AI and the Future of Baseball: How Artificial Intelligence Is Changing the Game We Love

If you tuned into this year’s World Series, you probably noticed something different — not on the field, but in the broadcast. Commentators suddenly had lightning-fast answers to hyper-specific questions: which lefty dominates in ninth-inning pressure, or how a reliever’s tiny spin-rate drop hints he’s running out of gas. That instant depth wasn’t luck or guesswork — it was artificial intelligence quietly changing how baseball tells its story.

LA Dodgers win back to back championships - MLB

That new level of precision storytelling didn’t come from a room of interns crunching numbers. It came from artificial intelligence — and it’s quietly revolutionizing baseball, from the broadcast booth to the dugout, and from scouting departments to player development labs.

Welcome to the age of AI-enhanced baseball, where algorithms and human instinct work together to make the game smarter, faster, and — surprisingly — even more human.

FOX’s Secret Weapon: Foresight in the Booth

When FOX Sports partnered with Google Cloud to launch FOX Foresight, the network wasn’t just looking for faster data. It wanted a way to give storytellers superpowers.

Built on Gemini and Vertex AI, Foresight is trained on decades of MLB data — every pitch, swing, and inning since Statcast began tracking them. It allows broadcasters to ask natural-language questions such as:

“Which left-handed batters in the playoffs this year had the best ninth-inning performance with runners on base?”

Within seconds, Foresight returns the answer — contextualized, weighted for significance, and ready for broadcast graphics.

For on-air talent like Alex Rodriguez or David Ortiz, this means moving beyond surface-level stats (“Player X is batting .300”) to craft data-driven narratives:

“Player X has the most extra-base hits by a left-handed batter in the ninth inning since 2018 — all against off-speed pitches below the zone.”

It’s storytelling elevated by AI. The system helps commentators connect trends and history in real time, turning numbers into narratives that deepen fans’ understanding of the game.

Meanwhile, a second system nicknamed Connie monitors FOX’s infrastructure behind the scenes. Connie is an “agent-style” AI that continuously scans video feeds and network connections. When something glitches, it reroutes or repairs data pathways automatically — ensuring viewers never miss a pitch.

Together, Foresight and Connie demonstrate how AI isn’t just about data; it’s about delivering a flawless, more insightful experience for fans.

AI Between the Chalk Lines: How Teams Are Using It to Win

While broadcasters use AI to tell better stories, teams use it to make better decisions.

Baseball’s long love affair with analytics — from the Moneyball era to today’s Statcast metrics — has evolved into something far more powerful. Machine learning, computer vision, and predictive modeling now help teams move beyond what happened to what’s about to happen.

1. Biomechanics and Movement Tracking

High-speed cameras and computer-vision algorithms now track every motion a player makes — every elbow angle, shoulder tilt, and hip rotation. AI models compare those motions against historical data to identify mechanical inefficiencies or injury risks before they surface.

If a pitcher’s arm slot dips by a fraction or their release point shifts slightly, AI can flag fatigue in real time. Coaches can intervene before velocity drops or command disappears.

For hitters, similar models analyze swing planes and contact patterns to optimize bat path and timing. What once required weeks of manual video review now happens automatically, giving coaches an entirely new level of insight.

It’s not just “how fast did he throw?” — it’s “how efficiently did he move to create that speed?”

2. Predictive Analytics for Matchups and Performance

In scouting and player development, AI has become a competitive differentiator.

Teams now feed thousands of variables — spin rate, release angle, pitch location, exit velocity, and even weather — into machine-learning models to simulate possible outcomes.

Imagine a model that can answer:

“Given this pitcher’s spin rate and arm slot, how likely is our left-handed slugger to hit a ball harder than 100 mph in a 2-1 count?”

Those predictions can shape entire game plans.

The Texas Rangers, for example, have built AI infrastructure that parses thousands of scouting reports and performance records to uncover undervalued players. That same system now informs their live strategy, predicting matchup advantages and helping optimize bullpen usage and pinch-hitting choices.

This is where baseball’s future lies: in predictive context, not just descriptive stats.

