Updates

How Possession Adjusted Shot Frequency Impacts Shots on Target

The idea behind this much, much overdue post is to explore how time-to-shoot impacts shot quality. Due to lack of xG data, I’ll use shots on target for now. I’ll compare these with how many goals were also scored, to understand how goals are impacted by TTS and shots on target, are teams who shoot more frequently converting more of their shots on target than ones who shoot less frequently? That’s kind of the premise of doing this analysis.

More importantly I’ll be trying to explore how being at home or not impacts this, we’ll see how away teams generally tend to perform and what type of approach are they forced to, or choose to take when attacking the home team.

One may point out how is this much different from looking at just shots and shots on target, because after all, TTS is a function of the number of shots. The difference is that TTS is possession adjusted, so a lower TTS doesn’t necessarily mean a higher number of shots on target, it introduces also how well a team used the possession that they had. As such, if a team has a low TTS and high number of shots on target, they’ve actually made use of the possession very well – as opposed to a team who has a lower number of shots on target.

Right from the start, before I even start looking at any data, I’m inclined to say that this will take the shape of a very roughly normally distributed curve.

My thinking behind this is that if a team takes an unusually low time to shoot, it’s likely because they’re shooting from outside the box OR taking really xG shots. And thus not many of them would end up on target. As we move into a range where time to shoot is relatively higher, but still probably normal, I’d think most teams are getting wiser and picking and choosing their opportunities to have a go and not wildly shooting as with teams with super low TTS numbers. After some point, though, diminishing returns will start showing and teams who are too ponderous on the ball will fail to register a good number of shots on target just because they take too long in shooting.

All the data I’ve got is from https://football-lineups.com, I web-scraped all the fixtures from the 2016/17 season and collected the shots, shots on target, and possession stats to calculate the TTS for each team in each fixture.
This is the notebook if you’re looking for the code:
https://github.com/Yatin-Kapur/tts-sot/blob/master/Code.ipynb

The calculation for TTS was fairly easy; team possession * (90 * 60) / shots.
Where team possession is a decimal between 0.00 to 1.00 and the 90 * 60 is to represent the seconds in a game.
I took data from the top 5 leagues for each game in the 2016/17 season

The result of plotting this data came out as such:

Home team data for SOT plotted against TTS.
Home team data for Goals plotted against TTS.

We observe a very basic trend, that generally when a team has more shots on target it implies that they took less time to shoot. The home team is regularly getting a higher number of shots on target whilst shooting at a good rate. It’s also worth noting that although it’s a relatively clear negative relationship, it does slightly stabilize at 9+ shots on target. This could suggest that teams who are shooting at a rate of 100-200seconds/shot have better quality shooters in their team who are able to hit the target more often as opposed to teams who are near 1-4SOT and still taking at 100-200seconds/shot showing how although they are making good use of their time on the ball with good shooting numbers, they are not keeping hold of the ball enough. In order to get into the higher numbers of shots on target, they likely need players who can hold the ball better as opposed to better finishers.

As for goals, we can observe a similar shape, the less time a team takes to shoot, the more goals they take a similar shape. I’m interested to understand why there’s a spread from the 4-6 goals mark. My assumption is that there are teams who rely on taking a lot of shots to score that many goals, and there are teams who take a relatively lower number of shots but are taking higher percentage chances.

It would be good to observe of xG changes the graphs’ shapes as we can then clearly define high/low quality chances and shots.

 

Away team data on SOT plotted against TTS.

The away team data is fascinating, it looks similar but there are differences in that the away team is rarely able to get 10+ shots on target. The away team approach to play is also probably quite different in that they don’t always look to shoot as frequently, but they much rather prefer taking better type of shots. This is reflected in how there is a lesser of a negative trend and more of a stable trend, although still declining in the number of shots on target taken. I’d also assume that the away teams have less possession, and this impacts their time to shoot which seems to be higher on average than the home team. So we can sort of tell that the away team will have less possession and be more picky of when to shoot. I think this is a typical trait where a team that has less of the ball wants to make the most of it and not rush into chances, as we saw with the home team who posted lower TTS numbers.

The goals data is really similar to the SOT for the away team as well, as you’d expect after looking at the home team data.

Away team data on Goals plotted against TTS.

After writing this I felt slightly dissatisfied because it wasn’t thorough enough and didn’t point to anything concrete as I was hoping when I started out. Nevertheless, I’ll try to do more and better things from here on out. I expected the graphs to come out as a bell curve but that wasn’t the case, I should have thought about how TTS is a function of shots taken and why that would impact the graphs in the way that it did. Clearly, more shots = less TTS and more shots = more shots on target, but I hope this helped in understanding how TTS takes into account the possession statistics which can dictate the way a team selects their shots.


Follow this blog and connect with me on twitter!
https://twitter.com/Yatin_Kapur

 

Leave a Reply