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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

Golf Analytics with R: Performance, Strategy & Data-Driven Improvement Golf analytics with R helps golfers, coaches, and analysts turn real data into actionable improvements. This page explains the workflow—cleaning, visualization, modeling, and dashboards—and links to a complete, hands-on guide with annotated code and real datasets. golf analytics with R R golf programming golf data analysis

Boxing Analytics with R – Data Science for Fight Performance Boxing Analytics with R: Data Science for Fight Performance and Strategy Boxing analytics with R helps fighters, coaches, and analysts transform raw fight data into actionable insights. From punch counts and timing to opponent behavior and strategy, this guide shows how to use R for

Baseball Analytics with R – Practical Guide to Sabermetrics Baseball Analytics with R: Sabermetrics & Data Science Made Practical Baseball analytics with R transforms raw MLB data into actionable insights for evaluating players, comparing teams, and improving game strategy. This guide covers the complete workflow — data cleaning, sabermetrics, visualization, modeling, and reporting — with