Introduction

Pass maps are a powerful visualization tool in football analytics, helping analysts and coaches understand passing patterns, player connectivity, and team dynamics. Using StatsBomb Open Data, we can generate detailed pass maps that reveal strategic insights about a team’s playstyle and individual player tendencies.

This guide will introduce StatsBomb’s Open Data, how to access it, and how to create pass maps using Python and visualization tools like Matplotlib and Seaborn.

Interactive Data Filtering & Visualization

StatsBomb data can be analyzed using Python. Below is a guide on setting up the StatsBomb Python API and visualizing pass maps.

Creating a Pass Map

A pass map visualizes successful passes made by a player or team in a given match. Here’s an example using Python:

import pandas as pd

import matplotlib.pyplot as plt

import seaborn as sns

from statsbombpy import sb

# Load event data for a specific match

match_id = 3795220

events = sb.events(match_id=match_id)

# Filter passes

passes = events[events['type'] == 'Pass']

# Plot pass map

fig, ax = plt.subplots(figsize=(10, 6))

plt.xlim(0, 120)

plt.ylim(0, 80)

# Plot passes

for _, row in passes.iterrows():

    plt.plot([row['location'][0], row['pass_end_location'][0]], 

             [row['location'][1], row['pass_end_location'][1]], 

             color='blue', alpha=0.5, lw=2)

plt.title("Pass Map for Match {}".format(match_id))

plt.show()

Advanced Visualization: Pass Maps by Player

Using Seaborn, we can create individual pass maps for specific players.

player_name = "Lionel Messi"

player_passes = passes[passes["player"] == player_name]

fig, ax = plt.subplots(figsize=(10, 6))

sns.scatterplot(x=player_passes["location"].apply(lambda x: x[0]),

                y=player_passes["location"].apply(lambda x: x[1]),

                color="red", s=100, label=player_name)

plt.title(f"Pass Map for {player_name}")

plt.show()

360 Data: Adding Player Positioning

StatsBomb also provides 360 freeze frame data, which includes the locations of players at key moments.

frames = sb.frames(match_id=3795220)

# Merge event data with frame data

pass_frames = frames[frames['type'] == 'Pass']

pass_frames = pass_frames.merge(passes, on='id')

# Visualize pass map with player positions

fig, ax = plt.subplots(figsize=(10, 6))

sns.scatterplot(x=pass_frames["location"].apply(lambda x: x[0]),

                y=pass_frames["location"].apply(lambda x: x[1]),

                color="green", label="Player Positions")

plt.title("Pass Map with Player Positions")

plt.show()

Resources & Further Reading

• StatsBomb Open Data: GitHub Repo

• StatsBombPy API: Python Library

• Football Analytics Guide: Getting Started with Football Analytics