This year would be the year that I would immerse myself in set pieces. I know that is something I’ve said a lot and it was one of my goals this year — but life has a funny way of disrupting all plans. Now, this year I’ve done a lot on metric development and before the year closes I wanted to share one last metric development/methodology with you that concerns set pieces.

I’ve done a few things on set pieces:

• Corner Threat Score: Measuring the threat a player generates from taking inswinging and outswinging corners
• Free kick Threat Score: Measuring the threat a player generates from indirect free kicks
• Actionable analysis: Individual Header Rating (IHR) determines choices in blockers vs runners
• Throw-in success: generating shots through emphasis on throw-in routines
• Corner Possession Index: measuring a team’s possession quality after an attacking corner.

But, I want to do a final one that goes further and proceeds on the thought pattern of the Individual Header Rating (IHR). I have looked at an individual level what players can contribute in terms of headers, but how do teams rate in set pieces? That’s what I’m trying to illustrate by introducing SPER. A way of rating Set Piece Expected Goals Differences between teams.

Why this metric for set piece analysis?

There isn’t a great necessity for this specific metric. One of the reasons is for me to try and make a power ranking based on expected goals for set pieces. However, with this insight we can create a way of rating team’s on their expected goals performance on set pieces and evaluate whether to rely on them.

In other words, with this metric, we can draw conclusions on how well teams are doing with their set piece xG. This can lead us to a few conclusions as to why teams need to rely on their set piece routines or to improve their open play, as to spread their win chances.

With this metric, we can combine this with the Individual Header Rating and gain meaningful analysis for set pieces, both on individual and team level.

Data collection and representation

The data used for this project comes from Opta/Statsperform and was collected on Thursday 18th of December 2024. All of the data is raw event data and from that XY-data, all the metrics have been developed, plotted, manipulated and calculated.

The data is from the Eredivisie 2024–2025 season and both contains match-level data as well as season-level data. There aren’t any filters used for value, but this is something that can be done in the next implementation of the score, as I will explain further on in this article.

This focuses on how teams are doing so that’s why the set piece xG are generated for each team and not for the individual players. We can split them for players, but it will not be as representative. The xG generated from set pieces often is the consequence of a good delivery as well, which would be nullified if we look at it from an individual angle.

There are different providers out there offering the XY-data, but I am sticking to Opta data for event data as all my research with event data has been done with Opta and therefore the continuity will improve the credibility of this work in line with my earlier work.

What is categorised as set piece?

This might seem like a very straightforward question, but one we need to talk about regardless. In the data there are different filters for our xG:

So, we need to make a distinction between different plays. We will focus on set pieces, but as you can see there are different variables:

• SetPiece: Shot occurred from a crossed free kick
• FromCorner: Shot occurred from a corner
• DirectFreeKick: Shot occurred directly from a free kick
• ThrowinSetPiece: Shot came from a throw-in set piece

These definitions come from Tom: https://github.com/tomh05/football-scores/blob/master/data/reference/opta-qualifiers.csv

I am excluding penalty. By all means it is a set piece, but the isolation of the play means that it’s such a big impact on the expected goals, that I will exclude it. It doesn’t say anything about the quality of play, but rather about the quality of kicktacking.

Methodology

There are a few steps I need to take before I get the SPER from my original raw data. The first step is to convert the shots I have into shots with an expected goal value. Which shots are listed?

• Miss: Any shot on goal which goes wide or over the goal
• Post: Whenever the ball hits the frame of the goal
• Attempt saved: Shot saved — this event is for the player who made the shot.
• Goal: all goals

We take these 4 events and convert them to shots with added value, by putting them through my own xG model that’s trained on 400.000 shots in the Eredivisie. We then get an excel/csv file like this:

This is the core we are working with, but the next step is to also calculate and generate what that means per game for each side. This will add to the xG values, but also win chance in % and the Expected Points given based on that expected goals.

So, now I have the two Excel files that form the base of my methodology and calculation. From here on, I’m going to focus on creating a new rating: SPER.

That has to happen in Python, because that’s my coding language of choice, and will need a few things.

This analysis combines match results and expected goals (xG) data using the Glicko-2 rating system to dynamically evaluate team performance. Match outcomes are determined by comparing xG values of home and away teams. A win, draw, or loss is assigned based on xG values:

Additionally, xG data for specific play types, such as “FromCorner” and “SetPiece,” is filtered and averaged for each team.

