Matching Items (5)

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MLB Team Relocation to Charlotte, NC- A Marketing Proposal

Description

Entering into my final year of W. P. Carey, I decided I wanted my thesis to combine what I've learned over the course of my undergraduate Marketing degree with my

Entering into my final year of W. P. Carey, I decided I wanted my thesis to combine what I've learned over the course of my undergraduate Marketing degree with my passion for baseball. Furthermore, I wanted my thesis to contain both a research element and creative application. I felt the best way to achieve the integration of these goals was to research and then select an MLB team to relocate to a more attractive American market. After performing research to determine an ideal team and city for relocation, I created a comprehensive marketing strategy to best cater this team for its new market. The first half of my thesis focuses entirely on the research required to select an optimal team and attractive market for relocation. I begin my thesis by performing an external analysis of the current MLB landscape. To elaborate, I gathered W-L records and fan attendance records for all 30 MLB teams between 2000 and 2016. I also collected the most recent team revenues and valuations before putting all of this data in Excel to create visual graphs. Using this data, I determine a list of the top 4 most attractive teams for relocation based on consistently poor performance in the metrics I collected data on. After selecting the Tampa Bay Rays as the ideal team to relocate, I then dive deeper into the organization through an internal analysis. Then, I focus on performing an external analysis of the most attractive markets for relocation before ultimately selecting Charlotte, NC as the best city. My research ends with a comprehensive external analysis of the Charlotte, NC market to help in creating a brand that caters to the makeup and culture of the distinct city. My analysis of Charlotte focuses on the city's demographics, population growth, local economy, political environment and trends that could impact target market segments. After performing extensive research on identifying the best team and city for a relocation, I switch gears to developing a comprehensive marketing strategy to best help the team achieve success in its new market. This begins with creating a unifying segmentation, targeting, and positioning strategy to outline the direction the team will take. These strategies place tremendous emphasis on the need for the Charlotte team to create an "irresistible cultural experience" that expands the traditional MLB mold to attract young Millennial fans to games that normally wouldn't be interested in attending games. Next, I begin by developing key elements of the brand including the team name, logos, uniforms, sponsors, and stadium. With the stadium, I even go as far as determining an ideal location along with unique features, such as lawn seating and even local vendors that have appeared on Food Network to add to the cultural experience of the brand. Then, I focus on a unifying initial marketing campaign through TV/print ads, radio ads, social media, and public relations to help the team seamlessly transition into its new home. My thesis ends with recommendations for future steps to take to ensure the relocated organization achieves lasting success in its new city.

Contributors

Agent

Created

Date Created
  • 2016-12

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Analytics of the Prospect Draft in Major League Baseball

Description

Our research encompassed the prospect draft in baseball and looked at what type of player teams drafted to maximize value. We wanted to know which position returned the best value

Our research encompassed the prospect draft in baseball and looked at what type of player teams drafted to maximize value. We wanted to know which position returned the best value to the team that drafted them, and which level is safer to draft players from, college or high school. We decided to look at draft data from 2006-2010 for the first ten rounds of players selected. Because there is only a monetary cap on players drafted in the first ten rounds we restricted our data to these players. Once we set up the parameters we compiled a spreadsheet of these players with both their signing bonuses and their wins above replacement (WAR). This allowed us to see how much a team was spending per win at the major league level. After the data was compiled we made pivot tables and graphs to visually represent our data and better understand the numbers. We found that the worst position that MLB teams could draft would be high school second baseman. They returned the lowest WAR of any player that we looked at. In general though high school players were more costly to sign and had lower WARs than their college counterparts making them, on average, a worse pick value wise. The best position you could pick was college shortstops. They had the trifecta of the best signability of all players, along with one of the highest WARs and lowest signing bonuses. These were three of the main factors that you want with your draft pick and they ranked near the top in all three categories. This research can help give guidelines to Major League teams as they go to select players in the draft. While there are always going to be exceptions to trends, by following the enclosed research teams can minimize risk in the draft.

