Euros prediction

I wasn’t going to do much about this (and probably, I shouldn’t have done anything and used my time more wisely…), but a couple of friends/colleagues have actually asked me if I had done it and Italy did so well in their first outing, that I was up last night to whip something up… 😉 (in case it’s not clear yet, yes: this is a post on using Bayesian modelling to predict the outcome of football games, specifically for the ongoing Euro championships).

So: it’s actually very fortunate that lots of the relevant data are fairly easy to get. Kaggle have a dataset including all international games from 1972 to 2021 (before the Euros began last week). They also have a dataset with the FIFA ranking for each of the national teams. And the games schedule is also available from a simple Google search.

These three datasets can be combined to create the actual data to analyse and use for prediction of the Euro championship games. In particular, I have constructed a “long” format data where each game is replicated twice and the two rows are from the “point of view” of each opponent — like below. I have filtered only the data from 2010 onward.

## # A tibble: 5,384 × 17
##       ID date       Team             Opponent          Goal tournament  Home  form diff_point diff_rank days_since_last city     country  rank total_points previous_points rank_date 
##    <int> <date>     <chr>            <chr>            <int> <chr>      <dbl> <dbl>      <int>     <int>           <dbl> <chr>    <chr>   <int>        <int>           <int> <date>    
##  1     1 2010-03-03 Albania          Northern Ireland     1 Friendly       1  5.39       -413       -57             109 Tirana   Albania    96          335             336 2010-03-03
##  2     1 2010-03-03 Northern Ireland Albania              0 Friendly       0  2.09        413        57             109 Tirana   Albania    39          748             729 2010-03-03
##  3     2 2010-03-03 Armenia          Belarus              1 Friendly       0  9.22        -80       -24             140 Antalya  Turkey    103          320             321 2010-03-03
##  4     2 2010-03-03 Belarus          Armenia              3 Friendly       0  4.29         80        24             105 Antalya  Turkey     79          400             397 2010-03-03
##  5     3 2010-03-03 Austria          Denmark              2 Friendly       1  6.37       -231       -23             105 Vienna   Austria    56          567             523 2010-03-03
##  6     3 2010-03-03 Denmark          Austria              1 Friendly       0  6.84        231        23             140 Vienna   Austria    33          798             827 2010-03-03
##  7     4 2010-03-03 Belgium          Croatia              0 Friendly       1 15.3        -559       -57             109 Brussels Belgium    68          491             491 2010-03-03
##  8     4 2010-03-03 Croatia          Belgium              1 Friendly       0  6.88        559        57             109 Brussels Belgium    11         1050            1053 2010-03-03
##  9     5 2010-03-03 Cyprus           Iceland              0 Friendly       1 10.9         140        25             140 Larnaca  Cyprus     66          495             471 2010-03-03
## 10     5 2010-03-03 Iceland          Cyprus               0 Friendly       0 10.1        -140       -25             109 Larnaca  Cyprus     91          355             349 2010-03-03
## # … with 5,374 more rows

In this table, ID is equal to 1 in the first two rows, to indicate that the first game is played between Albania and Northern Ireland. In the game, Albania scored 1, while Northern Ireland scored 0. The game was a Friendly match and because it was played in Tirana, Albania, the variable Home is set to 1 for Albania and 0 for their opponents. Also, using the FIFA ranking data, I’ve calculated the actual rank and total points at the time of the game and then reconstructed the difference in points and ranks between the two teams (respectively in the variables diff_point and diff_rank).

I’ve also computed the number of days since the last game was played by either teams involved (stored in the variable days_since_last). I’m using this in combination with another derived variable, form, which I have computed to give an indication of how good a run a team are having. In particular, form is defined as the weighted sum of the points accrued in the last 3 games, where the weights are computed as a function of the difference in “strength” (according to the total FIFA points each team had, going into the game). So, for each game I compute the proportion of points over the total for the two teams and use its inverse to weigh the number of points actually won in the game (3 for a win, 1 for a draw and 0 for a loss); this is meant to imply that if you beat a team that is way worse than you, that should count for much less than when you’re a crappy team and beat the World Champions…

With data formatted in this way, I’m ready to run a model that is based on a relatively standard format (and the specific Bayesian version we implemented here). The model assumes that the number of goals scored in each game is \(y_{g}\)in game \(g=1,\ldots,G\) (because of the long format in which I’ve constructed the dataset, this is the same as assuming two variables, one for each team involved in the game). I model the observed data using a Poisson distributions, \(y_{g}\sim\mbox{Poisson}(\theta_{g})\) with a structure on the distribution of the “scoring rates” \(\theta_{g}\): \[\log(\theta_{g})=\beta_0+\beta_1\mbox{Home}_{g}+\beta_2\mbox{Form}_{g}+\beta_3\mbox{Diff_point}_{g}+\beta_4\mbox{Diff_rank}_{g}+\beta_5\mbox{Days_since_last}_{g}+\beta_6\mbox{Tournament}_{g}+\text{att}_{g\text{Team}[g]}+\text{def}_{g\text{Opponent}[g]}.\]

There’s a bunch of “fixed effects” (in fact, I am slightly abusing the notation here: Tournament is a categorical variable and so there are in fact a set of indicators to describe the incremental effect with respect to the baseline type of tournament) and two “random effects” (\(\text{att}_{g\text{Team}[g]}\) and \(\text{def}_{g\text{Opponent}[g]}\)) that represent, respectively, the attacking strength of the first team and the defending strength of their opponent. These are modelled assuming an exchangeable structure, which implies a level of correlation across the output for the two teams involved in the same game.

