luxury-lakehouse/expected-threat-grids

Expected Threat (xT) Grids

Markov chain expected threat grids computed via value iteration — 192 cells per competition on a 12×16 grid. Each cell quantifies the probability that a possession starting in that zone will end in a goal, derived from observed transition and shot frequencies across ~4,900 matches.

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

Quick Start

from datasets import load_dataset
import pandas as pd

ds = load_dataset("luxury-lakehouse/expected-threat-grids")
df = ds["train"].to_pandas()

# Pivot the global grid into a 12x16 matrix (attacking direction left-to-right)
global_grid = df[df["competition_id"] == "global"]
xt_matrix = global_grid.pivot(index="zone_y", columns="zone_x", values="xt_value")
print(xt_matrix)

Explore interactively: Soccer Analytics App

What Is This Dataset?

Expected Threat (xT) is a Markov chain model that assigns a goal-scoring threat value to every zone on the pitch. The pitch is discretized into a 12×16 grid (192 cells), and transition probabilities between zones are estimated from observed event data. Value iteration propagates goal-scoring probability backward from the opponent goal to produce an expected threat surface.

Each cell represents a ~8.75m × 4.25m pitch zone. A player who receives the ball in a zone with xT = 0.05 is in a location from which possessions historically result in a goal 5% of the time.

Singh, K. (2018). Introducing Expected Threat (xT). karun.in/blog/expected-threat.html

Data Fields

Column	Type	Description
zone_x	int	Grid x-coordinate (0–11, left-to-right in attacking direction)
zone_y	int	Grid y-coordinate (0–15, pitch width)
xt_value	float	Expected threat value (0–1, higher = more threatening)
competition_id	string	Competition identifier, or "global" for the cross-competition aggregate

Coordinate System

The grid maps to the SPADL academic standard: 105×68 meters. Each of the 192 cells covers approximately 8.75m (length) × 4.25m (width). zone_x = 0 is the defending goal line; zone_x = 11 is nearest the attacking goal. zone_y = 0 is the left touchline; zone_y = 15 is the right touchline.

Data Sources

Source	Matches	License
StatsBomb Open Data	~3,000	CC-BY 4.0
Wyscout Public Dataset	~1,900	CC-BY-NC 4.0

All event data is converted to SPADL format before transition/shot frequency estimation.

Companion Resources

Resource	Description
SPADL/VAEP Action Values	Per-action VAEP scores from the same source events
OBSO Trained Grids	Reachability, EPV, and completion grids for OBSO computation

Limitations

• Spatial resolution: The 12×16 grid is deliberately coarse. Sub-zone variation (e.g., center vs. wing within a cell) is averaged away.
• Competition-agnostic global grid: The "global" grid pools all competitions. Tactical differences between leagues (e.g., Bundesliga pressing vs. Serie A low-block) are smoothed out.
• Static model: xT values are computed from full-season aggregates. They do not adapt to in-game state (score, time, personnel).
• Open data only: Trained on publicly available StatsBomb and Wyscout data. Commercial datasets with richer coverage may yield different threat surfaces.

Citation

If you use this dataset, please cite the original xT blog post:

@misc{singh2018expected,
  title={Introducing Expected Threat (xT)},
  author={Singh, Karun},
  year={2018},
  url={https://karun.in/blog/expected-threat.html}
}

More Information

Explore interactively: Soccer Analytics App

• License: MIT
• Publish script: scripts/compute_xt_grid_hf.py