Closing the Door.

One of the most fun aspects of football data analysis is when the team you're part of derives some exciting newly derived metrics from the raw data that allows you to look at old problems with a new light.

Some real heavy data lifting has been put into deriving our Non Shot expected goals model. So first a quick recap on what it does.

Whenever the ball is moved around the pitch there is a likelihood of scoring  from each location it finds itself in. We express this value as non shot xG and the difference between these values when an action is completed is the change in NSxG via that action.

There's also a "risk/reward" aspect for when you concede possession.

Finally, each team has (nearly always) a different NSxG for the same pitch location, because one major input is the distance to your opponents goal.

We've mainly looked at passing and ball carrying, so far, quantifying the differing importance to your side of moving the ball five yards out of your own penalty area or five yards into your opponents. But there's an obvious extension of this that flips the focus and examines how well a team prevents an opponent progression the ball.

This isn't just by making passing difficult, it's also by making it harder or easier for opponents to carry the ball forward as well.

It used to be call closing a player down, it's called any manner of terms nowadays.

Here's how sides are fairing in preventing ball progression in 2019/20.

The first thing you need is a benchmark figure to measure how well a side is closing down the opposition.

There's only been nine matches played by each Premier League team to date and they may have played a bunch of sides who aren't that good or willing to play out from the back, so we need to find a set of figures that reflect this possible imbalance of intent and talent.

Let's take Manchester United. They've played nine teams, Chelsea, CP, Leicester, Newcastle, Southampton, WHU, Arsenal, Wolves & Liverpool.

Those teams, in turn have also played nine teams (except Arsenal, who play tonight), that's 80 teams of which nine are Manchester United.

That's almost guaranteed to include every Premier League team at least once and makes up a decent sample of around 70-80 games depending upon how you slice it.

We therefore, we took those 71 non Manchester United matches played by Manchester United's opponents and looked at the "risk/reward" ball progression via both passes and ball carries for 100 pitch segments.

For each segment we calculated the average NS xG gained (or lost) per 100 pass & carry attempts. That was our baseline for United's opponents progression against a broad selection of opponents this season.

Then we repeated the exercise, but for these sides in their matches against Manchester United and ran a heat map to see where on the field these teams were finding it difficult to progress the ball against United and where they were having a easier time compared to their benchmark numbers against the rest of their opponents.

This is what it looks like ( ignore the numbers for now).

The red areas are where United's opponents are progressing the ball at lower levels against United than they've managed as a group against a basket of 71 other Premier League sides. Blue, they're doing better.

It's a pretty stark and clear picture of where on the field United have been making it difficult for their opponents to get the ball into more dangerous areas. Firstly, beginning in front of their opponent's own box and then aggressively in front of United's own. They aren't too fussed about targeting wide positions on halfway and not too good(?) at stopping runs or passes from the bye-line & in the box.

Here's Everton and they do harry the opposition, but it's a much more chaotic process, with very little structure, especially compared to United's disciplined approach.

And finally, here's Aston Villa.

There's no overt closing down of the opposition until they reach the box, at which point it seems to become all hands to the pump.

Passing Risk Reward in the Premier League

The availability of richer data sources has naturally led to an interest in passing and ball progression.

The generally quoted passing metrics still gravitate towards event data such as goal attempts and actual scores as the major framework.

Passes that lead to a potential goal scoring attempt predominate in most current passing metrics and little has been done to differentiate between the contribution made by individual players involved in these possession chains.

In contrast, we've broken down the value of each pass attempted by referencing how likely a possession anywhere on the pitch has historically led to a goal, whether or not the possession ultimately result in an attempt on goal.

This so called non shot xG metric not only allows a route to value every ball progression, be it a pass or a carry, but also quantifies individual involvement, rather than sharing the credit equally between all those participating in the possession.

However, as often is the case in football metrics, only one side of the ball has been investigated.

Each pass attempt comes with a risk and reward.

The player attempting the pass has custody of a valuable team resource, namely the non shot xG value for possession of the ball at that precise position on the field.

The potential reward in making a progressive pass is to advance the ball to a more dangerous area of the field.

And the ever present risk is the cost of a turnover. The passing team lose the NS xG value they had by owning the ball and the opponents gain their own NS xG by taking possession of the ball.

Weighing a player's NS xG leger is problematical, but one way to express the risk reward balance of a players passing performance is to add up the NS xG value of every progressive pass they complete and compare this to the sum of the NS xG he loses through incomplete passes, along with the NS xG gained by the opponent taking possession of his errant attempts.

