Dr Florentina Hettinga investigates how we can measure, evaluate and improve athletic performance.
Historically, apart from boxing and wrestling, completing a certain distance as fast as possible by any mode of transport was one of the first sporting events. Running is of course the most basic and obvious sport activity, but throughout the years, novel modes of transport were introduced and consequently transferred into sports. For example, both cycling and speed skating were originally introduced and developed as modes of transport, but are now popular Olympic sports disciplines.
However, even when the task is simply to go from A to B as fast as possible by any means of transport, understanding and optimizing human performance is not an easy task as multiple aspects can be of influence. Think of the physical characteristics of the athlete, aerodynamics, environmental characteristics such as terrain, fatigue, and/or the presence of opponents. To pace your race optimally, both internal (related to the athlete) and external (related to the environment) factors are important.
In a time-trial of any of these sports, the process of how and when to invest energy is crucial and dependant of a variety of aspects related to physiology, biomechanics and psychology. Pacing behaviour in time-trial sports is therefore a well-researched topic since the 90-s and scientists started to explore optimal pacing by experimentally imposing different pacing strategies.
As you can imagine, this was pretty time consuming and athletes were not always willing to repetitively perform time trials with different pacing strategies just to explore which one would be optimal.
Fortunately, computer and programming technological innovations were upcoming, allowing scientists to model athletes, e.g. by using an energy flow model. By modelling an athlete in terms of energy, scientists could have virtual athletes complete over a 1000 time trials with different strategies and under different circumstances. These models could be used to predict optimal performance. Generally, results indicated that a fast start strategy was optimal for distances shorter than 2min. How fast exactly remains up for debate. For longer distances, an even-paced strategy was advised. In addition, when wind or hills are included in the game, eg in riding stages in the Tour de France, athletes should adapt to the variations in wind and gradient by adopting a variable power strategy, parallelling the changes in the environment.
If we think of practical implications, we see that nowadays, cycling ergometers have been developed incorporating these environmental circumstances. In addition, equipment is allowing athletes to race against virtual athletes: Avatars are projected on screen in front of the athletes, creating an indoor setting in which athletes can train realistically.
Though a lot is known on time trial performance nowadays, understanding how athletes respond to the actions of their opponents, what optimal race tactics are, and how these are dependant on internal as well as external characteristics (such as opponents) is still rather unexplored. Science as well as practice has shown that athletes perform better when they compete against an opponent. With the Sports, Performance and Fatigue research unit of the University of Essex (Centre of Sport and Exercise Science, School of Biological Sciences), we are exploring how, when and why athletes respond to their opponents and also what information is relevant in making their decisions.
For example, the presence of opponents affects pacing and tactics in sports such as short track speed skating. Where for time trial performance below 2 minutes duration a fast start is advised, it becomes evident that the early stages of the race are not as important when racing against direct opponents. In short track speed skating, where athletes need to beat their opponents in several heats of head-to-head competition; it is in the final stages that competition is decided.
If we look into how future winners are positioning themselves throughout their short track speed skating races, we see that positions early in the race are unrelated to final performance. To win, athletes are advised to be in foremost positions when entering the last 5-6 laps. As can be seen in this chart, a very low % of future winners is already in position in the early stages if the race (adapted from this and this research).
Currently, many more studies are ongoing, exploring optimal strategies related to how and when to invest energy when racing against opponents. Advances in technology have created multiple opportunities to better understand as well as optimize performance. We only just started…