The scaling factor (0.1) ensures the adjustment is proportional and keeps outcomes between 0 and 1.

The Glicko-2 system updates team ratings after each match using adjusted outcomes. Each team has a rating (R), a rating deviation (RD), and a volatility (σ). Updates are based on the opponent’s rating and RD, incorporating the adjusted match outcome (S). The system calculates the new rating as:

This is obviously for all the technical mathematicians out there, however, this is the calculation that has been done in Python, but via code. By converting this into Python code, we get a ready to use excel that we can use in the analysis.

Analysis

With the data in the excel file, I know have ratings for every matchday for every team in the Eredivisie. It shows how well every team is doing and how they are progressing over the first half of the 2024–2025 season.

In the bargraph above you can see the final ratings for the Eredivisie with an average included. In the bargraph previous ratings are also included for every team. Feyenoord, FC Twente, AZ, PSV and Ajax are the best teams in terms of SPER. Fortuna Sittard, PEC Zwolle, NAC Breda and RKC Waalwijk are the worst.

In the line graph above, you can see how the ratings evolve over the course of the season for the top 5 teams in the Eredivisie for SPER. As we can see Feyenoord steadily becomes better, while PSV jave a more interesting trajectory with starting high and climbing again. Ajax really sinks in the last few weeks.

Final thoughts

The Glicko-2 scoring system provides a clear way to rank Eredivisie teams by combining match results and average expected goals (xG). It adjusts ratings dynamically, considering form and opponent strength, while xG adds context by reflecting goal-scoring chances. This approach gives a better understanding of team performance than traditional standings. However, its accuracy depends on reliable xG data and the chosen scaling factor for adjustments. Overall, the system is practical for tracking team progress and comparing strengths, offering useful insights for fans and analysts.

This might be my best and scariest project up to date. Scary because it can be full of flaws, but also best because I feel this will change something in the way we approach passing networks. Not that I think I will innovate the analytics space, but because I have been trying to find a way to create something meaningful from passing networks away from the aesthetics on social media. Because, I’m a firm believer we can create something meaningful from it and gather valuable information, you just need to know where to look and what the aim is.

In this article, I will show you a way that you can use to create off-ball value from passing networks by creating metrics from it and then going to an analysis that will lead to calculations for an impact store. That sounds very vague, but it will become more clear — I sure hope so at least lol — at the end of this article. It will have some logical steps to ensure it remains transparent at all times.

Why this development in passing network analysis?

I eluded to the fact a little bit, but the reason for this analysis is predominantly selfish. I wanted to see if I could create something meaningful from passing networks and test myself to create an off-ball value from event data. The reason for that is that I believe we have incredible data on how valuable possession/actions are with the ball, but far too few without the ball.

The next step for me is to show that with an out of the box thinking, there can open a world that offers much more metrics and paths for data analysis, beyond the aesthetically pleasing passing networks we have seen on social media — which I’m guilty of as well — which don’t add a whole lot. So, I wanted to challenge myself and see what we can extract from the passing network interconnectivity and calculations to develop new metrics and work with that.

Data collection and representation

The data used for this project comes from Opta/Statsperform and was collected on Saturday 14th of December 2024. All of the data is raw event data and from that XY-data, all the metrics have been developed, plotted, manipulated and calculated.

The data is from the Eredivisie 2024–2025 season and both contains match-level data as well as season-level data. There aren’t any filters used for value, but this is something that can be done in the next implementation of the score, as I will explain further on in this article.

There are different providers out there offering the XY-data, but I am sticking to Opta data for event data as all my research with event data has been done with Opta and therefore the continuity will improve the credibility of this work in line with my earlier work.

Passing networks: what are they?

American Soccer Analysis (ASA) said it quite clearly for me as follows:

“The passing network is simply a graphic that aims to describe how the players on a team were actually positioned during a match. Using event data (a documentation of every pass, shot, defensive action, etc. that took place during a game), the location of each player on the field is found by looking at the average x- and y-coordinates of the passes that person played during the match. Then, lines are drawn between players, where the thickness — and sometimes color — of each line signifies various attributes about the passes that took place between those players.

The most common and basic style of passing network simply shows these average player locations and lines between them, where the thickness of the line denotes the amount of passes completed between each set of players.”

