In a rapidly evolving technology landscape, the intersection of artificial intelligence (AI) and sports science is receiving more attention than ever before. A recent scoping review conducted by researchers S. Kranzinger, C. Halmich, and D. Hofer delves deep into the realm of explainable artificial intelligence in sports science, revealing the complex ways in which AI can enhance athletic performance, improve training plans, and reshape the future of this industry. This study not only highlights advances in AI technology, but also the critical importance of transparency and interpretability in AI-driven solutions, a theme that is increasingly resonating across a variety of scientific disciplines.
Artificial intelligence is a cornerstone of modern sports science, with algorithms designed to analyze vast data sets generated by athletes. From wearables that quantify an athlete’s performance metrics to software that predicts injury risk, the role of AI in optimizing athletic performance is no longer a matter of speculation. The researchers emphasize that a deep understanding of how these AI systems work is essential for coaches, athletes, and officials alike. This is where the concept of explainability in AI becomes important. It bridges the gap between complex algorithms and actionable insights, empowering human users to make informed decisions based on AI-driven observations.
This review scrutinizes various applications of AI in sports, including injury prediction models, performance analysis tools, and interactive coaching strategies. Traditionally, athletes relied heavily on subjective evaluation and intuition based on experience. However, with the integration of AI, sports scientists now have access to a comprehensive suite of objective metrics. These metrics reveal patterns and insights that even the most discerning eye may miss, providing a more systematic approach to training and improving performance. However, as research has shown, many AI systems are opaque, which can make practitioners reluctant to fully trust these technologies.
One of the most interesting findings of this review is the delicate relationship between player health and AI applications. By employing machine learning algorithms, researchers developed a predictive model that can predict injury risk based on historical data and real-time monitoring. Athletes now benefit from customized training sessions that take their individual risk factors into account, allowing them to optimize their performance while minimizing the chance of injury. While these advances are promising, this review calls attention to the need for explainable AI to foster trust. Coaches and athletes need to understand why certain predictions are made in order to effectively follow them.
In addition, the authors also discuss ethical considerations regarding the use of AI in sports. As with any technology related to data, aspects such as privacy and consent will be of paramount importance. As athletes share personal data that may be used to analyze their performance, questions arise about ownership and control of this information. This review advocates for transparent data use policies that not only comply with legal standards but also promote ethical practices. AI explainability allows stakeholders to ensure that athletes are fully informed participants in the data collection process.
The review also highlights the increasing reliance on AI for tactical decision-making during competitions. Coaches can now use real-time data analysis to make informed decisions and change strategies on the fly. This real-time capability is revolutionizing the way sports are played, but it also comes with challenges. Strategists must interpret AI recommendations within the broader context of team dynamics and human intuition. This study argues that explainable AI can facilitate this process, allowing coaches to leverage AI insights while aligning them with their professional knowledge and understanding of the game.
Furthermore, the potential of AI extends to fan engagement and experiences in sports science. Using AI-driven insights, teams can tailor the fan experience and deliver personalized content and interactive features that resonate with viewers. This transformative approach has the potential to redefine how fans perceive and engage with sports. The review highlights that while teams and organizations leverage AI to enhance fan engagement, they must also ensure that the technology they use is easy to understand and transparent to maintain trust and loyalty among supporters.
The review concludes with an energetic call for continued research and development in the field of explainable AI in sports science. As technology continues to evolve, so must the frameworks that manage its applications. The authors urge the scientific community to prioritize the development of explainable AI tools that not only improve athletic performance, but do so in a sustainable and ethical manner. By investing in transparency, they argue, the sports industry can harness the full potential of AI while fostering a culture of trust and collaboration among athletes, coaches, and fans.
In summary, the scoping review by Kranzinger, Halmich, and Hofer vividly paints a picture of the future of sports science through the lens of explainable artificial intelligence. This highlights the critical need for transparency in AI applications and ensures that the benefits of this cutting-edge technology do not come at the expense of ethical standards or stakeholder trust. As the conversation around AI in sports continues to unfold, one thing is clear. That means the integration of explainable AI will play a pivotal role in shaping the future of sports performance and engagement.
As the dawn of a new era in sports science approaches and these technologies continue to develop, we are on a mission to explore not only what AI can do in sports, but also how it can be done responsibly and ethically. Therefore, as we anticipate future developments in this field, the challenge remains to ensure that the power of AI can be harnessed to improve the human experience both on and off the field.
Research theme: Explainable artificial intelligence in sports science
Article title: A scoping review of explainable artificial intelligence in sports science
Article references:
Kranzinger, S., Halmich, C., Hofer, D. et al. A scoping review of explainable artificial intelligence in sports science.
Discob Artif Inter (2025). https://doi.org/10.1007/s44163-025-00709-8
image credits:AI generation
Toi: 10.1007/s44163-025-00709-8
keyword: Explainable AI, sports science, performance analytics, athletic training, injury prediction, ethical considerations, data transparency, fan engagement.
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