DUBLIN, March 15, 2019 /PRNewswire/ — The “Artificial Intelligence in Sports Market: AI in Sports by Technology, Applications, Sports Level (Olympic, Professional, College), Sports Type, User Type (Owner, Coach, Player, Spectator), Use Case, Deployment, Region and Country 2019-2024” report has been added to ResearchAndMarkets.com’s offering.
- AI improves the value of cross-training by team role/position between 9 and 32 percent
- Up to 65% of long-term cognitive dysfunction due to concussions is preventable through the use of AI
- AI in sports will improve individual and team performance by an average of 17% and 28% respectively
- Top benefits of AI in sports include performance improvement, injury prevention, and recruitment
- AI will improve revenue, reduce operational costs, and improve valuations of professional sports teams
This is the only research available that focuses on Artificial Intelligence (AI) in the sports industry. AI in the sports market represents a substantial opportunity for operational improvements including efficiency and effectiveness enhancements that ultimately lead to substantive team game performance.
This provides an assessment of the technologies, companies, strategies and solutions involved in leveraging artificial intelligence in sports market. The report analyzes AI in the sports market by sports level, type of sport, user type, and deployment options.
The report provides AI in sports market sizing for the aforementioned as well as a forecast for AI in the sports market by region and country from 2019 to 2024. It is important to note that certain countries focus on very specific sports, so AI in sports will vary significantly on a country by country basis and not just by comparative population or per capita GDP.
Improving the overall efficiency and effectiveness of teams and individual athletes has big implications as sports-related activities and events have become a major industry in the last few decades. Professional sports in particular have become a big business with the asset value of major teams at well over $1 billion each, generating triple-digit millions in revenue annually.
For example, the New England Patriots (American) football team is valued at roughly $3.8 billion, and generates over $500 million in total revenue annually. With about $103 million in revenue due to gate receipts, it is clear that a large portion of professional sports teams rely on non-venue related revenue including sponsorship, media rights, and merchandising. With the level of financials involved in a given organization, AI in the sports market is a meaningful investment for most team owners.
Sports at the Olympic, professional, and collegiate levels has become very data-driven as decisions ranging from recruitment and training to strategy and in-game tactics rely upon statistics and a dynamic set of variables including personnel, game conditions, and scenarios. Would be Olympians depend on sponsors, trainers, and coaches for major funding and support.
Sponsorship is a multi-million investment for each athlete, underscoring the need to make the best decisions possible for sovereign nations and companies involved in deciding who will be developed with the intent of representing a country in a given sport and sporting event for the Olympics. Wise implementation of AI in the sports market represents a means of sponsoring countries, companies, and wealthy benefactors to maximize their investment in the best world athletes.
At the collegiate level, a great deal is at stake in terms of recruiting athletes to become professionals. There is also great importance for National Collegiate Athletic Association division IA teams who vie for various milestones such as winning seasons, division leadership, league championships, playoff appearances, and championships.
Much is at stake from an alumni goodwill perspective, which translates into donations for sporting programs, which funds university and college development. AI in the sports market at the collegiate level provides this type of indirect benefit as college sports programs must be careful to not step over the line in terms of rules regarding financial benefits to players.
- The only report of its type focusing on AI in the sports market
- Understand how AI in sports will improve sports operations
- Identify opportunities and challenges of implementing AI in sports
- Understand how AI in sports relies upon other supporting technologies
Key Topics Covered
1. Executive Summary
2.1. Why AI in Sports?
2.2. Risks and Benefits
2.3. Opportunities and Challenges
3. AI in Sports and Related Technologies
3.1. AI and Computing
3.1.1. Machine Learning
3.1.2. Data Analytics
3.1.3. Natural Language Processing
3.1.4. Cognitive Computing
3.1.5. Computer Vision
3.2. Data Solutions
3.2.1. Data Analytics
3.2.2. Data as a Service
3.2.3. Decisions as a Service
3.3. Internet of Things
3.3.1. Wearable Devices
3.3.2. M2M Connectivity
3.3.3. IoT Messaging
3.3.4. IoT Command and Control
4. AI Applications
4.1. AI in Sports Recruitment
4.2. AI in Performance Improvement
4.3. AI in Game Planning
4.4. AI in Game Tactics
4.5. AI in Injury Prevention
5. AI in Sports by Level
5.5. High School
5.6. Middle School
5.7. Early Childhood Sports and Fitness
6. AI in Sports by Type
6.5. Football (American)
6.8. Hockey (Field)
6.9. Hockey (Ice)
6.10. Mixed Martial Arts
6.11. Racing (automobiles)
6.12. Racing (horses)
6.15. Soccer (association football)
6.16. Table Tennis (ping pong)
7. AI in Sports Operations
7.1. Long Term Planning
7.1.1. Team Planning
7.1.2. Budget Planning
7.1.4. Long Term Injury Prevention
7.2. Game Strategy
7.2.1. Game Preparation
7.2.2. Game Plan Development
7.2.3. Evaluating the Data
7.2.4. AI Enabled VR Simulations
7.3. Game Tactics
7.3.1. Game Plan Execution
7.3.2. In-game Adjustments
7.3.3. Improved Communication
8. AI in Sports Spectatorship
8.1. During the Game
8.1.1. Interactive Sports
8.1.2. Game Watching
8.1.3. Game Attendance
8.2. Between Game Engagement
8.2.1. Player, Coach, and Fan Interaction
8.2.2. Predicting Outcomes
8.3. Other Fan Involvement
8.3.1. Fantasy Sports
8.3.3. Traditional Sports and eSports
9. AI Company Analysis
9.1. 24/7.ai Inc.
