Working Group 4

Common data collection and analysis on sports practice for the university system BI

All Working Groups

Universities participating to the group

Alma Mater Studiorum Università di Bologna, Università di Bergamo, Università di Ferrara, Università di Modena e Reggio, Università di Roma Foro Italico, Università di Sassari, Università di Siena, Università di Trieste.


The comparison within the network and the effort of the working group allow the identification of useful information - both at the University level and in the University System - to a conscious action of strategic administration of university sport and of relations management inter-organizational and internal / external communication of the results achieved.

The identification of common data on sports practice finds a possible limit in the differences that occur within the university system with respect to the objectives pursued by each university in promoting university sport.

We therefore propose the structuring of data collection and analysis models of a multidimensional nature, suitable for considering different aspects related to inputs, outputs, process variables, satisfaction levels, outcome and impacts of university sports practice. One must be aware that an articulated data collection and analysis model is an ambitious project for the working group; on the other hand, the result could be suitable to dynamically support processes of strategic change at the level of general system and individual university with respect to a topic on which awareness is relatively recent.

At the same time, the work could be carried out in a modular way, drawing a model in its general lines and gradually proceeding to deepen its individual parts.


To define the structuring, construction and use of data-sets useful for each university and the university system governing university sport, managing institutional or commercial partnerships, and representing the value created by each university and the university system in favour of students, employees and communities as part of social reporting initiatives.


  1. Identification of information useful for managing university sports, in particular information relating to:
    • levels of participation of students, employees and citizenship
    • the lifestyles of students, with particular reference to matriculated students and undergraduates
    • levels of satisfaction with the university sports experience
    • the relationships between participation in sports activities and academic performance
    • levels of direct participation in the organization of sports activities by students and student associations
  2. Identification of objective parameters to be assigned to the University Sport Centres (CUS) within the annual programs, for example in relation to:
    • Sports initiatives (tournaments, courses, events)
    • Competitive performance
    • Conditions of economic accessibility for students and employees
  3. Identification of information useful to support commercial relationships with partner companies:
    • Data on the sports student population, appropriately segmented by gender, origin, etc.
  4. Identification of information useful for expressing the value generated by universities and the university system (individually and as a system) in university sports:
    • Outcome and social impact of the sports offer
    • Results related to specific SDGs (sustainable development objectives) of the United Nations, such as for example 3. Health and well-being (3.d. Strengthen the capacity [...] to signal in advance, reduce and manage health risks, both at national and global level); 10. Reduce inequalities (10.2., Strengthen and promote the social, economic and political inclusion of all, regardless of age, sex, disability, race, ethnicity, origin, religion, economic status or otherwise), 11 Cities and communities sustainable (11.6 reducing the negative per capita environmental impact of cities, paying particular attention to air quality and the management of urban waste and other waste)
  5. Identification of organizational and technological solutions for data collection and processing:
    • How and when to distribute data collection questionnaires
    • Technological tools such as badges for detecting active participation, apps, etc.

Macro activities

  1. Sharing the status at the individual universities
  2. Identification of best practices
  3. Model processing
  4. Insights related to individual parts of the model

Group coordinator

Prof. Maurizio Marano