The Sports AI Market: 2019 Insights – AI Will Improve Revenue, Reduce Operational Costs, and Improve Valuations of Professional Sports Teams

The Sports AI Market: 2019 Insights - AI Will Improve Revenue, Reduce Operational Costs, and Improve Valuations of Professional Sports Teams

DUBLIN–(BUSINESS WIRE)–Mar 14, 2019–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’s offering.

Key Findings

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.

Market Insights

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.

Report Benefits

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

Topics Covered

1. Executive Summary

2. Introduction2.1. Why AI in Sports? 2.2. Risks and Benefits 2.3. Opportunities and Challenges

3. AI in Sports and Related Technologies3.1. 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 Applications4.1. 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 Level5.1. Olympic 5.2. Private 5.3. Professional 5.4. Collegiate 5.5. High School 5.6. Middle School 5.7. Early Childhood Sports and Fitness

6. AI in Sports by Type6.1. Baseball 6.2. Basketball 6.3. Boxing 6.4. Cricket 6.5. Football (American) 6.6. Golf 6.7. Gymnastics 6.8. Hockey (Field) 6.9. Hockey (Ice) 6.10. Mixed Martial Arts 6.11. Racing (automobiles) 6.12. Racing (horses) 6.13. Rugby 6.14. Skiing 6.15. Soccer (association football) 6.16. Table Tennis (ping pong) 6.17. Tennis 6.18. Volleyball 6.19. Wrestling

7. AI in Sports Operations7.1. Long Term Planning 7.1.1. Team Planning 7.1.2. Budget Planning 7.1.3. Recruitment 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 Spectatorship8.1. 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 8.3.2. Gambling 8.3.3. Traditional Sports and eSports

9. AI Company Analysis9.1. 24/ Inc. 9.2. Active.Ai 9.3. Advanced Micro Devices (AMD) Inc. 9.4. AIBrian Inc. 9.5. Amazon Inc. 9.6. Anodot 9.7. AOL Inc. 9.8. Apple Inc. 9.9. ARM Limited 9.10. Atmel Corporation 9.11. Baidu Inc. 9.12. Cisco Systems 9.13. DeepScale 9.14. Digital Reasoning Systems Inc. 9.15. Directly 9.16. Facebook Inc. 9.17. Fujitsu Ltd. 9.18. Gamaya 9.19. Gemalto N.V. 9.20. General Electric (GE) 9.21. General Vision Inc. 9.22. Google Inc. 9.23. Graphcore 9.24. 9.25. Haier Group Corporation 9.26. Haptik 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 9.33. InteliWISE 9.34. IPsoft Inc. 9.35. iRobot Corp. 9.36. Juniper Networks, Inc. 9.37. Koninklijke Philips N.V 9.38. Kreditech 9.39. KUKA AG 9.40. Leap Motion Inc. 9.41. LG Electronics 9.42. Lockheed Martin 9.43. MAANA 9.44. Micron Technology 9.45. Microsoft Corporation 9.46. MicroStrategy Incorporated 9.47. Miele 9.48. Motion Controls Robotics Inc. 9.49. 9.50. Neurala and many more…

10. AI in Sports Market Analysis and Forecasts 2019 – 202410.1. 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 10.3.1. Olympic 10.3.2. Private Teams 10.3.3. Professional 10.3.4. Collegiate 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 10.4.1. Baseball 10.4.2. Basketball 10.4.3. Boxing 10.4.4. Cricket 10.4.5. Football (American) 10.4.6. Golf 10.4.7. Gymnastics 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) 10.4.13. Rugby 10.4.14. Skiing 10.4.15. Soccer (Association Football) 10.4.16. Table Tennis (Ping Pong) 10.4.17. Tennis 10.4.18. Volleyball 10.4.19. Wrestling 10.5. AI in Sports Market by User Type 2019 – 2024 10.5.1. Owners 10.5.2. Coaches 10.5.3. Players 10.5.4. Spectators 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 10.8.2. Europe 10.8.3. Asia Pacific 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

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