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# TRIMPS to TSS

I’ve been interested in using TRIMPS as a way to estimate TSS on mtb and cx training rides. In season these account for over 50% of my training volume, which I right now guesstimate IF for to get a TSS estimation.

I attached a plot comparing TRIMP and TSS calculations for all the outdoor rides I have over the last 3 months with power and HR data. I’m using the below calculation over each data sample in the Powertap file:

TRIMP: = t * %HRR * 0.64 *e^(1.92(%HRR))

where:
%HRR = (MaxHR – HR)/(MaxHR – RestHR)

For the attached chart the trendline slope = 0.38, and r^2 = 0.91. So from this dataset I should be able to multiply a TRIMPS value from a HR monitor by 0.38 and get an approximate TSS value. Obviously I need to get some more data points from recent rides. It’s worth noting the relationship is much different if I use files from isopower indoor rides, or rides from a year ago when my FTP was 20 watts lower.

### Re: TRIMPS to TSS

Quote:
 Originally Posted by strader For the attached chart the trendline slope = 0.38, and r^2 = 0.91. So from this dataset I should be able to multiply a TRIMPS value from a HR monitor by 0.38 and get an approximate TSS value. Obviously I need to get some more data points from recent rides. It’s worth noting the relationship is much different if I use files from isopower indoor rides, or rides from a year ago when my FTP was 20 watts lower.
How do the TRIMPS based TSS estimations differ from your RPE based estimations?
When you say the relationship is different for isopower indoors rides and when your FTP was lower, in what way? Different slope? Different R^2?

### Re: TRIMPS to TSS

Quote:
 Originally Posted by Fightin Boba How do the TRIMPS based TSS estimations differ from your RPE based estimations?
I'm working on putting together a chart with "estimated" IF/TSS versus actual TSS to see how well they match up. For the estimated IF I'm going to round IF to the nearest 0.1 to approximate what a good guess would be. So if the actual ride IF is 0.73 it gets rounded to 0.7 and TSS gets calculated from that. If the guesstimated TSS correlates better than TRIMPS then I probably won't pursue this any further.

Quote:
 Originally Posted by Fightin Boba When you say the relationship is different for isopower indoors rides and when your FTP was lower, in what way? Different slope? Different R^2?
The indoor workouts have a TSS/TRIMPS slope of 0.32 and oddly enough a R^2 of 0.88. I would have thought that the correlation would be much tighter indoors. I think part of the problem on the trainer is I screw around with variables that manipulate HR, i.e. hold cadence over 100 rpm, slow cadence, round pedal stroke, etc. It's hard not to with all the variables from the Computrainer Coach Software staring me in the face.
I attached a pic of the chart for the indoor workouts.

### Re: TRIMPS to TSS

Quote:
 Originally Posted by strader the relationship is much different if I use files from isopower indoor rides, or rides from a year ago when my FTP was 20 watts lower.[/size][/font]
No surprises there, really. Training tends to increase the % of heart rate reserve at which LT occurs, and the heart rate-power relationship is different for steady-state vs. variable intensity exercise.

### As good as a guess?

I made a chart using the set of data in post 1 using a TSS calculated from a "estimated" intensity factor (IF) plotted against the actual TSS value. Since I can't really create a estimate for I ride I already know the IF for, what I did was round the IF to one digit using excel to simulate a good (+/- 0.05) estimate of IF. So a ride with a 0.84 IF gets rounded to 0.8, 0.68 gets rounded to 0.7, etc. I should note that every ride was rounded to a IF of either 0.7 or 0.8 (I don't do a lot of really hard or really easy rides).
This is pretty much the guideline I follow now to estimate TSS for mountain bike rides:
1.0 = short track, cyclocross race
0.9 = XC race 1-2 hr
0.8 = Fast trail ride
0.7 = 3+ hr endurance ride

I was really suprised how well the estimated TSS value tracks with actual. It looks like there is little reason to guess IF closer than 1 decimal place.

