Methodology
FleetEdge combines certificate data, race results, and environmental context into a consistent performance-archetype framework.
FleetEdge's Multi-dimensional Performance Archetype Engine combines three independent data authorities into a single analytical framework. The preceding pages — Hull Efficiency, Rating & Classification, The Physics — each describe one layer of input. This page shows what happens when they converge.
Ocean and atmosphere, sampled at the race.
ERA5 Atmosphere ERA5 is the ECMWF global atmospheric reanalysis — wind, wave, pressure, and boundary-layer state on a common hourly grid, retrospectively computed against the full observation network. FleetEdge samples ERA5 at race-leg centroids and venue bounding boxes, timed to each race's actual window, and associates the atmospheric state per boat via race participation. Atmospheric enrichment is corpus-wide: every scored race carries ERA5-derived dimensions, and weather-dependent features fold into the archetype engine uniformly.
HYCOM Ocean HYCOM GLBy 93.0 is the Hybrid Coordinate Ocean Model global reanalysis at 1/12° horizontal resolution — surface currents, temperature, and sea-surface height retrospectively computed against the global ocean-observation network. FleetEdge samples HYCOM at the same race-leg centroids and venue bounding boxes as ERA5, extracting surface-current magnitude and direction timed to each boat's actual race geometry rather than to a generic venue average.
Coverage posture. ERA5 atmospheric enrichment covers the scored race set. HYCOM ocean-current enrichment focuses on the five IRC offshore classics where tidal flow is a first-order variable rather than a rounding error — Cowes, Fastnet, Rolex Middle Sea, Sydney–Hobart, and RORC Caribbean 600. Both layers fold into the unified environmental input to the archetype framework, and current-derived context is labeled where it is used. See Ocean Intelligence for the race-by-race current treatment.
The signals that describe performance.
Each boat in the fleet is described by a set of features drawn from the three data authorities — the kinds of parameters naval architects, rating engineers, and physicists use to characterise a sailing yacht's performance envelope. FleetEdge groups them into five families.
| Family | What it captures | Source Authority |
|---|---|---|
| Hull efficiency | Length, displacement, wetted-surface, and form indicators from rating certificates. | ORC Certificate |
| Stability | Righting-moment and angle-of-vanishing-stability indicators from rating certificates. | ORC Certificate |
| Rig performance | Sail-area and rig-proportion indicators. | ORC Certificate |
| Rating position | Corrected-time and rating-band context. | ORC Scoring |
| Physics delta | FleetEdge/SailEdge comparative performance indicators. | SailEdge vs ORC baseline |
Finding the axes that matter.
The raw signals overlap. Hull length tracks displacement; rig height tracks sail area. FleetEdge reduces a broad set of yacht, rating, race, and environmental signals into a smaller number of stable performance factors — the axes along which boats genuinely differ from one another, stripped of correlation and noise.
A few stable factors capture most of how boats differ. The most important axis is the hull-size-to-rig-power ratio; another captures stability strategy — how the boat generates righting moment relative to its displacement.
Further factors resolve progressively finer distinctions in appendage configuration, rating position, and the physics-delta patterns that separate boats within the same broad category.
Many signals → a few stable performance factors
Eleven natural groupings.
The performance factors define a space in which every boat occupies a specific position. Boats that share structural strategies group together in this space. The question is where to draw the boundaries.
FleetEdge uses multiple independent clustering checks and accepts only stable groupings — those that persist regardless of the method. This eliminates method-dependent artifacts and produces stable, reproducible archetype assignments.
Performance factors → multiple independent clustering checks → consensus → 11 archetypes
Five structural families, eleven archetypes.
Aero-Driven
AEROMAX and AEROBLADE — boats whose identity is set by rig power, with sail-area efficiency and upwind VMG characteristics that dominate the structural signature.
Balance-Sensitive
KEELFLEX and STEELCORE — designs that operate inside a narrow stability envelope where small changes in heel angle and trim produce large changes in speed.
Downwind-Optimized
GLIDEFORM and GRAVITYRUN — hull forms that trade upwind rig power for low-drag profiles and reaching-and-running leverage, with strong downwind VMG relative to upwind.
Platform-Rigid
IRONWIND, STEELFORM, and DEEPFRAME — stiff, high-righting-moment platforms with predictable behavior under load and conservative rig-to-displacement ratios.
Mixed-Mode-Power
STORMLINE and HEADFORCE — designs that combine high rig power with sensitivity traits (heel response, drag penalties, condition dependence) and the widest spread between best and worst corrected-time finishes in the fleet.
Signal strengthens with data.
Archetype assignments are not static labels. As new race data is processed, confidence accumulates. A boat with few observations has a provisional assignment — the structural position is identified, but the confidence bounds are wide.
Confidence increases as a boat accumulates observations across varied conditions — a boat with many observations has a high-confidence profile, with a stable archetype assignment and precisely resolved coordinates.
The methodology itself is validated against the fleet. The variance the factors explain is not a target — it is a measurement of how well the structure fits the fleet.
The eleven archetypes are not imposed — they emerge from the data through consensus clustering. As the fleet grows and new events are processed, these numbers update. The methodology is transparent and reproducible.
Three floors. Every page obeys the same rule.
Structural readouts — archetype distribution, designer or class signatures, dimensional profiles — publish for any fleet that exists. Describing what a fleet IS requires only that it exists.
Comparative or result-ranking narratives — who finished where, which archetype performed better on this course, who closed the gap — require a sufficient number of boats. Comparison across too few boats is anecdote, not analysis.
Crew-attribution or performance-residual narratives — decomposing a result into what the boat gave and what the crew added — require a substantial number of boats within a single event. Below that, noise dominates the residual.
Cohorts below the applicable floor carry a Low-sample badge and limit themselves to the structural tier; comparative narrative is suppressed. The policy itself is the public invariant every page is measured against today.
Five public evidence badges.
Every analytical card on FleetEdge carries a badge telling you what kind of claim it is. The lexicon is closed: five values, stable, auditable.
- Latest published view
- Live fleet-wide measurement. Results are stable enough for comparative use.
- Validation
- Methodology is under on-site validation. Results are directional while the validation run completes.
- Structural-only
- Dimensional or archetype analysis without race-result attribution. Describes what the fleet IS, not what it did.
- Low-sample
- Cohort is below the narrative floor for comparative analysis. The card ships a structural readout only; no comparative takeaways.
- Pre-race scenario
- Forward-looking analysis using forecast or historical-prior inputs. Not a measurement; a projection under stated conditions.
See Methodology in the Sydney Hobart fleet.
Sydney Hobart racing brings methodology into sharp relief — see how the physics meets the fleet's regime and rivalries.
Explore Sydney HobartRigorous methodology. Transparent pipeline. Every boat measured the same way.
A consistent performance-archetype framework, applied to every boat in the ORC fleet.