PlayField: An Adaptable Framework for Integrative Sports Data Analysis

• Type: Article in International Conference Proceedings Book
• Date: 2024

Authors

Pinto, F (Author) Other; Lima, B (Author) FEUP

Conference proceedings International

Title: Proceedings - 2024 IEEE/ACM International Conference on Big Data Computing, Applications and Technologies, BDCAT 2024

Pages: 107-112

11th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies, BDCAT 2024

Sharjah, 16 December 2024 through 19 December 2024

Indexing

Scopus - 0 Citations

Other information

Authenticus ID: P-018-HA6

DOI: 10.1109/bdcat63179.2024.00028

Abstract (EN): As sports analytics evolve to include a broad spectrum of data from diverse sources, the challenge of integrating heterogeneous data becomes pronounced. Current methods struggle with flexibility and rapid adaptation to new data formats, risking data integrity and accuracy. This paper introduces PlayField, a framework designed to robustly handle diverse sports data through adaptable configuration and an automated API. PlayField ensures precise data integration and supports manual interventions for data integrity, making it essential for accurate and comprehensive sports analysis. A case study with ZeroZero demonstrates the framework's capability to improve data integration efficiency significantly, showcasing its potential for advanced analytics in sports. © 2024 IEEE.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 5