Visualization cluster

Elite football academies collect large volumes of multimodal longitudinal data across physical,

anthropometric, and cognitive domains, yet much of it remains underutilised due to a lack of

integrated analytical tools. Analysis of this data is challenging because it combines heterogen-

eous data sources that differ in structure, measurement frequency, and scale, while developmental

comparisons are further complicated by individual differences in biological maturation. To help

academy staff at Feyenoord Rotterdam gain insight into youth player development and support

talent identification decisions, an interactive visual analytics system was developed. The sys-

tem integrates GPS tracking, anthropometric measurements, performance testing, and cognitive

assessments into a unified longitudinal structure, and incorporates biological age alongside chrono-

logical age to enable fairer developmental comparisons. At its core is a configurable role fit model

that selects relevant performance parameters, assigns weights to reflect the demands of a specific

playing role, and aggregates the resulting scores into a single role fit score that represents how

well a player matches that role profile. Complementary visualizations allow users to move between

current-state evaluation and longitudinal trajectory analysis within a single visual analytics tool.

This supports both current-state comparison between players and the contextualisation of indi-

vidual trajectories relative to cohort references and previously observed development trajectories.

The utility of the system is demonstrated through two analytical use cases and a user study with

four domain experts; the study yielded consistently positive ratings across all analytical tasks.