Athlete Monitoring: Data Analysis and Visualization
Introduction – Part 1
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.
- Jovanovic, M. (2020). Strength Training Manual: The Agile Periodization Approach (Volume One & Two: Theory). Paperback
- Jovanovic, M. (2020). bmbstats: Bootstrap Magnitude-based Statistics for Sports Scientists. Online
- McGuigan, M. (2017). Monitoring Training and Performance in Athletes. Human Kinetics. Paperback
- Delaney, J. (2018). The paradox of “invisible” monitoring: The less you do, the more you do! Link
- Jovanovic, M. (2014). Analysis of Metabolic Power Data Using Power-Duration Profile in Team Sports. Link
- Jovanovic, M. (2013). Real-Time Fatigue Monitoring using Metabolic Power and CP/W’. Link
- Jovanovic, M. (2020). How to Design Wellness Questionnaire? Find out what are the best practices and how to avoid common mistakes. Link
- Jovanovic, M. (2020). Extending the Classical Test Theory with Circular Performance Model. Link
- Morin, JB. (2020). The “in-situ” sprint profile for team sports: testing players without testing them? Link
- Borsboom, D. Latent Variable Theory. Measurement: Interdisciplinary Research & Perspective 6: 25–53, 2008.
- Borsboom, D. Measuring the mind: conceptual issues in modern psychometrics. Cambridge: Cambridge University Press, 2009.
- Borsboom, D, Mellenbergh, GJ, and van Heerden, J. The theoretical status of latent variables. Psychological Review 110: 203–219, 2003.
- Fried, EI. Lack of theory building and testing impedes progress in the factor and network literature. PsyArXiv, 2020. Available from: https://osf.io/zg84s
- 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
- 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.
- 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
- Guyon, H, Kop, J-L, Juhel, J, and Falissard, B. Measurement, ontology, and epistemology: Psychology needs pragmatism-realism. Theory & Psychology 28: 149–171, 2018.
- Phillips, LD. A theory of requisite decision models. Acta Psychologica 56: 29–48, 1984.
- Smaldino, P. (2017). Models are stupid, and we need more of them. Computational Social Psychology, 311–331. https://doi.org/10.4324/9781315173726
- Yarkoni, T. Implicit realism impedes progress in psychology: Comment on Fried (2020). PsyArXiv, 2020. Available from: https://osf.io/xj5uq