Are Swimming’s Fitness and Competitive Industries Data Fit for AI? – Part 2

Introduction

Welcome back! In Part 1, we highlighted why data quality is critical for AI-driven solutions, explored the risks of poor data, and outlined key principles for building AI-ready data structures. Now, it’s time to shift focus from theory to practice. In this second instalment, we take a closer look at the current state of swimming training session data—uncovering gaps, inconsistencies, and missed opportunities. We’ll also explore the potential for a unified framework, with references to Wise Racer’s zone-based systems, and finally address the big question: Is the swimming industry truly ready to embrace AI?

By the end of this post, you will have a clearer picture of the barriers that still exist and actionable insights into how coaches, organizations, and stakeholders can drive the next phase of data-centric innovation across the sport.

Sections Covered in Part 2

• Section 4: The Current State of Swimming Training Session Data Management
  Evaluates how sessions are currently documented, stored, and interpreted—highlighting the inconsistencies and gaps preventing effective data usage.
• Section 5: Opportunity—Setting the Stage for a Unified Framework Explores how combining technological advancements with training zone frameworks can result in standardized, shareable, and actionable data for coaches and athletes.
• Section 6: So, Is the Swimming Industry Data Fit for AI?
  Answers the key question driving this series. Emphasizes the role of swimming coaches, administrators, and innovators in fostering collaboration, adopting universal standards, and ensuring that the sport is fully prepared to harness AI.

Section 4: The Current State of Swimming Training Session Data Management

To build effective AI/ML solutions in swimming, we first need to understand the challenges of gathering, storing, and using training session data in real-world settings. This section analyses the current state of swimming data management through eight core pillars of high-quality data. These pillars highlight common pitfalls faced by coaches, athletes, and sports scientists, as well as the impact of these pitfalls on creating scalable and personalized AI applications.

1. Intrinsic Quality Intrinsic quality refers to the accuracy, consistency, and completeness of raw data values. In swimming, this quality is often compromised by fragmented record-keeping methods and the complexity of digitizing existing training logs. For example, session plans may be handwritten on notebooks or saved as spreadsheets using coach-specific shorthand, which introduces errors during digitization. Additionally, important metrics like lap times, stroke counts, heart rates, and measure units for distance, volume and intensity are sometimes missing or vaguely recorded. Without precise and complete data, AI models struggle to identify meaningful patterns, hindering their ability to make accurate performance predictions and guide training decisions.

2. Contextual Quality
   Contextual quality ensures that data is relevant, timely, and suitable for the specific task at hand. Many swimming session plans are generic and fail to consider individual athlete needs, such as age, gender, injury history, or skill level. This lack of specificity limits AI systems’ ability to customize recommendations across swimmer profiles. Ambiguity in descriptions like “build effort” or “as fast as possible” further complicates intensity analysis. Similarly, failure to record the phase of training—whether it’s off-season, peak competition, or recovery—removes important temporal context. For AI insights to be effective, data must reflect the athlete’s current state and long-term objectives.

3. Representational Quality
   Representational quality deals with how well data is formatted and structured for easy interpretation. Inconsistent representation of session details across teams, such as the use of abbreviations like “DKOB”, "OUS", "UK", or “choice” among a myriad of others can lead to confusion. Moreover, session plans often include nested sets or intervals, which are difficult to capture in flat formats like spreadsheets. Without a standardized data schema, important hierarchical relationships between warm-ups, main sets, complimentary sets, and cooldowns may be lost. Poor representational quality limits AI’s ability to analyse how different components of a workout contribute to overall performance.

4. Accessibility
   Accessibility ensures that data is easily available to authorized users while maintaining security and privacy. One major challenge in swimming is fragmented data storage, with session logs often spread across personal notebooks, apps, and spreadsheets. This lack of centralization creates data silos, preventing comprehensive analysis. Furthermore, session descriptions are created in different languages, and may contain inconsistent terminology, making it difficult for AI tools to interpret them accurately. Improving accessibility requires centralizing data in secure, shared environments where it can be leveraged by coaches, scientists, and athletes without barriers.

5. Data Lifecycle Management
   Data lifecycle management involves tracking data from its creation to its eventual analysis, feedback, and archiving. In many programs, key metrics like in-session and post-session heart rates or rest periods are collected but not consistently fed back into future planning. This feedback gap limits the potential for AI systems to learn and improve over time. In addition, warm-ups and cooldowns are often tracked with less precision than main sets, creating blind spots in workload and recovery monitoring. A closed-loop lifecycle management system is essential for ensuring that both AI systems and coaching strategies evolve based on new data.

