A practical, code-first walkthrough for fetching rich football data, building models, and producing clear visuals entirely in R. What You’ll Learn Everything below is copy-ready R. Adjust team/season names to your league of interest. Setup Get Data with worldfootballR worldfootballR provides friendly functions to pull leagues, teams, players, and match-level tables from FBref and Transfermarkt.

Basketball has become one of the most data-driven sports in the world. From the NBA to NCAA programs, teams now rely heavily on advanced metrics to make smarter decisions on the court. With the rise of open data sources, it’s easier than ever for analysts, students, and even fans to explore the game using modern

If you are interested in football data analytics, the worldfootballR package in R is one of the most powerful tools you can use. It allows you to collect and analyze soccer data from FBref, Transfermarkt, and Understat with just a few lines of code. In this step-by-step guide, we will show you how to install

worldfootballR: The Complete Guide for Soccer Data in R worldfootballR is one of the most popular R packages for collecting and analyzing soccer data. Whether you are a data scientist, a football analyst, or just a fan who loves statistics, this package makes it simple to pull structured data from FBref, Transfermarkt, and Understat. With

A hands-on, reproducible guide to loading NFL play-by-play, computing EPA/success, win probability, and fourth-down decisions — all with tidy R workflows. Why this guide NFL play-by-play (PBP) data enables powerful, interpretable metrics: Expected Points Added (EPA), success rate, win probability (WP), and fourth-down decision models. This tutorial shows a clean path to: Setup: packages &

A practical, reproducible walkthrough to pull open football data, build tidy datasets, and produce actionable xG-based insights and visuals — all in R with worldfootballR. Why this guide Football data is abundant, but turning it into clear, reproducible insights is the real edge. In this tutorial you will: You can adapt the same steps to

Sports Analytics with R – Practical Multi-Sport Guide Sports Analytics with R: Multi-Sport Performance, Strategy & Data Science sports analytics with R helps analysts, coaches, and learners turn raw match data into clear, repeatable insights across football, basketball, tennis, golf, boxing, and baseball. This article outlines an end-to-end workflow—cleaning, visualization, modeling, and dashboards—and links to

Tennis Analytics with R – Practical Guide to Player Stats & Strategy Tennis Analytics with R: Player Performance, Match Strategy & Data Science tennis analytics with R turns raw match data into actionable insights. Learn how to clean and structure tennis datasets, visualize performance across surfaces, model outcomes, and build dashboards—then go deeper with a

NBA Analytics with R – Practical Guide to Basketball Data & Strategy NBA Analytics with R: Player Performance, Team Strategy & Data Science NBA analytics with R helps analysts, coaches, and data-driven fans turn raw basketball data into clear, repeatable insights. This guide outlines the full workflow—loading, cleaning, modeling, visualization, and reporting—and links to a