Were Analytics the Real MVP of the Super Bowl?
As the Eagles readied to celebrate the franchise’s first Vince Lombardi trophy, an unlikely candidate basked in the glow of being declared the game’s Most Valuable Player. Surely it was Nick Foles who, on his way to upsetting one of the NFL’s elite franchises threw and caught a touchdown in the same big game, was the true MVP. But was he?
In the days leading up to the Super Bowl, the New York Times published an article about how the Eagles leveraged analytics to secure a Super Bowl berth. The team relied, in part, on probabilistic models that leveraged years of play data to calculate likely outcomes, given a specific set of circumstances. They found that while enumerating outcomes and optimizing for success, the models would, in many cases, recommend plays that bucked the common wisdom. Indeed, we saw the Eagles run plays and make decisions throughout the season that, to the outside observer, may have seemed mind-boggling, overly-aggressive, or risky. Of course, the outside observer did not have access to the play-by-play analytics. Yet, in many instances, these data-driven decisions produced favorable results. So it seems that analytics were the real MVP, right? Well, not entirely.
As we have written in the past, the most effective analytics platforms provide guidance and should never be solely relied upon by employers when making decisions. This analytics concept rings as true in football as it does in business. The New York Times article talks about how mathematical models can serve to defend a playmaking decision that defies traditional football logic. For example, why would any team go for it on fourth and one, deep in their own zone, during their first possession in overtime? What if the analytics suggested going for it was more likely to result in success? If it fails, well, the football pundits will have a lot to talk about.
Coaches and players weigh the analytics, examine the play conditions, and gauge on-field personnel’s ability to perform. In order words, the team uses analytics as a guide and, taking into account other “soft” variables and experience, makes a decision that is right for the team at that time. This same strategy leads to success in the business world. Modern companies hold a wealth of data that can be used to inform decisions with cutting edge analytics, but data-driven insights must be balanced with current business conditions in order to contribute to success. If this balancing act works on the grand stage of professional football, it can work for your organization.
Indeed, we may soon see a day when football stars raise the Super Bowl MVP trophy locked arm-in-arm with their data science team. Until then, congratulations, Mr. Foles.