The Performance ManagerTM

Andrew R. Coggan, Ph.D.

Cardiovascular Imaging Laboratory

Washington University School of Medicine

St. Louis, MO 63021

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 Eric Banister’s impulse-response model or some variation thereof.

Impulse-response model of training adaptation

Banister et al., Aust J Sports Med 7:57, 1975

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

Busso et al., J Appl Physiol 92:572, 2002

Prediction of training-induced changes in performance using impulse-response model

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

Representative studies from the literature

Study SubjectsTraining program

τa (τ1) τf (τ2) ka (k1) kf (k2)

Busso et al., 1991

Initially untrained

men (n=8)

Constant-load

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 40-60 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 40-60 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

Representative studies from the literature (con’t)

Study SubjectsTraining 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 4 y

50±8 19±8 0.01±0.01 0.05±0.03

Impulse-response model of training adaptation

Banister et al., Aust J Sports Med 7:57, 1975

Performance Manager

Coggan, 2004

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

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 the progressive overload principle is applied in a rational manner

• Planning a taper in an attempt to peak for a particular event

Performance Manager chartfor an elite track cyclist (2002 season)

Performance Manager chartfor an elite track cyclist (2002 season) (con’t)

Performance versus TSB: effect of durationDuration of effort TSB at time of PB

5 s 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

Performance Manager chart for a masters cyclist (2004 season)

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

Performance Manager chart for a masters cyclist (2005 season)

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

Performance versus TSB: effect of durationDuration of effort TSB for 2004 PB TSB for 2005 PB

5 s 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)

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

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

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

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”

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

Special thanks to the “beta testers” of the Performance Manager

Hunter AllenTom AnhaltGavin AtkinsAndy BirkoLindsay EdwardsMark EwersSam CallanChris CleelandTony GellerDave HarrisDave JordaanKirby KriegerChris MerriamJim MillerChris Mayhew

Dave MartinScott MartinPhil McKnightRick MurphyTerry RitterBen SharpAlex SimmonsPhil SkibaRic SternBob TobinJohn VerheulFrank OvertonLynda WallenfellsMike Zagorski