We attempt to analyze the effect of fatigue on free throw efficiency in the National Basketball Association (NBA) using play-by-play data from regular-season, regulation-length games in the 2016-2017, 2017-2018, and 2018-2019 seasons. Using both regression and tree-based statistical methods, we analyze the relationship between minutes played total and minutes played continuously at the time of free throw attempts on players' odds of making an attempt, while controlling for prior free throw shooting ability, longer-term fatigue, and other game factors. Our results offer strong evidence that short-term activity after periods of inactivity positively affects free throw efficiency, while longer-term fatigue has no effect.

This report attempts to understand the effects of the many aspects that pertain to a woman’s path into the construction industry and their role in limiting women’s overall representation in the construction industry. More specifically, it aims to understand how upbringing, background, and culture impact women that do pursue careers in the construction industry. This paper presents some of the current and prominent issues being faced by women in in the construction industry, including those in the trades. These issues then contribute to their lack of representation and forceful exit. Additionally, it assesses personal narratives from a localized group of women who are currently employed at a large construction company. This information and these narratives are analyzed jointly to try and gain a better understanding of the current challenges being faced by women in comparison to those reported previously. This joint comparison allows for a deeper understanding of women’s perception of the construction industry as a whole.

Of the many retirement savings options available, defined benefit pension plans were once a retirement income staple. Due to the highs and lows of the economic cycle, defined benefit pension plans have become severely underfunded. A series of inadequate contributions, enabled by weak funding and risk management policies, poses uncertainty for the retirement of many. The cost of paying pension benefits rises as defined benefit pension plans become increasingly underfunded, burdening the employers who continue to pay them. However, without increasing these already unaffordable pension benefits alongside inflation, they become less valuable to retirees. As pension benefits lose their value and the costs of retirement, such as healthcare and assisted living, increase, defined benefit pension plans may not provide the retirement security that was once promised.

Th NTRU cryptosystem is a lattice-based encryption scheme. Several parameters determine the speed, size, correctness rate and security of the algorithm. These parameters need to be carefully selected for the algorithm to function correctly. This thesis includes a short overview of the NTRU algorithm and its mathematical background before discussing the results of experimentally testing various different parameter sets for NTRU and determining the effect that different relationships between these parameters have on the overall effectiveness of NTRU.

College athletics are a multi-billion dollar industry featuring hard-working student-athletes competing at a high level for national championships across a variety of different sports. Across the college sports landscape, coaches and players are always seeking an edge they can gain in order to obtain a competitive advantage over their opponents. While this may sound nefarious, the vast amounts of data about these games and student-athletes can be used to glean insights about the sports themselves in order to help student-athletes be more successful. Data analytics can be used to make sense of the available data by creating models and using other tools available that can predict how student-athletes and their teams will do in the future based on the data gathered from how they have performed in the past.
Colleges and universities across the country compete in a vast array of sports. As a result of these differences, the sports with the largest amounts of data available will be the more popular college sports, such as football, men’s and women’s basketball, baseball and softball. Arizona State University, as a member of the Pac-12 conference, has a storied athletic tradition and decades of history in all of these sports, providing a large amount of data that can be used to analyze student-athlete success in these sports and help predict future success. However, data is available from numerous other college athletic programs that could provide a much larger sample to help predict with greater accuracy why certain teams and student-athletes are more successful than others.
The explosion of analytics across the sports world has resulted in a new focus on utilizing statistical techniques to improve all aspects of different sports. Sports science has influenced medical departments, and model-building has been used to determine optimal in-game strategy and predict the outcomes of future games based on team strength. It is this latter approach that has become the focus of this paper, with football being used as a subject due to its vast popularity and massive supply of easily accessible data.

College athletics are a multi-billion dollar industry featuring hard-working student-athletes competing at a high level for national championships across a variety of different sports. Across the college sports landscape, coaches and players are always seeking an edge they can gain in order to obtain a competitive advantage over their opponents. While this may sound nefarious, the vast amounts of data about these games and student-athletes can be used to glean insights about the sports themselves in order to help student-athletes be more successful. Data analytics can be used to make sense of the available data by creating models and using other tools available that can predict how student-athletes and their teams will do in the future based on the data gathered from how they have performed in the past. Colleges and universities across the country compete in a vast array of sports. As a result of these differences, the sports with the largest amounts of data available will be the more popular college sports, such as football, men’s and women’s basketball, baseball and softball. Arizona State University, as a member of the Pac-12 conference, has a storied athletic tradition and decades of history in all of these sports, providing a large amount of data that can be used to analyze student-athlete success in these sports and help predict future success. However, data is available from numerous other college athletic programs that could provide a much larger sample to help predict with greater accuracy why certain teams and student-athletes are more successful than others. The explosion of analytics across the sports world has resulted in a new focus on utilizing statistical techniques to improve all aspects of different sports. Sports science has influenced medical departments, and model-building has been used to determine optimal in-game strategy and predict the outcomes of future games based on team strength. It is this latter approach that has become the focus of this paper, with football being used as a subject due to its vast popularity and massive supply of easily accessible data.

College athletics are a multi-billion dollar industry featuring hard-working student-athletes competing at a high level for national championships across a variety of different sports. Across the college sports landscape, coaches and players are always seeking an edge they can gain in order to obtain a competitive advantage over their opponents. While this may sound nefarious, the vast amounts of data about these games and student-athletes can be used to glean insights about the sports themselves in order to help student-athletes be more successful. Data analytics can be used to make sense of the available data by creating models and using other tools available that can predict how student-athletes and their teams will do in the future based on the data gathered from how they have performed in the past. Colleges and universities across the country compete in a vast array of sports. As a result of these differences, the sports with the largest amounts of data available will be the more popular college sports, such as football, men’s and women’s basketball, baseball and softball. Arizona State University, as a member of the Pac-12 conference, has a storied athletic tradition and decades of history in all of these sports, providing a large amount of data that can be used to analyze student-athlete success in these sports and help predict future success. However, data is available from numerous other college athletic programs that could provide a much larger sample to help predict with greater accuracy why certain teams and student-athletes are more successful than others. The explosion of analytics across the sports world has resulted in a new focus on utilizing statistical techniques to improve all aspects of different sports. Sports science has influenced medical departments, and model-building has been used to determine optimal in-game strategy and predict the outcomes of future games based on team strength. It is this latter approach that has become the focus of this paper, with football being used as a subject due to its vast popularity and massive supply of easily accessible data.

The objective of this study was to investigate if 911 operators experience similar stressors and amounts of stress as law enforcement, fire, and EMS personnel. To accomplish this, I conducted a focus group to obtain information about similar stressors experienced by all three areas of emergency services. Then I utilized this information to form a survey to quantify the amounts of stress experienced by emergency service personnel. My findings indicate that the stress experience is similar.

Substance use disorders account for billions of dollars annually in emergency and inpatient healthcare, not taking into account the healthcare costs of the disorders with which substance use disorders are associated with increased risks of developing. However, while treatment for these disorders shows a decreasing action on health costs, a low percentage of affected individuals receive treatment, despite many insurance payers providing coverage for treatments of this nature. Thus, this maintains the issues under the current healthcare system of mitigatable, generally higher, healthcare costs and increased health risks for individuals with substance use disorders.

A growing body of research suggests that there is more to course assessment than homework scores and test performance. This paper contributes to the empirical literature in economics and education by evaluating the impact of racial and gender congruency on the performance of ASU students. Expanding on previous research which only covered elementary and high school, we are able to draw conclusions and policy recommendations to solve the racial achievement gap in the USA.