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The NBA Draft has become one of the most exciting and unique events in sports. Draft decisions are so monumental; so crucial to be right, so disastrous to be wrong. The purpose of this project is to build a model that would help teams to predict which types of players

The NBA Draft has become one of the most exciting and unique events in sports. Draft decisions are so monumental; so crucial to be right, so disastrous to be wrong. The purpose of this project is to build a model that would help teams to predict which types of players perform at a high level upon entering the league. By using regression analysis to predict the rookie year PER (performance efficiency rating) as a dependent variable, teams would have some idea of whether their rookies were underperforming, excelling, or performing at a level they could expect. The independent variables and their statistical significance could help answer a host of questions that front offices have about players: If a player came from a worse conference, can we expect them to have a harder time adjusting? Will their shorter wingspan have a negative effect on their play in the NBA? Do guards or forwards tend to have higher PERs upon entering the league? To answer these questions, I've gathered data on every first round NBA draft pick from 2001-2014 who played at least one season of Division 1 NCAA basketball. The data consist of anthropometric measurements (height, wingspan, standing reach, etc.), NBA draft combine results (agility drills, sprint times, etc.) and their college statistics per 40 minutes in their final season of college basketball (points, rebounds, assist-to-turnover ratio, etc.). I then separated the data into seven different sets: aggregate, backcourt, frontcourt, guard, wing, forward, and big. For each of these data sets, I built a predictive model for rookie PER. In doing so, I aimed to gain both a broad understanding of what factors lead to translation of college basketball play to professional play, and also a precise understanding of how those factors change for each distinct position.
ContributorsMurphy, Benjamin Joseph (Author) / Goegan, Brian (Thesis director) / Marburger, Daniel (Committee member) / Economics Program in CLAS (Contributor) / School of Historical, Philosophical and Religious Studies (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
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The NBA operates under a unique system with both forms of the salary cap. The league has a team salary cap that sets a limit that teams can spend on their entire roster. The NBA has a soft cap and a luxury tax system, meaning if teams spend over a

The NBA operates under a unique system with both forms of the salary cap. The league has a team salary cap that sets a limit that teams can spend on their entire roster. The NBA has a soft cap and a luxury tax system, meaning if teams spend over a determined amount, they are taxed for the salaries in excess. The league also has a player salary cap. The 1999 NBA collective bargaining agreement first introduced the individual player salary cap in the league. This cap sets a limit on what the best players can earn, otherwise known as the maximum contract. In an economic system with a soft team cap, the introduction of the player salary cap has important implications. The stated outcome of such a salary cap is to improve competitive balance and better distribute star players throughout the league. This study evaluated the 1990-2015 regular seasons to measure the impact of the player salary cap on competitive balance, the distribution of team payrolls, and the dispersion of star players. In accordance with the Rottenberg's invariance hypothesis, the player salary cap has hurt the players and benefited the owners by redistributing income from one party to the other, without impacting the distribution of talent in the league. The rule change has not affected competitive balance, while team payrolls have converged and star players have become more dispersed throughout the league. These changes hurt the league overall, preventing the maximization of revenues. Despite this inefficiency, the chance of the league moving to eliminate the player salary cap is low.
ContributorsWelu, Brian Andrew (Author) / Marburger, Daniel (Thesis director) / Goegan, Brian (Committee member) / Sandra Day O'Connor College of Law (Contributor) / Department of Economics (Contributor) / School of Historical, Philosophical and Religious Studies (Contributor) / W. P. Carey School of Business (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
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This study examines the economic impact of the opioid crisis in the United States. Primarily testing the years 2007-2018, I gathered data from the Census Bureau, Centers for Disease Control, and Kaiser Family Foundation in order to examine the relative impact of a one dollar increase in GDP per Capita

This study examines the economic impact of the opioid crisis in the United States. Primarily testing the years 2007-2018, I gathered data from the Census Bureau, Centers for Disease Control, and Kaiser Family Foundation in order to examine the relative impact of a one dollar increase in GDP per Capita on the death rates caused by opioids. By implementing a fixed-effects panel data design, I regressed deaths on GDP per Capita while holding the following constant: population, U.S. retail opioid prescriptions per 100 people, annual average unemployment rate, percent of the population that is Caucasian, and percent of the population that is male. I found that GDP per Capita and opioid related deaths are negatively correlated, meaning that with every additional person dying from opioids, GDP per capita decreases. The finding of this research is important because opioid overdose is harmful to society, as U.S. life expectancy is consistently dropping as opioid death rates rise. Increasing awareness on this topic can help prevent misuse and the overall reduction in opioid related deaths.
ContributorsRavi, Ritika Lisa (Author) / Goegan, Brian (Thesis director) / Hill, John (Committee member) / Department of Economics (Contributor) / Department of Information Systems (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
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Description

