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Hampden-Sydney Competes in COMAP Mathematical Contest in Modeling

Three teams from Hampden-Sydney College are competing in the Consortium for Mathematics and Its Applications (COMAP) Mathematical Contest in Modeling (MCM):

  • Team Alpha: Matthew Carrington, Miguel Mogollon, and Tian Shihao.
  • Team Beta: Cameron Auker, Nathan Parr, and Douglas Vermilya.
  • Team Gamma: Paul Cottrell and Ke Shang.
The COMAP MCM is a prestigious international contest in which teams from across the globe work on open-ended problems in applied mathematics.  The contest runs from February 18 through February 22.  The H-SC teams will be working intensively to develop mathematical models and computer simulations of real-world problems.  Here are the 2010 MCM problems:

PROBLEM A: The Sweet Spot

Explain the “sweet spot” on a baseball bat.

Every hitter knows that there is a spot on the fat part of a baseball bat where maximum power is transferred to the ball when hit. Why isn’t this spot at the end of the bat? A simple explanation based on torque might seem to identify the end of the bat as the sweet spot, but this is known to be empirically incorrect. Develop a model that helps explain this empirical finding.

Some players believe that “corking” a bat (hollowing out a cylinder in the head of the bat and filling it with cork or rubber, then replacing a wood cap) enhances the “sweet spot” effect. Augment your model to confirm or deny this effect. Does this explain why Major League Baseball prohibits “corking”?

Does the material out of which the bat is constructed matter? That is, does this model predict different behavior for wood (usually ash) or metal (usually aluminum) bats? Is this why Major League Baseball prohibits metal bats?

PROBLEM B: Criminology

In 1981 Peter Sutcliffe was convicted of thirteen murders and subjecting a number of other people to vicious attacks. One of the methods used to narrow the search for Mr. Sutcliffe was to find a “center of mass” of the locations of the attacks. In the end, the suspect happened to live in the same town predicted by this technique. Since that time, a number of more sophisticated techniques have been developed to determine the “geographical profile” of a suspected serial criminal based on the locations of the crimes.

Your team has been asked by a local police agency to develop a method to aid in their investigations of serial criminals. The approach that you develop should make use of at least two different schemes to generate a geographical profile. You should develop a technique to combine the results of the different schemes and generate a useful prediction for law enforcement officers. The prediction should provide some kind of estimate or guidance about possible locations of the next crime based on the time and locations of the past crime scenes. If you make use of any other evidence in your estimate, you must provide specific details about how you incorporate the extra information. Your method should also provide some kind of estimate about how reliable the estimate will be in a given situation, including appropriate warnings.

In addition to the required one-page summary, your report should include an additional two-page executive summary. The executive summary should provide a broad overview of the potential issues. It should provide an overview of your approach and describe situations when it is an appropriate tool and situations in which it is not an appropriate tool. The executive summary will be read by a chief of police and should include technical details appropriate to the intended audience.

Fascinating stuff.  Join us in wishing the best to the H-SC “mathletes” as they grapple with these challenging problems.  Stay tuned for updates!
UPDATE: Teams Alpha and Gamma have chosen to work on Problem A, while Team Beta is working on Problem B.

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