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Describing and Understanding Pacing Strategies during Athletic Competition

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Abstract

It is widely recognized that an athlete’s ‘pacing strategy’, or how an athlete distributes work and energy throughout an exercise task, can have a significant impact on performance. By applying mathematical modelling (i.e. power/velocity and force/time relationships) to athletic performances, coaches and researchers have observed a variety of pacing strategies. These include the negative, all-out, positive, even, parabolic-shaped and variable pacing strategies. Research suggests that extremely short-duration events (≤30 seconds) may benefit from an explosive ‘all—out’ strategy, whereas during prolonged events (>2 minutes), performance times may be improved if athletes distribute their pace more evenly. Knowledge pertaining to optimal pacing strategies during middle—distance (1.5–2 minutes) and ultra-endurance (>4 hours) events is currently lacking. However, evidence suggests that during these events well trained athletes tend to adopt a positive pacing strategy, whereby after peak speed is reached, the athlete progressively slows. The underlying mechanisms influencing the regulation of pace during exercise are currently unclear. It has been suggested, however, that self-selected exercise intensity is regulated within the brain based on a complex algorithm involving peripheral sensory feedback and the anticipated workload remaining. Furthermore, it seems that the rate and capacity limitations of anaerobic and aerobic energy supply/utilization are particularly influential in dictating the optimal pacing strategy during exercise. This article outlines the various pacing profiles that have previously been observed and discusses possible factors influencing the self-selection of such strategies.

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References

  1. van Ingen Schenau GJ, de Koning JJ, de Groot G. The distribution of anaerobic energy in 1000 and 4000 metre cycling bouts. Int J Sports Med 1992; 13 (6): 447–51

    Article  PubMed  Google Scholar 

  2. Foster C, de Koning JJ, Hettinga F, et al. Effect of competitive distance on energy expenditure during simulated competition. Int J Sports Med 2004; 25 (3): 198–204

    Article  PubMed  CAS  Google Scholar 

  3. Foster C, Hoyos J, Earnest C, et al. Regulation of energy expenditure during prolonged athletic competition. Med Sci Sports Exerc 2005; 37 (4): 670–5

    Article  PubMed  Google Scholar 

  4. Marino FE. Anticipatory regulation and avoidance of catastrophe during exercise—induced hyperthermia. Comp Biochem Physiol B Biochem Mol Biol 2004; 139 (4): 561–9

    Article  PubMed  Google Scholar 

  5. Tucker R, Marle T, Lambert EV, et al. The rate of heat storage mediates the anticipatory reduction in exercise workrate during cycling in the heat at a fixed rating of perceived exertion. J Physiol (Lond) 2006; 574 (3): 905–15

    Article  CAS  Google Scholar 

  6. Atkinson G, Davison R, Jeukendrup A, et al. Science and cycling: current knowledge and future directions for research. J Sports Sci 2003; 21 (9): 767–87

    Article  PubMed  Google Scholar 

  7. de Koning JJ, Bobbert MF, Foster C. Determination of optimal pacing strategy in track cycling with an energy flow model. J Sci Med Sport 1999; 2 (3): 266–77

    Article  PubMed  Google Scholar 

  8. Foster C, Snyder AC, Thompson NN, et al. Effect of pacing strategy on cycle time trial performance. Med Sci Sports Exerc 1993; 25 (3): 383–8

    PubMed  CAS  Google Scholar 

  9. Foster C, Schrager M, Snyder AC, et al. Pacing strategy and athletic performance. Sports Med 1994; 17 (2): 77–85

    Article  PubMed  CAS  Google Scholar 

  10. Atkinson G, Edwards B. Pacing strategy and cycling performance: field data from the 1997 British 16 km time—trial championship [abstract]. In: Sargeant AJ, Siddons H, editors. Proceedings of the Third Annual Congress of the European College of Sports Science. Liverpool: Centre for Health Care Development, 1998: 211

    Google Scholar 

  11. St Clair Gibson A, Lambert EV, Rauch LHG, et al. The role of information processing between the brain and peripheral physiological systems in pacing and perception of effort. Sports Med 2006; 36 (8): 705–22

    Article  Google Scholar 

  12. Abbiss CR, Laursen PB. Models to explain fatigue during prolonged endurance cycling. Sports Med 2005; 35 (10): 865–98

