Report on Current Developments in Esports and Sports Science Research
General Direction of the Field
The recent developments in the intersection of esports and sports science research are notably advancing the field towards more standardized and structured training methodologies, enhanced public perception analysis, and the integration of advanced technologies such as deep learning for sports analytics. The field is moving towards a more interdisciplinary approach, leveraging insights from psychology, nutrition, informatics, and data science to create comprehensive training regimes and performance metrics for esports athletes.
One of the key trends is the standardization of training systems for esports players, drawing parallels from traditional sports sciences. This involves the development of exercise regimes and periodization strategies tailored specifically for esports, which have been largely fragmented across various research areas. The challenge lies in assembling these fragmented insights into a cohesive theory of performance enhancement in esports.
Another significant direction is the exploration of public perceptions and value co-creation within the esports ecosystem. Studies are increasingly focusing on how social media and event logistics influence public opinion and promote esports events and brands. This includes the strategic use of large language models (LLMs) and topic modeling techniques to analyze and shape public perceptions, particularly in the context of major events like the Asian Games.
The integration of deep learning and AI technologies into sports analytics is also a burgeoning area. Research is being directed towards developing automated systems for action spotting and performance analysis in sports videos, which can significantly enhance coaching support, fan engagement, and sports analytics. The creation of large-scale datasets and benchmarks for sports video understanding is paving the way for more sophisticated and accurate AI-driven sports analysis tools.
Noteworthy Developments
Esports Training and Periodization: The field is making strides towards unifying fragmented research areas into a cohesive theory of performance enhancement in esports, though significant work remains in assembling these insights into practical training regimes.
Public Perceptions and Value Co-Creation: Studies using LLM-enhanced topic modeling are providing valuable insights into how social media and event logistics influence public opinion and promote esports, highlighting the importance of cross-subcultural collaborations.
Deep Learning in Sports Analytics: The development of large-scale datasets and benchmarks for sports video understanding is driving advancements in automated action spotting and performance analysis, making AI-driven sports analytics a viable and increasingly accurate tool for the industry.
