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Linear Regression Modeling for Soccer Player Performance Prediction in the EPL

Linear regression is commonly used in machine learning to solve prediction problems. The aim of this project is to predict EPL football player scores based on various factors. Furthermore, this method helps us understand how to model soccer player performance based on different factors. We use Python to build the model, making it easy for beginners to learn about linear regression. In addition, this project uses real-world data to improve learning and practice regression analysis.

Project Overview

This project focuses on building a multiple linear regression model to predict EPL soccer player scores. We use a dataset that includes attributes like player costs, goals, and shots per game. Moreover, the goal is to establish meaningful relationships between these factors and a player's score. This analysis helps team managers and scouts make better recruitment decisions.


This project covers key machine-learning ideas. It teaches data cleaning and regression analysis. You also learn how to check if a model works well. Beginners get hands-on practice with linear regression. They also understand how to measure a model's performance.


Prerequisites

We suggest having a basic understanding of Python, statistics, and machine learning before starting this project. It's helpful to know about model evaluation, visualization, and data preparation methods. You will need libraries like Matplotlib, NumPy, Pandas, and Scikit-learn for this project. Understanding ordinary least squares (OLS) regression and regression analysis is also helpful.


You can easily write and execute Python code by using Google Colab or Jupyter Notebook to run the code. You also learn important statistics like R-squared, modified R-squared, and p-values. These help you better understand the model's results.


Approach

In this project, we use multiple linear regression to predict EPL football player scores. We chose this method because it is simple to use. It shows how factors like player costs, shots per game, and goals impact the player's score.


You can also use other methods to predict player performance. These methods include decision trees, random forests, or neural networks. However, linear regression provides a simple and clear model. It helps you easily understand the connections between features and results. This makes it an excellent choice for beginners.


Workflow and Methodology

The overall workflow of this project includes:

  • Problem definition: Predicting EPL soccer player scores.
  • Data collection and preprocessing: First, we collect and preprocess the data, ensuring it is clean and ready for modeling.
  • Data splitting: Next, we split the dataset into training and testing sets.
  • Model building: We build a multiple linear regression model using ordinary least squares (OLS) regression
  • Model evaluation: Next, we check how the model performs using R-squared and mean-squared error (MSE).

The methodology involves:

  • Data handling: Cleaning, transforming, and splitting the data.
  • Model selection: Choosing the linear regression model due to its interpretability.
  • Training and evaluation: Training the model and validating its performance on the test set.

Additionally, other methods, such as random forest regression or neural networks, could be used to solve the problem. However, we chose this algorithm because it is simple and explains how different features relate to the target variable.


Data Collection

Data Preparation

First, we analyzed some players from EPL teams to create a dataset. After completing the analysis, we created a dataset with specific features. Moreover, the features we included in our dataset are:

  • Player's Name
  • Club
  • Distance Covered (in Kms)
  • Goals per Minute Ratio
  • Shots per Game
  • Agent Fee
  • BMI
  • Cost
  • Previous Club Cost
  • Height (Squared)

We analyzed these features and added the values of all players' characteristics to the dataset. The final dataset is now ready for use in the model.


Data Preparation Workflow

The data preparation workflow involves several steps to ensure the dataset is properly structured for the model:

Code Explanation

STEP 1:

You can mount your Google Drive in a Google Colab notebook with this block of code. This lets users easily view files saved in Google Drive within Colab. They can modify and analyze the data. Users can also train models using the files.

from google.colab import drive
drive.mount('/content/drive')
import warnings
warnings.filterwarnings('ignore')

Install required packages

These commands install the necessary Python libraries. They include numpy, seaborn, matplotlib, statsmodels, pandas, scipy, and scikit-learn. We use 'pip' to install them. This sets up the environment for data analysis, modeling, and visualization.

!pip install numpy
!pip install seaborn
!pip install matplotlib
!pip install statsmodels
!pip install pandas
!pip install scipy
!pip install scikit_learn

Import required packages

This code imports libraries for handling data, modeling, and creating visuals. It prepares the environment for data analysis and plotting tasks.

Code Editor