This is the first post in our ‘ML in the Wild’ series, exploring how machine learning works in everyday applications.
Every split-second decision in sports can determine victory or defeat. Coaches desperately need to predict what will happen next – will this tackle succeed? Will this defensive formation stop the play? Will this strategy work? But human intuition can only process so much information in real-time.
Machine learning in sports can process dozens of factors simultaneously to predict outcomes before they unfold. For example, the NFL has partnered with Amazon Web Services (AWS) to develop machine learning models for sports data analysis. Using detailed information tracking each player’s position, speed, and movement, usually from wearable sensors and stadium systems during games, AWS has built tackle probability models that analyze game dynamics in real-time. This project shows how AWS uses machine learning algorithms trained on huge amounts of player data to give insights that help understand the game better.
You may wonder, if we already have experienced coaches and analysts, why do we need machine learning? Here’s why: humans can only track a few variables at once, but sports involve dozens of players, speeds, positions, and game situations all changing at once. A coach might notice a player looks tired, but machine learning can detect subtle patterns in movement, positioning, and game context that predict outcomes before they become obvious. Here’s how machine learning is helping sports technology predict everything from tackle success to play outcomes long before they become apparent.
How Machine Learning Works in Sports
Machine learning in sports involves collecting huge amounts of data related to player movements, game situations, training loads, and historical outcomes. With this data, algorithms can spot patterns that predict everything from tackle success to optimal play calls to training schedules.
How It Works
Machine learning models are “trained“, which means they learn from data collected from wearable sensors or video footage. From a football player, for example, the data collected might include:
- Player position, speed, and acceleration during both games and training
- Defensive positioning and blocking patterns to reveal likelihood of successful plays
- Movement patterns and game context to predict play outcomes and performance trends
Machine learning models in sports use historical data: information about past plays, game situations, and training outcomes. By analyzing this data, they can spot patterns that predict future results. This could be through sensors, game film, or medical reports. The results can then inform coaches and athletes about smarter strategies, injury prevention, and improving performance.
The Techniques Making It Possible
AWS used SageMaker, their machine learning service, to build sports analytics models that learn patterns from data, like noticing that certain defensive formations always lead to missed tackles. These systems use algorithms called XGBoost and Random Forest, which work like decision trees asking yes/no questions: “Is the player running fast? Are defenders nearby? Then there’s a high tackle probability.”
XGBoost is like having thousands of expert coaches each making predictions, then combining their best guesses for the most accurate result. Random Forest creates multiple decision trees and averages their predictions to avoid the mistakes any single analysis might make.
The key is feature engineering, which transforms basic numbers (like “10 yards from sideline”) into meaningful insights (like “player is trapped between defenders”). AWS transforms basic numbers into meaningful insights that reveal game dynamics. The same techniques help identify potential injury risks by analyzing sleep, workout intensity, and movement patterns to flag when athletes might need extra attention.
You can use it too!
This NFL example illustrates a broader trend in sports technology. There are many apps that use machine learning to predict how much weight you should lift next time, or figure out which exercises will help you reach your goals faster. Apps like Tempo (the ‘smart home gym’), FitBod, and Strava create workouts based on your current performance using these same techniques. Tempo uses computer vision to analyze your form in real-time, while FitBod learns from your workout history to suggest optimal weights and exercises.
With these new products and applications, users now have access to the kind of personalized training that used to cost hundreds of dollars per session with elite performance coaches.
The shift from reactive to predictive decision-making in sports is gaining momentum. Rather than just responding to what happens, we’re starting to predict outcomes before they occur, and before human observers can identify the patterns. And with this sports technology becoming publicly available, it is already starting to change how we think about fitness and athletic performance.
If you found these sports examples helpful for understanding how machine learning works in practice, I cover similar real-world applications in my weekly newsletter – from healthcare to finance to retail. It’s designed for people who want to understand ML without the jargon and complexity.
Curious how they pulled this off? Check out AWS’s behind-the-scenes look at building tackle probability.
