BA 1530: Coursework Assignment - Business-Style Report - Report Writing Assignment Help

Assignment Task:

Assignment Task:
The task involves the use of spreadsheet data analytics capabilities to carry out a sabermetrics analysis to compare offensive performance in the 2017 and 2018 seasons in Major League Baseball and to answer other questions about the 2018 season’s offensive performance as detailed in the Information to Complete Task section on pages 2–5. You will write a business-style report in Microsoft Word or similar that documents your findings using a maximum of 2000 words. It is crucial that you read all the information in the Information to Complete Task section on pages 2–5 before you start writing the report as it gives details and hints for what you need to do to fulfil the assignment brief. In your report, you need the following sections: 
 
• Introduction
Briefly outline the situation. You need not provide much background — you can assume that the readers are familiar with baseball — but you need to show that you have understood the situation and what is required of you 

• Body (BA 1530)
Briefly summarise the purpose of the analysis and the analytical methods you have used – you should not give technical details of how to use a spreadsheet to do the analysis
Answer and discuss the various questions, supporting the points you make with copies of relevant sections of data from your spreadsheet
Extra credit for preparing and incorporating charts which help to visualise some of the points you make
Discuss policy implications for baseball of your answers where relevant
Outline a proposal to the Baseball Commissioner for implementation of relevant information systems which might be suitable to (a) track rule changes and (b) automate the analyses you have performed manually in Excel 

• Conclusion
Summarise the conclusions you have drawn
Summarise the policy implications for baseball where appropriate, and the proposals for relevant information systems to (a) track rule changes and (b) automate analyses 

• References
I do not expect you to use very many in-text references; you must provide a reference list at the end of the report 

Marking Criteria
The submitted and assessed part of this coursework is a business-style report, rather than an academic essay so the marking criteria are different from those usually required for an academic essay.  Marking criteria are shown in the Marking Rubric on the next page.

To Be Submitted 
1. Your report. It is this report on which your mark will be based. 
2. Your spreadsheet file. This is submitted to prove that you have carried out the spreadsheet work yourself.  It will only be reviewed if it is necessary to rule out plagiarism. 
 
You must submit the assignment electronically via the appropriate link on the VLE. The timely submission of assignments is your responsibility, and excuses — such as a problem on your PC or other devices, or losing a file — will not be accepted.   You will receive an electronic submission receipt ID via email.  It is your responsibility to keep this safe as proof of submission.  You are also strongly recommended to keep a copy of all submitted assignments.

Information to Complete Task: (BA 1530)
Almost every year the MLB Players' Association, Major League Baseball and Commissioner Rob Manfred announced a series of rule changes, and 2018 was no exception (Adler, 2018).  The focus for the last couple of seasons has been on the pace of play. “Baseball games take longer than ever to produce fewer and fewer runs and balls in play” (Verducci, 2015). It was hoped that changes made for the 2018 season might further improve pace of play and offensive performance during the season. 

Sabermetrics is the “the search for objective knowledge about baseball”, especially baseball statistics that measure in-game activity. Sabermetricians collect and summarise the relevant data from this in-game activity to answer specific questions. The term is derived from the acronym SABR, which stands for the Society for American Baseball Research, founded in 1971 (Birnbaum, n.d.).

Offensive performance data is now available for the 2018 season and for the prior season based on data taken from Lahman’s Baseball Database (Lahman, n.d.). In this assignment, as noted above, you will use spreadsheet data analytics capabilities to carry out a sabermetrics analysis to compare offensive performance in the past two baseball seasons and to answer other questions about the 2018 season’s offensive performance.

1. The Baseball-Analysis Data :

A spreadsheet file named Baseball.xlsx has been provided on the VLE to assist you. Download this file and save it. This file contains data for batters and their performance during the 2017 and 2018 seasons. Figure 1 shows the first few records of player data in the 2018 tab. It shows both biographical information and data for each player’s offensive performance in the 2018 season.

Columns in the 2018 table are as follows: 
• PlayerID — a unique code is assigned to each player 
• Last Name — the player’s last name 
• First Initial — the initial letter of the player’s first name 
• Team — the player’s team 
• League — teams are in two leagues: American (AL) and National (NL) 
• Position — the player’s position in the field 
• BirthYear — the year in which the player was born 
• PA — the number of plate appearances for a player this season; in other words, the number of times a player came to bat 
• AB — the number of at-bats the player had this season. This number is less than plate appearances because walks and some other appearances do not count as an at-bat • R — the number of runs the player scored in the season 
• H — the number of hits the player had in the season. A hit occurs when a player hits the ball and reaches base as a direct result. Hits are either singles, doubles, triples, or home runs 
• 1B — the number of singles (one-base hits) the batter had this season 
• 2B — the number of doubles (two-base hits) the batter had this season 
• 3B — the number of triples (three-base hits) the batter had this season 
• HR — the number of home runs the batter had this season 
• BB — the number of times the batter reached first base via a walk 
• SO — the number of times the batter struck out

The file has another tab called 2017 that contains the same types of biographical and offensive performance data as the 2018 table. Thus, two years of offensive performance data are available. Some players are in the starting line-up almost every day, and they accumulate hundreds of plate appearances in a season. Other players only appear in games when the starters need a rest. Player data is included in a tab only if the player had at least 100 plate appearances in each of the past two seasons. 
 
2. Data Analytics Using a Spreadsheet: (BA 1530)

a) Calculating Performance Measures
In this task, you will perform calculations to compute and display a range of performance measures for each player in both the 2017 and 2018 tabs. Your output should look like that in Figure 2 which shows only the first few records and columns for the 2018 tab.

