Overview This project involves analyzing football match data to gain insights into player movements, team formations, and game strategies. It includes visualizations of player positions, heatmaps of activity areas, and animations showing player movements over time. The project aims to provide a comprehensive understanding of football tactics and player performance.
Load the Data:
Make sure the data files are in the data directory.
Run the Jupyter Notebook:
bash Copy code jupyter notebook Open the notebook football_analysis.ipynb and execute the cells.
Generate Visualizations:
The code will automatically generate visualizations such as player positions, heatmaps, and trajectories.
Data The dataset used in this project includes:
Player Position Data: Coordinates of player positions during matches. Event Data: Events such as goals, assists, and key passes. Match Data: Metadata about the matches including teams, dates, and scores. The data files can be found in the data directory. Note that large data files are not included in the repository for performance reasons. You may need to acquire the data separately.
Analysis This project includes several key analyses:
Player Position Analysis: Visualization of average player positions on the field. Heatmaps: Areas of high player activity during matches. Trajectories: Paths of player movements over time. Animations: Animated visualizations showing changes in player positions. Visualizations Player Positions: Scatter plots showing average positions of players. Heatmaps: Color-coded maps indicating areas of high activity. Player Trajectories: Line plots illustrating player movements. Animations: Animated visualizations of player movements and heatmaps.