Sports Analytics Market Size is Witnessing to Grow at a CAGR of 27.3%, to Reach $8.4 billion by 2026 - Report by MarketsandMarkets™

Opportunities in Sports Analytics Market:- Rising Internet Proliferation and Growing Usage of Connected and Integrated Technologies


Chicago, Oct. 19, 2022 (GLOBE NEWSWIRE) -- 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, according to a new report by MarketsandMarkets™. 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.

Browse in-depth TOC on "Sports Analytics Market”
223-Tables
51- Figures
237- Pages

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Scope of the Report:

Report Metrics Details
Market size available for years 2015–2026
Base year considered 2020
Forecast period 2021–2026
Forecast units USD Million
Segments covered Sports Type, Application, Component, Deployment Mode, End Users and Region
Largest Market North America
Sports Type Individual Sports, Teams Sports, eSports
Geographies covered North America, Europe, APAC, MEA, and Latin America
Companies covered IBM (US), Zebra Technologies (US), Catapult (Australia), EXL (US), SAS Institute (US), HCL (India), ChyronHego (US), GlobalStep (US), Statsperform (US), Exasol (Germany), Trumedia Networks (US), Dataart (US), Orreco (Ireland), Quants4sports (Italy), Salesforce (US), Sportradar (Switzerland), Whoop (US), Kitman Labs (Ireland), HUDL (US), Trace (US), Kinduct (Canada), Uplift Labs (US), Sportlogiq (Canada), Sporting Algorithms (US), L2P Limited (Europe).

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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.

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 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.

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The Sports Analytics Market by application has been categorized into performance analysis, player and team valuation, player fitness and safety, fan engagement, broadcast management and other applications. The fan engagement application is expected to witness the highest growth rate, while the performance analysis is expected to have the largest market size during the forecast period. The larger market size of the performance analysis can be attributed to the fast use of smartphones and wearable devices.

The Sports Analytics Market has been segmented into five major regions: North America, Europe, APAC, Latin America, and MEA. 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.

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