| ---
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| language: [en]
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| license: mit
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| task_categories: [tabular-regression]
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| tags:
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| - sports-analytics
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| - soccer
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| - football
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| - obso
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| - epv
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| - transition
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| - reachability
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| - analytics
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| size_categories: [10K<n<100K]
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| configs:
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| - config_name: default
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| data_files:
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| - split: train
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| path: "data/*.parquet"
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| ---
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|
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| # OBSO Trained Grids — Reachability, EPV, and Completion Matrices
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| Pre-trained grid artifacts for **Off-Ball Scoring Opportunity (OBSO)** computation: ball reachability surfaces, expected possession value (EPV) grids, and pass completion probability matrices. These are the static lookup tables that power real-time OBSO evaluation — derived from observed passing, shooting, and transition patterns across ~4,900 open-data matches.
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| Part of the (Right! Luxury!) Lakehouse soccer analytics platform.
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|
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| ## Quick Start
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| ```python
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| from huggingface_hub import hf_hub_download
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| import pandas as pd
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| repo = "luxury-lakehouse/obso-trained-grids"
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| # Load the global reachability grid (100x64)
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| reach_path = hf_hub_download(repo, "data/reachability_grid_global.parquet", repo_type="dataset")
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| reach_df = pd.read_parquet(reach_path)
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| reach_matrix = reach_df.pivot(index="zone_y", columns="zone_x", values="reachability")
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| print(f"Reachability grid shape: {reach_matrix.shape}") # (100, 64)
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| # Load the global EPV grid (50x32)
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| epv_path = hf_hub_download(repo, "data/epv_grid_global.parquet", repo_type="dataset")
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| epv_df = pd.read_parquet(epv_path)
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| epv_matrix = epv_df.pivot(index="zone_y", columns="zone_x", values="epv_value")
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| print(f"EPV grid shape: {epv_matrix.shape}") # (50, 32)
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| ```
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| > **Explore interactively:** [HF Space demo](https://huggingface.co/spaces/luxury-lakehouse/soccer-analytics-demo)
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|
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| ## What Is This Dataset?
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| The OBSO model evaluates off-ball scoring opportunities by combining three components:
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| 1. **Reachability** — the probability that a ball played to a given zone can be controlled by the receiving team, based on observed reception patterns.
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| 2. **Expected Possession Value (EPV)** — the probability that a possession in a given zone will result in a goal, estimated via value iteration over transition and shot frequencies.
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| 3. **Pass Completion Probability** — the likelihood that a pass from one zone to another is completed, estimated from observed pass outcomes.
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| These grids are pre-computed from event data and serve as static inputs to the real-time OBSO pipeline, which combines them with dynamic pitch control surfaces.
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| ## Data Fields
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| The dataset contains three types of grid artifacts at different resolutions, plus per-competition variants.
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| ### Reachability Grid (`reachability_grid_global.parquet`)
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| | Column | Type | Description |
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| |--------|------|-------------|
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| | `zone_y` | `int` | Grid row index (0–99) |
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| | `zone_x` | `int` | Grid column index (0–63) |
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| | `reachability` | `float` | Ball reachability probability (0–1) |
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| Grid resolution: **100×64** (105m ÷ 100 = 1.05m per row, 68m ÷ 64 = 1.0625m per column).
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| ### EPV Grid (`epv_grid_global.parquet`)
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| | Column | Type | Description |
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| |--------|------|-------------|
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| | `zone_y` | `int` | Grid row index (0–49) |
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| | `zone_x` | `int` | Grid column index (0–31) |
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| | `epv_value` | `float` | Expected possession value (0–1, higher = closer to goal) |
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| Grid resolution: **50×32** (105m ÷ 50 = 2.1m per row, 68m ÷ 32 = 2.125m per column).
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| ### Completion Matrix (`completion_matrix_global.parquet`)
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| | Column | Type | Description |
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| |--------|------|-------------|
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| | `origin_zone` | `int` | Flat index of the origin zone (0–399) |
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| | `target_zone` | `int` | Flat index of the target zone (0–399) |
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| | `probability` | `float` | Pass completion probability (row-normalized, 0–1) |
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| Zone grid: **25×16 = 400 zones** (105m ÷ 25 = 4.2m per row, 68m ÷ 16 = 4.25m per column).
