The Powermeter Thread

[CoachFergie]

THE RELIABILITY OF POWER OUTPUT AND PERFORMANCE TIME DURING SIMULATED DYNAMIC GRADIENT CYCLING TIME TRIALS
Authors: CLARK, B.1, PATON, C.D.2, O'BRIEN, B.J.1 - [Contact]
Institution: 1 UNIVERSITY OF BALLARAT (BALLARAT, AUSTRALIA) 2 EIT (HAWKES BAY, NEW ZEALAND)
Department: SCHOOL OF HEALTH SCIENCES
Country: AUSTRALIA
Abstract text
THE RELIABILITY OF POWER OUTPUT AND PERFORMANCE TIME DURING SIMULATED DYNAMIC GRADIENT CYCLING TIME TRIALS Introduction Laboratory assessment of physiology and performance forms an integral part of athlete preparation for competition. Establishing the physiological capacity and performance standard of cyclists, allows sports scientists and coaches to formulate and implement structured training programs and subsequently assess the effectiveness of those programs. Cycling performance assessment is generally characterised by simulated time trials of various duration generally completed under controlled conditions in a laboratory. The purpose of this investigation was to establish the reliability of simulated cycling time trials completed on a course of varying gradient. Methods Twenty competitive cyclists (Age: 32 ± 12 years, weight 73 ± 11 kg, height 178 ± 5 cm) completed four simulated cycling time trials over a 20km course with numerous and un-regimented changes in gradient (both ascents and descents). The time trials were completed over a 5 week period to establish short and long term reliability. Results Performance time was highly reliable across all trials (TT1-TT2 CV= 1± 0.5%, TT2-TT3 CV= 1.4 ± 0.7%, TT3-TT4 CV= 1.5 ± 0.8%).Similarly, average power output highly reliable across all trials although somewhat less so than performance time (TT1-TT2 CV= 1.9 ± 1.0%, TT2-TT3 CV= 2.5 ± 1.3%, TT3-TT4 CV= 2.2 ± 1.2%). Discussion The major finding of the present study is a new laboratory based simulated cycling time trial performed on a course of varying gradient is a highly reliable test of the performance standard of competitive cyclists. In the present study, a course was designed to closely mimic the natural changes in gradient faced by cyclists in competitive situations thus improving the ecological validity of laboratory performance assessment. The average CV for performance time (1.3%) from the new protocol investigated by this study was found to be similar to, if not lower than the average CV reported for the same measure from previous investigations on constant gradient protocols (Sporer, 2007; Zavorsky, 2007; Smith, 2001; Nooreen, 2010). Correspondingly the average CV for average power output (2.2%) was also found to be similar to or better than the average CV reported from studies on constant gradient (Sporer, 2007; Zavorsky, 2007; Smith, 2001; Nooreen, 2010) and constant gradient dynamic protocols (Abiss, 2008). References Sporer, B. C., & McKenzie, D. C. (2007). Int J Sports Med, 28(11), 940-944. Zavorsky, G. S., Murias, J. M., Gow, J., Kim, D. J., Poulin-Harnois, C., Kubow, S., & Lands, L. C. (2007). Int J Sports Med, 28(9), 743-748. Smith, M. F., Davison, R. C. R., Balmer, J., & Bird, S. R. (2001). Int J Sports Med, 22(04), 270,274. Noreen, E., Yamamoto, K., & Clair, K. (2010). European Journal Of Applied Physiology, 110(3), 499-506. Abbiss, C. R., Levin, G., McGuigan, M. R., & Laursen, P. B. (2008). Int J Sports Med, 29(7), 574-578.

Topic: TRAINING AND TESTING
Keyword I: PERFORMANCE ASSESSMENT
Keyword II: SIMULATED TIME TRIAL
Keyword III: CYCLING

 

[Alex Simmons/RST]

Pithy Power Proverb: The best predictor of performance is performance itself. - A. Coggan

 

[CoachFergie]

Pithy Power Proverb: The best predictor of performance is performance itself. - A. Coggan

Would appear the Spanish are allowed to reference RATWAPM, much be a French thing that they can't.

