The sports analytics market size to grow from USD 2.5 billion in 2021 to USD 8.4 billion by 2026, at a Compound Annual Growth Rate (CAGR) of 27.3% during the forecast period. Various factors such as increasing spending on adoption of newer technologies, changing landscape of customer intelligence to drive the market, and proliferation of customer channels are expected to drive the adoption of sports analytics technologies and services.
Traditional analytics platforms use static and stored data to analyze simple or complex patterns and react to any business situation. These platforms take days to analyze and weeks to act on the stored data. Sports analytics technology is the core enabler of big data, enabling businesses to use historical data and combine it with customer insights to predict future events. Big data is an ever-increasing technology that assists businesses in optimizing processes and minimizing operational costs. The combination of real-time data streams, Artificial Intelligence (AI), Machine Learning (ML), and sports analytics can deliver competitive business advantages. Traditional analytics and BI systems use the deductive approach for analyzing data. This approach works well with structured data. Sports analytics, on the other hand, applies the inductive reasoning analysis approach, which deals with large datasets derived from ML, robotics, sensors, and AI. It uses algorithms that carry out complex calculations on large data sets and discover interrelationships and patterns between them. Big data offers the capability to gather, manage, and examine data across business verticals, such as banking, healthcare, and agriculture, which has made it a trending topic in Information Technology (IT) for a decade. It has numerous applications, and one such field where it has revolutionized the entire landscape is the sports industry. Data has always been crucial in sports to gain strategic decision-making capabilities and formulate other business strategies. Sports generate a large amount of data related to players, team performance, and the audience. Big data has made it easier and quicker for coaches, team managers, and sports associations to analyze the collected data and make optimum use of it. It further helps reshape existing business models, where sports are viewed as both commercial and technological platforms. To transform sports arenas into a testbed facility for the implementation of IoT on a wide-scale deployment, the technology platform is expected to comprise a robust Wireless Fidelity (Wi-Fi) network, mobile and cloud computing, and Internet of Things (IoT) technologies. IoT would eventually digitalize stadium operations and provide fans with an enhanced connected experience. A smart stadium, therefore, would use sophisticated technologies and robust infrastructure to enhance its operations to lure fans to the stadiums.
In-depth interviews were conducted with Chief Executive Officers (CEOs), innovation and technology directors, system integrators, and executives from various key organizations operating in the sports analytics market.
The report includes the study of key players offering sports analytics solutions and services. It profiles major vendors in the global sports analytics market. The major vendors in the global sports analytics market include IBM (US), SAS Institute (US), Salesforce (US), EXL (US), GlobalStep (US), Catapult (US), HCL (India), ChyronHego (US), Stats Perform (US), TruMedia Network (US), DataArt (US), Orreco (Ireland), Quant4Sport (Italy), Zebra Technologies (US), and Exasol (Germany).
The market study covers the sports analytics market across segments. It aims at estimating the market size and the growth potential of this market across different segments, such as components, deployment mode, organization size, application, vertical, and region. It includes an in-depth competitive analysis of the key players in the market, along with their company profiles, key observations related to product and business offerings, recent developments, and key market strategies.
The report would provide the market leaders/new entrants in this market with information on the closest approximations of the revenue numbers for the overall sports analytics market and its subsegments. It would help stakeholders understand the competitive landscape and gain more insights better to position their business and plan suitable go-to-market strategies. It also helps stakeholders understand the pulse of the market and provides them with information on key market drivers, restraints, challenges, and opportunities.
Traditional analytics platforms use static and stored data to analyze simple or complex patterns and react to any business situation. These platforms take days to analyze and weeks to act on the stored data. Sports analytics technology is the core enabler of big data, enabling businesses to use historical data and combine it with customer insights to predict future events. Big data is an ever-increasing technology that assists businesses in optimizing processes and minimizing operational costs. The combination of real-time data streams, Artificial Intelligence (AI), Machine Learning (ML), and sports analytics can deliver competitive business advantages. Traditional analytics and BI systems use the deductive approach for analyzing data. This approach works well with structured data. Sports analytics, on the other hand, applies the inductive reasoning analysis approach, which deals with large datasets derived from ML, robotics, sensors, and AI. It uses algorithms that carry out complex calculations on large data sets and discover interrelationships and patterns between them. Big data offers the capability to gather, manage, and examine data across business verticals, such as banking, healthcare, and agriculture, which has made it a trending topic in Information Technology (IT) for a decade. It has numerous applications, and one such field where it has revolutionized the entire landscape is the sports industry. Data has always been crucial in sports to gain strategic decision-making capabilities and formulate other business strategies. Sports generate a large amount of data related to players, team performance, and the audience. Big data has made it easier and quicker for coaches, team managers, and sports associations to analyze the collected data and make optimum use of it. It further helps reshape existing business models, where sports are viewed as both commercial and technological platforms. To transform sports arenas into a testbed facility for the implementation of IoT on a wide-scale deployment, the technology platform is expected to comprise a robust Wireless Fidelity (Wi-Fi) network, mobile and cloud computing, and Internet of Things (IoT) technologies. IoT would eventually digitalize stadium operations and provide fans with an enhanced connected experience. A smart stadium, therefore, would use sophisticated technologies and robust infrastructure to enhance its operations to lure fans to the stadiums.
