| # read in two functions | |
| source("generate_runs.R") | |
| source("retro_game_work.R") | |
| library(dplyr) | |
| # Phillies game plays on April 16, 2023 | |
| plays <- c("HR", "1B", "BB", "BB", "1B", "OUT", "1B", |
| 2021 through July 27th | |
| ====================== | |
| +-------------+-----------+----------+------------+------------+ | |
| | Certainty | NumBets | Stake | Winnings | ROI | | |
| |-------------+-----------+----------+------------+------------| | |
| | Pr > 0.0 | 1395 | 162761 | 1366 | 0.00839267 | | |
| | Pr > 0.05 | 439 | 48121 | 4786 | 0.0994576 | | |
| | Pr > 0.10 | 125 | 13991 | 1503 | 0.107426 | | |
| | Pr > 0.15 | 64 | 7294 | 226 | 0.0309844 | |
| ###### | |
| # NOTE: this includes calculaiton for my first Wins Above Replacement approach, as well as the current DRA- approach | |
| ###### | |
| games = mlbgame.day(2021,7,30) | |
| daily_game_id = 1 | |
| unique_row_id = 1. # just to make sure i have the same format as legacy spreadsheet | |
| location_index = 0 |
This page has been updated a lot in the past 3 years. Older revisions you might like more than this one:
| library(tidyverse) | |
| library(espnscrapeR) | |
| library(gt) | |
| all_win_rate <- scrape_espn_win_rate() | |
| wide_win_rate <- all_win_rate %>% | |
| pivot_wider(names_from = stat, values_from = win_pct, id_col = team) %>% | |
| set_names(nm=c("team", "prwr", "rswr", "pbwr", "rbwr")) %>% | |
| mutate(prwr_rk = min_rank(desc(prwr)), .before = prwr) %>% |
| # Main function to get Retrosheet game scores | |
| get_scores <- function(season){ | |
| require(dplyr) | |
| require(readr) | |
| load_gamelog <- function(season) { | |
| glheaders <- read_csv("https://raw.githubusercontent.com/beanumber/baseball_R/master/data/game_log_header.csv") | |
| remote <- paste0("http://www.retrosheet.org/gamelogs/gl", | |
| season, ".zip") | |
| local <- paste0("gl", season, ".zip") |
| library(tidyverse) | |
| library(tidyr) | |
| library(dplyr) | |
| library(na.tools) | |
| seasons <- seq(2014, 2019); | |
| # iteration count | |
| i = 0 | |
| df <- NULL; |
--> A beginner's guide to nflfastR <--
I get a lot of questions about how to get nflscrapR up and running. This guide is intended to help new users build interesting tables or charts from the ground up, taking the raw nflscrapR data.
Quick word if you're new to programming: all of this is happening in R. Obviously, you need to install R on your computer to do any of this. Make sure you save what you're doing in a script (in R, File --> New script) so you can save your work and run multiple lines of code at once. To run code from a script, highlight what you want, right click, and select Run line. As you go through your R journey, you might get stuck and have to google a bunch of things, but that's totally okay and normal. That's how I wrote this thing!
