The paper should be typed, 12 pt. font, double spaced, and the whole assignment in one single PDF file, including copies of all supporting material identified below. Do not ask me to accept assignments that are not combined, and please do not exceed the space limit of four written pages–my grading will stop there.
This project does not require an intimate knowledge of baseball or even econometrics, but you should be familiar with many aspects of the game. The project below will ANALYZE the most recent season and how it impacts player pay for this upcoming season. You will assume the role of General Manager trying to decide which players to select for the 2018 season using their performance in the 2017 season. Your analysis will be based on a simplified version of the methods described in the article “An Economic Evaluation of the Moneyball Hypothesis.”
Your analysis will use statistics from the previous season to predict salaries/performance for the current regular season. Be very careful that you are using the correct seasons on the website below. Once the MLB season begins, the websites will update based on current performance. Ensure that the pages below show you the right season for analysis. Do not use data from Spring Training or Postseason. You’ll want to use the regular season statistics only.
Six web sites will be of use in doing so. I have included the data years you will need to analyze. Be sure the data source is correctly visible:
6. 2017 doc.
Here is what to do:
Read the Hakes & Sauer (2006) paper and focus on the process of valuing baseball players. You should also read the FiveThirtyEight article to understand why this process was so revolutionary. You will be asked to summarize both of these articles in your final write-up. Afterward, begin collecting data using Microsoft Excel. Collecting your data in a single Excel file with different tabs for the Teams Sheet and Players Sheet. If you do not read the articles, it will be obvious when you try writing your paper.
Teams Sheet Tab:
The first sheet will relate the teams’ effectiveness to winning and revenue. We will use this information to determine the value of winning for a team. This performance analysis should be conducted for the 2017 season.
Obtain team OBP and SLG from the team batting database above, along with total home attendance, winning percentage, and the average team ticket price from the appropriate links.
Put the team name in the first column of the sheet, their regular season winning percentage in the second column, OBP in the third column, SLG in the fourth, and leave the fifth column blank for now. Then put total home attendance in the sixth column, ticket price in the seventh column, and calculate total team ticket revenue and put that in the eighth column.
The “Economic Evaluation” article indicated that OBP was twice as important as SLG in producing runs and wins. Thus, create a simple “index” of offensive production, 100*(2*OBP + SLG), and put that in your fifth column. Multiplying by 100 just makes the numbers easier to read and interpret.
Create a scatterplot that relates the index you have just created, on the x-axis, to the team’s revenue, on the y-axis. Include the Excel trendline and display the equation of the trendline (this is an option in the chart design / layout tab). There is a visual “outlier” in your data, remove that team from your charts, but not your data table. Leave the outlying team out of all your team analyses. The slope of the trendline tells you how much each additional point of the index would be worth in revenue, in dollars. Use the coefficient estimate on the trendline as your estimate the value of increases in the index of team offensive production. What is the impact of a one-unit increase in team index on total revenue?
Create another scatterplot that relates the index you have just created, on the x-axis, to the team’s winning percentage, on the y-axis. Include the Excel trendline and the equation, as before. If the index increases by 1, how much does the team’s winning percentage increase by?
Create another scatterplot of win percentage (x-axis) and revenue (y-axis), and include the Excel trendline and the equation, as before. If the team’s winning percentage increases by 0.01, how much does the team’s revenue increase by?
Player Sheet Tab:
The second sheet of your spreadsheet will have you calculate an individual player’s marginal product and then relate salaries to player effectiveness.
Download the list of free agents for the start of the 2018 season and choose 20 players at random. (All pitchers should have been taken off the list.) Work out a process you will use to pick your players it should be a random process and use it to choose your 20. Please describe this process in your write-up. The list shows all players eligible for free agency (Links to an external site.)Links to an external site.. Make sure your 20 players have at 1 year of MLB experience and are signed for the 2018 season. The list includes everyone eligible to play, but it doesn’t mean all of them will be “hired” for the 2018 season.
Put the player name in the first column, their 2016 on-base percentage in the second column, their 2016 slugging percentage in the third column, their 2017 on-base percentage in the fourth column, their 2017 slugging percentage in the fifth column, and their 2018 average salary in the sixth column.
In the seventh and eighth columns, calculate the player’s index of offensive production for 2016 and 2017, just as you did above for the team. In the ninth and tenth column, calculate the player’s “marginal product” for the 2016 and 2017 season. The marginal product of a player will be the amount that they increase the team’s offensive index, instead of a player at the “Mendoza Line” of a .250 OBP and .300 SLG. Recognizing that the average starter takes about 1/10 of all of the team’s at-bats, calculate the difference between each player’s index and the index of the Mendoza Line player, and then divide this by ten.
Now, in the last column, compute that player’s marginal revenue product (MRP), the extra revenue the player brings in for the team, but only for the 2017 season. MRP is the value of the player’s marginal product and the value of increases in that marginal product, which you calculated on the previous sheet, plus the league minimum of $550,000, for which we assume any team can get a player at the Mendoza Line. Hint: A Mendoza Player would have an MRP of $550,000.
First, make a scatterplot relating a players 2016 index (x-axis) to their 2017 index (y-axis). Use the correlation function in Excel (CORREL) to compute the correlation between all players’ performance leading up to their free agency year. Based on the calculation, how closely are the two connected? Summarize the Scorecasting excerpt related to batting averages and player effort. Do your results seem to indicate a similar pattern happened for your player in regards to “strategic effort”?
Make a scatterplot of your player’s 2017 MRP (their predicted salary) and the players’ actual salary for 2018. Again, use the correlation function in Excel (CORREL) to compute the correlation between all of the players’ MRP and their salaries. Based on your calculation, how closely are the two related? Why might MRP calculations not be perfect predictors of future salaries?
Your Write Up:
Write out, in four double-spaced, typewritten pages, 1) what you did, including writing out any formulas you utilized in your spreadsheets; 2) why you did the items that you did, including an explanation and justification of the formulas you used; and 3) answer questions above and 4) summarize with a conclusion on the correlation between estimated MRP and salary. For an introduction, you should summarize the FiveThirtyEight article.
To write out the formulas, you may use traditional algebraic notation or copy the Excel formulas out of your spreadsheet. Include a References page (APA or MLA) for any material that is referenced in the paper, including data sources provided above. Include an Appendix after your references that has your data and graphs nicely presented. The work in your Reerences and Appendix does not count against your four-page limit.
Everything should be combined together into a single PDF file and follow the formatting specified at the top of this assignment. Take care to make your data & graphs clear and visually pleasing: in business, presentation matters. Screenshots of data and graphs will be considered unprofessional.
As with all assignments, you are invited to come see me during office hours with questions. Please get started early, that gives you time to work through any complications you may run into.
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