# Quantifying Football Shooting Precision: The Expected Shot Impact Timing (xSIT) Approach
## 4 Practical Applications of the Model  
The xSIT model (Figure 7) was designed to evaluate shooter performance by integrating key contextual and execution-based variables. In this study, critical inputs included the shooter's position, the GK's positioning, the spatial distribution of other players, and the ball velocity.  
To illustrate the model's application, two examples of shots (one resulting in a shot on target and the other in a shot off target) were presented, detailing the resulting xG, xSIT and xGOT values derived from each scenario. Figure 8 presents an illustrative example  
Figure 6: Representation of all players on the field with the xSIT calculation (xSIT = 0.69), where each player’s range of movement was determined by their distance from the shooter and their individual reaction time.  
Figure 7: Representation of a shot on target illustrating the temporal segments associated with each advanced metric (xGOT, xSIT and xG), highlighting the specific phases of the shot analyzed by each and their respective measurement objectives.  
of the xSIT metric calculation, alongside the established xG and xGOT metrics commonly referenced in the scientific literature: Figure 8A illustrates a shot that misses the target but is taken under favorable conditions, as the shooter has a largely unobstructed shooting area. In this scenario, the advanced metrics yielded the following values: xG = 0.02, xSIT = 0.69, and xGOT = 0. Figure 8B, by contrast, depicts a shot that is directed on target but taken under high defensive pressure, with multiple opposing players surrounding the shooter. The corresponding metric values in this case were: xG = 0.70, xSIT = 0.22, and xGOT = 0.63.  
Based on the model's estimations, its principal practical application lies in supporting coaches and performance analysts in the systematic evaluation of shooting behavior, both within their own team and among opposing players. This enables the development of tailored training protocols and tactical strategies, grounded in objective data. By identifying individual shooting tendencies, such as spatial preferences, execution timing, and technical patterns, training interventions can be customized to reinforce specific strengths and address detected weaknesses. In addition, the model provides valuable insights for tactical decision making, allowing for the anticipation of potential constraints imposed by opposing defensive systems and for the strategic modulation of shooting techniques in response to the behavior of defenders and GKs.  
Beyond tactical preparation, the xSIT model contributes to the enhancement of in-game decision making. Training scenarios can be designed to simulate high-pressure conditions, fostering players' ability to execute technically optimal actions in real time. This promotes more effective shot selection and spatial awareness under defensive pressure.  
Ultimately, the integration of this model into performance workflows enhances both offensive and defensive planning. By leveraging these data-driven insights, teams can gain a competitive advantage through improved match preparation, adaptive in-game strategies, and optimized player development.