Football Analytics with Python & R - Helion

ebook

Autor: Eric A. Eager, Richard A. Erickson
ISBN: 9781492099581
stron: 352, Format: ebook
Data wydania: 2023-08-15
Księgarnia: Helion

Cena książki: 211,65 zł (poprzednio: 246,10 zł)
Oszczędzasz: 14% (-34,45 zł)

Osoby, które kupiły tę książkę, wybierały także »

Baseball is not the only sport to use "moneyball." American football fans, teams, and gamblers are increasingly using data to gain an edge against the competition. Professional and college teams use data to help select players and identify team needs. Fans use data to guide fantasy team picks and strategies. Sports bettors and fantasy football players are using data to help inform decision making. This concise book provides a clear introduction to using statistical models to analyze football data.

Whether your goal is to produce a winning team, dominate your fantasy football league, qualify for an entry-level football analyst position, or simply learn R and Python using fun example cases, this book is your starting place. You'll learn how to:

Apply basic statistical concepts to football datasets
Describe football data with quantitative methods
Create efficient workflows that offer reproducible results
Use data science skills such as web scraping, manipulating data, and plotting data
Implement statistical models for football data
Link data summaries and model outputs to create reports or presentations using tools such as R Markdown and R Shiny
And more

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Spis treści

Football Analytics with Python & R eBook -- spis treści

Preface
- Who This Book Is For
- Who This Book Is Not For
- How We Think About Data and How to Use This Book
- A Football Example
- What You Will Learn from Our Book
- Conventions Used in This Book
- Using Code Examples
- OReilly Online Learning
- How to Contact Us
- Acknowledgments
1. Football Analytics
- Baseball Has the Three True Outcomes: Does Football?
- Do Running Backs Matter?
- How Data Can Help Us Contextualize Passing Statistics
- Can You Beat the Odds?
- Do Teams Beat the Draft?
- Tools for Football Analytics
- First Steps in Python and R
- Example Data: Who Throws Deep?
  - nflfastR in R
  - nfl_data_py in Python
- Data Science Tools Used in This Chapter
- Suggested Readings
2. Exploratory Data Analysis: Stable Versus Unstable Quarterback Statistics
- Defining Questions
- Obtaining and Filtering Data
- Summarizing Data
- Plotting Data
  - Histograms
  - Boxplots
- Player-Level Stability of Passing Yards per Attempt
  - Deep Passes Versus Short Passes
  - So, What Should We Do with This Insight?
- Data Science Tools Used in This Chapter
- Exercises
- Suggested Readings
3. Simple Linear Regression: Rushing Yards Over Expected
- Exploratory Data Analysis
- Simple Linear Regression
- Who Was the Best in RYOE?
- Is RYOE a Better Metric?
- Data Science Tools Used in This Chapter
- Exercises
- Suggested Readings
4. Multiple Regression: Rushing Yards Over Expected
- Definition of Multiple Linear Regression
- Exploratory Data Analysis
- Applying Multiple Linear Regression
- Analyzing RYOE
- So, Do Running Backs Matter?
- Assumption of Linearity
- Data Science Tools Used in This Chapter
- Exercises
- Suggested Readings
5. Generalized Linear Models: Completion Percentage over Expected
- Generalized Linear Models
- Building a GLM
- GLM Application to Completion Percentage
- Is CPOE More Stable Than Completion Percentage?
- A Question About Residual Metrics
- A Brief Primer on Odds Ratios
- Data Science Tools Used in This Chapter
- Exercises
- Suggested Readings
6. Using Data Science for Sports Betting: Poisson Regression and Passing Touchdowns
- The Main Markets in Football
- Application of Poisson Regression: Prop Markets
- The Poisson Distribution
- Individual Player Markets and Modeling
- Poisson Regression Coefficients
- Closing Thoughts on GLMs
- Data Science Tools Used in This Chapter
- Exercises
- Suggested Readings
7. Web Scraping: Obtaining and Analyzing Draft Picks
- Web Scraping with Python
- Web Scraping in R
- Analyzing the NFL Draft
- The Jets/Colts 2018 Trade Evaluated
- Are Some Teams Better at Drafting Players Than Others?
- Data Science Tools Used in This Chapter
- Exercises
- Suggested Readings
8. Principal Component Analysis and Clustering: Player Attributes
- Web Scraping and Visualizing NFL Scouting Combine Data
- Introduction to PCA
- PCA on All Data
- Clustering Combine Data
  - Clustering Combine Data in Python
  - Clustering Combine Data in R
  - Closing Thoughts on Clustering
- Data Science Tools Used in This Chapter
- Exercises
- Suggested Readings
9. Advanced Tools and Next Steps
- Advanced Modeling Tools
  - Time Series Analysis
  - Multivariate Statistics Beyond PCA
  - Quantile Regression
  - Bayesian Statistics and Hierarchical Models
  - Survival Analysis/Time-to-Event
  - Bayesian Networks/Structural Equation Modeling
  - Machine Learning
- Command Line Tools
  - Bash Example
  - Suggested Readings for bash
- Version Control
  - Git
  - GitHub and GitLab
  - GitHub Web Pages and Résumés
- Suggested Reading for Git
- Style Guides and Linting
- Packages
  - Suggested Readings for Packages
- Computer Environments
- Interactives and Report Tools to Share Data
- Artificial Intelligence Tools
- Conclusion
A. Python and R Basics
- Obtaining Python and R
- Local Installation
  - Cloud-Based Options
- Scripts
- Packages in Python and R
- nflfastR and nfl_data_py Tips
- Integrated Development Environments
- Basic Python Data Types
- Basic R Data Types
B. Summary Statistics and Data Wrangling: Passing the Ball
- Basic Statistics
  - Averages
  - Variability and Distribution
  - Uncertainty Around Estimates
- Filtering and Selecting Columns
- Calculating Summary Statistics with Python and R
- A Note About Presenting Summary Statistics
- Improving Your Presentation
- Exercises
- Suggested Readings
C. Data-Wrangling Fundamentals
- Logic Operators
- Filtering and Sorting Data
- Cleaning
- Piping in R
- Checking and Cleaning Data for Outliers
- Merging Multiple Datasets
Glossary
Index