7:10 am - Tue, Sep 9, 2014
3 notes
Not every human activity needs to be justified, medicalized or financialized, some just need to be done for the sake of doing them.
12:45 pm - Mon, Sep 8, 2014
1 note

Fast climb fast analysis Vuelta Stage 16

According to the estimates, Contador and Froome were shockingly fast. The remainder of the contenders fell inline with what we’ve been seeing so far.

input data courtesy of @ammattipyoraily

10:06 pm - Sun, Sep 7, 2014

2014 Vuelta Stg 14 and 15 Performance Analysis

image

Adding stage 14 and 15 into the analysis the power numbers reinforce the impression of a cautious race. Typically, the deeper that riders go trying to push the limits of their ability the more likely we are to see some erratic performances. With 5  finishing climbs completed, the estimates have a distinct lack of of this erraticness. Instead, the power estimates are falling incredibly neatly on a power duration curve with a large amount of clustering any given climb. This is not entirely surprising considering the definitive climb of the race (Ancares) does not come until stage 20. Waiting for stage 20 however, will surely leave some wondering what if.

image

As for the pVAM, the longer climbs according to the model have not been particularly impressive. Again, the short climbs are in a faded color as the model validity may be fairly question at these very short durations.

8:51 am - Sat, Sep 6, 2014

Cyclists needed for cycling performance study

We are currently recruiting participants for a performance modelling study.

Requirements for participation are that you race and train with a power meter.

Please contact Dr Jonathan Baker; jrb07@aber.ac.uk for more information.

7:11 am - Fri, Sep 5, 2014

Friday Journal club ?

do the metrics not work ?

or

is that not the point of the metrics ?

10:06 pm - Thu, Sep 4, 2014
Q: It seems CP models attempt to derive the CP value by extrapolating from a series of MMP values. The 'slope' of these MMP values can effect the derived CP. It is also suggested that some cyclists have a low CP but high W' or visa-versa. Could it be that CP value is actually 'constant' but the duration for which efforts can be sustained at the CP level is the variable? IE We should have a "CP Time"?
Anonymous

Yes there is clearly a Critical Time at play when it comes to fitting the Critical Power model. This issue is one of the major reasons for using the Veloclinic Plot. 

some_text

From the power duration perspective, a more robust definition of Critical Power might be a threshold phenomena above which an identifiable W’ exists for a relatively broad range of power ie little penalty for intermittency within the Super Critical range.

For the explanation of the plot see: http://veloclinic.com/veloclinic-plot-w-cp-subtraction-plot/

For more on intermittency see: http://veloclinic.com/rethinking-intermittent-modelling/

9:56 pm

2014 Vuelta Brief Analysis through Stage 11

With 3 finishing climbs done and analyzed the power duration curve is starting to take shape. 

Overall, the performances have been more clustered than spread with the exception of Froome and Valverde falling off the pace on stage 9. Estimate wise, stage 11, the longest of the three was also the most pedestrian but road surface and tactics may have also been a larger than usual factor.

In terms of the pVAM model, only stage 11 is long enough to fall within the range that the model was intended for. I did leave the others stages in but I faded them out a bit as a reminder to not get to confident about the model result there.

2:07 am - Tue, Sep 2, 2014
7:41 pm - Mon, Sep 1, 2014

Identifying the underlying trends in performance analysis by Dynamic Factor Analysis

Continuing the theme investigating the MARSS package for useful tools above is a mock-up of how I anticipate part 2 of cycling performance studies will play out. 

(To sign up for the study if you haven’t remember to give Jon a shout http://veloclinic.com/cyclists-needed-performance-modelling-study/)

So as I alluded to in the last post (http://veloclinic.tumblr.com/post/96390104623/noise-probability-and-deterministic-skeletons-in) determining the utility of models and their parameters is a multi-factorial probabilistic issue were first principles and CVs may not necessarily be the ultimate best predictors of utility.

Instead, I’m keeping an open mind (aka shotgun or fishing experiment approach) to considering a wide array of candidate models for validation in Part 1 of the study. The issue then is how to go about declaring winners without making a priori declarations about the targets before hand.

One method is illustrated by the figures above. Here I used the phytoplankton data in MARSS as a mock up for some of the potential parameters that may come out of candidate models (humor me and just go along with this). 

As you can see in the first figure we have several years of data with what looks like a combination of seasonal variation and multi-year trends. One assumptions that can be made is that all of the observed parameters are slightly different views of a lesser number of underlying hidden states.

One of the way to estimate the number of hidden states is to use a Dynamic Factor Analysis. The second plot shows that for this data set that there are two underlying hidden states.

What we can also see is that the parameters load differently relative to these hidden states. Pmax and W1 load heavily on hidden state 1 while Tau1 and CP load heavily on hidden state 2. W2 loads heavily on both. 

From this factor analysis we have an indication that when it comes to performance (in this mock up) there are really just two hidden states that need to be monitored compressed down from the 5 potential parameters.

With the dynamic factor analysis done, there is a higher level of confidence that the hidden states represent orthogonal parameters that can then be used to monitor training response. 

3:50 pm
1 note

Uncanny resmemblances

http://www.ncbi.nlm.nih.gov/pubmed/19777251

as pointed out before the WKO4 model appears to be made up of the stringing together of 4 models that based on the distribution of their residuals function like a pmax model, a CP1-10 model, a CP3-30 model, and something that slopes down to cross the measured curve at the 45-60 minute range. the overlay of the CP models and the WkO4 model is previously illustrated in this post http://veloclinic.tumblr.com/post/72305824574/for-educational-purposes-only-post-contains-only

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