Introduction
Modern track and field coaching exists at the intersection of tradition and transformation. On one side stands the time-tested expertise of world-class coaches: the pattern recognition, the tacit knowledge, the athlete relationship. On the other, a technological revolution driven by data science, computer vision, and machine learning is rapidly reshaping how we understand performance, fatigue, and injury risk.
The convergence of these domains is no longer speculative. It is operational. At Athleet.AI, our core belief is simple: coaches know how to coach, but they are often under-equipped to process and act upon the increasing volumes of data now generated in the training environment. We seek to bridge that gap with tools that do not just offer metrics, but insights that are contextually meaningful and personalised to each athlete’s baseline.
This article explores the transformative potential of AI in track and field, with a specific focus on injury prediction and movement pattern recognition through unstructured video. Using a real-world case study from an elite pole vault environment, we present a framework for understanding how artificial intelligence can augment human decision-making and change the coaching process itself.
The Data Dilemma in High-Performance Athletics
Over the past decade, access to training data has expanded exponentially. Wearable sensors, GPS tracking, force platforms, timing gates, and video analysis software now permeate every level of performance sport. However, the volume and variety of this data often outpaces the human capacity to interpret it effectively. This is particularly evident in unstructured data forms such as freeform video, where the absence of time-locked sensor data, kinematic markers, or labelled annotations renders traditional analysis approaches impractical.
Unstructured video, however, contains latent insights. Changes in an athlete’s ground contact pattern, take-off angles, joint sequencing, or compensatory mechanics can all appear visibly long before they are expressed through pain or dysfunction. Historically, such changes have only been detectable to highly experienced coaches working within small athlete-to-coach ratios. Artificial intelligence, particularly computer vision models trained on millions of movement examples, now offers an alternative: the capacity to analyse, compare, and pattern-match unstructured footage at scale.
Case Study: Pole Vault, Pattern Deviation, and a Predictive Insight
In mid-season preparation, an internationally renowned pole vault coach submitted a collection of training clips to the Athleet.AI platform. The footage, captured on handheld devices, was unstructured, lacked metadata, and represented a mix of performance settings. Within 90 minutes of upload, our AI model flagged a consistent deviation in movement symmetry. The system highlighted three key observations:
- Reduced hip extension at toe-off on the right leg
- Compensatory pelvic tilt during bar clearance
- Subtle asymmetry in stride length during the approach phase
The model tagged this as a potential adductor or pelvic loading issue and generated a non-diagnostic recommendation for further review.
When presented to the coach, the insight was initially dismissed. The athlete appeared to be moving fluidly. No complaints had been made. The physiotherapy team had not reported any concerns.
Four days later, the athlete was withdrawn from training with a confirmed right adductor tear. In this case, the AI had detected a latent risk factor based solely on video evidence, and surfaced it before clinical symptoms emerged.
This was not a case of AI replacing clinical diagnosis. Rather, it functioned as an early-warning tool, offering a lead indicator that would otherwise have gone unnoticed. It was a probabilistic insight, not a deterministic forecast, yet it proved accurate.
Understanding AI-Driven Movement Pattern Analysis
The foundation of this capability lies in the marriage of computer vision, deep learning, and biomechanical modelling.
Athleet.AI’s system applies convolutional neural networks (CNNs) and transformer-based architectures to extract joint trajectories, segmental timing, and spatiotemporal features from video frames. It then applies unsupervised clustering and longitudinal pattern recognition to detect anomalies relative to an individual’s baseline. Importantly, the system does not rely solely on comparison with population norms, but adapts to intra-athlete variability, which is a critical feature in personalised injury risk modelling.
