Football Analytics – Part Three: PDO

If you haven’t already, you can read the first two parts of this series here and here.

In Part Two I mentioned that the two most important requirements of any metric are its repeatability and predictability. Just to be difficult, this next metric I’ll discuss, PDO, is not widely recognised as being either especially repeatable or predictable.

*Warning. PDO is a complicated subject.*

So what is PDO?

Confusingly enough, PDO doesn’t really stand for anything. It’s a metric borrowed from ice hockey across the pond, brought into football by James Grayson, and it’s generally used to gauge how lucky a team is.

It’s calculated by adding a team’s save percentage to its conversion percentage. Save percentage is (100-(Goals Conceded/Shots on Target against)*100) and conversion percentage is ((Goals Scored/Shots on Target for) *100).

So let’s say Liverpool are saving 60% of the shots on target they face, and scoring 33% of the shots on target they take, their PDO would be 93.

Because one team’s PDO directly affects the opposition, the average for teams is always 100.

PDO SOTR

All graphs are provided by James Grayson.

As the above graph shows, there’s pretty much no correlation between how good a team’s SOTR (covered in Part Two) is, and its PDO, indicating that PDO is determined by luck, not quality. (Don’t be put off by the numbers on the ‘y’ axis, some analysts have PDO’s average at 1000, others at 100. I use 100 because I think it’s easier to understand.) Better teams generally do have a slightly better PDO than worse teams, partly due to the fact that they can create more high-quality chances, but the differences are negligible.

PDO repeatability.jpg

Furthermore, there’s little to no repeatability in PDO numbers, as shown above by the weak correlation. This reinforces the fact that PDO is not something that can be controlled and skill-driven, but something driven by luck.

What PDO is though, is consistent.

PDO Distribution.jpg

As you can see, very few teams are expected to deviate largely from the PDO average of 100.

What does it mean?

PDO assumes that finishing and saving are random and tend to even out, or regress to the mean if you want a fancier term, over a period of time, affecting sides both offensively and defensively. If a side is scoring 50% of their shots on target or conceding 50% of its shots on target against, it’s likely that this is unsustainable and can’t be maintained, causing them to regress to the mean, thereby affecting their PDO.

A good example of this is Arsenal during the early part of this season. Having failed to score against West Ham in their opening game, they followed this up with a good win at Palace, a 0-0 at home to Liverpool, and wins over Newcastle and Stoke. The media tore their hair out at Wenger’s refusal to buy a striker after Arsenal had over 20 shots in both the Newcastle and Stoke games but only scored 3 goals, claiming that Arsenal had no chance of winning the title. In reality, Arsenal’s PDO was unsustainably low, and it quickly regressed to the mean, as PDO often does, with the Gunners putting 5 past Leicester.

There are exceptions to this rule of course. Good teams can generally sustain a PDO of >100 with a great ‘keeper and hot striker who has a great season or is just bloody brilliant, and Tony Pulis’ Stoke City sides had a strange habit of always having high PDO values under his tenure, implying that there are ways in which you can consciously influence your PDO. But examples like this are generally anomalies, and it’s a safe bet to expect a side with a PDO of 110 or 90 to regress to the mean.

The trouble is, you can’t predict with certainty if a side will regress, when a side will regress or to what extent the side will regress. Which complicates things. But one can make a reasonable assumption about all three things.

If it’s not repeatable, or a measure of how good a team is, what use is it?

PDO is a huge driver of narratives in football. Every manager sacked before Christmas in the Premier League was managing a side with a PDO of less than 100, showing the huge role it has in determining results, which in turn, influence people’s perceptions of events. A side putting together a few wins on the spin could be powered by unsustainable finishing/save percentages, indicating that they might not be as good as pundits and fans think. PDO allows you to estimate how lucky a side has been, giving you a better sense of their true quality.

As with everything in football analytics, it’s all about context.

Thanks for reading.

If you’ve got any questions, follow and DM me @OneShortCorner .

Further reading on PDO can be found

From @11Tegen11 here.

And from James Grayson here, here and here.

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2 thoughts on “Football Analytics – Part Three: PDO

  1. Pingback: PDO – Introduction – Football Analytics in English and 日本語

  2. Pingback: PDOを紹介します – Football Analytics in English and 日本語

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