Special Topics in Sports Engineering

Date: June 3 until June 15 , 2019
Location: Delft University of Technology 

As from 2015, the TU Delft Sports Engineering Institute will organize an international 2-week intense course on Sports Engineering for MSc students, in close collaboration with Sheffield Hallam University and VU University, Faculty of Human Movement Sciences.  Prof DirkJan Veeger is chairing this course for the TU Delft Sports Engineering Institute. 
If you are interested in joining, or have any question, please contact the Sports Engineering Institute, or Prof DirkJan Veeger directly.

Course contents

Special Topics in Sports Engineering is an inter-university course for Master students in Mechanical Engineering, Movement Sciences, Sport Sciences and other related MSc programmes. The course is organised as a two-week intensive course, and comprises lectures, demonstrations, practicals, hands-on research and a final field test. The course will be taught by staff from Delft University of Technology, Sheffield Hallam University and VU Amsterdam. The course is organised around a basic theme relevant for sports engineering.

During the course students will work out what aspects determine cycling performance, and collect data (through experiments or literature research) that are needed to develop / feed a simulation programme for the estimation of the optimal bike – rider combination and the maximal performance humanly possible. The course’s final activity will be a test ride to quantify the differences between actual performance and predicted performance.

In this course, students will have to answer the question:

Given a particular bike, what will be your own predicted time over a distance of 650 m. and how well does this match reality? For an impression of the final tests in 2017-2018 see this video:

The prediction should be based on a power-based simulation model of cycling and the relevant bike- and rider dependent parameters, which have to be collected experimentally. The same does of course apply to the measurement of “reality” 

Answering this question will require insight in relevant parameters, but also collecting these parameters, for each individual student with his or her individual bike. The bike in question can be chosen freely and might therefore be a top-end racing bike as well as your grand mothers shopping bike …

Study goals

After following this course, students should understand the complexity of maximizing sports performance and the importance of the inclusion of material – athlete interaction. More specifically, students should be:

  • Familiar with the Power Equation[1] concept and be able to apply this to cycling;
  • have knowledge of methodological aspects of sports research, in particular error propagation, man – machine interaction (closed loop complexity), measurement techniques, internal and external validity.
  • have insight in the organizational and psychological complexities of sports innovation.
  • able to measure key parameters needed for power equations, related to their own field and have experience in the measurement of key parameters in adjacent fields[2];
  • able to provide a cycling performance simulation programme with the parameters necessary to evaluate performance on a realistic level;
  • able to collect and present to fellow group members, data on parameters for such a simulation program.
  • present research findings through an individual portfolio, and a group presentation/poster/brief oral.
Educational methods
  • lectures
  • practicals
  • group work

To successfully finish the course, students should be able to present a portfolio that at least comprises the following:

  • overview of scientific literature studied. This should be at least five scientific papers on experimental studies covering the range of the course. Students should be prepared to be questioned on these papers following portfolio submission.
  • Own test results for the physiological parameter collection experiment.
  • Test results + description for the drag test experiment
  • Description of the cycle performance modelling process, including the final model prediction. This description should include

    • description of data input including a short description of the procedure along which those were obtained. These data should be individualized for both rider and bike
    • A prediction of cycling performance (i.e. a model result)

  • Short description of individual experimental results, comparison with model prediction and critical appraisal of the differences between predictions and experiment.

This course is taught in block format: as a two-week intensive inter-university course