This dissertation includes two essays. In the first essay, I use Major League Baseball ticket data, both in the primary market and in the secondary market, from one anonymous franchise in the 2011 season to study how the franchise can price dynamically to increase its revenue. Compared using a uniform price schedule over time, my model proposes that the franchise can see increased revenue by decreasing ticket prices as the game day approaches. In the counterfactual experiment, the revenue for the franchise can increase by approximately 6.93% as long as the assumption holds that consumers are not strategic in either market. However, if consumers are strategic in purchasing tickets, the revenue for the franchise will increase by around 3.67%. In the second essay, I focus further on the secondary market using both listing and transaction data from StubHub to study different pricing strategies for the different types of sellers. The data show that the sellers on StubHub can be separated into two types: single sellers and brokers. The single sellers sell tickets in just one or two games during the whole season. The brokers sell many tickets in a given game and also sell tickets in most of the games during the season. I use the data to estimate the probability of sale by the probit model first and then calculate the optimal prices for each listing on each day. The benchmark model shows that brokers price more optimally (meaning smaller expected profit losses) on the final day of sales. However, the two types of sellers have similar expected profit losses on other days.
