A data set on the odds for relegation and winning the table for English Premier League clubs for the 2024-25 season. Data are useful for illustrating what exactly odds are.
Format
A data frame with 20 observations on the following 7 variables.
cluba character communicating the name of the club in the Premier League
bet365_ra numeric vector for the odds against relegation, by way of Bet365
betfair_ra numeric vector for the odds against relegation, by way of Betfair
unibet_ra numeric vector for the odds against relegation, by way of Unibet
bet365_wa numeric vector for the odds against winning the table, by way of Bet365
betfair_wa numeric vector for the odds against winning the table, by way of Betfair
unibet_wa numeric vector for the odds against winning the table, by way of Unibet
Details
Data come oddschecker.com as of Oct. 20, 2024, assuming these
are preseason odds. Raw data are available on the project's website for your
consideration. Don't bet on sports, unless you've been visited by Biff Tannen
from the future.
Fractions have been converted into decimals for ease of maintaining the data. Raw odds (in fraction form) for those clubs most likely to be relegated are available in the raw data files on the project's Github.
Odds are typically(?) communicated as "odds against" in the sports betting world. It's why the highest odds for relegation and lowest odds for winning coincide with the biggest, most successful clubs. Context clues help, and are useful for understanding what these odds are saying.
It's possible that the language "win outright" is doing some heavy-lifting in how Bet365 lets you place wagers on winning the table.
I'm also aware of the reason these odds do not sum to 1, and in fact exceed
If anything, I think "overrounding" is its own pedagogical tool for why odds can be wonky things to learn in relation to its use in the statistical modeling context.