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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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!!
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2. Data are highly variable!
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“Tools” for analyzing powermeter data
1) Power profiling
2) Normalized power
3) Training stress score
4) Quadrant analysis
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“Tools” for analyzing powermeter data
1) Power profiling
2) Normalized power
3) Training stress score
4) Quadrant analysis
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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.
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Average power =
273 W
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Kinetics of PCr resynthesis
Coggan et al., J Appl Physiol 1993; 75:2125-2133
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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
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Data smoothed using 30 s rolling ave.
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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
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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
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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
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Normalized
power = 301 W
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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
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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
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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
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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
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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?