Analytics on FIFA 2019 Players!. Football analytics and modelling of the... | by Dhanush | Towards Data Science

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.
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')
Best players in various aspect?
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.
2. Most Preferred Foot of the Players?
3. Effect of football 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.
3. Does Age have an Impact on Potential?
It can be observed from the barplot that with increase in age, the potential of the player tends to fall.
It can be observed from the heatmap that the Overall performance is positively correlated with majority of the performance indicators.
Third stage of CRISP-DM is data preparation. The data is cleaned (handling the categorical data and missing values to predict overall performance) and prepared to achieve the outcome. Linear Regression model is built to predict the overall performance of a player based on the performance scores.
Further we have fitted the data into various models — Random forest regressor, K neared neighbours and Decision tree regressor and evaluated the models using the following metrics:
Mean Absolute ErrorR SquareMean Square Error
Mean Absolute Error
R Square
Mean Square Error
In this article, we performed simple data analysis on the FIFA 2019 complete player dataset .
We looked at which player is good at a specific performance indicator. For Example: L. Messi is best at Finishing.We then looked at how age has an influence on the potential of the player i.e. with increase in age, potential of the player decreases.Build a model that predicts the overall performance of a player, given his skill scores for each of the performance indicator.Finally, we evaluated our linear regression model against KNearestRegressor, DecisionTreeRegressor, RandomForestRegressor
We looked at which player is good at a specific performance indicator. For Example: L. Messi is best at Finishing.
We then looked at how age has an influence on the potential of the player i.e. with increase in age, potential of the player decreases.
Build a model that predicts the overall performance of a player, given his skill scores for each of the performance indicator.
Finally, we evaluated our linear regression model against KNearestRegressor, DecisionTreeRegressor, RandomForestRegressor
The findings here are observational, a lot more analysis remains:
How will YOU solve the Problem?
All the codes are available in my Github Repository.