Social and Behavioral Sciences Commons^™

Applying The Data: Predictive Analytics In Sport, Anthony Teeter, Margo Bergman

Access*: Interdisciplinary Journal of Student Research and Scholarship

The history of wagering predictions and their impact on wide reaching disciplines such as statistics and economics dates to at least the 1700’s, if not before. Predicting the outcomes of sports is a multibillion-dollar business that capitalizes on these tools but is in constant development with the addition of big data analytics methods. Sportsline.com, a popular website for fantasy sports leagues, provides odds predictions in multiple sports, produces proprietary computer models of both winning and losing teams, and provides specific point estimates. To test likely candidates for inclusion in these prediction algorithms, the authors developed a computer model, and test …

Data, Stats, Go: Navigating The Intersections Of Cataloging, E-Resource, And Web Analytics Reporting, Rachel S. Evans, Wendy Moore, Jessica Pasquale, Andre Davison

Presentations

Do you trudge through gathering statistics at fiscal or calendar year-end? Do you wonder why you track certain things, thinking many seem outdated or irrelevant? Many places seem to keep counting certain statistics because "that's what they've always done." For e-resources, how do you integrate those with physical counts and reconcile the variations (updated e-resources versus re-cataloged physical items)? What about repository downloads and other web traffic? The quantity of stats that libraries track is staggering and keeps growing. This program will encourage attendees to stop and evaluate what and why they're gathering data and help identify possible alternatives to …

Southwest Pacific Tropical Cyclone Frequency And Intensity Related To Observed And Modeled Geophysical And Aerosol Variables, Rupsa Bhowmick

LSU Doctoral Dissertations

The dissertation focuses on western region of Southwest Pacific Ocean (SWPO)

basin (135E - 180, and 5S - 35S) tropical cyclone (TC) climatology using observed

and modeled data. The classification-based machine learning approach

identifies the synoptic geophysical and aerosol environment favorable or unfavorable

for TC intensification and intensity change prior to landfall incorporating

observational and satellite data. A multiple poisson regression model with varying

temporal monthly lags was used to build a relationship between the number of

monthly TC days with basin wide average dust aerosol optical depth (AOD), sea

surface temperature (SST), and upper ocean temperature (UOT). This idea …

Analyzing Competitive Balance In Professional Sport, Kevin Alwell

Honors Scholar Theses

In this paper we review several measures to statistically analyze competitive balance and report which leagues have a wider variance of performance amongst its competitors. Each league seeks to maintain high levels of parity, making matches and overall season more unpredictable and appealing to the general audience. Here we quantify competitive advantage across major sports leagues in numbers using several statistical methods in order for leagues to optimize their revenue.

Boom Or Bust: Examining The Relationship Between High School Recruiting Rankings And The Nfl Draft, Nicholas E. Tice

Senior Theses

The goal of this thesis is to model the probability of a high school football player’s chance of being drafted based on information taken from their recruiting profile. The response variable is binary and defined as drafted (1) or undrafted (0). The independent variables were collected by scraping data from the recruiting websites including height, weight, position, hometown, recruiting grade and other socioeconomic factors based on the player’s high school. 247Sports and ESPN were the two recruiting services used and compared in this study. Because of the binary nature of the dependent variable, logistic regression and decision trees were chosen …

The Importance Of Type I Error Rates When Studying Bias In Monte Carlo Studies In Statistics, Michael Harwell

Journal of Modern Applied Statistical Methods

Two common outcomes of Monte Carlo studies in statistics are bias and Type I error rate. Several versions of bias statistics exist but all employ arbitrary cutoffs for deciding when bias is ignorable or non-ignorable. This article argues Type I error rates should be used when assessing bias.

The Author’S Reflections On No B.S. (Bad Stats): Black People Need People Who Believe In Black People Enough Not To Believe Every Bad Thing They Hear About Black People, Ivory A. Toldson

Numeracy

Toldson, Ivory. A. 2019. No BS (Bad Stats): Black People Need People Who Believe in Black People Enough Not to Believe Every Bad Thing They Hear About Black People (Boston, MA: Brill-Sense) 194 pp. ISBN 978-9004397026.

This essay provides an introduction to No BS (Bad Stats): Black People Need People Who Believe in Black People Enough Not to Believe Every Bad Thing They Hear About Black People. In the essay, the author discusses how cynical views about the educational potential of Black children motivated him to write a book that challenges negative statistics. The essay also outlines the harmful …

Should We Expect Each Year In The Next Decade (2019–28) To Be Ranked Among The Top 10 Warmest Years Globally?, Anthony Arguez, Shannan Hurley, Anand Inamdar, Laurel Mahoney, Ahira Sanchez-Lugo, Lilian Yang

Political Science & Geography Faculty Publications

Annual rankings of global temperature are widely cited by media and the general public, not only to place the most recent year in a historical perspective, but also as a first-order metric of recent climate change that is easily digestible by the general public. Moreover, all annual NOAAGlobalTemp anomalies from 1880 (the earliest reading available) through the mid-1970s are well below anomalies of the top 10 warmest years in Table 1, even when considering the uncertainty of the NOAAGlobalTemp time series values. While we expect the algorithm's performance to be largely independent of any changes made to the way that …