- Docente: David Neil Manners
- Credits: 4
- SSD: M-EDF/01
- Language: Italian
- Teaching Mode: Traditional lectures
- Campus: Bologna
- Corso: Second cycle degree programme (LM) in Sciences and Techniques of Sports Activities (cod. 9069)
Learning outcomes
The course introduces students to technological solutions and advanced methods for analyzing data obtained from the main protocols used in exercise science, such as movement analysis and training supervision. These objectives will be achieved through individual exercises using advanced analysis tools.
Course contents
Introduction to the course. Data analytics in sports organizations.
Data formats. Excel for data recording. Data cleaning.
Descriptive, predictive, and prescriptive analytics. Adapting analysis to the objective.
Reports and dashboards: Power BI. The fundamentals; data import and transformation.
Creating tables, graphs, KPI cards. Relational models. Exercise: dashboards for single-session indicators.
From Excel formulas to programming basics.
Advanced dashboards. DAX for custom metrics. Exercise: monthly load trend analysis.
Reviewing and optimizing dashboards. Revision of previously built models. Avoiding common mistakes.
An overview of protocols. Individual tests, biomechanics, spirometry data, GPS, etc.
Preparing for the final project. Organizing the assignment. Final project workshop.
Beyond dashboards. Programming environments and markdown.
Final project presentations. Concluding remarks.
Readings/Bibliography
The course does not have a set text. For the curious student who would like to delve deeper into some of the topics covered in the course, I recommend:
“Moneyball: The Art of Winning an Unfair Game” by Michael Lewis (2004) – a curious episode in the history of sports analytics, later dramatized in the film “Moneyball” (2011)
“Sports Analytics: A Guide for Coaches, Managers, and Other Decision Makers” by Benjamin Alamar (2013) – an overview of the use of data analytics in professional sports
“Soccermatics: mathematical adventures in the beautiful game” by David Sumpter (2017) – how mathematics can help in sports analysis (and more specifically, soccer)
Teaching methods
Lectures.
Case studies illustrated by a professional football “data analyst”
In-class exercises
Work in pairs or individually to prepare a presentation on a self-chosen data collection (after instructor approval), consisting of three phases: data transformation into a form useful for analysis, statistical/graphical analysis, and presentation preparation.
Assessment methods
Attendance and participation in group activities will constitute the first element of evaluation for the final assessment.
The final exam will consist of two components:
1. The submission of preparatory materials for the final presentation (data, source code, other documentation), to be submitted electronically along with the presentation.
2. A short presentation in which candidates demonstrate their ability to communicate the results of their analysis of acquired data in a real-world context.
For students unable to participate in all group activities, the first and third components of the exam will be replaced by a more in-depth oral exam that will test the knowledge and skills acquired during the course.
Teaching tools
Copies of the slides shown in class (available from the EOL application).
Some exercises will be conducted in class, so students are required to bring a laptop with internet access.
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Students with specific learning disabilities are asked to contact the instructor in advance to discuss possible modifications to the materials, additional support, and the exam format.
Office hours
See the website of David Neil Manners