If the trend that led to the movie Moneyball introduced computer analysis to baseball, then AI is building on that groundwork to dictate scouting, training, and other on-the-field functions. It’s a bold new world out there and nobody knows where it’s going to go.
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It’s interesting too, because the Moneyball approach, which emphasized certain statistics over others and teams relying more and more on numbers – and the analysis of those numbers – had naysayers saying things that some are now talking about with the incorporation of AI into the sport. At the top of that list: “we still need scouts.” Tell that to the many scouts who were edged out of jobs as teams downsized their scouting departments.
How AI Changed the Way Teams Scout
Years back, the Phillies had a young prospect named Jason Donald. The Phillies drafted him in the third round of the 2006 Draft, so he was pretty highly touted. He never reached the majors with Philadelphia, but was traded to Cleveland in the Roy Oswalt trade and played 170 games for Cleveland over parts of three seasons. When he was coming through the program at Lakewood, which was then Low-A ball but is not High-A and known as Jersey Shore, he would retreat to his locker after each game and take detailed notes on every pitcher he faced and how they pitched him. Before each game he would look at those notes to refresh himself on pitchers he might see in that game. I often think about Jason Donald because he was what many call a baseball rat; he loved the game nearly to the point of obsession. Many of us can relate. What he was doing then was a pen and paper version of what computers started to do for hitters, scouts, and teams.
I wonder what Jason Donald would think of the information that computers of today, assisted by AI, spit out for those same people. I don’t recall seeing anything about “spin rate” or such things in his notes.
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Whether it was done by a player taking their own notes or by a grizzled old guy in a baseball hat with a radar gun, scouting used to be built on intuition, long bus rides, and stacks of notebook pages (for Jason Donald it was binders full of pages). Now teams are layering that traditional eye test with machine learning models that comb through video, Statcast feeds, and scouting reports to find hidden value. These systems can flag pitchers whose arm actions predict durability concerns or hitters whose batted-ball profiles project an uptick in power. Front offices use AI to speed up comparisons across thousands of prospects and to generate objective scouting summaries that help shape draft boards and international signings. This isn’t about replacing scouts, there’s that thought again. It’s about giving them better tools to prioritize who to see in person.
Biomechanics: tiny motions, big decisions
Teams are investing in biomechanical analysis the way they invest in analytics. High-speed cameras, marker-less motion capture, and wearable sensors let clubs measure arm slot, elbow torque, hip-shoulder separation, and other micro-movements that were once guesswork. That data helps pinpoint inefficient mechanics that raise injury risk and reveals movement signatures correlated with success. Clubs like the Dodgers and other organizations integrate motion capture into spring training and minor-league development, using AI models to flag worrisome deviations and to recommend mechanical tweaks. The goal is to make pitching and hitting more reproducible and less risky.
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Wearable GPS units can now track players and give coaches baseline readings on their breaks to a ball, reaction times, and the speed that they can achieve when running all-out. Meredith Sholder of the US Olympic Field Hockey team told me about those innovations over a year ago when she talked about the training that she was put through. Coaches can also use the technology not only to find flaws and correct motions, but can tell when a player is not reaching their peak achievements, which in some cases can point to the early stages of an injury.
Injury prevention: predicting the avoidable
One of the clearest promises of AI and biomechanics is injury prevention. Rather than waiting for a pitcher’s arm to break down, clubs are building predictive models that combine workload history, biomechanical markers, and recovery metrics to estimate injury risk. Real-time systems can alert staff when a pitcher’s torque spikes or when a hitter’s timing patterns indicate fatigue. Some academic groups and teams have piloted sleeve and sensor systems that track elbow valgus torque – an outward force applied to a joint, particularly the elbow, that stresses the inside (medial) structures and compresses the outside (lateral) structures. It is a key factor in many throwing injuries because it puts significant strain on the ulnar collateral ligament (UCL), leading to potential tears. This type of stress is especially high during the cocking and acceleration phases of throwing and arm velocity during both games and bullpen sessions. When those numbers cross certain thresholds, trainers can intervene with adjusted workloads or corrective exercises. That approach won’t eliminate injuries, but it can reduce the frequency of spikes that lead to serious problems.
From data to on-the-ground change
Translating model output into coaching action is where things get interesting and difficult. Coaches have to balance the desire to preserve a pitcher’s arm against the competitive need to win today. That tension means AI tools are most valuable when they produce actionable, trustable recommendations. Teams have taken different routes; some use the models to inform individual development plans in the minors. Others use them for in-season workload decisions, like limiting a reliever’s high-intensity throws after a series of heavy usage. The best programs pair a data scientist with a biomechanics professional, and a coach so that the solution fits the player and the team.
Beyond the physical: cognitive and recovery tech
AI isn’t only watching muscles and joints. Companies are applying brain-signal analytics and cognitive sensors to measure reaction time, decision making, and mental fatigue. Those metrics are starting to show up in prep academies and college programs and will likely feed pro scouting soon. Cognitive readiness, when added to physical load data, gives a fuller picture of whether a player is truly prepared to perform or is at an elevated risk of failure or injury. Teams are also combining sleep, nutrition, and biometric data into recovery models so that training plans are tailored to how a player actually responds to stress.
What this means for the game
The practical upshot is that teams can now make more precise development choices. Prospects who once might have been labeled risky because of a funky delivery are getting individualized plans that correct dangerous movement while preserving effectiveness. Veterans who historically played through pain might be given targeted off-days and rehab protocols informed by real metrics. It also changes scouting philosophy. Players with clean biomechanics and a pattern of manageable workloads look more attractive in models, even if their traditional stats don’t jump off the page.
That said, these systems are not magic. Data quality, model bias, and organizational buy-in still limit impact. Some clubs are early adopters and already seeing benefits. Others are cautious, worried about over analyzing models to limited samples. But the direction is clear: teams that can blend human judgment with AI-driven biomechanics and injury monitoring will have a competitive edge in identifying talent and keeping it healthy.
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