Goalplayer Value Added (GV+): measuring passing contribution in the build-up for goalkeepers

It has been a hot minute since we spoke about goalkeeper data did we not? Goalkeeper data is not as evolved as field players. The data that is commonly used in goalkeeper evaluation is the shot-stopping data. I can write about that here too, but other people have done that better and in more detail in other blogs or on websites. I prefer looking at data that does not look at shot-stopping, but on-ball metrics for goalkeepers. Specifically, data that deals with actions with the feet of goalkeepers.

There are numerous ways of giving value or worth to on-ball actions, but I wanted to see how much passing adds value to the build-up of a team. I want to categorise the passing length, locations and impact on build up to measure how active a goalkeeper contributes to that. In the fashion of my latest articles, I will approach this from a theoretical, data and mathematical approach.

How qualified am I to talk about goalkeeper data? I am not sure, but I have written some articles earlier about goalkeepers:

• Goalkeeper Sweeper Pass Score: Measuring how a sweeping action can contribute to progression
• Using Standard Deviation and Mean Absolute Deviation to rate Goalkeeper’s shot-stopping
• Goalkeeper data analysis: WSL 22/23
• Complex GK Union: Unveiling the unresolved aspects of shot-stopping data in Football

Contents

1. Why this new metric?
2. Data collection
3. Methodology: Calculations
4. Analysis: GV+
5. Final thoughts

Adapting Basketball Data Analytics to Football: Spatial Dynamics of Man-Marking with Average…

Why this new metric?

The Goalplayer Value Added (GV+) metric provides a quantitative evaluation of a goalkeeper’s contribution to their team’s build-up play and defensive organisation, extending beyond traditional shot-stopping measures. By incorporating weighted assessments of passing, claiming, and tackling actions, GV+ offers a comprehensive analysis of a goalkeeper’s influence on ball progression and defensive interventions.

Pass contributions are evaluated based on length (short, medium, long), accuracy, and spatial context (pitch thirds and half-spaces), capturing the risk and reward of distribution. Claims are assessed by type (e.g., high or low) and location, reflecting a goalkeeper’s ability to relieve pressure and initiate counter-attacks. Tackles are weighted by positional zones to account for their defensive significance.

This metric enables detailed comparisons of goalkeepers, highlighting their role as proactive playmakers and defenders. GV+ supports data-driven scouting, performance appraisal, and tactical planning by contextualising a goalkeeper’s impact across all phases of play.

Data collection

The data used in this analysis consists of different data sources. For the player-level data I used Wyscout and Statsperform/Opta. All data is based on goalkeepers in the Eredivisie 2024–2025 with at least 500 minutes played for their club. I will use this data to showcase which goalkeepers are the best in shot-stopping using the two different data sources.

For the event data I used Statsperform/Opta and there is no minutes played filter on it, but I have only focused it on the Eredivisie 2024–2025 season. This will be used to actually make the new metric by focusing on the passing.

Methodology

So how do we go about getting this specific metric? The first step is to look at the data we need for it:

• Passes: just the raw passes and whether they are completed or not
• PlayerId and TeamId
• Start location and End location of the passes

With that data we first going to calculate three different forms of passes. Short passes, which will be shorter than 5 meters. Medium passes that will be between 5 and 15 meters. Long passes that will be more than 15 meters.

The next thing we need to is to look at the locations. A short pass starting higher up the pitch can mean something different than a medium passes starting deep, so we need to make these distinctions. We will divide the pitch into 18 zones as illustrated below.

What I want for our build-up by goalkeepers, is to see where the end location of the ball is going to be. I will only focus on goalkeepers’ passes that will end in either defensive third or in the middle third as it gives a better idea of progression from build-up, rather than a long ball.

All of these options need different weights. This means we have a lot of options to weigh.

As shown in the table above, by giving weights to the passes, we can add value to each pass made. However, we don’t only add passes to our calculations that are successful. The successful passes are added as +, but the unsuccessful passes, will be listed as -. These weights will be converted into a number.

Now we have all the weights and type of passes we are going to use, we can go over to the calculations.

In this formula, we calculate the sum of all actions. The weight of the pass is quite evident, as it is one of the weights we can have given above in the table. The pass value corresponds with a score divided by the total of passes. This score is either + or -, depending on the outcome of the pass. It’s worth mentioning that the + or -, will only be added in the final score, not in the weights. Every pass has the same weight, but the outcome dictates whether it’s positive or negative.

Before we calculate the GV+, I’m going to assess the threat of the pass as well with Expected Threat. The reason I’m doing this is to see how much attacking danger a goalkeeper can add with passing.

The basic idea behind xT is to divide the pitch into a grid, with each cell assigned a probability of an action initiated there to result in a goal in the next N actions. This approach allows us to value not only parts of the pitch from which scoring directly is more likely, but also those from which an assist is most likely to happen. Actions that move the ball, such as passes and dribbles (also referred to as ball carries), can then be valued based solely on their start and end points, by taking the difference in xT between the start and end cell. Basically, this term tells us which option a player is most likely to choose when in a certain cell, and how valuable those options are. The latter term is the one that allows xT to credit valuable passes that enable further actions such as key passes and shots. (Soccerment)

So before we calculate GV+, let’s see what we have now:

1. Pass types
2. Pass locations
3. Weights
4. xT

Now we have to create a score, but there are different ways of doing that. We are going to use the mean. I’ve taken z-scores because I think seeing how a player is compared to the mean instead of the average will help us better in processing the quality of said player and it gives a good tool to get every data metric in the right numerical outlet to calculate our score later on.

Z-scores vs other scores. Source: Wikipedia

We are looking for the mean, which is 0 and the deviations to the negative are players that score under the mean and the deviations are players that score above the mean. The latter are the players we are going to focus on in terms of wanting to see the quality. By calculating the z-scores for every metric, we have a solid ground to calculate our score via means.

I’m going to use the weighted mean to create the GV+. GV and xT get a weight of 1, while the weight as shown in the table before gets a weight of 2:

By doing this, I get a new score that gives me the GV+, my new metric I have been looking for. After having done that, we can start with the analysis.

Analysis

We now have all the goalkeepers with their playing minutes and how much they contribute to build-up actions. In the scatterplot below you can see how they rank.

As we can see in the scatterplot we see the correlation between minutes played and the total of GV+ a player has. That’s only logical because more passes lead to more xT or more positive outcomes. We now have to fix two things:

1. We need to have a minimum amount of minutes for representative analysis. I will set it at 500 minutes played in the current season.
2. I want to calculate the per 90 metric, as it gives a closer idea of how a player does per game/90 minutes and adds value for future evaluation.

As you can see in the bar graph above, we now have all goalkeepers with at least 500 minutes played in the Eredivisie 2024–2025 with the per 90 values calculated. This gives a more complete idea of how much they contribute to the build-up per 90 minutes in the games and how much their passing adds value to it.

Final thoughts

Adding value to the passes in the build-up shows that a goalkeeper can be integral to possession phases with his/her feet. Goalkeeping is more than just shot-stopping and this value shows that.

There are challenges though I would like to tackle in version 2.0 of this metric. I want to include more quality in the index to assure it’s more quality-based and less quantity-based. I would also like to see where we can make better distinctions in zones rather than only thirds. Room for thought for sure, in 2025!