So last time in this series I covered expected goals, and you can read about it here, where I briefly mention game state, but only that it would be covered in the future. Well luckily for you lot, this is that time.
As with most things concerning football analytics, the terminology is more confusing than the actual concept itself. Game state refers, surprise, surprise, to the state of the game in which two teams are playing. If we’re going to start off simplistically, there are three generic types of game state that a team can find itself in; winning, drawing or losing.
We can break down these game states further into categories determined by the match’s goal difference for greater detail. So a side that is 3-1 up will be in a game state of +2, having scored two more goals than the opposition.
Why does this concept matter?
Game state matters because the way teams respond to changing game states creates score effects. Any score effects outside of the game states -2 to +2 (so anything except -2, -1, 0, +1, +2) should be taken with a pinch of salt, due to small sample size.
Firstly, the following score effects are not necessarily applicable to every club and situation, they’re based on several years of theory and testing. Elite clubs are often able to sustain similar shot levels at any game state.
One seemingly obvious effect is that a side which is a goal down is more likely to take more shots. If you haven’t read about either TSR or SoTR, my piece on them is here.
The above graph was made by the excellent @11tegen11, whose piece on game states you can read here. Sides that are in game states of -2 or -1 tend to outshoot their opposition, presumably because they need to get back into the game, resulting in more shoots against an opposition that has no need to chase the game, and who are more likely to be defending in a good defensive structure.
Sides which are 3 goals or more down tend to get heavily outshot, normally because the 3 goal deficit is indicative of their inferior strength compared to the opposition, who can easily dominate them.
It’s a fairly similar story with SoTR (graph again from 11Tegen). But why are do teams 2 goals down get fewer shots on target off than their opponents, when they appear to get off more shots?
Being behind, theoretically, one would think, would lead to lower shot quality from the team behind, as with more players behind the ball, the leading team should be able to deny the opposition space to get shots away. It would also have the added effect of forcing a trailing team to take more hopeful efforts from further out and from poor angles if they were unable to work the ball into good shooting positions because of the defence. The winning side would probably also be able to create better quality chances on the counter-attack as the opposition would likely throw more men forward, allowing them to take shots from better angles which have a higher chance of going on target.
Indeed, Michael Caley mentions here that game state is significant for the expected goals value of regular shots (not headers, shots assisted by crosses or set-plays). Although he admits the effect is small, he attributes it to the ‘still unaccounted-for slight differences in defensive pressure applied by teams trailing or leading a match,’ which, if you remember how we don’t have off-the-ball tracking to measure pressure, makes sense.
Basically, if you’re winning, the quality of your chances by xG will be slightly higher than at a neutral/negative game state, because it’s presumed there will be less defensive pressure on the shooter.
This graph illustrates the importance of game state in conversion rate. Conversion rate’s kind of a big deal, and it’s not surprising, having discussed it above, to see sides in the lead score a higher % of their shots. Why? Because of the likelihood of the trailing opposition throwing men forward leaving themselves exposed to counter-attacks which can more easily create high-quality chances.
Another graph now (sorry), this from this StatsBomb.com piece by Ben Pugsley, who has done a ton of work on game states and runs this excellent site, which has this cool page detailing all of the shenanigans that take place at different game states.
I spoke about PDO in Part 3 of this series, and it’s amazed me to see Leicester continue to hold of PDO of over 110 throughtout this season, even though this ‘should’ be unsustainable. But when you consider that Leicester have only trailed for 361 minutes this term and have been at +1 game state for longer than anyone else, and thinking about what the above graphs have shown, a portion of their ‘over-performance’ in PDO can be explained. This is because their opponents will be taking more poor quality shots (due to the Leicester defensive structure) because they’re behind for a long time, allowing Leicester to rack up a high save % and conversion % when utilising their counter-attacks.
Context is king
Score effects can have the ability to make a team look better or worse than it actually is. Liverpool have been one of the strongest shot teams in the league this season, but the fact that they’ve only been leading for 23.8 minutes per game necessitates greater shot volume, so maybe they’re not as good as TSR says? In contrast, Leicester, who’ve spent only 10.9 minutes per 90 trailing this season, might not need to take as many shots as Liverpool, causing them to look like a weaker team using metrics such as TSR or SoTR.
For those who don’t follow them (and you should) @Stats4Footy has done several articles on penalties in football, including this one, which looks at penalty success rates at different game states. It threw up this graph:
Is this evidence of the presence of pressure on the shoulders of a penalty taker? I don’t know, but it’s something intriguing I thought to end this piece on.
Thanks very much for reading. If you’ve got any questions, don’t hesitate to contact me @OneShortCorner.
If you want to read more about game states, I did something a bit more specific on Arsenal and Manchester City back in December.