This study examined reliability and validity of the Fulltrack AI application to identify cricket ball landing position (line, length). Nine hundred and thirty-two deliveries were compared to 3D motion capture, the criterion measure, with 836 included in analysis (516 bowled (pace = 420, spin = 96), 320 SidearmTM; 301 facing a batter). Agreement analysis indicated an intraclass correlation coefficient of >0.96 for raw and filter 3D line and length data, compared to Fulltrack AI. The coefficient of variation was acceptable for length (<10%) and larger for line (23.82%), albeit with a smaller standard error of measurement (SEM = 0.05 m), improving with outliers removed. Bland-Altman plots confirmed good statistical agreement between devices, with limits of agreement largely within maximal allowable difference values. There are potential practical application considerations, given SEM = 0.47 m for length (diameter of seven cricket balls); with greater variability detecting length closer to the batters-end, and line closer to the bowlers-end. Validity, using a generalised additive model, showed no significant differences between devices (p > 0.05), with no condition-based interaction effects. The Fulltrack AI application enables ecologically valid assessment of bowling performance. Considering the trade-off between this and the accuracy of information is warranted when deciding how best to apply it to coaching environments to support augmented feedback.
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