The association between data science and sports may take time to come to mind when hearing about your favorite player or team’s hundredth goal, highest career score, latest win or loss, or the likelihood of success against a particular opponent. However, data and associated machine learning (ML), deep learning (DL), and artificial intelligence (AI) play a more critical role in sports than we might think. Not just scoring and performance but calculating correlations and forecasting based on parameters such as weather conditions, referee decisions, player strengths and weaknesses, performance, and history. Data science also plays a vital role in a live game or match venues such as stadiums in terms of increasing operating efficiency, fan engagement, environmental insight, safety, and security.
Specific analyst teams in the major leagues, college sports, and stadium management calculate statistics and probabilities using AI-driven data insight to optimize in real-time and longer term. Just as real-time data is used in manufacturing or even industrial plants to improve production, so are game or match decisions influenced by data insight. Actionable real-time data is the key to better, more informed decision-making for improved outcomes, regardless of the application. Making optimizations is not a guessing game and is not achieved through manual calculation. Today’s technology allows more automated, faster, efficient, and intelligent data gathering and deciphering.
Data science is the entire lifecycle of capturing, maintaining, processing, analyzing, and communicating data into actionable intelligence and is made possible through purpose-built hardware and software supported by edge infrastructure. Application-specific hardware reduces latency allowing direct data capture and real-time analysis of data at the edge. Regarding computing power, CPU and GPU accelerators also provide industry-leading processing speed to handle massive amounts of data.
There is a vast range of technology available in the market that supports the AI, ML, and DL needs of data science. Arrow works together with technology partners such as HP to deliver both hardware and software solutions that facilitate data science workflows and tailors solutions to the needs of data science and analytics teams and specific applications.
Together, Arrow and HP Data Science solutions are unlocking new levels of performance for customers, large and small, by improving efficiency, reducing the time to completion, and providing visibility into concurrent workflows through dedicated data science hardware and software products.
In a recent webinar, Build, Train and Deploy: 5 Ways to Optimize your Data Science Project, with Brad Franko from HP Data Science Solutions and Nick Wan from the Cincinnati Reds, we discuss ways to optimize your data science projects, data challenges, use cases, products, and solutions. Listen now and discover how to optimize your data.
