Global AI in Sports Market Size, Share & Trends Analysis Report, by Component, by Deployment Model, by Technology, by Application, by Game Type, and Regional Forecasts 2022-2032

Description

The global AI in Sports market, valued at approximately USD 1.11 billion in 2023, is poised for exponential growth at a compound annual growth rate (CAGR) of 16.7% during the forecast period from 2024 to 2032. This growth is primarily driven by the increasing demand for player tracking, performance monitoring, and real-time data analytics, coupled with the surge in adoption of AI-powered chatbots and virtual assistants to enhance fan engagement.

Key determinants for the market include the proliferation of AI in predictive analytics, enabling enhanced game strategies and planning. Additionally, advancements in AI technologies such as machine learning, natural language processing (NLP), and computer vision are revolutionizing various facets of sports, from injury prevention to recruitment analytics.

The integration of AI in sports facilitates personalized coaching, game performance optimization, and data-driven decision-making, offering transformative potential across various sports disciplines. However, challenges such as the high cost of implementation, maintenance, and a lack of skilled AI professionals remain as obstacles.

North America currently dominates the AI in Sports market, underpinned by robust technological infrastructure and early adoption of AI-based solutions. Asia-Pacific, on the other hand, is anticipated to exhibit the fastest growth, fueled by heavy investments in automation and AI to enhance sports productivity and fan experiences.

Major market players like Catapult Group International Ltd., IBM Corporation, Microsoft Corporation, and Sportradar AG are actively expanding their AI portfolios through strategic partnerships, product innovations, and acquisitions. These companies are focused on leveraging AI for real-time data analysis, injury simulations, and fan engagement tools, making the industry highly competitive.

The market segmentation reveals that software dominates by component, with cloud deployment gaining traction due to scalability and cost-effectiveness. Applications such as performance improvement and injury prevention are rapidly becoming pivotal in professional sports, further driving the adoption of AI solutions.

Major Market Players in the Report

Catapult Group International Ltd.
IBM Corporation
Microsoft Corporation
SAP SE
Sportradar AG
Stats Perform
Trumedia Networks
Facebook Inc.
Salesforce.com Inc.
SAS Institute Inc.

The detailed segments and sub-segments of the market are explained below:

By Component:

Software
Services

By Deployment Model:

Cloud
On-Premises

By Technology:

Machine Learning
Natural Language Processing (NLP)
Computer Vision
Data Analytics
Others

By Application:

Game Planning
Performance Improvement
Injury Prevention
Sports Recruitment
Others

By Game Type:

Football
Cricket
Tennis
Basketball
Baseball
Others

By Region:

North America
- U.S.
- Canada
Europe
- UK
- Germany
- France
- Italy
Asia Pacific
- China
- India
- Japan
- Australia
- South Korea
Latin America
- Brazil
Middle East & Africa
- UAE
- South Africa

Years considered for the study:

Historical year: 2022
Base year: 2023
Forecast period: 2024 to 2032

Key Takeaways:

Detailed revenue analysis for each market segment over a 10-year forecast period.
Regional-level insights into market dynamics and competitive landscape.
Strategic recommendations to capitalize on emerging opportunities.

Chapter 1: Global AI in Sports Market Executive Summary

1.1. Global AI in Sports Market Size & Forecast (2022-2032)
1.2. Regional Summary
1.3. Segmental Summary
- 1.3.1. By Component
- 1.3.2. By Deployment Model
- 1.3.3. By Technology
- 1.3.4. By Application
- 1.3.5. By Game Type
1.4. Key Trends
1.5. Recession Impact
1.6. Analyst Recommendation & Conclusion

Chapter 2: Global AI in Sports Market Definition and Research Assumptions

2.1. Research Objective
2.2. Market Definition
2.3. Research Assumptions
- 2.3.1. Inclusion & Exclusion
- 2.3.2. Limitations
- 2.3.3. Supply Side Analysis
  - 2.3.3.1. Availability
  - 2.3.3.2. Infrastructure
  - 2.3.3.3. Regulatory Environment
  - 2.3.3.4. Market Competition
  - 2.3.3.5. Economic Viability (Consumer's Perspective)
- 2.3.4. Demand Side Analysis
  - 2.3.4.1. Regulatory Frameworks
  - 2.3.4.2. Technological Advancements
  - 2.3.4.3. Environmental Considerations
  - 2.3.4.4. Consumer Awareness & Acceptance
2.4. Estimation Methodology
2.5. Years Considered for the Study
2.6. Currency Conversion Rates