A NEW WAY TO QUANTIFY TRAINING LOAD IN SPORT: THE “Z-INDEX”
Authors: ZABALA, M., MORENTE-SÁNCHEZ, J. - [Contact]
Institution: FACULTY OF SPORT SCIENCES - UNIVERSITY OF GRANADA
Department: PHYSICAL EDUCATION AND SPORTS
Country: SPAIN
Abstract text
A NEW WAY TO QUANTIFY TRAINING LOAD IN SPORT: THE “Z-INDEX” Zabala, M.1,2 & Morente-Sánchez, J.1 1 Faculty of Sport Sciences, University of Granada (Spain) 2 Spanish Cycling Federation, Madrid (Spain) Introduction Training load is an important issue related to adequately apply the optimum stimulus to get the best work-recovery relationship to improve performance. In some middle-long distance sports like cycling or running, some indexes in relation to the time spent working are used as Heart Rate (TRIMP) [1], RPE (Foster) [2], relative Power output in cycling (TSS -Training Stress Score-) [3], or Time/km in running (multiplying them by time in minutes or seconds). The aim of this study was to integrate those indexes that describe the same event from different but complementary perspectives (physiological, physical, and perceptual perspectives) to create a more complete index of training load. Methods Twelve under-23 elite road cyclists, and 14 recreational triathletes, (mean age: 19.67±1.12 years, and 27.67±3.12 years, respectively) participated in the study. Training was monitored during a total of 20±2 training sessions measuring TRIMP, Foster, and TSS or time/km x min. Then a new score was got weighting each value in a scale from 0-10 (Log10) and then getting the average of the indexes multiplied by 10 to get a final score in %: for cyclists, the so-called “Z-Index”=[(log10 of TRIMP + log10 of Foster + log10 of TSS)/3*10], and for runners, “Z-Index”=[(log10 of TRIMP + log10 of Foster + log10 of (Time/km x min))/3*10]. Descriptive and correlation statistics was carried out. Results Values in % of daily, weekly or monthly training loads were calculated in relation to the training plan and sessions. The relation of the subjective plan and the later training load showed that sometimes the proposed training load was less or more than the one measured after the workouts (10 to 20% difference, correlation of r=0,80**). When relating the different conventional indexes to the Z-index, significant and high correlations were found in cycling (r=0.75** for TRIMP, r=0.79** for Foster, r=0.81** for TSS), and running (r=0.80** for TRIMP, r=0.79** for Foster, r=0.80** for km/time x min). All the athletes stated that the new index was “easy, useful, and practical”. Discussion Z-index is a very easy to understand value calculated taking into account the most feasible indexes that can be got from training using specific variables for each sport -cycling or running-and that could be used in Swimming adapting the formula from running but using the specific units (e.g. time in seconds / 25-50-100m). This index can be someway elitist, but there are many athletes nowadays that can afford GPS devices, powermeters, or just a chronometer. More research is needed to validate and develop the original formula. References 1. Manzi V. et al. Am J Physiol Heart Circ Physiol 2009: 296, H1733–40. 2. Foster C. Med Sci Sport Exerc 1998 Jul 30(7): 1164-8. 3. Allen H & Coggan A. 2006 Velopress.

 

[FrankDay]

An abstract from a study presented at European College of Sport Sciences in Barcelona...

