Gray, Dianna P.

Committee Member

Sharp, Linda, A.

Committee Member

Hutchinson, Susan R.


Sport and Exercise Science


University of Northern Colorado

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Greeley (Colo.)


University of Northern Colorado

Date Created





299 pages

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Born digital


Technological advances in data storage and processing have led to more sophisticated ticket pricing strategies in professional sport. Sport organizations are beginning to adopt a form of revenue management known as dynamic ticket pricing. Effective pricing strategies such as dynamic ticket pricing require an in-depth understanding of the nature of advance ticket inventory and accurate forecasting models to predict remaining inventory at various time horizons prior to game time. The purpose of this study was to gain an understanding of the nature of advance seat section ticket inventory. The study built on and contributed to work in sport revenue management. Although studies of sport revenue management have examined the applicability of revenue management in a sport context, there has not been a study of advance seat section ticket inventory despite the fact that sport organizations utilize price discrimination strategies at the seat section level. As such, this study provided additional insight into the applicability and potential effectiveness of a sport revenue management strategy. The methodological focus on forecasting models and accuracy enabled another contribution. A 3x3x6x7 full factorial research design examined the accuracy of various forecasting models under different data strategies, time horizons, model parameters, and levels of the values of T and K used in the moving average and exponential smoothing forecasting models. Statistically reliable differences existed between data strategies with the classical pickup data strategy providing the best forecasts of final game day inventory. Within the classical pickup strategy, no reliable differences in forecast models were detected nor were forecasts found to significantly differ when changing the value of T or K. Finally, forecast accuracy was shown to follow the theoretically predicted best to worst pattern as days out increased. A profile analysis of seat section ticket inventory showed seat sections exhibit different slopes and changes in slope over time. The general pattern of ticket inventory followed a linear trend but with varying slopes. Steeper slopes were found at 20, 10, and 5 days out followed by a leveling out between 5 and 3 days out which was then followed by steeper slopes from 3 days to game day. This finding suggested that optimizing a sport revenue management plan should include forecasting at the seat section level.

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