Data-Driven Approaches for Soccer Match Analysis: an e-Science Perspective

• Applicant: Professor Dr. Daniel Memmert
• Subject Area: Image and Language Processing, Computer Graphics and Visualisation, Human Computer Interaction, Ubiquitous and Wearable Computing
• Term: since 2020
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 432920202

Huge volumes of Complex Data time series have been generated in various sport-related applications. These data require the definition of effective and efficient services for appropriate data storage, retrieval, and analysis. The term Complex Data refers to the universe of potentially non-structured, multimodal data, and therefore covers a wide variety of data types such as text, sound, image, video, graphs or positional information. Therefore, the present research proposal addresses research issues related to the specification and implementation of appropriate systems to handle large-scale Complex Data time series collections using an e-Science perspective. We expect to benefit from recent advances in the state of art on sport analysis and computer science. In particular, with respect to feature extraction, machine learning, and data fusion making use of data sets from sport science applications related to soccer match analysis.

• DFG Programme: Research Grants
• International Connection: Brazil
• Partner Organisation: Fundaçao de Amparo a Pesquisa do Estado de Sao Paulo - FAPESP
• Co-Investigators: Dr. Robert Rein, Ph.D.; Dr. Fabian Wunderlich
• Cooperation Partners: Professor Felipe Moura, Ph.D.; Professor Paulo Roberto Pereira Santiago, Ph.D.; Professor Ricardo da Silva Torres