# Leveraging the Chain on Goals Model in Football: Applications for Attack and Defensive Play
## 1 Introduction  
Football data analysis refers to the process of collecting, processing, and interpreting statistical information related to football matches, players, and teams to gain insights that can enhance performance, decision-making, and strategy. With advancements in technology and data science, football data analysis has become a crucial tool for clubs, coaches, and scouts to improve everything from game tactics to player development [1, 2]. In the early days of football, the use of data was extremely limited, often revolving around simple metrics such as goals scored, assists, yellow and red cards, shots on target, or possession percentage [3]. Coaches relied on their experience and intuition, while performance assessments were mostly subjective. Tactics were developed based on observation rather than quantifiable metrics [3]. In the 1990s, football saw a growing interest in statistical tracking. The introduction of companies like Opta in 1996 marked a turning point [4]. These companies began to record and analyze more detailed aspects of the game, such as passing accuracy, tackles and interceptions, distance covered, or number of crosses and corners [4; 5]. These data allowed clubs to start making more informed decisions about player performances and match strategies. The early 2010s marked the beginning of the Big Data revolution in football. Clubs began to accumulate vast amounts of data, not just from matches but also from training sessions. Simultaneously, the introduction of advanced statistical models revolutionized how performance was measured, as they highlighted the role of the shot, specifically the characteristics before and after the shot [6; 7; 8; 9; 10]. These included expected goals (xG) [11; 12] or expected goals on target (xGOT) [13]. xG is a statistical metric used in football to measure the quality of scoring chances and the likelihood of a given shot resulting in a goal. The value of this metric assigned to a shot represents its \"goal probability\" and is determined by various factors such as the distance of the shot, the shooting angle, the type of shot (e.g., header or strike), the positioning of defenders, and the play sequence leading up to the attempt [14]. xG quantifies the likelihood of a shot resulting in a goal on a scale from 0 to 1, where a higher xG value indicates a greater probability of scoring [9; 10]. In contrast, the expected goals on target (xGOT) metric offers a more detailed analysis by incorporating not only the quality of the scoring opportunity but also the execution of the shot itself. xGOT assesses the probability of a goal after the shot has been taken and is on target, considering factors such as shot placement, power, and the goalkeeper's positioning and reaction time. This refinement providers xGOT a deeper layer of analysis, focusing on the interplay between shooting quality and goalkeeping effectiveness [13]. Following this approach, in 2024, [PERSON] and [PERSON] [15] introduced an analysis model that emphasizes the process over the outcomes, aiming to offer more comprehensive insight into players' or teams' performance during a match or throughout season. This model, referred to as the \"chain on goals model\", differs from other models by incorporating multiple metrics instead of relying on a single one [6; 11; 12]. The model was structured around four primary metrics: goals scored, possession value (PV), xG, and xGOT. Each metric functions as a link in a chain, with the model premised on the idea that these interconnected data points collectively form a comprehensive representation of performance. Furthermore, the integrated analysis of these metrics offers opportunities to derive novel and insightful information relevant to football analytics. This chain on goals model was designed to provide a more precise evaluation of player and team performance in football matches.