3. Defensive Positioning and Real-Time Strategy

Every batted ball in the Statcast era has created a treasure trove of spatial data.

AI models use that data to identify where each hitter is most likely to put the ball, down to individual counts and pitcher types. That’s why you’ll see infielders taking a few extra steps toward the gap or an outfielder creeping in before the pitch — it’s not guesswork, it’s probability in motion.

Some teams are now experimenting with reinforcement-learning systems that test thousands of defensive alignments in simulations. The goal: minimize expected runs allowed for every possible hitter-pitcher-count combination.

It’s not just analytics; it’s algorithmic chess — and the defense is learning to move in perfect sync.

4. AI-Powered Scouting and Opponent Analysis

Scouts still travel with clipboards and radar guns, but now they also have access to large language models that digest and summarize years of unstructured data.

An AI assistant can instantly pull insights like:

“Hitter A: above-average bat speed, struggles on high fastballs, improved plate discipline since July, most productive on low-and-away breaking balls.”

That synthesis used to take hours of film study. Now it’s nearly instantaneous.

When the Rangers implemented their LLM-powered scouting tool, they found it didn’t replace scouts — it enhanced them. Human evaluators still provide nuance and emotional intelligence, while the AI adds speed and pattern recognition across thousands of players.

In a world where draft classes are massive and minor-league data is endless, AI gives front offices something they’ve never had before: clarity.

From Pre-Game Prep to Real-Time Decisions

Traditionally, analytics informed decisions before or after games — not during them. But as computing power grows and data pipelines accelerate, real-time decision support is becoming possible.

Picture this: it’s the 7th inning, two outs, runner on second. Your starter’s spin rate has dipped 6% over his last ten pitches. AI detects that his arm angle is shifting — a pattern that historically precedes elevated fastballs and fatigue.

At the same moment, it projects that your setup reliever has a 34% higher chance of inducing weak contact from the next batter.

The manager still makes the call — but now it’s informed by live predictive context instead of gut instinct alone.

This is the subtle but seismic shift AI is bringing to dugouts: data doesn’t replace intuition; it refines it.

Case Study: The Oakland Ballers’ AI Manager

In 2024, an independent team in the Pioneer League — the Oakland Ballers — made baseball history by letting an AI system, AaronLytics, manage a live game.

AaronLytics handled lineup construction, pitching changes, and substitutions in real time. When the starting pitcher showed signs of fatigue in the eighth inning, the AI recommended a reliever. The reliever shut down the side. Later, AaronLytics called for a pinch-runner who stole second base — a decision based on the algorithm’s analysis of the opposing pitcher’s delivery time and the catcher’s pop time.

The Ballers won the game.

It wasn’t perfect — the system glitched once, briefly misreading a substitution — but the experiment proved something important: AI can function as a real-time tactical assistant, capable of seeing patterns faster than humans.

While no MLB team has gone that far, the groundwork is being laid. Dugouts may one day feature AI copilots that analyze data streams mid-inning, providing managers with real-time probabilities and fatigue forecasts.

Why AI Won’t Replace Baseball’s Soul

The biggest misconception about AI in sports is that it will strip the human element away. But in baseball, it’s actually amplifying it.

AI doesn’t replace intuition, emotion, or rivalry — it enhances them. It gives managers and broadcasters sharper tools to understand what they already feel.

A broadcaster using FOX Foresight isn’t becoming robotic — they’re gaining context that lets them tell a richer story. A manager consulting a predictive fatigue model isn’t abdicating control — they’re protecting a pitcher’s arm before it’s too late.

The data isn’t dictating decisions; it’s deepening them.

As one analyst put it, “AI doesn’t change the game — it changes how we see it.”

The Art of Storytelling Meets the Science of Data

Baseball broadcasts have always been theater — full of suspense, emotion, and nostalgia.

AI now adds a new act to that play: instant historical context. Instead of merely noting that a player hit a home run, broadcasters can reveal that it was the first time in a decade a rookie did it off a 100-mph fastball in extra innings.

