📊 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'))
📥 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()
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:
- 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.")
Data filtered to 194 records and normalized.
📊 Step 4: Visualization Setup¶
We need to define:
- Questions: A dictionary mapping section labels to lists of metrics.
- 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'}
🎨 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
)
/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()
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