Data Analytics Blog - Real-World Applications | MicroStrategy

Welcome to the Modern Sports Franchise: Building a Better Team with Analytics

So far in our series on the modern sports franchise, we have explored how professional sports franchises can embrace analytics to change the way they think about business from both a management and an operational point of view. Today, we’re going to look at how analytics impacts the way that organizations use data to make personnel decisions—from scouting and player development, to roster composition and lineup optimization.

Nearly all personnel decisions that an organization makes start with scouting. As more and more money has been poured into pro sports, the price of established talent has increased dramatically, making amateur scouting essential to a team’s on-field success. Smart organizations realize that successfully developing young players is the most cost-effective way of putting a competitive product on the field year after year and are investing massive amounts of time and money in scouting. 


But how do teams with limited resources effectively scout the youth ranks of a sport like soccer that is played nearly everywhere on Earth? The sheer volume of amateur players, leagues, statistics, and scouting reports can be overwhelming. To deal with this, organizations need to be creative. Several English Premier League teams are using the player database in the game “Football Manager” to keep tabs on amateur players across the globe. The database is sourced from over 1,300 amateur scouts and is a wealth of knowledge for teams that lack the means to have a local scouting presence in remote locations. 

Although financial juggernauts, like the New York Yankees, could once dominate the competition by flexing their financial muscles in the transfer and free agent markets, those days are coming to an end. Despite committing nearly a half billion dollars in contracts over the course of the 2013 offseason, the Yankees actually managed to get worse in 2014—a spectacular example of the limits of this approach to building a team. At the opposite end of the spectrum, you have a team like the NBA’s Toronto Raptors using a first round pick to select a Brazilian teenager with tremendous potential but, by their own admission, “maybe a 2 percent chance” to fulfill on his promise. Paying top dollar for established talent is easy, identifying potential and correctly projecting tomorrow’s superstars from today’s teenagers is hard.  It requires tremendous amounts of data, hundreds of hours of study, and a smart approach to analytics. 

Player Development

For franchises to succeed with this method of team building, they need to turn talented young players into meaningful contributors at the sport’s highest level. This makes player performance tracking and development a critical organizational function. As I’m writing this, the Milwaukee Brewers (one of 30 franchises in Major League Baseball) have 131 position players and 132 pitchers affiliated with their organization across 8 different leagues. Needless to say, an organization of this size requires both skill and diligence to manage. Dashboards, like the one below, can be used to track player performance across the various leagues, and analytics can be applied to statistics to project player development and future performance based off of historical data. This type of analysis helps organizations more accurately assign value to their players and leads to better, more informed roster decisions. 

Roster Composition

This brings us to the most controversial aspect of professional sports—roster composition. When it comes to putting a team together everyone has an opinion, from reporters, to analysts, to your buddy John. But what actually goes into putting together a roster? How do organizations decide which players get a spot with the club—and how much they should spend on them?  This process was once dominated by lawyers, but today statisticians are playing an ever greater role in determining how much teams should pay their athletes. But it goes further than just individual players. How is it that some organizations manage to notoriously underperform expectations, cough **the Dallas Cowboys**cough, while others like the Oakland A’s always seem to be more than the sum of their collective parts? The idea that different players have different values based on the skillset and performance of their teammates is fascinating to me, and I think that the study of roster optimization and the impact that individual players have on their team’s overall performance will be the next major growth area in sports analytics. 

In this blog series, we’ve looked at a lot of ways analytics impacts the modern sports franchise, but we’ve only scratched the surface. That’s the beauty of this type of analytics. Sports data is so prevalent and widely available, new use cases and analytical techniques are being thought of all the time. So, while this may be the last post in this series—it is by no means the end of the discussion. We encourage you to join us in the MicroStrategy Community to continue the conversation!

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