expected_points_build: Builds an Expected Points Model
In mattmills49/MELLS: Build CFB points scored prediction model

Description Usage Arguments Value Examples

Takes in play by play data and drive info to estimate the value of each spot on the field. The model is built using a B-spline regression model predicting the number of points scored on the drives based on the current field position.

1	expected_points_build(plays, drives)

`plays`	the "play.csv" data frame for the years to use to build the model
`drives`	the "drive.csv" data frame

a named vector where the names are the yardlines on the field and the values are the estimated point values associated with that spot.

plays <- readin("play", 2014)
drives <- readin("drive", 2014)
epa_values <- expected_points_build(plays, drives)
## Not run: ggplot2::qplot(x = as.numeric(names(epa_values)), y = epa_values, xlab = "Distance From End Zone", ylab = "Expected Points", geom = "line", main = "Expected Points Model Results")