Chapter 3: Global AI in Sports Market Dynamics

3.1. Market Drivers
- 3.1.1. Demand for real-time player monitoring and tracking
- 3.1.2. Growth in fan engagement tools using chatbots and virtual assistants
- 3.1.3. Increasing application of predictive analytics in sports
3.2. Market Challenges
- 3.2.1. High costs of implementation and maintenance
- 3.2.2. Lack of trained AI professionals
3.3. Market Opportunities
- 3.3.1. Adoption of AI for future game predictions
- 3.3.2. Investment in automated sports analytics solutions

Chapter 4: Global AI in Sports Market Industry Analysis

4.1. Porter's 5 Force Model
- 4.1.1. Bargaining Power of Suppliers
- 4.1.2. Bargaining Power of Buyers
- 4.1.3. Threat of New Entrants
- 4.1.4. Threat of Substitutes
- 4.1.5. Competitive Rivalry
- 4.1.6. Futuristic Approach to Porter's 5 Force Model
- 4.1.7. Porter's 5 Force Impact Analysis
4.2. PESTEL Analysis
- 4.2.1. Political
- 4.2.2. Economic
- 4.2.3. Social
- 4.2.4. Technological
- 4.2.5. Environmental
- 4.2.6. Legal
4.3. Top Investment Opportunities
4.4. Top Winning Strategies
4.5. Disruptive Trends
4.6. Industry Expert Perspective
4.7. Analyst Recommendation & Conclusion

Chapter 5: Global AI in Sports Market Size & Forecast by Component (2022-2032)

5.1. Segment Dashboard
5.2. Global AI in Sports Market: Component Revenue Trend Analysis, 2022 & 2032 (USD Billion)
- 5.2.1. Software
- 5.2.2. Services

Chapter 6: Global AI in Sports Market Size & Forecast by Deployment Model (2022-2032)

6.1. Segment Dashboard
6.2. Global AI in Sports Market: Deployment Model Revenue Trend Analysis, 2022 & 2032 (USD Billion)
- 6.2.1. Cloud
- 6.2.2. On-Premises

Chapter 7: Global AI in Sports Market Size & Forecast by Technology (2022-2032)

7.1. Segment Dashboard
7.2. Global AI in Sports Market: Technology Revenue Trend Analysis, 2022 & 2032 (USD Billion)
- 7.2.1. Machine Learning
- 7.2.2. Natural Language Processing
- 7.2.3. Computer Vision
- 7.2.4. Data Analytics
- 7.2.5. Others

Chapter 8: Global AI in Sports Market Size & Forecast by Application (2022-2032)

8.1. Segment Dashboard
8.2. Global AI in Sports Market: Application Revenue Trend Analysis, 2022 & 2032 (USD Billion)
- 8.2.1. Game Planning
- 8.2.2. Performance Improvement
- 8.2.3. Injury Prevention
- 8.2.4. Sports Recruitment
- 8.2.5. Others

Chapter 9: Global AI in Sports Market Size & Forecast by Game Type (2022-2032)

9.1. Segment Dashboard
9.2. Global AI in Sports Market: Game Type Revenue Trend Analysis, 2022 & 2032 (USD Billion)
- 9.2.1. Football
- 9.2.2. Cricket
- 9.2.3. Basketball
- 9.2.4. Tennis
- 9.2.5. Baseball
- 9.2.6. Others

Chapter 10: Global AI in Sports Market Size & Forecast by Region (2022-2032)

10.1. North America AI in Sports Market
- 10.1.1. U.S. AI in Sports Market
- 10.1.2. Canada AI in Sports Market
10.2. Europe AI in Sports Market
- 10.2.1. UK AI in Sports Market
- 10.2.2. Germany AI in Sports Market
10.3. Asia Pacific AI in Sports Market
- 10.3.1. China AI in Sports Market
- 10.3.2. India AI in Sports Market
- 10.3.3. Japan AI in Sports Market
10.4. Latin America AI in Sports Market
- 10.4.1. Brazil AI in Sports Market
10.5. Middle East & Africa AI in Sports Market
- 10.5.1. UAE AI in Sports Market
- 10.5.2. South Africa AI in Sports Market

Chapter 11: Competitive Intelligence

11.1. Key Company SWOT Analysis
- 11.1.1. IBM Corporation
- 11.1.2. Sportradar AG
- 11.1.3. Microsoft Corporation
11.2. Top Market Strategies
11.3. Company Profiles
- 11.3.1. Catapult Group International Ltd.
- 11.3.2. Facebook Inc.
- 11.3.3. SAS Institute Inc.
- 11.3.4. Stats Perform

Chapter 12: Research Process

12.1. Research Process
- 12.1.1. Data Mining
- 12.1.2. Analysis
- 12.1.3. Market Estimation
- 12.1.4. Validation
- 12.1.5. Publishing
12.2. Research Attributes