    Article  PubMed  Google Scholar 

  13. St Clair Gibson A, Lambert MI, Noakes TD. Neural control of force output during maximal and submaximal exercise. Sports Med 2001; 31 (9): 637–50

    Article  Google Scholar 

  14. Padilla S, Mujika I, Orbananos J, et al. Exercise intensity during competition time trials in professional road cycling. Med Sci Sports Exerc 2000; 32 (4): 850–6

    Article  PubMed  CAS  Google Scholar 

  15. Wilberg RB, Pratt J. A survey of the race profiles of cyclists in the pursuit and kilo track events. Can J Sport Sci 1988; 13 (4): 208–13

    PubMed  CAS  Google Scholar 

  16. Coyle EF. Physiological determinants of endurance exercise performance. J Sci Med Sport 1999; 2 (3): 181–9

    Article  PubMed  CAS  Google Scholar 

  17. Billat LV, Koralsztein JP, Morton RH. Time in human endurance models: from empirical models to physiological models. Sports Med 1999; 27 (6): 359–79

    Article  PubMed  CAS  Google Scholar 

  18. Swain DP. A model for optimizing cycling performance by varying power on hills and in wind. Med Sci Sports Exerc 1997; 29 (8): 1104–8

    Article  PubMed  CAS  Google Scholar 

  19. Faria EW, Parker DL, Faria IE. The science of cycling: factors affecting performance: part 2. Sports Med 2005; 35 (4): 313–37

    Article  PubMed  Google Scholar 

  20. Arsac LM, Locatelli E. Modeling the energetics of 100−m running by using speed curves of world champions. J Appl Physiol 2002; 92 (5): 1781–8

    PubMed  Google Scholar 

  21. Candau RB, Grappe F, Menard M, et al. Simplified deceleration method for assessment of resistive forces in cycling. Med Sci Sports Exerc 1999; 31 (10): 1441–7

    Article  PubMed  CAS  Google Scholar 

  22. Smith MF, Davison RC, Balmer J, et al. Reliability of mean power recorded during indoor and outdoor self—paced 40 km cycling time—trials. Int J Sports Med 2001; 22 (4): 270–4

    Article  PubMed  CAS  Google Scholar 

  23. Balmer J, Davison RC, Bird SR. Peak power predicts performance power during an outdoor 16.1−km cycling time trial. Med Sci Sports Exerc 2000; 32 (8): 1485–90

    Article  PubMed  CAS  Google Scholar 

  24. Keller JB. Optimal velocity in a race. Am Math Monthly 1974; 81 (5): 474–80

    Article  Google Scholar 

  25. Sandals LE, Wood DM, Draper SB, et al. Influence of pacing strategy on oxygen uptake during treadmill middle—distance running. Int J Sports Med 2006; 27 (1): 37–42

    Article  PubMed  CAS  Google Scholar 

  26. Mattern CO, Kenefick RW, Kertzer R, et al. Impact of starting strategy on cycling performance. Int J Sports Med 2001; 22 (5): 350–5

    Article  PubMed  CAS  Google Scholar 

  27. Robinson S, Robinson D, Mountjoy RJ, et al. Influence of fatigue on the efficiency of men during exhausting runs. J Appl Physiol 1958; 12: 197–201

    PubMed  CAS  Google Scholar 

  28. Tucker R, Rauch L, Harley YXR, et al. Impaired exercise performance in the heat is associated with an anticipatory reduction in skeletal muscle recruitment. Pflugers Arch 2004; 448: 422–30

    Article  PubMed  CAS  Google Scholar 

  29. Albertus Y, Tucker R, Gibson ASC, et al. Effect of distance feedback on pacing strategy and perceived exertion during cycling. Med Sci Sports Exerc 2005; 37 (3): 461–8

    Article  PubMed  Google Scholar 

  30. Nikolopoulos V, Arkinstall MJ, Hawley JA. Pacing strategy in simulated cycle time—trials is based on perceived rather than actual distance. J Sci Med Sport 2001; 4 (2): 212–9

    Article  PubMed  CAS  Google Scholar 

  31. Kay D, Marino FE, Cannon J, et al. Evidence for neuromuscular fatigue during high—intensity cycling in warm, humid conditions. Eur J Appl Physiol 2001; 84 (1-2): 115–21

