📍 Recipe: Cumulative xG by Time per Team

In this example, we’ll load event data for a match from StatsBomb’s open dataset, extract shots and their xG values, and calculate the cumulative expected goals (xG) for each team over time.

We’ll then plot the result using a stepped line chart in matplotlib to show the ebb and flow of attacking threat.

🧰 What You’ll Learn

• How to access StatsBomb data using Flow.statsbomb methods

• How to filter for shot events and extract time and xG values

• Use .group_by(...).cumulative(...) to compute cumulative stats

• How to add a zero point to each team’s cumulative xG so the chart starts at (0,0) using .pipe()

• How to convert to pandas and visualize using matplotlib

Imports

from penaltyblog.matchflow import Flow, get_field, where_equals
import matplotlib.pyplot as plt

Filter Shot Events

match_id = 22912  # Champions League Final 2018/2019

shots = (
    Flow.statsbomb.events(match_id)
    .filter(where_equals("type.name", "Shot"))
    .assign(
        team=lambda r: get_field(r, "team.name"),
        xg=lambda r: get_field(r, "shot.statsbomb_xg") or 0,
        time=lambda r: r.get("minute", 0) * 60 + r.get("second", 0),
    )
    .select("time", "team", "xg")
    .sort_by("time")
)

Group by Team and Compute Cumulative xG

Note how we use the .pipe() method to add the zero point to each team’s cumulative xG so the chart starts at (0,0)

def add_zero_point(flow: Flow) -> Flow:
    teams = flow.select("team").distinct().collect()
    zeros = [{"team": t["team"], "time": 0, "cumulative_xg": 0.0} for t in teams]
    return Flow.from_records(zeros).concat(flow)


cum_xg = (
    shots.group_by("team")
    .cumulative(field="xg", alias="cumulative_xg")
    .pipe(add_zero_point)
    .sort_by("time")
)

Plot Chart

fig, ax = plt.subplots(figsize=(8, 5))

for team, records in cum_xg.grouped("team"):
    times = [r["time"] for r in records]
    xgs = [r["cumulative_xg"] for r in records]
    ax.step(times, xgs, where="post", label=team)

ax.set_xlabel("Match Time (seconds)")
ax.set_ylabel("Cumulative xG")
ax.set_title("Cumulative xG by Team")
ax.legend()

/Users/martin/repos/penaltyblog/venv/lib/python3.13/site-packages/statsbombpy/api_client.py:21: NoAuthWarning: credentials were not supplied. open data access only
  warnings.warn(

<matplotlib.legend.Legend at 0x113b516a0>