📊 OFF BALL RUN VISUALIZATION¶

This notebook demonstrates how to use the radar plot from skillcorner viz with the new schema of Game Intelligence 2.0, for Off Ball Runs in particular

📋 Step 1: Libraries and Path Setup¶

In [1]:

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import sys
import os
from skillcorner.client import SkillcornerClient

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

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

📥 Step 2: Load Data¶

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

In [ ]:

data_path = '../../../data/aggregates/aus1league_obraggregates_20242025.csv'
df = pd.read_csv(data_path)

# Use the below process if you have SkillCorner Credentials and want more data 

# client=SkillcornerClient(username=XXX,password=XXX')
# response=client.get_metrics_gi_ip_off_ball_runs(params={'competition_edition': '387,390',
#                                                'group_by':'player,competition_edition,position_group',
#                                                'variants':'obr_type',
#                                                'playing_time__gte':'60',
#                                                'average_per':'p30tip'
#                                                })
#
# df=pd.DataFrame(response)

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

data_path = '../../../data/aggregates/aus1league_obraggregates_20242025.csv' df = pd.read_csv(data_path) # Use the below process if you have SkillCorner Credentials and want more data # client=SkillcornerClient(username=XXX,password=XXX') # response=client.get_metrics_gi_ip_off_ball_runs(params={'competition_edition': '387,390', # 'group_by':'player,competition_edition,position_group', # 'variants':'obr_type', # 'playing_time__gte':'60', # 'average_per':'p30tip' # }) # # df=pd.DataFrame(response) print(f"Loaded {len(df)} rows") df.head()

Loaded 407 rows

Out[ ]:

	competition_edition_id	competition_id	competition_name	season_id	season_name	player_id	player_name	player_short_name	player_birthdate	team_id	...	behindrun_count_dangerous_received_p30tip	comingshortrun_count_dangerous_received_p30tip	crossreceiverrun_count_dangerous_received_p30tip	droppingoffrun_count_dangerous_received_p30tip	overlaprun_count_dangerous_received_p30tip	pullinghalfspacerun_count_dangerous_received_p30tip	pullingwiderun_count_dangerous_received_p30tip	aheadoftheballrun_count_dangerous_received_p30tip	supportrun_count_dangerous_received_p30tip	underlaprun_count_dangerous_received_p30tip
0	870	61	AUS - A-League	95	2024/2025	211	Adam Taggart	A. Taggart	1993-06-02	871	...	1.62	0.0	1.11	0.0	0.0	0.0	0.0	1.21	0.40	0.0
1	870	61	AUS - A-League	95	2024/2025	218	Adama Traoré	A. Traoré	1990-02-03	868	...	0.00	0.0	0.57	0.0	0.0	0.0	0.0	0.57	0.00	0.0
2	870	61	AUS - A-League	95	2024/2025	2759	Dino Arslanagić	D. Arslanagić	1993-04-24	1804	...	0.00	0.0	0.00	0.0	0.0	0.0	0.0	0.16	0.00	0.0
3	870	61	AUS - A-League	95	2024/2025	2858	Douglas Costa de Souza	Douglas Costa	1990-09-14	869	...	0.71	0.0	0.00	0.0	0.0	0.0	0.0	0.35	0.35	0.0
4	870	61	AUS - A-League	95	2024/2025	2858	Douglas Costa de Souza	Douglas Costa	1990-09-14	869	...	0.00	0.0	0.00	0.0	0.0	0.0	0.0	0.00	0.00	0.0

5 rows × 130 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.

In this example the data is already normalized so no need to do other operations

In [16]:

# Initial filtering
df_filtered = df[(df['performance_count'] >= 5)].copy()

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

# Initial filtering df_filtered = df[(df['performance_count'] >= 5)].copy() print(f"Data filtered to {len(df_filtered)} records and normalized.")

Data filtered to 194 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 [17]:

from skillcornerviz.standard_plots import radar_plot as rad

RUNS = {'crossreceiverrun_count_p30tip': 'Cross Receiver',
        'behindrun_count_p30tip': ' In Behind',
        'aheadoftheballrun_count_p30tip': 'Ahead Of The Ball',
        'overlaprun_count_p30tip': 'Overlap',
        'underlaprun_count_p30tip': 'Underlap',
        'supportrun_count_p30tip': 'Support',
        'comingshortrun_count_p30tip': 'Coming Short',
        'droppingoffrun_count_p30tip': 'Dropping Off',
        'pullinghalfspacerun_count_p30tip': 'Pulling Half-Space',
        'pullingwiderun_count_p30tip': 'Pulling Wide'}

from skillcornerviz.standard_plots import radar_plot as rad RUNS = {'crossreceiverrun_count_p30tip': 'Cross Receiver', 'behindrun_count_p30tip': ' In Behind', 'aheadoftheballrun_count_p30tip': 'Ahead Of The Ball', 'overlaprun_count_p30tip': 'Overlap', 'underlaprun_count_p30tip': 'Underlap', 'supportrun_count_p30tip': 'Support', 'comingshortrun_count_p30tip': 'Coming Short', 'droppingoffrun_count_p30tip': 'Dropping Off', 'pullinghalfspacerun_count_p30tip': 'Pulling Half-Space', 'pullingwiderun_count_p30tip': 'Pulling Wide'}

