Here's a breakdown of how Major League Baseball (MLB) utilizes Google Cloud: Youtube Video here
1. Enhanced Broadcasts & In-Game Analysis (Statcast):
* Real-time Data Analysis: MLB uses Google Cloud's Vertex AI and BigQuery to power Statcast, their data capture and analysis system. Statcast analyzes 15 million data points per game in real time. This allows for the calculation of advanced statistics that were previously impossible, like catch probability or steal success rate.
* Improved Stats Delivery: Google Cloud's infrastructure improves the speed of stat delivery by 300ms, enabling real-time analysis of events like ball/strike calls. This makes broadcasts more engaging and informative.
* Predictive Models: Statcast uses past data to train custom models and neural networks, allowing MLB to analyze player performance, categorize pitch types with high accuracy, and even predict future performance. They are continuously evolving Statcast to capture even more data, such as bat tracking.
* Data Processing at the Edge: MLB uses Google Distributed Cloud to process data closer to the stadiums, reducing latency and enabling real-time, data-driven decisions during games.
2. Personalized Fan Experiences:
* Data-Driven Content Recommendations: MLB uses Vertex AI to analyze massive amounts of fan data to understand individual preferences. This allows them to deliver personalized content recommendations, making the fan experience more engaging and relevant.
* Customized Content Delivery: Media CDN is used to power live game streaming and deliver personalized content, ensuring fans see the content they are most interested in.
* Improved Fan Engagement: By providing personalized experiences, MLB aims to maximize the time fans spend interacting with their platform, increasing engagement and satisfaction.
* Global Fan Reach: MLB is leveraging Vertex AI's translation capabilities to make baseball more accessible to international fans by translating content and making baseball vocabulary and culture accessible to a global audience. They combine automated translation with a dedicated in-house team of native Spanish speakers to ensure the quality of the translations at the local level.
3. Data Infrastructure and Security:
* Scalable and Secure Platform: MLB chose Google Cloud because it provides the building blocks for a robust and scalable cloud infrastructure. They needed a platform that could handle petabyte-scale data and analytics across all 30 teams and over 4,000 games per season.
* Building vs. Buying: MLB preferred to build their own solutions rather than using off-the-shelf products. Google Cloud provided the necessary tools and support to do this.
* Data Security: Protecting fan data is a top priority for MLB. They chose Google Cloud because of its strong security features, which help future-proof their security model.
* Modern Infrastructure: MLB transitioned the majority of its applications to Google Cloud, using Anthos as a unified platform for development and deployment, and Google Kubernetes Engine to run containerized applications, enabling the team to scale resources up or down based on demand.
* BigQuery as a Game Changer: MLB uses BigQuery as a central data warehouse to analyze the massive amounts of data they collect. They consider it a "game changer" for understanding trends and uncovering impactful insights within the game.
In summary: MLB leverages Google Cloud to enhance every aspect of the game, from improving the quality of broadcasts and in-game analysis to creating personalized fan experiences and ensuring data security. The partnership allows MLB to use data to tell the stories behind the game and connect with fans on a deeper level.
