Football Players Statistics Analysis
Football Players Statistics Analysis
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This project explores a dataset of football players to understand trends and patterns using statistical tools. We analyze categorical and numerical variables, create visualizations, and interpret key insights from the data.
This project explores a dataset of football players to understand trends and patterns using statistical tools. We analyze categorical and numerical variables, create visualizations, and interpret key insights from the data.
🞅Dataset Source & Link
▢The dataset used in this project contains detailed statistics of football players, including both categorical and numerical variables. It was sourced from Kaggle - FIFA 21 Complete Player Dataset, which is a publicly available and trusted platform for data.
▢The dataset contains over 15,000 rows and more than 100 columns, out of which we selected at least 5 relevant columns — including player positions, nationality, overall rating, age, and value — for our statistical analysis.
⊡DATASET LINK (click the logo)
📊 Dataset: FIFA 21 Player Stats
📊 Dataset: FIFA 21 Player Stats
The dataset used for this project is sourced from Kaggle - FIFA 21 Complete Player Dataset.
It includes data from FIFA 21, covering more than 100,000 player records across multiple years. This dataset includes a variety of attributes like name, nationality, age, club, player position, height, weight, and performance stats.
The dataset contains:
Categorical variables like: Nationality, Club, Position, Preferred Foot
Numerical variables like: Age, Height, Weight, Overall, Potential
This dataset allows us to perform statistical exploration using both categorical and numerical data — ideal for foundational analysis
💡 Explanation
💡 Explanation
⚝ Categorical variables are those that represent groups or categories, such as Position, Nationality, and Preferred Foot.
⚝ Numerical variables represent measurable quantities like Age, Weight, and Height.
⚝ Variables like Age and Overall are discrete, as they are counted in whole numbers.
⚝ Height and Weight are continuous, since they can take any value in a range.
⚝ All numerical variables in this dataset follow the ratio scale, as they have a true zero point and measurable intervals.
Distribution of Players by Preferred Foot
Distribution of Players by Preferred Foot
Player Positions (Pie Chart)
Player Positions (Pie Chart)
Age Distribution (Histogram)
Age Distribution (Histogram)
Top 10 Countries with the Most Players (Bar Chart)
Top 10 Countries with the Most Players (Bar Chart)
Height vs Weight (Scatter Plot)
Height vs Weight (Scatter Plot)
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