Non significant results from one way repeated measures ANOVAs - what now?

I am currently writing my thesis for an MSc in Sport Performance. My data collection involved running my participants through 5 different warm-up protocols (including baseline) and testing them in 3 different ways - stride to sprint times, jog to sprint times and agility times.
I ran three one-way ANOVAs, one for each of the tests. All obeyed Mauchly's test of sphericity, and all returned non significant results, indicating that the null hypotheses cannot be rejected.
My university's notes are unclear as to how to proceed. Do I just say "Well, results were not significantly different for each test after exposure to each warm up protocol, here's why I think that may be" or do I look for more subtle trends in the data? Is there any post-hoc work to be done at all?


P.S. was using 3 separate one-way ANOVAs even appropriate?


Less is more. Stay pure. Stay poor.
Well when looking at your measures is there quite a bit of dispersion around the parameters? Could your study just have been under-powered?
Thanks for responding.
To be honest, I've never used statistics before, so I'm not sure what dispersion around parameters actually means. In terms of power, I had 17 participants which my supervisor told me should be plenty.


Less is more. Stay pure. Stay poor.
Dispersion is just the variance/standard deviation/ for conversaion's sake we will say confidence interval, so if you plot your data using box plots or histograms stack on top of each other, are the means obviously different but the variability or overlap? So does it seem like the groups actually differ, but since you only have a few people they are not that precise or their is some overlap? So you have a total of 17 participants in how many groups and how many time points?