Crew Intelligence
When you account for the boat, what remains is the crew.
When you account for hull efficiency, rig power, stability characteristics, and rating position — the design contributions that FleetEdge extracts across 31 dimensional features — what remains is the crew's contribution. Tactics, execution, preparation, and the decisions that separate identical boats on the racecourse.
This is the crew performance residual: the gap between what the boat's design profile predicts and what the crew actually achieved.
It is not a single number but a multi-dimensional signal — a crew that excels at upwind VMG extraction produces a different residual signature than one that excels at downwind positioning or tactical decision-making in shifty conditions.
The residual is meaningful only when the design contribution is measured accurately. That is why FleetEdge begins with the archetype framework — you cannot isolate what the crew adds until you understand what the boat gives.
Same metric. Different meaning.
Upwind Precision
Crew residual is what's left when SailEdge's physics model has explained everything it can about a boat's expected speed. When a boat sails better than its predicted potential, the residual is positive; when worse, negative. The residual is per-boat per-race, not a property of the archetype the boat belongs to.
Consider one example boat from the fleet — an AEROMAX-class design with a large, efficient rig. Across this boat's race history, the positive residual concentrates in upwind VMG: across many beats, this crew has consistently extracted more upwind speed than the rig's measured efficiency alone predicts. That observation is specific to this boat and this crew. It is not a claim about AEROMAX boats in general — other AEROMAX boats with different crews show residuals in different dimensions.
What the example illustrates is the method, not a prediction: PPI's three-pillar decomposition isolates a residual that, on a specific boat across enough races, becomes observable as a pattern. The structural archetype is what the boat is; the residual is what the crew does. They are different things.
Downwind Positioning
A second example tells a different story. An IRONWIND-class boat — big rig on a stiff, high-RM platform — shows its strongest positive residual not in upwind VMG but in downwind positioning. Across a season's worth of races, the gap between this boat's actual finishes and SailEdge's predictions is largest on the run legs.
The structural facts about IRONWIND — stable depower, low heel amplification, predictable load handling — are the same across every IRONWIND in the fleet. What this particular crew does with that platform on the run legs is theirs alone: the angle decisions, the gybe timing, the ability to hold pressure through manoeuvres. PPI isolates the residual; the crew earns it.
Two different boats, two different residual signatures, two different stories about what their crews do well. The archetype tells you what the boat is. The residual tells you what the crew has built on top of it.
Where we are today.
FleetEdge's crew intelligence framework exists at two levels. The dimensional decomposition and archetype assignment are production capabilities — every boat in the fleet receives a 31-feature profile and an archetype assignment based on its ORC certificate and SailEdge physics predictions. This is live for the entire fleet.
The crew performance residual — the isolation of crew contribution from design contribution — requires race results. Boats with results across any fleet or event receive crew residual measurement. As the results dataset grows, the residual estimates sharpen: more observations, more conditions, more confidence.
What FleetEdge delivers today: dimensional profiling and archetype assignment for the full fleet, and crew performance residual measurement for boats with race results — including ORC Worlds, Fastnet, Sydney Hobart, Middle Sea Race, and every other event FleetEdge measures. The analytical framework is complete; the signal improves with every race added to the dataset.
Noise becomes signal.
A single race is an anecdote — wind shifts, current, and luck contribute as much as skill. But when you accumulate observations across an entire season, the noise averages out and the persistent signal remains. A crew that consistently outperforms its boat's dimensional profile across varied conditions is demonstrating real, measurable skill.
The confidence in crew residual estimates increases with three factors: the number of races observed, the variety of conditions encountered, and the depth of the archetype cluster.
A boat with 20 race observations in its archetype cluster of 170 boats produces a more reliable residual than one with 3 observations in a cluster of 40. FleetEdge formalises this into three confidence tiers — low, medium, and high — based on observation count, condition variety, and venue diversity. The Performance Potential Index page details the full confidence framework.
See Crew Intelligence in the USA ORC fleet.
USA ORC racing brings crew intelligence into sharp relief — see how the physics meets the fleet's regime and rivalries.
Explore USA ORCMeasure what the crew contributes.
FleetEdge isolates the crew signal from the design — the payoff of the ORC promise.