There is no end to the amount of sheer analysis that you can do with pass data.

You can analyse by location, angle, length as well as accuracy and which players are making passes. With that data, you can then look at making pass maps which can give you an overall picture in terms of passing shapes during the defensive and attacking phases, and answer questions like which players are busiest in possession? Do they play long or short passes? Which direction are their passes? Do they attempt easy or more difficult passes? Are the opposition busier on one side compared to the other and where does their main threat come from in the final third?

When talking about the final third area, the PassScore model can help answer some more critical questions about the opposition. Which players are the most dangerous? Which players get the ball into the best goalscoring positions? Where, on the pitch, are these passes played from and where are they aimed? If you can answer all of these questions using a single pass model you can gain a much better idea about your opposition prior to a match.

Without going into the PassScore model too much, as a dedicated article about it can be read here, in basic terms, it scores every pass and gives it a value, ranging from around -4.4 to +4.4. A positive 4.4 pass score is a pass played into a superb goalscoring position, inside the six-yard area. The negative value works in the opposite way. If an unsuccessful pass is given away in a goalscoring position according to the xG model, then it will be given a negative value. For example, if a goalkeeper makes a pass that is unsuccessful and intercepted close to his own, this will be scored negatively and close to the -4.4 bottom limit.

Given that the PassScore model looks at passes that help to contribute more directly to creating chances and scoring goals, passes played within the middle third are all scored zero. That’s because the expected goals value for that zone is <1%. In simple terms, less than 1 in 100 shots are expected to be scored from this area of the pitch.

Okay, so which players are most dangerous for the opposition? And on the whole, where do teams make their most valuable passes from?

Firstly, you can make quick pass maps for teams where the players are colour coded based on their PassScore:

The tweet above shows overall pass maps for 12 of the 24 Championship teams last season. Darker green indicates players with a greater contribution to playing successful passes into better goalscoring positions. This provides a useful overview of a team but you’ll want to delve deeper into individual teams and players.

You could create pass maps based on all passes that score >2.5 to understand which players are critical to a team’s build-up and where they average these passes from.

But firstly, we’ll create heatmaps for teams and compare them side by side:

In this example, we’re comparing Leeds United and Derby County, two sides who faced each other in the playoffs last season. The colour scales are relevant across both teams so if one team perform much better — playing more passes into good positions, this will affect the colour scale of the other team and vice versa.

In the comparison above, we can quickly see that Leeds United make more high-scoring passes compared to Derby. Their most successful area is from the right half-space and that’s no surprise given that that is where Pablo Hernandez does his mastery. So tactically, when assessing an opposition, that could be identified as a key area to keep covered and to apply close pressure on the ball.

It has to be said, that that is an obvious one. We all know of Hernandez’s quality but this can be applied to all teams within the Championship to quickly identify their most threatening areas.

Here’s another side-by-side team comparison, this time looking at Swansea and Sheffield United.

The first comparison that can quickly be made here is how Swansea make more valuable passes from the inside channels while Sheffield United play passes or most likely crosses from wider positions. It’s also worth remembering here that filters can be applied to include or exclude crosses, head passes and so on.

To delve further into the detail, you can then focus on a particular team and then a particular player within that team.

Talking of Pablo Hernandez earlier, we can look at his PassScore heatmap and compare it side-by-side another player, a fellow Leeds team-mate or an opposition player like Swansea’s Bersant Celina.

As expected, we can see above Hernandez’s dominance on that right flank and right half-space. Swansea’s Bersant Celina also has a sweet spot in that left half-space — the dark green square. As well as pretty visuals, you can rely more on the numbers, sorting teams’ players by PassScore to identify a team’s strongest and also weakest players and both ends of the pitch.

Doing so will help you to choose which players you want to zone in and create heatmaps for to answer the critical questions.

You can also identify those players that might not be obvious players that contribute to goalscoring chances. Swansea’s Mike van der Voorn and Sheffield United’s Chris Basham are two of the best defensive contributors to getting the ball into good goalscoring positions and their heatmaps help to identify where they’re most dangerous.

As you can see above, van der Hoorn played his valuable passes from deeper positions near the halfway line compared to Basham who bombed forward down the right channel.

If you then want to delve even further, you can look at the passes played from these key areas — those darkest green and look at where those passes are aimed towards. Does van der Hoorn play these deep passes into the central areas or into the wider channels more? Does Basham play crosses or is he more of a short passer in these wide areas? PassScore won’t give you all the answers but hopefully, it’ll give you a good starting point to work from.

In terms of weaknesses, you can also use PassScore to identify whether or not teams and players are vulnerable in certain positions on the pitch. Are certain players weak under pressure? Do they concede possession with poor passes deep in their own half? Is the goalkeeper’s distribution poor?

The tweet above shows two images, the first showing a list of players in descending order of those who have made the riskiest passes near to their own goal. The players that have made unsuccessful passes in dangerous areas close to their own goal. Ipswich’s Matthew Pennington is top of the list with 8. It might be worth teams looking at these particular passes and watching them to understand why and how these unsuccessful passes came about. Was he tightly pressed? Or is he simply weak on the ball? Is he then a target to press?

Swansea’s Connor Roberts is also in this list. It’s worth pointing out that his excellent attacking passing scores ensure that he’ll always score a positive value overall. But when you apply selective filters, like selecting passes with a -2.5 value or less as per the player list above, you can identify players making these poor passes. Roberts has been caught out on some occasions when teams have applied a high, intense press in Swansea’s defensive third. For the opposition, this could be a target pressing area, for Swansea themselves, this could be identified for an area for improvement when building from the back.

Last but not least, the chart below shows a quick scatter graph of all Championship teams last season, their total PassScore, how far up the pitch, on average, do they play their best passes and the average length of these passes. Quickly, you can see which teams play longer passes (crosses?) — are these from deeper positions or closer to the byline?

In the top right area of the chart, these are the teams that play more of their most valuable passes from closer to the byline and play shorter passes (fewer crosses and more interplay in and around the box). Champions Norwich City appear in this quadrant as well as Leeds United, Swansea, Brentford and Nottingham Forest. Derby County were the surprising team here, they play their attacking passes from deeper positions but ideally, surely you want to be getting closer to goal before playing these passes? They’re likely to be easier to make and therefore more likely to be successful. It would then be useful to look at these deeper passes in more detail, are they chipped into central areas or are they more deep crosses?


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