luxury-lakehouse/pitch-control-tracking

Pitch Control Tracking Data

Per-player per-frame pitch control values from ~38 million rows of professional soccer tracking data across 20 matches from three providers. Computed using the Spearman (2017) physics-based model — each row contains one player's position, velocity, and the home-team control probability at that location.

Part of the (Right! Luxury!) Lakehouse soccer analytics platform.

Quick Start

from datasets import load_dataset

ds = load_dataset("luxury-lakehouse/pitch-control-tracking")
df = ds["train"].to_pandas()

# Average home-team pitch control per match
home_control = df.groupby("match_id")["pitch_control_value"].mean()
print(home_control.describe())

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What is Pitch Control?

Pitch control quantifies which team controls each location on the pitch at every moment in a match. The Spearman (2017) physics-based model estimates control by computing the time for each player to intercept a given point, accounting for reaction time and maximum acceleration kinematics. A logistic influence function converts time-to-intercept into a control probability, and each player's contribution is the fraction of their team's total influence at that location.

The result is a per-player control value: the home-team control probability [0, 1] at that player's current position at that instant in time.

Data Fields

Column	Type	Description
tracking_id	string	Primary key — unique player-frame identifier
match_id	string	Match identifier (prefixed by provider)
player_id	string	Player identifier
team	string	Team affiliation (home or away)
period	int	Match period (1 or 2)
frame	int	Frame number within the period
timestamp_seconds	double	Timestamp in seconds from period start
x	double	Player X coordinate (StatsBomb 120-yard scale)
y	double	Player Y coordinate (StatsBomb 80-yard scale)
ball_x	double	Ball X coordinate
ball_y	double	Ball Y coordinate
velocity_x	double	Player velocity X (coordinate-units/second)
velocity_y	double	Player velocity Y (coordinate-units/second)
speed_ms	double	Player speed in m/s
pitch_control_value	double	Home-team control probability [0, 1] at this player's position
source_provider	string	Tracking data provider (metrica, idsse, skillcorner)
frame_rate	bigint	Frame rate in fps (25 for Metrica/IDSSE, 10 for SkillCorner)

Coordinate System

All coordinates use the StatsBomb 120×80 yards scale. All three tracking providers are normalized to this coordinate system during ingestion. The origin (0, 0) is at the bottom-left corner of the pitch; x runs along the length (0–120 yards), y along the width (0–80 yards).

Model

The pitch control values are computed using the Spearman (2017) physics-based model:

1. Time-to-intercept: For each player and each target location, compute the minimum time to reach that point given a reaction time plus kinematics under maximum acceleration.
2. Logistic influence function: Convert time-to-intercept to a control probability via a sigmoid function.
3. Per-player control: Each player's pitch control value is the fraction of their team's total logistic influence at the player's current position, relative to all players on the pitch.

The result is bounded [0, 1] where 1 = home team has full control and 0 = away team has full control.

Data Sources

Provider	Matches	Frame Rate	Competition
Metrica Sports	3	25 fps	Open sample matches
IDSSE Bundesliga	7	25 fps	German Bundesliga
SkillCorner A-League	10	10 fps	Australian A-League

All providers use standardized tracking formats normalized to the StatsBomb coordinate scale.

Limitations

• Small sample: Only 20 matches have full tracking data. Patterns may not generalize across leagues or tactical systems.
• Variable frame rate: Metrica and IDSSE track at 25 fps; SkillCorner at 10 fps. Time-series analyses should account for this difference.
• Velocity estimation: Velocity vectors are estimated from positional differences and may differ in smoothing method between providers.
• Physics model approximation: The Spearman (2017) model assumes constant maximum acceleration and uniform reaction time. It does not account for player fatigue, directional momentum, or tactical intent.
• No goalkeeper distinction: Goalkeepers are treated identically to outfield players in the control model.

Citation

If you use this dataset, please cite the original pitch control paper:

@inproceedings{spearman2017beyond,
  title={Beyond Expected Goals},
  author={Spearman, William},
  booktitle={MIT Sloan Sports Analytics Conference},
  year={2017}
}

More Information

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• License: MIT