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| ### Per-Competition Variants
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| Per-competition files (`*_all.parquet`) include an additional `competition_id` column. These allow competition-specific OBSO computation where sufficient data exists.
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|
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| ### Coordinate System
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| All grids map to the **SPADL 105×68 meters** pitch coordinate space. Grid resolution varies by artifact type (see tables above). The origin (0, 0) is at the bottom-left corner of the attacking team's half.
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| ## Data Sources
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| | Source | Matches | License |
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| |--------|---------|---------|
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| | [StatsBomb Open Data](https://github.com/statsbomb/open-data) | ~3,000 | CC-BY 4.0 |
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| | [Wyscout Public Dataset](https://figshare.com/collections/Soccer_match_event_dataset/4415000) | ~1,900 | CC-BY-NC 4.0 |
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| All event data is converted to SPADL format before grid estimation.
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| ## Companion Resources
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| | Resource | Description |
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| |----------|-------------|
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| | [OBSO/PAUSA Values](https://huggingface.co/datasets/luxury-lakehouse/obso-pausa-values) | Per-pass OBSO and PAUSA scores computed using these grids |
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| | [Expected Threat (xT) Grids](https://huggingface.co/datasets/luxury-lakehouse/expected-threat-grids) | Markov chain xT grids from the same source data |
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| | [SPADL/VAEP Action Values](https://huggingface.co/datasets/luxury-lakehouse/spadl-vaep-action-values) | Per-action VAEP scores from the same source events |
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| ## Limitations
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| - **Open data only**: Grids are trained on publicly available StatsBomb and Wyscout data. Commercial datasets with denser event coverage may yield different surfaces.
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| - **Competition-agnostic global grids**: The global variants pool all competitions. League-specific tactical patterns (e.g., high-press vs. low-block) are averaged away.
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| - **Static estimates**: Grids are computed from full-season aggregates. They do not adapt to in-game state (score, time, fatigue, personnel).
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| - **Resolution trade-offs**: Each grid uses a different resolution optimized for its purpose. Interpolation is required when combining grids of different resolutions.
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| - **No goalkeeper modeling**: Reachability and EPV grids do not distinguish goalkeeper positioning from outfield player patterns.
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|
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| ## Citation
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| If you use this dataset, please cite the underlying models:
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|
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| ```bibtex
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| @misc{singh2018expected,
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| title={Introducing Expected Threat (xT)},
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| author={Singh, Karun},
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| year={2018},
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| url={https://karun.in/blog/expected-threat.html}
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| }
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| ```
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| ```bibtex
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| @inproceedings{spearman2018beyond,
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| title={Beyond Expected Goals},
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| author={Spearman, William},
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| booktitle={MIT Sloan Sports Analytics Conference},
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| year={2018}
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| }
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| ```
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|
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| ```bibtex
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| @inproceedings{fernandez2018wide,
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| title={Wide Open Spaces: A statistical technique for measuring space creation in professional soccer},
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| author={Fernandez, Javier and Bornn, Luke},
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| booktitle={MIT Sloan Sports Analytics Conference},
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| year={2018}
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| }
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| ```
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|
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| ```bibtex
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| @inproceedings{lee2026pausa,
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| title={Valuing La Pausa: Quantifying the Timing and Quality of Soccer Passes Using Off-Ball Scoring Opportunities},
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| author={Lee, Minho and Jo, Hyunsung and Hong, Seungwon and Bauer, Pascal and Ko, Sangkuk},
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| booktitle={MIT Sloan Sports Analytics Conference},
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| year={2026}
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| }
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| ```
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|
|
| ## More Information
|
|
|
| > **Explore interactively:** [HF Space demo](https://huggingface.co/spaces/luxury-lakehouse/soccer-analytics-demo)
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|
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| - **License**: [MIT](https://opensource.org/licenses/MIT)
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| - **Publish script**: `scripts/compute_epv_transition_hf.py`
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|
