Packages Needed: tidyverse

library(tidyverse)
library(readxl)

1 Read Data In

WNBA <- read_xlsx("Data/WNBA.xlsx")
NBA <- read_xlsx("Data/NBA.xlsx")

2 Filter Data

WNBA400 <- WNBA %>% 
  filter(MP >= 400)

NBA1000 <- NBA %>% 
  filter(MP >= 1000)

3 NBA Regression

##  (Intercept)           FG          FGA           FT          FTA          ORB 
## -13.48442326  -0.14858126  -0.98827249   0.24640792  -0.44554616   0.46914799 
##          DRB          AST          STL          BLK          TOV           PF 
##   0.21856746   0.52798205   0.80738069   0.25279600  -1.60086566   0.04301587 
##          PTS 
##   1.00000000

4 WNBA Regression

## (Intercept)          FG         FGA          FT         FTA         ORB 
##  -6.5224804  -0.2960982  -0.8131592   0.1429953  -0.4431946   0.5238405 
##         DRB         AST         STL         BLK         TOV          PF 
##   0.2090862   0.5620121   0.8658215   0.3371603  -1.5332580  -0.1401101 
##         PTS 
##   1.0000000

5 Comparison

## (Intercept)          FG         FGA          FT         FTA         ORB 
##   0.4837048   1.9928367   0.8228087   0.5803193   0.9947221   1.1165783 
##         DRB         AST         STL         BLK         TOV          PF 
##   0.9566211   1.0644530   1.0723832   1.3337250   0.9577680   3.2571728 
##         PTS 
##   1.0000000

6 Transform Current Coefficients

## (Intercept)          FG         FGA          FT         FTA         ORB 
##        0.68       26.77       35.58       26.32       25.47       42.74 
##         DRB         AST         STL         BLK         TOV          PF 
##       11.45       29.70       47.30       38.00       47.90       18.80 
##         PTS 
##       41.10

7 Explanation

I took a regression of the stats included in PER against the wins score. I then took the coefficients and scaled them against the largest coefficient. I did this for the NBA and WNBA and then divided the WNBA by the NBA to create scalers for the WNBA against the known NBA PER. These then were scaled against the NBA PER coefficients. They are as follows:

## (Intercept)          FG         FGA          FT         FTA         ORB 
##   0.3289193  53.3482374  29.2755340  15.2740033  25.3355723  47.7225562 
##         DRB         AST         STL         BLK         TOV          PF 
##  10.9533116  31.6142546  50.7237264  50.6815507  45.8770895  61.2348486 
##         PTS 
##  41.1000000