This project evaluates Messi's passing ability using open-data from statsbombpy and Statsbomb360. The open data can be accessed using the following link: https://github.com/statsbomb/open-data.
In this project, I use the programming language Python to write the code for my analysis within Jupyter Notebooks.
matplotlib.pyplot - a collection of functions used to create different displays using raw data
pandas - used to create dataframes to store Statsbomb data
numpy - used to support large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays
mplsoccer - used to import a two-dimensional soccer pitch to plot the location of data
statsbombpy - used to access and import public data provided by Statsbomb using Python
rcParams - used to import sophisticated text styles for aesthetics
json - used to directly access and import json files within the notebook using Python
IPython.display - used to display images for raw data within a notebook
os - used for accessing the directory folder and using its contents
This repository contains code for analyzing soccer data using Python libraries. The code is organized into different functions, each serving a specific purpose.
The world_cup_final_passing_analysis function retrieves event data from the 2022 World Cup Final between Argentina and France and generates a passing network visualization for Argentina, as well as a plot of Messi's passes using the matplotlib and mplsoccer libraries. Visualizations include total passes, passes by foot, and progressive passes. This is supported by data from https://fbref.com/en/comps/1/passing/World-Cup-Stats.
The messi_incomplete_passes_analysis function analyzes Messi's incomplete passes during the 2020-21 La Liga season and generates a scatter plot of their location using the statsbombpy and seaborn libraries.
The player_heatmap_analysis function analyzes a player's heatmap based on their event data, allowing for customization of the heatmap colors and style.
The team_shot_analysis function combines Messi's passes with a team's shot data and generates a visualization of their shot locations on the field, providing insights into their attacking patterns and scoring efficiency.
The 'match 360 Analysis' function combines event data with Statsbomb360 data to plot the location of passes as well as the location of surrounding players from both teams.
Please refer to the code comments and function documentation for further details on how to use each function. Make sure to install the required libraries before running the code.
To provide a more complete analysis on this topic, I would run linear regression to evaluate the relationships between the passing variables. Based on the results from this analysis, I would update my existing analysis and gain better insights into what other factors are including within a player's passing ability.
Additionally, valuing actions would be effective within the smaller sample size that is used throughout this project. This is because specific actions can be measured to evaluate how they affect the probability of the team scoring. Valuable metrics such as expected threat xT are included in this area of analysis.