Basketball Data Science-With Applications in R

✍ Scribed by Paola Zuccolotto (Author); Marica Manisera (Author)

Publisher: Chapman and Hall/CRC
Year: 2020
Leaves: 245
Edition: 1
Category: Library

No coin nor oath required. For personal study only.

✦ Synopsis

Using data from one season of NBA games, Basketball Data Science: With Applications in R is the perfect book for anyone interested in learning and applying data analytics in basketball. Whether assessing the spatial performance of an NBA player’s shots or doing an analysis of the impact of high pressure game situations on the probability of scoring, this book discusses a variety of case studies and hands-on examples using a custom R package. The codes are supplied so readers can reproduce the analyses themselves or create their own. Assuming a basic statistical knowledge, Basketball Data Science with R is suitable for students, technicians, coaches, data analysts and applied researchers.

Features:

· One of the first books to provide statistical and data mining methods for the growing field of analytics in basketball.

· Presents tools for modelling graphs and figures to visualize the data.

· Includes real world case studies and examples, such as estimations of scoring probability using the Golden State Warriors as a test case.

· Provides the source code and data so readers can do their own analyses on NBA teams and players.

✦ Table of Contents

1. Introduction. 2. Finding Groups in Data. 3. Finding Structures in Data with Machine Learning. 4. Modelling Relationships in Basketball. 5. Concluding Remarks and Future Perspectives.

📜 SIMILAR VOLUMES

Basketball Data Science: With Applicatio

📁 Basketball Data Science: With Applications in R

✍ Paola Zuccolotto; Marica Manisera 📂 Library 📅 2020 🏛 CRC Press 🌐 English

Using data from one season of NBA games, Basketball Data Science: With Applications in R is the perfect book for anyone interested in learning and applying data analytics in basketball. Whether assessing the spatial performance of an MBA player's shots or doing an analysis of the impact of high pres

Basketball Data Science: With Applicatio

📁 Basketball Data Science: With Applications in R (Chapman & Hall/CRC Data Science Series)

✍ Paola Zuccolotto, Marica Manisera 📂 Library 📅 2020 🏛 Chapman and Hall/CRC 🌐 English

<p>Using data from one season of NBA games, <strong>Basketball Data Science: With Applications in R</strong> is the perfect book for anyone interested in learning and applying data analytics in basketball. Whether assessing the spatial performance of an NBA player’s shots or doing an analysis of the

Spatial Data Science: With Applications

📁 Spatial Data Science: With Applications in R

✍ Edzer Pebesma, Roger Bivand 📂 Library 📅 2023 🏛 CRC Press 🌐 English

Spatial Data Science introduces fundamental aspects of spatial data that every data scientist should know before they start working with spatial data. These aspects include how geometries are represented, coordinate reference systems (projections, datums), the fact that the Earth is round and its co

Telling Stories with Data With Applicati

📁 Telling Stories with Data With Applications in R

✍ Rohan Alexander 📂 Library 📅 2023 🏛 CRC Press 🌐 English

The book equips students with the end-to-end skills needed to do data science. That means gathering, cleaning, preparing, and sharing data, then using statistical models to analyse data, writing about the results of those models, drawing conclusions from them, and finally, using the cloud to put a m

Model-Based Clustering and Classificatio

📁 Model-Based Clustering and Classification for Data Science: With Applications in R

✍ Charles Bouveyron; Gilles Celeux; T. Brendan Murphy; Adrian E. Raftery 📂 Library 📅 2019 🏛 Cambridge University Press 🌐 English

Cluster analysis finds groups in data automatically. Most methods have been heuristic and leave open such central questions as: how many clusters are there? Which method should I use? How should I handle outliers? Classification assigns new observations to groups given previously classified observat

Model-Based Clustering and Classificatio

📁 Model-Based Clustering and Classification for Data Science: With Applications in R

✍ Charles Bouveyron, Gilles Celeux, T. Brendan Murphy, Adrian E. Raftery 📂 Library 📅 2019 🏛 Cambridge University Press 🌐 English