- Docente: David Neil Manners
- Credits: 4
- SSD: MEDF-01/A
- Language: Italian
- Teaching Mode: In-person learning (entirely or partially)
- Campus: Bologna
- Corso: Second cycle degree programme (LM) in Sciences and Techniques of Sports Activities (cod. 6783)
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 cleansing.
Descriptive and predictive analysis. Knowing how to adapt analysis to specific purposes.
Reports and dashboards: Power BI. The fundamentals; importing and transforming data.
Creating tables, graphs, and KPI cards. Relational models. Exercise: Dashboards for indicators related to a single session.
From Excel formulas to programming basics.
Advanced dashboards. DAX for custom metrics. Exercise: Monthly analysis of load trends.
Review and optimization of dashboards. Review of built models. Avoiding common mistakes.
Applying analysis in a real-world context, with case studies.
Workshop on the final project.
Presentation of final projects. 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.
Illustrated case studies by a professional in the field
In-class exercises
Small group work to prepare a presentation on a freely chosen data collection, approved by the instructor, 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 any students unable to participate in all group activities, the exam will consist of an oral exam that will test the knowledge and skills acquired during the course, and a practical test of the ability to use an IT tool to manage and transform data for analytical purposes.
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Regarding learning assessment, the use of artificial intelligence (AI) is encouraged for developing formulas in spreadsheets and programming scripts, which are necessary to transform data and complete the analysis required for the final exam.
Limited, declared, and non-substantial use of AI for support activities (synthesis, reformulations) is permitted. Substantial use for completing parts of the exam (documentation, presentation) is not permitted.
Teaching tools
Copies of the slides shown in class (available on the EOL application) and related notes.
Various datasets for practical exercises, each accompanied by a sheet of guided exercises.
Some exercises will be conducted in the classroom, so students are required to bring a laptop with internet access.
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Students with learning disabilities (LD) or temporary or permanent disabilities: please contact the relevant University office in advance (https://site.unibo.it/studenti-con-disabilita-e-dsa/it). They will advise the affected students of any adaptations, which must be submitted to the instructor for approval 15 days in advance, who will evaluate their suitability, also taking into account the learning objectives of the course.
Office hours
See the website of David Neil Manners