During the 2025 World Series, FOX’s on-air team used AI to highlight “micro-battles” inside each at-bat — why a hitter’s confidence rose against late-inning sliders, or how a reliever’s spin-axis drift was changing his pitch shape.

This is baseball storytelling in 4K: data, emotion, and timing, all synchronized through AI. Fans don’t just watch the game; they understand it.

The Silent MVP: AI Behind the Broadcast

Behind every perfect feed and smooth highlight reel, another AI system keeps things running.

FOX’s Connie monitors dozens of live video and data streams during every postseason game. It detects latency spikes, bandwidth drops, and other anomalies in real time, automatically rerouting or self-healing the connection before a single frame is lost.

What used to cause 30-second delays or missed replays now resolves invisibly. For fans, it means uninterrupted coverage. For broadcasters, it means confidence that their systems can handle terabytes of live data without failure.

It’s the kind of AI no one sees — but everyone benefits from.

Draft Day: Where AI Meets Human Judgment

Every MLB Draft blends art and science, and AI is quickly becoming a crucial brushstroke.

Machine learning models evaluate thousands of variables — from exit velocity and biomechanics to college-level plate discipline and even emotional resilience. These systems can detect hidden correlations that predict long-term success or risk.

Teams like the Dodgers, Rays, and Rangers use these insights to spot undervalued players, cross-check scouting reports, and simulate career trajectories under different development paths.

The next evolution will be generative simulation: AI tools that model how a player might perform across environments — different ballparks, coaching staffs, or even climate conditions.

It’s one thing to draft based on stats; it’s another to simulate a player’s future self before they ever reach the majors.

Ethics at the Plate: The Questions AI Raises

With power comes responsibility.

As AI grows more predictive, it raises ethical challenges. If an algorithm forecasts a player’s decline or injury risk, how should teams use that information? Should it influence contract negotiations or playing time?

There are also privacy concerns. Biomechanical and biometric data are deeply personal. MLB and the Players Association are beginning to draft policies governing how player data is collected, shared, and applied.

Some insiders advocate for a centralized MLB “AI ethics board” to standardize model validation and protect player rights. Others argue that open-source transparency will ensure fairness and prevent data monopolies among wealthy teams.

Whatever the approach, baseball must now navigate the same frontier that every AI-driven industry faces: how to use data without losing humanity.

The Next Inning: Where AI Is Taking the Game

Looking ahead, AI’s presence in baseball will only deepen — and in some cases, become visible to fans.

Possible near-future innovations include:

• Augmented reality overlays: Live broadcasts showing pitch prediction zones, defensive probabilities, or expected outcomes in real time.
• Wearable sensors: Player uniforms feeding fatigue and stress data to AI models that alert coaches before injuries happen.
• Simulated strategy engines: Teams running predictive “digital twins” of games to test bullpen decisions or lineup orders before they happen.
• AI-assisted umpiring: Automated strike zones paired with models that interpret swing intent and deception, blending human judgment with machine precision.
• Emotion analytics: Computer vision detecting team morale or focus based on facial cues and body language — a futuristic take on reading momentum.

Each innovation moves baseball closer to a digital twin of itself — a real-time mirror that captures every detail of performance and emotion.

The Human Heart of a Digital Game

For all its algorithms and neural networks, baseball remains a sport of moments — unpredictable, emotional, and alive.

AI can project probabilities, but it can’t predict magic: the underdog rookie who hits a walk-off, or the veteran who finds one last gear under the October lights.

That’s the beauty of the sport — and the reason AI fits so well within it. The technology doesn’t erase the wonder; it amplifies it. By revealing patterns we never noticed, it helps us appreciate the brilliance and fragility of human performance even more.

As we move into this next era — where FOX Foresight, Connie, and even AI “managers” like AaronLytics coexist with dugouts, scouts, and broadcasters — one thing is clear: the game is learning faster than ever.

But the heartbeat of baseball still belongs to the players, the fans, and the timeless drama between pitcher and hitter.

AI is just helping us see it — and feel it — a little more clearly.