This hands-on tutorial walks through acquiring sports data for analysis in R—either by downloading CSVs from public sources or by using R-based APIs and web scraping. You’ll learn how to load raw data, deal with common “messiness” (types, missing values, inconsistent formats), and produce a tidy dataset that’s ready for analysis and visualization. What you’ll […]

An overview of essential R libraries for sports analytics with examples. Table of Contents tidyverse: Data Wrangling ggplot2: Data Visualization Shiny: Interactive Dashboards worldfootballR: Soccer Data nflfastR: NFL Play-by-Play Data Conclusion tidyverse: Data Wrangling The tidyverse is a collection of packages for data science. It includes dplyr, readr, and tidyr, making it a must for

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

A practical, code-first guide to collecting soccer data, building tidy datasets, and producing shareable analysis in R using worldfootballR. Want the full end-to-end project template (clean folders, reusable functions, and advanced modeling examples)? 📘 Mastering Football Data with worldfootballR — practical pipelines with examples from FBref, Transfermarkt, and Understat. Table of Contents What is worldfootballR?

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