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Off-the-ball behaviors in elite football using spatiotemporal tracking data: a scoping review
Francesco Esposito1,2, Maurizio Bertollo1,3, Dario Pompa1
1Department of Medicine and Aging Sciences, University "G. d'Annunzio" Of Chieti-Pescara, Chieti, Italy.
Abstract:
Off-the-ball behaviors are central to tactical and technical performance in professional football, shaping individual and collective movements supporting coordinated play. Although spatiotemporal tracking data has made these dynamics observable, the literature remains fragmented, with inconsistent definitions and narrow analytical approaches. This scoping review summarizes how off-the-ball behaviors have been examined through tracking data and highlights computational methods and limitations.Following PRISMA-ScR, a systematic search was conducted in PubMed, Scopus, Web of Science and SportDiscus up to January 2026. Eligible studies involved elite players, tracking data, and analyzed off-the-ball behaviors while providing technical, tactical or kinematic insights. Peer-reviewed empirical studies in English were included. Search identified 4283 records, of which 32 met the criteria. Publications increased after 2020, covering European leagues such as Germany, Spain and the other major competitions. Across studies, 7705 matches were analyzed, with sample sizes ranging from single matches to over 4,000 matches. Tracking systems sampled at various frequencies, with 25 Hz being the most common, and all studies combined positional and event data. Off-the-ball behaviors were assessed at player, subgroup and team levels across phases of play, revealing methodological heterogeneity.Research on off-the-ball behaviors employs diverse spatiotemporal metrics and modeling techniques but remains dispersed across disconnected frameworks. Defensive phases are examined more frequently than attacking ones, and common limitations include context-specific samples, simplified outcomes and limited integration of opponents and situational constraints. Future work would benefit from shared conceptual frameworks, richer contextual modeling and methods that balance sophistication with interpretability for football analytics.
