Artificial Intelligence (AI) in Sports Market was US$ xx Mn in 2018 and is expected to reach US$ xx Mn by 2026, growing with a CAGR of xx% during the forecast years.
Artificial Intelligence (AI) is primarily being employed in the sports industry for tracking player performance and for improving the health of the player by providing suggestions on the injury. Moreover, AI and machine learning, from chatbots to network vision concept, are applied in several sports enterprise applications to improve sports planning. There are several major areas in the sport industry in which AI, including computer vision, automated journalism, marketing, and wearable technology play a key role.
Artificial Intelligence (AI) in Sports Market Segmentations |
|
| By Offerings | 1. Hardware |
| 1. Sensors | |
| 2. Processors | |
| 3. Others | |
| 2. Software | |
| 1. AI Platforms | |
| 1. Application Program Interface (API) | |
| 2. Machine Learning Framework | |
| 2. AI Solution | |
| 3. Services | |
| 1. Deployment & Integration | |
| 2. Support & Maintenance | |
| By Technology | 1. AI and Computing |
| 1. Natural Learning Processing | |
| 2. Data Analytics | |
| 3. Natural Language Processing | |
| 4. Cognitive Computing | |
| 5. Computer Vision | |
| 2. Data Solutions | |
| 1. Data Analytics | |
| 2. Data as a Service | |
| 3. Decisions as a Service | |
| 3. Internet of Things | |
| 1. Wearable Devices | |
| 2. M2M Connectivity | |
| 3. IoT Messaging | |
| By Application | 1. Sports Recruitment |
| 2. Performance Improvement | |
| 3. Scenario Analysis | |
| 4. Injury Prevention | |
| 5. Game Tactics | |
| By Operations | 1. Long Term Planning |
| 1. Team Planning | |
| 2. Budget Planning | |
| 3. Recruitment | |
| 4. Long Term Injury Prevention | |
| 2. Game Strategy | |
| 1. Game Preparation | |
| 2. Game Plan Development | |
| 3. Evaluating the Data | |
| 4. AI-Enabled VR Simulations | |
| 3. Game Tactics | |
| 1. Game Plan Execution | |
| 2. In-game Adjustments | |
| 3. Improved Communication | |
| By Spectatorship | 1. During the Game |
| 1. Interactive Sports | |
| 2. Game Watching | |
| 3. Game Attendance | |
| 2. Between Game Engagement | |
| 1. Player, Coach, and Fan Interaction | |
| 2. Predicting Outcomes | |
| 3. Other Fan Involvement | |
| 1. Fantasy Sports | |
| 2. Gambling | |
| 3. Traditional Sports and eSports | |
| By Sports Type | 1. Cricket |
| 2. Football | |
| 3. Basketball | |
| 4. Tennis | |
| 5. Baseball | |
| 6. Others | |
| By Region | 1. North America (US and Canada) |
| 2. Europe (UK, Germany, France and Rest of Europe) | |
| 3. Asia Pacific (China, Japan, India and Rest of Asia Pacific) | |
| 4. Latin America (Brazil, Mexico and Rest of Latin America) | |
| 5. Middle East & Africa (GCC and Rest of Middle East & Africa) | |
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Adoption of AI in sports helps in engaging fans and collecting essential information during the match to provide real-time insights for enhancing the game. Data is used to get insights, which are then shared with team players and strategists through the mobile app. Real-time insights are used to enhance the execution of the player and consequently, the team. This information supports the team players in analyzing the previous game patterns, and this can help predict the opponent’s progress through high-level analytics and provide a useful plan for the game. Sports analysts can compare previous data and improve the performance of a team.