Abstract
Objective: To assess the attitudes and knowledge of behavioral health technicians (BHTs)
towards opioid overdose management and to assess the effect of online training on opioid
overdose response on BHTs’ attitudes and knowledge, and the confidence to identify and
respond to opioid overdose situations.

Design/Methods: Pre-intervention Opioid Overdose Knowledge Scale (OOKS) and Opioid
Overdose Attitude

Abstract
Objective: To assess the attitudes and knowledge of behavioral health technicians (BHTs)
towards opioid overdose management and to assess the effect of online training on opioid
overdose response on BHTs’ attitudes and knowledge, and the confidence to identify and
respond to opioid overdose situations.

Design/Methods: Pre-intervention Opioid Overdose Knowledge Scale (OOKS) and Opioid
Overdose Attitude Scale (OOAS) surveys were administered electronically to five BHTs in
2020. Data obtained were de-identified. Comparisons between responses to pre-and post-surveys questions were carried out using the standardized Wilcoxon signed-rank statistical test(z). This study was conducted in a residential treatment center (RTC) with the institutional review board's approval from Arizona State University. BHTs aged 18 years and above, working at this RTC were included in the study.

Interventions: An online training was provided on opioid overdose response (OOR) and
naloxone administration and on when to refer patients with opioid use disorder (OUD) for
medication-assisted treatment.

Results: Compared to the pre-intervention surveys, the BHTs showed significant improvements
in attitudes on the overall score on the OOAS (mean= 26.4 ± 13.1; 95% CI = 10.1 - 42.7; z =
2.02; p = 0.043) and significant improvement in knowledge on the OOKS (mean= 10.6 ± 6.5;
95% CI = 2.5 – 18.7; z =2.02, p = 0.043).

Conclusions and Relevance: Training BHTs working in an RTC on opioid overdose response is
effective in increasing attitudes and knowledge related to opioid overdose management. opioid
overdose reversal in RTCs.

Keywords: Naloxone, opioid overdose, overdose education, overdose response program

ContributorsQuie, Georgette (Author) / Guthery, Ann (Thesis advisor)
Created2021-04-12
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Description
Objective: To assess the attitudes and knowledge of behavioral health technicians (BHTs) towards opioid overdose management and to assess the effect of online training on opioid overdose response on BHTs’ attitudes and knowledge, and the confidence to identify and respond to opioid overdose situations. Design/Methods: Pre-intervention Opioid Overdose Knowledge Scale (OOKS) and Opioid Overdose Attitude

Objective: To assess the attitudes and knowledge of behavioral health technicians (BHTs) towards opioid overdose management and to assess the effect of online training on opioid overdose response on BHTs’ attitudes and knowledge, and the confidence to identify and respond to opioid overdose situations. Design/Methods: Pre-intervention Opioid Overdose Knowledge Scale (OOKS) and Opioid Overdose Attitude Scale (OOAS) surveys were administered electronically to five BHTs in 2020. Data obtained were de-identified. Comparisons between responses to pre-and post-surveys questions were carried out using the standardized Wilcoxon signed-rank statistical test(z). This study was conducted in a residential treatment center (RTC) with the institutional review board's approval from Arizona State University. BHTs aged 18 years and above, working at this RTC were included in the study. Interventions: An online training was provided on opioid overdose response (OOR) and naloxone administration and on when to refer patients with opioid use disorder (OUD) for medication-assisted treatment. Results: Compared to the pre-intervention surveys, the BHTs showed significant improvements in attitudes on the overall score on the OOAS (mean= 26.4 ± 13.1; 95% CI = 10.1 - 42.7; z = 2.02; p = 0.043) and significant improvement in knowledge on the OOKS (mean= 10.6 ± 6.5; 95% CI = 2.5 – 18.7; z =2.02, p = 0.043). Conclusions and Relevance: Training BHTs working in an RTC on opioid overdose response is effective in increasing attitudes and knowledge related to opioid overdose management. opioid overdose reversal in RTCs.
Created2021-04-12