Index
Symbols
- ! (exclamation point), nflfastR in R
- != (not equal to) operator, Filtering and Sorting Data
- " (double quotation marks), nflfastR in R, Filtering and Sorting Data
- # (pound sign/hashtag), First Steps in Python and R
- & (and) operator, Logic Operators
- ' (single quotation marks), nflfastR in R, Filtering and Sorting Data
- () (parentheses), in order of operations, Filtering and Sorting Data
- < (less than) operator, Logic Operators
- <= (less than or equal to) operator, Logic Operators
- == (equals) operator, Logic Operators
- > (greater than) operator, Logic Operators
- >= (greater than or equal to) operator, Logic Operators
- (backslash), as line break, Filtering and Sorting Data
- ` (backtick), Web Scraping and Visualizing NFL Scouting Combine Data
- | (or) operator, Logic Operators
- |> (pipe) operator, Piping in R
A
- absolute paths, Bash Example
- accessing dataframes, Cleaning
- Adams, Cooper, Football Analytics
- add-ons (see packages)
- Advanced Football Analytics website, Football Analytics
- advanced tools
- artificial intelligence, Artificial Intelligence Tools
- command lines, Command Line Tools-Suggested Readings for bash
- computer environments, Computer Environments
- documentation, Advanced Tools and Next Steps
- interactives, Interactives and Report Tools to Share Data
- for modeling, Advanced Modeling Tools-Machine Learning
- packages, Packages
- style guides, Style Guides and Linting
- types of, Advanced Tools and Next Steps
- version control, Version Control-Suggested Reading for Git
- aggregating data
- aggressiveness statistics, nflfastR in R
- air yards, How Data Can Help Us Contextualize Passing Statistics
- Alstott, Mike, Simple Linear Regression: Rushing Yards Over Expected
- analysis of variance (ANOVA), Exploratory Data Analysis: Stable Versus Unstable Quarterback Statistics
- analytics (see football analytics)
- and (&) operator, Logic Operators
- argument names, Filtering and Sorting Data
- Arnette, Damon, Are Some Teams Better at Drafting Players Than Others?
- arrays in Python, Basic Python Data Types
- artificial intelligence (AI) tools, Artificial Intelligence Tools
- assumption of linearity, Assumption of Linearity-Assumption of Linearity
- auto-correlations, Time Series Analysis
- average depth of target (aDOT), nflfastR in R-nflfastR in R
- averages, calculating, Averages-Averages
B
- backslash (), as line break, Filtering and Sorting Data
- backtick (`), Web Scraping and Visualizing NFL Scouting Combine Data
- Baldwin, Ben, Football Analytics, So, Do Running Backs Matter?, Generalized Linear Models: Completion Percentage over Expected, Analyzing the NFL Draft
- Baltimore Ravens, touchdowns per game, Poisson Regression Coefficients-Poisson Regression Coefficients
- Barkley, Saquon, Do Running Backs Matter?
- baseball analytics, Baseball Has the Three True Outcomes: Does Football?
- bash shell, Command Line Tools-Suggested Readings for bash
- batch files, Scripts
- Bayesian networks, Bayesian Networks/Structural Equation Modeling
- Bayesian statistics, Bayesian Statistics and Hierarchical Models-Bayesian Statistics and Hierarchical Models
- Belichick, Bill, Do Teams Beat the Draft?
- Bell, Le'Veon, Analyzing RYOE, So, Do Running Backs Matter?
- betting (see sports betting)
- Big Data Bowl, Simple Linear Regression: Rushing Yards Over Expected-Simple Linear Regression: Rushing Yards Over Expected
- bimodal distributions, Web Scraping and Visualizing NFL Scouting Combine Data
- binning and averaging, Exploratory Data Analysis-Exploratory Data Analysis
- binomial distributions, Generalized Linear Models
- bins, Histograms, Histograms
- Bioconductor, Packages in Python and R
- Bitbucket, GitHub and GitLab
- bits, origin of term, Exploratory Data Analysis: Stable Versus Unstable Quarterback Statistics
- bookies, Using Data Science for Sports Betting: Poisson Regression and Passing Touchdowns
- books, Using Data Science for Sports Betting: Poisson Regression and Passing Touchdowns
- Boolean objects
- bounded values, Generalized Linear Models
- box-and-whisker plots (see boxplots)
- boxplots, Exploratory Data Analysis: Stable Versus Unstable Quarterback Statistics, Boxplots-Boxplots, Variability and Distribution
- Bradford, Sam, Simple Linear Regression: Rushing Yards Over Expected, Generalized Linear Models: Completion Percentage over Expected, GLM Application to Completion Percentage, GLM Application to Completion Percentage
- Brady, Tom, Deep Passes Versus Short Passes-Deep Passes Versus Short Passes, Analyzing the NFL Draft
- Brees, Drew, GLM Application to Completion Percentage, GLM Application to Completion Percentage, Is CPOE More Stable Than Completion Percentage?
