Statistics Slam Dunk: Statistical analysis with R on real NBA data 9781633438682
Learn statistics by analyzing professional basketball data! In this action-packed book, you’ll build your skills in expl
157 103
English Pages 951 Year 2024
Table of contents :
about the cover illustration
1 Getting started
1.1 Brief introductions to R and RStudio
1.2 Why R?
Visualizing data
Installing and using packages to extend R’s functional footprint
Networking with other users
Interacting with big data
Landing a job
1.3 How this book works
2 Exploring data
2.1 Loading packages
2.2 Importing data
2.3 Wrangling data
Removing variables
Removing observations
Viewing data
Converting variable types
Creating derived variables
2.4 Variable breakdown
2.5 Exploratory data analysis
Computing basic statistics
Returning data
Computing and visualizing frequency distributions
Computing and visualizing correlations
Computing and visualizing means and medians
2.6 Writing data
3 Segmentation analysis
3.1 More on tanking and the draft
3.2 Loading packages
3.3 Importing and viewing data
3.4 Creating another derived variable
3.5 Visualizing means and medians
Regular season games played
Minutes played per game
Career win shares
Win shares every 48 minutes
3.6 Preliminary conclusions
3.7 Sankey diagram
3.8 Expected value analysis
3.9 Hierarchical clustering
4 Constrained optimization
4.1 What is constrained optimization?
4.2 Loading packages
4.3 Importing data
4.4 Knowing the data
4.5 Visualizing the data
Density plots
Boxplots
Correlation plot
Bar chart
4.6 Constrained optimization setup
4.7 Constrained optimization construction
4.8 Results
5 Regression models
5.1 Loading packages
5.2 Importing data
5.3 Knowing the data
5.4 Identifying outliers
Prototype
Identifying other outliers
5.5 Checking for normality
Prototype
Checking other distributions for normality
5.6 Visualizing and testing correlations
Prototype
Visualizing and testing other correlations
5.7 Multiple linear regression
Subsetting data into train and test
Fitting the model
Returning and interpreting the results
Checking for multicollinearity
Running and interpreting model diagnostics
Comparing models
Predicting
5.8 Regression tree
6 More wrangling and visualizing data
6.1 Loading packages
6.2 Importing data
6.3 Wrangling data
Subsetting data sets
Joining data sets
6.4 Analysis
First quarter
Second quarter
Third quarter
Fourth quarter
Comparing best and worst teams
Second-half results
7 T-testing and effect size testing
7.1 Loading packages
7.2 Importing data
7.3 Wrangling data
7.4 Analysis on 2018-19 data
2018-19 regular season analysis
2019 postseason analysis
Effect size testing
7.5 Analysis on 2019-20 data
2019-20 regular season analysis (pre-COVID)
2019-20 regular season analysis (post-COVID)
More effect size testing
8 Optimal stopping
8.1 Loading packages
8.2 Importing images
8.3 Importing and viewing data
8.4 Exploring and wrangling data
8.5 Analysis
Milwaukee Bucks
Atlanta Hawks
Charlotte Hornets
NBA
9 Chi-square testing and more effect size testing
9.1 Loading packages
9.2 Importing data
9.3 Wrangling data
9.4 Computing permutations
9.5 Visualizing results
Creating a data source
Visualizing the results
Conclusions
9.6 Statistical test of significance
Creating a contingency table and a balloon plot
Running a chi-square test
Creating a mosaic plot
9.7 Effect size testing
10 Doing more with ggplot
10.1 Loading packages
10.2 Importing and viewing data
10.3 Salaries and salary cap analysis
10.4 Analysis
Plotting and computing correlations between team payrolls and regular season wins
Payrolls versus end-of-season results
Payroll comparisons
11 K-means clustering
11.1 Loading packages
11.2 Importing data
11.3 A primer on standard deviations and z-scores
11.4 Analysis
Wrangling data
Evaluating payrolls and wins
11.5 K-means clustering
More data wrangling
K-means clustering
12 Computing and plotting inequality
12.1 Gini coefficients and Lorenz curves
12.2 Loading packages
12.3 Importing and viewing data
12.4 Wrangling data
12.5 Gini coefficients
12.6 Lorenz curves
12.7 Salary inequality and championships
Wrangling data
T-test
Effect size testing
12.8 Salary inequality and wins and losses
T-test
Effect size testing
12.9 Gini coefficient bands versus winning percentage
13 More with Gini coefficients and Lorenz curves
13.1 Loading packages
13.2 Importing and viewing data
13.3 Wrangling data
13.4 Gini coefficients
13.5 Lorenz curves
13.6 For loops
Simple demonstration
Applying what we’ve learned
13.7 User-defined functions
13.8 Win share inequality and championships
Wrangling data
T-test
Effect size testing
13.9 Win share inequality and wins and losses
T-test
Effect size testing
13.10 Gini coefficient bands versus winning percentage
