Beyond the Game: AI’s Impact on the Sports Industry
An Exploration of Artificial Intelligence in Sports
Artificial intelligence (AI) has rapidly become a driving force in the sports industry, fundamentally reshaping competitive strategies and operational performance, through technology and data-driven decision-making. This piece examines how AI-driven insights are redefining the sports landscape by transforming traditional approaches to performance analysis, strategic planning, athlete management, and business practices.
According to a report by Allied Market Research, the value of the global AI sports market was $2.2 billion in 2022, by 2032 the market is expected to grow to $29.7 billion dollars (Yogendra & Sumant, 2024).
Over the past few years, significant AI advancements have spurred enthusiasm and dedicated inquiry into its practical applications within the sports industry.
For instance, modern AI systems have emerged as powerful tools in real-time game analysis. "AI can analyse game data in real-time, helping the coaching team to adjust tactics dynamically and optimize player performance, a stark contrast to traditional approaches that rely solely on human expertise and are often inefficient and subject to bias." (Qian Chen, 2024). This capability enhances the precision of tactical adjustments during live matches and fosters a more agile response to evolving game scenarios.
In this piece, we highlight key areas where players and franchises must harness AI to remain competitive both on the field and in business:
Performance Success
Optimising Fan Engagement
Revenue Generation
Performance Success
The Role of Data Analytics and AI
Data analytics lies at the core of AI applications in sport. The global sports analytics market, valued at $3.52 billion in 2023, is projected to grow at a compound annual growth rate (CAGR) of 21.5% from 2024 to 2030 (Grand View Research, 2023).
The integration of AI into sports analytics not only enhances performance but also redefines how teams approach competition, training, and talent development. As the sports analytics market expands, AI’s role in uncovering new dimensions of performance optimisation will only continue to grow.
Key AI Applications for Improving Results
Player Scouting and Recruitment
AI has transformed player recruitment by providing data-driven assessments of performance, strengths, and team compatibility, reducing the risks of costly transfers. In football, clubs like Sevilla have partnered with IBM to integrate generative AI, improving the precision and efficiency of talent identification (IBM, 2023).
Unlike traditional scouting, which relies on subjective evaluations, AI aggregates match statistics, biometric data, and video analysis for a more objective assessment. Predictive modelling further enhances recruitment by forecasting player performance in different tactical systems and competitive environments.
Research shows that machine learning can identify promising young talent by analysing key performance metrics like speed, agility, passing accuracy, and decision-making (Farkas & O’Donoghue, 2020).
Player Analysis
1. Player Health Assessment and Management
Injury prevention is another critical area in which AI has shown immense promise. Sports injuries can sideline players for extended periods and negatively impact careers. Past methods, such as physical screenings and manual data analysis, often struggle to predict when injuries will occur. By contrast, machine learning models can analyse large-scale biomechanical data, identifying subtle patterns that may indicate injury predisposition.
Rabotti et al. (2020) reviewed AI-driven wearables and found these devices can monitor athletes in real time, predicting issues such as stress fractures and ligament strains before they manifest. This proactive approach not only prolongs athletes’ careers but also reduces both the financial and physical toll on sports organisations. In collaboration with Amazon Web Services, the NFL developed the “Digital Athlete,” an AI tool using TV footage and sensors in football gear to enhance player safety.
2. Player Performance Review
AI revolutionises sports performance analysis by automating data-driven insights, eliminating the inefficiencies and errors of manual methods. The player tracking market reflects this shift, projected to grow at a 24.9% CAGR over the next five years (MarketsandMarkets, 2023). Such systems enhance performance, refine tactics, and optimize decision-making across various sports.
For example, RaceWatch in Formula 1, analyses real-time telemetry, weather, and competitor data to refine race strategies, optimize pit stops, and improve outcomes—demonstrating AI’s transformative role in high-stakes sports.
Coaching
While data analytics have been instrumental to coach for decades, AI delivers two transformative benefits. First, it can handle far larger and more complex information than established alternatives, unveiling insights that might otherwise remain hidden. Second, AI translates these insights into clear, actionable language, enabling quick and confident decisions in high-pressure moments. Importantly, AI does not replace human judgement; instead, it serves as a strategic tool that coaches can selectively integrate into their decision-making processes Cossich et al., 2023).
