Wharton Moneyball Training Camp

     This past week, I was fortunate enough to be able to learn about data analytics in sports through the Moneyball Training Camp taught by Professor Adi Wyner. Throughout the one week, it was packed with many great insights in the sports industry as well as information on how sports teams use data in order to come to conclusions about their own team or other teams. At the base of all data analytics is the use of the computer program R. R is commonly used for data analytics, and I was able to learn how to use R to create graphs as well as showcase how a certain player might perform in the future based on past results. Daily, I would receive a live lecture from professor Wyner as well as Erig Eager, the VP of Research and Development at Pro Football Focus. I learned many different topics ranging from standard deviation all the way to probability. Using the knowledge learned from them as well as R, I was put to the task of creating a project to answer a sports question. With my group, we decided to see whether Shohei Ohtani or Jacob deGrom should be the most valuable player. Ultimately, based on regression data, team success, and hitting/pitching stats, we came to the conclusion that although both players are amazing, deGrom should have a slight edge over Ohtani as the most bauble player. It was great to work in a group on this project because we all had varying ideas about who should be the most valuable, but when we finally got all the important data that we were looking for, we were able to come to an easy consensus. In my opinion, the most exciting part of the week was the guest speakers. The guest speakers came from extremely reputable sports organizations such as the Washington Nationals, Washington Wizards, and the Los Angeles Lakers. Listening to the Assistant GM of the Washington Nationals, Samuel Modry-Cohen, was extremely enjoyable because as an avid fan of baseball, I was able to hear about the insights that teams use that most people would never hear about. For example, there is a company that runs data analytics for baseball teams, but they only take one team per division in order to prevent taking direct competitors. Although the information I learned this week pretty much only relates to sports, I really enjoyed taking the class as it opened up my eyes to the hidden part of sports that many people do not bother to think about. Sports is not just about pure skill from the players, but the data and research team in the shadow of the players also plays a key impact in the results that these players have in their career. The knowledge I learned while taking this camp can also be used to when buying sports cards. Using data from previous years, in R, we are able to create graphs that can predict future stats of players. Obviously it is not 100% accurate and never will be, but it is a great way to get a feeling as to whether a certain player is worth investing in or not.​