I exported my metrics data (TRIMPS, etc.) and uploaded it to SAS. I cleaned the data w.r.tt files in which my HR monitor was off, rides with no powerdata (so no TSS to estimate), etc. I also had a few rides with TSS above 300. Since I do not do rides in winter times with TSS above 300 I excluded those data, because if I don’t do those rides it’s no use of trying to predict them. The calculated TSS_Friel were later merged to the GC aggregate file. Below some statistics w.r.t the rides.
Average HR is least correlated with TSS (as expected of course). TRIMPS and the TSS_Friel derived metric are most correlated with TSS. In itself a correlation does not say much, only that there seems to be a relation between the two. To get a better visual sight on the relation of the metrics with TSS I ran some scatterplots depicted in the figure below (I also included a regression spline for visual sake). I have looked at all the measures, but only two the two best related TRIMPS and TSS_Friel.
You see that the relation between TRIMPS / TSS_Friel is not linear with the actual TSS score so a linear equation is not a good idea. The R2 of TRIMPS and TSS_Friel under the above models are 0.8 and 0.83. You also see some outlier points going towards the top right corner. In the figure below you see the scatterplots of the error terms.