Basketball Data Science

Basketball Data Science

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 ...

Author: Paola Zuccolotto

Publisher: CRC Press

ISBN: 9780429894268

Category: Business & Economics

Page: 219

View: 267

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 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.
Categories: Business & Economics

Basketball Data Science

Basketball Data Science

Therefore, Data Science has no ambition to replace basketball experts, but rather aims to support them in their choices and decisions, trying to give a quantitative answer to the questions they pose. Figure 1.1 illustrates what we call ...

Author: Paola Zuccolotto

Publisher: CRC Press

ISBN: 9780429894251

Category: Business & Economics

Page: 264

View: 799

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 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.
Categories: Business & Economics

Recent Advances in Data Science

Recent Advances in Data Science

Skinner, B., Guy, S.J.: A Method for using player tracking data in basketball to learn player skills and predict team performance. PLoS ONE 10(9), e0136393 (2015) 9. Zou, S.: Open source data science pipeline for developing “Moneyball” ...

Author: Fei Han

Publisher: Springer Nature

ISBN: 9789811587603

Category: Artificial intelligence

Page: 287

View: 583

This book constitutes selected papers of the Third International Conference on Data Science, Medicine and Bioinformatics, IDMB 2019, held in Nanning, China, in June 2019. The 19 full papers and 1 short paper were carefully reviewed and selected from 93 submissions. The papers are organized according to the following topical sections: business data science: fintech, management, and analytics.- health and biological data science.- novel data science theory and applications.
Categories: Artificial intelligence

New Statistical Developments in Data Science

New Statistical Developments in Data Science

In: Statistics and Data Science: New Challenges, New Generations SIS2017 Proceeding. Firenze Uiversity Press. e-ISBN: 978-88-6453-521-0 (2017) Metulini, R., Manisera, M., Zuccolotto, P.: Sensor analytics in basketball.

Author: Alessandra Petrucci

Publisher: Springer Nature

ISBN: 9783030211585

Category: Computers

Page: 479

View: 893

This volume collects the extended versions of papers presented at the SIS Conference “Statistics and Data Science: new challenges, new generations”, held in Florence, Italy on June 28-30, 2017. Highlighting the central role of statistics and data analysis methods in the era of Data Science, the contributions offer an essential overview of the latest developments in various areas of statistics research. The 35 contributions have been divided into six parts, each of which focuses on a core area contributing to “Data Science”. The book covers topics including strong statistical methodologies, Bayesian approaches, applications in population and social studies, studies in economics and finance, techniques of sample design and mathematical statistics. Though the book is mainly intended for researchers interested in the latest frontiers of Statistics and Data Analysis, it also offers valuable supplementary material for students of the disciplines dealt with here. Lastly, it will help Statisticians and Data Scientists recognize their counterparts’ fundamental role.
Categories: Computers

Statistical Foundations of Data Science

Statistical Foundations of Data Science

CHAPMAN & HALL/CRC DATA SCIENCE SERIES Reflecting the interdisciplinary nature of the field, this book series ... David J. Marchette Basketball Data Science With Applications in R Paola Zuccolotto and Marcia Manisera JavaScript for Data ...

Author: Jianqing Fan

Publisher: CRC Press

ISBN: 9780429527616

Category: Mathematics

Page: 752

View: 885

Statistical Foundations of Data Science gives a thorough introduction to commonly used statistical models, contemporary statistical machine learning techniques and algorithms, along with their mathematical insights and statistical theories. It aims to serve as a graduate-level textbook and a research monograph on high-dimensional statistics, sparsity and covariance learning, machine learning, and statistical inference. It includes ample exercises that involve both theoretical studies as well as empirical applications. The book begins with an introduction to the stylized features of big data and their impacts on statistical analysis. It then introduces multiple linear regression and expands the techniques of model building via nonparametric regression and kernel tricks. It provides a comprehensive account on sparsity explorations and model selections for multiple regression, generalized linear models, quantile regression, robust regression, hazards regression, among others. High-dimensional inference is also thoroughly addressed and so is feature screening. The book also provides a comprehensive account on high-dimensional covariance estimation, learning latent factors and hidden structures, as well as their applications to statistical estimation, inference, prediction and machine learning problems. It also introduces thoroughly statistical machine learning theory and methods for classification, clustering, and prediction. These include CART, random forests, boosting, support vector machines, clustering algorithms, sparse PCA, and deep learning.
Categories: Mathematics

Mastering Python for Data Science

Mastering Python for Data Science

To build the multiple linear regression model, we'll utilize the NBA's basketball data to predict the average points scored per game The following are the column descriptions of the data: • height: This refers to the height in feet ...

