Athlete Monitoring: Data Analysis and Visualization

In this first module, I will (hopefully) provide a “Big Picture” overview of what athlete monitoring is, as well as introduce a few theoretical models that guide my rationale and inferences.In the first video, I will introduce the Circular Causal Model (see figure below) and Circular Performance Model. Funny enough, I just noticed how similar they are, although they cover different phenomena. I also touch on the topic of transparency of theory – hence the reason why I shared these models, so you can easily see where my ideas are coming from.

I have also provided an extensive list of resources, not only for this lecture but for the whole course. I will continue adding resources underneath each lecture if needed.

References

Books

1. Jovanovic, M. (2020). Strength Training Manual: The Agile Periodization Approach (Volume One & Two: Theory). Paperback
2. Jovanovic, M. (2020). bmbstats: Bootstrap Magnitude-based Statistics for Sports Scientists. Online
3. McGuigan, M. (2017). Monitoring Training and Performance in Athletes. Human Kinetics. Paperback

Blogs

1. Delaney, J. (2018). The paradox of “invisible” monitoring: The less you do, the more you do! Link
2. Jovanovic, M. (2014). Analysis of Metabolic Power Data Using Power-Duration Profile in Team Sports. Link
3. Jovanovic, M. (2013). Real-Time Fatigue Monitoring using Metabolic Power and CP/W’. Link
4. Jovanovic, M. (2020). How to Design Wellness Questionnaire? Find out what are the best practices and how to avoid common mistakes. Link
5. Jovanovic, M. (2020). Extending the Classical Test Theory with Circular Performance Model. Link
6. Morin, JB. (2020). The “in-situ” sprint profile for team sports: testing players without testing them? Link

Papers

1. Borsboom, D. Latent Variable Theory. Measurement: Interdisciplinary Research & Perspective 6: 25–53, 2008.
2. Borsboom, D. Measuring the mind: conceptual issues in modern psychometrics. Cambridge: Cambridge University Press, 2009.
3. Borsboom, D, Mellenbergh, GJ, and van Heerden, J. The theoretical status of latent variables. Psychological Review 110: 203–219, 2003.
4. Fried, EI. Lack of theory building and testing impedes progress in the factor and network literature. PsyArXiv, 2020. Available from: https://osf.io/zg84s
5. Fried, EI. Theories and models: What they are, what they are for, and what they are about. PsyArXiv, 2020. Available from: https://osf.io/dt6ev
6. Gelman, A and Hennig, C. Beyond subjective and objective in statistics. Journal of the Royal Statistical Society: Series A (Statistics in Society) 180: 967–1033, 2017.
7. Guyon, H, Falissard, B, and Kop, J-L. Modeling Psychological Attributes in Psychology – An Epistemological Discussion: Network Analysis vs. Latent Variables. Frontiers in Psychology 8, 2017.Available from: http://journal.frontiersin.org/article/10.3389/fpsyg.2017.00798/full
8. Guyon, H, Kop, J-L, Juhel, J, and Falissard, B. Measurement, ontology, and epistemology: Psychology needs pragmatism-realism. Theory & Psychology 28: 149–171, 2018.
9. Phillips, LD. A theory of requisite decision models. Acta Psychologica 56: 29–48, 1984.
10. Smaldino, P. (2017). Models are stupid, and we need more of them. Computational Social Psychology, 311–331. https://doi.org/10.4324/9781315173726
11. Yarkoni, T. Implicit realism impedes progress in psychology: Comment on Fried (2020). PsyArXiv, 2020. Available from: https://osf.io/xj5uq