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Applying Quant Techniques To College Football's BCS (Alabama Vs. Notre Dame)

In previous sports-related posts, I applied a quant analysis of sports psychology concepts to predict the winner of various sports championships. In this article, we took this a step further and used quant investment techniques (including probabilistic Monte Carlo methods) to develop a football simulator to take a look at the big game between Alabama and Notre Dame in this year's college football national championship BCS game.

Here is an excerpt from the article in the NY Times.

... our work relates key statistics to sports psychology concepts like leadership, consistency and minimizing errors. We have also developed a football simulator based on a probabilistic Monte Carlo model to use in conjunction with our championship factors.

Simulation: We developed a football simulator that plays out thousands of games relatively quickly. The simulator is a probabilistic Monte Carlo model that uses statistics and results from the regular season.

... football games can be modeled based on certain random variables, statistics and our championship factors. The final score that came up most frequently on our simulator was Alabama 27, Notre Dame 14. As expected, we got a cloud of widely varying results, but this is the center of the model's probability distribution.

Edge: Alabama

Read more here:

Carlton J. Chin, a portfolio strategist and fund manager, and Jay P. Granat, psychotherapist, are authors of "Who Will Win the Big Game? A Psychological & Mathematical Method." They have previously written about the N.C.A.A. men's basketball tournament, the N.B.A. finals and the Super Bowl.