This process reflects principles observed in recent sports science literature. For example:
- Bartlett et al. (2019) emphasise the limitations of relying purely on mechanical thresholds for injury prediction, advocating instead for the integration of neuromechanical and contextual variables
- Bahr & Krosshaug (2005) identified that the majority of non-contact injuries follow subtle biomechanical deviations, often visible in hindsight but hard to catch in real time
- Claudino et al. (2019), in a meta-analysis on AI in sport, conclude that machine learning models outperform traditional regression approaches in classifying injury risk, particularly when fed multimodal data sources
Athleet.AI incorporates these research findings into practice, using probabilistic models to generate alerts based on data volatility, not just absolute thresholds. This enables coaches to view injury as a process, not a binary event.
The Therapist Who Knows Before the Athlete
Let us return to the imagined but increasingly realistic scenario.
A therapist walks into the changing room and says, “I’m here to treat your injury.”
The athlete looks confused. “But I’m not injured.”
The therapist responds, “Correct. Not yet.”
While it may seem intrusive, this scenario exemplifies a paradigm shift. Injury management in sport is no longer reactive. When AI systems detect precursors to injury — movement drift, micro-asymmetries, compensatory neuromuscular responses — they provide coaches and practitioners with a lead time for intervention.
Early-stage interventions might include:
- Adjusted session loads
- Technique drills to re-establish joint symmetry
- Targeted S&C exercises to reduce unilateral fatigue
- Physiotherapy to address movement inefficiencies
In this model, the athlete is protected before damage occurs. The performance plan is updated in real time. The coaching process becomes dynamic and responsive, rather than retrospective and reactive.
Coaching Implications: Augmenting, Not Replacing
AI in coaching is not a replacement for experience. It is an augmentation of it.
Elite coaches are already adept at pattern recognition. What AI offers is a broader canvas. It enables the same coach to scale their attention across multiple athletes, identify patterns that would otherwise be lost in noise, and respond to subtle changes before they manifest as performance drops or injuries.
Within Athleet.AI, this plays out through three layers of functionality:
Coaching Function | AI Capability |
|---|---|
Performance Surveillance | Continuous monitoring of movement patterns, alert generation when deviation exceeds personalised variance thresholds |
Programme Decision-Making | Adaptive recommendations for volume, intensity, and content based on fatigue markers, biomechanical integrity, and historical trends |
Communication and Planning | Integrated dashboards for therapists, S&C coaches, and parents with shared visualisations and AI-generated insight summaries |
Each of these layers supports the human decision-maker. They do not make decisions autonomously. They present insight, rationale, and historical context — so the coach retains authority but gains clarity.
Ethical Considerations and Data Integrity
A common concern in AI-enabled coaching is the reduction of athletes to datapoints. At Athleet.AI, we take this risk seriously. All insights are interpretable, not black-box. Athletes retain agency. Data is stored securely. No outputs are generated without context.
We advocate for three guiding principles:
- Transparency — every AI flag must include a rationale that can be understood by the coach and athlete
- Control — coaches decide how and when to act; AI never triggers changes automatically
- Privacy — all data is encrypted, and athletes can request removal at any time
This ensures that trust is built, not eroded, by the use of artificial intelligence in sport.
Coaching in the Era of Intelligent Insight
The case of the pole vault coach who doubted, then witnessed the system’s insight prove correct, is not unusual. It reflects a broader trend in elite sport. Coaches are beginning to see that artificial intelligence is not a threat to their role, but a tool that strengthens it.
Athleet.AI is built for these coaches. It is not a platform for algorithmic automation. It is a system for human-led performance decision-making, enhanced by structured insight, predictive modelling, and pattern recognition that no individual could conduct alone.
Whether the athlete is 15 or 25, whether they are running 800 metres or vaulting five metres, the need is the same: greater visibility, earlier intervention, and smarter planning.
And when the therapist walks into the room and says, “You’re not injured… yet,” it no longer needs to be a warning. It can be the start of a conversation that keeps the athlete on track, both literally and figuratively.
If you would like this expanded into a journal submission, white paper, or coaching presentation deck for use at clinics or governing body CPD events, I would be happy to assist.