In the image above, I’ve created a passnetwork on a pitch from the game AZ Alkmaar vs Ajax 2–1 (December 8th, 2024) and it shows AZ. Now this shows the combinations and directions of the passing combinations, including the average positions.

This is something we see a lot in articles and social media and data reports, but we want to add value to this. Sometimes this happens by any type of value: Expected Threat (xT), Expected Goal Chain, Goals added (G+) or On-Ball Value (OBV). This gives us more meaning and context about the networks, but in my opinion it’s quite limited in the way it tells us about value away from possession. These are possession-based values.

Methodology Part I: Creating metrics from passing networks

So in the passing network we have calculated the average positon of the 11 starters per team and what the passing combinations are. This is now visual, but we want to take a step back and see a few different things we can create into metrics:

• In-degree centrality: The total weight of incoming edges to a player (i.e., the number of passes they received).
• Out-degree centrality: The total weight of outgoing edges from a player (i.e., the number of passes they made).
• Betweenness centrality: Measures how often a player lies on the shortest path between other players in the network.
• Closeness centrality: The average shortest path from a player to all other players in the network.
• Eigenvector centrality: Measures the influence of a player in the network, taking into account not just the number of connections they have but also the importance of the players they are connected to
• Clustering coefficient: Measures the tendency of a player to be part of passing triangles or localized groups (i.e., whether their connections form closed loops).

These are metrics that player-based and focus on how a player is in possession and out of possession. This distinction is important to us, to have an idea where to look later as we approach out of possession metrics.

These metrics are calculated in Python by analysis — using code- the relations between players in terms of passing and their average positions.

Next to player-level data, there are so data metrics that are team-based. These are the following I’ve managed to calculate:

• Network density: Measures the overall connectivity of the team, defined as the ratio of actual passes (edges) to the maximum possible connections.
• Network reciprocity: Proportion of passes that are reciprocated (player A passes to B, and B passes back to A).
• Network Assortativity: Measures if high-degree players tend to pass to other high-degree players

Analysis I: Adjust passing networks with player-receiving values

We calculate the newly developed metrics and form them into a CSV/Excel file, whatever your preference is for analysis. It will look like this:

As you can see we have the distinction between passing and receiving in general. We want to focus on betweenness, which is important: A player with high betweenness centrality is a key link in the team, acting as a bridge between other players. This highlights their importance in maintaining the flow of play.

If we look at this specific game, we can see that Clasie is the player who is most important as the key link in the team, followed by Mijnans and Penetra. It’s not weird that the two midfielders are linked so importantly, but the central defenders getting the ball so often, means something for the security and risk-averse style of the play.

We can also use any of the other metrics to illustrate how a player is doing, but you get my drift: value is there to be added.

Of course, this is on a player level, how does this translate to team-level for this specific game?

On their own these metrics mean nothing, they have to be in relation to other games that AZ has played or have to be benchmarked against the whole league. They however do tell us something about how close the average positions of the players are in relation to each other: a high density means that the team has good ball circulation and most players are connected through passes. A low density may indicate a more direct, counterattacking style with fewer connections. And, that’s something that’s valuable in the bigger picture.

Analysis II: Player comparisons

We also want to see what the relation is between passing and receiving for the key players. We will look at Betweenness and Closeness, which give value if you are closest to receive or pass the ball to the closest: are these key players equally good in both or do you find different outcomes?

If we look at the scatterplot above, we don’t see many outliers and we only see the top 10 players. However, an interesting conclusions we can draw from this is that players are more likely to score higher on passing the ball to the closest teammate, than they are to receive it from the closest teammate. Tristan Gooijer (PEC Zwolle) being the exception.

If you look one step further and compare Eigenvector centrality to Clustering Coefficient, we get some different insights. Eigenvector focuses on a key player also linking with other key players, while clustering coefficient focuses on how well a player is connected in passing triangles.

As you can see in the scatterplot above, the relations are quite different here. The most important players are more likely to be included in the passing triangles, confirming that key players will always look for each other.

Methodology Part II: Creating OBIS

Now, I want to take the next step and the last step. We want to see how we can create an off-ball value score from passing networks. We can do that as follows. We will filter the new metrics and choose those we think we will help in calculating that metric:

• In-degree centrality: A player with high in-degree centrality is frequently targeted by teammates and serves as a passing hub or focal point.
• Betweenness: A player with high betweenness centrality is a key link in the team, acting as a bridge between other players. This highlights their importance in maintaining the flow of play.
• Eigenvector: A player with high eigenvector centrality is well-connected to other influential players. They amplify their team’s passing efficiency by linking with key teammates.

