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April 18, 2021
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April 18, 2021


Congratulations. You are a finalist in for a data analyst
position for a Major League Baseball (MLB) team. As you prepare for the final
round of interviews, you’ve been asked to use the above data set to create a
series of analytics / dashboards to help show how well the team is doing in two
important KPIs: home-game attendance and salaries.Within the MLB, the San Francisco Giants are in the:League = National LeagueDivision = West (W) DivisionThe intended audience for this dashboard is the Director of Analytics. Limitations: Clearly this project is limited in terms of scope of data. In the
real world setting there would be ticket sales, customer demographic
information, television viewership ratings, social media mentions / hits, and a
whole host of additional data to churn through. But (realistically) like any
project, it’s good to start with a piece of the puzzle at a time, and in
sequence. So consider this an initial step in what could be a much larger
project. Two files are needed for this submission: Your Power BI dashboard file and the
answers to the questions below (in a Word document). Broadly speaking this project’s learning outcomes include:

Assigning KPIsTrend analysisComparative analysisCreating columns and
measuresCreating relationships
between multiple data sourcesCreating the best
visualization to appropriately show the data

Hint: Use the TeamsMostRecent table as your centralized table
that all others are related to (connected with). But only connect Salaries to
Team_Statistics and Team_Statistics to TeamsMostRecent as you don’t want to
have unnecessary relationships that will cause a circular logic in your design.

Hint2: you will need to create a new column to join the Salaries
and Team_statistics tables together. What 2 (or more fields) create a unique
identifier for each individual row that exists in both of
these tables? You will need to use this field to join these tables together.

Analytics portion:

Get a sense of the data to start. Create a
matrix that has every ball club, each year (2006-2014) and total games played.
This will allow you to see if there are any significant gaps in the data. Are
there? Explain.a. Choose the most appropriate visualization
to show the total attendance for the team from 2006 – 2014. What’s
their trend? b. Choose the most appropriate visualization to show the total
attendance for each year and each club in their division attendance for 2006 – 2014. What is the trend for
the team?  Which team came closest to surpassing them in attendance and in
what year? c. Choose the most appropriate visualization to show how the
team’s attendance average (combined for all years, 06-14)
compares with the attendance average of all other teams in the League. Sort by
average attendance in descending order (Most to Least). How are they ranked?
Overall is their attendance numbers considered “good” or
“bad”?  How do you know? Plot all stadium addresses on a map. Include
the club names in the legend. Include in the plot the average attendance (for the
size of the circles). How does the team compare with other clubs in the region?
Between the visualizations you created in problems 2 and 3, which do you think
best tells the “attendance” story and why?a.Show the total salary for every ball club in
2013. (2014 salary information is incomplete for some clubs.) Who are the top 5
ball clubs that have the highest salary? Who has the least? Where does the team
reside in the salary “rankings”? Highlight where they rank. b. Show
what the Team salary has been like for the last 5 years. What is the trend?
c. Again for 2013, show the number of wins per ball club,
highlight where the Team ranks. Create a measure that calculates Salaries
by Wins to measure how much each win “costs” based on the player
salary (total salary / total wins). Where does the team rank? Describe your
findings. a.Select the best visualization type to show
the average salary per player by every ball club in 2013. Which club has the
highest average salary? The Lowest? Where does the team sit? b. Create a slicer
by ball club name and create an additional visualization that lists out all
ball players and total salary. Filter on the team. And sort by salary in
descending order (highest to lowest). Who were the top 5 players by salary in
2013? What was the most common amount (mode) paid to the players? Create your own visualization here that does
not repeat one of the above questions. And describe what you are showing, the
question being addressed and the results of the analysis.
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