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Dove Golf·Deterministic fitting model·Brand-neutral outputs

A fitter-style engine that converts your answers into a testable shaft + build blueprint.

DoveFit™ Engine is a deterministic scoring model grounded in ball-flight constraints. It maps measurable tendencies (tempo, miss, strike, flight window, speed proxies) into a repeatable equipment profile: weight band, flex band, stability/torque bias, launch/spin direction, and setup guidance.

Same inputs → same outputPhysics-aware heuristicsTransparent structureNo pay-to-play
Output
Blueprint
Model
Deterministic
Goal
Reduce guesswork
Scientific posture

We treat fitting like an inference problem: measure signal → normalize → score → map into practical test bands. The goal is not “perfect numbers,” but repeatable guidance you can verify with real shots.

1) What we measure

Short questions. High signal. Built for real golfers.

We ask for variables that materially influence delivery and dispersion. Some are direct measurements (mph). Others are practical proxies (carry → speed) designed to capture signal without turning this into a 10-minute intake form.

Driver inputs
  • v driver speed (mph) or estimated from carry
  • m miss tendency (slice / hook / two-way dispersion)
  • t transition / tempo (smooth ↔ aggressive)
  • s strike pattern (center / heel / toe / inconsistent)
  • f typical flight window (low / mid / high)
  • w₀ current shaft weight class (optional anchor)
  • c physical constraints (comfort bias)
Iron inputs
  • vᵢ iron speed proxy (or carry proxy)
  • p peak height tendency (low / mid / high)
  • sᵢ strike tendency (thin / fat / center / inconsistent)
  • fat fatigue sensitivity (none ↔ high)
  • w₀ᵢ current iron shaft weight (optional anchor)
  • c physical constraints (comfort bias)
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This tool is honest about reality: some misses are equipment-sensitive, others are mostly mechanics. The engine outputs the best-fit profile given your answers — and flags when mechanics likely dominate.

2) The science we anchor to

Ball-flight constraints, impact stability, and repeatability.

Ball-flight constraints

We frame fit around outcomes you can validate: launch window, spin tendency, curvature, and carry stability — not “magic shafts.”

  • Launch vs spin is governed largely by delivered loft & spin loft
  • Curvature responds to face-to-path and strike/gear effect
  • Carry stability is driven by strike efficiency + repeatable delivery
Shaft as timing + stability

We treat the shaft as a component that influences timing, closure feel, and impact stability under load — especially under transition and fatigue.

  • weight is the biggest lever for tempo, sequencing, and strike quality
  • profile (stiffness distribution) impacts load/unload timing
  • torque influences closure feel and perceived stability
Head as stability + bias

Head geometry changes forgiveness and flight bias. We model it as a stability and launch/spin component that interacts with strike tendencies.

  • CG/MOI influences twist resistance on off-center hits
  • Loft/lie/face angle influences start line + curvature
  • Face tech influences ball speed retention across the face

3) How we convert answers into signals

We normalize subjective answers into variables a model can reason about.

Deterministic engines require structured inputs. Each answer is normalized into signals (tiers, bands, and directional biases) that combine consistently into a final blueprint.

Speed tier

Speed is the strongest baseline for weight and flex.

tier(v) ∈ {slow, mid, fast, very_fast}
If mph is unknown, we estimate via carry proxies.
Stability demand

Two-way dispersion and aggressive transitions increase stability demand.

stab = g(m, t, s)
Hook risk → anti-left bias. Slice/heel/toe variance → stability + forgiveness bias.
Launch direction

Flight window + goal tune launch/spin directionally (up/down, more/less spin).

launch = h(f, goal)
Low flight → add launch. High flight → control spin/launch.

4) How we score and produce the blueprint

Transparent structure — with internal calibration.

We compute intermediate scores and map them into labeled test bands that match real-world fitting: weight ranges, flex bands, stability/torque direction, and launch/spin direction. These outputs are designed to be verifiable in a bay or on-course.

Profile generation

Conceptually, we combine speed tier, stability demand, launch direction, and comfort bias into a profile that targets weight/flex/stability.

Conceptual form
weightScore = a·tier(v) + b·tempo + c·comfort + d·anchorWeight
flexScore = e·tier(v) + f·transition
stabScore = g·miss + h·tempo + i·strikeVariance
launchScore = j·flight + k·goal
map scores → labeled bands (example: 60–70g, Stiff, stable / low-torque)

We publish the structure so it’s understandable. Calibration coefficients remain internal to preserve consistency and resist “gaming.”

Fit score & confidence

Fit score is a conservative compatibility marker based on alignment and signal quality. It is not a promise of performance.

Calibration idea
score = base + 36·(alignment − 0.5) + 20·(signalQuality − 0.5)
− 10·volatility − 2.5·unknownCount
Clamped to a conservative range.
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If everything is always “perfect,” nothing is believable. We bias toward conservative, testable recommendations.

5) What you get

A blueprint you can test — not hype you have to believe.

The output is a fit blueprint that tells you what to target. No paid placements. No brand pushing. Just fit traits you can validate with your own swings.

Driver blueprint
  • Weight band and flex band
  • Stability and torque direction
  • Launch/spin direction
  • Setup starting point based on miss + flight
Iron blueprint
  • Weight band and flex band
  • Peak height / launch direction
  • Material bias (steel vs graphite)
  • Fatigue/comfort bias
Why this result
  • Shows what drove stability vs launch vs weight
  • Flags when mechanics likely dominate
  • Gives clear next steps for validation

Assumptions & limits

Honest boundaries improve trust and usability.

What this is great at
  • Weight/flex/stability direction for amateurs without full LM data
  • Conservative starting points for testing and fitter conversations
  • Spotting mismatches (too light, too soft, too unstable, too spinny)
Where you still need real measurement
  • Precise launch/spin optimization at the edge of your speed window
  • Lie/loft gapping decisions and detailed wedge matrix work
  • When strike is highly volatile (major face/low-point control issues)

Founder note

A Southern California family company built to fight marketing hype.

J
Joshua
Founder · Engineer · Amateur golfer
Free for public use
Mission

Give golfers a data-driven fitting blueprint that’s deterministic, transparent, and not tied to brand incentives.

Philosophy

The dove is intentionally minimal: calm signal over loud marketing. One mark, one idea — clarity.

Why I built Dove Golf

I’m a Southern California golfer who got tired of the same cycle: new “must-buy” shafts every year, influencer hype, and advice that’s hard to verify. Equipment matters — but the signal is often buried under marketing.

So I built a deterministic engine that turns a golfer’s tendencies into a blueprint you can actually test. It’s designed to be practical, conservative, and brand-neutral. If you want to validate it, you can — with your own swings.

No pay-to-play
No sponsored recommendations mixed into the core output.
Deterministic
Same answers produce the same blueprint every time.
Built to test
Outputs are bands you can validate in a bay or on-course.
Transparency policy

If Dove Golf ever adds partner catalogs or sponsored placements, they will be clearly labeled and separated from the core DoveFit™ Engine output. The engine’s structure remains consistent and interpretable.