Classification in football

Classification in football: Activity classification using sensor data in football

Kaketsis, Georgios (TU Delft Electrical Engineering, Mathematics and Computer Science; TU Delft Delft Institute of Applied Mathematics)

Jansen, Kaspar (mentor)
Söhl, Jakob (graduation committee)
Jongbloed, Geurt (graduation committee)

Delft University of Technology

2020-07-01

The goal of this report is to present and describe the effort surrounding the completion of the Master Thesis Project of classification in football. Classification is a procedure which belongs in the field of Statistics. The objective is to capture, detect and distinguish certain actions relative to the environment of analysis. In our case we are focused on classification for actions related to football, using sensor data. As a first step we introduce the motivation that enforces our project and also the nature of the sensors that provide us with the information for our analysis. We follow with some enlightening review of previous research on motion recognition related projects, in order to have some supplemental information that will provide us with experience that will serve as guidance and further direction. Afterwards we introduce the methodology of the classification. The methodology includes all the models and tools needed to achieve a precise and robust classification outcome. As a next step, we dive through the details of the experiment we are going to analyse, while we explain the process followed in order to create a refined data set that will consist the input of our models. After the description of the data and the preprocessing procedure, we present the results obtained by the analysis along with the evaluation and relative comparisons. Finally, we give the most important conclusions we reached in the whole process along with some proposals for future improvements.

Classification
Applied mathematics
Stochastics
Statistics
Data Science
Sports
Football
Soccer
Sensors
IMU

http://resolver.tudelft.nl/uuid:e2fde103-0a8a-42a2-a2c3-6e99f82650d0

Student theses

master thesis

© 2020 Georgios Kaketsis