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The Performance Manager TM

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

  3. Impulse-response model of training adaptation Banister et al., AustJ 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

  8. Representative studies from the literature (con’t)

  9. Impulse-response model of training adaptation Performance Manager  Coggan, 2004 Banister et al., AustJ 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 the progressive overload principle is applied in a rational manner • Planning a taper in an attempt to peak for a particular event

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

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

  14. Performance versus TSB: effect of duration

  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

  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