An Introduction to Sports Analytics

Angel Guo, the author of this resource, is our Founder and Editor-in-Chief.

Baseball culture has always been known to stick to tradition, even to this day. It makes sense that General Manager Billy Beane was met with strong criticism when he took an unorthodox approach to drafting and acquiring several small-name players and giving up big veterans for the 2002 Oakland A’s team. The New York Yankees had a monopoly over player acquisition as the franchise with the largest budget, essentially able to bid over any player they wanted. Faced with a stuck-in-the-rut team and a small budget, Billy Beane turned the Oakland A’s around by focusing on sabermetrics and revolutionized baseball with the Moneyball Effect.  

Instead of acquiring star players that were known for their flashiness, Beane searched for players who statistically played a consistent game and were, in-theory, compatible with each other. With the help of his assistant and Harvard statistician, Paul DePodesta, the duo looked at obscure player statistics beyond simple batting averages, like walks and on-base averages. DePodesta believed that the A’s could find a combination of players that would produce similar results that allowed big-hit players high trade bids. Most importantly, other teams wouldn’t pay mind to underdog players, and the A’s could acquire them for cheap. This method was beneficial for these small-name players because it would give them the opportunity to come into the light and showcase their skills, but was a huge financial risk for the franchise’s future. Despite a slow start to the season, the 2002 Oakland A’s achieved a 20 game win streak, breaking the AL record.

Billy Beane’s Moneyball tactics changed the course of sports analytics and player evaluation. Management teams are now using player statistics to evaluate, analyze, and extrapolate different conclusions about how players can fit into a team. All factors have to be considered when it comes to these evaluations. For instance, a basketball player can have a high field goal percentage (the percentage of successful shots made), but having a higher field goal percentage doesn’t always mean this player was the better shooter. Sports analysts must also consider where these baskets were being made on the court. Shaquille O’Neal knows how to play big under the rim, and a dump to him can almost guarantee points, but he is infamously known for his low free throw percentage. Today, players must be looked at holistically, with statistics, flashiness, and sportsmanship all considered.

I had the opportunity to learn more about sports analytics at Wharton Moneyball Academy hosted by UPenn during a one week intensive online course this June. We began with an introduction to statistical models and, throughout the program, we learned how these models are applied to player analytics. We also investigated how areas of bias can influence statistics. Furthermore, we talked about how human emotions and certain situations can create anomaly data points. Our statistics professor, Adi Wyner, introduced us to R, a popular statistical program, where we got to learn how to code our own commands to compute and organize data. Lastly, I was able to network with sports analysts and broadcasters who were guest speakers at our program. During their talks, they discussed the future of sports analytics and brought up how the rise of technology allows statisticians to collect data that is difficult to collect by eye. Statisticians hope to use this information to help athletes optimize and hone in on their skills. 

I particularly enjoyed this course because it allowed me to have a better understanding of the game and see the aspects that managers look for in a player when scouting. I was intrigued when we picked out anomaly data points while looking at players’ trends, because I realized that a story can be extrapolated from a player’s data, showing what makes each player truly special. Computer Science isn’t one of my main interests, but I was surprised to see how easy it was to navigate R, the statistical program we worked with throughout the course. At the end of the week, our professors had us present a group project that examined statistical trends in sports. Our project focused on Drew Brees’ dynasty, and we looked at his 2006-2020 seasons to analyze the relationship between passing completion percentage and touchdowns scored. After comparing this with the NFL averages, we then used a regression line to plot the data, and we found a positive correlation between Brees’ passing success and the touchdowns scored. This data suggests that Brees had a larger impact on the Saints than other quarterbacks for their own teams.

The UPenn Wharton Moneyball Academy gave an insightful introduction to sports analytics, and I highly recommend this program to students who love working with numbers, discovering trends, and predicting outcomes.

Sources for Sports Business and Analytics

Front Office Sports https://frontofficesports.com

Sports Business Journal https://www.sportsbusinessjournal.com/Daily.aspx

Five Thirty Eight https://fivethirtyeight.com

Wharton Sports Analytics and Business Initiative https://wsb.wharton.upenn.edu/?_gl=1%2A14dlo4l%2A_ga%2AMTM1Njk5MzMyLjE2Njc2MTM1Mjc.%2A_ga_2QNGY0KQFG%2AMTY2NzYxMzUyNy4xLjEuMTY2NzYxMzUzMS41Ni4wLjA.%2A_ga_B5B4E387GY%2AMTY2NzYxMzUyNy4xLjEuMTY2NzYxMzUzMS41Ni4wLjA.