The readme.md on the GitHub repo should describe the project, what you learned and how it is pertinent to your career as a data scientist.

This project looks at how physical performance metrics taken from the UVA Men’s Basketball team with Catapult devices show long term player development. The metric that we decided to analyze was jump intensity by looking at the proportion of high and low band jumps. We felt that this metric gives a good sense of the physical output that players are producing which tells us about their athletic development. Through this we were able to deduce trends across players who only had data for one season versus two. Our primary takeaway is that two seasons of playing with UVA correlated with less inconsistency and overall stronger player development as compared with playing for just one season. Obviously this analysis is not perfect as other factors such as strength of schedule, player health, and team demand can influence a player’s jump performance. Since the data is long-term, however, we feel that these variances are leveled out.

From a data science perspective, this project offered us the ability to apply our knowledge to real-world performance data to hopefully give the UVA basketball team some insight and influence player recruitment/coaching. It deepened our ability to turn raw data into insights with practical value and helped us improve as analytical thinkers. Not only did we find an answer to a question, we also formulated the question itself which demonstrates our ability to deeply understand and apply our skills to a dataset. We learned about the beginning to end process of working on a data project, an experience that we will all look back on for advice in our future data science ventures.