I hope all our American readers had a great Thanksgiving, and everyone else from around the world had a great Thursday.
Another week of Premier League matches are coming up, which means so are my simulated results for each match.
Week 13 Simulated Odds:
The closest matchup of the weekend is Swansea City vs Bournemouth. If Swansea want to avoid relegation, this is really the type of match that they need to win.
Arsenal face Burnley on Sunday. and win in 5,536 of the 10,000 simulations. The most common results were 0-1 and 1-2 in favor of Arsenal, but there is also a 15.5% chance that Arsenal win the match by 3 or more goals.
Simulated finish and points
Using those same 10,000 simulations, I was able to generate data for the entire league for the season. I put a few tables together to show the results in an easily understood (hopefully) visual format.
This table shows the chances of a team’s finishing the season in each of the 20 places in the table:
This chart, a “box and whisker” plot, shows the range of probable points for each team in the league based on the simulations. Gray represents the lower half of the midrange of possible points (25-50%), yellow the upper half (50-75%):
This last table is pretty straightforward. It’s the odds of a team winning the title, finishing in the top four, or being relegated, and the average number of points earned in the 10,000 simulations run for this data:
Team ratings and expected points
Next up: team ratings, which are derived from the inputs that go into the model.
These team rankings use data from the 2015-16 season through the current season. Data that is more recent is weighted more heavily. Rankings are compared to league average, and scaled so that 100 is average. Each point above or below 100 represents 1 percent better or worse than the average team.
You’ll note that this means that Southampton are almost a perfectly average Premier League team. Up The Averages!
Last but not least is expected points for each team this season.
There are two expected points totals shown here. The first is based on the pre-match odds that I do before every match, as I have done above, and the second is based on the post-match xG that each team produces. More information on xPoints can be found here.