Artificial Intelligence (AI) in Sports Market based on Technology
Adoption of technology in sports is gaining momentum with increasing digitalization across the globe. With the adoption of AI and machine learning applications in various sports, sports organizations or academies can use their data to improve every area of their operations. From player recruitment and performance to ticket sales, predictive analytics can help make targeted decisions and strategic changes that impact every area of a sports organization. This technology is being adopted across various sports to enhance the game pattern. For instance, in football, new devices are used for different reasons such as to help referees in decision-making and to quantify the athletes’ performance during a game, thus helping the coach to set the training program and the game strategy.
Predictive analytics is gaining popularity in sports as they use statistical tools and models to provide insights into future events for making predictions. The coach and the sport analyst deploy these analytics for understanding the past game events, performance of athletes or players, and the performance of a team. Based on this understanding, predictions are made for future sport events and sports organizations can utilize the algorithm to give possible outcomes, which results in better decision making.
Sports Insights
The global AI in sports market based on sports type has been segmented into football, cricket, baseball, basketball, and others. The other segment includes rugby, swimming, hockey, and boxing. The football segment is expected to witness a high CAGR over the coming years, owing to the rising popularity of the game mainly in European countries such as the U.K., Spain, and Germany. These countries conduct several football leagues including the English Premier League, La Liga, and Bundesliga. Analytics has been adopted in football for gaining insights on different fields including acceleration attained, passing trend, player’s health, and the number of touch-down passes.
North America is dominating the global Artificial Intelligence (AI) in Sports Market
North America was dominating the market in the year 2018 and is expected to show similar growth trends during the forecast years. In the U.S., growing focus on technological advancements and the rising adoption of technology in sports are propelling the market growth. Moreover, changing market environment and higher technology adoption rates across the region are increasing the adoption of AI in sports across North America. Recently, several clubs in ice hockey have started investing in analytics, while American Football remains an influential market.
Europe is also expected to witness high growth in the global AI in sport market. The New England Patriots use an approach to season ticket holder retention that is based on a variety of behavioural metrics. For instance, their models include variables such as game attendance, purchases of team merchandise, attendance at special season ticket holder events, and attendance at concerts or soccer games at the Gillette Stadium.
Company Profiles and Competitive Intelligence
The key players operating in the global AI in the Sports market includes 24/7.ai Inc., Amazon Inc., Apple Inc., Anodot, Facebook Inc., Fujitsu Ltd., Cisco Systems, DeepScale, Atmel Corporation, ARM Limited, Microsoft Corporation, and Micron Technology, among others.
