Quantitative analytics case study

Quantitative Probability & EV Engine
1 model, every market

An in-house quantitative engine that builds a full probability distribution over a match, prices every derived market from that one coherent model, anchors to margin-free market consensus, and surfaces only positive expected-value selections, validated by calibration and closing-line value rather than short-term results.

Quantitative Analytics Quantitative ModelingProbabilityPython
Industry
Quantitative Analytics
Timeline
Ongoing in-house quantitative R&D build
Outcome
1 model, every market
Result snapshot

1 model, every market

One probability map prices the entire board consistently, and success is judged by process: calibration (do 60% picks win about 60% of the time?) and closing-line value (did we beat the market price?), rather than by individual results. Every prediction is stored so it can be audited and back-tested later.

Every market priced consistently from one internally coherent model
Value defined as true probability beating the implied price, not as confidence
A validation loop built on calibration and CLV, with every prediction auditable
/ The challenge

Where the bottleneck actually was

Pricing many correlated markets consistently from a single coherent view of an event is genuinely hard. Without one underlying probability model, decisions drift toward narrative and gut feeling instead of whether a price is actually mispriced, so a "good call" becomes just a confident guess.

Selections drift toward narrative instead of whether a price is genuinely mispriced.
Pricing 10+ correlated markets consistently from one model is hard to get right.
Research narratives tend to override the math unless they are strictly bounded.
/ What we built

A system built around the real workflow

We built a decision engine, not a tips channel. A single Poisson outcome grid, driven by expected goals and Elo-style strength ratings, is computed once, and every market is derived directly from it so the logic stays internally consistent. Market odds are stripped of margin to form a fair-probability anchor, and bounded research signals can nudge the numbers but can never override the math. For each selection it outputs probability, fair odds, expected value, confidence, a CORE or LOTTERY grade, and a fractional-Kelly stake. It is built for disciplined, auditable analysis and is explicitly not financial advice or a guarantee of outcomes.

Module 01
A single Poisson outcome grid from which every market is derived
Module 02
Expected goals plus Elo-style strength ratings to drive the grid
Module 03
Margin-free market consensus as the probability anchor
Module 04
Bounded research adjustments that prime but never override the model
Module 05
EV, confidence, risk grade, and fractional-Kelly staking with CORE / LOTTERY classification
Module 06
Calibration and closing-line-value tracking for honest, long-run validation
Build profile
Stack
Poisson modelingExpected goals (xG)PythonSupabase PostgresFractional KellyCalibration & CLV
Proof source
Naurra in-house build
Quantitative probability & EV engine
Related pages
Next step

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