RESULT OF AN INDIVIDUALIZED CYCLING TRAINING PROGRAM THROUGH POWER METERS
Authors: FERNÁNDEZ-MONTILLA, J.A., LÓPEZ-GRUESO, R., SARABIA, J.M., ARACIL, A., GUILLÉN, S., PASTOR, D., MOYA, M. - [Contact]
Institution: GIAFIS, SPORTS RESEARCH CENTRE, UNIVERSIDAD MIGUEL HERNÁNDEZ DE ELCHE (SPAIN)
Department: PSICOLOGIA DE SALUD
Country: SPAIN
Abstract text
Introduction The theoretical framework for quantifying the training proposed by Dr. Coggan (Allen and Coggan, 2010) parts of the biparametric models theory applied to cycling and bike segment in triathlon. The aim of the study was to evaluate the influence that an individualized training program at different percentage of functional threshold power (FTP) Methods Fourteen triathletes (aged 38.12 ± 6.37 years) were divided into two groups (Control -C- and Experimental, -E-) and developed six weeks of planned workouts, with a pre and post-test in both cycle ergometer (Monark ergomedic 839) and field tests (Powertap, CycleOps, Madison, USA). The program focuses on the critical pedaling power with normalized power (NP) and decreasing by 30% the intensity factor (IF), developing a working method based on the and training stress score (TSS) on the basis of an individualized FTP for each subject (E) versus usual training group (C). The variables evaluated were: maximal oxygen uptake (VO2max), time to exhaustion (tExh), power generated at the second ventilatory threshold (wVT2), lactate threshold (LT) on a cycle ergometer test, and volume in kilometers (Vkm), absolute and relative power generated in the FTP field test (wFTP and w/kgFTP). Results The average values show no significant differences between both groups in the different tests and variables assessed at the pre y post- tests. However, a beneficial trend (in favor of experimental group) is clearly marked in all of them: wFTP (C: 256.6±22.4 to 260.0±16.9w vs E: 236.0±19.3 to 248.0±25.0w), w/kgFTP (C: 3.34±0.32 to 3.33±0.27w/kg vs E: 3.53±0.21 to 3.67±0.20w/kg), tExh (C: 14.71±1.08 to 14.89±1.43min vs E: 12.29±1.89 to 13.50±1.86min), wVT2 (C: 267.9±12.2 to 271.4±17.3w vs E: 239.3±19.3 to 260.7±24.4w), wLT (C: 200.0±0.0 to 204.2±19.0w vs E: 158.3±34.0 to 170.8±43.0w), w/kgLT (C: 2.60±0.08 to 2.60±0.27w/kg vs E: 2.42±0.53 to 2.63±0.73w/kg) and lactate production at LT kgLT (C: 2.23±0.37 to 1.95±0.61mM vs E: 2.12±0.60 to 2.32±0.73mM). The decline in VO2max had a negative trend less marked in the experimental group also (C: 55.1±2.1 to 54.0±2.2 ml/kg/min vs E: 55.4±3.5 to 55.0±3.9ml/kg/min, p=0.088). Discussion For group C which was training a larger volume at different IF and not individualized %UPF (but the same NP) is not guaranteed performance improvement. This group even manifests a negative trend, while group E, with relatively lower but personalized volumes, shows a slight improving trend, although not significant. References Allen H, Coggan A. (2010). Training and racing with a power meter. Velopress, Colorado.

Topic: TRAINING AND TESTING
Keyword I: FTP
Keyword II: BIPARAMETRIC MODELS THEORY
Keyword III: TRIATHLON

Fergie, I am really interested in hearing your take on this study. Especially your take on this statement:

Results The average values show no significant differences between both groups in the different tests and variables assessed at the pre y post- tests. However, a beneficial trend (in favor of experimental group) is clearly marked in all of them

 

[CoachFergie]

Fergie, I am really interested in hearing your take on this study. Especially your take on this statement:

The method you use to quantify training load, deliver training intensity and gain feedback while training has no significant effect on outcome. The principle of specificity with how you train for each cycling event hasn't changed. Also principles of individuality, progressive overload and recovery.

Common sense stuff really. Look forward to reading the full paper.

 

[CoachFergie]

That study also used Triathletes as subjects. They also combine Swimming and Running and depending on the type of event they are training for tend to have far less variation in power in training and racing. Bike racing, especially most forms of mass start racing is another story.

Invited Commentary: Distribution of Power Output when Establishing a Breakaway in Cycling.

Abbiss CR, Menaspà P, Villerius V, Martin DT
Centre for Exercise and Sports Science Research; School of Exercise and Health Sciences, Edith Cowan University, Joondalup, WA, Australia.

International Journal of Sports Physiology and Performance [2013]

A number of laboratory based performance tests have been designed to mimic the dynamic and stochastic nature of road cycling. However, the distribution of power output and thus physical demands of high-intensity surges performed in order to establish a breakaway during actual competitive road cycling is unclear. Review of data from professional road cycling events has indicated that numerous short duration (5-15 s), high intensity (~9.5 to 14 W.kg-1) surges are typically observed in the 5 to 10 minutes prior to athletes establishing a breakaway (i.e. riding away from a group of cyclists). Following this initial high-intensity effort, power output declines but remained high (~450 to 500W) for a further 30 s to 5 min, depending on race dynamics (i.e. the response of the chase group). Due to the significant influence competitors have on pacing strategies, it is difficult for laboratory-based performance tests to precisely replicate this aspect of mass-start competitive road cycling. Further research examining the distribution of power output during competitive road racing is needed in order refine laboratory-based simulated stochastic performance trials and better understand the factors important to the success of a breakaway.

 

[CoachFergie]

Not that this hasn't been studied before...