A prime example is AlphaPlay, which secured paying clients across the U.S., South America, and Europe in 2024. It uses advanced algorithms to identify patterns in player development, talent identification, and tactics. By presenting complex data as straightforward, actionable insights, AlphaPlay empowers coaches to optimise performance and exploit opponents’ weaknesses. This accessibility is particularly valuable for women’s sports teams and minor league organisations with limited budgets or technical resources.
Case Study: Liverpool FC and DeepMind's TacticAI
A compelling illustration of AI’s potential is Liverpool FC’s collaboration with Google-owned DeepMind on an AI-powered system called TacticAI. Designed to advise coaches on corner kick strategies, TacticAI analyses historical and real-time match data to offer tactical recommendations. This collaboration highlights how AI can amplify human coaching expertise.
Optimising Fan Engagement
A comprehensive international study by Morning Consult, involving 18,000 sports fans across 10 countries, shows how viewing habits and fan engagement are evolving. The study found a growing preference for personalised, time-efficient digital experiences and revealed that emerging technologies like AI are playing an increasingly important role (Armonk, 2024).
A generational gap was evident:
- Younger fans (18-29 years): More open to AI-driven features, using mobile devices as their primary viewing platform, and subscribing to streaming services. Around 58% believe AI will positively impact sports, reflecting their enthusiasm for technological innovation.
- Older fans (45+ years): More inclined to stick with traditional content consumption, such as linear broadcasting. While 40% of respondents over 55 see potential benefits in AI, their adoption of digital platforms and AI-driven features lags behind younger fans.
Noah Syken, Vice President of Sports and Entertainment Partnerships at IBM, underscored these trends: “Fans worldwide are increasingly seeking platforms and solutions that foster deeper connections with their favourite sports and athletes. IBM’s study shows a growing acceptance of AI-driven technologies to deliver these experiences.
Through our sports and entertainment partnerships, IBM is committed to equipping our partners with cutting-edge technologies, such as our AI and data platform Watsonx, to meet evolving fan expectations” (Armonk, 2024).
Generative AI-Driven Fan Experiences
Viewing Experience
By analysing a fan’s viewing history, favourite teams, and preferred players, generative AI can create personalised content such as game highlights and real-time commentary. For instance, a fan might request commentary delivered in the style of their favourite comedian, adding a unique, immersive twist to the experience.
A pioneering demonstration of this technology took place during the Paris Olympics with NBC’s “Your Daily Olympic Recap on Peacock.” Using an AI-generated clone of commentator Al Michaels, the platform tapped into over 5,000 hours of coverage to produce personalised daily recaps. These could be tailored in more than seven million ways—showcasing how scalable and adaptable generative AI can be in delivering bespoke content.
AI is also revolutionising sports through virtual reality (VR) and augmented reality (AR). VR technology allows fans to immerse themselves in a stadium environment with a 360-degree view of the action, while AI personalises that virtual setting to match individual preferences. AR enables fans to access real-time statistics, player information, and interactive games, adding an extra dimension to live match engagement (Armonk, 2024)
Moreover, AI can deliver targeted advertisements and relevant offers with minimal interruption. A fan might be prompted to reorder food during a match based on previous buying patterns or receive personalised reminders tied to products or services they genuinely need. Additionally, AI-powered translations of player interviews and strategic insights expand a sport’s global reach, fostering inclusivity by bridging cultural and linguistic barriers.
Case Study: LaLiga
LaLiga demonstrates how AI can enhance fan engagement through its Beyond Stats software portal. By analysing real-time data on player performance and goal probabilities, LaLiga offers personalised content and live broadcasts enriched with AI-generated graphics.
These innovations provide fans with deeper insights and a more immersive experience.
The In-Person Experience
AI not only enhances digital engagement but also optimises stadium operations. Several U.S. football clubs, for example, have adopted facial recognition for seamless entry. Fans upload their photo and ticket barcode to an AI-supported platform in advance; on arrival, cameras at the stadium verify their identity and grant access. This eliminates lengthy queues and streamlines the entry process.
AI also strengthens safety measures by analysing real-time security camera feeds to detect overcrowding.
