Using Grey Models to Predict Some Indicators of Al-shorta Football Club in the Iraq

Abstract

The Iraqi Football League faces significant challenges regarding data quality, as statistical records exhibit evident deficiencies in accuracy and completeness. These problems primarily stem from the absence of a systematics data recording system, coupled with limited awareness of the importance of precise sports statistics. In this context, grey models as an effective solution for handling incomplete data through specialized mathematical modeling processes in grey system whitening. This research applied four principal grey models: the grey model GM(1,1), grey model GM(1,2), grey model GM(2,1), and grey model VG(1,1). These models were selected for their capability to analyze systems with incomplete information by transforming grey data into measurable predictive models.The comparative analysis of the four grey models employed two key evaluation metrics: Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). Results demonstrated the superior performance of the GM(1,1) model in terms of both accuracy and ease of implementation. This advantage stems from its simpler mathematical structure and greater efficiency in processing limited datasets compared to other models. The findings confirm the potential of grey models, particularly the GM(1,1) model, as a practical tool for supporting technical decision-making in Al-shorta Football Club. Furthermore, they highlight the need to develop enhanced statistical data collection systems to improve the accuracy of future analyses.