Fifa 2022 Player Analysis

Name

Jacob Kim, Anirit Bansal, Sydney Imokawa

Inspiration

Our inspiration for this project came from our love for Fifa video games.

What it does

Our project analyzes the individual player data from the 2022 Fifa video game. We create two machine learning models to understand what impacts player stats and contract values.

How we built it

We created our project using Edstem workspace and created three different methods, one for each research question we had. We utilized the SciKitLearn library, matplotlib, and seaborn for our models and graphs.

Challenges we ran into

Our team ran into some issues with testing the accuracy of our code initially, and filtering/manipulating the data so it works with our programs.

Accomplishments that we're proud of

We are super proud of our linear regression model graphs and the fact that we were able to get things done with some time left over.

What we learned

Our team learned that player attributes correlating stronger with teamwork driven traits have the larger impact on a player's overall rating. We also learned that the scarcity of a position, in terms of available players, impacts the average contract valuation of a player.

What's next for Fifa 2022 Player Analysis

Our team believes that this analysis can inspire others to further research other player statistics to help shape their decision making process when creating a fantasy team or Fifa player.

Built With

• matplotlib
• python
• scikitlearn
• seaborn