FedCal/sport-intelligence-benchmark

Sport Intelligence Benchmark

Ensemble V2 LightGBM meta-learner methodology and a synthetic reproduction dataset for football match outcome prediction, reported as two distinct, deliberately never-reconciled results (D-07). Reproducible: python run_benchmark.py --seed 42 against the shipped synthetic sample (data/derived_sample.csv, itself generated via python export_dataset.py --rows 4000 --seed 42 --output data/derived_sample.csv) prints a Brier score (3-class summed, range 0.0-2.0) that any third party can verify end to end. Production: Brier 0.5783 on the real 97,000-match production corpus — this figure is measured on non-redistributable data and cannot be verified by third parties; it is reported for transparency, not for independent reproduction. See DATA_PROVENANCE.md for the full reconciliation. Code: MIT. Contents of data/: CC-BY-4.0.

Metodologia del meta-learner ensemble V2 LightGBM e un dataset sintetico di riproduzione per la previsione degli esiti di partite di calcio. Il risultato di produzione (Brier 0.5783 su circa 97.000 partite reali) e il risultato sintetico riproducibile sono dichiarati come due numeri distinti, mai riconciliati.

This is a self-deposited research artifact — an author-submitted Zenodo record, not a publication reviewed by an independent third party before release.

Synthetic sample disclosure: the file(s) under data/ in this mirror are a fully synthetic reproduction sample (see DATA_PROVENANCE.md in the Files below) — not a copy, subset, or export of the production dataset. The headline production figure (Brier 0.5783 on ~97,000 real matches) is reported here for transparency but is measured on non-redistributable data and cannot be verified by third parties — only the synthetic in-sample number is independently reproducible from what this repository ships (python run_benchmark.py --seed 42).

Source & Attribution

• Canonical page (IT): https://federicocalo.dev/blog/sport-intelligence-benchmark-97k-partite
• Canonical page (EN): https://federicocalo.dev/en/blog/sport-intelligence-benchmark-97k-matches
• Author: Federico Calò — https://federicocalo.dev (Wikidata Q139562320, ORCID 0009-0004-4102-281X)
• Licence: This Hugging Face front-matter declares cc-by-4.0 — the licence of the data you download here (this repository's dual licence: code MIT, data CC-BY-4.0). The corresponding Zenodo deposit's structured license field instead reports mit-license, because Zenodo's structured field records the code licence; the data licence lives in prose there (LICENSE, README.md, and the data-directory LICENSE). This divergence between the two records is deliberate, not an inconsistency — on Hugging Face, what a user actually downloads is the data, so the front-matter reflects the data licence.
• Methodology: see DATA_PROVENANCE.md in the Files below

Files

• README.md — this file (the Hugging Face dataset card)
• CITATION.cff
• DATA_PROVENANCE.md
• LICENSE
• REPO_README.md
• data/LICENSE
• data/derived_sample.csv
• export_dataset.py
• requirements.txt
• run_benchmark.py
• tests/test_reproduce.py

Citation

Zenodo is the DOI of record for this artifact — cite the concept DOI below, not this Hugging Face page (Hugging Face mints one DOI per revision, with the previous one becoming outdated, so it has no stable identifier semantics):

Federico Calò, "Sport Intelligence Benchmark", Zenodo, 2026. https://doi.org/10.5281/zenodo.21602378

• Concept DOI (stable, cite this): https://doi.org/10.5281/zenodo.21602378
• Source code / machine-readable CITATION.cff: https://github.com/fedcal/sport-intelligence-benchmark