add_preseason: Initialize Pre Season Projections into modeling format
In mattmills49/MELLS: Build CFB points scored prediction model

Description Usage Arguments Value Examples

Takes the preseason projections for teams based on the generate_preseason function and puts them in the proper format to use to build the team model estimates. It does this by "building" a game's worth of plays for each team from their preseason forecasts. See the vignette for more info.

1 2	add_preseason(run_plays, pass_plays, drives, preseason_vals, seed = round(runif(1) * 1e+07))

`run_plays`	the cleaned run plays after the `add_epa` function
`pass_plays`	the cleaned pass plays after the `add_epa` function
`drives`	the fixed drives after the `fix_drives` function
`preseason_vals`	the output from one of the `generate_preseason_` functions
`seed`	the random seed to use. Default is a random seed

A list containing the initial run, pass, and drive values.

years <- 2013:2014
plays <- readin("play", years)
teams <- readin("team", years)
runs <- readin("rush", years)
pass <- readin("pass", years)
games <- readin("game", years)
conf <- readin("conference", years)

epa_model <- expected_points_build(plays[plays$Year != 2014, ], drives[drives$Year != 2014, ])
fixed_games <- fix_games(games)
drives <- fix_drives(fixed_games, drives)
model_plays <- combine_run_pass(runs, pass, fixed_games) %>% remove_garbage %>% fix_fcs(teams, conf) %>% add_epa(epa_model)
model_values <- generate_preseason_mlm(run_plays = model_plays[["run_info"]], pass_plays = model_plays[["pass_info"]]) %>% add_preseason(run_plays = model_plays[["run_info"]], pass_plays = model_plays[["pass_info"]], drives = drives, preseason_vals = .)