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Covid-19 is unlike any coronavirus we have seen before, characterized mostly by the ease with which it spreads. This analysis utilizes an SEIR model built to accommodate various populations to understand how different testing and infection rates may affect hospitalization and death. This analysis finds that infection rates have a

Covid-19 is unlike any coronavirus we have seen before, characterized mostly by the ease with which it spreads. This analysis utilizes an SEIR model built to accommodate various populations to understand how different testing and infection rates may affect hospitalization and death. This analysis finds that infection rates have a significant impact on Covid-19 impact regardless of the population whereas the impact that testing rates have in this simulation is not as pronounced. Thus, policy-makers should focus on decreasing infection rates through targeted lockdowns and vaccine rollout to contain the virus, and decrease its spread.

Created2021-05
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
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The findings of this project show that through the use of principal component analysis and K-Means clustering, NBA players can be algorithmically classified in distinct clusters, representing a player archetype. Individual player data for the 2018-2019 regular season was collected for 150 players, and this included regular per game statistics,

The findings of this project show that through the use of principal component analysis and K-Means clustering, NBA players can be algorithmically classified in distinct clusters, representing a player archetype. Individual player data for the 2018-2019 regular season was collected for 150 players, and this included regular per game statistics, such as rebounds, assists, field goals, etc., and advanced statistics, such as usage percentage, win shares, and value over replacement players. The analysis was achieved using the statistical programming language R on the integrated development environment RStudio. The principal component analysis was computed first in order to produce a set of five principal components, which explain roughly 82.20% of the total variance within the player data. These five principal components were then used as the parameters the players were clustered against in the K-Means clustering algorithm implemented in R. It was determined that eight clusters would best represent the groupings of the players, and eight clusters were created with a unique set of players belonging to each one. Each cluster was analyzed based on the players making up the cluster and a player archetype was established to define each of the clusters. The reasoning behind the player archetypes given to each cluster was explained, providing details as to why the players were clustered together and the main data features that influenced the clustering results. Besides two of the clusters, the archetypes were proven to be independent of the player's position. The clustering results can be expanded on in the future to include a larger sample size of players, and it can be used to make inferences regarding NBA roster construction. The clustering can highlight key weaknesses in rosters and show which combinations of player archetypes lead to team success.
ContributorsElam, Mason Matthew (Author) / Armbruster, Dieter (Thesis director) / Gel, Esma (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05