- Brown, Jim, So, Do Running Backs Matter?
- Burke, Brian, Football Analytics
- Burrow, Joe, GLM Application to Completion Percentage, A Question About Residual Metrics
- business intelligence (BI) tools, Tools for Football Analytics
- buy-low candidates, Exploratory Data Analysis: Stable Versus Unstable Quarterback Statistics
- bytes, Exploratory Data Analysis: Stable Versus Unstable Quarterback Statistics
C
- Carl, Sebastian, Football Analytics
- Carnegie Mellon Sports Analytics Conference, Football Analytics
- Carroll, Bob, Football Analytics
- Carter, Virgil, Football Analytics
- cd command (bash), Bash Example
- Chahouri, George, Football Analytics
- characters in R, Basic R Data Types
- ChatGPT, Artificial Intelligence Tools
- cheeseheads, First Steps in Python and R
- Chicago Bears, draft pick trade with Oakland/Las Vegas Raiders, Are Some Teams Better at Drafting Players Than Others?
- Chubb, Nick, Analyzing RYOE
- cleaning data, Cleaning-Cleaning
- Cleveland Browns, drafting struggles, Are Some Teams Better at Drafting Players Than Others?, Are Some Teams Better at Drafting Players Than Others?
- closing line, Using Data Science for Sports Betting: Poisson Regression and Passing Touchdowns
- cloud-based installation of Python/R, Obtaining Python and R-Cloud-Based Options
- clustering, Principal Component Analysis and Clustering: Player Attributes, Closing Thoughts on Clustering
- code golf, Merging Multiple Datasets
- Codey, Artificial Intelligence Tools
- coefficient of variation, Variability and Distribution
- coefficients
- collinearity, Principal Component Analysis and Clustering: Player Attributes
- Collinsworth, Cris, A Note About Presenting Summary Statistics
- colorblindness, PCA on All Data
- combine (see NFL Scouting Combine)
- command lines, Command Line Tools-Suggested Readings for bash
- commands, bash, Bash Example
- comments, First Steps in Python and R
- completion percentage and air yards, Generalized Linear Models: Completion Percentage over Expected-Generalized Linear Models: Completion Percentage over Expected
- completion percentage over expected (CPOE), How Data Can Help Us Contextualize Passing Statistics, Generalized Linear Models: Completion Percentage over Expected
- Comprehensive R Archive Network (CRAN), Packages in Python and R
- computer environments, Computer Environments
- Conda, Computer Environments
- confidence interval (CI), Simple Linear Regression, Are Some Teams Better at Drafting Players Than Others?, Uncertainty Around Estimates-Uncertainty Around Estimates
- Conner, James, So, Do Running Backs Matter?
- console, Command Line Tools
- context, importance of, A Note About Presenting Summary Statistics
- continuous predictor variables, Definition of Multiple Linear Regression-Definition of Multiple Linear Regression
- contrasts, Definition of Multiple Linear Regression
- Cook's distance, Assumption of Linearity
- Cousins, Kirk, Analyzing the NFL Draft
- cp command (bash), Bash Example
- cumulative density function (CDF), Individual Player Markets and Modeling
D
- Dallas Cowboys
- data cleaning (see cleaning data; data wrangling)
- data dictionary, Web Scraping with Python
- data files, saving as outputs, Cleaning
- data manipulating (see data wrangling)
- data mutating (see data wrangling)
- data pipelines, Summary Statistics and Data Wrangling: Passing the Ball
- data types
- data wrangling
- cleaning, Cleaning-Cleaning
- defined, Data-Wrangling Fundamentals
- filtering, Filtering and Sorting Data-Filtering and Sorting Data
- logic operators for, Logic Operators-Logic Operators
- merging datasets, Merging Multiple Datasets-Merging Multiple Datasets
- outliers, Checking and Cleaning Data for Outliers
- passing statistics, Obtaining and Filtering Data
- piping in R, Piping in R
- rushing statistics, Exploratory Data Analysis-Exploratory Data Analysis
- terminology, Summary Statistics and Data Wrangling: Passing the Ball
- tools for, Summary Statistics and Data Wrangling: Passing the Ball-Summary Statistics and Data Wrangling: Passing the Ball
- dataframes
- datasets
- deep passes, stability analysis of, Deep Passes Versus Short Passes-Deep Passes Versus Short Passes
- defining questions, Defining Questions
- degrees of freedom, Simple Linear Regression, Simple Linear Regression, Variability and Distribution
- dependent variables (see response variables)
- design matrix, Definition of Multiple Linear Regression
- Detroit Lions
- dictionaries in Python, Basic Python Data Types
- dimensionality reduction, Principal Component Analysis and Clustering: Player Attributes
- dimensions, Introduction to PCA
- directories in bash, Bash Example-Bash Example
- discrete predictor variables, Definition of Multiple Linear Regression-Definition of Multiple Linear Regression
- distance, Simple Linear Regression: Rushing Yards Over Expected
- distributions, Variability and Distribution-Variability and Distribution
- Docker, Computer Environments
- documentation
- double quotation marks ("), nflfastR in R, Filtering and Sorting Data
- downs, Simple Linear Regression: Rushing Yards Over Expected, Multiple Regression: Rushing Yards Over Expected
- draft approximate value (DrAV), Analyzing the NFL Draft, The Jets/Colts 2018 Trade Evaluated-The Jets/Colts 2018 Trade Evaluated
- draft capital, Do Teams Beat the Draft?