The rows we want to predict are stored at the end of the dataset — in this case the number of goals scored is set to NA.

## # A tibble: 72 × 17
##       ID date       Team        Opponent     Goal tournament  Home  form diff_point diff_rank days_since_last city             country     rank total_points previous_points rank_date 
##    <int> <date>     <chr>       <chr>       <int> <chr>      <dbl> <dbl>      <int>     <int>           <dbl> <chr>            <chr>      <int>        <int>           <int> <date>    
##  1  2657 2021-06-11 Italy       Turkey         NA Official       1 15.2         137        22               7 Rome             Italy          7         1642            1642 2021-05-27
##  2  2657 2021-06-11 Turkey      Italy          NA Official       0 11.9        -137       -22               8 Rome             Italy         29         1505            1505 2021-05-27
##  3  2658 2021-06-12 Switzerland Wales          NA Official       0 15.5          36         4               9 Baku             Azerbaijan    13         1606            1606 2021-05-27
##  4  2658 2021-06-12 Wales       Switzerland    NA Official       0  7.66        -36        -4               7 Baku             Azerbaijan    17         1570            1570 2021-05-27
##  5  2659 2021-06-12 Denmark     Finland        NA Official       1 13.4         221        44               6 Copenhagen       Denmark       10         1631            1631 2021-05-27
##  6  2659 2021-06-12 Finland     Denmark        NA Official       0  0          -221       -44               8 Copenhagen       Denmark       54         1410            1410 2021-05-27
##  7  2660 2021-06-12 Belgium     Russia         NA Official       0 12.6         321        37               6 Saint Petersburg Russia         1         1783            1783 2021-05-27
##  8  2660 2021-06-12 Russia      Belgium        NA Official       1  7.81       -321       -37               7 Saint Petersburg Russia        38         1462            1462 2021-05-27
##  9  2661 2021-06-13 Croatia     England        NA Official       0  6.56        -81       -10               7 London           England       14         1605            1605 2021-05-27
## 10  2661 2021-06-13 England     Croatia        NA Official       1 17.1          81        10               7 London           England        4         1686            1686 2021-05-27
## # … with 62 more rows

I used INLA to fit the model — this is very quick and I could re-use some of the code I have written for this, so that was handy… Leonardo has done something very similar, using rstan.

The nice thing about this model is that you can predict a weath of outputs and summarise them nicely. For instance, one important metric is of course based on the estimate of the (posterior) probability that either of the two teams would win the match, or the probability of a draw. But with the Bayesian model I set up, I can also predict the full joint posterior predictive distribution of the number of goals scored by the two teams and visualise it (together with the marginal distributions, which are depicted as the top and side histograms below).

For example, the emphatic 3-0 win for Italy against Turkey was, numerically, not the most likely outcome, according to the model. The probability that Italy would win was estimated to be very high (almost 60%), but the model (and perhaps, I too) was expecting Italy to score fewer goals (the most likely outcome was a 1-0 win for Italy). The observed result was not impossible under the model (it had almost an 8% chance as opposed to a 13.5% chance for the modal result).

The prediction can be obtained for the first round of games in the Group stage (as below). As the games are played, I can update the dataset and re-run the model (which in INLA is pretty fast) to predict the next round of games (according to the historic data as well as the last performances).

I can make some comments on the (other) games that have already been played:

  • Switzerland-Wales (1-1). The model was predicting a slight higher chance of winning for Switzerland (that’s kind of contrary to Leonardo’s prediction) and was favouring a 1-0 win for them. However, both a 0-0 draw and the actual 1-1 result were relatively highly likely (with probabilities of 13.5% and 13%, respectively).
  • Denmark-Finland (0-1). The model doesn’t get that right — but this was a very special game for what has happened to Christian Eriksen. The model gives the Danes the home advantage and uses the fact that they are ranked much higher than the Finn’s team and so predicts a 2-0 as the most likely outcome. The observed 0-1 was rather unlikely given the model, but again, I don’t think this game was in any way generated by the “normal” process that the model assumes…
  • Belgium-Russia (3-0). That’s an interesting one — I think mostly because of the slightly over-estimation of Russian’s chances. Belgium were still favourite (with a probability of winning of over 50%), but I think in terms of prediction of the goal scored, the modal value for Russia was kind of overestimated due to the home effect (the game was played in St Petersburg), which in fact never materialised.
  • England-Croatia (1-0). Here the model was bang-on. The most likely outcome was the one that was, in fact, observed. England were favourite to win by a large margin and even a bigger win (2-0) would have been supported by the model.
  • Austria-North Macedonia (3-1). The actual outcome wasn’t very likely according to the model, which supported mostly a 1-0 win for the Austrian. Other outputs would have been aligned to the model’s prediction, including a 0-0 or a 1-1 draw, which probably wouldn’t have been out of North Macedonia’s grasp (they conceded two relatively late goals).
  • Netherlands-Ukraine (3-2). The Dutch were highly tipped to win by the model (giving them almost a 60% chance). But the numerical outcome of the game was a lot more uncertain. A 2-0 win for Netherlands was given almost a 10% chance by the model (the Dutch were 2-0 up before the Ukraininans came back to 2-2, which was given a 4.5% chance by the model).

Of the games that are yet to be played, France-Germany is an interesting one: the model seems to suggest a very tight outcome (given they are both very strong teams) and the modal outcome is a 1-1 draw. However, it gives Germany a slightly higher chance overall — I think this is due to the fact that the game is played in Munich (so Germany take advantage of the “home effect” — though the number of fans allowed in the stadium is of course reduced…) and that Germany are historically very good in “official” competitions (e.g. the Euros or the World Cup).

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