Full Description
Sports Analytics in Practice with R A practical guide for those looking to employ the latest and leading analytical software in sport
In the last twenty years, sports organizations have become a data-driven business. Before this, most decisions in sports were qualitatively driven by subject-matter experts. In the years since numerous teams found success with "Money Ball" analytical perspectives, the industry has sought to advance its analytical acumen to improve on- and off-field outcomes. The increasing demand for data to inform decisions for coaches, scouts, and players before and during sporting events has led to intriguing efforts to build upon this quantitative approach.
As this methodology for assessing performance has matured and grown in importance, so too has the open-source R software emerged as one of the leading analytical software packages. In fact, R is a top 10 programming language that is useful in academia and industry for statistics, machine learning, and rapid prototyping. Sports Analytics in Practice with R neatly marries these two advances to teach basic analytics for sports-related use—from cricket to baseball, from basketball to tennis, from soccer to sports gambling, and more.
Sports Analytics in Practice with R readers will also find:
A broad perspective of sports, focusing on a wide range of sports rather than just one
The first book of its kind that features coding examples
Case study approach throughout the book
Companion website including data sets to work through alongside the explanations
Sports Analytics in Practice with R is a helpful tool for students and professionals in the sports management field, but also for sports enthusiasts who have a coding background.
Contents
Preface vii
Author Biography ix
Foreword xii
1 Introduction to R 1
2 Data Visualization: Best Practices 25
3 Geospatial Data: Understanding Changing Baseball Player Behavior 55
4 Evaluating Players for the Football Draft 91
5 Logistic Regression: Explaining Basketball Wins and Losses with Coefficients 133
6 Gauging Fan Sentiment in Cricket 155
7 Gambling Optimization 191
8 Exploratory Data Analysis: Searching Data for Opponent Insights 227
Index 253
