To better understand the outcome of the complex graphs obtained, we introduce the Geometric Activity Performance Index (GAPI) defined as the ratio of the positive predictive performance area divided by the negative predictive performance area. It is a single number that is directly proportional to the positive predictive performance area. Indeed, the higher the GAPI, the bigger this area. Based on this propriety, we aim to identify the GAPI which was the strongest correlated with our main outcome of interest: performance.
Data Interrelations In Sport Science
To obtain a better overview of the training adaptation ongoing in each athlete, we consider combinations of the collected parameters rather than single parameter separately. We considered combinations of two training and/or recovery parameters and added personal best (%PBT) as the third parameter in each combination in order to confront the first two parameters with our performance outcome. For each combination and using Artificial Neural Networks, a geometric optimization, we then provide three-dimensional graphs using each parameter as a different time series.
In 2017, a consensus statement on training monitoring stated that “monitoring athletes’ training load is essential for determining whether they are adapting to their training program, understanding individual responses to training, assessing fatigue and the associated need for recovery, and minimizing the risk of overtraining, injury, and illness” By doing so, training monitoring could also aim to enhance athletic performance. Recently and thanks to the new digital technologies, training monitoring has become increasingly popular in the world of sports. A lot of parameters are as useful to track in order to achieve an efficient athlete- and performance-oriented training monitoring.