To make the score, I have a formula:

We have the three metrics as described above and they all have a weight. Some metrics are more important for the score than others. In this instance, In-degree has a weight of 0.5, Betweenness a weight of 0.3 and Eigenvector a weight of 0.2.

Analysis III: Off-Ball Impact Score (OBIS)

If we look at the total season so far and the OBIS, we can see that these 15 players score highest in this metric. We want to convert this into a score from 0–100 and if we do that, we get the following scores for the top 25:

Obviously,, this is a season score and we can also look at this from a individual level. And, with that I mean the match level. How do we add value to the passing network with using OBIS.

We are looking at a different game and this time it is the SC Heerenveen — PSV game that ended 1–0 for the hosts. We will focus on PSV.

In the pitch above you can see the passing network of PSV in their game against Heerenveen, but with the nodes coloured based on the OBIS they had in this particular game. In other words, which player had the most impact in not passing, but receiving the ball.

Final thoughts

OBIS is a promising metric for evaluating player performance by combining key network-based metrics like in-degree, betweenness, and eigenvector centrality. By weighting these factors and normalizing them, OBIS provides an insightful measure of player influence on the field. However, further refinement could enhance its accuracy and adaptability. Incorporating additional metrics (pass completion, defensive actions) and considering context-dependent factors (game state, opponent strength) would improve OBIS’s ability to reflect a player’s true impact. Additionally, using machine learning to fine-tune weightings and integrate spatial data could offer a more nuanced, dynamic representation of player performance.

Data helps make decisions in football, but most of the data out there helps analyse teams and players in post-match analysis. This means that we look after the events and determine performances. This gives us a scope of how players have done in a particular time period or how a team has faired against other teams.

I promise you that at some point, I will stop creating new metrics or presenting you with player scores. But I just love them. The reason for that is that event data allows us to play around and make our own metrics. You can tailor the data to the needs you need, but you have to stay vigilant: it’s incredibly prejudiced and subjective. Data also has to deal with narratives and that’s something I hope to give you all if you find these articles interesting to read.

Sometimes I wonder what I really love to write about. And, in all honesty, I don’t usually write about the stuff that truly fascinates me. My mind goes to the audience — you — to discuss what I think will do well for my audience. That’s a good way of approaching the market. Attacking numbers or players always do well, but I think that the attacking pieces are plenty and those of defensive data aren’t. So, that’s why I want to look at data that truly fascinates me and I enjoy defensive data.

For this article, I’m going to have a look at long passes or long balls. The aim is to create a metric via a score that judges not the length of the passes or the end location, but rather looks at the starting location. I have always been fascinated with central defenders who dribble up the pitch or defensive midfielders who distribute from deep, so with this, we can measure how they contribute to their team’s attack.

Data

The data I’m going to use for this specific research is event data from Opta. This was collected from the 2024–2025 season and focuses on the Austrian Bundesliga. I won’t make any distinction for position here, because in terms of average position it will most likely be central defenders dribbling in, defensive midfielders dropping or fullbacks/wingbacks from the half spaces.

Event data will be manipulated so it can be used as match data. This means that will go from XY data to metrics, which is more usable in this kind of visualisation like tables and scatterplots.

Methodology

The aim is to go from event data to results that are totals or per 90 metrics. I have previously made metrics via Opta event data and using standard qualifiers to determine long passes. Which will be helpful.

So what’s a long pass? That’s the first question we have to ask ourselves in this regard. We can determine a long pass from the total passes when:

• A pass is a ground pass over 45 meter
• A pass is a high pass over 25 meter

From that we get a total number of passes based on those qualifiers. But what we are going to do next is to filter those passes. I can do that in the first step already, but since I already have that information I will do it after. I will determine the start location area to make sure I’m getting the right ones.

I will calculate that through Python where I put the event data through a set of calculations. Without limitations we get this:

Of course this is impossible to distinguish and doesn’t give a lot of meaning to what we are trying to achieve. We want to look at start locations that are in the middle third. From the start location in the middle third, we can see the total long passes in the Austrian Bundesliga so far.

In the image above we can clearly see all passes that fit the begin location, but what we also see is that passes have that particular start location, but not the progressive end location. Many of the passes go backwards and that’s not what we want, so we have to change that.

