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A good point. The solution depends on what type of data is used to profile. This article and technique was born out of a collaborative project Robin completed at the Institute of Sport where I oversee the S&C department. Initial data collection was done on female field sport athletes (international level) and Robin used the fastest sprint so that each of the splits are actually connected in reality – the outcomes are the direct result of the cumulative sum of the splits. One solution to this is to use the range i.e. show the max and min values for each data point.
If data are averaged (from 3 trials lets say) then the SD of the Z score is appropriate.
We haven’t included any error bars in reporting charts at this point however – we think it might make the charts “too busy” and over complicate things. Ultimately the data has to be accessible to the coach so there is sometimes a tradeoff of true validity and data presentation. But the question is a good one. It is key that coaches understand the variability of the test or the technology and we should be mindful of this when trying to assess meaningful change.
A key point that must be explicitly stated is that the inferences are only as reliable as the raw data! One needs to be very careful not to use “bad” trials i.e. where there is an issue with the start or first step or if the athlete doesn’t run maximally for the entire sprint – this skews things significantly.
