Recent developments in \ac{ML} have paved the way for unprecedented possibilities in the field of data analytics in numerous team sports, such as American football, baseball, and basketball. In more recent years, \ac{ML} techniques have been applied to football as professional teams got inspired to collect enormous quantities of data to evaluate the performance of their players and their applied tactics.
However, research in performance analytics has primarily focused on the development of offline evaluation techniques, while online evaluation of a game has remained largely unexplored.
To attract the attention of executive decision-makers in football, research requires the creation of comprehensive metrics that can give answers to the continuous calls of coaches and trainers to evaluate within-game performance.
This work therefore aimed at finding an online evaluation technique to capture the complex aspects and highly non-linear dynamics of the sport in an interpretable way. \\
To achieve this goal, an online predictive model was developed using an \ac{ANN} approach, which is able to give an instantaneous value to every moment in the game through the computation of the \ac{ED}. The \ac{ED} is a comprehensive metric created in this work, which defines a form of relative, goal-related danger and encapsulates the complex spatio-temporal characteristics of the 22 players on the pitch.
The \acp{ANN} have been trained and evaluated on two newly created datasets of simulated data which contain a combined total of 72 games and include the tracking data of the players and ball as well information on in-game events such as shots, passes, and goals.
The extensiveness of the datasets enabled the creation of a novel \ac{MDP} that describes the complicated dynamics of the system.
The developed \ac{MDP} model is a crucial component of this work and served as the basis for the design of the \ac{ED} and allowed the creation of the \ac{ANN}-based prediction model.
The prediction pipeline was tested over five different prediction horizons, ranging from 5 to 45 minutes. Accurate predictive performance was achieved over all horizons,
thereby showing the \ac{ANN} architecture was able to account for all the nuances within the wide range of games of the datasets.
The predictor functions were also tested on a small, additional dataset of unknown opponents to evaluate online performance and test robustness against unknown opponents. Experiments on this dataset demonstrated that reasonably accurate predictions can be made as long as the prediction horizon is not larger than 15 minutes. \\
Results achieved in this work show that a \ac{ANN}-based approach to online predictive modelling can achieve accurate results. However, future research should focus on validating the performance of the \acp{ANN} on a more extensive dataset of unknown opponents and, ultimately, on a dataset of real-life football games. In addition, the online prediction pipeline of this work can be extended to an online decision-making model with the purpose of changing tactics dynamically during the game.