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Winning Combination for FIFA EWorld Cup


Individual Project - Pranav Prajapati

Project Overview


The Fédération Internationale de Football Association (FIFA) is well known for organizing global soccer events like the world cup and euros. Due to the increasing popularity of video games and especially FIFA SOCCER game, FIFA has started a virtual world cup tournament called as FIFA EWorld Cup. With prize money as big as 100000 dollars, players can earn up to 2 million dollars per year.

There are many factors to consider when building the perfect team to win matches. One cannot have only top players at every position as there are many constraints laid on the candidates. Hence every player has to find the right combination and select players according to his style of play.



The Exploratory Data Analysis and Interactive visualizations will help a certain user understand the roster of the players available and what kind of strategy the user can use to build a team on a certain budget.

The Regression Analysis will give the user complete knowledge of whether his players can be traded at what price and how much potential do all the young players can achieve.

Tools Used



Visualization Libraries : Matplotlib and Plotly


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Prepare Data


All the Features in the dataset

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A look at highly correlated numerical features

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Histogram of players age

We can see most players have age in the early twenties.

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Distribution of age in top clubs

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Let's look at who has the oldest players. You would not want to play with these teams as old players will lose their skills.

Let's look at who has the youngest players. You would not want to play with these teams as the young players are not skilled yet.

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The Best Player per position

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Top 3 Features per position

Some additional Data Cleaning

Top 10 Players with the best growth potential

We can see A.Dabo is the highest rated at 60 and has the best growth.

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Nationalities of FIFA 19 Players

Feel free to rotate and zooom into the plot

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Which country has the top talent?

Below graph shows the RATING vs COUNT graph.

Spain and Brazil have the best talent.

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Always having the best players may not be a good decision. The team balance can get compromised. This plot shows how a player's potential increases.

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Which Clubs have the best young talent?

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The Top young Players can always be a great addition since they will always grow

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You also want players to have unique skills like scoring Penalties and Free Kicks

We can compare some of the top young players's skill . If you find defensive players with good penalty and free kick skills, then its a certain buy!

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Who are the quick, agile and strong young players ?

A Good Combination of all three will be a must have!

X axis - Ball Control

Y axis - Dribbling

Z axis - Shot Power

Zoom in and discover the player!

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Wages and Price comparison of players

There are some players who are important figures in thier own team so although thier wages are high, their value will be lower. Such players are a good buy !

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Another Proof as to why one cannot just select all players from a team but has to look for different players in each team to get the right combination

The below graph shoes all the top teams and their combined player values. Not much different!

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What is the most important position on the pitch ?

As we can see that players with great 'Finishing' and 'Composure' ability are the ones with highest value. If anyone wants a high value player, it should be a goal scorer!

X Axis - Finishing

Y Axis - Composure

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Do I have enough options if not for the top players

As we can see below we have plenty of talent pool to choose from.

The X axis depicts the overall rating and y gives the count

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Regression Analysis to Predict Ratings


A small study to predict Ratings

Creation of Simplified Positions, Converting some features to Binary

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Splitting Data into Test and Train

Printing Shape

Linear Regression

Those are great results!

Let's see which are the most important features


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Visualizing the Fit

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Additional Visualizations


I found this radar/polar chart project on github. These Visualizations can help the user find their favorite player's information.

Search Player ID with below code and use that ID to see their main skills

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Example: Lionel Messi

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Hence with this study we can see that Potential of Players is a great indicator that if anyone wants to be good at team building, more emphasis must be on young talent.

Future Scope


There can be a real time recommendation system that can help give gamers the right information about the player they want to play with. We can also use this study for other sports like Football, Baseball, Basketball etc.



This Tableau dashboard is related to the same dataset and also very intuitive. Have a look at it. https://public.tableau.com/en-us/s/gallery/fifa-19-top-ranked-players