Section A
You are asked to obtain and analyse a dataset in Microsoft Excel (or other spreadsheet
software of your choice), and provide insight and commentary on your analysis.
Download the csv file containing results of Bundesliga 1 during the 2021/22 season from
https://www.football-data.co.uk/germanym.php
Note that the file is a csv (comma separated variable), and can be opened in Excel. Please
search the internet for “open a csv file in Excel” if you are struggling. On the same website,
there is a text file defining the variables in the dataset.
You will need to use a combination of formulas, functions, and pivot tables to answer these
questions in Excel
This coursework forms 100% of the final mark for the module. Submission deadline is 12
noon Friday 22th March 2023. This coursework requires online submission. You must
submit via Turnitin. If a copy is not submitted to Turnitin the assessment will not be marked.
This is a written assignment and your answers should be provided in a document with your
answers clearly numbered. Include text, tables and figures (charts) in your document where
and when appropriate.
There are two sections.

.

a) What are the average goals per game scored by home and away teams? Why is there a
difference?

(3 marks)

b) What is the league home team win percentage, draw percentage and away team win
percentage?

(3 marks)

2
c) How many goals per game, on average, did Bayern Munich score at home, compared to
away from home? What is strange about these numbers?

(2 marks)

d) What are Bayern Munich’s and Dortmund’s win percentage when playing at home, and
what is their win percentage when playing away? Comment on the difference.
(2 marks)

e) Of all teams in the league, which team has
(i) the lowest goals scored per game, and what is the value?
(ii) the highest goals conceded at home, and what is the value?
(iii) the highest average winning odds when playing at home?

(6 marks)
You want to build a model to predict the goal difference between the two teams in a match.
As a starter you examine the relationship between the difference in home and away odds, and
goal difference.
(i) Plot goal difference as a function of difference in the home and away team
winning odds (odds difference) according to Bet365.

(3 marks)
(ii) What is the correlation between the goal difference and the odds difference?
What does this value mean?

(2 marks)
(iii) Fit a linear regression model and provide the “summary output” table from the
fitted regression model.

(2 marks)

(iv) Is the Intercept term statistically significant? Why do you say this?

(2 marks)
(v) Is the odds difference statistically significant? Is the sign of the estimated
coefficient as you would expect? Explain your answer.

(2 marks)
(vi) Given the estimated coefficients, in a match which has home team wining odds of
1.4, and away team winning odds of 7, what is the predicted goal difference?
(2 marks)

3
Follow the video on “Forecasting football in Excel” available on the Canvas page for this
module. Use these data to build a forecasting model, as shown in the video.

f) Use the model to estimate:
(i) the probability of Bayern Munch winning a home match against Wolfsburg.
(4 marks)

(ii) the probability of the scoreline in the match being 2-2

(4 marks)

g) Using references to the scientific literature, in 300 words or less, discuss one of the
following topics:
(i) How the forecasting model above can be improved.
(ii) How forecasting models are employed in anti-match fixing processes.
(iii) A forecasting model in a sport of your choice.

(14 marks)

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Section B
In 1,000 words or less, please discuss one of the following.
(i) In a team sport, explain why we cannot evaluate a managerial turnover decision by
comparing team performance before and after such a decision. Explain how the
economic literature tackles this concern and discuss the main empirical results.
(ii) Find an example where sports data can be used to investigate the presence of racial or
gender discrimination. Discuss the employed analytical methods from a critical
perspective.
Where appropriate you should support your work using relevant citations (news articles,
websites, academic articles, and books are all acceptable forms of evidence), examples, and
data.