Statistics Slam Dunk: Statistical analysis with R on real NBA data

Statistics Slam Dunk....1 brief contents....8 contents....10 foreword....17 preface....19 acknowledgments....21 about this book....23 Who should read this book....23 How this book is organized: A road map....24 About the code....27 liveBook discussion forum....27 about the author....28 about the cover illustration....29 Chapter 1: Getting started....31 1.1 Brief introductions to R and RStudio....32 1.2 Why R?....34 1.2.1 Visualizing data....34 1.2.2 Installing and using packages to extend R’s functional footprint....37 1.2.3 Networking with other users....38 1.2.4 Interacting with big data....39 1.2.5 Landing a job....39 1.3 How this book works....39 Chapter 2: Exploring data....44 2.1 Loading packages....45 2.2 Importing data....46 2.3 Wrangling data....47 2.3.1 Removing variables....48 2.3.2 Removing observations....48 2.3.3 Viewing data....49 2.3.4 Converting variable types....50 2.3.5 Creating derived variables....51 2.4 Variable breakdown....53 2.5 Exploratory data analysis....55 2.5.1 Computing basic statistics....55 2.5.2 Returning data....58 2.5.3 Computing and visualizing frequency distributions....59 2.5.4 Computing and visualizing correlations....72 2.5.5 Computing and visualizing means and medians....75 2.6 Writing data....81 Chapter 3: Segmentation analysis....83 3.1 More on tanking and the draft....84 3.2 Loading packages....85 3.3 Importing and viewing data....86 3.4 Creating another derived variable....87 3.5 Visualizing means and medians....88 3.5.1 Regular season games played....88 3.5.2 Minutes played per game....92 3.5.3 Career win shares....94 3.5.4 Win shares every 48 minutes....96 3.6 Preliminary conclusions....98 3.7 Sankey diagram....99 3.8 Expected value analysis....104 3.9 Hierarchical clustering....110 Chapter 4: Constrained optimization....116 4.1 What is constrained optimization?....117 4.2 Loading packages....118 4.3 Importing data....119 4.4 Knowing the data....119 4.5 Visualizing the data....122 4.5.1 Density plots....122 4.5.2 Boxplots....125 4.5.3 Correlation plot....127 4.5.4 Bar chart....129 4.6 Constrained optimization setup....132 4.7 Constrained optimization construction....134 4.8 Results....139 Chapter 5: Regression models....142 5.1 Loading packages....144 5.2 Importing data....144 5.3 Knowing the data....145 5.4 Identifying outliers....148 5.4.1 Prototype....148 5.4.2 Identifying other outliers....152 5.5 Checking for normality....157 5.5.1 Prototype....158 5.5.2 Checking other distributions for normality....159 5.6 Visualizing and testing correlations....163 5.6.1 Prototype....164 5.6.2 Visualizing and testing other correlations....165 5.7 Multiple linear regression....167 5.7.1 Subsetting data into train and test....167 5.7.2 Fitting the model....168 5.7.3 Returning and interpreting the results....168 5.7.4 Checking for multicollinearity....171 5.7.5 Running and interpreting model diagnostics....172 5.7.6 Comparing models....173 5.7.7 Predicting....176 5.8 Regression tree....180 Chapter 6: More wrangling and visualizing data....184 6.1 Loading packages....185 6.2 Importing data....185 6.3 Wrangling data....186 6.3.1 Subsetting data sets....186 6.3.2 Joining data sets....187 6.4 Analysis....191 6.4.1 First quarter....191 6.4.2 Second quarter....195 6.4.3 Third quarter....198 6.4.4 Fourth quarter....199 6.4.5 Comparing best and worst teams....201 6.4.6 Second-half results....211 Chapter 7: T-testing and effect size testing....217 7.1 Loading packages....218 7.2 Importing data....219 7.3 Wrangling data....219 7.4 Analysis on 2018–19 data....221 7.4.1 2018–19 regular season analysis....221 7.4.2 2019 postseason analysis....227 7.4.3 Effect size testing....231 7.5 Analysis on 2019–20 data....233 7.5.1 2019–20 regular season analysis (pre-COVID)....233 7.5.2 2019–20 regular season analysis (post-COVID)....238 7.5.3 More effect size testing....241 Chapter 8: Optimal stopping....244 8.1 Loading packages....245 8.2 Importing images....245 8.3 Importing and viewing data....246 8.4 Exploring and wrangling data....247 8.5 Analysis....252 8.5.1 Milwaukee Bucks....252 8.5.2 Atlanta Hawks....259 8.5.3 Charlotte Hornets....263 8.5.4 NBA....267 Chapter 9: Chi-square testing and more effect size testing....271 9.1 Loading packages....272 9.2 Importing data....273 9.3 Wrangling data....274 9.4 Computing permutations....277 9.5 Visualizing results....279 9.5.1 Creating a data source....280 9.5.2 Visualizing the results....281 9.5.3 Conclusions....283 9.6 Statistical test of significance....285 9.6.1 Creating a contingency table and a balloon plot....286 9.6.2 Running a chi-square test....288 9.6.3 Creating a mosaic plot....288 9.7 Effect size testing....289 Chapter 10: Doing more with ggplot2....291 10.1 Loading packages....292 10.2 Importing and viewing data....292 10.3 Salaries and salary cap analysis....294 10.4 Analysis....300 10.4.1 Plotting and computing correlations between team payrolls and regular season wins....301 10.4.2 Payrolls versus