### Re: As good as a guess?

Quote:
 Originally Posted by strader I made a chart using the set of data in post 1 using a TSS calculated from a "estimated" intensity factor (IF) plotted against the actual TSS value. Since I can't really create a estimate for I ride I already know the IF for, what I did was round the IF to one digit using excel to simulate a good (+/- 0.05) estimate of IF. So a ride with a 0.84 IF gets rounded to 0.8, 0.68 gets rounded to 0.7, etc. I should note that every ride was rounded to a IF of either 0.7 or 0.8 (I don't do a lot of really hard or really easy rides). This is pretty much the guideline I follow now to estimate TSS for mountain bike rides: 1.0 = short track, cyclocross race 0.9 = XC race 1-2 hr 0.8 = Fast trail ride 0.7 = 3+ hr endurance ride I was really suprised how well the estimated TSS value tracks with actual. It looks like there is little reason to guess IF closer than 1 decimal place.
that would be good for me as I have the same dilemma. however, looking over past rides where I've used RPE and compared the ride to equal difficulty road rides (where I had definite values) I have estimated much lower TSS than you're IF values would have otherwise given me. is there anyone who has ACTUAL power data from MTB rides/races that could assert the more/less validity of these IF values?

### Re: As good as a guess?

Quote:
 Originally Posted by kaparzo that would be good for me as I have the same dilemma. however, looking over past rides where I've used RPE and compared the ride to equal difficulty road rides (where I had definite values) I have estimated much lower TSS than you're IF values would have otherwise given me. is there anyone who has ACTUAL power data from MTB rides/races that could assert the more/less validity of these IF values?
Dave Harris at wattage forum is your man.

### Re: TRIMPS to TSS

Quote:
 Originally Posted by strader ...I’m using the below calculation over each data sample in the Powertap file:TRIMP: = t * %HRR * 0.64 *e^(1.92(%HRR))where:%HRR = (MaxHR – HR)/(MaxHR – RestHR)
I'm just starting to read some of the TSS and TRIMP literature and I'm wondering where the 0.64 coefficient comes from. In Banister's work he presents this equation without the 0.64. His equation, based on my research, seems to originate from Green et al. (1983) relating blood lactate concentration and training intensity. See here for more details and references.

Anyway, I published a free web app that uses a TRIMP score with the training stress balance (TSB) algorithm presented by Coggan. The TRIMP score in the app is normalized to a (user-defined) functional threshold TRIMP score (e.g. 1 hr time trial or 10k race). I was mostly looking for a way to get a ballpark estimate based on very limited data (i.e. average or per-interval heart rate) for a workout:

TSB TRIMP Web App

Feel free to try it out. Obviously, it is no where near as sophisticated or accurate as using power. But it works well for other disciplines/cross-training, especially when all you have is a heart rate data and don't have time to fiddle with synching the data.

But, I would be interested to know where the 0.64 comes from, is that your empirical coefficient? In the system I'm playing with (in the web app) the linear coefficient drops out since the workout TRIMP score is normalized to a functional threshold TRIMP score.

### Re: TRIMPS to TSS

Quote:
 Originally Posted by MontanaJeff I published a free web app that uses a TRIMP score with the training stress balance (TSB) algorithm presented by Coggan.
Free or not, it would be nice if you gave credit where credit is due, and appropriately cite me as the originater of the CTL/ATL/TSB concept.

### Re: TRIMPS to TSS

Quote:
 Originally Posted by acoggan Free or not, it would be nice if you gave credit where credit is due, and appropriately cite me as the originater of the CTL/ATL/TSB concept.
Absolutely.....

I referenced you and Training Peaks software in my blog post. I'll also make sure it is visible in the free web app as well. Sorry for not making it clear and thanks for all the work you've done and making it freely available.

Do you have a particular journal reference you would prefer me to reference?

Also, do you know where the 0.64 coefficient comes from? And what do you think about normalizing the TRIMP score?
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