Global Artificial Intelligence in Sports Market Size, Share & Industry Analysis Report By Offering (Solution and Services), By Sports Type (Team Sports, Esports, and Individual), By Technology (Gen AI and Other AI), By Regional Outlook and Forecast, 2025

The Global Artificial Intelligence in Sports Market size is expected to reach $34.41 billion by 2032, rising at a market growth of 20.8% CAGR during the forecast period.

The adoption of AI-powered video analytics, wearables, and predictive modeling tools has significantly improved team coordination and tactical decision-making, making team sports a primary area of investment and innovation in the AI sports market. Thus, the team sports segment acquired the 46% revenue share in the artificial intelligence in sports market in 2024. This dominance reflects the widespread application of AI technologies across various team-based sports such as football, basketball, baseball, and cricket. AI is being utilized for player performance tracking, game strategy optimization, injury prevention, and fan experience enhancement.

COVID 19 Impact Analysis

During the COVID-19 pandemic, the sports industry experienced a significant decline in revenue due to the cancellation or postponement of major events, tournaments, and league matches worldwide. As a consequence, clubs, organizations, and broadcasters faced financial strain, leading to a temporary reduction in investments in advanced technologies, including artificial intelligence. Budgets earmarked for innovation, performance analytics, and fan engagement tools were reallocated to critical operations and pandemic response efforts. This reallocation caused delays or cancellations in AI-related initiatives across various sports domains. Thus, the COVID-19 pandemic had a negative impact on the market.

Market Growth Factors

Artificial Intelligence has revolutionized performance analytics in the sports industry, enabling teams, coaches, and athletes to achieve unprecedented levels of precision and efficiency in training and game-day decision-making. One of the most powerful applications of AI lies in real-time data tracking—gathered from wearables, motion sensors, and computer vision systems—used to monitor biomechanics, fatigue levels, heart rate variability, and movement efficiency. Therefore, by transforming performance tracking and tailoring injury-preventive strategies through intelligent, predictive systems, AI has become an indispensable ally in maximizing athlete longevity and effectiveness.

Additionally, AI is at the heart of a dramatic shift in how fans engage with sports, personalizing experiences and making interactions more immersive, dynamic, and satisfying. One of the key enablers here is recommendation engines powered by AI, like those used in entertainment platforms. These systems analyze fans' behavior across apps, social media, ticketing portals, and e-commerce platforms to recommend content, merchandise, and match experiences uniquely suited to each user’s preferences. Whether it’s highlighting a favorite player’s stats, replaying iconic moments, or sending notifications about upcoming matches, AI personalizes every touchpoint in the fan journey. Thus, by turning passive viewers into interactive participants through personalized content and immersive technologies, AI is redefining fan engagement in sports as a deeply connected, dynamic experience.

Market Restraining Factors

However, one of the most critical restraints hampering the widespread adoption of AI in sports is the issue of data privacy and ethical use of athlete and consumer data. In modern sports ecosystems, AI applications heavily depend on real-time data collection — from biometric data during training, to behavioral data collected through fan engagement platforms. Wearable sensors, smart stadiums, and tracking cameras generate volumes of highly sensitive information, much of which pertains to individuals' health, location, and private routines. When such data is analyzed by AI systems for performance enhancement or audience targeting, it raises important questions about consent, ownership, and risk of misuse. In conclusion, unless data governance frameworks and ethical standards are significantly strengthened, the risks associated with data privacy and ethical misuse will continue to restrict the pace and scope of AI adoption in the sports industry.

Value Chain Analysis

The value chain of the Artificial Intelligence in Sports Market starts with Inbound Logistics, where data from sensors, cameras, and wearables is collected and sourced for processing. In Operations, AI models analyze player performance, game strategies, and injury risks using machine learning and computer vision. Outbound Logistics involves delivering these insights through dashboards, apps, or live broadcasts. Marketing & Sales promote AI-powered solutions to teams, leagues, and broadcasters. Finally, Service includes continuous model training, tech support, and performance updates for optimal user experience.

Market Share Analysis

The leading players in the market are competing with diverse innovative offerings to remain competitive in the market. The above illustration shows the percentage of revenue shared by some of the leading companies in the market. The leading players of the market are adopting various strategies in order to cater demand coming from the different industries. The key developmental strategies in the market are Partnerships & Collaborations.

Offering Outlook

Based on offering, the artificial intelligence in sports market is characterized into solution and services. The solution segment garnered 59% revenue share in the artificial intelligence in sports market in 2024. This segment includes AI-driven tools and platforms that assist in decision-making, performance analysis, player tracking, strategy development, and predictive modeling. The growing adoption of AI technologies by teams, leagues, and broadcasting partners to enhance on-field strategies and fan engagement contributed heavily to the prominence of this segment.

