RIDSS: A Role-Based Decision-Support Platform for Formula 1 Team Operations

Formula 1 has become an increasingly data-intensive motorsport, generating detailed telemetry, timing, tyre, and race information that supports both engineering analysis and strategic decision-making. The growing accessibility of historical Formu-la 1 data through tools such as FastF1 has enabled a body of research applying computational and statistical methods to race performance, telemetry behaviour, tyre degradation, and pit-stop strategy. Existing studies have demonstrated the value of this data for identifying strategic differences through telemetry and tyre analysis, for transforming raw telemetry into pit-stop deci-sion support using deep-learning architectures, and for model-ling tyre degradation as a dynamic process using state-space approaches. While these studies establish a strong technical foundation for using Formula 1 data in engineering and strategic contexts, each addresses an isolated analytical problem rather than the broader operational workflow of a motorsport team. Specifically, existing work does not address role-specific access to motorsport information, integration of telemetry and strategy analysis within a common workflow, or the operational man-agement functions — such as vehicle maintenance, team admin-istration, and reporting — that a complete motorsport platform requires. This review identifies these gaps and proposes RIDSS (Race Intelligence Decision Support System), a role-based deci-sion-support platform that integrates telemetry visualization, engineering analysis, race-strategy support, vehicle maintenance, team management, and access control within a single applica-tion. Rather than introducing a new telemetry-analysis algo-rithm, RIDSS's contribution lies in structuring existing motor-sport data-analysis concepts into a unified, role-based software architecture, built initially on deterministic analytical methods with the capacity to incorporate predictive models in future de-velopment.