Update 2 – World Cup Qatar 2022 Predictions with footBayes/Stan

Time to update our World Cup 2022 model!

The DIBP (diagonal-inflated bivariate Poisson) model performed very well in the first match-day of the group stage in terms of predictive accuracy – consider that the ‘peudo R-squared’, namely the geometric mean of the probabilities assigned from the model to the ‘true’ final match results, is about 0.4, whereas, on average, the main bookmakers got 0.36.

It’s now time to re-fit the model after the first 16 group stage games with the footBayes R package and obtain the probabilistic predictions for the second match-day. Here there are the posterior predictive match probabilities for the held-out matches of the Qatar 2022 group stage played from November 25th to November 28th, along with some ppd ‘chessboard plots’ for the exact outcomes in gray-scale color – ‘mlo’ in the table denotes the ‘most likely result’ , whereas darker regions in the plots correspond to more likely results.

Plot/table updates: (see Andrew’ suggestions from the previous post, we’re still developing these plots to improve their appearance, see below some more notes). In the plots below, the first team listed in each sub-title is the ‘favorite’ (x-axis), whereas the second team is the ‘underdog’ (y-axis). The 2-way grid displays the 16 held-out matches in such a way that closer matches appear at the top-left of the grid, whereas more unbalanced matches (‘blowouts’) appear at the bottom-right.  The matches are then ordered from top-left to bottom-right in terms of increasing winning probability for the favorite teams. The table reports instead the matches according to a chronological order.

The most unbalanced game seems Brazil-Switzerland, where the Brazil is the favorite team with an associated winning probability about 71%. The closest game seems Iran-Wales – Iran just won with two goals of margin scored in the last ten minutes! – whereas France is given only 44% probability of winning against Denmark. Argentina seems to be ahead against Mexico, whereas Spain seems to have a non-negligible advantage in the match against Germany.

Another predictive note: Regarding ‘most-likely-outcomes’ (mlo here above), the model ‘guessed’ 4 ‘mlo’ out of 16 in the previous match-day.

You find the complete results, R code and analysis here.

Some more technical notes/suggestions about the table and the plots above:

  • We replaced ‘home’ and ‘away’ by ‘favorite’ and ‘underdog’.
  • I find difficult to handle ‘xlab’ and ‘ylab’ in faceted plots with ggplot2! (A better solution could be in fact to directly put the team names on each of the axes of the sub-plots).
  • The occurrence ‘4’ actually stands for ‘4+’, meaning that it captures the probability of scoring ‘4 or more goals’ (I did not like the thick ‘4+’ in the plot, for this reason we just set ‘4’, however we could improve this).
  • We could consider adding some global ‘x’ and ‘y’-axes with probability margins between underdog and  favorite. Thus, for Brazil-Switzerland, we should have a thick on the x-axis at approximately 62%, whereas for Iran-Wales at 5%.

For other technical notes and model limitations check the previous post.

Next steps: we are going to update the predictions for the third match-day and even compute some World Cup winning probabilities through a ahead-simulation of the whole tournament.

Stay tuned!

7 thoughts on “Update 2 – World Cup Qatar 2022 Predictions with footBayes/Stan

  1. Leo:

    I’m surprised that, under your model, Japan is given less than 70% chance of beating Costa Rica, that England is given less than 70% chance of beating the United States, and that the Netherlands is given less than a 70% chance of beating Ecuador. I’m not saying you’re wrong, and I haven’t checked the betting odds–I’m guessing that your model is just fine and my intuition about Pr(upset) is wrong. Either way, this is interesting to me. Maybe my intuition is missing the high probability of a tie.

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