Averia Padgett: Pick a Sport, Any Sport
Sports analytics is growing field. I have created a variety of modules and code that take sports data and analyzes equations, analyzes variables, and even helps create regression models. I will demonstrate the modules with some specific examples I used to refine my code. The sports I will be focusing on are football, basketball, and baseball.
Cescily Metzgar: Covering All The Bases: Markov Chains and Baseball
Baseball has been studied by statisticians for decades, and today more than ever they are concerned with creating the most accurate models possible. The best models today all make use of Markov Chains. I will talk about how Markov Chains are used in modeling baseball, as well as how to create transition matrices for both team and individual players data. I will also be discussing how to create and make use of expected run distributions. Comparisons between run distributions of players and of teams will allow us to make conclusions about the data that we can compare to actual season statistics to assess the quality of the developed model.