success-rate.R

library(ggplot2)

APR <- read.csv("./data/2016DI_Datafile5SquadFiles_20161212.csv")
APR.Football <- subset(
  APR, SPORT_CODE == 4
  )[c("SCL_UNITID", "SCL_NAME", "D1_FB_CONF_16", "APR_RATE_2016_1000",
      "APR_RATE_2015_1000", "APR_RATE_2014_1000", "APR_RATE_2013_1000",
      "APR_RATE_2012_1000", "APR_RATE_2011_1000", "APR_RATE_2010_1000",
      "APR_RATE_2009_1000", "APR_RATE_2008_1000", "APR_RATE_2007_1000")]

APR.Football$APR_AVG <- rowMeans(
  APR.Football[
    c("APR_RATE_2016_1000", "APR_RATE_2015_1000", "APR_RATE_2014_1000",
      "APR_RATE_2013_1000", "APR_RATE_2012_1000", "APR_RATE_2011_1000",
      "APR_RATE_2010_1000", "APR_RATE_2009_1000", "APR_RATE_2008_1000",
      "APR_RATE_2007_1000")
    ]
)

WL <- read.csv("./data/records.csv", header = TRUE)

all <- merge(
  APR.Football, WL, by = "SCL_UNITID"
  )[c("Team.name", "D1_FB_CONF_16", "Win.Pct", "APR_AVG")]

ggplot(all, aes(APR_AVG, Win.Pct)) +
  geom_point(aes(colour = all$D1_FB_CONF_16)) +
  xlab("Academic Progress Rate") +
  ylab("Win Percent") +
  labs(colour = "Conferences") +
  geom_text(data = subset(all, APR_AVG > 980), aes(label = Team.name))

cor(all$APR_AVG, all$Win.Pct) ## 0.3592891