Analytics on FIFA 2019 Players!

Analytics on FIFA 2019 Players!

Football analytics and modelling of the FIFA. In this post we will perform simple data analysis and modelling of the FIFA 2019 complete player dataset following the CRISP-DM process . The dataset has been collected Kaggle. Dataset contains 1 CSV file.

Football analytics and modelling of the FIFA

In this post we will perform simple data analysis and modelling of the FIFA 2019 complete player dataset following the CRISP-DM process . The dataset has been collected Kaggle. Dataset contains 1 CSV file.

FIFA 2019 is football simulation video game developed as a part of Electronic Arts’ FIFA series. It is the 26th instalment in the FIFA series selling over approximately 20 million units.

Let’s dive in!

In a sport like football, each player adds a significant value to the team’s success. It is important to understand player’s skills. How age could play an impact on potential of the player ? Which player is best at which profile? The study also focuses on evaluating the player’s overall performance based on the performance indicators and how various models evaluates on the prepared data.

Data Understanding

As a second stage of CRISP DM, it is important to explore the data and address data mining questions using data visualizaton and querying. The data set consist of 89 columns but we will limit ourself to the following columns:

Index(['Name', 'Age', 'Overall', 'Potential', 'Value', 'Wage', 'Special',
       'Preferred Foot', 'International Reputation', 'Weak Foot',
       'Skill Moves', 'Crossing', 'Finishing', 'HeadingAccuracy',
       'ShortPassing', 'Volleys', 'Dribbling', 'Curve', 'FKAccuracy',
       'LongPassing', 'BallControl', 'Acceleration', 'SprintSpeed', 'Agility',
       'Reactions', 'Balance', 'ShotPower', 'Jumping', 'Stamina', 'Strength',
       'LongShots', 'Aggression', 'Interceptions', 'Positioning', 'Vision',
       'Penalties', 'Composure', 'Marking', 'StandingTackle', 'SlidingTackle',
       'GKDiving', 'GKHandling', 'GKKicking', 'GKPositioning', 'GKReflexes'],
      dtype='object')
  1. Best players in various aspect?

It can be observed that the following players are rated the best at the respective fields. It can be seen that K. Mbappé has the highest potential, Cristiano Ronaldo has the best skill moves, Naido has the highest heading accuracy etc.

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Figure 1: Best Player based on their performance score

2. Most Preferred Foot of the Players?

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Figure 2: Most Preferred Foot of the Player

3. Effect of football foot on player’s potential

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FIgure 3: Impact of Foot on player’s Potential

It can be observed from the above plot, that the potential of the hardly depends on whether the player is left foot or a right foot.

data-science python football machine-learning

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