Athlete Monitoring: Data Analysis and Visualization
Introduction – Part 2
In the second video, I am providing a basic overview of the three tasks of the statistical analysis, three types of machine learning, and finally, how we can use athlete monitoring – or in other words, for what problems that we have. Besides bmbstats book, I also suggest checking the books below.
Papers & Posts
- Jovanovic, M. (2017). UNCERTAINTY, HEURISTICS AND INJURY PREDICTION. Aspetar Journal. Link
- Jovanovic, M. (2018). Predicting non-contact hamstring injuries by using training load data and machine learning models. Link
- Jovanovic, M. (2018). Data Preparation for Injury Prediction. SPSR – 2018, Aug, 34, v1. Link
- Jovanović, M. bmbstats: Bootstrap Magnitude-based Statistics for Sports Scientists. Mladen Jovanović, 2020.
- Foreman, JW. Data smart: using data science to transform information into insight. Hoboken, New Jersey: John Wiley & Sons, 2014.
- Spiegelhalter, D. The art of statistics: how to learn from data. New York: Basic Books, an imprint of Perseus Books, a subsidiary of Hachette Book Group, 2019.
- Gelman, A, Hill, J, and Vehtari, A. Regression and Other Stories. S.l.: Cambridge University Press, 2020.
- McElreath, R. Statistical rethinking: a Bayesian course with examples in R and Stan. 2nd ed. Boca Raton: Taylor and Francis, CRC Press, 2020.
- James, G, Witten, D, Hastie, T, and Tibshirani, R. An Introduction to Statistical Learning: with Applications in R. 1st ed. 2013, Corr. 7th printing 2017 edition. New York: Springer, 2017.
- Kuhn, M and Johnson, K. Applied Predictive Modeling. 1st ed. 2013, Corr. 2nd printing 2016 edition. New York: Springer, 2018.