6. Ethical and Legal Compliance
   Athlete privacy, data ownership, and regulatory compliance are critical to maintaining trust in AI applications. Issues often arise when swimmers change teams or when minors are involved, raising questions about who owns the data and how it can be shared. Without clear guidelines, organizations may be hesitant to collaborate or pool their data for AI development. Robust privacy policies and informed consent processes can help mitigate these risks while fostering more effective data-sharing practices.

7. Continuous Monitoring and Improvement
   Given the dynamic nature of swimming data—new sensors, changing training programs, and evolving goals—continuous monitoring is vital. However, many teams lack frameworks for regular data audits and improvements. Incomplete metrics and recurring data gaps go unnoticed, leading to unreliable analyses. Continuous monitoring protocols can help detect anomalies, such as implausibly short lap times or missing rest data, and refine data-collection methods accordingly. This iterative approach ensures that data quality remains high as conditions evolve.

8. Integration of Domain Knowledge
   Integrating domain expertise ensures that AI systems can interpret ambiguous or complex data correctly. Coaches, sports scientists, and athletes provide critical insights that AI alone cannot capture. For example, terms like “build”, “feel power”, or "cruise" may carry different meanings depending on the swimmer’s level or training context. Without expert input, AI models risk misinterpreting such instructions. Collaboration with domain experts ensures that AI-generated recommendations align with practical coaching principles, making them more effective and trustworthy.

By analysing the current challenges in managing swimming training session data, we can identify where improvements are needed to build AI-ready datasets. From standardizing data formats and contextualizing metrics to centralizing data storage and fostering collaboration, addressing these challenges will help bridge the gap between raw performance data and actionable AI insights.

Section 5: So, Is the Swimming Industry Data Fit for AI?

After exploring the importance of data quality (Part 1) and analysing the current state of swimming session data management (Part 2), we return to the central question: Is the swimming industry ready to fully leverage AI? The short answer: Not yet—but it’s on its way.

The Importance of Coach Leadership Coaches are the gatekeepers of swimming training data. As the primary creators and custodians of training plans, their role is pivotal in driving data-driven advancements. By adopting standardized intensity zones, well-structured session plans, and comprehensive outcome tracking, coaches lay the foundation for accurate, high-quality data collection. With this strong data backbone, AI/ML tools can deliver valuable insights, from real-time technique feedback to predictive models for managing long-term fatigue and optimizing peak performance.

Scaling Benefits for All Stakeholders When the swimming industry aligns around high-quality, structured data, the benefits extend across all levels of the sport:

• Athletes: Receive personalized training plans that reflect their individual goals and abilities, enhancing both performance and injury prevention.
• Coaches and Clubs: Streamline the session planning process, reduce administrative burdens, and gain access to advanced performance analytics for individuals and teams.
• Organizations and Federations: Can pool anonymized data across regions to fuel large-scale research, inform national training programs, and develop best practices for all levels of competition—from age-group events to elite international meets.

Keeping It Simple and Universal The key to success is designing simple, intuitive data structures that are easy to adopt while maintaining the depth of expert knowledge. This doesn’t mean oversimplifying or losing valuable insights. Instead, it’s about making data collection and management accessible to all stakeholders. By using standardized terminology, consistent intensity zones, and well-defined data frameworks, coaches, athletes, and technology developers can collaborate within a common language that bridges expertise and technology.

Section 6: Opportunity—Setting the Stage for a Unified Framework

Our exploration of data quality challenges reveals a crucial insight: creating high-calibre AI/ML solutions in swimming isn’t just about better sensors, computer vision, more detailed spreadsheets, or digitising the workouts of the best swimmers on the world. The real opportunity lies in establishing a unified framework—a shared blueprint that standardizes how training sessions are planned, recorded, and analysed. When swimming professionals and technology experts collaborate around common standards, they can unlock richer, more reliable data, benefitting everyone from elite athletes chasing records to fitness enthusiasts seeking steady improvements.

