Tagged: Guest Article
18/06/2017 at 02:20 #19882
The old adage in team sports is that “speed kills” but what if faulty interpretation of speed testing data is killing our ability to meaningfully assess and assist our athlete’s sprint performance?
The typical approach for speed assessment of team sport athletes is to test athletes over a 40 m distance with their “output” tracked at various 10 m intervals in between – giving the coach 10 m, 20 m, 30 m and/or 40 m sprint times. Often in the team sport environment we look at 10 m time as a global indicator of acceleration with our total 40 m times highlighting top speed characteristics. ‘’We typically use rankings to benchmark players against each other or against “normative” data we have accrued over time. Ranking lists are shared and promoted to praise the best accelerators or our top speed kings.
[See the full post at: Sprint Variability Profiling: New Insights From Speed Testing Data]18/06/2017 at 09:15 #19889
This is a fair point and is a potential “limitation” of the technique. Robin developed this technique and as he describes it, the group used to generate the Z scores should “make sense”. If you have a back row in a group with outside backs then it wouldn’t be very surprising if the profile highlights weaknesses in the 20-30 and 30-40 m splits. We have included in the text “A good rule of thumb” which would be to match athletes / players based on general speed qualities that are desirable for their position. Hopefully the article shows a step by step process to interpreting the plots mixing qualitative (where is there a deficit) and quantitative (what is the absolute difference? Is it meaningful?).
It can’t be stressed enough though – the normative group is key. This technique is not going to be meaningful if you have small sample size in your normative group or the members of your normative group have fundamentally different training goals and performance demands.18/06/2017 at 09:43 #19891
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.18/06/2017 at 11:18 #19893
This method can be combined with other popular sprint profiling methods e.g FV profiling. In fact, the distance time data from timing gates etc can be used to perform FV profiling also. Robin has done some specific validation of this technique:
An important difference is that FV profiling requires a “true” first split time (where timing is initiated by the first movement of the athlete) whereas this is not a requirement of variability profiling (as long as the test set up is consistent, it’s fine). FV profiling calculates the macroscopic external mechanical capabilities of an athlete’s neuromuscular system throughout the acceleration phase of sprinting. In summary the methods are highly compatible as they yield distinct information.
The sprint variability profiling using the timing gates can also, easily, be combined with other data. For example, in a collaborative project at the Irish Institute of Sport, Robin combined the timing gate data with stride length, stride frequency and contact time data from an optojump system (this article was born out of this collaborative project). This extra layer of data can be analysed in a very similar z-score manner helping to identify which athletes might be stride length dominant, which might be stride frequency dominant or which athletes might need to work on longer or shorter contact times.
More data can blur the coaches vision and make everything more difficult to interpret. But I think the z-score analysis presented in the article can help add clarity.23/06/2022 at 00:57 #35830
How did you manage to create the excel diagram for the splits and outcome? It’s very tricky to add in the 10-20m splits in between the 10m and 20m outcome. Did you but the splits on a secondary axis? But how it is that the splits find the corresponding y-axis naming e.g. 10-20m and letting one left out (20m split) before adding the point above the 20-30m split y axis?
Thank you for the response.
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