Forecasting Attendance Demand of Sports Games

Abstract

Demand information is a major tool for decision makers and sports administrators. It is also required by dynamic ticket pricing models of sports events, which have been an increasingly popular research topic. It can be inferred that the accuracy of the demand forecasting plays a crucial role on critical decisions. Considering this point, soft computing techniques, artificial neural networks and adaptive neuro-fuzzy inference system (ANFIS), are utilized to forecast attendance demand of sports games in this study. Classical regression models have been used in almost all of the studies conducted for the same purpose. It is aimed to propose competitive alternative forecasting approaches. To do so, multiple models are developed to forecast attendance demand rate of sports events. To generalize the use of the proposed approaches, as a general term, the demand rate is considered. By multiplying the demand rate by the total number of tickets offered for sale, the number of spectators (attendance demand) is obtained. The proposed approaches can be used for almost all sports disciplines by making some modifications. Demand factors of indoor and outdoor sports games may differ. Therefore, the determinants of demand are selected accordingly. The real data of the sports clubs are used to evaluate the forecasting results of the models. The mean absolute percentage error (MAPE) is used for that purpose. As a result, the MAPE values of the proposed models are below %10 that means the proposed models can be utilized for demand forecasting purposes. The proposed models may be utilized in other industries as well.