The Science of Greyhound Form

Research suggests that true edges in greyhound markets are found not just in reading the form guide, but in understanding the structural dynamics of data. From the critical "3-to-6 game" window to the manipulation of data feeds by sophisticated organizations, this dashboard deconstructs the key elements of professional analysis.

The Sweet Spot

3 - 6 Games

The window of maximum predictive reliability. Beyond this, noise increases significantly.

Class Volatility

Grade A vs. D

Quantifying how form translation differs massively between elite and lower grades.

Data Complexity

100+ Variables

A comprehensive catalog of physical, environmental, and statistical factors.

Why "Data is King"

In a market saturated with basic information, the real value lies in proprietary data manipulation. Organizations that control the source feed and apply sectional analysis to long-term databases create a barrier to entry that standard form analysis cannot breach.

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The Recency Window & Layoffs

Our research indicates a specific "decay curve" in form reliability. While history matters, the immediate physiological state of a greyhound is best represented by the last 3 to 6 races. This section visualizes why older form is deceptive and how long layoffs shatter predictive models.

Form Relevance Decay

Model: Recent_Wgt_v4

Insight: The correlation between past performance and future result peaks at 4 games back. By 10 games back, the data is mostly noise unless contextualized by grade changes.

Impact of Days Since Last Run

Metric: Win % Delta

Long layoffs (>28 days) introduce massive variance. While fresh dogs can sprint well, their sectional endurance typically suffers, creating profitable "Lay" opportunities for quants.

Sectional Analysis: The Hidden Story

The Split (Early)

Crucial in lower grades where trouble is common. A dog that leads consistently avoids the "traffic jam" variable.

Mid-Race Pace

Often ignored by the public. This is where fitness form from the last 3-6 games shows up most clearly.

Run Home (Closing)

Heavily impacted by layoffs. A dog returning from a break often fades here, regardless of class.

Grade Dynamics & Class Volatility

Not all form is created equal. A "win" in A-Class is a fundamentally different data point than a win in D-Class. This section explores how form translates when dogs move up or down the grading ladder, highlighting the "Class Trap."

Scenario Modeling

Select Current Grade Movement Direction

Select a scenario to see the analysis.

Win Probability Matrix: Grade Transitions

The A-Class Anomaly

In A-Class races, form is remarkably consistent. The dogs are elite athletes with stable routines. Statistical noise is lower here. However, margins are tighter. An A-Class dog dropping to B is often over-bet by the public, offering value in opposing them if they have 2+ losses in recent form.

The "Mix" Effect

When grades form a "mixed race" (e.g., A/B), the data suggests prioritizing the lower-grade dog with upward momentum (improving sectionals) over the higher-grade dog with downward momentum (recent losses). Sectional times are the only truth-teller here; finishing position is misleading.

The 100 Factor Model

You requested a breakdown of the sheer volume of data points involved. While the public looks at 5 or 6 factors, algorithmic models ingest over 100. This explorer highlights the depth of data required to truly "control the output."

All Factors

The Data Hierarchy

Why do organizations and quants win? Because they don't just consume data; they originate it. This diagram illustrates the flow of information from the track to the market, highlighting where the "Edge" is captured and protected.

RAW INPUT

The Source

Track conditions, weigh-in (actual), kennel behavior, weather variance.

▶ Data is chaotic & unstructured

➔ Latency: 0ms

THE QUANTS

Proprietary Ingestion

Private feeds, video analysis (sectional scraping), physiological modeling.

★ The Edge is created here

➔ Latency: +5 mins

PUBLIC MARKET

Bookmakers & Totes

Adjusted odds based on "The Source" money flow + public sentiment.

● The Edge is already gone

Manipulation & Feed Control

Quants with "good sources" often receive data points (e.g., a dog's weight change or minor injury) before the public feed updates. They manipulate the market by feeding liquidity early, shaping the odds to induce public error.

Protected Edges

Inside knowledge isn't just "fixing" races; it's knowing that Dog A changed feed brands 3 days ago, or the track surface was watered unevenly in Lane 1. This data never makes it to the standard form guide.