kaylabolden/project_fifa_moneyball

Project_FIFA_MoneyBall

The challenge

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

Possible Outcomes

• Rank players by 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.

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.

Dataset

In this project, you will use the provided fifa21_male2.csv dataset.

Here details about the dataset can be found here as well.

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

3. 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

4. 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

Instructions & Scope

• You CAN'T CODE until your project is planned.

• Create a *.gitignore* file and include it in your repository.

• You should include a linear regression question(s) on the data.

Deliverables

• Repository with your workflow + documentation + code. This repository must contain:

  • README: it is mandatory to present the project.

    • What made you decide to do this project?
    • Objetive
    • Used tools
    • Workflow
    • Results and conlusions (this is not so obligatory, it is a more personal decision)

    ⚠️ Readme will be the first thing that people will see from us. A person who knows nothing about this project should be able to read your readme and know what you have done.

• A SQL Database with the FIFA_MoneyBall, using the learned statements during the last weeks

• A well-commented Jupyter notebook with your analysis.

  ⚠️ Remember, you could use the markdown cells in jupyter!!

• The final dataset after all cleaning and transformations.

• To send us your work... as always a pull request where in the comments leave us the link to your repo.

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.