oddsmaker is a set of functions that enables the collection, tracking, and analysis of the public betting consensus for individual NFL game spreads and over-unders.
From the website oddsshark.com: "consensus is ... the percentage of the general public betting on each side of a matchup or total. You can bet with or against the public. When you bet against the public, it is called 'fading' the public."
The inverse of the popular gambling adage "the house always wins," would probably be something like "the public always loses." While this isn't strictly true (or a good analogy), it prompts the question: can the public consensus picks be used to better inform our own? The purpose of the oddsmaker package is to provide trended spread, over-under, and the respective consensus data to help us make better informed picks each week of the NFL season.
The NFL "Pick Consensus" page on Oddsshark shows a single point-in-time "snapshot" of the share of bets are going for either side for any given game.
This is helpful, but variation over time could add important context, such as:
- Where did the line open?
- Has public consensus changed over time?
- What is the relationship between % share of bets and the line?
All of these are still observational but more data could help us make better decisions.
Each day (or multiple times a day), run both the odds_consensus and nfl_season_schedule scripts, providing the required arguments for each, respectively. When done, check to ensure that the dates and times in each table match and then join the data sets. Keep in-mind that the weekly "flex" game will create the need for a manual fix in your final data set if one site updates the date/time and the other does not.
picks_con<- oddsmaker::picks_consensus(season = 2022)
picks_con |> glimpse()
sched_2022 <- oddsmaker::nfl_season_schedule(year = 2022)
sched_2022 |> glimpse()
final<- sched_2022 |>
mutate(game_time= as_datetime(format(game_time, format="%Y-%m-%d %I:%M:%S"))) |>
inner_join(picks_con, by= c('team_abbrev' = 'team', 'home_away', 'game_time')) |>
select(-c(10,18), team_full= team, team= team_abbrev, game_num = game_num.x) |>
mutate(matchup= str_replace(matchup, pattern = 'VS', replacement = '@')) |>
select(date_pulled, everything())
final |> glimpse()
After you've run the code several times, you'll now have enough data to compile a data set:
# create a csv for the most recent join
time<- lubridate::now()
final |> print(n=Inf)
final |> write_csv(glue::glue('season_2022/oddsshark_{time}.csv'))
# create a list of all of the files you've run/created over time
files<- paste('season_2023/', list.files('season_2023'), sep = "")
files
# combine all files into a single master data frame object
master<-map(.x = files, .f= read_csv) |>
bind_rows(.id= 'id') |>
mutate(team= str_extract(team, '^([A-Z]{3}|[A-Z]{2})')) |>
arrange(game_num)
# write that data frame to a master csv
master |> write_csv('master_2022.csv')
glimpse(master)
With data compiled in a single dataframe, we can now build a timeseries of our data:
master |>
oddsmaker::gridiron_all(week_no = 20)
And we can do the same for the over-under:
master |>
oddsmaker::gridiron_ou(week_no = 20)
Or you can use the app, "app.R" script.