Name: Python Sports Analytics: Build Predictive Models for NFL & Fantasy Football
Availability: InStock

About Course

Merge data science and sports entertainment. Learn how to scrape live NFL stats APIs, construct regression prediction models in Python, and build optimized lineups to dominate Fantasy Football leagues.

Course Curriculum

Module 1: Python Data Stack Setup – Pandas, NumPy, and Jupyter Notebooks.
Module 2: Scraping NFL Data APIs – Interfacing with live stats, roster changes, and weather overlays.
Module 3: Algorithmic Player Predictions – Linear Regression, Machine Learning, and Performance Projections.
Module 4: Portfolio Optimization – Linear Programming for DFS, Drafts, and Sports Analytics Consulting.

What You Get:

Jupyter Notebook Templates • 12 Live Coding Modules • Real-time API Integration Codes • Fantasy Football Optimizer Script • Certificate of Completion.

Course Content

Module 1: Data Science Environment Setup
Install and configure Python, Jupyter Notebooks, Pandas, and NumPy for sports analytics.

Installing Anaconda and Jupyter
Crash Course in Pandas

Module 2: Scraping Live NFL APIs
Interface with real-time NFL statistics APIs to pull game-day rosters and historical performance.

Module 3: Advanced Feature Engineering
Create custom metrics like Expected Points Added (EPA) and Completion Percentage Over Expected (CPOE).

Module 4: Machine Learning & Regression
Build predictive models using Scikit-Learn to forecast player yardage and touchdowns.

Module 5: Fantasy Football Lineup Optimization
Use linear programming to automatically generate the optimal Daily Fantasy Sports (DFS) lineup.

Module 6: Deployment & Automation
Deploy your scripts to the cloud to run automatically every Sunday morning.

Student Ratings & Reviews

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Python Sports Analytics: Build Predictive Models for NFL & Fantasy Football

About Course

Course Curriculum

What You Get:

Course Content

Module 1: Data Science Environment Setup
Install and configure Python, Jupyter Notebooks, Pandas, and NumPy for sports analytics.

Installing Anaconda and Jupyter

Crash Course in Pandas

Module 2: Scraping Live NFL APIs
Interface with real-time NFL statistics APIs to pull game-day rosters and historical performance.

Connecting to the NFLFastR API

Data Cleaning and Normalization

Module 3: Advanced Feature Engineering
Create custom metrics like Expected Points Added (EPA) and Completion Percentage Over Expected (CPOE).

Calculating EPA

Weather and Stadium Factors

Module 4: Machine Learning & Regression
Build predictive models using Scikit-Learn to forecast player yardage and touchdowns.

Linear and Multiple Regression

Random Forests and Gradient Boosting

Module 5: Fantasy Football Lineup Optimization
Use linear programming to automatically generate the optimal Daily Fantasy Sports (DFS) lineup.

Introduction to PuLP

Solving for Maximum Expected Value

Module 6: Deployment & Automation
Deploy your scripts to the cloud to run automatically every Sunday morning.

Deploying to AWS Lambda

Automated Email Alerts

Start Learning Today & Achieve Success

FROYOO

About Course

Course Curriculum

What You Get:

Course Content

Module 1: Data Science Environment Setup Install and configure Python, Jupyter Notebooks, Pandas, and NumPy for sports analytics.

Installing Anaconda and Jupyter

Crash Course in Pandas

Module 2: Scraping Live NFL APIs Interface with real-time NFL statistics APIs to pull game-day rosters and historical performance.

Connecting to the NFLFastR API

Data Cleaning and Normalization

Module 3: Advanced Feature Engineering Create custom metrics like Expected Points Added (EPA) and Completion Percentage Over Expected (CPOE).

Calculating EPA

Weather and Stadium Factors

Module 4: Machine Learning & Regression Build predictive models using Scikit-Learn to forecast player yardage and touchdowns.

Linear and Multiple Regression

Random Forests and Gradient Boosting

Module 5: Fantasy Football Lineup Optimization Use linear programming to automatically generate the optimal Daily Fantasy Sports (DFS) lineup.

Introduction to PuLP

Solving for Maximum Expected Value

Module 6: Deployment & Automation Deploy your scripts to the cloud to run automatically every Sunday morning.

Deploying to AWS Lambda

Automated Email Alerts

Student Ratings & Reviews

Start Learning Today & Achieve Success

FROYOO

Module 1: Data Science Environment Setup
Install and configure Python, Jupyter Notebooks, Pandas, and NumPy for sports analytics.

Module 2: Scraping Live NFL APIs
Interface with real-time NFL statistics APIs to pull game-day rosters and historical performance.

Module 3: Advanced Feature Engineering
Create custom metrics like Expected Points Added (EPA) and Completion Percentage Over Expected (CPOE).

Module 4: Machine Learning & Regression
Build predictive models using Scikit-Learn to forecast player yardage and touchdowns.

Module 5: Fantasy Football Lineup Optimization
Use linear programming to automatically generate the optimal Daily Fantasy Sports (DFS) lineup.

Module 6: Deployment & Automation
Deploy your scripts to the cloud to run automatically every Sunday morning.