Matching Items (5)
Filtering by

Clear all filters

Description

An examination upon the historical evolution of the quarterback reveals that there were three foundational cycles leading up to 2007 which established the model for the mobile quarterback in the NFL. These were especially marked by exceptional quarterbacks breaking molds and pioneering African American quarterbacks overcoming racial stigma. Since 2007,

An examination upon the historical evolution of the quarterback reveals that there were three foundational cycles leading up to 2007 which established the model for the mobile quarterback in the NFL. These were especially marked by exceptional quarterbacks breaking molds and pioneering African American quarterbacks overcoming racial stigma. Since 2007, there has been a steady trend of mobile quarterbacks replacing pocket passers, especially among playoff teams. Using k-means clustering, three different categories of quarterbacks were established: pocket passers, scramblers, and dual-threats. After evaluating various player metrics describing quarterback mobility, using yards per game, run-to-pass ratio, scramble rate, and designed run rate on third down produced the best model. This yielded an accurate prediction of covariance and a good overall fit. Teams with dual-threat quarterbacks had more success than other quarterback types on third-and-medium for dropbacks, third-and-long for designed runs, and explosive plays (plays which gain 20+ yards) on designed runs, passes, and quarterback scrambles. An examination into the schematic tendencies using film reveals that mobile quarterbacks allow the offense to have more freedom in its play calling and reduces the margin of error for defenses. Alongside the NFL’s increased focus on the concept of positionless football, this provides the framework for what this thesis calls the “Slashback Offense,” in which the offense utilizes a young, athletic quarterback in multiple positions in conjunction with a mobile starting quarterback. This can enhance option plays, establish the threat of another passer, and reduce the physical burden on the starting quarterback.

ContributorsWelco, Bennett (Author) / McIntosh, Daniel (Thesis director) / McCreless, Tamuchin (Committee member) / Kollmann, Brett (Committee member) / Barrett, The Honors College (Contributor) / Department of Psychology (Contributor) / School of Sustainable Engineering & Built Envirnmt (Contributor)
Created2023-05
ContributorsWelco, Bennett (Author) / McIntosh, Daniel (Thesis director) / McCreless, Tamuchin (Committee member) / Kollmann, Brett (Committee member) / Barrett, The Honors College (Contributor) / Department of Psychology (Contributor) / School of Sustainable Engineering & Built Envirnmt (Contributor)
Created2023-05
ContributorsWelco, Bennett (Author) / McIntosh, Daniel (Thesis director) / McCreless, Tamuchin (Committee member) / Kollmann, Brett (Committee member) / Barrett, The Honors College (Contributor) / Department of Psychology (Contributor) / School of Sustainable Engineering & Built Envirnmt (Contributor)
Created2023-05
131857-Thumbnail Image.png
Description
Appointment scheduling in health care systems is a well-established domain, however, the top commercial services neglect scheduling analytics. This project explores the benefit of utilizing data analysis to equip health care offices with insights on how to improve their existing schedules. The insights are generated by comparing patients’ preferred appointment

Appointment scheduling in health care systems is a well-established domain, however, the top commercial services neglect scheduling analytics. This project explores the benefit of utilizing data analysis to equip health care offices with insights on how to improve their existing schedules. The insights are generated by comparing patients’ preferred appointment times with the current schedule coverage and calculating utilization of past appointments. While untested in the field, the project yielded promising results using generated sample data as a proof of concept for the benefits of using data analytics to remove deficiencies in a health care office’s schedule.
ContributorsBowman, Jedde James (Author) / Chen, Yinong (Thesis director) / Balasooriya, Janaka (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
131260-Thumbnail Image.png
Description
Machine learning is the process of training a computer with algorithms to learn from data and make informed predictions. In a world where large amounts of data are constantly collected, machine learning is an important tool to analyze this data to find patterns and learn useful information from it. Machine

Machine learning is the process of training a computer with algorithms to learn from data and make informed predictions. In a world where large amounts of data are constantly collected, machine learning is an important tool to analyze this data to find patterns and learn useful information from it. Machine learning applications expand to numerous fields; however, I chose to focus on machine learning with a business perspective for this thesis, specifically e-commerce.

The e-commerce market utilizes information to target customers and drive business. More and more online services have become available, allowing consumers to make purchases and interact with an online system. For example, Amazon is one of the largest Internet-based retail companies. As people shop through this website, Amazon gathers huge amounts of data on its customers from personal information to shopping history to viewing history. After purchasing a product, the customer may leave reviews and give a rating based on their experience. Performing analytics on all of this data can provide insights into making more informed business and marketing decisions that can lead to business growth and also improve the customer experience.
For this thesis, I have trained binary classification models on a publicly available product review dataset from Amazon to predict whether a review has a positive or negative sentiment. The sentiment analysis process includes analyzing and encoding the human language, then extracting the sentiment from the resulting values. In the business world, sentiment analysis provides value by revealing insights into customer opinions and their behaviors. In this thesis, I will explain how to perform a sentiment analysis and analyze several different machine learning models. The algorithms for which I compared the results are KNN, Logistic Regression, Decision Trees, Random Forest, Naïve Bayes, Linear Support Vector Machines, and Support Vector Machines with an RBF kernel.
ContributorsMadaan, Shreya (Author) / Meuth, Ryan (Thesis director) / Nakamura, Mutsumi (Committee member) / Computer Science and Engineering Program (Contributor, Contributor) / Dean, W.P. Carey School of Business (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05