I have been monitoring scoring first in association football.
In the 2017-2018 season I used the Worldfootball.net website to record goal scoring timings in six European leagues: EPL; Ligue 1; Bundesliga; Serie A; Eredivisie; and Primera.
I looked at the following events in games played in these leagues:
- score first win (SFW)
- score first draw (SFD)
- score first lose (SFL)
- 0 v 0 game (0goals)
I wondered if these measures could inform a naive Bayes approach to probabilistic behaviours in association football. I am thinking that for the 2018-2019 season I will use these prior probabilities to monitor teams’ progress.
For the six leagues combined, I have these median probabilities:
- SFW 0.64
- SFD 0.19
- SFL 0.12
- 0goals 0.07
I have a measure for scoring first and not losing (SFNL) (a sum SFW + SFD) and my median for the six leagues is 0.82.
My median probability profiles for each of the six leagues is:
For the champions of each league, my probability profiles are:
For the bottom team in each of these leagues, my probability profiles are:
I am hopeful that these data might be of interest to anyone undertaking a Bayes approach to goal scoring.
In my own work, I am keen to see how early we can confirm the likely trajectory of any team in each of these six leagues.