You should create new columns in each tab, one for each of the following calculations: 
• Age — this is calculated from the player’s birth year and the season year of either 2017 or 2018 as appropriate;  
• TotalBases — this value is the number of bases accumulated by the player’s hits. A single (1B) counts as one base, a double (2B) counts as two bases, a triple (3B) counts as three bases, and a home run (HR) counts as four bases.
• batting average — this value is the ratio of the player’s (H) hits to at-bats (AB). 
• Slugging%— this value is the ratio of the player’s total bases to at-bats (AB). If two players have the same number of hits (H) and at-bats, they will have the same batting average. However, a player who has more doubles, triples, and home runs will have a higher slugging percentage than a singles hitter. 
• OnBase% — this value is the player’s hits (H) plus walks (BB) divided by at-bats (AB) plus walks (BB); it shows how frequently the player reached base.  
• OPS — this value is the sum of the player’s slugging percentage and on-base percentage. OPS is a combined measure of a player’s ability to get on base and hit for power.
 
b) Using Data Tables and Pivot Tables to Gather Data
Now use the data tables and pivot tables to gather data needed to answer league officials’ questions. There is information on pages 4 and 5 on how to use a spreadsheet to answer the questions. 
 
1. League officials want to know if offensive performance decreased this season compared with last season. You can answer this question using data table analysis.  

2. Who were the league leaders this year and last year in batting average, OPS, and on-base percentage? You can answer this question using data table analysis. 

3. Traditional baseball fans consider batting average the best measure of offensive performance. However, many modern baseball analysts say that OPS is a broader and therefore better measure. Which measure is actually better? To shed light on the question, use data table analysis to create two all-star teams. One team contains the players with the highest OPS at each position. The other team contains the players with the highest batting average at each position. If the same players make both teams, you might reasonably conclude that the two measures are equally powerful. But, if the two teams have mostly different players, the two measures must be telling different stories 
 
4. Baseball fans and officials sometimes say that a player’s best offensive season occurs when the player is 27 years old. Was this statement truer for this year’s players or last year’s players? You can answer this question by examining the average OPS for both years using pivot tables. 

5. What were team batting averages this year versus last year? Which teams improved their batting average this year? You can answer these questions using pivot table analysis.

Pivot Table Analyses (BA 1530)
Use pivot tables to analyse two sets of data: (1) Player performances at the age of 27 versus those in other years, and (2) team batting averages 2017 and 2018. 

4. Is offensive performance best when the player is 27 years old? 
a. Assume that OPS is the best performance measure to use. Using 2018’s data, create a pivot table on a separate sheet that shows the average OPS by player age. (For example, show the average OPS for all players who are 25 years old, 26, 27, and so on.) The table should also display a count of players at each age.  

i. Age should be the Rows field. 
ii. OPS and PlayerID should be the Value (Data) fields. 
iii. Change the summary types using the Value field settings to make sure you have Average for OPS and Count for PlayerID
iv. You should only show the top eight values by Average of OPS. This is done at different stages depending on whether you are using Microsoft Excel or LibreOffice Calc: 
1. For Microsoft Office: after the pivot table has been created, the Age column should have a drop-down arrow; use it to set the relevant Value Filter. 
2. For LibreOffice Calc: during pivot table creation, double-click on Age and then on Options, so that you can set the relevant options in the Show Automatically section. 

v. In the pivot table, you can change the name of the Row Labels column to Age if necessary. 
vi. Sort the pivot table by the values in the Average of OPS column. This is done in different ways depending on whether you are using Microsoft Excel or LibreOffice Calc:  
1. For Microsoft Office: in the pivot table, right-click the top value cell in the Average of OPS column and then sort from largest to smallest. The player age with the best average OPS will appear at the top row of the pivot table values.  
2. For LibreOffice Calc: select the data cells (excluding the heading and Total Result rows), then do a custom sort sorting by the row containing the Average- OPS. 

b. Apply the same procedure for 2017’s values and then copy the table data and paste it into the sheet that contains 2018’s pivot table data so you can compare the averages for two years.  

c. Use a calculation to compare the averages for the two years. 

d. Below the tables, insert a note that states your conclusion about the 27th year rule—does it apply or not?  

5. What were team batting averages this year versus last year? Which teams improved their batting average this year? 

a. Using separate sheets for each year, create pivot tables that show the average batting average of each team.  
b. Copy 2017’s pivot table to the sheet that contains 2018’s pivot table for comparative purposes.  
c. Using formulas, compute the difference in averages for each team.  
d. In the next adjacent column, use an IF() function to show the word ‘Improved’ or ‘not improved’ as appropriate to show which teams had a better or worse batting average in 2018 than 2017. 
e. Use the Countif() function to compute the number of teams that had an improved batting average, and display that value at the bottom of the Improved? column. 

Marking Rubric:

This BA 1530: Report Writing Assignment has been solved by our Report Writing Experts at My Uni Paper. Our Assignment Writing Experts are efficient to provide a fresh solution to this question. We are serving more than 10000+ Students in Australia, UK & US by helping them to score HD in their academics. Our experts are well trained to follow all marking rubrics & referencing style.

Be it a used or new solution, the quality of the work submitted by our assignment experts remains unhampered. You may continue to expect the same or even better quality with the used and new assignment solution files respectively. There’s one thing to be noticed that you could choose one between the two and acquire an HD either way. You could choose a new assignment solution file to get yourself an exclusive, plagiarism (with free Turnitin file), expert quality assignment or order an old solution file that was considered worthy of the highest distinction.