1. MANSOUR SHARAHILI 1 Modelling and optimising the sport and exercise training process With thanks to professor Philip Scarf

2. The aim of the study Our aim is to relate training to performance and provide a quantitative model that can be used to optimize training in advance of a major competition using power output-heart rate data collected every 5 seconds. 2

3. The general structure of the thesis Performance measurement. Training measurement. Relate performance to training using a statistical model. 3

4. The relationship between training and performance Highly individualised. (genetic factors, individual training background, technical factors). The amount and type of training. Level of performance at the beginning of training. Random increase in training may lead to over-training which increase the likelihood of injury. 4

5. 5 The components of training Fitness (benefit)Fatigue (dis-benefit)

6. The study data 6 Rider Age (years) Height (m) Weight (kg) Rider Age (years) Height (m) Weight (kg) 145183.074.3627183.771.8 252175.074.5740177.575.5 335181.071.0834182.077.0 442178.578.2934185.588.2 521171.460.91029174.571.5

7. The study data 7

8. 8 The number of sessions and the period in day for each rider’s training schedule

9. Power output and heart rate monitors 9 A B C

10. Power output and heart rate monitors calculate: 10 Power output (watts). Heart rate (beat per minute). Cadence (revolutions per minute). Speed (miles or kilometres per hour). Temperature (Fahrenheit or Celsius).

11. The measurement of training Banister, et al. (1975) proposed a measure of training that calculates the accumulative effect of all training sessions carried out up to time t. This measure had a number of components. The first one is the measurement of the amount of training for a single session, called the training load of that session. The second component is how training accumulates for a sequence of sessions over time. 11

12. 12 Measurement for a single session The accumulation of training (ATE) for all sessions The measurement of training

13. The measurement for a single session training impulse (TRIMP) 13

14. 14

15. 15 Rider Max(HR)Rest (HR) Rider Max(HR)Rest (HR) 118045618739 220348718749 318245817342 419242919253 5184421017442 Maximum and resting heart rate (beats per minute) for each rider.

16. 16 The accumulated training effect (ATE) Banister model The accumulated training effect (ATE) Banister model Where: is the accumulated training effect (ATE) at time t. is the known training load during session i. is the number of sessions up to time t. corresponds to net training effect at time t=0 of sessions in and are the fitness and fatigue decay time constants, respectively. is the benefit scale parameter and is the disbenefit scale parameter

17. Banister model 17

18. 18 An example of the Banister curve for a single session (A) and for a progressive training schedule of 200 days (B) with unit training load for each session, and with default parameters as: A B

19. Measuring performance Performance can be measured in a standard way by asking an athlete to swim or ride or run (depending on the type of sport) a particular specified distance. A difficulty with this approach is that performance measurements may underestimate the actual capability or readiness- to-perform. 19

20. 20 Some performance measures: Average power (AP) Normalised power (NP) Critical power (CP)

21. 21 The relationship between power output and heart rate The relationship between power output and heart rate is strongly linear Grazzi et al. (1999). However, There is also a delay or time lag between the change in power output and the heart rate response. We investigate different lags of some seconds (0, 10, 15, 20, 25 and 30 seconds) between power output and heart rate and find the strongest relationship when the lag is 15 seconds for almost all sessions.

22. A new measure of performance 22 The histogram of polling power output for one rider (rider 3)

23. A new measure of performance 23 Power output versus heart rate for a single session

24. Performance measure for all sessions for one rider 24

25. Determining the Banister model parameters If the Banister model parameters are known, training can be optimised. 25

26. The statistical model 26 where and

27. 27 Results Rider 15.7 (0.97) 23 (13.5) 2.6 (4.2) 2 (3.8) 149 (3.1) -0.0033 (0.0019) -1.74 24.5 (1.5) 5.7 (3) 2.8 (28) 0.4 (1.4) 174 (4.0) -0.0250 (0.0100) -2.50 34.6 (1.3) 8 (32) 3.2 (16) 2.2 (4.7) 156 (3.4) -0.0140 (0.0900) -0.16 44.8 (1.6) 228 (791) 0.93 (1.2) 67 (144) 164 (3.4) -0.0011 (0.0004) -2.75 538 (5.8) 89 (139) 7.2 (13) 57 (18) 168 (10.0) -0.0010 (0.0020) -0.50 68.3 (1.1) 74 (14) 1.2 (0.2) 43 (14) 186 (4.4) -0.0230 (0.0100) -2.30 74.1 (1.1) 13 (7) 1.9 (1.1) 7 (3.7) 159 (2.2) -0.0210 (0.0200) -1.05 85.3 (0.7) 6.3 (7) 2.1 (1.7) 0.12 (0.07) 145 (2.3) -0.0100 (0.0050) -2.00 94.6 (0.7) 9 (3) 1.1 (0.4) 6 (3.6) 156 (2.0) -0.0100 (0.0100) 106 (0.6) 96 (72) 1.4 (1.2) 42 (50) 142 (2.4) -0.0013 (0.0030) -0.43

28. 28 Performance measure and the accumulated training effect versus days for one rider (rider 6)

29. The statistical significance of the training effect 29

30. The practical significance of the training effect 30 we determine the change in power output (power gain) between the start of the training and the point at which the rider is most trained. where

31. 31 we would accept that there is a significant practical effect of the accumulated training effect (ATE) on performance.

32. 32 Rider 12.31-0.00303290232910.08 22.45-0.0250786483070.16 31.61-0.0140430102910.03 41.11-0.0011735592460.04 51.83-0.0010002800 63.35-0.023015101163840.30 71.85-0.00207376273230.08 83.11-0.0100798252740.09 92.34-0.0100692162140.08 102.43-0.00104937122600.05 Practical significance

33. Limitations of the study Many parameters in the Banister model The data are very noisy No training diaries were recorded Monitoring a young rider for a period of time (e.g. 2 years) may give better estimates for our model 33

34. Conclusion In conclusion, there is much work that remains to be done. This thesis makes a start at optimising training schedules, and in cycling in particular. We have suggested some key points which should be taken into account to develop this work and contribute to the knowledge. 34

35. Future work To obtain new data on a developing rider. To study the effect of temperature on the relationship between power output and heart rate. 35

36. Thank you 36