New wave of data and analytics in baseball opens door to marketing insights
There continues to be an explosion in both data and its accessibility in Major League Baseball over the past two decades. The processing power of computers allows for analysis once only dreamed of. What used to be painstaking research either is already available or can easily be monitored with a fitness tracker.
Being a lifelong baseball fan and always enjoying watching the movie “Moneyball,” I continue to be struck with how statistics have been gleaned from every aspect of the game. So, I’ve wracked my brain and challenged myself to identify a new stat that would be a) interesting and b) useful. But everywhere I look, a stat seems to exist, some more popular than others.
I found one potential stat that doesn’t seem to be reflected when it comes to tracking outfielders, something that might be worth expanding on. Outfielders take their positions on the field based on the propensity of where each batter tends to hit outfield balls as well as their power and whether they are a righty or lefty. Also tracked is the cumulative distance outfielders run in each game, by position: left, right and center. All of this is measured via Catch Probability and Outs Above Average. Also tracked are Feet Saved or Lost on Outfield Plays due to reaction and route.
It is fascinating to understand how far each outfielder runs in a game vs. other outfielders. This data serves to better position them on the field with the goal of garnering more outs. What I don’t see in these outfield stats are a correlation to assists. It would be interesting to understand if assists improve based on these data insights.
An assist is credited to each fielder who throws or deflects a batted or thrown ball in such a way that a putout results. A putout is given to a defensive player who records an out by one of the following methods: Tagging a runner with the ball when he is not touching a base (a tagout) or catching a batted or thrown ball and tagging a base to put out a batter or runner (a force out).
There is another way this data can be used to benefit a brand such as Nike, the official shoe of Major League Baseball. There could very well be a limit on how many steps a player can make before his shoes officially wear out. By tracking this information, players will know the optimal time to replace their shoes. This will help them perform at their very best level and could improve Nike’s sales if the numbers reveal that shoes need to be changed more often.
As a lifelong baseball fan, I have long embraced the era of Sabermetrics. It helps keep my math skills sharp. As a marketer, the use of data is a critical part of what we do. In this case, perhaps Nike’s shoe sales increase. Regardless, whoever can mine the data to garner the greatest insights will win the game.
Zach Rosenberg is founder of Zach Rosenberg Consulting Inc.