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Social and Behavioral Sciences Commons™
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Full-Text Articles in Social and Behavioral Sciences
Analytical Study To Determine Significant Causes Of Increased No-Hitters In The 2021 Major League Baseball Season, Joel Robison
Analytical Study To Determine Significant Causes Of Increased No-Hitters In The 2021 Major League Baseball Season, Joel Robison
Honors Projects
Why were there so many no-hitters in the 2021 MLB season? This project focuses on possible significant causes to the record-breaking number of no-hitters pitched in the 2021 Major League Baseball season. Specifically, this project takes an analytical look at the recent trends in launch angles and spin rates to determine if there are any significant causes to the increased number of no-hitters in baseball. The random nature and unpredictability of the game of baseball make it almost impossible to come to any solid conclusions.
Conceptual Distinction Between The Critical P Value And The Type I Error Rate In Permutation Testing, Richard B. Anderson
Conceptual Distinction Between The Critical P Value And The Type I Error Rate In Permutation Testing, Richard B. Anderson
Psychology Faculty Publications
To counter past assertions that permutation testing is not distribution-free, this article clarifies that the critical p value (alpha) in permutation testing is not a Type I error rate and that a test's validity is independent of the concept of Type I error.
Data Clustering For Fitting Parameters Of A Markov Chain Model Of Multi-Game Playoff Series, Christopher M. Rump
Data Clustering For Fitting Parameters Of A Markov Chain Model Of Multi-Game Playoff Series, Christopher M. Rump
Applied Statistics and Operations Research Faculty Publications
We propose a Markov chain model of a best-of-7 game playoff series that involves game-togame dependence on the current status of the series. To create a relatively parsimonious model, we seek to group transition probabilities of the Markov chain into clusters of similar game-winning frequency. To do so, we formulate a binary optimization problem to minimize several measures of cluster dissimilarity. We apply these techniques on Major League Baseball (MLB) data and test the goodness of fit to historical playoff outcomes. These state-dependent Markov models improve significantly on probability models based solely on home-away game dependence. It turns out that …