07/06/2016 at 22:46 #18076
Hi all I am a Sports Doc from the UK I currently work as the Doc for British Triathlon however I have worked at a national level in rugby union and league
Over the past few years I have had an increasing interest in monitoring players and the practical benefits from the medical perspective for example looking at injuries like muscle strains, tendon pain and stress fractures.
I like the idea of Tim Gabbetts work ie the differential between acute and chronic work load however it seems like its only one factor in a Gazpacho.
Many clubs I work with are very into the GPS, they have come up with a large number of theories about monitoring this metabolic load or that inertial movement analysis or the other high intensity running distance.
We know in the medical field that one of the risks of developing an overuse injury is change.
Change could be anything from training volume, to shoes, to style, to diet, to wt loss, wt gain,
Of course these changes can be measured and I think that’s why you sports scientists like these variables because they can be measured.
However we can measure many variables and then become either confused by them or hold too much faith in them because we thought we saw a pattern.
What is the current consensus on load monitoring in GPS terms
I am referring to Di Prampero (2005) and Osgnach (2009) metabolic load metrics is it worth monitoring if so how should it be analysed and when are you sports scientists concerned, how do you express this concern and to whom ? the Doc Coach Athlete ?
What does the Doc do ?
Coach do ?
Athlete do ?
thanks for helping an old Doc to understand10/06/2016 at 20:17 #18094
Thanks for the post. I am developing one injury prediction model using GPS metrics, RPE and Wellness scores – although predictions are perfect for “training dataset”, the predictions for the “hold out” data set are not great (AUC 0.7). But again it could be due small data set (one season is training data set, and another season is holdout).
Generally, we are juggling with models and assumptions. These needs to be validated with the real supervised model. The problem is that there are a LOT of factors involved and we might be dealing with ignorance problem.
I guess Gabbett model (actually Banister) of checking the ATL and CTL is a generally good/useful strategy, but we still don’t have a proof it works, even if Gabbett et al. published studies on it, they haven’t used holdout data set.
Hope this helps,
Mladen17/06/2016 at 22:45 #18133
Thanks Mladen would you like to test the theory with several clubs to increase the sample size? We have access to many
I tend to agree with you on prediction its hard, plus who will listen to the prediction?
Athletes want to play
Coaches want athletes to play
its difficult one eh01/08/2016 at 15:56 #18336
I am new to the forum. Took a while since Mladen didn’t want to make it any cheaper. 😉
Speaking of ATL:CTL – Ratio:
I set up a simple database with our position measurement metrics and a simple google docs form to track sRPE. That works very good so far. The only problem is that the model works only in the retrospective. That is the reason why I find it difficult to make good derivations for the upcoming week.
I guess it is a nice tool so far and I have to work longer with it to check if it works for us in practice.
Has anyone of you good experience with the approach over a certain period of time? 5-7 months?
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