This year I am participating in Simon Gleave‘s Premier League prediction competition. It is an interesting initiative, as both statistical models and and more informal approaches are compared.
Last time I participated in something like this was midway trough the last Premier League season for statsbomb.com’s compilation. This time, however, the predictions are made before the first match has been played. To be honest, I think it is futile to try to model and predict an unplayed season since any model based only on previous results will necessarily reproduce what has already happened. This approach will work OK for predicting the result of the next couple of matches midway trough a season, but making predictions for the start of a season is really hard since the teams have brought inn some new players and gotten rid of other and perhaps also changed managers and so on. And not to forget that we also try predict results 9 months into the future.
When May comes and my predictions are completely wrong, I am not going to be embarrassed.
Last time I wanted to use the Conway-Maxwell-Poisson model, but I did not get it to work when I included data from several seasons plus data from the Championship. I still did not get it to work properly, but this time I tried a different approach to estimate the parameters. I ended up using a two-step approach, where I first estimate the attack and defense parameters with the independent Poisson model, and then, keeping those parameters fixed, I estimated the dispersion parameter by itself.
To fit the model I used Premier League data from the 2010-11 season to the 2015-16 season. I also included data from the 2015-16 season of the Championship (including the playoff) to be able to get some information on the promoted teams. I used the Dixon-Coles weighting scheme with \(\xi = 0.0019\). I used a separate parameter for home field advantage for Premier League and the Championship. I also used separate dispersion parameters for the two divisions.
I estimated the dispersion parameter for the Premier League to be 1.103, about the same as I previously estimated in some individual Premier League seasons, indicating some underdispersion in the goals. Interestingly, the dispersion parameter for the Championship was only 1.015.
Anyway, here are my projected league table with expected (or average) point totals. This is completely based on the model, I have not done any adjustments to it.
| Team | Points |
|---|---|
| Manchester City | 73.70 |
| Arsenal | 69.73 |
| Leicester City | 64.12 |
| Manchester United | 63.95 |
| Chelsea | 63.84 |
| Tottenham | 62.53 |
| Southampton | 60.51 |
| Liverpool | 60.37 |
| Everton | 51.48 |
| West Ham | 51.12 |
| Middlesbrough | 46.30 |
| Swansea | 44.59 |
| Burnley | 44.20 |
| Stoke City | 42.99 |
| Hull | 42.49 |
| Crystal Palace | 41.33 |
| Watford | 41.23 |
| Sunderland | 39.83 |
| West Bromwich Albion | 39.21 |
| Bournemouth | 36.37 |
Hi, thanks for your post. I would like to ask you something about your methodology in predicting future events.
You have used your model and some previous data from Premier League and Championship. This gives you a model containing a superset of teams.
let’s say that you have 40 teams in your trained model, and now you want to predict the next year’s standings, using 20 out of these 40 teams. How to do do that?
did you manually get all the offence/defence params for these 20 teams, plus the extra home factors, manually program the double Poisson for instance, and then run your simulations?
To make predictions I basically just look up the parameters and plug them into the formula for the Poisson intensity. For an entire season it is easy, since every team plays each other twice. Then it is just a matter of predicting the outcome of all combinations. But otherwise you would need a table with the remaining fixtures. And yes i used simulations.