- draft picks
- Dunn, Warrick, Simple Linear Regression: Rushing Yards Over Expected
E
- ease of passing, So, Do Running Backs Matter?
- edge, in sports betting, Using Data Science for Sports Betting: Poisson Regression and Passing Touchdowns, Cleaning
- eigenvalues, PCA on All Data
- eigenvectors, PCA on All Data
- Elliott, Ezekiel, Who Was the Best in RYOE?, Analyzing RYOE, So, Do Running Backs Matter?
- equals (==) operator, Logic Operators
- Euclidean distance, Multivariate Statistics Beyond PCA
- exclamation point (!), nflfastR in R
- expected completion percentage, Is CPOE More Stable Than Completion Percentage?
- expected goals (xG), Football Analytics
- expected points, Football Analytics-Football Analytics
- expected points added (EPA), Football Analytics, Summarizing Data
- explanatory variables (see predictor variables)
- exploratory data analysis (EDA), Exploratory Data Analysis: Stable Versus Unstable Quarterback Statistics
F
- factorials, The Poisson Distribution
- family, Generalized Linear Models
- Faulk, Marshall, So, Do Running Backs Matter?
- favorites, The Main Markets in Football
- features (see predictor variables; response variables)
- Fields, Justin, Analyzing RYOE
- filepaths, Bash Example
- filtering
- Fitzgerald, Jason, The Jets/Colts 2018 Trade Evaluated
- Fitzpatrick, Ryan, Player-Level Stability of Passing Yards per Attempt, GLM Application to Completion Percentage
- floating-point numbers
- football analytics
- football markets, The Main Markets in Football-Application of Poisson Regression: Prop Markets
- for loops, Individual Player Markets and Modeling-Individual Player Markets and Modeling
- forward pass, origin of, Do Running Backs Matter?
- 4th Down Bot, Football Analytics
- frequentist statistics, Bayesian Statistics and Hierarchical Models
- full joins, Merging Multiple Datasets-Merging Multiple Datasets
- Fuller, Kendall, So, What Should We Do with This Insight?
G
- Galton, Francis, Simple Linear Regression: Rushing Yards Over Expected
- gambling (see sports betting)
- gamma regression, Closing Thoughts on GLMs
- generalized additive mixed-effect models (GAMMs), Closing Thoughts on GLMs
- generalized additive models (GAMs), Closing Thoughts on GLMs
- generalized linear models (GLMs), Closing Thoughts on GLMs-Closing Thoughts on GLMs
- geometric mean, Averages
- Gettleman, Dave, Do Running Backs Matter?
- ggplot2 tool, Histograms, Suggested Readings
- Git, Version Control-Suggested Reading for Git
- Git for Windows, Command Line Tools
- GitHub, GitHub and GitLab
- GitHub Copilot, Artificial Intelligence Tools
- GitHub Desktop, Git
- GitLab, GitHub and GitLab
- greater than (>) operator, Logic Operators
- greater than or equal to (>=) operator, Logic Operators
- Green Bay Packers
- grouping data
H
- Harris, Franco, So, Do Running Backs Matter?
- hashtag (#), First Steps in Python and R
- help files, Python and R Basics
- Henry, Derrick, Simple Linear Regression: Rushing Yards Over Expected, Who Was the Best in RYOE?, Analyzing RYOE, So, Do Running Backs Matter?
- Hermsmeyer, Josh, How Data Can Help Us Contextualize Passing Statistics
- hexbin plots, Building a GLM
- The Hidden Game of Football (Carroll, Palmer, Thorn), Football Analytics
- Hill, Taysom, Analyzing RYOE, GLM Application to Completion Percentage
- histograms, Histograms-Histograms
- history of football analytics, Football Analytics-Football Analytics
- hold, Individual Player Markets and Modeling
- home-field advantage, A Football Example-A Football Example
- Horowitz, Max, Football Analytics
- house advantage, Can You Beat the Odds?, The Main Markets in Football
I
- IDEs (integrated development environments), Integrated Development Environments-Integrated Development Environments
- importing pandas package, nfl_data_py in Python
- improving statistics presentations, Improving Your Presentation
- in operators, Logic Operators
- independent variables (see predictor variables)
- Indianapolis Colts, draft pick trade with New York Jets, Analyzing the NFL Draft, The Jets/Colts 2018 Trade Evaluated-The Jets/Colts 2018 Trade Evaluated
- individual player markets, Individual Player Markets and Modeling-Individual Player Markets and Modeling
- inner joins, Merging Multiple Datasets
- install.packages() function, Packages in Python and R
- installing
- integers
- integrated development environments (IDEs), Integrated Development Environments-Integrated Development Environments
- interactions, Applying Multiple Linear Regression
- interactives, Interactives and Report Tools to Share Data
- intercept, Definition of Multiple Linear Regression
- intermediate objects versus piping objects, Histograms
- interquartile range (IQR), Summarizing Data, Variability and Distribution
J
- Jackson, Bo, Do Running Backs Matter?