Sports Type Outlook

On the basis of sports type, the artificial intelligence in sports market is classified into team sports, esports, and individual. The individual segment held 23% revenue share in the artificial intelligence in sports market in 2024. This segment encompasses sports such as tennis, golf, athletics, boxing, and others where athletes compete individually rather than as part of a team. AI technologies in this space are primarily focused on personalized training programs, performance monitoring, biomechanics analysis, and real-time feedback systems.

Technology Outlook

By technology, the market is divided into gen AI and other AI. The other AI segment garnered 47% revenue share in the artificial intelligence in the sports market in 2024. These solutions form the foundation of most AI applications in sports today ranging from real-time game analysis, player tracking, and scouting to fan sentiment analysis and operational optimization. These tools are well-established and continue to evolve with advancements in algorithmic accuracy, processing speed, and data availability.

Use Case Title Confidential

Entities Involved Confidential

Objective To optimize player performance, team strategy, and officiating using traditional AI technologies such as machine learning, computer vision, and predictive modeling.

Context and Background Before GenAI, sports teams relied on traditional AI for analytics, injury prevention, and officiating. In 2025, these remain core, using supervised learning, reinforcement learning for simulations, and AI-enhanced video/sensor analytics.

Description (Examples)

Predictive workload models using biometric sensor data

• Computer vision-based player positioning for offside and goal-line decisions
• ML-powered opponent scouting and match preparation
• Real-time win probability engines during broadcasts
• Fan behavior prediction and ticket demand forecasting Used by NFL, FIFA, Bayern Munich, and Manchester City

Key Capabilities Deployed • Supervised and unsupervised learning pipelines

• Optical tracking integrated with real-time dashboards

• Rule-based AI for officiating (VAR, goal-line tech)

• ML-based revenue forecasting and fan analytics

Benefits • Improved coaching precision and training outcomes

• Greater transparency and fairness in officiating

• Enhanced predictive insights into player and team performance

• Data-informed decision-making across operations

Source Confidential

Regional Outlook

Region-wise, the artificial intelligence in the sports market is analyzed across North America, Europe, Asia Pacific, and LAMEA. The North America segment recorded 38% revenue share in the artificial intelligence in the sports market in 2024. The region’s leadership can be attributed to early adoption of advanced technologies, high investments in sports analytics, and the presence of major leagues such as the NFL, NBA, MLB, and NHL. Professional teams across the U.S. and Canada are increasingly utilizing AI for player performance optimization, fan engagement, injury prevention, and tactical decision-making.

Recent Strategies Deployed in the Market

• Mar-2025: Catapult Group International Ltd. unveiled Vector 8, an advanced athlete monitoring system designed to enhance real-time decision-making. It offers faster data analysis, improved workflow efficiency, and more precise performance insights. The system features cutting-edge wearables, a powerful dock, and an expansive receiver network, supporting up to 120 athletes.
• Nov-2021: Catapult Group International Ltd. unveiled new features to enhance baseball performance analysis and injury prevention. These updates allow coaches and players to track advanced metrics, providing insights into player mechanics, workload, and fatigue to improve training programs and reduce injury risks. The innovations aim to support better decision-making and overall athlete care.
• Oct-2021: Stats Perform Group teamed up with TruMedia, a leader in advanced sports analytics. The collaboration focused on further developing their joint initiative, the ProVision sports analytics platform, designed to deliver cutting-edge insights and data-driven tools for enhancing team performance, strategy, and decision-making in professional sports.
• Oct-2021: Stats Perform Group teamed up with K-Sport to expand the use of wearable performance technology. This collaboration aimed to deliver advanced player tracking and monitoring solutions to over 200 professional football and rugby teams, enhancing athlete performance analysis and training through innovative, data-driven wearable devices.
• Oct-2021: Stats Perform Group announced the partnership with K-Sport. This will continue to provide innovative data and analytics solutions for K-Sport’s sports-related offerings, enhancing their services and fan engagement initiatives. The partnership reflects a mutual commitment to driving innovation and elevating sports experiences through advanced technology.

List of Key Companies Profiled

• IBM Corporation
• Microsoft Corporation
• Trumedia Networks, Inc.
• Google LLC (Alphabet Inc.)
• SAP SE
• Stats Perform Group
• SAS Institute Inc.
• Oracle Corporation
• Sportradar Group AG
• Catapult Group International Ltd.

Global Artificial Intelligence in Sports Market Report Segmentation

By Offering

• Solution
• Services

By Sports Type

• Team Sports
• Esports
• Individual

By Technology

• Gen AI
• Other AI

By Geography

• North America

US

Canada

Mexico

Rest of North America

• Europe

Germany

UK

France

Russia

Spain

Italy

Rest of Europe

• Asia Pacific

China

Japan

India

South Korea

Australia

Malaysia

Rest of Asia Pacific

• LAMEA

Brazil

Argentina

UAE

Saudi Arabia

South Africa

Nigeria

Rest of LAMEA