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Chapter 1 Research Scope
1.1 Market Segmentation Scope
1.2 Market Regional Scope
Chapter 2 Executive Summary
2.1 Market Summary
2.1.1 Global AI in Sports Market, 2016-2026, (US$ Mn)
2.1 Market Snapshot: Global AI in Sports Market
2.2 Market Dynamics (DRO)
2.3 Global AI in Sports Market, by Segment, 2018
2.3.1 Global AI in Sports Market, by Offerings, 2018, (US$ Mn)
2.3.2 Global AI in Sports Market, by Technology, 2018, (US$ Mn)
2.3.3 Global AI in Sports Market, by Application, 2018, (US$ Mn)
2.3.4 Global AI in Sports Market, by Operations, 2018, (US$ Mn)
2.3.5 Global AI in Sports Market, by Spectatorship, 2018, (US$ Mn)
2.3.6 Global AI in Sports Market, by Region, 2018 (US$ Mn)
2.4 Premium Insights
2.4.1 AI in Sports Market In Developed Vs. Developing Economies, 2018 vs 2026
2.4.2 Global AI in Sports Market: Technology Integration
2.4.3 Global AI in Sports Market: Regulatory Implications
2.4.4 Global AI in Sports Market: Who’s Winning & Who’s Losing
2.4.5 Global AI in Sports Market: Regional Life Cycle Analysis
Chapter 3 Market Dynamics
3.1 Market Overview
3.2 Market Drivers
3.2.1 Driver 1
3.2.2 Driver 2
3.2.3 Driver 3
3.3 Market Restraints
3.3.1 Restraint 1
3.3.2 Restraint 2
3.4 Market Opportunities
3.4.1 Opportunity 1
3.4.2 Opportunity 2
3.5 Key Trends and Impact on Growth Trajectory
3.6 Industry Value Chain Analysis
3.6.1 Analyst’s Views
3.7 Industry SWOT Analysis
Chapter 4 Global AI in Sports Market, by Offerings
4.1 Market Overview, by Offerings
4.1.1 Incremental Opportunity, by Offerings, 2018-2026
4.2 Hardware
4.2.1 Global AI in Sports Market, by Hardware, 2016-2026, (US$ Mn)
4.2.2 Sensors
4.2.3 Processors
4.2.4 Others
4.3 Software
4.3.1 Global AI in Sports Market, by Software, 2016-2026, (US$ Mn)
4.3.2 AI Platforms
4.3.2.1 Application Program Interface (API)
4.3.2.2 Machine Learning Framework
4.3.3 AI Solution
4.4 Services
4.4.1 Global AI in Sports Market, by Services, 2016-2026, (US$ Mn)
4.4.1 Deployment & Integration
4.4.2 Support & Maintenance
Chapter 5 Global AI in Sports Market, by Technology
5.1 Market Overview, by Technology
5.1.1 Incremental Opportunity, by Technology, 2018-2026
5.2 AI and Computing
5.2.1 Global AI in Sports Market, by AI and Computing, 2016-2026, (US$ Mn)
5.2.2 Natural Learning Processing
5.2.3 Data Analytics
5.2.4 Natural Language Processing
5.2.5 Cognitive Computing
5.2.6 Computer Vision
5.3 Data Solutions
5.3.1 Global AI in Sports Market, by Data Solutions, 2016-2026, (US$ Mn)
5.3.2 Data Analytics
5.3.3 Data as a Service
5.3.4 Decisions as a Service
5.4 Internet of Things
5.4.1 Global AI in Sports Market, by Internet of Things, 2016-2026, (US$ Mn)
5.4.2 Wearable Devices
5.4.3 M2M Connectivity
5.4.4 IoT Messaging
Chapter 6 Global AI in Sports Market, by Application
6.1 Market Overview, by Application
6.1.1 Global AI in Sports Market, by Application, 2016-2026 (US$ Mn)
6.1.2 Incremental Opportunity, by Application , 2018-2026
6.2 Sports Recruitment
6.2.1 Global AI in Sports Market, by Sports Recruitment, 2016-2026, (US$ Mn)
6.3 Performance Improvement
6.3.1 Global AI in Sports Market, by Performance Improvement, 2016-2026 (US$ Mn)
6.4 Scenario Analysis
