Football fans watching the Super Bowl have a chance to see how statistical analysis can help predict the game’s outcome. 

A group of NDSU statistics students is using statistical modeling to show how several key in-game statistics can impact the final margin of score and a team’s overall probability of winning. They are scheduled to present and discuss their work Wednesday, Jan. 28, at 3:30 p.m. in Morrill Hall room 103.

Joe Roith, a doctoral student in the Department of Statistics, will lead the discussion by using two statistical models and key New England and Seattle regular season and playoff statistics to predict what fans can expect from the Super Bowl and how much it can change depending on how each team plays on game day.

For example, in any given NFL game, a team with one more turnover than its opponent is essentially giving up 3.9 points in the final score margin, decreasing the odds of winning by 75 percent, Roith said.

Six graduate students, Jennifer Johnson, Scot Jones, Wenting Wang, Nick Taylor, Feifei Huang and John Lauman-Beltz, also are scheduled to present and discuss their statistical models for the game. The students are advised by Rhonda Magel, chair of statistics at NDSU.

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