One of the most exciting Super Bowls ever just ended. Fans will be talking about this game for a long time. Should the Seattle Seahawks have run the ball? Were they thinking of the clock too much?
What are the odds of scoring a touchdown from "second and one?" Numbers and probabilistic methods can give us a sense what these chances are...
Earlier in the fourth quarter, what were the odds that Tom Brady and the New England Patriots would come from behind after being down two scores? Numbers, probabilities, and statistics can give us a good sense of expectations. Additional research can then guide data-driven decisions -- decisions that are informed by objective measures and analytics.
Similar to other sports analysts, we have developed probabilistic models that can yield interesting and useful information. The chart above is a popular model used for various sporting events. This win probability chart for the Super Bowl is from Advanced Football Analytics. Our models use similar approaches, including Monte Carlo approaches, to uncover realistic expectations for games, seasons -- and to study in-game strategy.
This win probability chart shows how New England's dominance in the first half gave way to quick scores by the Seahawks. Seattle's strong third quarter reduced the Patriots' win probability to the 10% range. New England's two scores in the fourth quarter then got the Patriots to the 80% range before the wild ending had the chart flip-flopping like crazy.
Our "Who Will Win" Super Bowl quant fact prediction did not work out this time. There is a lot of randomness in sports, which is just one of the reasons why we love sports! Numbers and sports analytics can help sports organizations get an edge in the competitive sports world.
Carlton Chin is a fund manager, MIT-trained quantitative analyst, and co-author of “Who Will Win the Big Game?" He has been quoted by the Wall St. Journal, New York Times, and ESPN -- and has worked with sports organizations, including the Sacramento Kings on Draft 3.0.