Inverse Suitability: Identifying Condition Difficulty and Rider Skill from Behavioural Outcomes via Continuous-Item Response Theory

Suitability scoring for outdoor activities (kitesurfing, paragliding, ski touring) maps environmental conditions to a go/no-go verdict via expert-defined curves.

These curves conflate two distinct quantities: the intrinsic difficulty of a condition and the skill of the person facing it.

We introduce Inverse Suitability, a continuous-item Item Response Theory (IRT) model that identifies both from behavioural outcomes alone.

Each outcome is a triple (rider r, condition metric x at site s, binary outcome y); we model P(y=1) = sigma(a (theta_r - delta(x, s))), where theta_r is latent rider skill, delta(x, s) is a latent difficulty function anchored to a physics-derived expert curve as its prior, and a is a discrimination parameter.

The formulation is strictly more general than a single suitability curve, which it recovers exactly when skill is integrated out under the population distribution.

Parameters are estimated by marginal maximum likelihood with Gauss-Hermite quadrature; identification holds when the rider-by-condition incidence graph is connected, with a documented single-curve fallback otherwise.

We validate via synthetic recovery: on a reference cohort (80 riders times 30 outcomes) the model recovers latent skill at r = 0.96, locates the difficulty minimum within 3 units of ground truth, and improves held-out Brier Skill Score by +0.33 over the expert-curve baseline.

The recovered difficulty function defines a measurable, site-level construct, an intrinsic difficulty atlas, that existing meteorological observation networks do not capture.

All results reproduce from a single command on synthetic data, requiring no proprietary observations.