- Jackson, Lamar, Who Was the Best in RYOE?, Analyzing RYOE
- Jacobs, Josh, Are Some Teams Better at Drafting Players Than Others?
- Jimmy Johnson chart, Analyzing the NFL Draft
- Johnson, Calvin, Closing Thoughts on Clustering
- Johnson, Jimmy, Analyzing the NFL Draft
- joins
- Jones, Jerry, So, Do Running Backs Matter?, Analyzing the NFL Draft
- Jupyter Notebook, Interactives and Report Tools to Share Data, Scripts
L
- Landry, Tom, Analyzing the NFL Draft
- Lange, Gregg, Exploratory Data Analysis: Stable Versus Unstable Quarterback Statistics
- LaTeX, Interactives and Report Tools to Share Data
- left datasets, Merging Multiple Datasets
- left joins, Merging Multiple Datasets
- less than (<) operator, Logic Operators
- less than or equal to (<=) operator, Logic Operators
- line breaks, Summarizing Data, Applying Multiple Linear Regression, Filtering and Sorting Data
- linear regression, Generalized Linear Models
- link functions, Generalized Linear Models
- linting, Style Guides and Linting
- Linux, Version Control
- lists
- loading
- local installation of Python/R, Obtaining Python and R-Cloud-Based Options
- log(0) function, Analyzing the NFL Draft
- logic operators
- logical operators in R, Basic R Data Types
- logistic regression, Generalized Linear Models
- logit, Generalized Linear Models
- lognormal regression, Closing Thoughts on GLMs
- long passes, Obtaining and Filtering Data
- longitudinal data, Using Data Science for Sports Betting: Poisson Regression and Passing Touchdowns
- Lopez, Michael, Simple Linear Regression: Rushing Yards Over Expected, Analyzing the NFL Draft
- ls command (bash), Bash Example
- Luck, Andrew, Analyzing the NFL Draft
M
- machine learning, Machine Learning
- Mack, Khalil, Are Some Teams Better at Drafting Players Than Others?
- macOS, plotting data, Histograms
- Mahomes, Patrick, Player-Level Stability of Passing Yards per Attempt, Simple Linear Regression: Rushing Yards Over Expected, A Question About Residual Metrics, Individual Player Markets and Modeling-Individual Player Markets and Modeling, The Jets/Colts 2018 Trade Evaluated, Averages
- markets in football betting, The Main Markets in Football-Application of Poisson Regression: Prop Markets
- Massey, Cade, Analyzing the NFL Draft
- matplotlib package, Poisson Regression Coefficients
- matrices, Basic R Data Types
- McCoy, Mike, Analyzing the NFL Draft
- mean, Averages-Averages
- median, Averages
- merging datasets, Merging Multiple Datasets-Merging Multiple Datasets
- metadata, Obtaining and Filtering Data, Web Scraping with Python
- Microsoft PowerShell, Command Line Tools
- MIT Sloan Sports Analytics Conference, Football Analytics
- mode, Averages
- model matrix, Definition of Multiple Linear Regression
- moneyline market, The Main Markets in Football
- Moss, Randy, Closing Thoughts on Clustering
- Mostert, Raheem, Analyzing RYOE
- moving mean, Analyzing the NFL Draft
- multiple linear regression
- multivariate statistics, Multivariate Statistics Beyond PCA
- munging (see data wrangling)
- Murray, Kyler, Who Was the Best in RYOE?, Analyzing RYOE
N
- naked mean, Uncertainty Around Estimates
- naming conventions, Obtaining and Filtering Data
- negative binomial distributions, Closing Thoughts on GLMs
- Nerd-to-Human Translator (Baldwin), So, Do Running Backs Matter?
- nested loops, Individual Player Markets and Modeling
- New England Statistics Symposium, Football Analytics
- New York Giants, draft picks, Do Running Backs Matter?
