senimtra/data-mini-project-fifa

6 hours challenge - perform an end-to-end analysis putting into practice what you have learned so far.

Mini-project Fifa

6 hours challenge

Perform an end-to-end analysis putting into practice what you have learned so far. You will apply descriptive statistics or predictive techniques and present your results to the class.

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Possible Outcomes

• Predict which players are being priced below/above market value.
• Highlight the top players for their outstanding performances over a discrete season.
• Decide when to transfer a player.
• Decide the best replacement for a transferred player.

You might suggest your own outcomes. Check with instructional staff before committing to a new option.

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Objectives

• Ask interesting and thoughtful questions and find the data to answer them.
• Focus on improving in areas that are hard for you or learning more about something with which you feel comfortable.
• Apply the statistical and machine learning techniques we have learned.
• Create useful and clear graphs.
• Present your insights in a thoughtful, clear, and accurate way.

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Dataset

Details about the dataset can be found here as well [https://www.kaggle.com/ekrembayar/fifa-21-complete-player-dataset?select=fifa21_male2.csv]

This data set includes:

1. EA Sports FIFA 19 Game data:

• Player Name
• Club of the Player
• League
• Position
• Pace
• Shooting
• Passing
• Dribbling
• Defending
• Physical

2. Transfermarkt extra info by player:

• Date of Birth
• Nationality
• Height
• Foot
• Day Joined the current club
• Day of Contract End
• Market Value of the Player
• Instagram and Facebook data by player:
• Number of followers on Instagram
• Number of likes on Facebook of the club in which the player has a contract
• ESPN FC data from the past 5 years performance of each player
• GS: Games Started
• SB: Games Substituted
• G: Goals Scored
• A: Assists
• SH: Shots
• SG: Shots on Goal
• FC: Fouls Committed
• FS: Fouls Suffered
• YC: Yellow Cards
• RC: Red Cards

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Deliverables

• A well-commented Jupyter notebook with your analysis.
• The final dataset after all cleaning and transformations.
• Repository with your workflow + documentation + code.
• A 5 minute presentation of your objectives, main findings and challenges.

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Tips & Tricks

• Organize yourself (don’t get lost!). Respect deadlines.
• Ask for help but don’t forget that Google is your friend.
• Define a simple approach first. You never know how the data can betray you. 😉
• Document your work.
• Learn about the problem and what research has been done before you.
• Before making a graph, think about what you want to represent.