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Volleyball Analytics Volleyball Analytics with R: A Practical, End-to-End Playbook Build a full volleyball analytics workflow in R: data collection, cleaning, scouting reports, skill KPIs, rotation/lineup analysis, sideout & transition, serve/receive, visualization, dashboards, and predictive modeling. Table of Contents Why Volleyball Analytics (and Why R) Volleyball Data Model: Events, Rally, Set, Match Data Sources: Manual […]

Rugby is a sport defined by collisions, structure, and constant tactical adaptation. Unlike many other invasion sports, rugby alternates between highly structured moments—scrums, lineouts, restarts—and extended passages of chaotic open play. Each phase generates rich performance data: tackles, rucks, carries, kicks, meters gained, penalties conceded, turnovers, and spatial changes in territory. Despite this richness, rugby

Focus keyphrase: cricket analytics in R • Secondary: R cricket data analysis • Package: cricketdata Cricket analytics is no longer limited to season averages and simple leaderboards. With modern ball-by-ball datasets, we can quantify tempo, isolate phase-specific skills, evaluate matchups, and model outcomes under uncertainty. R is a strong environment for this work because it

From the dead-ball era to the launch-angle revolution Baseball has always evolved, but few trends tell a clearer story of transformation than the rise of the home run.A century ago, hitting even a handful of home runs in a season was an achievement. Today, it’s an expectation. Thanks to better training, analytics, and even changes

If you love sports analytics, this post will show you how to predict NBA matchups using real ESPN data and the R ecosystem — specifically hoopR, tidyverse, and ggplot2. We’ll use a simple Poisson model to simulate thousands of possible scores, compute win probabilities, and visualize expected outcomes for any matchup — all inside R.

Ever wondered how to pull and visualize NBA player stats using R?With just a few lines of code and the {hoopR} package, you can load real NBA data directly from ESPN and create professional-looking visualizations in minutes. Here’s how to build a quick chart of the Top 10 NBA Scorers (2023–24) using tidyverse and hoopR.

R is a versatile programming language designed for statistical computing and graphics. It can act as a calculator, provide numerical and graphical summaries of data and handle a variety of specific analyses:contentReference[oaicite:0]{index=0}. Whether you’re exploring data, running statistical tests or building predictive models, R offers built-in functions and packages to streamline the workflow. The summary()

A hands-on tutorial with reproducible R code for play-by-play analysis, shot charts, and data-driven decision making. This post accompanies and expands on the book listed here: Basketball Analytics with R — Product Page Table of Contents Setup & Data Ingestion Cleaning and Tidy Structures Lineups and On/Off Impact Shot Charts and Efficiency Maps Pace, Four

In analytics teams, the question often arises: “If I already know R, do I still need SQL?” The short answer is: yes—most of the time. R and SQL solve different—yet complementary—problems. SQL is the lingua franca for querying and shaping data where it lives (databases, data warehouses), while R excels at modeling, visualization, and reproducible

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