This is May from the GLB Division Lakehouse Department.
We share "How the Texas Rangers Revolutionized Baseball Analytics with a Modern Data Lakehouse" based on reports from members attending the local Data + AI SUMMIT2023 (DAIS).
In this session, Texas Rangers R&D Assistant Director Alexander Booth and Data Engineer Oliver Dykstra introduce the Texas Rangers' data strategy. It is of great interest to baseball fans, those interested in data analysis, and sports team managers.
Texas Rangers Data Strategy Background
Rebuilding from a losing streak and the road to No. 1
We will introduce the history of the Texas Rangers' data strategy, from their past losing streak to their current No. 1 win. Follow the journey of how a once underperforming team transformed with the help of data analytics.
History of baseball statistics
Baseball statistics began over 150 years ago. During its history, new indicators were introduced by statisticians such as Bill James, John Ford, and Pete Palmer in the 1970s and 1980s. These metrics will change the traditional way baseball is evaluated.
- Bill James: Founding the field Sabermetrics and proposing new metrics
- John Ford: Developed a metric to predict team wins and losses
- Pete Palmer: Suggested Metrics for Evaluating Player Performance
These metrics will change the traditional way baseball is evaluated.
Texas Rangers Data Strategy and Data Lakehouse
Introducing a data lake house
The Texas Rangers have implemented a state-of-the-art data lakehouse to take advantage of data analytics. A data lake house is a system for efficiently managing and analyzing large amounts of data, and combines the functions of a data warehouse and a data lake.
Data Lake House Features: 1. Efficient management and analysis of large amounts of data 2. A system that combines data warehouse and data lake functions 3. Enables flexible data analysis
Empower your team with data analytics
With Data Lakehouse, the Texas Rangers are able to analyze player performance and team tactics in greater detail. This allows us to clearly understand the strengths and weaknesses of our players and optimize the tactics of the entire team.
Examples of strengthening teams with data analytics: 1. Player performance analysis: clearly grasp the strengths and weaknesses of players 2. Optimizing Team Tactics: Planning Tactics Based on Data 3. Opponent analysis: Understand the characteristics and weaknesses of the opposing team and develop tactics
Strengthening the team through these data analyzes led to the Texas Rangers winning first place.
The Texas Rangers used a data strategy to rebuild their team and win first place. By introducing the data lakehouse and analyzing player performance and team tactics in detail, we have strengthened the team as a whole. In the future, it is expected that baseball will be developed using data analysis.
This content based on reports from members on site participating in DAIS sessions. During the DAIS period, articles related to the sessions will be posted on the special site below, so please take a look.
Translated by Johann
Thank you for your continued support!