European Journal of Applied Physiology and Occupational Physiology
May 1998, Volume 77, Issue 6, pp 492-497

The mathematics of breaking away and chasing in cycling

Tim Olds

Abstract
In cycling stage races a small group of riders will often form a “breakaway” and establish a lead over the main group. This paper examines the factors that affect the likelihood of success for the breakaway. A mathematical approach is used, drawing on a model of cycling previously developed and validated (Olds et al. J␣Appl Physiol 78:1596–1611, 1995). In a breakaway group, the power required to overcome air resistance is reduced because the lead can be shared, with trailing riders sheltering or drafting behind leading riders. The benefit of drafting can be quantified as a function of the distance between riders using previously obtained data. Of course, this advantage is even greater in the (larger) chasing group, so that eventually the chasing group will catch the breakaway, assuming identical bicycles and physiological characteristics. The question addressed is: what factors determine how great a lead the breakaway must have in order for the chasing group to be unable to catch the breakaway before the finish of the race? Demand-side simulations show that the critical factors are: the distance remaining in the race; the speed of the breakaway group; the number of riders in the chasing and breakaway groups; how closely riders in each group draft one another; the grade; surface roughness; as well as head- and cross-winds. When supply-side physiological factors are incorporated, the maximum sustainable speed and maximum lead time can be calculated.

 

[CoachFergie]

Long essay on racing and training with a Power Meter...

http://hig.diva-portal.org/smash/get/diva2:325072/FULLTEXT01

Abstract
Aim
This critical review is aimed at the methods, concepts and theories currently employed and
advocated by users of mobile power meters (PM) in cycling and the physiological and
pedagogical implications they have for training, racing and performance testing.

Method
The methods, concepts and theories reviewed were chosen since they generated the most
hits from a search on the Google Groups Wattage
(http://groups.google.com/group/wattage) discussion forum, which is the largest internet
forum for PM-related discussions. After the selection of methods, concepts and theories a
search in the data bases available at College of Dalarna was made to find if they had any
support in the scientific literature.

Results
The methods, concepts and theories included are:
1. Pacing strategy and racing tactics
2. General considerations for performance testing with a PM. Tests for alactic
anaerobic and maximal neuromuscular power, anaerobic lactic power, maximal
aerobic power and power related to VO2max and anaerobic threshold power.
Performance profiling, scaling and ratios for power output and interpreting changes
in testing results.
3. Quantifying the power output demands of racing for designing training programs
and other preparations.
4. Andrew Coggans and Richard Sterns power based training zones and the relationship
between power output and perceived exertion.
5. Analysis of training and racing power output with tools such as Normalized Power™,
Intensity Factor™, Quadrant Analysis™, energy expenditure and how pedaling
dynamics affect this analysis.
6. Planning, monitoring and managing the training process with tools such as Training
Stress Score™, Performance Manager Chart™ and “power-to-heart rate decoupling”.
7. Theoretical basis for the role of the PM in the inter-personal and intra-personal
communication.

Conclusions
This review has shown that there are several very promising methods, concepts and
theories related to the use of PM’s in cycling. Presently, however, most of these are in need
of further research to investigate their affect on performance and how to improve their
validity and reliability.

 

[Alex Simmons/RST]

Long essay on racing and training with a Power Meter...

http://hig.diva-portal.org/smash/get/diva2:325072/FULLTEXT01

I even get a mention

 

[CoachFergie]

Not sure about the comment that parents get pressured into buying their kids Powermeters. I have to apply pressure to make sure U17 and U15 parents don't buy power meters as they have other important things to learn before they get that specific.

 

[CoachFergie]

J Sports Sci. 2013 May 28. [Epub ahead of print]

Effects of magnitude and frequency of variations in external power output on simulated cycling time-trial performance.

Wells M, Atkinson G, Marwood S.

Abstract: Mechanical models of cycling time-trial performance have indicated adverse effects of variations in external power output on overall performance times. Nevertheless, the precise influences of the magnitude and number of these variations over different distances of time trial are unclear. A hypothetical cyclist (body mass 70 kg, bicycle mass 10 kg) was studied using a mathematical model of cycling, which included the effects of acceleration. Performance times were modelled over distances of 4-40 km, mean power outputs of 200-600 W, power variation amplitudes of 5-15% and variation frequencies of 2-32 per time-trial. Effects of a "fast-start" strategy were compared with those of a constant-power strategy. Varying power improved 4-km performance at all power outputs, with the greatest improvement being 0.90 s for ± 15% power variation. For distances of 16.1, 20 and 40 km, varying power by ± 15% increased times by 3.29, 4.46 and 10.43 s respectively, suggesting that in long-duration cycling in constant environmental conditions, cyclists should strive to reduce power variation to maximise performance. The novel finding of the present study is that these effects are augmented with increasing event distance, amplitude and period of variation. These two latter factors reflect a poor adherence to a constant speed.

