New insights in the field of sports analytics can come from all levels of play and it’s worth understanding where they are likely to emerge. While almost all data driven actions and decisions in the news come from teams in leagues like the NBA and MLB, the analysis in minor and developmental leagues may be even more useful.

Sports Analytics in Minor League Baseball

Though the Minor Leagues of professional baseball don’t bring in the same revenue or boast the same talent as their big league counterpart, the “farm system” serves as a vital resource for developing and discovering talent. With the possibility of finding tomorrow’s next star in the minors, teams need to use all available resources to scout players or they risk missing out on future talent. The biggest challenge in scouting Minor Leaguers is predicting how their stats in the lower leagues will translate to the MLB. For example, a hitter who hits a lot of home runs in the minors may not be as valuable to a major league club as a player with far fewer home runs, but a higher on-base percentage.

Statisticians Guy Stevens and Gabe Chandler, who also play and coach baseball respectively set out to find a solution to projecting success of Minor League players. In their paper that was published in the Journal of Quantitative Analysis in Sports, the statisticians used random forests to see which Minor League statistics had the strongest correlation with Major League production.[1] The combination of statistical and research abilities and deep knowledge of the sport allow for not just useful, but interesting and rigorous analysis from these and other talented writers.

The Only Rule Is It Has to Work

The Minor Leagues also serve as a great place for the use of sports analytics because of the ability to experiment without the fear of huge losses. Some teams in the MLB might be afraid to embrace the new wave of sports analytics because a lack of instant production can result in lost jobs or big hits to reputations for those who made an incorrect prediction. The prospect of losing games in the short term while losing money in the process causes some to shy away from using sports data analytics. Without the risk of losing big, Minor League teams and managers can experiment more and try things that their major league affiliates can’t.

This year baseball analysts Ben Lindbergh and Sam Miller got to take part in the ultimate Minor League experiment when the Sonoma Stompers let them apply their data backed insights to their organization. In their book “The Only Rule Is It Has to Work” the duo tells about their attempts to change the mindset of the Stomper’s management to apply less traditional methods in order to win games. While they faced strong opposition from the baseball traditionalists in the organization, Lindbergh and Miller were still able to sign players they found value in and implemented better data collection methods and analytics for the team.[2]

 Pushing Insights to the Limit

Although sports analytics seemingly revolves solely around baseball, experimentation in lower levels of sporting isn’t simply limited to the one sport. In the NBA’s Developmental League, or D-League, the Reno Bighorns have taken efficient basketball to an entirely new level. Modern basketball thinking tells teams to shoot as close to the basketball hoop as possible, or three-pointers and ideally nothing in between. Additionally, coach David Arseneault’s system calls for his players to do this at a tremendous pace, hoping that his teams can overload their opponents with shots. The system even has a formula for winning that almost guarantees a win if the athletes take 94 shots, half of the shots being three-pointers, rebound 33% of the missed shots, force 32 turnovers and take 25 more shots than their opponent. This “run-and-gun” strategy produces a nightly point total usually to 150 than the NBA average of 102.[3] But, while the high scores may be good for the team, many players feel that being part of an “experiment” makes it impossible for them to ever reach the NBA.

Whatever the sport, the minor leagues foster opportunities to try new things and experiment. Today this means that many teams can turn to the use of sports analytics to help build their rosters and make management decisions that, in the higher levels, some traditionalists would strongly oppose.  As we see more experimentation in the minor leagues, we can look forward to applications and insights from analyses that may get the attention of big league coaches and bring out the best in top athletes.

If you are interested in becoming a sports analyst, a degree that specializes in data science might be just the ticket.  Learn more about degrees in data science.


[1] “An Exploratory Study of Minor League Baseball Statistics : Journal of Quantitative Analysis in Sports.” An Exploratory Study of Minor League Baseball Statistics : Journal of Quantitative Analysis in Sports. N.p., 2012. Web. 26 Aug. 2016.

[2] “What a Minor League Moneyball Reveals About Predictive Analytics.” Harvard Business Review. N.p., 2016. Web. 26 Aug. 2016.

[3] Carpenter, Les. “140 Points a Game – but Are the Reno Bighorns a Basketball Experiment Too Far?” The Guardian. Guardian News and Media, 2015. Web. 26 Aug. 2016.