Goal Multiplicator: A New Metric Detecting The Quality And Importance Of A Goal

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Do you really think that Maradona’s “Goal of the Century” against England in the 1986 World Cup carries the same “value” as Bellingham’s goal against Osasuna during the 9th matchday of La Liga 2023/2024? Obviously, the answer is no. Yet, conventional international rankings classify both as “just” a goal, represented as “1” in scorer tables. At Brandsen Sports, a data analytics company, the aim is to take subjective descriptions and reduce them to quantifiable metrics, facilitating analysis, comparison, and overall scouting processes. This formed the rationale behind the development of the Goal Multiplicator (mG), a new metric designed to assess the importance, beauty, style, difficulty, and overall added value brought by a player when scoring a goal.

The Goal Multiplicator (mG) is defined as an innovative statistical metric that captures the added value brought by the scorer when a goal is scored. The concept of the Goal Multiplicator emerged a few months ago while I was analysing a match using Brandsen Sports metrics. During this analysis, I encountered a scenario where it seemed unjust to assign the same “value” of 1 to two vastly different goals in terms of difficulty, importance, and beauty. This inconsistency led me to contemplate a formula that could address this issue. Initially, it was a challenging task, but my background in economics provided a breakthrough. I decided to draw upon the Keynesian Multiplicator, a macroeconomic concept that measures the percentage growth in national income relative to the increase in one or more macroeconomic variables that constitute aggregate demand. Essentially, it quantifies how national income increases in response to the rise of certain macroeconomic factors. His aim was to adapt this concept to football, specifically focusing on scoring actions.

Why is it useful?

I argue that developing a new metric to objectively measure the “quality” of a goal scored can be exceptionally meaningful for data analysts and, particularly, for football clubs and federations in enhancing their scouting and decision-making processes. Specifically, it can help clubs avoid poor choices when acquiring new players, especially offensive players. For example, Real Madrid’s purchase of Luka Jovic in the summer of 2020 for €63 million exemplifies one of the most significant missteps by arguably the most prestigious club in football history. Jovic played 51 matches in 3 years, netting only 3 goals across all competitions. This issue stemmed not from injuries but from subpar scouting that perhaps could have been mitigated by utilising the Goal Multiplicator (mG). In this scenario, Real Madrid, using mG, would have been able to discern that the added value brought by Jovic in his 40 goals with Eintracht Frankfurt was less substantial than initially perceived. But let me delve deeper into what the multiplicator concretely entails.

Variables

To create this new metric, I used both established variables and new variables.

Established variables:

– xG (Expected Goal = it measures the quality of a chance created, before the shot);

– xGOT (Expected Goal on-target = it measures the probability of an on-target shot resulting in a goal);

– xT (Expected Threat = it measures the probability of scoring from a given rectangle that, together with the others, covers the entire playing field; table 1.)

New variables:

Shot quality: it measures the quality of the shot taken by the scorer, using xG and xGOT);

– Goal importance (= it measures the importance of a goal scored, for ex. Winning goal, draw goal, +2 goal…);

– Team ranking (= it measures the importance of the team against which the goal is scored, considering the category of scorer’s team);

– Time (= it measures the moment of the match in which the goal is scored divided by minutes);

– Carry (= it measures the added value of scorer’s carry with the ball before the shot, using xT).

To understand better I bring you the example of Bellingham’s double against Barcelona in season’s 1st Clàsico in October:

1st goal: mG = 2,2

2nd goal: mG = 1,2

In this case, the Goal Multiplicator metric enables us to discern the added value brought by Bellingham in his two goals against Barcelona. Rather than merely accounting for these as two standard goals or two “points” in the scorer’s table, the refined value is 3.4 (which is the sum of 2.2 and 1.2). Consequently, these goals collectively contribute an added value of 1.4. This enhanced approach transforms the concept of the scorer’s table, making it significantly more relevant and insightful for scouts, coaches, managers, and agents. Let’s see an example…

Sample Case

Scorers table of La Liga 2023/2024 first half of the season (after 21 matches):

Scorers Table	Scorers Table (by mG)
Dovbyk 13	Bellingham 18,9
Bellingham 13	Morata 18,6
Morata 13	Dovbyk 16,7

From this example, we can clearly see the advantages of the Goal Multiplicator. The right table takes on a different form compared to the classic one on the left. By calculating goals with the mG, we can better comprehend the “quality” of the goals scored, as it allows us to convert a complex action into a number, facilitating analysis and comparison. In this specific case, the quality of the goals scored by Bellingham and Morata is evidently higher than that of Dovbyk.

Bellingham has an added value of 5.9 (which is 18.9 minus 13), Morata 5.6 (18.6 minus 13), and Dovbyk a lesser 3.7 (16.7 minus 13). From this, we can easily calculate the Added Value Ratio (AVratio): Bellingham = 0.45 (5.9 divided by 13); Morata = 0.43 (5.6 divided by 13); and Dovbyk = 0.28 (3.7 divided by 13).

AVratio: The AVratio is calculated by dividing the total added value by all goals scored. This gives us a highly useful metric to understand the average added value brought by the player in his goal action. The higher the AVratio, the more the goal scored depends on the quality of the player. Traditional scorers’ tables merely show the number of goals scored by players; in this case, all three players scored 13 goals, but it doesn’t consider how these goals were scored.

Critics

The Goal Multiplicator is undoubtedly a complex metric and, as you might expect, it presents some imperfections—specifically, aspects that I haven’t yet been able to measure and therefore are not included in the formula. One crucial play situation that should necessarily be incorporated into this metric is the “unmark”. This is the situation where a player creates space for themselves before receiving the ball. Unfortunately, a precise metric to measure a player’s unmark movements has not yet been developed. However, I am confident that, with time, we will be able to assess the effectiveness of a player’s unmarking through the use of tracking data. Tracking data represents the new frontier in football analytics, enabling us to capture the spatial coordinates of all players and the ball at every moment during the game. Thanks to this emerging technology, he will be able to include and calculate player unmark movements, thereby enhancing the Goal Multiplicator to be more complete and realistic.

Conclusion

After perusing these five pages, I am confident you now understand why I decided to create this new metric and the breadth of its utility. In today’s football landscape, clubs, much like companies, must stay updated to maintain their competitive edge. It is no longer feasible for football clubs to make decisions without relying on data; every club is engaging in data-driven decision-making processes to varying degrees. The development of the Goal Multiplicator aligns perfectly with this modern approach, as its primary function is to offer new data to aid and refine the scouting processes of football clubs. At Brandsen Sports, we are firmly convinced of the metric’s potential, and we believe the Goal Multiplicator can serve as a foundation for the development of increasingly sophisticated and comprehensive metrics.

If you think goal multiplicator could be useful for you and you want more information don’t hesitate to contact Niccolò on niccolobecattini08@gmail.com.

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© Niccolò Becattini, Operational Manager and Analyst for Brandsen Sports. All rights reserved. This document is intended solely for informational and educational purposes, and any unauthorised use or reproduction of the content herein without the express written permission of Niccolò Becattini is strictly prohibited.