14 Intermediate and advanced modeling
14.1 Loading packages
14.2 Importing and wrangling data
Subsetting and reshaping our data
Extracting a substring to create a new variable
Joining data
Importing and wrangling additional data sets
Joining data (one more time)
Creating standardized variables
14.3 Exploring data
14.4 Correlations
Computing and plotting correlation coefficients
Running correlation tests
14.5 Analysis of variance models
Data wrangling and data visualization
One-way ANOVAs
14.6 Logistic regressions
Data wrangling
Model development
14.7 Paired data before and after
15 The Lindy effect
15.1 Loading packages
15.2 Importing and viewing data
15.3 Visualizing data
Creating and evaluating violin plots
Creating paired histograms
Printing our plots
15.4 Pareto charts
ggplot2 and ggQC packages
qcc package
16 Randomness versus causality
16.1 Loading packages
16.2 Importing and wrangling data
16.3 Rule of succession and the hot hand
16.4 Player-level analysis
Player 1 of 3: Giannis Antetokounmpo
Player 2 of 3: Julius Randle
Player 3 of 3: James Harden
16.5 League-wide analysis
17 Collective intelligence
17.1 Loading packages
17.2 Importing data
17.3 Wrangling data
17.4 Automated exploratory data analysis
Baseline EDA with tableone
Over/under EDA with DataExplorer
Point spread EDA with SmartEDA
17.5 Results
Over/under
Point spreads
18 Statistical dispersion methods
18.1 Loading a package
18.2 Importing data
18.3 Exploring and wrangling data
18.4 Measures of statistical dispersion and intra-season parity
Variance method
Standard deviation method
Range method
Mean absolute deviation method
Median absolute deviation method
18.5 Churn and inter-season parity
Data wrangling
Computing and visualizing churn
19 Data standardization
19.1 Loading a package
19.2 Importing and viewing data
19.3 Wrangling data
Treating duplicate records
Final trimmings
19.4 Standardizing data
Z-score method
Standard deviation method
Centering method
Range method
20 Finishing up
20.1 Cluster analysis
20.2 Significance testing
20.3 Effect size testing
20.4 Modeling
20.5 Operations research
20.6 Probability
20.7 Statistical dispersion
20.8 Standardization
20.9 Summary statistics and visualization
appendix More ggplot2 visualizations
index
about the cover illustration
1 Getting started
1.1 Brief introductions to R and RStudio
1.2 Why R?
Visualizing data
Installing and using packages to extend R’s functional footprint
Networking with other users
Interacting with big data
Landing a job
1.3 How this book works
2 Exploring data
2.1 Loading packages
2.2 Importing data
2.3 Wrangling data
Removing variables
Removing observations
Viewing data
Converting variable types
Creating derived variables
2.4 Variable breakdown
2.5 Exploratory data analysis
Computing basic statistics
Returning data
Computing and visualizing frequency distributions
Computing and visualizing correlations
Computing and visualizing means and medians
2.6 Writing data
3 Segmentation analysis
3.1 More on tanking and the draft
3.2 Loading packages
3.3 Importing and viewing data
3.4 Creating another derived variable
3.5 Visualizing means and medians
Regular season games played
Minutes played per game
Career win shares
Win shares every 48 minutes
3.6 Preliminary conclusions
3.7 Sankey diagram
3.8 Expected value analysis
3.9 Hierarchical clustering
4 Constrained optimization
4.1 What is constrained optimization?
4.2 Loading packages
4.3 Importing data
4.4 Knowing the data
4.5 Visualizing the data
Density plots
Boxplots
Correlation plot
Bar chart
4.6 Constrained optimization setup
4.7 Constrained optimization construction
4.8 Results
5 Regression models
5.1 Loading packages
5.2 Importing data
5.3 Knowing the data
5.4 Identifying outliers
Prototype
Identifying other outliers
5.5 Checking for normality
Prototype
Checking other distributions for normality
5.6 Visualizing and testing correlations
Prototype
Visualizing and testing other correlations
5.7 Multiple linear regression
Subsetting data into train and test
Fitting the model
Returning and interpreting the results
Checking for multicollinearity
Running and interpreting model diagnostics
Comparing models
Predicting
5.8 Regression tree
6 More wrangling and visualizing data
6.1 Loading packages
6.2 Importing data
6.3 Wrangling data
Subsetting data sets
Joining data sets
6.4 Analysis
First quarter
Second quarter
Third quarter
Fourth quarter
Comparing best and worst teams
Second-half results
7 T-testing and effect size testing
7.1 Loading packages
7.2 Importing data
7.3 Wrangling data
7.4 Analysis on 2018-19 data
2018-19 regular season analysis
2019 postseason analysis
Effect size testing
7.5 Analysis on 2019-20 data
2019-20 regular season analysis (pre-COVID)
