In mattmills49/MELLS: Build CFB points scored prediction model

Multilevel Expected points added Linear Least Squares

Description

The goal of this package is to ease the testing of my college football prediction points prediction model. The package will be built off the CFB Stats data.

Use

I expect the structure of the package to be small functions that can be piped together:

library(MELLS)
library(magrittr)

years <- 2005:2014
plays <- readin("play", years)
teams <- readin("team", years)
runs <- readin("rush", years)
pass <- readin("pass", years)
drives <- readin("drive", years)
games <- readin("game", years)
game_info <- readin("team-game-statistics", 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 = .)
weekly_values <- weekly_values_mlm(model_runs = model_values[["run_data"]], model_pass = model_values[["pass_data"]], model_drives = model_values[["drives_data"]])

This package will also contain some model testing functions and validation results.

Assumptions

• The expected points model is based on the points scored at the end of each possession and includes all field goals, punts, runs, passes, and penalties.

mattmills49/MELLS documentation built on May 21, 2019, 1:25 p.m.