6.4.1 Global AI in Sports Market, by Scenario Analysis, 2016-2026 (US$ Mn)
6.5 Injury Prevention
6.5.1 Global AI in Sports Market, by Injury Prevention, 2016-2026 (US$ Mn)
6.6 Game Tactics
6.6.1 Global AI in Sports Market, by Game Tactics, 2016-2026 (US$ Mn)
Chapter 7 Global AI in Sports Market, by Operations
7.1 Market Overview, by Operations
7.1.1 Global AI in Sports Market, by Operations , 2016-2026 (US$ Mn)
7.1.2 Incremental Opportunity, by Operations, 2018-2026
7.2 Long Term Planning
7.2.1 Global AI in Sports Market, by Long Term Planning, 2016-2026, (US$ Mn)
7.2.2 Team Planning
7.2.3 Budget Planning
7.2.4 Recruitment
7.2.5 Long Term Injury Prevention
7.3 Game Strategy
7.3.1 Global AI in Sports Market, by Game Strategy, 2016-2026, (US$ Mn)
7.3.2 Game Preparation
7.3.3 Game Plan Development
7.3.4 Evaluating the Data
7.3.5 AI-Enabled VR Simulations
7.4 Game Tactics
7.4.1 Global AI in Sports Market, by Game Tactics, 2016-2026, (US$ Mn)
7.4.2 Game Plan Execution
7.4.3 In-game Adjustments
7.4.4 Improved Communication
Chapter 8 Global AI in Sports Market, by Spectatorship
8.1 Market Overview, by Spectatorship
8.1.1 Global AI in Sports Market, by Spectatorship, 2016-2026 (US$ Mn)
8.1.2 Incremental Opportunity, by Spectatorship, 2018-2026
8.2 During the Game
8.2.1 Global AI in Sports Market, by During the Game, 2016-2026, (US$ Mn)
8.2.2 Interactive Sports
8.2.3 Game Watching
8.2.4 Game Attendance
8.3 Between Game Engagement
8.3.1 Global AI in Sports Market, by Between Game Engagement, 2016-2026, (US$ Mn)
8.3.2 Player, Coach, and Fan Interaction
8.3.3 Predicting Outcomes
8.4 Other Fan Involvement
8.4.1 Global AI in Sports Market, by Other Fan Involvement, 2016-2026, (US$ Mn)
8.4.2 Fantasy Sports
8.4.3 Gambling
8.4.4 Traditional Sports and eSports
Chapter 9 Global AI in Sports Market, by Sports Type
9.1 Market Overview, by Sports Type
9.1.1 Global AI in Sports Market, by Sports Type, 2016-2026 (US$ Mn)
9.1.2 Incremental Opportunity, by Sports Type, 2018-2026
9.2 Cricket
9.2.1 Global AI in Sports Market, by Cricket, 2016-2026, (US$ Mn)
9.3 Football
9.3.1 Global AI in Sports Market, by Football, 2016-2026, (US$ Mn)
9.4 Basketball
9.4.1 Global AI in Sports Market, by Basketball, 2016-2026, (US$ Mn)
9.5 Tennis
9.5.1 Global AI in Sports Market, by Tennis, 2016-2026, (US$ Mn)
9.6 Baseball
9.6.1 Global AI in Sports Market, by Baseball, 2016-2026, (US$ Mn)
9.7 Others
9.7.1 Global AI in Sports Market, by Others, 2016-2026, (US$ Mn)
Chapter 10 Global AI in Sports Market, by Region
10.1 Market Overview, by Region
10.1.1 Global AI in Sports Market, by Region, 2016-2026,
10.2 Attractive Investment Opportunity, by Region, 2018
10.3 North America AI in Sports Market
10.3.1 North America AI in Sports Market, by Offerings, 2016-2026 (US$ Mn)
10.3.2 North America AI in Sports Market, by Technology, 2016-2026 (US$ Mn)
10.3.3 North America AI in Sports Market, by Application , 2016-2026 (US$ Mn)
10.3.4 North America AI in Sports Market, by Operations , 2016-2026 (US$ Mn)
10.3.5 North America AI in Sports Market, by Spectatorship, 2016-2026 (US$ Mn)
10.3.6 North America AI in Sports Market, by Sports Type, 2016-2026 (US$ Mn)
10.3.7 North America AI in Sports Market, by Country, 2016-2026, (US$ Mn)
