They got the same results when they isolated the data for time trials. They didn't only look at road races.

They got the same results when they isolated the data for time trials. They didn't only look at road races.

I provided data to that study (though I don't know if I was included*). At no point in the time period had I done any formal testing. My power figures in races did increase as my CTL rose but still were well off fresh maximums (450 vs 500 for 5mp I think). 20MP not all that high though normalized figures were (usually ¬330-350AP, 400-420NP) as our racing is short and hilly.

I think the aim of the study was to post-collect a lot of data of normal training and racing (rather than a structured collection process) to determine whether there's a clear correlation.

*given how much data I was able to provide and that my sessions had comments for context I suspect I would have been

Yes. You posted the abstract of a study. What does this do to contribute to this thread? If you want to debate the performance manager chart, start a thread. Otherwise, provide some thoughts on the topic of internet vs academic papers.

He's formally known as "Trev the Rev" and a troll who thinks his view of rpe and heart rate is better than all the peer reviewed science so good luck asking him to keep on topic

Jctriguy wrote:

As a side note, have you noticed they call it a 'performance manager' not a race time predictor, or performance predictor. Might want to ponder that before you remove all doubt.

something better doesn't really exist yet, and so the PMC is all folks have.

have you noticed they call it a 'performance manager' not a race time predictor, or performance predictor.

I suspect that they may have overlooked some important relationships by focussing so much on correlations.

There are a lot of scientists, particularly in the UK (that I know of), doing sports science work, who are active on Twitter

Andrew Coggan wrote:

Dtyrrell wrote:

There are a lot of scientists, particularly in the UK (that I know of), doing sports science work, who are active on Twitter

Speaking of which, I found this entertaining...maybe you will as well:

http://genomebiology.com/2014/15/7/424

(BTW, my K index is <1...maybe I should start tweeting more?)

Andrew Coggan wrote:

(BTW, my K index is <1...maybe I should start tweeting more?)

Some follow ..

Well here is a paper which some might find entertaining.

If TrainingPeaks and Coggan's measures of fitness, freshness and fatigue can't predict performance what is the point of them?

""
Measures of training stress in cyclists do not usefully predict maximum mean power in competitions

H.A. Ferguson1, C.D. Paton2, W.G. Hopkins1

1Auckland Institute of Technology, Auckland, New Zealand; 2Eastern Institute of Technology, Napier, New Zealand.

Background
Many competitive cyclists use mobile ergometers to monitor power output during training and competition rides. A training-impulse model is then often used to combine a training-stress score from each ride into measures of "fitness" and "fatigue", the difference in which is interpreted as a measure of "freshness" that might predict competitive performance.

Purpose
To determine the effect of fitness, fatigue and freshness the day before cycling competitions on physiological indicators of performance in the competitions.

Methods
Twenty male and four female competitive cyclists (29 ± 9 y, 71 ± 7 kg, mean ± SD) provided recordings of their SRM or Quarq mobile ergometers for training rides, 55 time trials (16-40 km), and 447 mass-start road races over a 6- to 8-month period. TrainingPeaks software (version WKO+ 3) was used to synthesize fitness, fatigue and freshness scores on the day before each competition and to extract maximal mean power (MMP) for four durations (5 s, 60 s, 5 min, 20 min) from the competition ride. The within-cyclist relationship between each measure of training and each measure of performance was investigated by producing scatterplots of the deviations from each rider's mean training and mean performance measure for time trials and road races in single-day and multi-day competitions. Mixed modeling was used to quantify the relationship as the linear effect of a change of two within-cyclist standard deviations of the measure of training, assuming a smallest important change in performance of 1%.

Results
Individual typical variation in maximum mean power from competition to competition ranged from ±7.1% (5-min MMP) to ±14% (5-s MMP). Scatterplots were generally consistent with a random relationship between the indicators of performance in competitions and the measures of training the previous day, and all effects of training measures on performance measures were unclear.

Discussion
The uncertainty in the relationships between the measures of training and the measures of performance is due to the extremely poor reliability of the measures of maximum mean power. Contextual information about each competition ride might improve the reliability by helping to filter out or otherwise account for poorer performances. Alternatively other measures of performance from competitions are needed to determine whether fitness, fatigue and freshness usefully predict competition performance.

Conclusions
Maximum mean power in competitions is too unreliable to determine whether the measures of fitness, fatigue and freshness provided by mobile ergometers and TrainingPeaks software reflect readiness for competitions. ""

My view is that using power, heart rate & RPE is superior to using Power and RPE.