Jurgen Perl chaired these sessions and introduced the first presenter in the session, Dietmar Saupe (Universitat Konstanz). Dietmar noted the Wikipedia link to Dynamical Systems. He provided a mathematical introduction to dynamical systems.
The abstract for Dietmar’s presentation is:
Based on a physical model for the forces that must be applied by pedaling while cycling and a simple physiological model for the exertion of the athlete as a function of his/her accumulated power output, an optimal riding strategy for time trials on mountain ascents is computed. A combination of the two models leads to a mathematical optimization problem that can be solved numerically by discretization. The physical model depends most sensitively on an accurate estimation of the road slope on the course. For this purpose, we also present a new method that combines model-based slope estimations with noisy measurements from multiple GPS signals of differing quality. Altogether, we provide a means to analyze rider performance, to identify and quantify potential performance improvement, as well as to instruct the athlete exactly where and how to change his/her pacing strategy to achieve these gains.
He presented a Model-Based Optimisation of Pacing Strategies for Cycling Time Trials (work underway with colleagues Stefan Wolf and Thorsten Dahl) with particular reference to uphill time trials. He used example data from Schienenberg. Dietmar uses power and velocity data combined with GPS and DGPS data to have better estimate the of slope of a climb.
Dietmar discussed Monod and Scherrer’s (1965) and Morton’s (1996) three-parameter Hyperbolc Model and applied these to the Schienenberg simulator rides. Future work in Dietmar’s research group will seek to improve the endurance model, improve the visualisation of proposed velocity during the ride and then test it in the field.
Jaime Sampalo (Universidade de Trás-os-Montes – Vila Real) was the second presenter of the morning session. He discussed GPS data for player positioning in football and their tactical importance. Discussed distance between players in game situations and presented data from a six week intervention study. (See also, Kannekens et al 2011.)
Koen Lemmink (University of Gronigen) was the final presenter of the first morning session. The main focus Koen’s research is performance monitoring during matches and training in ball team sports, like soccer, field hockey, and baskeball. Koen discussed video (SportVU, ProZone, Amisco) and electronic (InMotio LPM, Global Sports) tracking systems and gave an example of the SportVU system at PSV Eindhoven.
Koen shared data from a study reported in Frencken, Lemmink, Delleman and Visscher (2011). The abstract for this paper is:
There is a need for a collective variable that captures the dynamics of team sports like soccer at match level. The centroid positions and surface areas of two soccer teams potentially describe the coordinated flow of attacking and defending in small-sided soccer games at team level. The aim of the present study was to identify an overall game pattern by establishing whether the proposed variables were linearly related between teams over the course of the game. In addition, we tried to identify patterns in the build-up of goals. A positive linear relation and a negative linear relation were hypothesized for the centroid positions and surface areas respectively. Finally, we hypothesized that deviations from these patterns are present in the build-up of goals. Ten young male elite soccer players (mean age 17.3, s=0.7) played three small-sided soccer games (4-a-side) of 8 minutes as part of their regular training routine. An innovative player tracking system, local position measurement (LPM), was used for obtaining player positions at 45 Hz per player. Pearson correlation coefficients were calculated to investigate the proposed linear relation of the key variables. Correlation coefficients indicate a strong positive linear relation during a whole game for the centroid position in all three games, with the strongest relation for the forward-backward direction (r>0.94). For 10 out of 19 goals a crossing of the centroids in this direction can be seen. No negative linear relation was found for surface area (−0.01 < r<0.07). From this study, we concluded that over the course of a whole small-sided game, the forward-backward motion of the centroids is most strongly linearly related. Furthermore, goals show a specific pattern in the forward-backward motion of the centroid. Therefore, surface area and particularly centroid position may provide a sound basis for a collective variable that captures the dynamics of attacking and defending in soccer at team level. Future research should develop these ideas further.
All three presentations stimulated a large number of questions.