New Open-Access Paper Published in Sensors Journal

We have just published an open-access paper in the Sensors journal regarding the effect of the flying start on the estimated short sprint parameters using timing gates. This is the third and final paper from my PhD project. What is novel about this paper is that we have compared the acceleration-velocity profile parameters (i.e., maximum-sprinting speed MSS and maximum-acceleration MAC) estimated using the timing gates, which served as the practical measure, against the laser gun, which served as the criterion measure. Multiple novel models were presented (i.e., estimated flying-distance model and time correction model), but unfortunately, we concluded that, given the athletes screened, timing gates, and protocols used, estimating maximum acceleration and its change using the timing gates is unreliable.


Short sprints are predominantly assessed using timing gates and analyzed through parameters of the mono-exponential equation, including estimated maximal sprinting speed (MSS) and relative acceleration (TAU), derived maximum acceleration (MAC), and relative propulsive maximal power (PMAX), further referred to as the No Correction model. However, the frequently recommended flying start technique introduces a bias during parameter estimation. To correct this, two additional models (Estimated TC and Estimated FD) were proposed. To estimate model precision and sensitivity to detect the change, 31 basketball players executed multiple 30 m sprints. Athlete performance was simultaneously measured by a laser gun and timing gates positioned at 5, 10, 20, and 30 m. Short sprint parameters were estimated using a laser gun, representing the criterion measure, and five different timing gate models, representing the practical measures. Only the MSS parameter demonstrated a high agreement between the laser gun and timing gate models, using the percent mean absolute difference (%MAD
) estimator (%MAD < 10%). The MSS parameter also showed the highest sensitivity, using the minimum detectable change estimator (%MDC95), with an estimated %MDC95 < 17%. Interestingly, sensitivity was the highest for the No Correction model (%MDC95 < 7%). All other parameters and models demonstrated an unsatisfying level of sensitivity. Thus, sports practitioners should be cautious when using timing gates to estimate maximum acceleration indices and changes in their respective levels.

Here is the link to the full paper:

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Published Paper on Bias in Sprint Modeling

I am happy to announce that my pre-print paper “Bias in estimated short sprint profiles using timing gates due to the flying start” has been published in Computer Methods in Biomechanics and Biomedical Engineering. This publication is my last requirement for publishing and defending my Ph.D. thesis. Hopefully, finishing everything during the first half of this year.

The Optimal Timing Gate Placement for Obtaining the Most Reliable and Sensitive Acceleration-Velocity Profile: A Simulation Study

I have explored the bias and sensitivity of the acceleration-velocity profiles (AVP) of the short sprints due to the flying start involved in using the timing gates in recently published papers and my PhD thesis. Besides using a common mono-exponential model, I have also created a few more models: time-correction, distance correction, time and distance correction, and flying distance correction models.

Effects of the Flying Start on Estimated Short Sprint Profiles Using Timing Gates

Over the last few years, I have been working on the short sprints profiling, thanks to the initial spark in 2020/21 by prof. Jason Vescovi who sent me a sample data. This resulted in the {shorts} R package hosted at the official CRAN repository, as well as few published papers (and few more in preparation), as well as the doctoral dissertation (thesis) that I have successfully defended on 21st of May, 2024 at the Faculty of Sport and Physical Education, University of Belgrade.


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