If you were scouting out an athletics team, you would need to know more than just how far each person has jumped or thrown once. You want to know who is the most consistent, who fouls the least and who comes through in the clutch. And you will need to decide which aspects are most important to you so you can find the right balance. In this R project, you will use dataframes and the `dplyr` package to find out who you should put on your team, and in doing so become the next "Moneyball" star manager of an athletics team. You will use performance data collated from jumps and throws events in the US from 2013-2017.
- 1Athletics needs a new breed of scouts and managers
- 2Managers love tidy data
- 3Every throw matters
- 4Find the clutch performers
- 5Pull the pieces together for a new look at the athletes
- 6Normalize the data to compare across stats
- 7What matters most when building your squad?
- 8Get to know your players
- 9Make your case to the front office
- 10Time to throw down
Sports Scientist and Entrepreneur
George Perry is a sports scientist and entrepreneur. Whether he is coaching his athletes, managing a team or writing about soccer, he lives to find the balance between heart-pounding passion for the game and data-driven insight. Before wandering into data science George was a Submarine Warfare Officer in the United States Navy, and earned degrees from Boston University and the University of Texas.