Making sense out of apparent chaos analyzing data from on-bike powermeters
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Making sense out of apparent chaos: analyzing data from on-bike powermeters Andrew R. Coggan, Ph.D. Cardiovascular Imaging Laboratory Washington University School of Medicine St. Louis, MO 63021
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Transcript of Making sense out of apparent chaos analyzing data from on-bike powermeters
Making sense out of apparent chaos:
analyzing data from on-bike
powermeters
Andrew R. Coggan, Ph.D.
Cardiovascular Imaging Laboratory
Washington University School of Medicine
St. Louis, MO 63021
On-bike powermeters: both a blessing and a curse
Powermeters provide a
detailed (e.g., second-by-
second) record of a
cyclist’s power, cadence,
heart rate, etc., during
each training session or
race, but...
1. Multiple variables/seconds x 3600 seconds/hour x
several hours/day x 365 days/year = a LOT of data!!
2. Data are highly variable!
“Tools” for analyzing powermeter data
1) Power profiling
2) Normalized power
3) Training stress score
4) Quadrant analysis
“Tools” for analyzing powermeter data
1) Power profiling
2) Normalized power
3) Training stress score
4) Quadrant analysis
What is normalized power?
Normalized power is an estimate of the power
that a rider could have maintained for the same
physiological “cost” if power had been perfectly
constant (e.g., as on an ergometer) instead of
variable.
Average power =
273 W
Kinetics of PCr resynthesis
Coggan et al., J Appl Physiol 1993; 75:2125-2133
Half-lives of other physiological responses
Power (force and/or velocity) (0 s)
PCr kinetics ~25 s
Heart rate/cardiac output: ~25 s
Sweating: ~25 s
VO2: ~30 s
VCO2: ~45 s
Ventilation: ~50 s
Temperature (core): ~70 s
Data smoothed using 30 s rolling ave.
VO2, heart rate, lactate, and RPE
as a function of power output
0
1
2
3
4
5
6
7
8
9
0 50 100 150 200 250 300 350 400 450
Power (W)
VO
2 (
L/m
in),
la
cta
te (
mM
), o
r R
PE
(U)
0
20
40
60
80
100
120
140
160
180
HR
(beats
/min
)
VO2 Blood lactate RPE Heart rate
VO2max
Lactate threshold
OBLA
Blood lactate-exercise intensity relationship
y = 3.94x3.91
R2 = 0.81
0
2
4
6
8
10
12
14
16
18
20
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6
Power/power at lactate threshold
Blo
od lacta
te (
mm
ol/L)
Coggan, unpublished observations
Steps to calculate normalized power
1) smooth the data using a 30 s rolling average to
take into account the time course of physiological
responses
2) Raise the data obtained in step 1 to the 4th power
take into account the non-linear nature of
physiological responses
3) take the average of the values obtained in step 2
4) reverse step 2 to obtain the normalized power
Normalized
power = 301 W
Relationship of average and normalized power to
maximal steady state power
y = 1.27x - 126
R2 = 0.73
y = 0.93x + 27
R2 = 0.93
0
100
200
300
400
500
0 100 200 300 400 500
Maximal steady state power (W)
Pow
er
during ~
1 h
race (
W)
Average power Normalized power
Coggan, unpublished observations
Relationship of normalized power to power at lactate
threshold (Dmax method)
y = 0.88x + 51
R2 = 0.91
0
100
200
300
400
500
0 100 200 300 400 500
Power at lactate threshold (Dmax method) (W)
Norm
aliz
ed p
ow
er
for
1 h
(W
)
Edwards et al., unpublished observations
Advantages of/uses for normalized power
• Allows more valid comparison of races or training
sessions with differing demands
– e.g., hilly vs. flat training rides, criteriums vs. TTs, outdoor vs.
indoor training
• Helpful in the design of novel interval workouts
– if normalized power for session (intervals plus recovery periods
combined) exceeds athlete’s power-duration curve, unlikely that
they will be able to complete workout as planned
Advantages of/uses for normalized power (con’t)
• Can be used to assess changes in fitness w/o need for
formal testing
– normalized power from hard ~1 h race provides estimate of
maximal steady state power
• May prove to be useful constraint when attempting to
model performance
– e.g., to determine optimal TT pacing strategy
Limitations of normalized power
• Essentially assumes that the net contribution from
anaerobic ATP production is negligible
– therefore not valid during shorter efforts in which contribution
from anaerobic capacity is significant (e.g., individual pursuit)
• Occasionally overestimates sustainable power
– is the algorithm biased, or are such data just statistical outliers?