- New York Jets, draft pick trade with Indianapolis Colts, Analyzing the NFL Draft, The Jets/Colts 2018 Trade Evaluated-The Jets/Colts 2018 Trade Evaluated
- Newton, Cam, Analyzing RYOE
- NFL Draft, Web Scraping: Obtaining and Analyzing Draft Picks
- NFL Scouting Combine, Web Scraping: Obtaining and Analyzing Draft Picks, Principal Component Analysis and Clustering: Player Attributes-Principal Component Analysis and Clustering: Player Attributes
- nflfastR package, Football Analytics, nflfastR in R-nflfastR in R
- nflscrapR package, Football Analytics
- nfl_data_py package, Football Analytics, nfl_data_py in Python-nfl_data_py in Python, nflfastR and nfl_data_py Tips
- noise, smoothing, Analyzing the NFL Draft
- normal distributions, Generalized Linear Models
- normalization, purpose of, Simple Linear Regression: Rushing Yards Over Expected
- not equal to (!=) operator, Filtering and Sorting Data
- null hypothesis significance testing (NHST), Simple Linear Regression
- numeric floating-point numbers, Basic R Data Types
O
- Oakland/Las Vegas Raiders
- odds ratios, A Brief Primer on Odds Ratios-A Brief Primer on Odds Ratios
- open source, Tools for Football Analytics
- or (|) operator, Logic Operators
- order of operations, Filtering and Sorting Data
- ordinal regression, Generalized Linear Models
- ordinary least-squares regression, Simple Linear Regression, Generalized Linear Models
- origination, Using Data Science for Sports Betting: Poisson Regression and Passing Touchdowns
- outer joins, Merging Multiple Datasets-Merging Multiple Datasets
- outliers, Boxplots, Checking and Cleaning Data for Outliers
P
- p-values, Simple Linear Regression
- packages, Packages
- Palmer, Pete, Football Analytics
- pandas package, importing, nfl_data_py in Python
- parentheses (), in order of operations, Filtering and Sorting Data
- pass depth, GLM Application to Completion Percentage
- passing statistics, How Data Can Help Us Contextualize Passing Statistics
- in 2016, Simple Linear Regression: Rushing Yards Over Expected
- air yards and completion percentage, Generalized Linear Models: Completion Percentage over Expected-Generalized Linear Models: Completion Percentage over Expected
- averages, Averages-Averages
- CPOE (see completion percentage over expected (CPOE))
- data cleaning/wrangling, Obtaining and Filtering Data
- ease of passing, So, Do Running Backs Matter?
- filtering data, Obtaining and Filtering Data-Obtaining and Filtering Data
- loading data, Obtaining and Filtering Data
- long versus short passes, Obtaining and Filtering Data
- with nflfastR, nflfastR in R-nflfastR in R
- with nfl_data_py, nfl_data_py in Python-nfl_data_py in Python
- plotting data
- stability analysis of, Player-Level Stability of Passing Yards per Attempt-So, What Should We Do with This Insight?
- summarizing data, Summarizing Data-Summarizing Data
- passing yards per attempt (see passing statistics)
- Pearson's correlation coefficient, Exploratory Data Analysis: Stable Versus Unstable Quarterback Statistics, Deep Passes Versus Short Passes
- Penny, Rashaad, Who Was the Best in RYOE?, Analyzing RYOE
- Peterson, Adrian, Do Running Backs Matter?, So, Do Running Backs Matter?
- pip command, Packages in Python and R
- pipe (|>) operator, Piping in R
- pipelines, Summary Statistics and Data Wrangling: Passing the Ball
- piping in R, Piping in R
- piping objects versus intermediate objects, Histograms
- Pittsburgh Steelers
- play-by-play (pbp) data
- player evaluation
- plot() function, Assumption of Linearity
- plotting data
- advantages of, Plotting Data
- in boxplots, Boxplots-Boxplots
- in histograms, Histograms-Histograms
- logistic regressions, Building a GLM-Building a GLM
- NFL Scouting Combine data, Web Scraping and Visualizing NFL Scouting Combine Data-Web Scraping and Visualizing NFL Scouting Combine Data, Introduction to PCA
- in principal component analysis, PCA on All Data-PCA on All Data
- R plot() function, Assumption of Linearity
- resources for information, Suggested Readings
- rushing statistics, Exploratory Data Analysis-Exploratory Data Analysis, Exploratory Data Analysis-Exploratory Data Analysis
- in scatterplots, Deep Passes Versus Short Passes-Deep Passes Versus Short Passes
- touchdowns per game, Poisson Regression Coefficients-Poisson Regression Coefficients
- point differential, Multiple Regression: Rushing Yards Over Expected
- point estimates, Simple Linear Regression
- point-spread market, The Main Markets in Football-The Main Markets in Football
- Poisson distribution, The Poisson Distribution-The Poisson Distribution
- Poisson regression
- Pollard, Tony, Who Was the Best in RYOE?, So, Do Running Backs Matter?