Contributors

Agent

Created

Date Created
  • 2017-05

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Relationship Between College Baseball Conferences and Average Offensive Production of Major League Baseball Players

Description

Beginning with the publication of Moneyball by Michael Lewis in 2003, the use of sabermetrics \u2014 the application of statistical analysis to baseball records - has exploded in major league

Beginning with the publication of Moneyball by Michael Lewis in 2003, the use of sabermetrics \u2014 the application of statistical analysis to baseball records - has exploded in major league front offices. Executives Billy Beane, Paul DePoedesta, and Theo Epstein are notable figures that have been successful in incorporating sabermetrics to their team's philosophy, resulting in playoff appearances and championship success. The competitive market of baseball, once dominated by the collusion of owners, now promotes innovative thought to analytically develop competitive advantages. The tiered economic payrolls of Major League Baseball (MLB) has created an environment in which large-market teams are capable of "buying" championships through the acquisition of the best available talent in free agency, and small-market teams are pushed to "build" championships through the drafting and systematic farming of high-school and college level players. The use of sabermetrics promotes both models of success \u2014 buying and building \u2014 by unbiasedly determining a player's productivity. The objective of this paper is to develop a regression-based predictive model that can be used by Majors League Baseball teams to forecast the MLB career average offensive performance of college baseball players from specific conferences. The development of this model required multiple tasks: I. Data was obtained from The Baseball Cube, a baseball records database providing both College and MLB data. II. Modifications to the data were applied to adjust for year-to-year formatting, a missing variable for seasons played, the presence of missing values, and to correct league identifiers. III. Evaluation of multiple offensive productivity models capable of handling the obtained dataset and regression forecasting technique. IV. SAS software was used to create the regression models and analyze the residuals for any irregularities or normality violations. The results of this paper find that there is a relationship between Division 1 collegiate baseball conferences and average career offensive productivity in Major Leagues Baseball, with the SEC having the most accurate reflection of performance.

Contributors

Agent

Created

Date Created
  • 2017-05

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MLB Expansion: Analyzing the cities, factors, implementation, and draft process

Description

The purpose of this thesis is to cover the multiple aspects of Major League Baseball Expansion from 30 to 32 teams. The thesis can be divided into two parts with

The purpose of this thesis is to cover the multiple aspects of Major League Baseball Expansion from 30 to 32 teams. The thesis can be divided into two parts with the first being the preparation and consideration for expansion, and the second half is about the execution and implementation of adding two expansion teams to the league.
For years, Commissioner Rob Manfred has hinted and brought about the idea of adding two more teams to Major League Baseball (Mitchell). The growth of the game is of utmost importance, and they have made many changes to try to expand the growth of fans the past few years particularly catered to new and young fans. New rules like a pitch clock and mound visit limitations are examples of in game changes made to speed up the game, but they have also experimented with spring training and regular season games internationally or at new venues. In just the past decade, games have been played or planned (due to COVID-19 cancellations) in Monterrey, Mexico City, London, Tokyo, San Juan, Montreal, Las Vegas, Williamsport, and even Iowa. With the exception of the Williamsport Little League Classic and the Field of Dreams game in Iowa, all these locations had games to see what the atmosphere and logistics would be like with expansion in mind as a possibility in the future. With this in mind, this thesis will analyze and come to a conclusion on the following cities for the best fits for expansion: Monterrey, Mexico City, San Juan, Vancouver, Montreal, Las Vegas, Portland, Nashville, Raleigh, and San Antonio.

Contributors

Agent

Created

Date Created
  • 2020-05

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A Study of Win Expectancy Estimators in Major League Baseball

Description

In recent years, advanced metrics have dominated the game of Major League Baseball. One such metric, the Pythagorean Win-Loss Formula, is commonly used by fans, reporters, analysts and teams alike

In recent years, advanced metrics have dominated the game of Major League Baseball. One such metric, the Pythagorean Win-Loss Formula, is commonly used by fans, reporters, analysts and teams alike to use a team’s runs scored and runs allowed to estimate their expected winning percentage. However, this method is not perfect, and shows notable room for improvement. One such area that could be improved is its ability to be affected drastically by a single blowout game, a game in which one team significantly outscores their opponent.<br/>We hypothesize that meaningless runs scored in blowouts are harming the predictive power of Pythagorean Win-Loss and similar win expectancy statistics such as the Linear Formula for Baseball and BaseRuns. We developed a win probability-based cutoff approach that tallied the score of each game once a certain win probability threshold was passed, effectively removing those meaningless runs from a team’s season-long runs scored and runs allowed totals. These truncated totals were then inserted into the Pythagorean Win-Loss and Linear Formulas and tested against the base models.<br/>The preliminary results show that, while certain runs are more meaningful than others depending on the situation in which they are scored, the base models more accurately predicted future record than our truncated versions. For now, there is not enough evidence to either confirm or reject our hypothesis. In this paper, we suggest several potential improvement strategies for the results.<br/>At the end, we address how these results speak to the importance of responsibility and restraint when using advanced statistics within reporting.

Contributors

Created

Date Created
  • 2021-05