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xGunners: Week 16 Simulated odds

Arsenal big road favorites going to Southampton while Manchester United are home underdogs in the Manchester Derby

Burnley v Arsenal - Premier League Photo by Alex Livesey/Getty Images

The weekend features a pair of Northwest Derbies with Manchester United hosting Manchester City and Liverpool hosting Everton.

The Saturday matches are pretty meh with only the Burnley vs Watford the only matchup between two teams in the top half of the table.

Week 16 Simulated Odds:

Arsenal travel to Saint Mary’s Stadium to face a Southampton team that I have rated as the best team outside of the “top 6” but they are a team that has really struggled to score goals lately.

The biggest favorites this weekend are Tottenham facing Stoke City at home trying to snap out of the funk that came with losing to Arsenal, followed by Liverpool taking on the now “Big Sam” lead Everton.

In the big match of the weekend Manchester City are road favorites against Manchester United. Manchester United were cut open by Arsenal’s attack over and over again only to be saved by David De Gea, if I were Jose Mourinho I would not try the sitting back and let the other team bombard my goal tactic again. You can only chance fate for so long.

On the Manchester City “invincible” watch, after another late win the latest simulation gives them a 0.35% (35 out of 10,000 simulations) chance to go without a loss this season. This weekend against Manchester United represent on the more stern tests they will face and a win or draw will see the odds rise close to 0.5%. I will keep looking at this and reporting the results until they lose... or don’t.

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:

Projected Finish

Team Title Top 4 Relegation Avg Sim Pts
Team Title Top 4 Relegation Avg Sim Pts
Manchester City 88.3% 99.8% 0.0% 87.48
Manchester United 8.1% 87.7% 0.0% 75.08
Liverpool 2.9% 74.5% 0.0% 71.91
Chelsea 2.8% 71.9% 0.0% 71.57
Arsenal 0.6% 43.1% 0.0% 70.42
Tottenham Hotspur 0.4% 32.3% 0.0% 65.57
Leicester City 0.0% 0.7% 0.8% 52.75
Everton 0.0% 0.5% 1.4% 50.91
Southampton 0.0% 0.4% 1.7% 49.85
Watford 0.0% 0.2% 2.3% 49.07
Burnley 0.0% 0.2% 2.2% 48.65
Bournemouth 0.0% 0.2% 2.8% 45.64
Stoke City 0.0% 0.0% 9.0% 44.78
Brighton & Hove Albion 0.0% 0.0% 15.9% 42.56
West Bromwich Albion 0.0% 0.0% 23.0% 40.94
Newcastle United 0.0% 0.0% 23.1% 40.87
Huddersfield Town 0.0% 0.0% 32.2% 40.62
West Ham United 0.0% 0.0% 51.7% 39.45
Crystal Palace 0.0% 0.0% 36.3% 38.76
Swansea City 0.0% 0.0% 75.9% 32.58

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.

That Manchester City team is really good, Arsenal jumped up to the second best team in the rankings on their very good and improving offense while the rest of the rankings remain roughly the same.

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.

If you are interested in the methodology of the model, or for any of the work I do here, you can find that on my personal blog.