LAUSR

Augmented feedback, including that provided using technology, can elicit multifaceted benefits on perceptual-motor learning and performance of sporting skills. However, current considerations of the applied value in supporting learning and teaching cricket skill is limited. This systematic review with meta-analysis aimed to understand the role and effectiveness of feedback-involved interventions on skill-based performance outcomes in cricket-related research. Six electronic databases were searched (SPORTDiscus, CINAHL, MEDLINE, Scopus, Web of Science and PsycINFO). Of 8,262 records identified, 11 studies met inclusion criteria; five of which were included in meta-analyses. Given no studies with an isolated feedback intervention-arm were identified, the two meta-analyses explored anticipation-based studies consisting of an intervention that included augmented feedback; positioned with respect to the key motor skill concepts of perception (anticipation accuracy) and action (performance success). Despite results highlighting improved performance outcomes for the feedback-involved intervention groups, with a large effect size for improved anticipation accuracy (Hedge’s g = 1.21, 95% CIs [-0.37, 2.78]) and a medium effect size for overall performance success (Hedge’s g = 0.55, 95% CIs [-0.39, 1.50]), results were not statistically significant and should be interpreted with caution given the wide confidence intervals. Considering the small number of studies available, in addition to the lack of isolated feedback protocols, further research is warranted to thoroughly explore the impact of augmented feedback on skill-based performance in cricket. Beyond the meta-analyses, the review also explored all included studies from an ecological dynamics perspective; presenting future avenues of research framed around evaluating the applied value of using augmented feedback (mediated with or without technology) for learning and teaching skill in cricket. Trial registration The protocol was preregistered with Open Science Framework (osf.io/384pd).