pinnacle.MLB: Market Odds Data from Pinnacle

#' MLB2016.
#'
#' Major League Baseball (MLB) data for the 2016 season.
#'
#' All wagering lines from Pinnacle for the 2016  MLB season
#'
#' @import tibble
#'
#' @format A tibble with 20 variables:
#' 
#' \describe{
#' \item{\code{GameID}}{same format as Retrosheets and BaseballReference data}
#' \item{\code{EventDateTimeUTC}}{Time of the game in UTC}
#' \item{\code{EventDateTimeET}}{Time of the game in Eastern Standardtime}
#' \item{\code{AwayTeam}}{Team name of the Away Team}
#' \item{\code{HomeTeam}}{Team name of the Home Team}
#' \item{\code{DoubleHeaderGame}}{Indicates if this was a double Header}
#' \item{\code{AwayStartingPitcher}}{Starting pitcher Away Team}
#' \item{\code{HomeStartingPitcher}}{Starting pitcher Home Team}
#' \item{\code{FinalScoreAway}}{Runs scored by Away Team}
#' \item{\code{FinalScoreHome}}{Runs scored by Home Team}
#' \item{\code{EnteredDateTimeUTC}}{Time of the wager line in UTC}
#' \item{\code{EnteredDateTimeET}}{Time of the wager line in Eastern Standardtime}
#' \item{\code{SpreadTeam1}}{Spread Handicap for Away Team}
#' \item{\code{SpreadUS1}}{Spread US odds for Away Team}
#' \item{\code{SpreadUS2}}{Spread US odds for Home Team}
#' \item{\code{MoneyUS1}}{Moneyline US odds for Away Team}
#' \item{\code{MoneyUS2}}{Moneyline US odds for Home Team}
#' \item{\code{TotalPoints}}{Total runs handicap}
#' \item{\code{TotalUSOver}}{Total runs US odds for Over}
#' \item{\code{TotalUSUnder}}{Total runs US odds for Under}
#' }
#' 
#' @examples 
#' if (require("tidyverse")) {
#' library(tidyverse)
#' # What was the range of expected total runs according to the prediction market at Pinnacle?
#'MLB2016 %>% 
#'  unnest() %>% 
#'  group_by(GameID) %>% 
#'  arrange(desc(EnteredDateTimeUTC)) %>% 
#'  slice(1) %>% 
#'  ungroup() %>% 
#'  group_by(TotalPoints) %>% 
#'  summarize(Count = n())
#' 
#' # How many games went Over/Under/Landed on the total?
#'MLB2016 %>% 
#'  unnest() %>% 
#'  group_by(GameID) %>% 
#'  arrange(desc(EnteredDateTimeUTC)) %>% 
#'  slice(1) %>% 
#'  ungroup() %>% 
#'  select(GameID,TotalPoints,FinalScoreAway,FinalScoreHome) %>% 
#'  mutate(TotalOutcome = case_when(
#'    FinalScoreAway + FinalScoreHome > TotalPoints ~ "Over",
#'    FinalScoreAway + FinalScoreHome < TotalPoints ~ "Under",
#'    FinalScoreAway + FinalScoreHome == TotalPoints ~ "Landed"
#'  )
#'  ) %>% 
#'  group_by(TotalPoints,TotalOutcome) %>% 
#'  summarize(Count = n()) %>% 
#'  print(n=100)
#' }
"MLB2016"


#' USA_Election_2016
#'
#' US Presidential Election data 2016.
#'
#' All lines from Pinnacle for the 2016 US Presidential Election
#'
#' @import tibble
#'
#' @format A data.frame with 5 variables:
#' \describe{
#' \item{\code{EnteredDateTime}}{Time of the wager line in UTC}
#' \item{\code{TeamName1}}{Team name of the Away Team}
#' \item{\code{TeamName2}}{Team name of the Home Team}
#' \item{\code{MoneyUS1}}{Moneyline US odds for Away Team}
#' \item{\code{MoneyUS2}}{Moneyline US odds for Home Team}
#' }
#' 
#' @examples 
#' if (require("odds.converter")) {
#' library(tidyverse)
#' # What is Hilary Clinton's the highest implied winning probability at Pinnacle?
#' 
#' USA_Election_2016[which.min(USA_Election_2016$MoneyUS1),"EnteredDateTime"]
#' odds.converter::odds.us2prob(min(USA_Election_2016$MoneyUS1))
#' }
#' 
#' # What time on election night that Trump's implied winning probability surpassed Clinton's?
#' if (require("tidyverse")) {
#' library(tidyverse)
#' USA_Election_2016 %>% 
#'  filter(MoneyUS1>MoneyUS2) %>%
#'  slice(1)
#' }
"USA_Election_2016"