9.3. Advanced Micro Devices (AMD) Inc.
9.4. AIBrian Inc.
9.5. Amazon Inc.
9.7. AOL Inc.
9.8. Apple Inc.
9.9. ARM Limited
9.10. Atmel Corporation
9.11. Baidu Inc.
9.12. Cisco Systems
9.14. Digital Reasoning Systems Inc.
9.16. Facebook Inc.
9.17. Fujitsu Ltd.
9.19. Gemalto N.V.
9.20. General Electric (GE)
9.21. General Vision Inc.
9.22. Google Inc.
9.25. Haier Group Corporation
9.27. Hewlett Packard Enterprise (HPE)
9.28. Huawei Technologies Co. Ltd.
9.29. IBM Corporation
9.30. Imagen Technologies
9.31. Inbenta Technologies Inc.
9.32. Intel Corporation
9.34. IPsoft Inc.
9.35. iRobot Corp.
9.36. Juniper Networks, Inc.
9.37. Koninklijke Philips N.V
9.39. KUKA AG
9.40. Leap Motion Inc.
9.41. LG Electronics
9.42. Lockheed Martin
9.44. Micron Technology
9.45. Microsoft Corporation
9.46. MicroStrategy Incorporated
9.48. Motion Controls Robotics Inc.
9.52. Next IT Corporation
9.53. Nokia Corporation
9.54. Nuance Communications Inc.
9.56. Omron Adept Technology
9.58. Oracle Corporation
9.59. Panasonic Corporation
9.61. PointGrab Ltd.
9.62. QlikTech International AB
9.63. Qualcomm Incorporated
9.64. Rethink Robotics
9.65. Rockwell Automation Inc.
9.67. Samsung Electronics Co Ltd.
9.69. SAS Institute Inc.
9.70. Sentient Technologies Holdings Limited
9.71. Siemens AG
9.72. Signal Media
9.73. SoftBank Robotics Holding Corp.
9.74. SparkCognition Inc.
9.79. Tesla Inc.
9.80. Texas Instruments Inc.
9.82. Umbo Computer Vision
9.83. Veros Systems Inc.
9.85. Wade & Wendy
9.86. Wind River Systems Inc.
9.88. Xiaomi Technology Co. Ltd.
9.89. XILINX Inc.
10. AI in Sports Market Analysis and Forecasts 2019 – 2024
10.1. Global Aggregate AI in Sports Market 2019 – 2024
10.2. AI in Sports Market by Technology 2019 – 2024
10.2.1. Machine Learning in Sports Market
10.2.2. NLP in Sports Market
10.2.3. Cognitive Computing in Sports Market
10.2.4. Computer Vision in Sports Market
10.2.5. Data as a Service in Sports Market
10.2.6. Decisions as a Service in Sports Market
10.3. AI in Sports Market by Sports Level 2019 – 2024
10.3.2. Private Teams
10.3.5. High School
10.3.6. Middle School
10.3.7. Early Child Sports and Fitness
10.4. AI in Sports Market by Type 2019 – 2024
10.4.5. Football (American)
10.4.8. Hockey (Field)
10.4.9. Hockey (Ice)
10.4.10. Mixed Martial Arts
10.4.11. Racing (Automobiles)
10.4.12. Racing (Horses)
10.4.15. Soccer (Association Football)
10.4.16. Table Tennis (Ping Pong)
10.5. AI in Sports Market by User Type 2019 – 2024
10.6. AI in Sports Market by Use Case 2019 – 2024
10.6.1. Performance Improvement
10.6.2. Long-term Injury Prevention
10.6.3. Game Planning and Preparation
10.6.4. In-game Decision Making
10.6.5. Personnel Management
10.7. AI in Sports by Deployment 2019 – 2024
10.7.1. Embedded AI Software
10.7.2. Decision Support Systems
10.7.3. Data as a Service
10.7.4. Decisions as a Service
10.8. AI in Sports by Region 2019 – 2024
10.8.1. North America
10.8.3. Asia Pac
10.8.4. Middle East and Africa
10.8.5. Latin America
11. Summary and Recommendations
12. Appendix: AI Technologies and Solutions
For more information about this report visit https://www.researchandmarkets.com/research/sb55hb/ai_in_the_global?w=5
Research and Markets also offers Custom Research services providing focused, comprehensive and tailored research.
Research and Markets
Laura Wood, Senior Manager
For E.S.T Office Hours Call +1-917-300-0470
For U.S./CAN Toll Free Call +1-800-526-8630
For GMT Office Hours Call +353-1-416-8900
U.S. Fax: 646-607-1907
Fax (outside U.S.): +353-1-481-1716
View original content:http://www.prnewswire.com/news-releases/ai-in-the-global-sports-market-by-technology-applications-sports-level-sports-type-user-type-use-case-deployment-region-and-country-2019-2024-300813095.html
SOURCE Research and Markets