How AI Is Transforming the Chicago Cubs' Game Strategy

How the Cubs Use Machine Learning to Predict Pitcher Fatigue and Optimize Bullpen Usage

The Chicago Cubs have deployed machine learning models that combine biomechanical sensor data with historical pitch outcomes to detect early signs of pitcher fatigue. These models analyze metrics such as pitch velocity, spin rate, and release point consistency, flagging deviations that often precede a decline in performance or increased injury risk. The system processes data in real time, giving the coaching staff actionable insights on when to replace a starter or which reliever to call.

Real-time fatigue predictions allow the Cubs to make data-driven bullpen decisions, reducing reliance on gut instinct and post-game analysis.

The Cubs' pitching staff saw a measurable improvement in late-inning effectiveness after adopting this system. By pulling starters before their mechanics deteriorate, the team has cut down on blown leads in the sixth and seventh innings. The model also integrates with Statcast data to correlate subtle changes in release point with increased probability of arm injuries, enabling proactive rest days.

• Biomechanical sensors and historical pitch data train models to forecast mechanical breakdown.
• Real-time fatigue predictions guide decisions on starter removal and reliever selection.
• Integration with Statcast velocity and spin-rate trends provides injury risk correlation.

AI-Driven Defensive Shifts: Real-Time Positioning Based on Batter Tendencies

Deep learning models trained on millions of batted ball events now dictate the Cubs' defensive alignment on every pitch. The system calculates optimal fielder positions for each opposing batter, adjusting for park dimensions, pitcher handedness, and game context such as the count and base runners. This approach moves beyond static shifts to a dynamic, pitch-by-pitch optimization.

The Cubs have estimated a 15% reduction in hits allowed on ground balls to the pull side since implementing the AI-driven shifts. Outfielders also benefit from probability cones that project where a line drive is most likely to land. The system updates in real time as the batter's tendencies evolve during the game, ensuring the defense stays ahead of adjustments.

By factoring in pitcher tendencies and ballpark spray charts, the Cubs' AI aligns fielders with precision that human intuition alone cannot match.

• Deep learning models process every batted ball event for real-time positioning.
• Adjustments account for park factors, pitcher tendencies, and game situation.
• Measurable 15% reduction in hits allowed on pull-side ground balls.

Injury Prevention Through Wearable Sensors and Neural Networks

Cubs players now wear smart sleeves and compression shirts equipped with accelerometers and electromyography sensors during games and practices. These devices capture muscle activation, joint angles, and acceleration at 200 Hz, feeding a neural network that identifies abnormal movement patterns. When the model detects a deviation from a player's baseline — such as a subtle change in hip rotation during a swing — it alerts the training staff before an injury occurs.

The system has delivered tangible results: the Cubs have reduced time spent on the injured list by an average of 20 days per player per season since its introduction. Soft-tissue injuries, which account for the majority of MLB disabled list stints, have dropped significantly. The approach mirrors advances seen in other sports; for example, Serena Williams: How AI is Changing Tennis Training demonstrates similar wearables for overuse detection.

Neural network models analyze streaming data to flag abnormal movement patterns that often precede soft-tissue injuries.

• Smart sleeves capture muscle activation, joint angles, and acceleration during play.
• Neural networks detect deviations from baseline movement for early injury warnings.
• Average 20-day reduction per player per season in injured list time since implementation.

Key Takeaways

• AI and machine learning give the Cubs a competitive edge in pitcher management, defensive alignment, and injury prevention.
• Real-time data integration from wearables and Statcast enables immediate tactical adjustments during games.
• Predictive models reduce reliance on human intuition, leading to more consistent and evidence-based decision-making.
• The Cubs' investment in AI technology has shown tangible ROI in terms of wins and player health.
• As AI continues to evolve, real-time video analytics and automated scouting reports are the next frontier for the team.
• Other MLB teams are now following the Cubs' blueprint for AI adoption in baseball operations — a trend also visible in other sports, as covered in Inside the AI Revolution: 60 Minutes Delivers Breakthroughs and Ethical Firestorm.