WorldCupPrediction | AIrgentina

Predicting results for the FIFA Men's 2022 World Cup and FIFA Women's 2023 World Cup.

Matches are predicted using a framework based on the team-level model in AIrsenal, a Python library for using Machine learning to pick a Fantasy Premier League team, which in turn uses bpl which is a numpyro implementation of our model.

This model is trained on international mens football results obtained from https://github.com/martj42/international_results and https://github.com/martj42/womens-international-results.

The original model is a version of Dixon and Coles.

In the media

There is some background information and description of the results here.

The New Scientist also has an article, as does The Daily Mail, which prompted one reader to enthuse: "What an absolute load of rubbish, but typical of the anti-English establishment today!", and another to ponder: "I have NEVER KNOW a Prediction from a Scientist to be Right Ever, But on this Prediction I may be wrong.". High praise indeed!

Our AIrgentina model came 6th in @Futbolmetrix1's World Cup 2022 Sophisticated Prediction Contest outperforming models developed by FiveThirtyEight, Opta and Betfair!

Installation

The easiest way to use the code is via poetry. If you have poetry installed, from this directory, you can do

poetry shell
poetry install

to first open a shell in a virtual environment, and then install the dependencies and the wcpredictor package.

Usage

Simulating a tournament multiple times

There are a couple of command-line applications that can be run when the wcpredictor package is installed as described above.

In order to simulate the 2022 Men's World Cup $N$ times, you can do

wcpred_run_simulations --num_simulations N --tournament_year 2022 --training_data_start <YYYY-MM-DD> --training_data_end <YYYY-MM-DD> --resume_from=Group --output_csv <outputfilename>

For the 2023 Women's World Cup, you can run

wcpred_run_simulations --womens --num_simulations N --tournament_year 2023  --years_training_data 20 --resume_from=Group --output_csv <outputfilename>

Notice here, instead of passing in the training start and end dates, we can simply pass how many years of training data to look backwards in time for. You can use the --resume_from CLI argument to specify at what point to simulate the tournament from.

The results, in the form of a table of how many times each team got to each stage of the competition, will be saved in the specified csv file. At present, the allowed values for tournament_year are "2014", "2018", "2022" (the default) and "2023".

⚠️ Warning: ⚠️ The simulations can use a lot of memory. If you want to run more than around 250 simulationns we recommend checking the help (wcpred_run_simulations --help) and setting the --num_thread and --per_tournament arguments (start low andd then slowly increase them whilst monitoring CPU/memory usage on your system).

Once you have a csv file saved from running the simulation, you can plot the top ten most frequent winners by running:

wcpred_plot_winners --input_csv <inputfilename> --output_png <outputfilename>

and the results will be saved in the specified png.

You can also make a plot showing how far in the tournament a selection of teams got, by running e.g.:

wcpred_plot_progress --input_csv <inputfilename> --output_png <outputfilename> --team_list "England,Wales"

Note that both these commands can be run with --help to see the options.

Running a single tournament

In a python session, to simulate the Men's 2022 World Cup, you can do something like:

>>> from wcpredictor import Tournament, get_and_train_model
>>> t = Tournament("2022") # can also choose "2018" or "2014"
>>> model = get_and_train_model(start_date="2016-06-01", end_date="2022-11-20") # choose dates for training data
>>> t.play_group_stage(model)
>>> # at this stage, we can look at how each group is doing
>>> print(t.groups["A"])
Position |  Team   | Points | GS |  GA
   1st    Netherlands   6      8     4
   2nd    Qatar         4      2     1
   3rd    Ecuador       4      1     5
   4th    Senegal       3      4     5
>>> # or, we can go ahead and play the knockout stages
>>> t.play_knockout_stages(model)
>>> print(t.winner)

Pre-tournament predictions:

FIFA Men's 2022 World Cup

How well did we do in 2022?

Our top two favourites to win the competition, Brazil (25%) and Belgium (19%) got knocked out early on in the competition, getting eliminated in the quarter-final and group stages, respectively. However, AIrgentina gave the eventual winners, Argentina, a 13% chance of winning the tournament.

For England and Wales, our model suggested their most likely outcomes were being knocked out at the quarter-finals and group stages respectively which unfortunately were their actual outcomes in Qatar.

We entered our pre-tournament predictions to @Futbolmetrix1's WC2022 Sophisticated Prediction Contest and in the end came 6th!

FIFA Women's 2023 World Cup

How well did we do in 2023?

We'll see 😉