The cloud segment to have the highest CAGR during the forecast period
By deployment mode, the sports analytics market has been segmented into on-premises and cloud. The CAGR of the cloud deployment mode is estimated to be the largest during the forecast period. Cloud-based services are provided directly through the cloud-deployed network connection. Cloud-based platforms are beneficial for organizations that have strict budgets for security investments. The cloud deployment mode is growing, as cloud-based sports analytics solutions are easy to maintain and upgrade.The individual sports segment to hold higher CAGR during the forecast period
The sports analytics market has been segmented by sports type into individual sports, team sports and eSports. The market for Individual Sport is expected to register a higher CAGR during the forecast period. These individual sports are early adopters of sports analytics solutions. They are faced with the troublesome task of effectively managing data because of the diverse nature of IT infrastructure, which is complex in nature.Among regions, APAC to hold highest CAGR during the forecast period
APAC is expected to grow at a good pace during the forecast period. The region will be booming, as it is experiencing a lot of new entrepreneur setups, who are adopting the newer technologies to have a competitive advantage over the established players. China, Japan, and India have displayed ample growth opportunities in the sports analytics market.Breakdown of primaries
In-depth interviews were conducted with Chief Executive Officers (CEOs), innovation and technology directors, system integrators, and executives from various key organizations operating in the sports analytics market.
- By Company: Tier I: 35%, Tier II: 45%, and Tier III: 20%
- By Designation: C-Level Executives: 35%, D-Level Executives: 25%, and Managers: 40%
- By Region: APAC: 25%, Europe: 30%, North America: 30%, MEA: 10%, Latin America: 5%
The report includes the study of key players offering sports analytics solutions and services. It profiles major vendors in the global sports analytics market. The major vendors in the global sports analytics market include IBM (US), SAS Institute (US), Salesforce (US), EXL (US), GlobalStep (US), Catapult (US), HCL (India), ChyronHego (US), Stats Perform (US), TruMedia Network (US), DataArt (US), Orreco (Ireland), Quant4Sport (Italy), Zebra Technologies (US), and Exasol (Germany).
Research Coverage
The market study covers the sports analytics market across segments. It aims at estimating the market size and the growth potential of this market across different segments, such as components, deployment mode, organization size, application, vertical, and region. It includes an in-depth competitive analysis of the key players in the market, along with their company profiles, key observations related to product and business offerings, recent developments, and key market strategies.
Key Benefits of Buying the Report
The report would provide the market leaders/new entrants in this market with information on the closest approximations of the revenue numbers for the overall sports analytics market and its subsegments. It would help stakeholders understand the competitive landscape and gain more insights better to position their business and plan suitable go-to-market strategies. It also helps stakeholders understand the pulse of the market and provides them with information on key market drivers, restraints, challenges, and opportunities.
Table of Contents
1 Introduction
2 Research Methodology
4 Premium Insights
5 Market Overview and Industry Trends
6 Sports Analytics Market, By Sports Type
7 Sports Analytics Market, By Application
8 Sports Analytics Market, By Deployment Model
9 Sports Analytics Market, By Component
10 Sports Analytics Market, By Region
11 Competitive Landscape
12 Company Profiles
13 Appendix
List of Tables
List of Figures
Companies Mentioned
- Ibm
- Sas Institute
- Hcl Technologies
- Salesforce
- Zebra Technologies
- Catapult
- Exl
- Chyronhego Corporation
- Global Step
- Stats Perform
- Exasol
- Dataart
- Trumedia Networks
- Orreco
- Quant4Sport
- Sportradar
- Whoop
- Kitman Labs
- Hudl
- Trace
- Kinduct
- Uplift Labs
- Sportlogiq
- Sporting Algorithms
- L2P Limited
Methodology
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Table Information
| Report Attribute | Details |
|---|---|
| No. of Pages | 238 |
| Published | March 2022 |
| Forecast Period | 2021 - 2026 |
| Estimated Market Value ( USD | $ 2.5 Billion |
| Forecasted Market Value ( USD | $ 8.4 Billion |
| Compound Annual Growth Rate | 27.3% |
| Regions Covered | Global |
| No. of Companies Mentioned | 25 |