Author: Samir Madhavan

Publisher: Packt Publishing Ltd

ISBN: 9781784392628

Category: Computers

Page: 294

View: 415

Explore the world of data science through Python and learn how to make sense of data About This Book Master data science methods using Python and its libraries Create data visualizations and mine for patterns Advanced techniques for the four fundamentals of Data Science with Python - data mining, data analysis, data visualization, and machine learning Who This Book Is For If you are a Python developer who wants to master the world of data science then this book is for you. Some knowledge of data science is assumed. What You Will Learn Manage data and perform linear algebra in Python Derive inferences from the analysis by performing inferential statistics Solve data science problems in Python Create high-end visualizations using Python Evaluate and apply the linear regression technique to estimate the relationships among variables. Build recommendation engines with the various collaborative filtering algorithms Apply the ensemble methods to improve your predictions Work with big data technologies to handle data at scale In Detail Data science is a relatively new knowledge domain which is used by various organizations to make data driven decisions. Data scientists have to wear various hats to work with data and to derive value from it. The Python programming language, beyond having conquered the scientific community in the last decade, is now an indispensable tool for the data science practitioner and a must-know tool for every aspiring data scientist. Using Python will offer you a fast, reliable, cross-platform, and mature environment for data analysis, machine learning, and algorithmic problem solving. This comprehensive guide helps you move beyond the hype and transcend the theory by providing you with a hands-on, advanced study of data science. Beginning with the essentials of Python in data science, you will learn to manage data and perform linear algebra in Python. You will move on to deriving inferences from the analysis by performing inferential statistics, and mining data to reveal hidden patterns and trends. You will use the matplot library to create high-end visualizations in Python and uncover the fundamentals of machine learning. Next, you will apply the linear regression technique and also learn to apply the logistic regression technique to your applications, before creating recommendation engines with various collaborative filtering algorithms and improving your predictions by applying the ensemble methods. Finally, you will perform K-means clustering, along with an analysis of unstructured data with different text mining techniques and leveraging the power of Python in big data analytics. Style and approach This book is an easy-to-follow, comprehensive guide on data science using Python. The topics covered in the book can all be used in real world scenarios.
Categories: Computers

The 9 Pitfalls of Data Science

The 9 Pitfalls of Data Science

Many athletes and fans believe in the hot hand; for example, that a basketball player who makes several shots in a row is very likely to make his next shot. Three psychologists (Thomas Gilovich, Robert Vallone, ...

Author: Gary Smith

Publisher: Oxford University Press

ISBN: 9780192582768

Category: Computers

Page: 240

View: 513

Data science has never had more influence on the world. Large companies are now seeing the benefit of employing data scientists to interpret the vast amounts of data that now exists. However, the field is so new and is evolving so rapidly that the analysis produced can be haphazard at best. The 9 Pitfalls of Data Science shows us real-world examples of what can go wrong. Written to be an entertaining read, this invaluable guide investigates the all too common mistakes of data scientists - who can be plagued by lazy thinking, whims, hunches, and prejudices - and indicates how they have been at the root of many disasters, including the Great Recession. Gary Smith and Jay Cordes emphasise how scientific rigor and critical thinking skills are indispensable in this age of Big Data, as machines often find meaningless patterns that can lead to dangerous false conclusions. The 9 Pitfalls of Data Science is loaded with entertaining tales of both successful and misguided approaches to interpreting data, both grand successes and epic failures. These cautionary tales will not only help data scientists be more effective, but also help the public distinguish between good and bad data science.
Categories: Computers

Data Analytics

Data Analytics

The series will publish cutting-edge research, industry applications, and textbooks in data science. ... Basketball Data Science With Applications in R Paola Zuccolotto and Marcia Manisera JavaScript for Data Science Maya Gans, ...

Author: Shuai Huang

Publisher: CRC Press

ISBN: 9781000372458

Category: Mathematics

Page: 273

View: 888

Data Analytics: A Small Data Approach is suitable for an introductory data analytics course to help students understand some main statistical learning models. It has many small datasets to guide students to work out pencil solutions of the models and then compare with results obtained from established R packages. Also, as data science practice is a process that should be told as a story, in this book there are many course materials about exploratory data analysis, residual analysis, and flowcharts to develop and validate models and data pipelines. The main models covered in this book include linear regression, logistic regression, tree models and random forests, ensemble learning, sparse learning, principal component analysis, kernel methods including the support vector machine and kernel regression, and deep learning. Each chapter introduces two or three techniques. For each technique, the book highlights the intuition and rationale first, then shows how mathematics is used to articulate the intuition and formulate the learning problem. R is used to implement the techniques on both simulated and real-world dataset. Python code is also available at the book’s website: http://dataanalyticsbook.info.
Categories: Mathematics

Public Policy Analytics

Public Policy Analytics

Published Titles Introduction to Data Science Data Analysis and Prediction Algorithms with R Rafael A. Irizarry Cybersecurity Analytics Rakesh M. Verma and David J. Marchette Basketball Data Science With Applications in R Paola ...

Author: Ken Steif

Publisher: CRC Press

ISBN: 9781000401615

Category: Business & Economics

Page: 228

View: 853

Public Policy Analytics: Code & Context for Data Science in Government teaches readers how to address complex public policy problems with data and analytics using reproducible methods in R. Each of the eight chapters provides a detailed case study, showing readers: how to develop exploratory indicators; understand ‘spatial process’ and develop spatial analytics; how to develop ‘useful’ predictive analytics; how to convey these outputs to non-technical decision-makers through the medium of data visualization; and why, ultimately, data science and ‘Planning’ are one and the same. A graduate-level introduction to data science, this book will appeal to researchers and data scientists at the intersection of data analytics and public policy, as well as readers who wish to understand how algorithms will affect the future of government.
Categories: Business & Economics

Explanatory Model Analysis

Explanatory Model Analysis

CHAPMAN & HALL/CRC DATA SCIENCE SERIES Reflecting the interdisciplinary nature of the field, this book series ... Basketball Data Science With Applications in R Paola Zuccolotto and Marcia Manisera JavaScript for Data Science Maya Gans, ...

Author: Przemyslaw Biecek

Publisher: CRC Press

ISBN: 9780429648731

Category: Business & Economics

Page: 362

View: 162

Explanatory Model Analysis Explore, Explain and Examine Predictive Models is a set of methods and tools designed to build better predictive models and to monitor their behaviour in a changing environment. Today, the true bottleneck in predictive modelling is neither the lack of data, nor the lack of computational power, nor inadequate algorithms, nor the lack of flexible models. It is the lack of tools for model exploration (extraction of relationships learned by the model), model explanation (understanding the key factors influencing model decisions) and model examination (identification of model weaknesses and evaluation of model's performance). This book presents a collection of model agnostic methods that may be used for any black-box model together with real-world applications to classification and regression problems.
Categories: Business & Economics