APPLYING THE PRINCIPLES OF JOHAN CRUYFF TO DATA SCIENCE
With such an ambitious goal, the Barça Innovation Hub has presented its research at this year’s world-renowned MIT Sloan Sports Analytics international conference.
26 February, 2019
WHICH VARIABLES DO WE TRAINERS LOOK AT TO HELP US MAKE DECISIONS?
Sports Performance
Team Sports
Team sports performance
Of all the variables provided by the systems that monitor and record our players’ activity, what is the most relevant information? This is a question that all coaches will probably face when they begin managing the large volumes of data generated in every training session. However, in most cases, the choice of variables does not actually follow a systematic process, but rather depends on the users’ personal preferences, which are sometimes haunted by trends in the use of variables that have not yet been sufficiently contrasted.
The high quantity of variables supplied by the technological tools available today has made it necessary to study and reduce the number of variables to be managed in the training process. This is why we must choose the most valid options available in order to understand the demands of the sports activity in question. We have to be practical and make this arduous recording process sustainable; in other words, we should take into account the available temporal, material, human and technological resources in such a way that the cost-benefit relationship is as efficient as possible.
Among other options, the implementation of principal components analysis makes it possible to identify the variables that supply the most information, differentiating between those that supply complementary information and those that supply redundant information. This drastically reduces the number of variables or dimensions to study. In the work being presented, the authors recorded different intensity measurements during training tasks and official soccer matches.
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The main findings of this study indicate that three main components are needed to summarize the different intensity measurements, and these offer different weightings or degrees of importance depending on the game format being studied. Regardless of the game format being studied, the 1st component accounts for 40% to 45% of the variance, with average metabolic power being the variable with the greatest weight. The 2nd component accounts for between 20% and 25% of the variance, and this is represented by speed variables (e.g., distance covered at high speed, distance covered at sprint, and peak speed). Lastly, the 3rd component accounts for between 10% and 15% of the variance, in which the inertial variables (dynamic stress load, or DSL, and impact) are grouped.
Thus a practical application of this study indicates that in order to collect the majority of the external load information, regardless of the game format being studied, we need to consider locomotor or strength variables, speed variables, and inertial variables. In short, we would need to consider the following indicators:
- Locomotor or strength variables: average metabolic power (AMP) or total distance covered or high metabolic load distance or distance covered in acceleration and high-intensity decelerations
- Speed variables: Peak speed reached or distance covered at high speed
- Inertial variables: Impact or IMP or dynamic stress load (DSL)
Nevertheless, when game formats change, the importance of the variables within the main component also changes. In small positional play of 4 vs. 4 + 3 in a space of 13 x 17 m, the speed variable that provides the most information is peak speed, followed by distance covered at high speed. For example, in 10 vs 10 situations played in 105 x 65 m, the variable that provides the most information within the second main component is distance covered at sprint, followed by distance covered at high speed, and peak speed. Thus the weight of each variable changes depending on the game format being studied.
In the process of choosing variables for the subsequent management of training and competition loads, we must try not to duplicate information; only the indicators needed to obtain valid and applicable information should be chosen (Castellano and Casamichana, 2016).
David Casamichana – Strength and Conditioning Coach at Real Sociedad
Antonio Gomez – Strength and Conditioning Coach at FC Barcelona
Julen Castellano – Head of the Doctoral Program in Physical Activity and Sports at the University of the Basque Country (UPV/EHU)
Andrés Martin – Strength and Conditioning Coach at FC Barcelona
References.
Gestión de carga de trabajo. Barca Innovation Hub. https://barcainnovationhub.com/es/product/certificado-en-gestion-de-la-carga-de-trabajo-en-futbol/
Castellano, J. and Casamichana, D. (2016). El arte de planificar en fútbol. Futboldelibro.
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