    Article  PubMed  CAS  Google Scholar 

  32. Tatterson AJ, Hahn AG, Martin DT, et al. Effects of heat stress on physiological responses and exercise performance in elite cyclists. J Sci Med Sport 2000; 3 (2): 186–93

    Article  PubMed  CAS  Google Scholar 

  33. Rauch HGL, St Clair Gibson A, Lambert EV, et al. A signalling role for muscle glycogen in the regulation of pace during prolonged exercise. Br J Sports Med 2005; 39 (1): 34–8

    Article  PubMed  CAS  Google Scholar 

  34. Tibshirani R. Who is the fastest man in the world? Am Statistician 1997; 51 (2): 106–11

    Google Scholar 

  35. Bishop D, Bonetti D, Dawson B. The influence of pacing strategy on V̇O2 and supramaximal kayak performance. Med Sci Sports Exerc 2002; 34 (6): 1041–7

    Article  PubMed  Google Scholar 

  36. Atkinson G, Brunskill A. Pacing strategies during a cycling time trial with simulated headwinds and tailwinds. Ergonomics 2000; 43 (10): 1449–60

    Article  PubMed  CAS  Google Scholar 

  37. Mureika JR. A simple model for predicting sprint race times accounting for energy loss on the curve. Can J Physiol 1997; 75: 837–51

    Article  CAS  Google Scholar 

  38. Yamamoto M, Kanehisa H. Dynamics of anaerobic and aerobic energy supplies during sustained high intensity exercise on cycle ergometer. Eur J Appl Physiol Occup Physiol 1995; 71 (4): 320–5

    Article  PubMed  CAS  Google Scholar 

  39. Thompson KG, Haljand R, Mac Laren DP. An analysis of selected kinematic variables in national and elite male and female 100−m and 200−m breaststroke swimmers. J Sports Sci 2000; 18 (6): 421–31

    Article  PubMed  CAS  Google Scholar 

  40. Garland SW. An analysis of the pacing strategy adopted by elite competitors in 2000 m rowing. Br J Sports Med 2005; 39 (1): 39–42

    Article  PubMed  CAS  Google Scholar 

  41. Thompson KG, Mac Laren DP, Lees A, et al. The effect of even, positive and negative pacing on metabolic, kinematic and temporal variables during breaststroke swimming. Eur J Appl Physiol 2003; 88 (4-5): 438–43

    Article  PubMed  CAS  Google Scholar 

  42. Thompson KG, Mac Laren DPM, Lees A, et al. The effects of changing pace on metabolism and stroke characteristics during high—speed breaststroke swimming. J Sports Sci 2004; 22 (2): 149–57

    Article  PubMed  Google Scholar 

  43. Noakes TD, St Clair Gibson A, Lambert EV. From catastrophe to complexity: a novel model of integrative central neural regulation of effort and fatigue during exercise in humans: summary and conclusions. Br J Sports Med 2005; 39 (2): 120–4

    Article  PubMed  CAS  Google Scholar 

  44. Gonzalez-Alonso J, Teller C, Andersen SL, et al. Influence of body temperature on the development of fatigue during prolonged exercise in the heat. J Appl Physiol 1999; 86 (3): 1032–9

    PubMed  CAS  Google Scholar 

  45. Nielsen B, Hales JR, Strange S, et al. Human circulatory and thermoregulatory adaptations with heat acclimation and exercise in a hot, dry environment. J Physiol (Lond) 1993; 460: 467–85

    CAS  Google Scholar 

  46. Laursen PB, Knez WL, Shing CM, et al. Relationship between laboratory—measured variables and heart rate during an ultraendurance triathlon. J Sports Sci 2005; 23 (10): 1111–20

    Article  PubMed  Google Scholar 

  47. Abbiss CR, Quod MJ, Martin DT, et al. Dynamic pacing strategies during the cycle phase of an Ironman triathlon. Med Sci Sports Exerc 2006; 38 (4): 726–34

    Article  PubMed  Google Scholar 

  48. Lambert MI, Dugas JP, Kirkman MC, et al. Changes in running speeds in a 100 km ultra—marathon race. J Sports Sci Med 2004; 3: 167–73

    Google Scholar 

  49. Laursen PB, Rhodes EC, Langill RH, et al. Relationship of exercise test variables to cycling performance in an Ironman triathlon. Eur J Appl Physiol 2002; 87 (4-5): 433–40

