“John had no guile. He didn’t understand it when people were mean to him because he could never be mean to anybody."—Suzyn Waldman
You may have noticed Ranger's comment to Duque's post that likely explains Hal's $3 billion loan.
He also sent me the below, what Chat GPT (or is it GTO?) has to say regarding AI use in baseball. This helps explain why so many decisions seem to fly in the face of what our lying eyes are telling us.
As a final grace note for those wary of AI, this answer to Ranger's question ends with "decision-making," which should not be hyphenated. AI... it's the footure, don't you know.
Yes. Today, every Major League Baseball team uses advanced analytics, and many are now incorporating AI and machine learning as part of those systems. AI hasn't replaced traditional baseball analytics—it has expanded what teams can analyze and predict.
Some of the main uses include:
Player evaluation: AI models analyze thousands of variables from tracking data, scouting reports, biomechanics, and historical performance to project how players will perform or develop.
Pitch design: Using high-speed cameras and ball-tracking systems such as Hawk-Eye Innovations, teams use AI to identify grip and release changes that can increase spin, movement, or deception.
Injury prevention: Machine learning analyzes workloads, biomechanics, sleep, recovery, and movement patterns to identify players at higher risk of injury before problems become obvious.
Game strategy: Teams simulate thousands of game scenarios to optimize defensive positioning, bullpen usage, pinch hitters, stolen-base decisions, and pitch sequencing.
Player development: AI helps identify subtle mechanical issues in swings and deliveries, allowing coaches to create individualized training plans.
Scouting: Teams use computer vision to process enormous amounts of video, automatically tagging pitches, swings, defensive plays, and player tendencies that scouts can review more efficiently.
AI works alongside several major data sources, including:
Pitch velocity, spin rate, and movement
Exit velocity and launch angle
Fielder positioning and reaction times
Biomechanical motion capture
Video analysis
Wearable sensor data (where permitted)
While all 30 clubs have analytics departments, several organizations are widely regarded as leaders:
Los Angeles Dodgers
Tampa Bay Rays
Houston Astros
Cleveland Guardians
New York Yankees
Boston Red Sox
These clubs invest heavily in data science, software engineering, biomechanics, and quantitative research.
One important point is that baseball decisions are rarely made by AI alone. Front offices typically combine AI-generated insights with the judgment of scouts, coaches, medical staff, and executives. AI can highlight patterns or probabilities that humans might miss, but personnel decisions, game strategy, and player development still rely heavily on human expertise.
In many ways, MLB is among the most data-driven professional sports, making it one of the earliest and most extensive adopters of AI-assisted decision-making.
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6 comments:
The Yankees should covert their bullpen into a data center.
They can't, El Duque. Governor Hochul has enacted a moratorium on data centers in the state of NY. They'd have to move to Hoboken.
No that's what parking lots are for...
As previously mentioned here - there are rumored to be as many as 5-6 sub-levels beneath the field at Yankee Stadium.
Ranger - you should scour the web to uncover more information about what exactly goes on in there.
Unfortunately, the AI machines cannot identify and quantify the human experience. The psychology and nuances of the everyday existence of mortal men. This analytical tool perceives the player as an android or robot. And that's a big fly in the ointment.
Also, AI can't analyze the rate of spin in my head from this top-heavy use of computer examination and evaluation.
Tampa and Florida data centers
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