MLB has collaborated with Google Cloud to power advanced real-time analytics, particularly through its Statcast system. Statcast is MLB’s premier tracking technology that captures detailed data from every game across all 30 ballparks.
A single MLB game generates more than 15 million data points—including pitch velocity, spin rates, player positioning, swing mechanics, exit velocities, and even 3D body tracking at high frame rates.
Using Google Cloud tools like Vertex AI, BigQuery, and other AI/ML infrastructure, MLB processes and analyzes this massive volume of data in real time. This enables: Computation of advanced, previously impossible statistics like catch probability, predicted steal success rates, pitch repertoire categorization, and bat tracking insights.
Faster delivery of stats with improvements like 300ms reductions in latency for certain analyses. Enhanced fan experiences, such as personalized highlights, real-time insights during broadcasts, and features like AI-driven content recommendations or predictive models.
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Support for teams, broadcasters, and apps by providing equal access to data for building custom models and strategies. This capability has been emphasized in Google Cloud’s official case studies and MLB announcements, with the partnership evolving since around 2020–2022 including expansions for media, personalization, and AI integration.
Recent examples include using AI for contextualizing stats during games and special events like the All-Star Game. It’s a prime example of how cloud-based AI is transforming sports analytics, turning raw data into actionable, engaging insights for players, teams, and fans alike.
The NBA has a prominent partnership with Amazon Web Services (AWS) for AI-driven analytics and cloud innovation. In October 2025, the NBA announced a multi-year deal making AWS the Official Cloud and Cloud AI Partner of the NBA and its affiliate leagues including the WNBA, NBA G League, Basketball Africa League, and NBA 2K League.
This collaboration powers NBA Inside the Game powered by AWS, a basketball intelligence platform that processes billions of data points from games to deliver real-time insights, advanced statistics, and interactive fan experiences.
Key highlights include: AI-Powered Advanced Stats debuting in the 2025-26 season and beyond, such as: Shot Difficulty (xFG%): Measures how challenging a shot is based on factors like defender proximity, shot type, and movement. Defensive Pressure: Quantifies defender impact.
Gravity: Tracks how players draw defenders and create space using AI to analyze data 60 times per second. Leverage Score: An AI system using counterfactual modeling to evaluate which players and possessions most influence game outcomes, assigning credit in real time across all on-court players.
These stats help quantify previously intangible aspects of performance, enhancing broadcasts, NBA App features, League Pass, and fan engagement. The platform leverages AWS’s machine learning, AI tools like Amazon Bedrock and SageMaker, and cloud infrastructure for scalable, low-latency analysis and personalized content.
This mirrors how the MLB uses Google Cloud for real-time Statcast data but the NBA’s league-wide initiative centers on AWS for fan-facing and backend analytics. For tracking data, the NBA has long partnered with Second Spectrum since around 2017 for optical player/ball tracking, which feeds into advanced analytics and has been expanded for innovations like enhanced graphics and coaching tools.
Other tech integrations exist, such as Microsoft Azure powering the NBA’s website, app, and personalized experiences; Alibaba Cloud handling AI and cloud for NBA China with fan engagement tools like real-time highlights and interactive content; and team-specific efforts like the Golden State Warriors using Google Cloud for AI-driven coaching and analytics.
The AWS partnership represents the NBA’s biggest push into AI-powered, league-wide analytics as of early 2026, transforming how fans, teams, and broadcasters interact with the game through deeper, data-driven storytelling.