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"# 📍 Recipe: Drawing a Pass Map\n",
"\n",
"This example demonstrates how to visualize a player's passes using `Flow` to process StatsBomb data and `mplsoccer` to draw the pitch.\n",
"\n",
"## 🧰 What You’ll Learn\n",
"\n",
"- How to filter for completed passes\n",
"- How to extract pass start and end locations\n",
"- How to use `.assign()` to extract nested data as an alternative to using `.select()` + `.rename()`\n",
"- How to use `.split_array()` to extract the location array into multiple fields\n",
"- How to optimize the flow for better performance\n",
"- How to plot them using the `penaltyblog` `Pitch` class\n",
"\n",
"## Imports"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "e6449b0d",
"metadata": {},
"outputs": [],
"source": [
"from IPython.display import HTML\n",
"from penaltyblog.matchflow import Flow, where_equals, get_field\n",
"from penaltyblog.viz import Pitch\n",
"import plotly.io as pio"
]
},
{
"cell_type": "markdown",
"id": "b6ae3204",
"metadata": {},
"source": [
"## Load the Data"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "76571641",
"metadata": {},
"outputs": [],
"source": [
"# Load events for a StatsBomb match\n",
"match_id = 22912 # Champions League Final 2018/2019\n",
"\n",
"flow = Flow.statsbomb.events(match_id)"
]
},
{
"cell_type": "markdown",
"id": "1ad09160",
"metadata": {},
"source": [
"## Extract the Passes"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "9b3cb682",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/martin/repos/penaltyblog/venv/lib/python3.13/site-packages/statsbombpy/api_client.py:21: NoAuthWarning: credentials were not supplied. open data access only\n",
" warnings.warn(\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'x': 60.0, 'y': 40.0, 'x2': 43.4, 'y2': 40.3}\n",
"{'x': 39.6, 'y': 63.4, 'x2': 39.8, 'y2': 55.0}\n",
"{'x': 86.5, 'y': 47.8, 'x2': 75.6, 'y2': 55.6}\n"
]
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"source": [
"passes = (\n",
" flow.filter(where_equals(\"player.name\", \"Harry Kane\"))\n",
" .filter(where_equals(\"type.name\", \"Pass\"))\n",
" .filter(lambda r: get_field(r, \"pass.outcome.name\") is None)\n",
" .split_array(\"location\", [\"start_x\", \"start_y\"])\n",
" .split_array(\"pass.end_location\", [\"end_x\", \"end_y\"])\n",
" .select(\"start_x\", \"start_y\", \"end_x\", \"end_y\")\n",
" .rename(start_x=\"x\", start_y=\"y\", end_x=\"x2\", end_y=\"y2\")\n",
" .dropna()\n",
")\n",
"\n",
"for pass_ in passes.head(3):\n",
" print(pass_)"
]
},
{
"cell_type": "markdown",
"id": "2e227238",
"metadata": {},
"source": [
"## Plotting the Passes"
]
},
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"execution_count": 4,
"id": "7300585b",
"metadata": {},
"outputs": [
{
"name": "stderr",
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"text": [
"/Users/martin/repos/penaltyblog/venv/lib/python3.13/site-packages/statsbombpy/api_client.py:21: NoAuthWarning:\n",
"\n",
"credentials were not supplied. open data access only\n",
"\n"
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"\n",
"\n",
"\n",
" \n",
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"source": [
"pitch = Pitch(\n",
" provider=\"statsbomb\",\n",
" orientation=\"horizontal\",\n",
" view=\"full\",\n",
" theme=\"night\",\n",
" show_axis=False,\n",
" show_legend=False,\n",
" width=400,\n",
" height=400,\n",
" title=\"Harry Kane – Completed Passes\",\n",
")\n",
"\n",
"pitch.plot_arrows(passes)\n",
"# NOTE: normally we'd just call `pitch.show()` here, but since\n",
"# we're exporting to HTML docs, we need to use `HTML` to export\n",
"# the plot\n",
"# pitch.show()\n",
"HTML(pio.to_html(pitch.fig, include_plotlyjs=\"cdn\"))"
]
}
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