A Shared Vision Despite the diversity in coaching methods and swimmer skill levels, there is widespread agreement that high-quality data is essential for tracking progress, reducing injury risks, and improving training effectiveness. Harmonizing the way key metrics like stroke counts, rest intervals, and intensity zones are captured can resolve many of the data issues we’ve discussed, including inconsistent terminology, lack of individualization, and incomplete rest and recovery data.

This isn’t simply a technology initiative—it’s a bridge between sports science and data science. Coaches, sports scientists, and software developers each bring valuable expertise, ensuring the framework reflects the practical realities of daily training sessions while remaining technically sound and scalable.

Building on a Training Zones Framework At Wise Racer, we’ve already taken steps toward standardization by introducing two key models:

1. The 9-Zone Performance Swimming Training Framework
   Designed for competitive athletes, this framework categorizes effort into nine zones, covering everything from low-intensity technique work to high-intensity sprints.
2. The 5-Zone Fitness Swimming Training Framework
   Built for fitness and recreational swimmers, this simplified system focuses on core intensity ranges, making it accessible to those who prioritize fitness improvements over competition.

These zone-based frameworks help swimmers, coaches, and stakeholders communicate effectively about intensity and effort. However, zones alone aren’t enough. For these frameworks to truly drive consistent outcomes, they must be paired with standardized data-collection protocols. This means clear definitions of each zone, uniform methods for logging sets and intervals, and a consistent approach to capturing athlete-specific contexts like injury history or training phases. With this structure, an athlete training in Zone 3 should represent the same physiological workload, regardless of the club or region.

The Path Forward In upcoming blog posts, we’ll provide guidelines for implementing a unified framework. This includes structuring training plans, logging session outcomes, and using data to adjust coaching decisions. We’ll also explore how consistent, high-quality data can supercharge AI/ML tools in swimming by enabling:

• Feedback Loops: Analysis of rest intervals, stroke efficiency, and heart-rate data to fine-tune training sessions as they happen.
• Predictive Analytics: AI models that forecast when a swimmer is at risk of hitting a performance plateau or suffering from overtraining, based on patterns in their data.
• Individualized Recommendations: Automated systems that adapt training plans to an athlete’s personal thresholds, whether they are a youth swimmer or a triathlete focused on long-distance open-water events.

Benefits for Institutions, Parents, Coaches, and Swimmers A structured, technology-driven approach to swimming training benefits every stakeholder in the ecosystem:

• Enhanced Personalization: By combining standardized zones with precise, athlete-specific data, coaches can tailor training sets and intensities to individual needs, maximizing performance without risking burnout.
• Efficient Workload Management: Improved tracking of rest, recovery, and workload data helps coaches prevent common overuse injuries through better cumulative load management.
• Easier Progress Tracking: With a unified data format, tracking a swimmer’s progress over weeks or seasons becomes straightforward, offering both coaches and parents a transparent view of performance trends.
• Collaborative Advancement: When multiple clubs, regions, or federations adopt similar frameworks, they can share and compare aggregated insights. This collaboration can spur innovation and raise competitive standards across the sport.

A Modernized Swim Culture A shared vision for data management, combined with standardized frameworks like Wise Racer’s 9-Zone and 5-Zone models, can boost how swimming is taught, trained, and evaluated. By adopting a universal data structure and aligning around effective training principles, the swimming community can create a more informed, inclusive, and dynamic environment. This will not only drive performance improvements but also foster long-term engagement at all levels—from grassroots programs to elite international competition.

Summary

Part 2 offers both a reality check and a roadmap for progress. We examine the fragmented state of current session data management and show how this disarray hampers effective AI adoption. However, the outlook isn’t bleak—we outline a hopeful path forward through harmonized data collection protocols, the integration of domain knowledge, and the application of Wise Racer’s intensity zone frameworks. By addressing these gaps, the swimming community can be on the path to unlock AI/ML-driven insights more effectively, enhancing performance and innovation across all levels of the sport.

But how do we design training sessions that meet the demands of the AI era? In the next instalment, we’ll introduce our comprehensive training session framework, discussing the key considerations and design choices aimed at meeting AI-ready requirements. Then, in the final instalment, we’ll showcase examples of how to apply this framework in practice—demonstrating how the training zones and session structures come together to drive meaningful improvements.

Call to action

This can’t be a solo effort—swimming needs your support.

If you're a coach, athlete, data scientist, sports scientist, sports director, or simply passionate about swimming and your trade, and you’d like to contribute to this conversation, please reach out! Your insights and expertise can help drive meaningful change.

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