Abstract:
Predictive systems have been employed to predict events and results in virtually all walks of life.
Football results prediction in particular has gained popularity in recent years. Statistical approaches have
shown complex and low prediction results. Data mining tools with insufficient features, however, have also
yielded low predictions. In our research, knowledge discovery in databases (KDD) has been used to develop a
football match result predictive model by gathering 9 features that affect the outcome of football matches. We
constructed a more comprehensive system with an improved prediction accuracy by using the features that
directly affect the result of a football match. Our prediction system for football match results was implemented
using both artificial neural network (ANN) and logistic regression (LR) techniques with Rapid Miner as a data
mining tool. The technique yielded 85% and 93% prediction accuracy for ANN and LR techniques respectively.
With this output, it is observed that the prediction accuracy is higher than those of existing systems.
