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PitchQuant’s central engineering idea is to let Python scripts do repeatable calculations while an LLM follows a prescribed checklist to interpret results and check evidence. Its author says the project analyzed roughly 227,000 football matches, but the public repository omits the raw dataset, so readers cannot reproduce the large-sample findings from the release alone. The reported backtests are historical project claims—not independently verified evidence of future performance or betting profit.
What PitchQuant is—and what the “220,000 matches” title means
PitchQuant is a public project for analyzing football odds and historical match data. Its article, dated September 19, 2026, describes coverage of 227,000 matches across six months; the repository and methodology document also cite 227,495 matches for some analyses. Those are figures attached to particular project summaries and backtests, not one universal count for every result. The project’s article and the repository explain its scope and approach.
The project’s framing is “Football is chaotic, markets are efficient.” That is the author’s way of describing the challenge, not an independently established law. The practical question is narrower: how can a workflow examine odds, probabilities, and historical outcomes systematically while making its calculations and decision rules easier to inspect?
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PitchQuant’s design separates numerical computation from rule-based interpretation. Python scripts calculate values such as probabilities and score estimates; a language model then works through a constrained sequence of checks rather than being asked to invent a prediction from an odds line. The repository describes versioned rule and skill files, JSON lookup tables derived from historical analysis, consistency checks, a generated checklist, and an archive of analyses.
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| Approach | Who does the arithmetic? | What happens to the evidence? | What can be repeated? |
|---|---|---|---|
| LLM as an unconstrained oracle | The model may be asked to produce calculations and a conclusion in one response. | The path from input to conclusion may be hard to audit unless the prompts and evidence are independently structured. | Consistent numerical output is not assured just by asking for it. |
| PitchQuant’s described constrained-runtime pattern | Deterministic Python scripts perform specified calculations; the LLM follows the project’s checklist. | Versioned rules, lookups, checks, and archived analyses are intended to leave a more inspectable trail. | Given the same inputs, script versions, and configuration, deterministic computations are designed to be repeatable. Independent reproduction of the project’s large historical findings is a separate issue because the raw data is not released. |
This division is useful beyond football: language models can help apply a process, but consequential arithmetic is easier to inspect when it is handled by explicit code. A constrained workflow does not, by itself, prove that the inputs are correct, that every rule is sound, or that the conclusions generalize.
What the analysis calculates
The repository describes a sequence that starts with data acquisition and probability calculations, then considers odds movement and scenario rules, league or international submodels, direction and goal analysis, score estimates, cross-checks, and archiving. Its listed methods include de-vigging bookmaker odds, Poisson score modelling with a Dixon–Coles adjustment for low scores, odds-movement analysis, market calibration, score lookup tables, and Kelly-criterion calculations used as a relative ranking signal. These are components of the project’s analysis workflow, not proof that any particular match outcome can be predicted reliably. The implementation and pipeline description are in the repository.
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The README labels the public release v1.2 and core model V3.5.76, and describes 414 checks. The project article describes 238 checks. Because the counts come from different project materials and snapshots, they should not be treated as two measurements of one identical release. The repository is mutable, so these labels and counts may change.
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What data and competitions it covers
The project names Football-Data.co.uk, ClubElo, Understat, API-Football, odds-api.io, The Odds API, and Chinese Sports Lottery public odds among its data sources. It lists the Premier League, La Liga, Bundesliga, Serie A, Ligue 1, Champions League, and Europa League, plus a separate Nations League submodel; it says other leagues are not calibrated. The repository does not include API keys or the raw match dataset. A setup example calls for Python 3.10 or later, repository scripts, an odds input file, and API keys for some live or supplemental sources. Those details are described in the project repository.
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What the reported backtests do—and do not—show
PitchQuant reports directional accuracy of roughly 55–58% in selected project summaries and a top-two score hit rate of about 30% on strong-signal matches. These are different metrics on different tasks and samples: a directional result is not an exact-score result, and a strong-signal subset is not the full match set. The project article and repository report those figures as methodology validation, not future-performance guarantees.
The project’s methodology document says its rules are intended to pass a time-based 70/30 training/test split, a paired significance threshold of p<0.05, and sample-size and rollback gates. These are the project’s documented criteria. They do not establish that the implementation or underlying data has been independently audited. Nor can an accuracy figure alone show that a system beats a market: interpretation depends on the target, baseline, sample, and evaluation method.
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The key reproducibility limit is explicit: the methodology document says the raw dataset of roughly 227,000 matches is omitted because of its size and source terms, preventing direct reruns of the large-sample analyses from the public release alone. Scripts, rules, and reported results can help readers understand the intended method, but they are not a substitute for access to the inputs needed to verify those results.
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The project’s methodology document says an online-learning layer scored 34.5% in a 30,000-match time-split test, compared with a 48.7% favourite baseline—a 14.2 percentage-point shortfall. The project says it demoted that layer to logging only. This is a worthwhile engineering lesson: evaluating components against a stated baseline can reveal when a more elaborate feature is less useful than a simpler reference. The figures are the project’s report, not an independent replication; see its methodology document.
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What a reader can take from the project
- Separate calculation from interpretation. Explicit scripts make arithmetic easier to inspect and rerun than calculations embedded in a free-form model response.
- Keep metrics attached to their definitions. Directional accuracy, score-hit rate, calibration, and baseline comparisons answer different questions; results for a selected subset should not be generalized to all matches.
- Treat backtest gates as process, not proof. Time splits and significance thresholds are useful design choices, but they cannot validate data or implementation that readers cannot inspect.
- Make negative findings visible. Logging rather than relying on the online-learning layer after its reported result is more informative than presenting only favorable components.
Use and risk
The PitchQuant repository notice states: “FOR ACADEMIC & EDUCATIONAL USE ONLY. Use for gambling/betting is strictly PROHIBITED. NOT betting advice.” That is the project’s own notice, not an independent regulator’s finding. Its repository also says long-term sports-lottery expected value is negative. Historical analysis cannot remove uncertainty from individual matches or establish betting profitability; the project itself prohibits betting use. Read the notice in the repository.
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