2006 USA Cycling 2nd Biannual International Cycling Summit, Colorado Springs, CO

1. The Performance ManagerTM
Andrew R. Coggan, Ph.D.
Cardiovascular Imaging Laboratory
Washington University School of Medicine
St. Louis, MO 63021

2. Scientific studies using mathematical modeling to
quantitatively relate training load to performance
• Approximately 30 English language papers
• Many different sports studied (i.e., weightlifting,
hammer throwing, running, swimming, cycling,
triathlon)
• Variety of mathematical approaches used (e.g., from
simple regression to neural networking)
• Vast majority have relied upon Banister’s impulseresponse model or some variation thereof.

3. Impulse-response model of training adaptation
Banister et al., Aust J Sports Med 7:57, 1975

4. Impulse-response model: effect of “square wave”
increase in training load to 100 units/d

5. Prediction of training-induced changes in
performance using impulse-response model
Busso et al., J Appl Physiol 92:572, 2002

6. Limitations to the impulse-response model
• Mathematically complex, yet overly simplified
• Requires frequent, quantitative measurement of
performance (i.e., 20-200 times every 60-90 d)
• Parameter estimates still may be insufficiently stable
(precise) to permit highly accurate prediction of future
performance
• Inter-study and inter-subject variability in parameter
estimates (esp. ka (k1) and kf (k2)) limits ability to apply
“generic” version of model

7. Representative studies from the literature
Subjects
Training
program
τa (τ1)
τf (τ2)
ka (k1)
kf (k2)
Initially
untrained
men (n=8)
Constantload
cycling
60 min/d,
4 d/wk, for
14 wk
38±9
2±2
0.048±0.019
0.117±0.114
Busso et al.,
1997
Recreational
cyclists (n=2)
Interval
cycling 4060 min/d,
3-5 d/wk
for 14 wk
60, 60
4, 6
0.0021,
0.0019
0.0078,
0.0073
Busso, 1993
Initially
untrained
men (n=6)
Interval
cycling 4060 min/d
for 15 wk
3 d/wk:
41±15
5 d/wk:
35±12
3 d/wk:
9±6
5 d/wk:
13±3
3 d/wk:
0.019±0.006
5 d/wk:
0.021±0.006
3 d/wk:
0.015±0.008
5 d/wk:
0.021±0.006
Study
Busso et al.,
1991

8. Representative studies from the literature (con’t)
Subjects
Training
program
τa (τ1)
τf (τ2)
ka (k1)
kf (k2)
Morton et
al., 1990
Initially
untrained
men (n=2)
Running
40-100
min/d, 7
d/wk, for 4
wk
40, 50
11, 11
1, 1
1.8, 2
Iñigo et al.,
1996
National and
international
level
swimmers
(n=18)
Swimming
35-40
km/wk for
44 wk
41±4
12±6
0.062±0.04
0.128±0.055
Hellard et
al., 2005
Olympic level
swimmers
(n=7)
Swimming
45-50
km/wk for
4y
50±8
19±8
0.01±0.01
0.05±0.03
Study

9. Impulse-response model of training adaptation
Performance Manager
∝
Coggan, 2004
Banister et al., Aust J Sports Med 7:57, 1975

10. Performance Manager: result of “square wave”
increase in training load to 100 TSS/d

11. Uses for the Performance Manager
• Determining optimal long-term training load
• Identifying periods of severe overreaching that may
lead to illness or overtraining
• Identifying periods of “training stagnation”
• Assuring that the progressive overload principle is
applied in a rationale manner
• Planning a taper in an attempt to peak for a particular
event

12. Performance Manager chart
for an elite track cyclist (2002 season)

13. Performance Manager chart
for an elite track cyclist (2002 season) (con’t)

14. Performance versus TSB: effect of duration
Duration of effort
TSB at time of PB
5s
35
10 s
35
20 s
35
30 s
35
1 min
34
2 min
34
5 min
33
5 min (normalized power)
34
10 min
6
10 min (normalized power)
5
20 min
(-19)
20 min (normalized power)
34
30 min
6
30 min (normalized power)
34
60 min
(-10)
60 min (normalized power)
34

15. Performance Manager chart for a masters cyclist
(2004 season)

16. Performance Manager chart for a masters cyclist
(2004 season) (con’t)

17. Performance Manager chart for a masters cyclist
(2005 season)

18. Performance Manager chart for a masters cyclist
(2005 season) (con’t)

19. Performance versus TSB: effect of duration
Duration of effort
TSB for 2004 PB
TSB for 2005 PB
5s
11
13
10 s
11
6
20 s
11
3
30 s
11
13
1 min
19
13
2 min
8
14
5 min
4
9
5 min (normalized power)
6
10
10 min
0
(-7)
10 min (normalized power)
19
10
20 min
0
(-4)
20 min (normalized power)
19
6
30 min
10
(-2)
30 min (normalized power)
1
6
60 min
3
(-2)
60 min (normalized power)
3
(-5)

20. TSB at time of personal best for power
(all durations)

21. TSB at time of personal best for power
(<5 min)

22. TSB at time of personal best for power
(>10 min)

23. Caveats and limitations
• Accuracy of predictions depends upon:
– Accuracy/completeness of underlying data
– Use of appropriate time constants (esp. for ATL)
• “Composition” of the training load still matters
• Training Manager helps you view the “forest”, but
you should never lose sight of the “trees”

24. Additional resources
• www.cyclingpeakssoftware.com/power411/
performancemanagerscience.asp
• www.cyclingpeakssoftware.com/power411/
performancemanager.asp
• www.cyclingpeakssoftware.com/power411/
howtoperformancemanager.asp
• www.cyclingpeakssoftware.com/support/
WKO+2_1_user_guide.pdf

25. Special thanks to the “beta testers” of the
Performance Manager
Hunter Allen
Tom Anhalt
Gavin Atkins
Andy Birko
Lindsay Edwards
Mark Ewers
Sam Callan
Chris Cleeland
Tony Geller
Dave Harris
Dave Jordaan
Kirby Krieger
Chris Merriam
Jim Miller
Chris Mayhew
Dave Martin
Scott Martin
Phil McKnight
Rick Murphy
Terry Ritter
Ben Sharp
Alex Simmons
Phil Skiba
Ric Stern
Bob Tobin
John Verheul
Frank Overton
Lynda Wallenfells
Mike Zagorski