Fantasy Premier League 2019/20 – Fixtures Analysis

BRIGHTON, ENGLAND - MAY 12: Manchester City pose with the Premier League trophy after the Premier League match between Brighton & Hove Albion and Manchester City at American Express Community Stadium on May 12, 2019 in Brighton, United Kingdom. (Photo by Michael Regan/Getty Images)
BRIGHTON, ENGLAND - MAY 12: Manchester City pose with the Premier League trophy after the Premier League match between Brighton & Hove Albion and Manchester City at American Express Community Stadium on May 12, 2019 in Brighton, United Kingdom. (Photo by Michael Regan/Getty Images) /
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Here are the fixture ratings for each team ranked from 0%-100% (0 being the easiest and 100 being the hardest).

The methodology is something I’ve played around with when trying to build a probabilistic model of the Premier League (yes I am THAT cool), which I hope to include in articles further down the line as I’m still refining it! If you’re not interested in the methodology and just want to read about the conclusions I have drawn, skip ahead to the next page!

The data I have used is simply goals scored and goals conceded home and away. I have only taken last year’s final league table as that is simpler, removes issues of inconsistency due to managerial changes (Allardyce to Silva or Mourinho to Solskjaer!) and in my experience of teams without any major changes, the averages do not move significantly over the course of a season.

Of course, I don’t have any data for the 3 promoted sides as they weren’t in the league last year, so as an estimate I have generated goals scored/conceded figure for each team based on their Championship goals statistics and how they compare with Cardiff, Fulham and Wolves.

For example, Sheffield United scored as many goals away from home last season as Wolves did in the year before, therefore I have used 19 goals (same as Wolves last year) as a rough projection. It’s not perfect but it’s a start and as the season progresses their own data will be used to supersede my estimations.

Using this data

Using this data, I have given each team a score for home and away, attack and defence. That score is their average goals scored/conceded per match divided by the overall average scored/conceded per match.

For example, Arsenal scored 42 goals at the Emirates last year, averaging 2.21 per game. In contrast, there were 596 goals scored over a total of 380 home matches, which is an average of 1.57, so Arsenal’s strength rating is 2.21/1.57, which equals 1.41.

Therefore, you can rate each team’s fixture difficulty by totalling the scores of their opponents for the next set of matches you are interested in. Higher scores are better for the attacking rating (suggests the opposition defences let in more goals than average), while lower scores are better for defensive ratings (suggests the opposition attacks score fewer than average).