Barrett, The Honors College Thesis/Creative Project Collection
Barrett, The Honors College at Arizona State University proudly showcases the work of undergraduate honors students by sharing this collection exclusively with the ASU community.
Barrett accepts high performing, academically engaged undergraduate students and works with them in collaboration with all of the other academic units at Arizona State University. All Barrett students complete a thesis or creative project which is an opportunity to explore an intellectual interest and produce an original piece of scholarly research. The thesis or creative project is supervised and defended in front of a faculty committee. Students are able to engage with professors who are nationally recognized in their fields and committed to working with honors students. Completing a Barrett thesis or creative project is an opportunity for undergraduate honors students to contribute to the ASU academic community in a meaningful way.
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- Creators: Computer Science and Engineering Program
For my Thesis Project, I worked to operationalize an algorithmic trading application called Trading Dawg. Over the year, I was able to implement several analysis models, including accuracy, performance, volume, and hyperparameter analysis. With these improvements, we are in a strong position to create valuable tools in the algorithmic trading space.
Historically, the predominant strategy for evaluating baseball pitchers has been through statistics created directly from the offensive production against the pitcher, such as ERA. Such statistics are inherently relative to the abilities and competition level of the opposing offense and the field defense, which the pitcher has no control over, making it difficult to compare pitchers across leagues. In this paper, I use cutting edge pitch-tracking data to develop a pitch evaluation model that is intrinsic to the attributes of the pitches themselves, and not influenced directly by the outcomes of each individual pitch. I train four different classifiers to predict the probability of each pitch belonging to different subsets of outcomes, then multiply the probability of each outcome by that outcome’s average run value to arrive at an expected run value for the pitch. I compare the performance of each classifier to a baseline, examine the most impactful features, and compare the top pitchers identified by the model to those identified by a different baseball statistics resource, ultimately concluding that three of the four classification models are productive and that the overall intrinsic evaluation model accurately identifies the sports top performers.