📊 Sectioned Summary Table Visualization¶

This notebook demonstrates how to use the sectioned_summary_table_viz.py tool. This visualization is designed to show a breakdown of multiple metrics across different categories (sections) for a specific group of players, using percentile ranks to compare them against the rest of the dataset.

📋 Step 1: Libraries and Path Setup¶

In [2]:

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import sys
import os

# Add the src directory to the path so we can import the visualization module
sys.path.append(os.path.abspath('../../../src'))

from visualization.sectioned_summary_table_viz import ranking_plot

import pandas as pd import numpy as np import matplotlib.pyplot as plt import sys import os # Add the src directory to the path so we can import the visualization module sys.path.append(os.path.abspath('../../../src')) from visualization.sectioned_summary_table_viz import ranking_plot

📥 Step 2: Load Data¶

We will load the AUS A-League physical aggregates for the 2024/2025 season.

In [3]:

data_path = '../../../data/aggregates/aus1league_physicalaggregates_20242025.csv'
df = pd.read_csv(data_path)

print(f"Loaded {len(df)} rows")
df.head()

data_path = '../../../data/aggregates/aus1league_physicalaggregates_20242025.csv' df = pd.read_csv(data_path) print(f"Loaded {len(df)} rows") df.head()

Loaded 406 rows

Out[3]:

	player_name	player_short_name	player_id	player_birthdate	team_name	team_id	competition_name	competition_id	season_name	season_id	...	sprint_distance_full_otip	sprint_count_full_otip	hi_distance_full_otip	hi_count_full_otip	medaccel_count_full_otip	highaccel_count_full_otip	meddecel_count_full_otip	highdecel_count_full_otip	explacceltohsr_count_full_otip	explacceltosprint_count_full_otip
0	Adam Taggart	A. Taggart	211	1993-06-02	Perth Glory Football Club	871	AUS - A-League	61	2024/2025	95	...	48.46	3.00	297.83	27.21	42.38	2.67	38.25	6.96	1.92	0.08
1	Adama Traoré	A. Traoré	218	1990-02-03	Melbourne Victory Football Club	868	AUS - A-League	61	2024/2025	95	...	69.67	4.00	315.33	25.00	37.83	2.00	25.67	4.50	1.17	0.50
2	Dino Arslanagić	D. Arslanagić	2759	1993-04-24	Macarthur FC	1804	AUS - A-League	61	2024/2025	95	...	29.12	1.25	223.25	16.88	45.12	1.75	30.75	3.50	1.25	0.00
3	Douglas Costa de Souza	Douglas Costa	2858	1990-09-14	Sydney Football Club	869	AUS - A-League	61	2024/2025	95	...	28.00	2.00	227.25	17.50	28.75	2.00	25.00	3.00	0.75	0.25
4	Douglas Costa de Souza	Douglas Costa	2858	1990-09-14	Sydney Football Club	869	AUS - A-League	61	2024/2025	95	...	1.00	0.00	170.00	16.00	26.00	0.00	21.00	2.00	0.00	0.00

5 rows × 65 columns

📂 Step 3: Normalization and Filtering¶

Matching the approach in the Z-score tutorial, we perform the following:

1. Filter for players with at least 5 matches.
2. Normalize cumulative metrics to Per 90 (P90) to allow fair comparison.

In [19]:

# Initial filtering
df_filtered = df[(df['count_match'] >= 5) & (df['minutes_full_all'] > 60)].copy()

# Normalize key metrics to P90
metrics_to_normalize = {
    'hi_count_full_all': 'hi_count_p90',
    'sprint_distance_full_all': 'sprint_distance_p90',
    'total_distance_full_all': 'total_dist_p90',
    'running_distance_full_all': 'running_dist_p90',
    'highaccel_count_full_all': 'highaccel_p90',
    'explacceltosprint_count_full_all': 'explacceltosprint_p90'
}

for raw_col, p90_col in metrics_to_normalize.items():
    df_filtered[p90_col] = (df_filtered[raw_col] / df_filtered['minutes_full_all']) * 90

print(f"Data filtered to {len(df_filtered)} records and normalized.")

