Sometimes life gets a little hectic for the S3 group and their advisers.  This blog post was written over the summer by 2014 Scots Science Scholar Evan Ezell, but seems to have gotten lost in the shuffle!.

Baseball is a very statistical game. There simply seems to be a statistic for everything. Of the most common statistics are batting average, on-base percentage, and slugging percentage. There are many more statistics that front offices among Major League Baseball teams use to help evaluate players and future success of prospective players. What does this have to do with science, technology, engineering, and math? How are baseball statistics related to real world applications?

There are many different ways Major League Baseball (MLB) teams arrange their lineups.  Teams have different payrolls which allow them different options. A smaller market team is not going to have the same opportunity as a large market team. This idea is the conventional across most baseball writers and fans, but is it really true? Professor of Maryville College of Math and Computer Science, Dr. Jeff Bay, taught us how to use a social statistic used to gauge economies to evaluate Major League Baseball lineups. This lecture that Dr. Bay presented changed my perspective on baseball statistics.

For the most part, MLB teams try to have their most productive hitters in the lineup spots three, four, and five. Players with very high on-base percentages usually bat first so the more productive spots can drive the leadoff runner home. Should general managers and owners of clubs spend more money to get a few superstar players? Or should they spread their resources to get a more balanced lineup?

We used the Gini coefficient to determine the answers to these questions. The Gini coefficient measures how far a particular economy is unevenly distributed from the perfect ratio of distribution. A perfect ratio for example would be ten percent of the population having ten percent of the wealth. The greater the Gini coefficient the further the economy deviates from the perfect distribution.

In our studies of National League MLB teams, we discovered that there was no correlation between the Gini coefficient and offensive production. This was surprisingly different than the traditional way of thinking. Smaller market teams were paying less money and still producing the same offensive production that big market teams like the Los Angeles Dodgers and Philadelphia Phillies spent two and three-times more to acquire.

Our S3 class followed this activity up by seeing a Tennessee Smokies’ game. The Tennesee Smokies are the AA affiliate of the Chicago Cubs. We enjoyed the game and seeing what takes place in order to make baseball statistics.

S3 at the Smokies game.