- Posit's Shiny, Interactives and Report Tools to Share Data
- pound sign (#), First Steps in Python and R
- PowerShell, Command Line Tools
- predictor variables, Simple Linear Regression: Rushing Yards Over Expected
- presenting statistics, A Note About Presenting Summary Statistics-Improving Your Presentation
- principal component analysis (PCA), Principal Component Analysis and Clustering: Player Attributes
- principal components (PCs), Introduction to PCA
- Pro Football Reference, Web Scraping: Obtaining and Analyzing Draft Picks
- probabilities
- probability distributions, The Poisson Distribution
- probability mass function (PMF), Individual Player Markets and Modeling
- Professional and Amateur Sports Protection Act of 1992 (PASPA), Can You Beat the Odds?, Using Data Science for Sports Betting: Poisson Regression and Passing Touchdowns
- programming languages, advantages of, Tools for Football Analytics-Tools for Football Analytics
- prop market, Application of Poisson Regression: Prop Markets-Application of Poisson Regression: Prop Markets
- Prophet, Time Series Analysis
- push, The Main Markets in Football
- pwd command (bash), Bash Example
- Python
- advantages of, Tools for Football Analytics-Tools for Football Analytics, Simple Linear Regression
- calculations in, First Steps in Python and R
- clustering data, Clustering Combine Data in Python-Clustering Combine Data in Python
- comments in, First Steps in Python and R
- comparison with R, Data-Wrangling Fundamentals
- data types, Basic Python Data Types-Basic Python Data Types
- filtering data, Obtaining and Filtering Data, Filtering and Selecting Columns-Filtering and Selecting Columns
- for loops, Individual Player Markets and Modeling-Individual Player Markets and Modeling
- grouping data, Player-Level Stability of Passing Yards per Attempt-Player-Level Stability of Passing Yards per Attempt
- help files, Python and R Basics
- installing, Obtaining Python and R-Cloud-Based Options
- line breaks, Summarizing Data
- loading packages, Obtaining and Filtering Data
- logic operators, Logic Operators-Logic Operators
- nfl_data_py package, nfl_data_py in Python-nfl_data_py in Python
- numbering, start of, Obtaining and Filtering Data
- as object-oriented language, Merging Multiple Datasets
- package installation, Example Data: Who Throws Deep?
- packages, Packages in Python and R-Packages in Python and R
- plotting data
- Pythonistas, First Steps in Python and R
- quotation marks in, nflfastR in R
- scripts, Scripts-Scripts
- simple linear regression, Simple Linear Regression-Simple Linear Regression
- summarizing data, Summarizing Data-Summarizing Data, Calculating Summary Statistics with Python and R-Calculating Summary Statistics with Python and R
- variable definition, First Steps in Python and R
- web scraping, Web Scraping with Python-Web Scraping with Python
- Pythonistas, First Steps in Python and R
Q
- quantile regression, Quantile Regression
- quantiles, Variability and Distribution
- quantreg package, Quantile Regression
- quarterbacks
- aggressiveness statistics, nflfastR in R
- draft pick valuation, The Jets/Colts 2018 Trade Evaluated
- passing statistics, How Data Can Help Us Contextualize Passing Statistics
- in 2016, Simple Linear Regression: Rushing Yards Over Expected
- air yards and completion percentage, Generalized Linear Models: Completion Percentage over Expected-Generalized Linear Models: Completion Percentage over Expected
- averages, Averages-Averages
- in boxplots, Boxplots-Boxplots
- CPOE (see completion percentage over expected (CPOE))
- data cleaning/wrangling, Obtaining and Filtering Data
- ease of passing, So, Do Running Backs Matter?
- filtering data, Obtaining and Filtering Data-Obtaining and Filtering Data
- in histograms, Histograms-Histograms
- loading data, Obtaining and Filtering Data
- long versus short passes, Obtaining and Filtering Data
- with nflfastR, nflfastR in R-nflfastR in R
- with nfl_data_py, nfl_data_py in Python-nfl_data_py in Python
- in scatterplots, Deep Passes Versus Short Passes-Deep Passes Versus Short Passes
- stability analysis of, Player-Level Stability of Passing Yards per Attempt-So, What Should We Do with This Insight?
- summarizing data, Summarizing Data-Summarizing Data
- rushing statistics, Who Was the Best in RYOE?, Analyzing RYOE
- as tight ends, Clustering Combine Data in R
- touchdown pass market, Application of Poisson Regression: Prop Markets-Application of Poisson Regression: Prop Markets
- quartiles, Variability and Distribution
- Quarto, Interactives and Report Tools to Share Data, Scripts
- questions, defining, Defining Questions
- quotation marks, nflfastR in R, Scripts, Filtering and Sorting Data
R
- R
- advantages of, Tools for Football Analytics-Tools for Football Analytics, Simple Linear Regression
- calculations in, First Steps in Python and R
- clustering data, Clustering Combine Data in R-Clustering Combine Data in R
- comments in, First Steps in Python and R
- comparison with Python, Data-Wrangling Fundamentals
- data types, Basic R Data Types-Basic R Data Types
- dataframes, nflfastR in R
- filtering data, Obtaining and Filtering Data, Filtering and Selecting Columns-Filtering and Selecting Columns
- as functional language, Merging Multiple Datasets
- grouping data, Player-Level Stability of Passing Yards per Attempt-Player-Level Stability of Passing Yards per Attempt
- help files, Python and R Basics
- installing, Obtaining Python and R-Cloud-Based Options
- line breaks, Summarizing Data
- loading packages, Obtaining and Filtering Data
- logic operators, Logic Operators-Logic Operators
- nflfastR package, nflfastR in R-nflfastR in R
- numbering, start of, Obtaining and Filtering Data
- package installation, Example Data: Who Throws Deep?
