297.201-2025 Semester 1 Massey University
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Project 1
Deadline: Hand in by midnight March 25th 2025
Evaluation: 33% of your final course grade.
Late Submission: Refer to the course guide.
Work This assignment is to be done individually.
Purpose: Gain experience in performing data wrangling, data visualization and introductory data 
analysis using Python with suitable libraries. Begin developing skills in formulating a 
problem from data in a given domain, asking questions of the data, and extracting 
insights from a real-world dataset. Learning outcomes 1, 3 and 4 from the course 
outline.
Please note that all data manipulation must be written in python code in the Jupyter Notebook environment. No marks 
will be awarded for any data wrangling that is completed in excel. 
Also, demonstrating your own skills, critical thinking and understanding is still the most important aspect of assessment, 
so you must keep a record of all your AI prompts and outputs, and submit these as an appendix to your completed 
assessment. Failure to keep an accurate record of your AI use for an assessment may be a breach of the Use of Artificial 
Intelligence in Assessment Policy, and of the Academic Integrity Policy. Refer to Stream as to the level of Generative AI 
use that is permitted and what this means. This particular assignment is designated as permitting ‘AI Planning’.
Download this Word document to guide you in creating your AI use statement.
In addition, do not copy the work of others – there are many ways to solve the problems below, and we expect that no 
two answers will produce the same code. Copying the work of others (even if object/variable names are changed) will be 
considered plagiarism.
The dataset problem domain: Analysis of Professional Tennis Match Results (ATP – men, or WTA - women)
You are asked to download a curated dataset on the topic of professional tennis and use your data wrangling and 
visualisation skills covered I the first few weeks, to answer a series of analytics questions. You do not have to be an 
expert in tennis to answer these questions and solve this assignment, but you will need to cover some basics. Once you 
enter the workforce as a data scientist, you will need to quickly learn about domains previously unknown to you in order 
to perform your job, so this is an exercise in practicing how to do this. Some helpful information on tennis and various 
tournaments can be found here:
https://www.olympics.com/en/news/tennis-rules-regulations-how-to-play-basics
https://www.tennisleo.com/basic-tennis-rules/
https://thetennisbros.com/tennis-tips/what-are-the-major-tournaments-in-tennis/
Keep in mind that some questions can be interpreted in slightly different ways and so depending on your interpretation 
and assumptions, you might come up with slightly different answers – this is perfectly acceptable. The purpose of this 
assignment is not to answer all questions in the ‘right’ way, but to develop your technical and problem solving skills. 
Therefore, you will not be marked down for having slightly different answers as long as you have stated your 
assumptions clearly and have gone about in a technically sound and reasonable manner in answering the questions.
The datasets we’re after can be found below.
Dataset source: http://tennis-data.co.uk/alldata.php
You will need to download this dataset from home since Massey’s filter restricts access to this website due to its 
categorization as a gambling site. 
297.201-2025 Semester 1 Massey University
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If for some reason, you’re unable to download it from home, please let us know. As a an alternatively, you can download 
the similar data from a GitHub source here, but there are some columns not present in this data, so we would prefer 
that you use the tennis-data.co.uk source instead:
For men: https://github.com/JeffSackmann/tennis_atp (use only: atp_matches_<xxxx>.csv)
For women: https://github.com/JeffSackmann/tennis_wta (use only: wta_matches_<xxxx>.csv)
Task 1: Wrangling, reshaping, EDA (20 marks)
- Collect data covering 10 years (2015 - 2024) from the above website. Read each excel dataset using Python and 
combine into a single dataset.
- Check that all the data has been read. Check that all the data in the combined dataset is in order based on the 
date column. 
- What other data-checking operations could you perform to make sure that the data is ready for analysis? Use 
various approaches to perform sanity checking on the data, including some plotting and discuss.
- Create EDA 6-8 visualisations of the dataset and explain each one. Be curious and creative. Ensure that the plots 
are clean and interpreted.
Task 2: Analysis questions and plotting (20 marks)
- Who are the top 10 players by total wins in the dataset, and how many wins do they have? Plot and discuss this.
- Who are the top 10 players according to the largest number of First Round tournament losses across all 10 
years? Plot and discuss this.
297.201-2025 Semester 1 Massey University
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- Identify the 5 biggest upsets for each year in the dataset based on ranking differentials. List player names, 
rankings, winner/loser, score, and tournament name and what the difference in the rankings was at the time – a 
table is fine. 
- Who were the top 10 players at year-end in 2019? How have their rankings changed over the period of 2015 to 
2024? Plot and discuss this.
Task 3: Advanced analysis questions (20 marks)
- Which tournaments have had on average the most upsets (where a lower-ranked player defeated a higher ranked player)? List the top 10 and plot their averages.
- Determine who the top 10 ranked players (by ranking) were at the end of 2024. Then calculate their head-to head win-loss record against each other for all the matches they played in 2024. Present this result and discuss.
- List the top 5 players who had the longest winning streaks between 2015 – 2024. List their names, the lengths 
of their winning streaks and the year(s) in which they occurred.
- In tennis, each set is played first to 6, but sometimes it is played to 7. A tiebreak is a set that someone wins 7-6 
and is different to someone winning a set 7-5. Tiebreaks are stressful and some players perform better than 
others in tiebreaks. Count how many tiebreaks each player in the entire dataset has played. Then, calculate the 
percentage of tiebreaks that each player has won. List the top 10 players according to the percentage of 
tiebreaks won.
Task 4: Open questions and analyses (30 marks)
- Come up with 3 more questions of your own. 
- Try to demonstrate the usage of more advanced data wrangling functionalities as you answer the questions like 
group by, pivots etc…
- Create several plots and discuss them.
A Jupyter notebook template will be provided for you. Please use it for this assignment.
Hand-in: Submit a single zipped file via the Stream assignment submission link. It should contain one notebook with all 
the answers embedded, and an HTML version of your notebook also with its output showing as well in case we have 
issues running your code. Also, you must submit the AI use statement.
Use of Generative AI in This Assignment
In industry, AI and online resources are commonly used to improve efficiency and productivity. However, at university, 
the primary goal is to develop your understanding and ability to work through problems independently. We need you to 
master these skills first, so that you will be able to use the AI tools more effectively and efficiently later on. This means 
that while AI can be a helpful tool for learning, it should not replace your own thought process or problem-solving efforts
as it will actually short-circuit your learning and development
.
Allowed Uses of AI for assignment 1
You may use AI along the lines of the following prompts to:
• Understand background knowledge related to professional tennis, tournament structures, and general 
concepts about tennis matches. 
o Example: “Explain the rules of a tennis match and how scoring works.”
• Seek feedback on your problem-solving approach without directly generating code. 
o Example: "I plan to find the top 10 players by total wins using pandas. Does this approach make sense?"
• Clarify error messages or debugging hints, as long as you are the one writing the code. 
o Example: “Why am I getting a KeyError in pandas when trying to merge two dataframes?”
• Find alternative ways to visualize data for inspiration, but not for direct copying. 
o Example: “What are common ways to visualize win-loss records in sports data?”
297.201-2025 Semester 1 Massey University
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Prohibited Uses of AI for assignment 1
You must NOT:
• Copy AI-generated code directly into your submission.
• Input the assignment questions directly into AI and use its responses as your own.
• Paraphrase AI-generated explanations/code and present them as original work.
• Ask AI to write step-by-step solutions to any of the assignment tasks.
• Academic Integrity & AI Use Statement

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