###### Abstract  
Evaluating player actions in football is essential for understanding match dynamics and optimizing team strategies. Traditional models, such as the widely adopted Expected Threat (xT) model, assign static threat values to pitch zones without considering real-time player positioning, leading to a limited representation of the evolving tactical context. To address this limitation, we introduce the Dynamic Expected Threat (DxT) model, which adjusts threat values dynamically by integrating off-ball player positions. DxT refines the probability of shooting and ball movement using an Expected Goals (xG) model that incorporates off-ball player positioning. Built on event data from professional football matches, which encompasses over 335,000 actions, our results demonstrate that DxT significantly improves upon traditional xT models by offering a more accurate and dynamic evaluation of action threats. This framework enhances tactical analysis, providing valuable insights for coaches, analysts, and scouting professionals seeking a more realistic approach to performance evaluation. fontsize  
beyond traditional metrics. [PERSON] [4] later popularized the term \"Expected Threat\" (xT), introducing an equation that computes xT components. Central to [PERSON]'s approach was the location of the ball, which allowed for a detailed grid of the pitch, assigning xT values to each grid cell. Despite its usefulness, the xT framework presents notable limitations. First, it is restricted to actions characterized by a well-defined start and end location, such as passes and carries. Second, it does not account for the threat conceded by a team, limiting its ability to evaluate how actions expose a team to danger. Finally, it overlooks the spatial configuration of off-ball players, whose positioning plays a crucial role in both creating and mitigating threats during a game. A notable advancement in football action evaluation is the VAEP model (Valuing Actions by Estimating Probabilities), introduced by [PERSON] et al. [5]. Unlike xT, VAEP overcomes key limitations by evaluating all types of actions, regardless of whether they have clearly defined start and end locations. Furthermore, it assesses not only the potential to create scoring opportunities but also the extent to which an action increases a team's vulnerability to conceding a goal. For instance, a pass may positively influence the probability of scoring in subsequent actions, yet at the same time, it may also expose the team to counterattacks if unsuccessful or poorly positioned. VAEP captures this dual nature by estimating the change in scoring and conceding probabilities before and after each action. The model incorporates contextual features such as action location, distance and angle to the goal, game tempo, time remaining, and current score differential, providing a comprehensive view of an action's impact within the match context. In addition to VAEP, other machine learning approaches, such as Deep Reinforcement Learning (DRL), have been explored for football action evaluation. [PERSON] et al. [6] employed Deep Reinforcement Learning (DRL) by training an action-value Q-function to evaluate actions, developing a neural network framework with a stacked two-tower LSTM architecture, one for each competing team. In a similar vein, [PERSON] and [PERSON] [7] employed a Markov model in hockey to compute Q-values for actions, which estimate the probability of scoring the next goal based on the current game context. [PERSON] and [PERSON] [8] also leveraged DRL to analyze decision-making, introducing a Decision Value (DV) metric that optimizes decision policies based on teammates' and opponents' positions. While these models have significantly improved action evaluation by integrating machine learning techniques and probabilistic frameworks, they often fail to account for the dynamic spatial context of player positioning despite its crucial influence on decision-making and game dynamics [9; 10; 11; 12; 13; 14].