How Artificial Intelligence Will Redefine Professional Sports

Artificial intelligence and virtual reality are redefining what it means to train, compete and recover. Welcome to the era of performance-enhancing algorithms.

When people think about artificial intelligence (AI) in sports, the images that come to mind tend to be cinematic. Robot referees making flawless calls, algorithms predicting every play before they happen, or android sprinters leaving humans in the dust. Those images grab headlines but they are the sci-fi version of AI. The real story of AI in athletics is quieter but no less profound, because the next frontier of performance is not about the body, but instead what the mind can do.

Virtual reality and artificial intelligence are synergistically helping teams quantify the focus, resilience, and psychological readiness of athletes, to understand what may enhance their mental performance during competition.

Peak performance is the mind

Mental toughness is the psychological backbone of elite athletes. It combines resilience, confidence, focus, and emotional control. These are qualities that distinguish serial champions from the rest, because beyond physical features, mental toughness enables consistent performance, helps overcome failure, and fuels the drive to push beyond limits. While some aspects of this mindset are innate, most athletes actively cultivate mental toughness through deliberate practice, using tools such as mindfulness, visualization, reframing challenges and self-reflection. Resilience is the capacity to persist through adversity. Control ensures emotional balance and clarity of thought when stakes are high. Confidence stems from deep trust in one’s preparation and abilities, while focus keeps attention anchored in the present moment. Finally, determination and drive, sustain motivation. For example, one large study found that mental toughness and dedication was nearly as predictive of elite performance as biomechanical factors like flexibility and movement symmetry (#here). That insight changes everything because equipped with the new breed of wearable sensors and cognitive analytics, coaches can now monitor emotional and psychological strain as closely as they track muscle fatigue. Recent work introduced ANXIETY-CARE, a context-aware framework that fuses electrodermal response (EDR) sweat sensing with multimodal biosignals and machine-learning classifiers to detect anxiety in real-world use. By gating different sensor branches based on wear location and activity context, the system achieved high accuracy (up to ~96% wrist, ~99% chest), demonstrating that context modeling significantly improved anxiety detection using wearable sensors (#here). Monitoring many individuals is essential. Work presenting HHISS (Human Heterogeneity Invariant Stress Sensing) as a framework that learns stress features that are invariant to person-specific differences, enables robust performance across unseen people, environments, and stressors. HHISS is feasible for mobile deployment, supporting scalable and reliable, real-world wearable stress analytics (#here).

Also, accelerometers and motion sensors can capture and analyze posture and activity patterns that correlated with anxiety or fatigue (#here). Recently-patented technology (WO2023015013A1 and US20240090807A1) will further accelerate the development and deployment of wearable device for stress detection and emotion recognition by monitoring motion data and other biosignals. Moreover, AI-powered emotionally intelligent voice stress analysis can interpret tone and speech cadence to infer emotional state. For example, a study in 2024 used speech signals to identify psychological stress through voice features (#here). Interestingly, Spotify has patented an invention that can monitor your speech to determine your emotions. Although they evidently want to use it to suggest music based on your emotional state (#here), the same tech. can be applied to elite athletes to increase their mental toughness. In a report of The Future Laboratory focusing on the UAE’s tech. intitative G42, Sarah Bailey, Director of Research for the Tennessee Titans said that “If the AI detects stress or fatigue, you go to a player and ask, how are you feeling today? (#here). Do you want some extra work on your left quad? The player is blown away, asking, how did you know that? Then the player gets it, they’re not just a test bunny, there is a point to all this data collection”. While at the Los Angeles Rams, Bailey’s previous role, the team was lowest on injury in the league three years in a row. And this subtle shift, from reactive to anticipatory coaching, will mark the dawn of cognitive performance analytics.

Virtual reality

For years, virtual reality (VR) in sports looked like a toy for gamers, not for elite sportspeople. But that view has recently crumble under hard-to-ignore data. A 2025 systematic review of VR and sports performance found that athletes using VR-based training show significant improvements in precision and shooting tasks, and immersive sensorimotor training reduced injury rates and improved on-field performance (#here). A controlled study on young soccer players compared traditional video training with immersive VR sessions. The VR group not only made smarter decisions on the field, they learned to scan faster, with more fixations of shorter duration, a pattern associated with elite playmakers (#here). The reason appears to be immersion because VR replicates the sensory conditions of real play more closely than a flat screen can. It creates what scientists call visual correspondence, the sense that the virtual action matches real-world physics and timing. In other words, VR can help enhance perceptual intelligence. A way to put it is “video teaches what you must do, while VR teaches you what it feels like to do it.” And the beauty of it is that this tech. is no longer just for pros. Affordable headsets and AI-based motion tracking are opening the door for schools, youth academies and even your endearing MAMIL husband (*) to train like a professional, ushering in a new era of athletic democratization.