    Article  PubMed  CAS  Google Scholar 

  50. O’Toole ML, Douglas PS, Hiller WD. Use of heart rate monitors by endurance athletes: lessons from triathletes. J Sports Med Phys Fitness 1998; 38 (3): 181–7

    PubMed  Google Scholar 

  51. Neumayr G, Pfister R, Mitterbauer G, et al. Effect of ultramarathon cycling on the heart rate in elite cyclists. Br J Sports Med 2004; 38 (1): 55–9

    Article  PubMed  CAS  Google Scholar 

  52. Neumayr G, Pfister R, Mitterbauer G, et al. Exercise intensity of cycle—touring events. Int J Sports Med 2002; 23 (7): 505–9

    Article  PubMed  CAS  Google Scholar 

  53. Coyle EF, Coggan AR. Effectiveness of carbohydrate feeding in delaying fatigue during prolonged exercise. Sports Med 1984; 1 (6): 446–58

    Article  PubMed  CAS  Google Scholar 

  54. Laursen PB, Rhodes EC. Factors affecting performance in an ultraendurance triathlon. Sports Med 2001; 31 (3): 195–209

    Article  PubMed  CAS  Google Scholar 

  55. Lepers R, Maffiuletti NA, Rochette L, et al. Neuromuscular fatigue during a long—duration cycling exercise. J Appl Physiol 2002; 92 (4): 1487–93

    PubMed  Google Scholar 

  56. Hausswirth C, Bigard AX, Guezennec CY. Relationship between running mechanics and energy cost of running at the end of a triathlon and a marathon. Int J Sports Med 1997; 18 (5): 330–9

    Article  PubMed  CAS  Google Scholar 

  57. St Clair Gibson A, Baden DA, Lambert MI, et al. The conscious perception of the sensation of fatigue. Sports Med 2003; 33 (3): 167–76

    Article  Google Scholar 

  58. Laursen PB, Suriano R, Quod MS, et al. Core temperature and hydration status during an Ironman triathlon. Br J Sports Med 2006; 40 (4): 320–5

    Article  PubMed  CAS  Google Scholar 

  59. Padilla S, Mujika I, Angulo F, et al. Scientific approach to the 1−h cycling world record: a case study. J Appl Physiol 2000; 89: 1522–7

    PubMed  CAS  Google Scholar 

  60. di Prampero PE, Cortili G, Mognoni P, et al. Equation of motion of a cyclist. J Appl Physiol 1979; 47 (1): 201–6

    PubMed  Google Scholar 

  61. Morton RH. The critical power and related whole—body bioenergetic models. Eur J Appl Physiol 2006; 96 (4): 339–54

    Article  PubMed  Google Scholar 

  62. Fukuba Y, Whipp BJ. A metabolic limit on the ability to make up for lost time in endurance events. J Appl Physiol 1999; 87 (2): 853–61

    PubMed  CAS  Google Scholar 

  63. Zamparo P, Bonifazi M, Faina M, et al. Energy cost of swimming of elite long—distance swimmers. Eur J Appl Physiol 2005; 94 (5-6): 697–704

    Article  PubMed  CAS  Google Scholar 

  64. Kennedy MD, Bell GJ. Development of race profiles for the performance of a simulated 2000−m rowing race. Can J Appl Physiol 2003; 28 (4): 536–46

    Article  PubMed  Google Scholar 

  65. Paterson S, Marino FE. Effects of deception of distance on prolonged cycling performance. Percept Mot Skills 2004; 98: 1017–26

    Article  PubMed  CAS  Google Scholar 

  66. Liedl MA, Swain DP, Branch JD. Physiological effects of constant versus variable power during endurance cycling. Med Sci Sports Exerc 1999; 31 (10): 1472–7

    Article  PubMed  CAS  Google Scholar 

  67. Atkinson G, Peacock O, Law M. Acceptability of power variation during a simulated hilly time trial. Int J Sports Med 2007; 28: 157–63

    Article  PubMed  CAS  Google Scholar 

  68. Noakes TD. Lore of running. 4th ed. Champaign (IL): Human Kinetics, 1985

    Google Scholar 

  69. Palmer GS, Borghouts LB, Noakes TD, et al. Metabolic and performance responses to constant—load vs variable—intensity exercise in trained cyclists. J Appl Physiol 1999; 87 (3): 1186–96

