Emerging portable technology provides highly predictive models of sprint performance in elite Australian track athletes: a foundation for measuring sprint performance in the field

Background The purpose of this study was to identify the key predictors of sprint performance in elite Australian track and field athletes using portable field-based technology. Methods Twenty-eight elite Australian track and field sprinters (16 male, 12 female, 69.21 ± 8.45 kg, 1.74 ± 6.88 metres) completed 2 x 60 metre sprints recorded via a MuscleLab LaserSpeed, SportScientia Techlayer and VueMotion kinogram. Results For 60 metre sprint time, the regression model was highly significant, F(5, 50) = 136.20, p < 0.001, explaining 93.2% of the variance (R² = 0.932), with maximal velocity (MaxV) as the strongest predictor (β = − 1.118, p < 0.001) and MaxStepFreq also significant (β = 0.210, p = 0.017). Prediction of MaxV across 60 metres was also strong (R² = 0.885, p < 0.001), with MaxStepFreq (β = 0.600) and min_accel_magnitude (β = 0.402) as key predictors. For the 20 metre sprint time, the model accounted for 74.7% of variance (R² = 0.747, p < 0.001), with MaxV_20m the only significant predictor (β = − 0.869, p < 0.001) and a subsequent model for MaxV_20m was also significant explaining 59.0% of the variance in MaxV_20m (R² = .590, Adjusted R² = .548) with the percent_maxV60 as the only statistically significant contributor (β = −0.237, p = .043). Conclusion These findings suggest that force-velocity metrics derived from portable instrumented insoles and infrared radar are critical for measuring sprint performance. This provides sport scientists and health clinicians a strong foundation for tracking performance changes in elite sprinting.