2019-20 regular season analysis (post-COVID)
More effect size testing
8 Optimal stopping
8.1 Loading packages
8.2 Importing images
8.3 Importing and viewing data
8.4 Exploring and wrangling data
8.5 Analysis
Milwaukee Bucks
Atlanta Hawks
Charlotte Hornets
NBA
9 Chi-square testing and more effect size testing
9.1 Loading packages
9.2 Importing data
9.3 Wrangling data
9.4 Computing permutations
9.5 Visualizing results
Creating a data source
Visualizing the results
Conclusions
9.6 Statistical test of significance
Creating a contingency table and a balloon plot
Running a chi-square test
Creating a mosaic plot
9.7 Effect size testing
10 Doing more with ggplot
10.1 Loading packages
10.2 Importing and viewing data
10.3 Salaries and salary cap analysis
10.4 Analysis
Plotting and computing correlations between team payrolls and regular season wins
Payrolls versus end-of-season results
Payroll comparisons
11 K-means clustering
11.1 Loading packages
11.2 Importing data
11.3 A primer on standard deviations and z-scores
11.4 Analysis
Wrangling data
Evaluating payrolls and wins
11.5 K-means clustering
More data wrangling
K-means clustering
12 Computing and plotting inequality
12.1 Gini coefficients and Lorenz curves
12.2 Loading packages
12.3 Importing and viewing data
12.4 Wrangling data
12.5 Gini coefficients
12.6 Lorenz curves
12.7 Salary inequality and championships
Wrangling data
T-test
Effect size testing
12.8 Salary inequality and wins and losses
T-test
Effect size testing
12.9 Gini coefficient bands versus winning percentage
13 More with Gini coefficients and Lorenz curves
13.1 Loading packages
13.2 Importing and viewing data
13.3 Wrangling data
13.4 Gini coefficients
13.5 Lorenz curves
13.6 For loops
Simple demonstration
Applying what we’ve learned
13.7 User-defined functions
13.8 Win share inequality and championships
Wrangling data
T-test
Effect size testing
13.9 Win share inequality and wins and losses
T-test
Effect size testing
13.10 Gini coefficient bands versus winning percentage
14 Intermediate and advanced modeling
14.1 Loading packages
14.2 Importing and wrangling data
Subsetting and reshaping our data
Extracting a substring to create a new variable
Joining data
Importing and wrangling additional data sets
Joining data (one more time)
Creating standardized variables
14.3 Exploring data
14.4 Correlations
Computing and plotting correlation coefficients
Running correlation tests
14.5 Analysis of variance models
Data wrangling and data visualization
One-way ANOVAs
14.6 Logistic regressions
Data wrangling
Model development
14.7 Paired data before and after
15 The Lindy effect
15.1 Loading packages
15.2 Importing and viewing data
15.3 Visualizing data
Creating and evaluating violin plots
Creating paired histograms
Printing our plots
15.4 Pareto charts
ggplot2 and ggQC packages
qcc package
16 Randomness versus causality
16.1 Loading packages
16.2 Importing and wrangling data
16.3 Rule of succession and the hot hand
16.4 Player-level analysis
Player 1 of 3: Giannis Antetokounmpo
Player 2 of 3: Julius Randle
Player 3 of 3: James Harden
16.5 League-wide analysis
17 Collective intelligence
17.1 Loading packages
17.2 Importing data
17.3 Wrangling data
17.4 Automated exploratory data analysis
Baseline EDA with tableone
Over/under EDA with DataExplorer
Point spread EDA with SmartEDA
17.5 Results
Over/under
Point spreads
18 Statistical dispersion methods
18.1 Loading a package
18.2 Importing data
18.3 Exploring and wrangling data
18.4 Measures of statistical dispersion and intra-season parity
Variance method
Standard deviation method
Range method
Mean absolute deviation method
Median absolute deviation method
18.5 Churn and inter-season parity
Data wrangling
Computing and visualizing churn
19 Data standardization
19.1 Loading a package
19.2 Importing and viewing data
19.3 Wrangling data
Treating duplicate records
Final trimmings
19.4 Standardizing data
Z-score method
Standard deviation method
Centering method
Range method
20 Finishing up
20.1 Cluster analysis
20.2 Significance testing
20.3 Effect size testing
20.4 Modeling
20.5 Operations research
20.6 Probability
20.7 Statistical dispersion
20.8 Standardization
20.9 Summary statistics and visualization
appendix More ggplot2 visualizations
index
![Statistics Slam Dunk: Statistical analysis with R on real NBA data [1 ed.]
1633438686, 9781633438682](https://ebin.pub/img/200x200/statistics-slam-dunk-statistical-analysis-with-r-on-real-nba-data-1nbsped-1633438686-9781633438682.jpg)
![Statistics With R: Solving Problems Using Real-World Data [1 ed.]
1506388159, 9781506388151](https://ebin.pub/img/200x200/statistics-with-r-solving-problems-using-real-world-data-1nbsped-1506388159-9781506388151.jpg)