- packages, Packages in Python and R-Packages in Python and R
- piping in, Piping in R
- plotting data
- quotation marks in, nflfastR in R
- scripts, Scripts-Scripts
- simple linear regression, Simple Linear Regression
- summarizing data, Summarizing Data-Summarizing Data, Calculating Summary Statistics with Python and R-Calculating Summary Statistics with Python and R
- useRs, First Steps in Python and R
- variable definition, First Steps in Python and R
- web scraping, Web Scraping in R-Web Scraping in R
- R Markdown, Interactives and Report Tools to Share Data, Scripts
- range, Variability and Distribution
- receiver separation, A Question About Residual Metrics
- regression
- gamma, Closing Thoughts on GLMs
- generalized linear models, Closing Thoughts on GLMs-Closing Thoughts on GLMs
- linear, Generalized Linear Models
- logistic, Generalized Linear Models
- lognormal, Closing Thoughts on GLMs
- multiple linear
- ordinal, Generalized Linear Models
- ordinary least-squares, Simple Linear Regression, Generalized Linear Models
- origin of term, Simple Linear Regression: Rushing Yards Over Expected
- Poisson
- purpose of, Simple Linear Regression: Rushing Yards Over Expected
- quantile, Quantile Regression
- resources for information, Suggested Readings, Suggested Readings
- simple linear
- regression candidates, So, What Should We Do with This Insight?
- regression toward the mean, Individual Player Markets and Modeling
- relative paths, Bash Example
- relative risk, Poisson Regression Coefficients
- reports, writing, Interactives and Report Tools to Share Data
- residuals
- resources for information
- football analytics, Suggested Readings, Conclusion
- generalized linear models, Suggested Readings, Suggested Readings
- NFL Scouting Combine, Principal Component Analysis and Clustering: Player Attributes
- plotting data, Suggested Readings
- probabilities, Suggested Readings
- regression, Suggested Readings, Suggested Readings
- running back valuation, Suggested Readings
- sports betting, Suggested Readings
- statistics, Suggested Readings, Suggested Readings
- web scraping, Suggested Readings
- response variables, Simple Linear Regression: Rushing Yards Over Expected, Simple Linear Regression
- resumes (GitHub), GitHub Web Pages and Résumés
- right datasets, Merging Multiple Datasets
- right joins, Merging Multiple Datasets
- right skew, Closing Thoughts on GLMs
- risk ratio, Poisson Regression Coefficients
- Riske, Timo, Analyzing the NFL Draft, Are Some Teams Better at Drafting Players Than Others?
- rm command (bash), Bash Example
- Rodgers, Aaron, Deep Passes Versus Short Passes-Deep Passes Versus Short Passes, GLM Application to Completion Percentage
- rolling average, Analyzing the NFL Draft
- Roosevelt, Teddy, Do Running Backs Matter?
- running average, Analyzing the NFL Draft
- running backs
- rushing yards over expected (RYOE)
- Ryan, Matt, Player-Level Stability of Passing Yards per Attempt, So, What Should We Do with This Insight?, GLM Application to Completion Percentage, GLM Application to Completion Percentage
S
- S language, Simple Linear Regression
- sabermetrics, Baseball Has the Three True Outcomes: Does Football?
- Sanders, Barry, Do Running Backs Matter?, So, Do Running Backs Matter?
- saving data files as outputs, Cleaning
- scaling, Introduction to PCA
- scatterplots, Deep Passes Versus Short Passes-Deep Passes Versus Short Passes
- scikit-learn package, Simple Linear Regression
- scouting
- scraping (see web scraping)
- scripts, Scripts-Scripts
- seaborn package, Histograms, Suggested Readings
- sell-high candidates, Exploratory Data Analysis: Stable Versus Unstable Quarterback Statistics
- set the line, Using Data Science for Sports Betting: Poisson Regression and Passing Touchdowns
- Seth, Tej, Simple Linear Regression: Rushing Yards Over Expected
- Shanahan, Kyle, Player-Level Stability of Passing Yards per Attempt
- shaping (see data wrangling)
- sharing data, Interactives and Report Tools to Share Data
- shell, Command Line Tools
- short passes, Obtaining and Filtering Data, Deep Passes Versus Short Passes-Deep Passes Versus Short Passes
- short-yardage backs, Simple Linear Regression: Rushing Yards Over Expected
- shot quality, Football Analytics
- significant digits, Averages
- Silver, Nate, Exploratory Data Analysis: Stable Versus Unstable Quarterback Statistics
- simple linear regression
- Simpson's paradox, Exploratory Data Analysis
- single quotation marks ('), nflfastR in R, Filtering and Sorting Data
- skill positions, Clustering Combine Data in Python
- slope, Definition of Multiple Linear Regression
- smart quotes, Scripts
- Smith, Alex, So, What Should We Do with This Insight?