A MAMIL at home, training like a pro.

The sports that may benefit the most are team sports, for example football/soccer, american football, or rugby, where VR tools are used to simulate game scenarios, enabling players to practise decision-making, spatial awareness and peripheral vision in repeatable virtual environment (#here). For example, a review found that football/soccer players using VR improved passing decision-making and visual search behavior compared to conventional video training (#here). Nevertheless, in individual precision sports, including tennis, golf or even archery, VR allows immersive repetition of the movement, sometimes with real-time feedback from sensors. For instance, a paper on “Enhancing Tennis Training with Real-Time Swing Data Visualisation in Immersive Virtual Reality” showed improved situational awareness and performance behaviors (#here). In seasonal sports, cycling, swimming, canoe slalom, VR has already been used for rehearsal of highly specific scenarios where access to real courses or conditional replication is challenging or costly. The Guardian reported that Australian Olympic swimmers used VR to visualize relay change-overs in three dimensions to practise timing without being in full physical sets (#here). For elite road cyclists, incorporating VR appears to increase training engagement and the ability to sustain high intensity training. This may make a significant difference in the off-season because indoor cycling can be tedious and mentally fatiguing. Here, VR adds stimulus, immersion and distraction, making the training more tolerable and motivating (#here). A study by University of Georgia found that during high-intensity cycling when participants used a VR environment, they reported less muscle pain and were able to maintain effort more easily. Moreover, VR-based cycling systems allow real-time tracking of key performance metrics combined with immersive visual feedback, which aids technique refinement and situational awareness. This level of immersive feedback supports tactical rehearsal and scenario-based recognition, for example drafting and positioning), which is harder to replicate repetitively to experiment variant outdoors at full speed (#here). However, there are obvious limitations to this tech. VR is a complementary rather than a full replacement for outdoor road riding. Accuracy of speed algorithms, motion simulation fidelity, and the “feel” of the real terrain will remain challenges to solve in the near future.

The crystal ball problem

Yet for all its triumphs, one of AI’s most coveted promises, predicting injuries before they happen, remains frustratingly out of reach. Machine learning models like Random Forest and XGBoost show early promise, with some studies reporting up to 85% accuracy for specific injuries. But across the field, the results are wildly inconsistent. The problem isn’t the algorithms, it is the annotation of data because there is no universal definition of injury. For example, some datasets lump contact and non-contact injuries together, whereas others measure injury level by days missed and others by self-reporting. The result is that every dataset speaks, so to say, a different language. Machine learning systems rely on large datasets with consistent annotations to detect patterns. In sports medicine, however, injury definitions differ widely. Some classify an injury only when it causes time lost from competition. Others include pain, imaging findings, or functional impairment even without missed playtime. Still others distinguish between acute, overuse, and chronic microtrauma, each captured differently in databases. Injury reporting also depends on staff, regulations, or cultural norms. A La Liga club in Spain may record minor strains systematically, while a youth academy in Moldova may record only hospitalization-level injuries. As a result, one dataset may call a muscle strain an injury, whereas another may not. Consequently, AI models trained on these inconsistent annotations cannot generalize reliably. Moreover, physiological signals are continuous, not binary. Without a standardized injury onset, it will be impossible to teach AI what patterns precede a dangerous event. Therefore, until sports science agrees on common definitions and standardized data-collection protocols, AI will remain more a weather vane than a crystal ball.

The data Doppelgänger and the domain expert

Where AI truly shines is in personalization. Enter the Digital Twin, a virtual replica of an athlete built from continuous streams of biometric, biomechanical, physiological and environmental data. But a digital twin does not just mirror performance, it can simulates it under various conditions. It lets athletes and coaches experiment in silico. It can predict how an athlete’s body might respond to an extra sprint, a long flight, a change in altitude, a poor night’s sleep, a change in diet, new medication, etc. In practical terms, this means a coach can test in advance various, even wild, training loads without actually running them. If the virtual model predicts excessive fatigue or risk, the plan is adjusted. This is performance forecasting at the individual level is like having a thousand copies of yourself performing countless training exercises under any imaginable condition. Powerful? Yes. Possible? Of course!

