Testing the year to year stability of snaps for the WR position

gistfile1.txt

###########################################
### Testing the year to year stability of
### snaps for the WR position
### Josh Hermsmeyer 10-5-18
###########################################

### SQL to generate the dataset from Armchair Analysis 
# SELECT a.player, a.snp, a.trg, b.fname, b.lname, b.pos1, c.seas
# FROM offense a
# INNER JOIN player b ON a.player = b.player
# INNER JOIN game c ON a.gid = c.gid
# WHERE pos1 = 'WR' AND a.snp > 0

# data can be found here: https://t.co/hF6Sc4RgHv
library(tidyverse)

### Read in the csv, create a season2 variable to join on
### and group and summarize by season

snaps <- read_csv('data/snaps_wr.csv') %>%
   mutate(season2 = seas + 1) %>%
   group_by(player, seas, season2) %>%
   summarize(snp = mean(snp),
             trg = mean(trg))

### Join the data on itself for a year over year analysis
snaps_joined <- snaps %>%
   left_join(snaps, by = c("season2" = "seas", "player")) %>%
   na.omit() %>%
   filter(snp.x >= 30)

### Create a simple model using snaps and targets per game predictors in year 1
### and targets per game in year y+1 as the response variable
model_snaps_trg <- lm(data = snaps_joined, trg.y ~ trg.x + snp.x)
summary(model_snaps_trg)

### Same as above just this time with targets per game alone
model_trg <- lm(data = snaps_joined, trg.y ~ trg.x)
summary(model_trg)