For example, in the nascent Premier League, Matteo Guendouzi's completed open play progressive passes have been received at areas on the field that totals 6.69 NS xG.

On the minus side, his picked off pass attempts has "lost" Arsenal 1.67 N xG. This is made up of loss of pitch position for Arsenal and the combined NS xG value for the opponent based on where possession is won.

Overall, and without regard for pass volume or minutes played, Guendouzi has a net positive 5.02 NS xG for Arsenal in 2019/10.

This puts him top of the Arsenal "risk/reward" passing charts and we feel is a much better single figure metric to describe a player's involvement in progressing his side towards the opponents goal.

Not only does it quantify individual involvement and utilses every pass attempted, it also penalises reckless or sloppy execution that leads to change of possession.

Here's the current pass risk/reward numbers for all 20 Premier League players with a minimum number of attempts.

Game State and Blocked Shots.

I've written a fair bit about game state and how it impacts on how a side approaches a match s the time elapses and occasionally the score line changes.

I don't use score differential to define "game state", instead I use a measure of how well each team is fairing based of their pre game expectation.

This can be defined as the expected points based on the current score and time elapsed or the expected success rate of a team, again when measured against a pre kick off baseline. The choice is entirely up to you.

The advantage of this approach is primarily when the game is tied (which it is for a fairly significant portion of most matches). Instead of counting offensive production for both sides at this score differential, there's usually a clear indication of which of the two teams is happier with the stalemate and which is not.

You also get a gradual movement of game state that incorporates the often omitted variable of time elapsed.

It's intuitive as to what might happen as game state ebbs and flows over the course of a match, as unhappy teams perhaps become more risk taking in order to change the current status quo, while pregame underdogs are forced or chose to attempt to bank their above expectation gains by becoming more defensive.

One slight problem with this approach is that it assumes a relatively balanced competitive edge between competing teams and further assumes that those needing to change the current scoreline are capable of attempting to do so.

Not to be harsh, but it's difficult to envisage a situation where Manchester City felt the need to protect a lead against say Newcastle or where Newcastle were technically able to up their attacking intent against the champions.

So often the presence of  clearly superior teams can skew conclusions. "Possession leads to wins" arose largely because better sides also had high levels of possession, but the possession was a byproduct of other things they did, rather than the primary driver of their results.

Remove Barca etc from the data and the relationship between possession and wins tended to disappear.

Therefore, firstly here's why "zero goal differential" (the game is level) shouldn't be regarded as a single game state.

Here's a sample of matches from the 2018/19 Premier League, involving games where one of the Big 6 wasn't playing. Thus the games weren't particularly one-sided from the outset.

Initially, I've simply counted the shot volume from regular play for teams when the score differential is zero (the game is level). The vertical axis records my version of changing game state, a larger negative value indicates that a team that is doing badly compared to the expectation at kickoff.

Typically, this may be when a home favourite is level a fair way into the game and a points expectation that may have been 1.75 expected points at 3 o'clock has fallen back towards one point as the clock ticks on towards 5.

Those above the blue score differential line of zero are doing better that they hoped for, they might have expected to average less than a point from such a game, but they are edging closer and closer to a point, with a possibility of nicking all three.

Each point represents a goal attempt and it's clear that the lions share are being taking by the disgruntled favs.

If we re-examine our intuition, it's likely that if the beneficiaries of the stalemate aren't taking that many shots in the match, they're doing things to prevent the ones at the other end going in.

Learning from the likes of Pulis and Dyche that will likely include blocking shots.

Next I built a simple xG model (just location & type), but also included the game state factor, not just at zero goal differential, but at all score differentials to see if it told anything about the likelihood a shot would be blocked or not.

I eliminated games where a red card had been shown, for obvious reasons.

The bottom line was that game state was a significant factor in correlating with whether an attempt was blocked or not, along with location and shot type. And the larger the decrease in a side's pre-match expectation when the attempt was taken, the more likely it became that the shot was blocked.

In short, without the superstar teams, run of the mill games appear to follow the "hold what we have" and "this is disappointing, let's crack on" mentality.

This is one route to improve the much criticised problem of single xG races, where one team scores early and then drops anchor, but whether it is a universal improvement to a predictive model is a question of over fitting the past and potentially screwing up the future.