10.3.7.1 U.S.
10.3.7.2 Canada
10.4 Europe AI in Sports Market
10.4.1 Europe AI in Sports Market, by Offerings, 2016-2026 (US$ Mn)
10.4.2 Europe AI in Sports Market, by Technology, 2016-2026 (US$ Mn)
10.4.3 Europe AI in Sports Market, by Application, 2016-2026 (US$ Mn)
10.4.4 Europe AI in Sports Market, by Operations, 2016-2026 (US$ Mn)
10.4.5 Europe AI in Sports Market, by Spectatorship, 2016-2026 (US$ Mn)
10.4.6 Europe AI in Sports Market, by Sports Type, 2016-2026 (US$ Mn)
10.4.7 Europe AI in Sports Market, by Country, 2016-2026, (US$ Mn)
10.4.7.1 U.K
10.4.7.2 Germany
10.4.7.3 France
10.4.7.4 Rest of Europe
10.5 Asia Pacific AI in Sports Market
10.5.1 Asia Pacific AI in Sports Market, by Offerings, 2016-2026 (US$ Mn)
10.5.2 Asia Pacific AI in Sports Market, by Technology, 2016-2026 (US$ Mn)
10.5.3 Asia Pacific AI in Sports Market, by Application, 2016-2026 (US$ Mn)
10.5.4 Asia Pacific AI in Sports Market, by Operations , 2016-2026 (US$ Mn)
10.5.5 Asia Pacific AI in Sports Market, by Spectatorship, 2016-2026 (US$ Mn)
10.5.6 Asia Pacific AI in Sports Market, by Sports Type, 2016-2026 (US$ Mn)
10.5.7 Asia Pacific AI in Sports Market, by Country, 2016-2026, (US$ Mn)
10.5.7.1 China
10.5.7.2 Japan
10.5.7.3 India
10.5.7.4 Rest of Asia Pacific
10.6 Latin America AI in Sports Market
10.6.1 Latin America AI in Sports Market, by Offerings, 2016-2026 (US$ Mn)
10.6.2 Latin America AI in Sports Market, by Technology, 2016-2026 (US$ Mn)
10.6.3 Latin America AI in Sports Market, by Application, 2016-2026 (US$ Mn)
10.6.4 Latin America AI in Sports Market, by Operations , 2016-2026 (US$ Mn)
10.6.5 Latin America AI in Sports Market, by Spectatorship, 2016-2026 (US$ Mn)
10.6.6 Latin America AI in Sports Market, by Sports Type, 2016-2026 (US$ Mn)
10.6.7 Latin America AI in Sports Market, by Country, 2016-2026 (US$ Mn)
10.6.7.1 Brazil
10.6.7.2 Mexico
10.6.7.3 Rest of Latin America
10.7 Middle East & Africa AI in Sports Market
10.7.1 Middle East & Africa AI in Sports Market, by Offerings, 2016-2026 (US$ Mn)
10.7.2 Middle East & Africa AI in Sports Market, by Technology, 2016-2026 (US$ Mn)
10.7.3 Middle East & Africa AI in Sports Market, by Application, 2016-2026 (US$ Mn)
10.7.4 Middle East & Africa AI in Sports Market, by Operations, 2016-2026 (US$ Mn)
10.7.5 Middle East & Africa AI in Sports Market, by Spectatorship, 2016-2026 (US$ Mn)
10.7.6 Middle East & Africa AI in Sports Market, by Sports Type, 2016-2026 (US$ Mn)
10.7.7 Middle East & Africa AI in Sports Market, by Region/Country, 2016-2026 (US$ Mn)
10.7.7.1 GCC
10.7.7.2 Rest of Middle East & Africa
Chapter 11 Competitive Intelligence
11.1 Market Players Present in Market Life Cycle
11.2 Top Players Comparison
11.3 Market Positioning of Key Players, 2018
11.4 Competitors Benchmarking
11.5 Market Players Mapping
11.5.1 By Offerings
11.5.2 By Technology
11.5.1 By Application
11.5.2 By Operations
11.5.3 By Spectatorship
11.5.4 By Sports Type
11.5.5 By Region
11.6 Strategies Adopted by Key Market Players
11.7 Market Share Analysis of Key Players, 2018
11.8 Recent Developments in the Market
11.8.1 Mergers & Acquisitions, Partnership, New Product Developments
Chapter 12 Company Profiles
12.1 24/7.ai Inc.