    PubMed  CAS  Google Scholar 

  70. Monod H, Scherrer J. The work capacity of a synergic muscular group. Ergonomics 1965; 8: 329–38

    Article  Google Scholar 

  71. Moritani T, Nagata A, de Vries HA, et al. Critical power as a measure of physical work capacity and anaerobic threshold. Ergonomics 1981; 24 (5): 339–50

    Article  PubMed  CAS  Google Scholar 

  72. Hill DW, Rose LE, Smith JC. Estimates of anaerobic capacity derived using different models of the power—time relationship [abstract]. Med Sci Sports Exerc 1993; 25: S108

    Google Scholar 

  73. Noakes TD, Peltonen JE, Rusko HK. Evidence that a central governor regulates exercise performance during acute hypoxia and hyperoxia. J Exp Biol 2001; 204 (Pt 18): 3225–34

    PubMed  CAS  Google Scholar 

  74. Lambert M, St Clair Gibson A, Noakes TD. Complex systems model of fatigue: integrative homoestatic control of peripheral physiological systems during exercise in humans. Br J Sports Med 2005; 39: 52–62

    Article  PubMed  CAS  Google Scholar 

  75. St Clair Gibson A, Noakes TD. Evidence for complex system integration and dynamic neural regulation of skeletal muscle recruitment during exercise in humans. Br J Sports Med 2004; 38: 797–806

    Article  Google Scholar 

  76. Ulmer HV. Concept of an extracellular regulation of muscular metabolic rate during heavy exercise in humans by psychophysiological feedback. Experientia 1996; 52: 416–20

    Article  PubMed  CAS  Google Scholar 

  77. St Clair Gibson A, Schabort EJ, Noakes TD. Reduced neuromuscular activity and force generation during prolonged cycling. Am J Physiol Regul Integr Comp Physiol 2001; 281 (1): R187–96

    Google Scholar 

  78. Hettinga FJ, De Koning JJ, Broersen FT, et al. Pacing strategy and the occurrence of fatigue in 4000−m cycling time trials. Med Sci Sports Exerc 2006; 38 (8): 1484–91

    Article  PubMed  Google Scholar 

  79. Hunter AM, St Clair Gibson A, Lambert MI, et al. Effects of supramaximal exercise on the electromyographic signal. Br J Sports Med 2003; 37 (4): 296–9

    Article  PubMed  CAS  Google Scholar 

  80. Ansley L, Schabort E, St Clair Gibson A, et al. Regulation of pacing strategies during successive 4−km time trials. Med Sci Sports Exerc 2004; 36 (10): 1819–25

    Article  PubMed  Google Scholar 

  81. Gandevia SC. Spinal and supraspinal factors in human muscle fatigue. Physiol Rev 2001; 81 (4): 1725–89

    PubMed  CAS  Google Scholar 

  82. Amann M, Eldridge MW, Lovering AT, et al. Arterial oxygenation influences central motor output and exercise performance via effects on peripheral locomotor muscle fatigue in humans. J Physiol 2006; 575 (Pt 3): 937–52

    Article  PubMed  CAS  Google Scholar 

  83. Behncke H. A mathematical model for the force and energetics in competitive running. J Math Biol 1993; 31 (8): 853–78

    Article  PubMed  CAS  Google Scholar 

  84. van Ingen Schenau GJ, de Koning JJ, de Groot G. A simulation of speed skating performances based on a power equation. Med Sci Sports Exerc 1990; 22 (5): 718–28

    Article  PubMed  Google Scholar 

  85. Perrey S, Grappe F, Girard A, et al. Physiological and metabolic responses of triathletes to a simulated 30−min time—trial in cycling at self—selected intensity. Int J Sports Med 2003; 24 (2): 138–43

    Article  PubMed  CAS  Google Scholar 

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Acknowledgements

Chris Abbiss is supported by an Australian Postgraduate Award (Department of Education, Science and Training, Australia) and an Edith Cowan University Excellence Award (ECU Postgraduate Scholarship Office, Edith Cowan University, Australia). There are no conflicts of interest that relate to the contents of this manuscript.

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Abbiss, C.R., Laursen, P.B. Describing and Understanding Pacing Strategies during Athletic Competition. Sports Med 38, 239–252 (2008). https://doi.org/10.2165/00007256-200838030-00004

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