Introduction to Sports Machine Learning

What is Machine Learning in Sports?

Machine learning (ML) has revolutionized sports analytics by enabling teams to extract insights from massive datasets that would be impossible to analyze manually. From predicting player performance to optimizing game strategies, ML algorithms help teams gain competitive advantages.

Types of Machine Learning

Supervised Learning: The algorithm learns from labeled data. Examples include:

• Predicting whether a pitch will be a ball or strike (classification)
• Projecting a player's future WAR (regression)
• Estimating win probability based on game state

Unsupervised Learning: Finding patterns in unlabeled data:

• Clustering players by similar skill profiles
• Identifying play types from tracking data
• Detecting anomalies in performance data

Common Algorithms in Sports Analytics

Key Takeaways

• ML enables analysis of complex, high-dimensional sports data
• Supervised learning requires labeled training data
• Start simple (linear models) before moving to complex algorithms
• Domain knowledge is essential for feature engineering
• Always validate models on held-out test data