In the image above you can see the corrected version. Now all the passes go forward and beyond the half of the pitch. It already gives us a better idea of progressive long passes, but not quite yet. We still have to filter out unsuccessful passes.

Now we have all the passes that we need to make the metric and create the score to see which players are doing the best in this metric.

Progressive Long Pass Score

So first we have a look at progressive long passes and the success rate of those passes. This gives us an idea of how successful players are when conducting this kind of pass.

In this scatterplot we see the relation the total of progressive long balls and their successrate. I have filtered for players that 5 or more long balls to not skew the data when we are looking at metrics and scores. The next step is to convert this to a score.

We will look at the metrics and give new weights. The successrate is leading, but the weights of the total progressive long balls plays a part too. The success rate will become more important in the score when the volume of long balls is higher.

When we have created the code, we see the following score and rank for the Austrian Bundesliga in terms of Progressive Long Ball Score.

If you want to know more about the Python code, you can subscribe to my Patreon here:

https://www.patreon.com/c/outswingerfc?source=post_page—–52ffeb371fcc——————————–

I’ve included the data and python code to my Patreon 🙂

In the new season 2024–2025 I want to try something new. I’ve been dabbling a lot in innovation (think of creating new metrics) and using analysis with existing data, but that’s not enough for me. It’s quite standard and I want to give more insight to what happens when you tailor your metrics whilst working for a club and make it actionable.

That’s the major issue with online content, it’s mostly present because it speaks to an audience — and I’m definitely part of that issue. I write not for myself only, but also in part because I know my audience would like the result of what I’m researching. I don’t think I can change that, but I can show you a little more about what the design of a metric means in the light of actual analysis within a club or organisation.

In this first installment I will focus on header ability. I want to create a metric that gives a rating to individual heading ability. With that rating that will change after each matchday, I can show probability of a player winning a duel. The actionable part here is that we can use that particular info when we are training and preparing for set pieces in the next game. IF we employ 3v3 runners vs blockers, we want the best winning probability in the air to maximise our delivery.

Why do we need a metric that measures ability

Football is a lot about tactics and avoiding trouble, but there are a lot of areas of the game where we have to focus on duels. It is a contact sport so we need to be aware that this will always be part of the game. To win critical battles on the pitch you need to make sure you can win little parts of it and that’s where aerial duels come into place. If you want to control the central areas of the pitch, you will have to deal with long goal kicks for example. This means there will be a battleground in those zones, so you need a strong force in the air.

I was trying to come up with ideas to look at aerial duels and came across this article about a metric designed by Statsbomb. Because, of course I was too late to actual invent a new idea.

Introducing HOPS: A New Way To Evaluate Heading Ability

It’s a very interesting concept and I wanted to recreate it, but also make it different. I want to look at aerial duels per 90, aerial duels won in % and height, but I also want to look further. I want to see if more metrics can be incorporated or see if different weights can be used for specific metrics. Recreating metrics is a great exercise for data analysis and data engineers, but more often than not you realise you are not completely satisfied with things that have been done before. That’s why I wanted to do it a bit different: I want to evaluate heading ability for individuals, with a link to probability and come with Power Ranking for Individual Set Piece Strength based on heading ability.

What do I need to make this happen?

There are several things I need to make this happen. First of all I need the full data of full season in a specific league or number of leagues. For this particular research, I’ve chosen the Eredivisie 2023–2024. This data can come from StatsBomb, Wyscout, Opta or any other data platform you are using. I’m using Wyscout data and specifically look at four specific metrics:

1. Height: the influence of height on the aerial duels is not to be underestimated. There is a correlation and we will see later what that looks like.
2. Aerial duels per 90: the number of aerial duels means something because it indicates how often you come in these duels and therefore your rating can constantly change.
3. Aerial duels won in %: yes the number of duels is very important, but the win percentage tells us everything about quality. And, that’s where the real advantage will come from.

All the players will have played at least 900 minutes and will be excluded when they are goalkeepers. Their aerial duels are from a completely different calibre and need to be addressed in another research.

Methodology

There isn’t one specific methodology, because I want to make different things. I will be making a rating (and a score based on that rating), a probability and a power ranking based on that.

For the rating I will make sure there is a weighting for the metrics used. I will give a weight of 0,5 to the height, a weight of 1 to the numbers of duels and a weight of 1,5 to the win percentage of those duels.

