This project looks at the change in strikeout patterns over the past 19 years of Major League Baseball. New research in 2001 revolutionized the pitching statistics field, and non-coincidentally, the number of strikeouts has ballooned since then. I first detail the statistical nature of the increase, looking at where the additional strikeouts are coming from. Then, a discussion of why this has happened, referencing changes in baseball strategy and talent usage optimization follows. The changes in the ways MLB teams use their pitching staffs are largely the cause of this increase. Similar research is cited to confirm that these strategy changes are valid and are having the effect of increasing strikeouts in the game. Strikeout numbers are then compared to other pitching statistics over the years to determine whether the increase has had any effect on other pitching metrics. Lastly, overall team success is looked at as a verification method as to whether the increased focus on increasing strikeouts has created positive results for major league teams. Teams making the MLB playoffs consistently ranked much higher than non-qualifying teams in terms of strikeout rates. Also included in the project are the details of data acquisition and manipulation, to ensure the figures used are valid. Ideas for future research and further work on the topic are included, as the amount of data available in this field is quite staggering. Further analysis could dive into the ways pitches themselves are changing, rather than looking at pitching outcomes. Overall, the project details and explains a major shift in the way baseball has been played over the last 19 years, complete with both pure data analysis and supplementary commentary and explanation

While former New York Yankees pitcher Goose Gossage unleashed his tirade on the deterioration of the unwritten rules of baseball and nerds ruining the sport about halfway through my writing of the paper, sentiments like his were inspiration for my topic: the evolution of statistics and data in baseball. By telling the story of how baseball data and statistics have evolved, my goal was to also demonstrate how they have been intertwined since the beginning—which would essentially mean that nerds have always been ruining the sport (if you subscribe to that kind of thought).

In the quest to showcase this, it was necessary to document how baseball prospers from numbers and numbers prosper from baseball. The relationship between the two is mutualistic. Furthermore, an all-encompassing historical look at how data and statistics in baseball have matured was a critical portion of the paper. With a metric such as batting average going from a radical new measure that posed a threat to the status quo, to a fiercely cherished statistic that was suddenly being unseated by advanced analytics, it shows the creation of new and destruction of old has been incessant. Innovators like Pete Palmer, Dick Cramer and Bill James played a large role in this process in the 1980s. Computers aided their effort and when paired with the Internet, unleashed the ability to crunch data to an even larger sector of the population. The unveiling of Statcast at the commencement of the 2015 season showed just how much potential there is for measuring previously unquantifiable baseball acts.

Essentially, there will always be people who mourn the presence of data and statistics in baseball. Despite this, the evolution story indicates baseball and numbers will be intertwined into the future, likely to an even greater extent than ever before, as technology and new philosophies become increasingly integrated into front offices and clubhouses.

ABSTRACT: After attending the Major League Winter Meetings in Orlando, Florida (the largest gathering of baseball executives in the world), it was affirmed how valuable a pitcher is to a team. "Which pitcher will be worth the pay or hurt your pay?" This is a multimillion-dollar question asked every season by managers, teams and organizations in Major League Baseball. This is a question that has been mulled over in the past, present and will be a highly regarded question in the future in the baseball industry. While technology is still being developed to analyze and test pitchers for the future, what can be done in the meantime without the bells and whistles? The purpose and objective of my thesis paper is to try to identify a recipe that can be used by any baseball team to compare pitchers without the use of very advance and expensive technology. The arm motion of a pitcher is crucial as poor mechanics can lead to an injured pitcher or even surgery, forcing a team to dig deep elsewhere. For my paper, I chose pitchers I had video access to from the 2013 season that include Diamondbacks pitchers: Patrick Corbin, Wade Miley, Josh Collmenter and Joe Thatcher. Then I chose two players I would like to further analyze: a knuckleball pitcher, R.A. Dickey, and a fastball/curveball pitcher, Stephen Strasburg. The data collected includes: angles of arm in motion also known as the jerk, stride, angle velocity, height, weight, number of games started/played in 2013, percentage of pitches thrown in a season, number of pitches thrown in a season, innings pitched (IP) and earned run average (ERA). The data was put in a table to compare pitchers in the now or the future.

This creative project explores the trend of designer/synthetic drug use in Arizona and nationwide. The project serves as "gap research" - bringing to light the problem of limited use statistics and constantly-changing drug chemical compounds. The project was thoroughly researched using media reports, psychology/drug addiction experts, community education organizers and available healthcare statistics. The results provided not definitive answer other than that more work needs to be done in the area of synthetic drug use. Parents and youth must educate themselves on the dangers of using these "legal" drugs.

This report examines the transformation of downtown Phoenix businesses between 2004 and 2013. The main factors at play during that time period are the introduction of Arizona State University to the downtown area, and the construction of Valley Metro Light Rail and the bulk of data was gleaned from US Census and City of Phoenix reports. During the period of the study, downtown Phoenix saw a shift toward more restaurants and arts and away from professional, technical and financial services. Food services jumped from eight to 12 percent of total businesses, while professional services declined from 32 to 29 percent. Certain business sectors were affected by the Recession, while others were seemingly impervious to the economic downturn. Of the sectors that saw the most growth through the period, restaurants were the most highly correlated with growth in ASU enrollment at 0.95 R. Meanwhile, the total number of businesses downtown decreased slightly, representing a negative correlation with ASU. However, the decline was so slight that ASU growth fails to account for the stagnation. Light rail ridership in the downtown area is not, on its own, highly correlated with downtown business growth. Only the Van Buren Junction, which includes both the Central and 1st Avenue stops, shows the same degree of correlation with businesses as ASU enrollment. Growth in ridership at the Van Buren Junction represents the vast majority of light rail growth in the area, and it is almost entirely linked to the spike in ASU enrollment. This suggests that ASU enrollment is a much more significant driver of business transformation than light rail. Neither ASU nor light rail can explain the totality of every shift in the downtown business landscape, but in certain sectors, namely restaurants and the arts, the extremely high correlations suggest a near indisputable connection. Because this system does not allow for the upload of excel, appendixes are available at: https://drive.google.com/folderview?id=0B6y9cOb9sqVnMHdzalNOSmxuZFE&usp=sharing