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# This is a script to guess _how_ the NET is computed
# Credit for almost all of this work goes to bchare: https://github.com/bchare/ncaabball
# This is an attempt to transform his Python code into R

# -----------------------------------------------------------
# Gamelogs are loaded from the following repo:
# This fetches data from cbbdata/torvik and saves a daily csv
# Job runs around 7 am ET each day to fetch data
gamelogs <- readr::read_csv(
  "https://raw.githubusercontent.com/gallochris/evdev-byc/refs/heads/main/data/cbb_daily_gamelog.csv"

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# First, use hoopR to grab the kenpom data for continuity and experience
meta_metrics <- hoopR::kp_height(min_year = 2024, max_year = 2024)

# now load the seed data and filter out teams that already lost
seeds <-
  readr::read_csv(
    "https://gist.githubusercontent.com/gallochris/86fc2a21dd4b30e3dd9ea79516594f05/raw/1e2dfa7eae2d196f5dee9ecf5fe95e5f76ba827d/ncaat_seeds.csv"
  ) |>
  dplyr::select(seed = Seed,
                team = Team,

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# use bigballR to fetch schedule 
schedule <-
  bigballR::get_team_schedule(season = "2023-24", team.name = "North Carolina")

# grab miami home game pbp
unc_miami_pbp <- bigballR::get_play_by_play(schedule$Game_ID[28])

# add index to determine first half and second half 
um_index <- which(unc_miami_pbp$Half_Status == 2)[1]

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# Attempt to recreate or riff on the plot from the Tufte book
# https://opticalacumen.blogspot.com/2014/07/data-visualization-in-1919.html

# Fetch the player minutes data by game over past three seasons
hubert <-
  cbbdata::cbd_torvik_game_factors(team = "North Carolina") |>
  dplyr::filter(year > 2021) |>
  dplyr::arrange(date) |>
  dplyr::mutate(off_ppp = format(round(off_ppp / 100, 2), nsmall = 2),
                def_ppp = format(round(def_ppp / 100, 2), nsmall = 2)) |>

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gallochris

library(rvest)
library(dplyr)
library(janitor)
library(stringr)

# load data
url <-
  'https://www.sports-reference.com/cfb/play-index/sgl_finder.cgi?request=1&match=game&year_min=2019&year_max=2021&school_id=north-carolina&c1stat=points&c1comp=gt&c1val=1&c2stat=opp_points&c2comp=gt&c2val=1&c3comp=gt&c4comp=gt&order_by=date_game#results'

mack_raw <- url %>%

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library(tidyverse)
library(gt) 

# make the data - https://www.wynnbet.com/theplaybook/posts/wynnbet-releases-odds-for-upcoming-men-s-college-basketball-season-01ffk34jmvek
odds <- tibble::tribble(
~team, ~win, ~nt, ~ff,
"Gonzaga",  33.5, 11.1, 33.3,
"UCLA",  31, 7.7,  25.0,
"Michigan",  27, 7.7,  25.0,
"Memphis", 32, 6.3,  20.0,

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library(tidyverse)
library(gt) 

# make the data
injury <- tibble::tribble(
~Opp,~Anthony,~Keeling,~Robinson,~Black,~Brooks,~Bacot,~Pierce,~Platek,~Francis,~Harris,~Manley,
"Notre Dame",37,21,0,33,39,23,20,26,0,0,0,
"at UNC-Wilmington",34,23,0,32,33,3,32,27,0,0,0,
"Gardner-Webb",33,30,0,34,30,23,21,23,0,0,0,
"Elon",36,25,0,27,33,26,23,18,0,0,0,

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Season	Result	UNC Score	Opp Score	Ref 1	Ref 2	Ref 3	Spread	Location
2019	L	73	74	Roger Ayers	Jamie Luckie	Clarence Armstrong	3.5	neutral
2019	W	79	70	Roger Ayers	Ted Valentine	Bill Covington Jr.	-4.5	home
2019	W	88	72	Mike Eades	Brian Dorsey	Ron Groover	9.5	away
2018	W	74	69	Mike Eades	Ted Valentine	Bert Smith	4	neutral
2018	L	64	74	Roger Ayers	Mike Eades	Raymond Styons	7.5	away
2018	W	82	78	Jeff Clark	Jamie Luckie	Ron Groover	-1.5	home
2017	L	83	93	Bria

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