March Madness Math


March Madness Math
A couple of self-described frustrated jocks—and business professors—teamed up to produce a model for predicting which college basketball teams would make it to the NCAA Tournament: The Dance Card. The most updated version of the Dance Card debuted in 2009, and has had a 94.4 percent accuracy rate.

As the 2013 Final Four are decided this weekend, the Dance Card’s mathematical model has been perfect.

College basketball is practically a religion in the Hoosier State during the NCAA's "March Madness." In 2013, however, top-ranked Indiana was brought down by Syracuse.Photograph by Michael Heinemann
College basketball is practically a religion in the Hoosier State during the NCAA’s “March Madness.” In 2013, however, top-ranked Indiana was brought down by Syracuse.
Photograph by Michael Heinemann

Discussion Ideas:

  • Jay Coleman, Mike DuMond, and Allen Lynch, who developed the “Dance Card” prediction tool, analyze “big data“—sets of mathematical data too large and too complex to process and visualize without sophisticated technology. What are some “big data” that the Dance Card analyzes?
    • team RPI (rankings percentage index), which measures the strength of a team’s schedule and how the team performs against that schedule
    • a team’s wins against top-seeded teams
    • strength of a team’s conference
  • “Sports is a natural fit” for big data analysis, the article says. Other analysts agree. Can students think of other ways basketball or other sports coaches might use big data to improve their teams or sell more tickets?
    • the article lists a few examples in basketball: Butler University uses statistics to help determine which players play best together, the Orlando Magic use big data to adjust prices for season ticket-holders, etc.
    • Moneyball, the best-selling book or movie, chronicles the maverick style of Oakland Athletics manager Billy Beane. Beane used sabermetrics (related to big data) to assemble one of the best baseball teams of 2002.
    • soccer analysts are increasingly considering how a team passes the ball as much or even more than how often they score (how frequently players pass, where they pass, in which situations they pass, etc.)
    • Formula 1 race cars are equipped with sophisticated sensors that track dozens of details on each lap (changes in pressures, angles, suspension, and aerodynamics) and compare it to others.
    • consider other sports that are popular in your school or community!
      • track or cross-country foot races
      • cycling
      • skiing
      • horse racing
      • gymnastics
      • swimming
      • football
      • tennis
  • Look at these fascinating maps compiled by Facebook. Do students’ March Madness loyalties match up with others in their community? Why do students think conference or team loyalties map the way they do?
    • Consider the way Facebook compiled its “big data”: Who fans “liked” and where they live.

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