 

[Ripper]

J Sports Sci. 2013 May 28. [Epub ahead of print]

Effects of magnitude and frequency of variations in external power output on simulated cycling time-trial performance.

Wells M, Atkinson G, Marwood S.

Abstract: Mechanical models of cycling time-trial performance have indicated adverse effects of variations in external power output on overall performance times. Nevertheless, the precise influences of the magnitude and number of these variations over different distances of time trial are unclear. A hypothetical cyclist (body mass 70 kg, bicycle mass 10 kg) was studied using a mathematical model of cycling, which included the effects of acceleration. Performance times were modelled over distances of 4-40 km, mean power outputs of 200-600 W, power variation amplitudes of 5-15% and variation frequencies of 2-32 per time-trial. Effects of a "fast-start" strategy were compared with those of a constant-power strategy. Varying power improved 4-km performance at all power outputs, with the greatest improvement being 0.90 s for ± 15% power variation. For distances of 16.1, 20 and 40 km, varying power by ± 15% increased times by 3.29, 4.46 and 10.43 s respectively, suggesting that in long-duration cycling in constant environmental conditions, cyclists should strive to reduce power variation to maximise performance. The novel finding of the present study is that these effects are augmented with increasing event distance, amplitude and period of variation. These two latter factors reflect a poor adherence to a constant speed.

Glad to see a study that confirms and teases out detailed nuances of some long touted theories. I wonder if this will generate some interesting training strategies with power meters ...

 

[FrankDay]

Glad to see a study that confirms and teases out detailed nuances of some long touted theories. I wonder if this will generate some interesting training strategies with power meters ...

It is your opinion that a mathematical model of a hypothetical rider confirms nuances of long held theories? What if some of the assumptions in that hypothetical model are not correct? IMHO, such a "study" is only useful once the model itself is validated.

 

[CoachFergie]

Another PM on the market.

http://www.bikeradar.com/road/news/...crank-to-hit-market-in-august-37743/#previous

 

[CoachFergie]

Volume 2 No 1 of Journal of Cycling Science is out. Includes this paper on internal and external power and uses Axis Cranks to measure.

http://www.jsc-journal.com/ojs/index.php?journal=JSC&page=article&op=view&path[]=41

 

[FrankDay]

Volume 2 No 1 of Journal of Cycling Science is out. Includes this paper on internal and external power and uses Axis Cranks to measure.

http://www.jsc-journal.com/ojs/index.php?journal=JSC&page=article&op=view&path[]=41

Differences in values for IP in the published literature, therefore, might not necessarily be caused by differences in participant characteristics, but rather differences in the accuracy of the variables that are input into the IP models.

Of course, in my opinion, the problem is the models themselves as most of these models ignore the effects of gravity and the cost of making the legs go up and down.

 

[Ripper]

This should be my new sig ...

Clear signs of mental deficiency? Comments like this wrt power meters - "most of these models ignore the effects of gravity and the cost of making the legs go up and down"

 

[FrankDay]

This should be my new sig ...

Clear signs of mental deficiency? Comments like this wrt power meters - "most of these models ignore the effects of gravity and the cost of making the legs go up and down"

This comment is not about the PM. Does anyone read what I write (or write about)?

 

[FrankDay]

Another PM on the market.

http://www.bikeradar.com/road/news/...crank-to-hit-market-in-august-37743/#previous

It appears this crank may come with software that will turn it into a second generation PM, similar to the, hopefully, soon to be available iCranks. I have revised my explanation as to why additional information (beyond just power or right and left power) may be useful if anyone is interested.

http://www.powercranks.com/icranks.html

 

[CoachFergie]

Analysis of Tour de France Power...

http://home.trainingpeaks.com/races/2013-tour-de-france.aspx

http://www.srm.de/news/road-cycling/

 

[CoachFergie]

Performance analysis groundwork of the Tour de France

http://www.sportsscientists.com/2013/07/the-power-of-tour-de-france-performance.html

These guys found agreement between Ferrari method and SRM. Another person found a 4.28% error in another.

 

[CoachFergie]

Good information for those new to power from Coach Chris Mayhew...

http://rouleurville.com/you-just-got-a-power-meter-now-what-by-chris-mayhew/

 

[CoachFergie]

Power meter analysis from BMX. In a recent seminar led by Dr Jim Martin he presented peak power data from BMX riders over 2700 watts!!!

