rprogrammingbooks

Related (on this site): Install & Use worldfootballR worldfootballR Guide Sports Analytics with R NFL Analytics with R Tennis Analytics with R Boxing Analytics with R Bayesian Sports Analytics (Book/Product) Contents Setup Get match data Feature engineering Model 1: Poisson goals (baseline) Model 2: Dixon–Coles adjustment (improves low scores) From scorelines to 1X2 probabilities Odds, […]

R DuckDB Parquet Calibration Ranking Bayesian Odds TS Backtesting Racing analytics as an inference-and-decision system Thoroughbred flat racing is not a binary classification problem. It is a multi-competitor outcome process with hierarchy (horse / trainer / jockey / track), time dependence (form cycles, market moves), and decision layers (how you act on probabilities). This macro

Table of Contents 1. Introduction to Machine Learning in Sports Analytics Machine Learning has transformed modern sports analytics. What was once limited to box scores and descriptive statistics has evolved into predictive modeling, simulation systems, optimization engines, and automated scouting pipelines. Today, teams, analysts, researchers, and performance departments rely on machine learning to gain measurable

R • Sports Analytics • Ratings • Monte Carlo • Forecasting Sports are noisy. Teams change. Injuries happen. Upsets happen. But uncertainty is not the enemy—it’s the input. In this hands-on guide you’ll build a practical sports prediction workflow in R using tidyverse, PlayerRatings, and NFLSimulatoR, then connect ratings to Monte Carlo simulations and forecasting

A good sports betting system is not a “pick-winners” machine. It’s an uncertainty engine: it turns data into probabilities, probabilities into expected value, and expected value into position sizes that survive variance. If you can do those three steps consistently, you can build a robust process— even if individual bets lose often. This post is

Boxing analysis is no longer just about punch totals or “who looked busier.” Modern fight analysis is data science: repeatable pipelines, validated data, explainable models, and performance indicators that translate into strategy. This post shows how to build a professional fight data science workflow in R—from raw data to metrics, modeling, and tactical insights—using code

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