12.1.1 24/7.ai Inc. Overview
12.1.2 Key Stakeholders/Person in 24/7.ai Inc.
12.1.3 24/7.ai Inc. Products Portfolio
12.1.4 24/7.ai Inc. Financial Overview
12.1.5 24/7.ai Inc. News/Recent Developments
12.1.6 24/7.ai Inc. SWOT Analysis
12.1.7 Analyst View
12.2 Amazon Inc.
12.2.1 Amazon Inc. Overview
12.2.2 Key Stakeholders/Person in Amazon Inc.
12.2.3 Amazon Inc. Products Portfolio
12.2.4 Amazon Inc. Financial Overview
12.2.5 Amazon Inc. News/Recent Developments
12.2.6 Amazon Inc. SWOT Analysis
12.2.7 Analyst View
12.3 APPLE INC.
12.3.1 APPLE INC. Overview
12.3.2 Key Stakeholders/Person in APPLE INC.
12.3.3 APPLE INC. Products Portfolio
12.3.4 APPLE INC. Financial Overview
12.3.5 APPLE INC. News/Recent Developments
12.3.6 APPLE INC. SWOT Analysis
12.3.7 Analyst View
12.4 Anodot
12.4.1 Anodot Overview
12.4.2 Key Stakeholders/Person in Anodot
12.4.3 Anodot Products Portfolio
12.4.4 Anodot Financial Overview
12.4.5 Anodot News/Recent Developments
12.4.6 Anodot SWOT Analysis
12.4.7 Analyst View
12.5 Facebook Inc.
12.5.1 Facebook Inc. Overview
12.5.2 Key Stakeholders/Person in Facebook Inc.
12.5.3 Facebook Inc. Products Portfolio
12.5.4 Facebook Inc. Financial Overview
12.5.5 Facebook Inc. News/Recent Developments
12.5.6 Facebook Inc. SWOT Analysis
12.5.7 Analyst View
12.6 Fujitsu Ltd.
12.6.1 Fujitsu Ltd. Overview
12.6.2 Key Stakeholders/Person in Fujitsu Ltd.
12.6.3 Fujitsu Ltd. Products Portfolio
12.6.4 Fujitsu Ltd. Financial Overview
12.6.5 Fujitsu Ltd. News/Recent Developments
12.6.6 Fujitsu Ltd. SWOT Analysis
12.6.7 Analyst View
12.7 Cisco Systems
12.7.1 Cisco Systems Overview
12.7.2 Key Stakeholders/Person in Cisco Systems
12.7.3 Cisco Systems Products Portfolio
12.7.4 Cisco Systems Financial Overview
12.7.5 Cisco Systems News/Recent Developments
12.7.6 Cisco Systems SWOT Analysis
12.7.7 Analyst View
12.8 DeepScale
12.8.1 DeepScale Overview
12.8.2 Key Stakeholders/Person in DeepScale
12.8.3 DeepScale Products Portfolio
12.8.4 DeepScale Financial Overview
12.8.5 DeepScale News/Recent Developments
12.8.6 DeepScale SWOT Analysis
12.8.7 Analyst View
12.9 Atmel Corporation
12.9.1 Atmel Corporation Overview
12.9.2 Key Stakeholders/Person in Atmel Corporation
12.9.3 Atmel Corporation Products Portfolio
12.9.4 Atmel Corporation Financial Overview
12.9.5 Atmel Corporation News/Recent Developments
12.9.6 Atmel Corporation SWOT Analysis
12.9.7 Analyst View
12.10 ARM Limited
12.10.1 ARM Limited Overview
12.10.2 Key Stakeholders/Person in ARM Limited
12.10.3 ARM Limited Products Portfolio
12.10.4 ARM Limited Financial Overview
12.10.5 ARM Limited News/Recent Developments
12.10.6 ARM Limited SWOT Analysis
12.10.7 Analyst View
Chapter 13 Research Methodology
13.1 Methodology/Research Approach
13.2 Market Size Estimation
13.3 Data Source
13.3.1 Secondary Sources
13.3.2 Primary Sources
13.4 Breakup of the Primary Profiles
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