Dr. Raffaele Landolfi, Medical Director at SSC Napoli, has shared his first-hand perspective on integrating AI into the club’s performance and medical ecosystem, as discussed at the Isokinetic Football Medicine 2023 conference (#here). Over just a few months the team learned how to interpret risk flags and graphs, distinguishing what was relevant. For example, Napoli focused on two distinct player-types. The first-team regulars exposed to match overload, and squad players with low match load but flagged repeatedly as high risk. Crucially, he emphasises the hybrid nature of the approach. AI requires human context to interpret data correctly. AI becomes an ally, not a replacement. This example illustrates how a top-flight football club can successfully partner with AI to overcome data-volume hurdles, tailor the system to the club’s unique player profile, and embedding a human-machine collaboration to enhance workload management, injury risk, and performance monitoring. Yet, all this power depends on one thing. Good quality and expertly annotated data. Acquiring data is becoming faster and less costly, tweaking algorithms is becoming easier, but labeling data by domain experts remains a bottleneck. As they say in the AI field “garbage in, garbage out”. AI’s effectiveness rises and falls with the quality of what we feed it. For example, a heart-rate spike could signal stress, exertion, or dehydration. However, only a domain expert can tell which interpretation is valid. Without such insight AI may well misclassify biometrics and, even worse, propagates errors. This is why domain expertise is and will remain irreplaceable no matter how good the algorithms will become. Experts are the ones that will always determine what counts as an injury event, a damage threshold, and a meaningful feature in the data. In prediction, for example, injury may mean lost play time, imaging evidence, or pain score. AI models are excellent in detecting correlations, but experts are the people that interpret causation. This is because domain knowledge ensures focusing on mechanisms rather than on irrelevant correlations. For example, the All Blacks wins most rugby matches, but it is the players that make it happen, not the color of their jersey. Otherwise, the German rugby national team will be able to beat Georgia just simply by donning a black shirt (of note, Georgia and Germany have historically met 11 times, with 11 wins for Georgia with a point difference of −468) (*). The reason is that experts understand the variables that matter and what are the confounders. And this is why expert humans will remain “in the loop”. Labeling remains the rate-limiting step of AI development. While unsupervised and self-supervised learning can reduce dependency on labeled data, ground truth verification still requires expert judgment. Without this step, performance plateaus quickly. Therefore, the frontier is not “AI vs. experts,” but AI with experts. AI systems will accelerate, not replace, domain expertise. AI can crunch numbers, but only humans can weigh values.

Culture eats AI for breakfast

Ironically, the biggest barrier to wearables and AI in sports is not technological, it’s human. A 2025 global survey of senior sports executives found that 35% cited resistance to change as the main obstacle to adopting AI (#here). By contrast, far fewer blamed cost or technical complexity. Why the reluctance? In large part it is trust. Seasoned coaches are used to intuition, not to algorithms. Integrating sensors, virtual reality and AI means re-training not just athletes but entire organizations to interpret and act on data differently. It also requires the introduction of AI literacy in an environment not used to it. Those who adapt, however, will find the gains to be staggering. Again, the G42-The-Future-of-Sports-and-AI study found that although “57% of coaches intend to implement an AI strategy in the next three years, lack of in-house talent will hold them back, and resistance to change across their organization will be their biggest barrier”.

A pro’s playground at your fingertips

Cheaper tech. is going to flatten the hierarchy of sport. The data and tools that once required million-dollar elite-sports facilities are becoming accessible to virtually everyone. Notable cases are markerless motion-capture systems. For example, DeepLabCut, developed by the research group of Mackenzie Mathis at École Polytechnique Fédérale de Lausanne in Switzerland (#here), can let anyone record a training session with a phone camera, and get biomechanical feedback that was once only reserved for well-funded Olympic teams. Furthermore, AI-driven scouting tools such as AiScout are redefining how talent is discovered, analyzing thousands of amateur submissions from around the world to find the “diamonds in the rough” (#here).

The same algorithms that help a striker perfect their timing are now helping a teenager in Nairobi submit their highlight reel. AI is making the walls between pros and us are crumbling (image capture from AiScout).

Limiting AI is the best for everyone

Integrating AI into sports is less a technical problem than a moral and economic one. How do we keep the pursuit of excellence without erasing individuality? The tension, the error, the risk. All of the messy and beautiful unpredictability of elite sports is why we want watch them. AI can measure the heartbeat of a golfer, but it can’t feel ours pumping in anticipation of the defining shot. It can model strategy, but it does not know what a victory means to fans. Therefore, the future of sport should always belong to those that illuminate it.