball tournament each year, and that emotion often clouds their judgment.

But three engineering professors at the Georgia Institute of Technology have created a computer ranking system, called LRMC, that consistently predicts NCAA basketball rankings more accurately than the AP poll of sportswriters and the ESPN/USA Today poll of coaches, formulas (the Ratings Percentage Index), other computer models (the Massey ratings and the Sagarin ratings), and even the tournament seeds themselves.

After correctly picking all four of this year's finalists, the LRMC method has now identified 30 of the last 36 Final Four participants (83 percent accuracy over the past nine years of NCAA tournaments) as one of the top two teams in their region. Over the same nine-year stretch, the seedings and polls have correctly identified only 23, and the RPI identified 21.

LRMC (Logistic Regression Markov Chain) is a college basketball rankings system designed to use only basic scoreboard data, including which teams played, which team had home court advantage and the margin of victory. It was originally designed by Joel Sokol and Paul Kvam and has been maintained and improved by Sokol and George Nemhauser, all three optimization and statistics professors in the Stewart School of Industrial and Systems Engineering at Georgia Tech.

"As fans, we only get to see most tournament teams two or three times at most during the season, so our gut feelings about a team are really colored by how well or poorly they played the few times we've been watching," says Sokol. "On the other hand, our system objectively measures each team's performance in every game it plays, and mathematically balances
all of those outcomes to determine an overall ranking."

LRMC seems to have a particular knack for predicting good bubble teams and identifying the top teams. In addition to correctly picking the Final Four, LRMC also correctly identified several over-rated and under-rated teams as potential upsets. First-round losers Drake (5-seed, LRMC #30), Vanderbilt (4-seed, LRMC #38), and Connecticut (4-seed, LRMC #26), as well
as second-round loser Georgetown (2-seed, LRMC #12), were all picked by LRMC as significantly over-rated teams.

On the other hand, teams like West Virginia (7-seed, LRMC #17), which defeated second-seeded Duke, and Kansas State (11-seed, LRMC #19), which defeated sixth-seeded USC, were correctly identified by LRMC as under-rated teams that could pull off one or more upsets.

But LRMC isn't perfect -- it picked Clemson as under-rated (upset in the first round) and Davidson wasn't identified as under-rated by any major ranking method, including LRMC.

LRMC differs from other computer rankings systems in two important ways. When determining the value of home court advantage, LRMC considers how much playing at home helps a team win rather than how many points playing on a home court is worth.

Georgia Tech researchers have also been able to show that very close games are often "toss-ups," meaning the better team barely wins more than half the time. So, they determined that winning a close game shouldn't be worth as much as winning easily, and losing a close game shouldn't hurt a team's ranking as much as losing badly. LRMC's ranking methodology takes
this into account.

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Study Reveals Strongest Predictors for Oscar Nominations

If you're an actor angling for an Academy Award nomination on Tuesday, you better hope you didn't leave the audience rolling in the aisles, suggests a new study from UCLA's California Center for Population Research.

"The odds of being nominated for an Academy Award are so much greater for performers who appear in dramas that — at least this time of year — it really pays to be a drama queen," says Gabriel Rossman, one of the study's two authors and an assistant professor of sociology at UCLA.

Albeit to a lesser degree, it also helps to have a major film distributor, prior Oscar nominations, a high spot in the pecking order in past movie credits, fewer films competing for attention and good collaborators. And it doesn't hurt to be a woman.

"A performer's odds of being nominated are largely set before the cameras even start rolling, back when the script was bought, the director was signed and the film was cast," says Nicole Esparza, the study's lead author and a Robert Wood Johnson Scholar in Health Policy Research at Harvard University. "It's surprising how many variables other than a performer's talent play a role in determining who gets nominated."

The 80th Academy Awards nominations are scheduled to be announced at 5:30 a.m. on Tuesday at the Academy's Samuel Goldwyn Theater in Beverly Hills.

Using Internet Movie Database (IMDb) records for every Oscar-eligible film made between the founding of the Academy of Motion Pictures Arts and Sciences in 1927 and 2005, Esparza and Rossman looked for conditions that improved the odds of a performer getting the nod.

The researchers looked at number of Oscar-eligible films in any given year, the distributors and studios behind each performer's films, the film's tone or subject matter, the cast size, the sex of the performer, the performer's contacts within the industry, and past Oscar nominations among a film's cast, directors and writers.

The single greatest predictor of a nomination proved to be serious subject matter — or at least a film that was classified by IMDb as a drama, despite the possible presence of comedic elements. In examining IMDb records on 171,539 performances by 39,518 actors in 19,351 Oscar-eligible films, the researchers found that actors were nine times more likely to receive a nomination for their work in a drama than in a non-drama.

"In the entertainment industry, there's long been a sense that the nomination process prefers dramas, but I don't think anybody is aware of the magnitude of the effect," Rossman says.

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