23-27 Mar 2020 Paris (France)

ICT Week AI & Sport

European ICT Week, Paris-Nord

 Artificial Intelligence  & Sport

 March 23 to 27, 2020 

Paris 13 University (Paris North)

IUT de Villetaneuse

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During the week, you will learn the many faces of Artificial Intelligence by working on a project to solve a problem with data coming from the sport sector. Expert lectures, workshops and working in a team are incorporated in the program to help you in this mission.


The registration fee of 250 euros includes 5 nights with breakfast, 5 lunches, 1 dinner, social and cultural activities. You will be invoiced by your own school.

Target population

  • 30 students,

Level of study

  • BAC +2 (IUT system in France)
  • Bachelor (second or third year)

Evaluation of the performance in football

No matter the sport, studying and improving the performance is a major asset towards success, whether individual or collective. However, there are many factors influencing the performance that are more or less manageable by sport scientists, coaches, or athletes themselves. For example, missing the rowing gold medal for few centimeters because of the sub-optimal shape of the boat, losing a collective game because of the lack of strategic study of the opposing team, or losing a 100m race for 3’’ with a starting position that was not prepared enough are really frustrating results considering we had potential solutions at our disposal.

This data challenge, in a smaller scale than the examples above, aims at studying one the step of the improvement in sport: the evaluation of the performance.

Several steps and some preliminary questions are required to reach this goal:

  1. Knowing your data: visualize, explore, synthetize.
  2. Are there clusters of similar patterns among football players?
  3. Can we predict the number of goals scored?
  4. Can we assess the field position of the player?
  5. Can we create a score that evaluates the annual performance for each player?
  6. Can we draw a map of the performances for a team?

The available data: name of the player, starting year of the championship (from 2009 to 2016), club, age, height, weight, field position, nb of game played, nb of minutes played, nb of assists, nb of yellow cards, red cards, average nb of shot per game, average % of successful pass, average nb of aerial won and nb of ‘man of the match’ awards.









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