🎨 Step 5: Render the Sectioned Summary Table¶

In [24]:

runs_df=df_filtered.copy()

fig, ax = rad.plot_radar(runs_df[runs_df['position_group'] == 'Wide Attacker'],
                         data_point_id='player_id',
                         label=18573, # Whcih data_point_id to highlight. If you use a name, use player_name as data_point_id
                         plot_title='Off-Ball Run Profile',
                         metrics=RUNS.keys(),
                         metric_labels=RUNS,
                         percentiles_precalculated=False, # Add this to make sure you visualize percentiles
                         suffix=' Runs P30 TIP',
                         filter_relevant=True, # Only color the run types that are important to you group like user group

                         # Create your own sample text
                         # positions='Wide Attackers',
                         # matches=5,
                         # minutes=60,
                         # competitions='AUS1',
                         # seasons='2024/2025', 
                         # add_sample_info=True
                         )

runs_df=df_filtered.copy() fig, ax = rad.plot_radar(runs_df[runs_df['position_group'] == 'Wide Attacker'], data_point_id='player_id', label=18573, # Whcih data_point_id to highlight. If you use a name, use player_name as data_point_id plot_title='Off-Ball Run Profile', metrics=RUNS.keys(), metric_labels=RUNS, percentiles_precalculated=False, # Add this to make sure you visualize percentiles suffix=' Runs P30 TIP', filter_relevant=True, # Only color the run types that are important to you group like user group # Create your own sample text # positions='Wide Attackers', # matches=5, # minutes=60, # competitions='AUS1', # seasons='2024/2025', # add_sample_info=True )

/Users/nano/PycharmProjects/opendata/venv/lib/python3.10/site-packages/skillcornerviz/utils/skillcorner_utils.py:107: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df[metric + '_pct'] = (df[metric].rank(pct=True, na_option='keep') * 100).round()
/Users/nano/PycharmProjects/opendata/venv/lib/python3.10/site-packages/skillcornerviz/utils/skillcorner_utils.py:107: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df[metric + '_pct'] = (df[metric].rank(pct=True, na_option='keep') * 100).round()
/Users/nano/PycharmProjects/opendata/venv/lib/python3.10/site-packages/skillcornerviz/utils/skillcorner_utils.py:107: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df[metric + '_pct'] = (df[metric].rank(pct=True, na_option='keep') * 100).round()
/Users/nano/PycharmProjects/opendata/venv/lib/python3.10/site-packages/skillcornerviz/utils/skillcorner_utils.py:107: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df[metric + '_pct'] = (df[metric].rank(pct=True, na_option='keep') * 100).round()
/Users/nano/PycharmProjects/opendata/venv/lib/python3.10/site-packages/skillcornerviz/utils/skillcorner_utils.py:107: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df[metric + '_pct'] = (df[metric].rank(pct=True, na_option='keep') * 100).round()
/Users/nano/PycharmProjects/opendata/venv/lib/python3.10/site-packages/skillcornerviz/utils/skillcorner_utils.py:107: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df[metric + '_pct'] = (df[metric].rank(pct=True, na_option='keep') * 100).round()
/Users/nano/PycharmProjects/opendata/venv/lib/python3.10/site-packages/skillcornerviz/utils/skillcorner_utils.py:107: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df[metric + '_pct'] = (df[metric].rank(pct=True, na_option='keep') * 100).round()
/Users/nano/PycharmProjects/opendata/venv/lib/python3.10/site-packages/skillcornerviz/utils/skillcorner_utils.py:107: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df[metric + '_pct'] = (df[metric].rank(pct=True, na_option='keep') * 100).round()
/Users/nano/PycharmProjects/opendata/venv/lib/python3.10/site-packages/skillcornerviz/utils/skillcorner_utils.py:107: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df[metric + '_pct'] = (df[metric].rank(pct=True, na_option='keep') * 100).round()
/Users/nano/PycharmProjects/opendata/venv/lib/python3.10/site-packages/skillcornerviz/utils/skillcorner_utils.py:107: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df[metric + '_pct'] = (df[metric].rank(pct=True, na_option='keep') * 100).round()

In [ ]: