Vegas Lines

OLD REPO -- SCRAPER NO LONGER WORKS. USE THE bettoR PACKAGE. Now the bettoR scraper function no longer works.

This function is used to scrape the historical Vegas Lines for NFL and NBA games

# Load libraries
library(lubridate)
library(tidyverse)
library(rvest)
library(magrittr)
library(knitr)

The magic

# this is the function
GetLines <- function(sport = "NBA", year, type = "both") {
  
  options(stringsAsFactors = FALSE)
  sport <- tolower(sport)
  
  # Get team urls and names
  url <- paste0("http://www.covers.com/pageLoader/pageLoader.aspx?page=/data/", sport, "/teams/teams.html")
  html <- suppressWarnings(readLines(url))
  links <- html[grep(paste0("/data/", sport, "/teams/team[0-9]"), html)]
  links <- unique(gsub(paste0('.*/data/', sport,'/teams/([^\"]*)\"[ ]*>([^<]*).*'), '\\1,\\2', links))
  links <- strsplit(links, ',')
  
  # Create URL stems for getting team records
  url.base <- paste(gsub("SPORT", sport, "http://www.covers.com/pageLoader/pageLoader.aspx?page=/data/SPORT/teams/pastresults/"), year - 1, '-', year, '/', sep = '')
  full.lines <- data.frame()
  
  # Put all team records in one data frame
  for (i in 1:length(links)) {
    
    url <- paste(url.base, links[[i]][1], sep = "")
    tables <- read_html(url) %>% html_table(header = TRUE, fill = TRUE) 
    
    # Get appropriate data: regular season, playoffs, or both
    if (type == 'regular season') {
      
      if (length(tables) == 0) {  # If the team didn't exist at the time, make an empty df
        temp <- data.frame(matrix(nrow = 1, ncol = 6, data = 0))
        temp <- temp[-1, ]
      } else {
        if (sport == 'nfl') {
          temp <- tables[[length(tables) - 1]]  # Regular season is always the second to last table
        } else if (sport == 'nba') {
          temp <- tables[[length(tables)]]      # Regular season is always the last table
        }
      }
      
    } else if (type == 'playoffs') {
      
      if (length(tables) == 1) {  # If the team didn't make the playoffs, make an empty df
        temp <- data.frame(matrix(nrow = 1, ncol = 6, data = 0))
        temp <- temp[-1, ]
      } else {
        temp <- tables[[1]]
      }
      
    } else {
      
      if (length(tables) == 0) {  # If the team didn't exist at the time, make an empty df
        temp <- data.frame(matrix(nrow = 1, ncol = 6, data = 0))
        temp <- temp[-1, ]
      } else {
        if (sport == 'nfl') {
          if (length(tables) == 2) {
            temp <- tables[[1]]
          } else {
            temp <- rbind(tables[[1]], tables[[2]])
          }
        } else if (sport == 'nba') {
          if (length(tables) == 1) {
            temp <- tables[[1]]
          } else {
            temp <- rbind(tables[[1]], tables[[2]])
          }
        }
      }
      
    }
    
    colnames(temp) <- c("date", "away.team", "score", "type", "home.line", "over.under")
    
    if (nrow(temp) > 0) {   # Add home team if the df has any info (we didn't ask for playoffs from a team that didn't make it)
      temp$home.team <- links[[i]][2]
    }
    
    try(full.lines <- rbind(full.lines, temp), silent = FALSE)
  }
  
  # Extra formatting for NFL df
  if (sport == 'nfl') {
    full.lines <- full.lines %>% filter(score != "BYE") 
    full.lines$date <- gsub("[A-Za-z ]*", "", full.lines$date)
  }
  
  # Format date
  full.lines$date <- mdy(full.lines$date)
  
  # Remove away games (the odds exist for home team already)
  full.lines <- full.lines[-grep('@', full.lines$away.team), ]
  
  # Create home score and away score columns
  full.lines$home.score <- as.numeric(gsub('[^0-9]*([0-9]+)-([0-9]+)[^0-9]*', '\\1', full.lines$score))
  full.lines$away.score <- as.numeric(gsub('[^0-9]*([0-9]+)-([0-9]+)[^0-9]*', '\\2', full.lines$score))
  
  # Fix line and over/under
  full.lines$home.line <- as.numeric(gsub('[^0-9\\.-]', '', full.lines$home.line))
  full.lines$over.under <- as.numeric(gsub('[^0-9\\.-]', '', full.lines$over.under))
  
  # Reorder columns, sort by date, and return
  full.lines <- full.lines[, c('date', 'type', 'home.team', 'away.team', 'home.score', 'away.score', 'home.line', 'over.under')]
  full.lines <- full.lines[order(full.lines$date), ]
  return(full.lines)
}

This is how to use the function

Parameters:

• sport: As of right now, only NFL and NBA have been fully tested... I will be adding NHL, NCAAF, NCAAB, and MLB eventually.
  • "NBA"
  • "NFL"
• year: season. (2012 would return the data from the 2011-2012 season)
• type:
  • "regular season"
  • "playoffs"
  • "both"

# example
NFL.2017 <- GetLines(sport = "NFL", year = 2017, type = "both")

Jackpot!!

kable(NFL.2017)

bonus function

This function will combine multiple seasons of data together.

Please be kind when scraping websites

GetLinesRange <- function(sport = "NBA", year.start, year.end, type = "both") {
  lines <- data.frame()
  
  for (year in year.start:year.end) {
    temp <- GetLines(sport, year, type)
    temp$season <- year
    lines <- rbind(lines, temp)
  }
  
  return(lines)
}