Categoria: Research & Analysis
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AERO: a new aerial skill metric
In this blog post, we introduce our new Aerial Elo Rating Optimization (AERO) index, a new metric focused on aerial skill. Aerial skill metrics and the case for an Elo rating Aerial duels can be a key event in football on both ends of the pitch, and the relative skill between defenders and forwards in…
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One-twos: a new metric for associative ball progression and chance creation
Ball progression and chance creation are key phases of play in football, which can be achieved in several ways. In this article, we introduce a new metric which quantifies a specific mode of associative ball progression and chance creation: one-twos. We describe our empirical definition of one-two in event data and characterize the resulting data…
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Introducing Gegenpressing Intensity (GPI)
Gegenpressing, or counterpressing, is now a well-established strategy in many possession-based teams. To capture this aspect of the game, we introduce a new metric, called Gegenpressing Intensity (GPI), which measures the fraction of times a team immediately attempts to regain the ball after losing possession in attacking areas, rather than falling back. We find that…
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Shot quality and results in football
We investigate the link between shot quality, as measured by the team average xG per shot, and both results (in terms of wins) and performance (measured with our Expected Points model), for the top 5 European leagues. We find a strong link between xG per shot difference (xG per shot minus xG per shot conceded,…
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The Very Exclusive xOVA Club
We introduced Expected Offensive Value Added, or ‘xOVA’, to facilitate scouting for creative and skilled players, who are able to have a great impact in the final third of the pitch. Therefore, forwards, wingers and attacking midfielders top our xOVA charts. For example, in our database, the highest seasonal xOVA (22.27) was produced by Lionel…
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Measuring pressing success: Buildup Disruption Percentage (BDP)
Pressing is a fundamental part of many teams’ game plan in modern football. At the same time, it is difficult to measure it accurately using event data. While PPDA is certainly a very useful metric, in our view it would be advisable to complement it with different, indirect measurements of how teams are able to…
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Soccerment's Expected Points model
Expected Goals (xG) models allow us to quantify the chance quality of individual shots. This gives us the chance to quantify the probability of a team winning, losing or drawing the match, based on the two team’s total xG during the match. In turn, this can be translated into an expectation value for points gained…
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Expected Threat
One of the cornerstones of measuring offensive contributions in football is based on a very simple fact: the likelihood of scoring a goal in the next few actions depends heavily on the distance to goal at any given moment. It follows, therefore, that the most valuable actions, besides those directly related to scoring such as…
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![[ITA] – Le statistiche avanzate di Soccerment](https://soccerment.com/wp-content/uploads/2021/08/Soccerment_Advanced_Metrics-11-1.png)
[ITA] – Le statistiche avanzate di Soccerment
Uno degli obiettivi principali dell’analisi dati applicata al calcio è di informare chi prende decisioni strategiche nel modo più oggettivo e imparziale possibile. Poiché il calcio è uno sport con punteggi bassi, dove il risultato è determinato da eventi rari soggetti ad un alto grado di casualità, statistiche come gol e assist sono spesso insufficienti…
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Soccerment's Advanced Metrics
One of the main goals of data analytics in football is to inform performance analysts and other decision-makers in the most objective and unbiased way possible. As football is a low-scoring sport with crucial events that are subject to a high degree of randomness, metrics such as goals scored and assists are often insufficient to…
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