Predicting-Baseball-Statistics

Classification and Regression Applications in Python Using scikit-learn and TensorFlow-Keras

This repository contains the prediction of baseball statistics using MLB Statcast Metrics.

Goals

• Using MLB Statcast Metrics, summarize and examine baseball statistics.

Classification

• Build and train models to predict home runs and extra-base hits implementing the following approaches:

  • Logistic Regression
  • k-Nearest Neighbors Classification
  • Decision Trees Classification
  • Random Forests Classification
  • Support Vector Machines Classification
  • XGBoost Classification
  • Neural Networks Classification

• Implement over-sampling for imbalanced data to improve the quality of predictive modeling (i.e., generalizability).

• Apply regularization and cross-validation techniques for model evaluation, selection, and optimization.

Regression

• Build and train models to predict hit distance implementing the following approaches:

  • Linear Regression
  • Decision Trees Regression
  • Random Forests Regression

• Apply regularization (Ridge, Lasso, Elastic Net) and cross-validation (k-fold) techniques for model evaluation, selection, and optimization.

Business Use Case

By implementing machine learning models to classify baseball player performance and predict hit distances, teams can enhance their decision-making processes, improve player development, and achieve better competitive outcomes, ultimately leading to greater success on and off the field.

1. Performance Optimization: By accurately predicting the likelihood of home runs and hit distances, teams can make informed decisions about player lineups and in-game strategies. This data-driven approach can lead to better game outcomes and a competitive edge.

2. Player Development: Insights from the predictive models can identify areas where players can improve their swing mechanics or approach at the plate. This targeted feedback can help players refine their skills, leading to enhanced performance and career longevity.

3. Injury Prevention: Understanding the impact of different pitch types, velocities, and swing mechanics on hit outcomes can help in designing training programs that minimize the risk of injury. For example, players can be trained to optimize their launch angles and exit velocities in ways that reduce physical strain.

4. Scouting and Recruitment: The models can be used to evaluate and compare potential recruits or trade targets. Teams can identify players with the greatest potential for hitting home runs and achieving long hit distances, ensuring more strategic and effective recruitment.

5. Fan Engagement: Advanced predictive analytics can be used to enhance fan experience through interactive and engaging content. Fans can access real-time predictions and insights during games, increasing their engagement and enjoyment.

6. Sponsorship and Marketing: Enhanced player performance data can be leveraged for marketing and sponsorship opportunities. Highlighting players' predictive performance metrics can attract sponsors and increase revenue through targeted marketing campaigns.

7. Game Strategy and Analysis: Coaches and analysts can use the models to develop more effective game strategies. For example, understanding how different pitches affect hit outcomes can inform pitching strategies and defensive alignments.

8. Competitive Advantage: Teams that leverage advanced machine learning models for performance prediction can gain a significant competitive advantage. The ability to make data-driven decisions that enhance player and team performance can lead to more wins and better overall team success.