# Initial filtering df_filtered = df[(df['count_match'] >= 5) & (df['minutes_full_all'] > 60)].copy() # Normalize key metrics to P90 metrics_to_normalize = { 'hi_count_full_all': 'hi_count_p90', 'sprint_distance_full_all': 'sprint_distance_p90', 'total_distance_full_all': 'total_dist_p90', 'running_distance_full_all': 'running_dist_p90', 'highaccel_count_full_all': 'highaccel_p90', 'explacceltosprint_count_full_all': 'explacceltosprint_p90' } for raw_col, p90_col in metrics_to_normalize.items(): df_filtered[p90_col] = (df_filtered[raw_col] / df_filtered['minutes_full_all']) * 90 print(f"Data filtered to {len(df_filtered)} records and normalized.")

Data filtered to 210 records and normalized.

📊 Step 4: Visualization Setup¶

We need to define:

1. Questions: A dictionary mapping section labels to lists of metrics.
2. Highlight Group: The list of players we want to display in the table.

In [24]:

# Sectioned metrics definition
questions = {
    'Intensity': ['hi_count_p90', 'sprint_distance_p90'],
    'Volume': ['total_dist_p90', 'total_metersperminute_full_tip','total_metersperminute_full_otip'],
    'Explosivity': ['highaccel_p90', 'explacceltosprint_p90']
}

# Metric labels for cleaner output
metric_labels = {
    'hi_count_p90': 'HI Count',
    'sprint_distance_p90': 'Sprint Distance',
    'total_dist_p90': 'Total Distance',
    'total_metersperminute_full_tip': 'Meters Per Minute TIP',
    'total_metersperminute_full_otip': 'Meters Per Minute OTIP',
    'highaccel_p90': 'High Accels',
    'explacceltosprint_p90': 'Explosive Accels'
}

# Select a group of players for comparison (e.g., players from Sydney FC)
highlight_players = df_filtered[df_filtered['team_name'] == 'Sydney Football Club']['player_name'].unique()[:6]

# Sectioned metrics definition questions = { 'Intensity': ['hi_count_p90', 'sprint_distance_p90'], 'Volume': ['total_dist_p90', 'total_metersperminute_full_tip','total_metersperminute_full_otip'], 'Explosivity': ['highaccel_p90', 'explacceltosprint_p90'] } # Metric labels for cleaner output metric_labels = { 'hi_count_p90': 'HI Count', 'sprint_distance_p90': 'Sprint Distance', 'total_dist_p90': 'Total Distance', 'total_metersperminute_full_tip': 'Meters Per Minute TIP', 'total_metersperminute_full_otip': 'Meters Per Minute OTIP', 'highaccel_p90': 'High Accels', 'explacceltosprint_p90': 'Explosive Accels' } # Select a group of players for comparison (e.g., players from Sydney FC) highlight_players = df_filtered[df_filtered['team_name'] == 'Sydney Football Club']['player_name'].unique()[:6]

🎨 Step 5: Render the Sectioned Summary Table¶

In [25]:

fig, ax = ranking_plot(
    df=df_filtered, 
    questions=questions, 
    highlight_group=highlight_players,
    metric_labels=metric_labels,
    figsize=(14, 8)
)

fig, ax = ranking_plot( df=df_filtered, questions=questions, highlight_group=highlight_players, metric_labels=metric_labels, figsize=(14, 8) )

Alternative Visualization: Bubble Mode¶

We can also use user_circles=True to render the ranks as expanding bubbles instead of bars.

In [26]:

fig, ax = ranking_plot(
    df=df_filtered, 
    questions=questions, 
    highlight_group=highlight_players,
    metric_labels=metric_labels,
    user_circles=True,
    figsize=(14, 8)
)

fig, ax = ranking_plot( df=df_filtered, questions=questions, highlight_group=highlight_players, metric_labels=metric_labels, user_circles=True, figsize=(14, 8) )

In [ ]: