SynthesisFrontiers in physiology2026
Deployability of machine-learning prediction models in elite and high-level team sports: a task-specific systematic review.
Synthesis in Frontiers in physiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Abstract
Machine-learning models are increasingly used for match forecasting and athlete monitoring in elite and high-level team sports, but high reported performance does not necessarily indicate readiness for real-world use. This systematic review evaluated whether models were developed and tested under conditions compatible with their intended decision time. Eight databases were searched, with searches completed by 12 July 2026. Eighty reports representing 77 studies contributed 87 analysis units: 55 Core, 23 post-event or high-leakage Comparators, and nine Boundary units. All 87 units were assessed for deployable prediction validity (DPV) across six domains, and 78 Core or Comparator units were also assessed with PROBAST. Amongst Core units, 26/55 (47.3%) were rated at least Moderate, including five Moderate-to-strong and 21 Moderate units. Pre-adjudication agreement for the overall DPV classification was 85/87 (97.7%; κ = 0.968; linearly weighted κ = 0.978), whilst domain-level exact agreement ranged from 75/87 to 87/87. In Systematic review registration: https://www.crd.york.ac.uk/PROSPERO/view/CRD420261425179
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