Wednesday 22 January 2014

Expected Goals Model with Game State

Here's a look at the latest update to model with Game State added to the equation. Game State is the current score in a match when a shot is taken. I've included three states::

  • Close - score is even, or one team is winning by a single goal
  • Up - Team is winning by two or more goals
  • Down - Team is losing by 2 or more goals
The logic is that when the score is "Close" teams don't convert their chances quite as easily as when they are leading, or ""Up". Also when teams are "Down" they convert a little better too. The following chart shows very basic conversion rates for all shots: 

The model is more sophisticated than this though. I look at Game State for each classification of shot, from the different zones, different assist types, etc., rather than just biasing each shot by a set value. Not sure if his is is necessary or not though. Anyway, the results...

xG Model wIth Game State

Pretty good, right? And it looks like an improvement (and is) over the non-Game State results I posted on Monday, However, a small confession - When I was adding in Game State I noticed some data data for Zone A shot (6 yard box) for the current season.had corrupted so had to fix that.  Here's what the chart from  Monday should have looked like...

xG model without Game State

Not a great deal of difference but I think importantly that the top clubs are just a little closer to the line with Game State included than without, which makes sense of course as it's these clubs that that are more often "Up" in Game State. Also, the total Goals for the Game State (GS) model is only 12 out (2573 expected compared to 2585 actual)  whereas non-Game State is 152 below the actual. The RMS error for the GS model is also a little better, 8.5 compared to 10.8.

Comparison with Most Basic Shot Model
One final thing to look at is how this model compares to just looking at expected goals from total shots, if all shots were considered equal...

The R^2 value is good! If you looked at this without the chart along you'd think you had a good relationship but as can be very clearly seen it's at the top end of the model where all the extra shot classification that gets built into the xG model proves to be of immense value. 

All very interesting but what next? Firstly, I'm really going to try and get this latest model built into the insideFPL stats and projections for Fantasy Premier League. I also want to take alook at he performance for the different models to from season to season.

I will also look at some data for individual players. Over the past few seasons players have racked up of hundred of shots and there is some very interesting stuff going on. Some players can be shown to have converted their shots at a rate twice that of the league average consistently over the seasons.

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