Staff can respond immediately, preventing risky build-ups and ensuring compliance with safety regulations. Furthermore, AI can predict patterns such as peak arrival times, helping operators meet demand for food, beverages, and merchandise efficiently.
This creates a smoother, more enjoyable experience for fans while maximising operational effectiveness (Kottke, 2022).
Revenue Generation (1/2)
Media
Artificial intelligence improves content distribution by automating key processes such as accessibility and localisation. It can utilises predictive analytics to anticipate consumer behaviour and identify emerging trends. Additionally, AI-powered semantic search engines enhance the user experience by enabling more intuitive content discovery. Streaming platforms leverage these predictive capabilities to forecast traffic patterns and specific content demand, leading to optimal allocation of network resources and improved service delivery. This improved quality and delivery of desired content leads to an increase in demand for those sports services that execute modern streaming effectively (Bantourakis et al., 2025).
Moreover, while live rights constituted 86% of the total value received by rights-holders in 2019, the average sports fan consumed only 55% of their sports video content live in 2024 (Two Circles, 2024). Predicting the trend of the rise of short form content this trend, firms such as Veritone unveiled products like SPORTX. SPORTX is a new short-form content marketplace tailored for the sports industry. The platform bridges the gap between sports content owners and producers and a network of media buyers. By leveraging advanced AI algorithms, SPORTX addresses challenges related to media rights management while offering personalised content recommendations to media buyers.
Advertising
AI is transforming sports advertising by delivering highly personalised, contextually relevant messages. By analysing viewer preferences in real time, AI ensures ads are targeted to individual interests, enhancing engagement and reducing irrelevant interruptions.
One of AI’s most significant capabilities is identifying optimal moments during a live event to display advertisements. By tracking game dynamics in real time, AI can place ads during periods of heightened excitement (for instance, right after a game-changing play), creating a seamless and impactful viewing experience. AI can also measure viewer sentiment to match ads with the emotional tone of the moment—whether motivational during tense phases or more light-hearted during celebratory pauses (Mogaji et al., 2019).
Moreover, AI can dynamically adjust in-stadium adverts to feature current sponsors during high-profile moments, boosting brand visibility for live and broadcast audiences. This adaptability ensures advertisers maximise their impact when fans are most engaged (Kottke, 2022).
Ticket Revenue
The Orlando Magic provides a clear example of AI’s potential to drive revenue and operational efficiency. Recognising the wealth of fan data at its disposal, Attendance, purchasing habits, seating preferences, and more, the organisation adopted an analytics-driven approach to predict consumer behaviour and refine marketing strategies. In a competitive market such as Orlando, where world-class theme parks vie for consumer spending, advanced analytics became a critical differentiator.
The Magic opened the Amway Center (officially spelled “Center” in the U.S.) in 2010 as a state-of-the-art “smart” arena and began testing variable ticket pricing with the help of SAS Data Management and SAS Analytics. This led to a 50% increase in ticket revenues in a single season. Since 2013–14, the team has achieved a 91% growth in ticket revenues and consistently ranks among the NBA’s top five for single-game ticket sales—demonstrating the value of an AI-driven, dynamic pricing strategy.
Concluding Thoughts
AI has firmly established itself as an evolving force in sports, and its influence will only deepen in the coming years. By offering advanced tools for performance optimisation, fan engagement, and revenue growth, AI is redefining the industry. Nonetheless, there are important challenges to address, including the interpretability of AI models (“black box” algorithms), ethical considerations related to data privacy and consent, and the delicate balance between automated decisions and human intuition.
However, many AI systems excel at generating highly accurate predictions, such as injury risks or tactical insights, without clearly explaining how they reached those conclusions (Rabotti et al., 2020). This can create mistrust among coaches, medical professionals, and other stakeholders who need to understand the rationale behind AI-driven recommendations. Researchers are thus advocating for more transparent, interpretable AI models to foster greater confidence.
Furthermore, ethical questions around massive data collection, especially biometric and psychological data, loom large. Robust security measures and privacy safeguards are essential to protect athletes and fans alike. Moreover, while AI augments human judgement by providing data-driven insights, over-reliance on automation risks undermining the intuitive and creative dimensions that define competitive sport (Bunker & Thorpe, 2022). Ultimately, the future lies in striking the right balance, harnessing AI’s potential while preserving the human element that makes sport so compelling.