After that I will use the glicko method to calculate the rating. I can also use ELO, but there is a reason I don’t. Glicko2 is more accurate in terms of predicting match outcomes or win probability and that’s essential for my process in doing this all.

Lastly, I will be making a power ranking and for all of these things I need to make a lot of calculations. These calculations will be made using Python.

Individual Header Rating (IHR)

By using glicko calculations I can calculate a rating based on the weights I’ve described above. By doing so I get a list of the players who score highest. For this example, I will take the top 15 performers according to this rating.

When we look at the table above we see the top 25 players and their rating. The rating is based on glicko2 method, which can be compared to an ELO rating, but again, the calculations are quite different.

When we want to look at an easier comparison for players instead of a ranking, we will convert them into a score from 0–100.

As you can see in the table above we have managed to show the top 10 players and their corresponding score. In this case we can then state that these players are most likely to win their aerial duels against other opposition.

Win Probability

We we will go into changing ratings and scores in a bit, but before we can get there, we need to talk about win probability. If we think a player is going to match up with a player during the length of the game, we can look at how likely it is that the a player will win.

I want to look at two players that might come across each other in a match. In the attacking side I’m going for Luuk de Jong (81,24) who is a threat in the air for PSV. He will play against Lutsharel Geertruida (83,85) of Feyenoord, who might come across to defend him and is very strong in the air too.

Using just the score, we can conclude that the win probability for Geertruida is 54,70% against 45,30% for De Jong. That matching up can be favorable for Feyenoord and PSV might want to think about a different match up. More on that later.

New ratings

So from that probability we move on to the new ratings and effectively making a power ranking. We will continue with these two players and look at the effect of the probability being the actual result.

Geertruida had the win probability and also won the aerial duels (absolute numbers in the majority) and that means his new rating is 2211,73 from 2210,45. De Jong lost and went from 2177,66 to 2176,38. In the grand scheme of things their ratings did change, but their place on the ranking did not. Geertruida stayed at place 33 while De Jong also stayed at place 40.

The place might stay the same, but over a whole season, things can fluctuate and become different. That’s how we can have an individual power ranking based on the Individual Header Rating.

Actionable analysis

Let’s pick Ajax for this analysis. They need to play against NEC in their next game and they need to know how well their own players are doing in the air and how NEC is doing as a whole.

In team performances for this metric, Ajax scores 7th and NEC scores 16th. From this you can conclude that Ajax is better in the air as a team that NEC is. That would lead to soft conclusion that this would also work to Ajax’s benefit in defending set pieces and attacking set pieces.

Now, let’s take a closer look at the individual players.

In the top 5, NEC has 3 defenders and Ajax 2. This means that in general we can see that NEC is stronger in the air with defending than Ajax. Ajax has more players overall who do well in the air in defence, but also in midfield and attack, while NEC isn’t that great in attack in comparison.

Suggestions could be that Ajax needs to focus more on the attacking and see solutions there, because the NEC defence is tough. Their attacking however isn’t as threatening, so the worry shouldn’t be there for Ajax.

The next step is how you match up in attacking set piece to make sure you create the upper hand. Let’s for a moment assume there are runners vs blockers of 2v2. If that would mean that Ross-Pereira would defend, Ajax would do well to get their highest rating players to go against them. That would lead to a higher probability.

Ross still has a higher winning probability, but it is much closer than when Ajax employ another player like Berghuis there;

If this were the case, according to our data, it would be very easy Ross to defend Berghuis. This would also lead to a difference in ratings after the game, but that’s purely for academic purposes.

Correlation between Height and Rating

Of course, there is a relation between the height of a player and the way it’s easier to win those duels in the air. You can see that in the scatterplot above. I don’t think it’s really weird to expect these results, but what can really help in this analysis is to look at outliers — they stand out and can lead to conclusions about someone’s ability to jump or go into an aerial duel. These outliers are marked in red.

Final thoughts

I had a lot of fun creating/writing this because this is something I use to determine my suggestions for set piece positioning to teams. Of course, data is just a part of the story and should always be backed with video to explain routines.

What I really wanted to show is that when you make metrics, there should be a practical use for it when you work in analysis with teams. It should add value to the process of the staff and the players. There are so many bugs, tweaks and turns I need to look at, but it’s an example of how making a metric can help in set piece analysis. Especially when most data metrics focus on delivery and shooting.