Artificial data in sports forecasting: a simulation framework for analysing predictive models in sports - Marc Garnica-Caparrós; Daniel Memmert; Fabian Wunderlich (Information Systems and e-Business Management, 2022)
Far-reaching decisions in organizations often rely on sophisticated methods of data analysis. However, data availability is not always given in complex real-world systems, and even available data may not fully reflect all the underlying processes. In these cases, artificial data can help shed light on pitfalls in decision making, and gain insights on optimized methods. The present paper uses the example of forecasts targeting the outcomes of sports events, representing a domain where despite the increasing complexity and coverage of models, the proposed methods may fail to identify the main sources of inaccuracy. While the actual outcome of the events provides a basis for validation, it remains unknown whether inaccurate forecasts source from misestimating the strength of each competitor, inaccurate forecasting methods or just from inherently random processes. To untangle this paradigm, the present paper proposes the design of a comprehensive simulation framework that models the sports forecasting process while having full control of all the underlying unknowns. A generalized model of the sports forecasting process is presented as the conceptual basis of the system and is supported by the main challenges of real-world data applications. The framework aims to provide a better understanding of rating procedures and forecasting techniques that will boost new developments and serve as a robust validation system accounting for the predictive quality of forecasts. As a proof of concept, a full data generation is showcased together with the main analytical advantages of using artificial data.
Keywords: 650; Simulation; Knowledge modelling; Information systems frameworks; Sports forecasting; Artificial data generation