Maximal torque and power pedaling rate relationships for high level BMX riders in field tests
Pierre Debraux, Aneliya Vilizarova Manolova, Mickael Soudain-Pineau, Christophe Hourde, William Bertucci

Abstract

The Bicycle Motocross race is an “all-out” sprint discipline with a race time not exceeding 40s. In high-level, the maximal power output during acceleration phase can be higher than 2000 W. The purpose of this study was to analyse the maximal torque- and power-pedaling rate relationships and anthropometric characteristics during 80m sprints. Seven elite riders performed three 80m sprints on a levelled ground. The maximal mechanical power output (PMAX), the mean pedaling rate (PRmean), the optimal pedaling rate (PROpt), the maximal theoretical pedaling rate (PR0), the maximal theoretical torque (T0), the time at 20m (t20) and the maximal velocity reached during 80m sprint (vMAX) were measured using PowerTap system and photoelectric cells. Moreover, the projected frontal area (Ap) was measured during the sprints by photographs. Significant correlations (P < 0.05) were observed between PMAX and vMAX (r = 0.99), vMAX and PMAX•Ap-1 (r = 0.87), vMAX and T0 (r = 0.97), vMAX and PRmean (r = 0.98) and t20 and vMAX (r = -0.99). Moreover there was a significant difference (P < 0.01) between PRmean and PROpt with PRmean significantly greater than PROpt (158 ± 9 vs. 122 ± 18 rpm). The main results of this study showed that PMAX, T0, PRmean, Ap and t20 were significant determining factors of performance in 80m sprint. Furthermore, a lower value of PRmean could permit to reduce the difference between PRmean and PROpt in order to maximize the power output during the sprint.


Keywords

power; optimal pedaling rate; torque; BMX; field testing; sprint

 

[acoggan]

Power meter analysis from BMX. In a recent seminar led by Dr Jim Martin he presented peak power data from BMX riders over 2700 watts!!!

Maximal torque and power pedaling rate relationships for high level BMX riders in field tests
Pierre Debraux, Aneliya Vilizarova Manolova, Mickael Soudain-Pineau, Christophe Hourde, William Bertucci

Abstract

The Bicycle Motocross race is an “all-out” sprint discipline with a race time not exceeding 40s. In high-level, the maximal power output during acceleration phase can be higher than 2000 W. The purpose of this study was to analyse the maximal torque- and power-pedaling rate relationships and anthropometric characteristics during 80m sprints. Seven elite riders performed three 80m sprints on a levelled ground. The maximal mechanical power output (PMAX), the mean pedaling rate (PRmean), the optimal pedaling rate (PROpt), the maximal theoretical pedaling rate (PR0), the maximal theoretical torque (T0), the time at 20m (t20) and the maximal velocity reached during 80m sprint (vMAX) were measured using PowerTap system and photoelectric cells. Moreover, the projected frontal area (Ap) was measured during the sprints by photographs. Significant correlations (P < 0.05) were observed between PMAX and vMAX (r = 0.99), vMAX and PMAX•Ap-1 (r = 0.87), vMAX and T0 (r = 0.97), vMAX and PRmean (r = 0.98) and t20 and vMAX (r = -0.99). Moreover there was a significant difference (P < 0.01) between PRmean and PROpt with PRmean significantly greater than PROpt (158 ± 9 vs. 122 ± 18 rpm). The main results of this study showed that PMAX, T0, PRmean, Ap and t20 were significant determining factors of performance in 80m sprint. Furthermore, a lower value of PRmean could permit to reduce the difference between PRmean and PROpt in order to maximize the power output during the sprint.


Keywords

power; optimal pedaling rate; torque; BMX; field testing; sprint

Sounds familiar (note the date, as well as the means of analysis):

http://www.trainingandracingwithapowermeter.com/2010/05/fatigability-and-bmx-performance-at.html

 

[acoggan]

Performance analysis groundwork of the Tour de France

http://www.sportsscientists.com/2013/07/the-power-of-tour-de-france-performance.html

These guys found agreement between Ferrari method and SRM. Another person found a 4.28% error in another.

Just as I (and others, e.g., Alex Simmons) have been pointing out all along: estimates of power based on rate of ascent are, on average, only good to +/- ~5%. Pretty hard to be confident who is/isn't doping based on such sloppy measurements!