end-of-season results....312 10.4.3 Payroll comparisons....315 Chapter 11: K-means clustering....323 11.1 Loading packages....324 11.2 Importing data....324 11.3 A primer on standard deviations and z-scores....325 11.4 Analysis....327 11.4.1 Wrangling data....328 11.4.2 Evaluating payrolls and wins....332 11.5 K-means clustering....337 11.5.1 More data wrangling....339 11.5.2 K-means clustering....341 Chapter 12: Computing and plotting inequality....352 12.1 Gini coefficients and Lorenz curves....353 12.2 Loading packages....354 12.3 Importing and viewing data....355 12.4 Wrangling data....357 12.5 Gini coefficients....363 12.6 Lorenz curves....367 12.7 Salary inequality and championships....371 12.7.1 Wrangling data....372 12.7.2 T-test....376 12.7.3 Effect size testing....379 12.8 Salary inequality and wins and losses....380 12.8.1 T-test....380 12.8.2 Effect size testing....382 12.9 Gini coefficient bands versus winning percentage....383 Chapter 13: More with Gini coefficients and Lorenz curves....388 13.1 Loading packages....389 13.2 Importing and viewing data....390 13.3 Wrangling data....390 13.4 Gini coefficients....396 13.5 Lorenz curves....399 13.6 For loops....403 13.6.1 Simple demonstration....404 13.6.2 Applying what we’ve learned....404 13.7 User-defined functions....408 13.8 Win share inequality and championships....411 13.8.1 Wrangling data....412 13.8.2 T-test....417 13.8.3 Effect size testing....420 13.9 Win share inequality and wins and losses....423 13.9.1 T-test....423 13.9.2 Effect size testing....426 13.10 Gini coefficient bands versus winning percentage....426 Chapter 14: Intermediate and advanced modeling....431 14.1 Loading packages....432 14.2 Importing and wrangling data....432 14.2.1 Subsetting and reshaping our data....433 14.2.2 Extracting a substring to create a new variable....435 14.2.3 Joining data....436 14.2.4 Importing and wrangling additional data sets....436 14.2.5 Joining data (one more time)....439 14.2.6 Creating standardized variables....440 14.3 Exploring data....441 14.4 Correlations....445 14.4.1 Computing and plotting correlation coefficients....445 14.4.2 Running correlation tests....449 14.5 Analysis of variance models....450 14.5.1 Data wrangling and data visualization....451 14.5.2 One-way ANOVAs....454 14.6 Logistic regressions....458 14.6.1 Data wrangling....459 14.6.2 Model development....460 14.7 Paired data before and after....471 Chapter 15: The Lindy effect....477 15.1 Loading packages....479 15.2 Importing and viewing data....479 15.3 Visualizing data....482 15.3.1 Creating and evaluating violin plots....483 15.3.2 Creating paired histograms....484 15.3.3 Printing our plots....485 15.4 Pareto charts....487 15.4.1 ggplot2 and ggQC packages....488 15.4.2 qcc package....490 Chapter 16: Randomness versus causality....494 16.1 Loading packages....495 16.2 Importing and wrangling data....496 16.3 Rule of succession and the hot hand....498 16.4 Player-level analysis....503 16.4.1 Player 1 of 3: Giannis Antetokounmpo....503 16.4.2 Player 2 of 3: Julius Randle....508 16.4.3 Player 3 of 3: James Harden....512 16.5 League-wide analysis....515 Chapter 17: Collective intelligence....520 17.1 Loading packages....521 17.2 Importing data....522 17.3 Wrangling data....522 17.4 Automated exploratory data analysis....526 17.4.1 Baseline EDA with tableone....526 17.4.2 Over/under EDA with DataExplorer....529 17.4.3 Point spread EDA with SmartEDA....543 17.5 Results....552 17.5.1 Over/under....553 17.5.2 Point spreads....564 Chapter 18: Statistical dispersion methods....570 18.1 Loading a package....571 18.2 Importing data....571 18.3 Exploring and wrangling data....572 18.4 Measures of statistical dispersion and intra-season parity....576 18.4.1 Variance method....576 18.4.2 Standard deviation method....579 18.4.3 Range method....581 18.4.4 Mean absolute deviation method....583 18.4.5 Median absolute deviation method....586 18.5 Churn and inter-season parity....588 18.5.1 Data wrangling....589 18.5.2 Computing and visualizing churn....590 Chapter 19: Data standardization....595 19.1 Loading a package....596 19.2 Importing and viewing data....597 19.3 Wrangling data....598 19.3.1 Treating duplicate records....598 19.3.2 Final trimmings....602 19.4 Standardizing data....603 19.4.1 Z-score method....605 19.4.2 Standard deviation method....608 19.4.3 Centering method....610 19.4.4 Range method....612 Chapter 20: Finishing up....616 20.1 Cluster analysis....617 20.2 Significance testing....619 20.3 Effect size testing....622 20.4 Modeling....624 20.5 Operations research....627 20.6 Probability....630 20.7 Statistical dispersion....632 20.8 Standardization....633 20.9 Summary statistics and visualization....635 Appendix: More ggplot2 visualizations....639 index....661 Numerics....661 A....661 B....661 C....661 D....662 E....664 F....664 G....664 H....665 I....665 J....665 K....665 L....666 M....666 N....667 O....667 P....667 Q....668 R....668 S....668 T....669 U....670 V....670 W....670 X....670 Y....670 Z....670