Teacher(s)
Benoit Nicolas; Hardwick Robert (coordinator);
Language
French
Prerequisites
The prerequisite(s) for this Teaching Unit (Unité d’enseignement – UE) for the programmes/courses that offer this Teaching Unit are specified at the end of this sheet.
Main themes
- Analysis of sports disciplines
- Planning and monitoring training load: different models
- Analysis of specific performances to deduce conclusions in terms of training or performance planning
- Identification of high sporting potential: definition of sporting potential, chronological age, biological age, relative age, biochronological age, biological age, relative age, bio--banding.
Learning outcomes
At the end of this learning unit, the student is able to : | |
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Content
This course builds on the knowledge acquired in the LEPHY2152 "Data Science" course and assumes a firm grasp of the associated skills.
The course aims to develop the skills required to implement a comprehensive Sports Analytics approach in the field of physical activity and sport. Students will learn to analyse sports data in order to produce insights that aid in understanding performance and support decision-making. The course covers the main stages of the analytical cycle, including defining a research question, collecting and preparing data, statistical analysis, visualising results and disseminating them to relevant users.
Students will deepen their proficiency in R and RStudio for the management and analysis of sports data, the creation of performance indicators, automated reporting and the development of interactive tools. Through the completion of a practical project, they will apply these skills in a real-world context to design an analytical solution that meets the needs of a stakeholder in the sports sector.
The course aims to develop the skills required to implement a comprehensive Sports Analytics approach in the field of physical activity and sport. Students will learn to analyse sports data in order to produce insights that aid in understanding performance and support decision-making. The course covers the main stages of the analytical cycle, including defining a research question, collecting and preparing data, statistical analysis, visualising results and disseminating them to relevant users.
Students will deepen their proficiency in R and RStudio for the management and analysis of sports data, the creation of performance indicators, automated reporting and the development of interactive tools. Through the completion of a practical project, they will apply these skills in a real-world context to design an analytical solution that meets the needs of a stakeholder in the sports sector.
Teaching methods
Lectures - practical sessions
Evaluation methods
☒ Assessment for this course is based on continuous assessment.
☒ Assessment components:
☒ group work
☒ report / dossier
☒ oral presentation
☒ Weighting : 100%
Further useful information: Students work in groups to develop and present a project involving a comprehensive process of sports data analysis. Assessment is based on two separate marks:
- A mid-term assessment (30%)
- A final assessment (70%)
Assessment in the second session will be based solely on the final project presentation (70 per cent) to the lecturers. As the mark for the mid-term assessment (30 per cent) cannot be improved, it will automatically be carried over.
Other information
The project is carried out in groups of two to three students.
The work involves transforming a set of sports data into useful information for decision-making in the context of training, performance or the monitoring of athletes.
Students can choose the sporting discipline, the dataset and the presentation format (analytical report in Quarto/RMarkdown, interactive dashboard or Shiny application).
This course is strictly reserved for FSM students. It is not open to other UCLouvain students.
The work involves transforming a set of sports data into useful information for decision-making in the context of training, performance or the monitoring of athletes.
Students can choose the sporting discipline, the dataset and the presentation format (analytical report in Quarto/RMarkdown, interactive dashboard or Shiny application).
This course is strictly reserved for FSM students. It is not open to other UCLouvain students.
Faculty or entity