- Smith, Emmitt, Do Running Backs Matter?, So, Do Running Backs Matter?
- smoothing noise, Analyzing the NFL Draft
- Society for American Baseball Research (SABR), Baseball Has the Three True Outcomes: Does Football?
- software, origin of term, Exploratory Data Analysis: Stable Versus Unstable Quarterback Statistics
- Spielberger, Brad, The Jets/Colts 2018 Trade Evaluated
- sports analytics (see football analytics)
- sports betting, Can You Beat the Odds?
- spread market, The Main Markets in Football-The Main Markets in Football
- spreadsheets
- stability analysis, Baseball Has the Three True Outcomes: Does Football?, Exploratory Data Analysis: Stable Versus Unstable Quarterback Statistics-Exploratory Data Analysis: Stable Versus Unstable Quarterback Statistics
- standard deviation, Variability and Distribution
- standard error (SE), Simple Linear Regression, Uncertainty Around Estimates-Uncertainty Around Estimates
- statistics
- statsmodels package, Quantile Regression
- sticky stats, Exploratory Data Analysis: Stable Versus Unstable Quarterback Statistics
- strings
- structural equation modeling, Bayesian Networks/Structural Equation Modeling
- style guides, Style Guides and Linting
- styles (of players), Principal Component Analysis and Clustering: Player Attributes
- summarizing data
- summary statistics (see statistics)
- supervised learning, Principal Component Analysis and Clustering: Player Attributes
- survival analysis, Survival Analysis/Time-to-Event
T
- t-distributions, Simple Linear Regression
- targets (see response variables)
- Taylor, Jim, So, Do Running Backs Matter?
- Taylor, Jonathan, Who Was the Best in RYOE?, Analyzing RYOE
- team proficiency in drafting, Are Some Teams Better at Drafting Players Than Others?-Are Some Teams Better at Drafting Players Than Others?
- terminal, Command Line Tools
- Thorn, John, Football Analytics
- three true outcomes, Baseball Has the Three True Outcomes: Does Football?
- tibbles, nflfastR in R
- Tice, Mike, Clustering Combine Data in R
- tidy datasets, Summary Statistics and Data Wrangling: Passing the Ball
- tidying (see data wrangling)
- tidyverse, nflfastR in R
- tight ends, Clustering Combine Data in R
- time series analysis, Time Series Analysis
- time-to-event analysis, Survival Analysis/Time-to-Event
- Torvalds, Linus, Version Control
- total market, The Main Markets in Football
- touchdown pass market, Application of Poisson Regression: Prop Markets-Application of Poisson Regression: Prop Markets
- touchdowns per game, Poisson Regression Coefficients-Poisson Regression Coefficients
- trend lines, Exploratory Data Analysis
- Tukey, John, Exploratory Data Analysis: Stable Versus Unstable Quarterback Statistics, Exploratory Data Analysis: Stable Versus Unstable Quarterback Statistics
- tweener players, Clustering Combine Data in R
V
- valuation of draft picks, Analyzing the NFL Draft-Analyzing the NFL Draft
- variability, Variability and Distribution-Variability and Distribution
- variable definition
- variance, Variability and Distribution
- vectorization, Individual Player Markets and Modeling
- vectors, Basic R Data Types
- Ventura, Sam, Football Analytics
- version control, Version Control-Suggested Reading for Git
- vigorish (vig), Can You Beat the Odds?, The Main Markets in Football
- vulturing, Simple Linear Regression: Rushing Yards Over Expected
W
- Walker, Herschel, Do Running Backs Matter?, Analyzing the NFL Draft
- Walsh, Bill, Football Analytics
- Watson, Deshaun, GLM Application to Completion Percentage, GLM Application to Completion Percentage
- web scraping
- West Coast offense, Football Analytics
- white space, Summarizing Data, Filtering and Sorting Data
- Wickham, Hadley, Summary Statistics and Data Wrangling: Passing the Ball, Data-Wrangling Fundamentals
- wide receivers, Closing Thoughts on Clustering
- win probability (WP), Football Analytics
- window, Analyzing the NFL Draft
- Windows Subsystem for Linux (WSL), Command Line Tools
- wins above replacement (WAR), Football Analytics
- Winston, Jameis, Player-Level Stability of Passing Yards per Attempt, GLM Application to Completion Percentage
- wisdom of crowds, Using Data Science for Sports Betting: Poisson Regression and Passing Touchdowns
- wrangling data (see data wrangling)
- writing reports, Interactives and Report Tools to Share Data
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