In the rapidly evolving world of data science and analytics, two languages stand out for statisticians and researchers: R and Julia. While R has been a trusted companion in statistical computing for decades, Julia has emerged as a modern language promising both speed and flexibility. This raises a crucial question: is Julia the future of […]

In the world of data science, two programming languages dominate the conversation: R and Python. Both have powerful ecosystems, passionate communities, and proven success in real-world analytics. Yet the question persists: which language is better for data science? The truth is that both R and Python excel in different areas. Choosing the right tool often

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

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 &