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\"The purpose of this text is to give a broad overview so that the reader can understand what drives the financial success or failure of any entity in the sport industry. It grounds students in foundational financial principles and concepts and demonstrates how they are applied in real-world sport management settings. This book takes practical applications a step further to encourage students to take a strategic organizational perspective to understanding sport finance. Readers will not only understand the what and how of sport finance, but also the why, creating a more valuable and accessible learning experience and better preparation for future careers. It provides readers with the basic skills needed to help raise, manage, and spend money in sport settings\"-- Provided by publisher.

Purpose – Two of the primary growth strategies for Major League Soccer (MLS) have been team expansion and the construction of soccer-specific stadiums. Therefore, the purpose of this paper is to determine the relationship between these factors and game-specific MLS spectator attendance. Design/methodology/approach – Two multiple regression models, one using multi-level mixed effects linear regression and another using interval regression, were developed to explain the variation in attendance utilizing the two factors of interest along with other control factors that have been identified as attendance determinants in previous literature. Game-specific data were collected for five MLS seasons, 2007-2011. Findings – The two regression models explained approximately 40 percent of the variation in spectator attendance and the results showed that expansion teams and soccer-specific stadiums were significantly related to attendance. However, the effect of soccer-specific stadiums was minimized due to the extreme success of the Seattle Sounders in drawing about twice as many fans as the next highest drawing franchise, yet playing in an American football stadium. Research limitations/implications – While many of the standard factors such as the presence of holidays and novelty players, competition from other professional teams, and day of week, competition from other professional teams; team quality failed to show significance. Expansion teams drew better than incumbent teams and the impact from soccer-specific stadia is weak given the success of the Seattle franchise (and possibly negative when excluding Seattle). Censoring of the dependent variable had a discernible impact on many of the attendance factors. Practical implications – These findings may be useful to managers of MLS and their teams along with other professional teams and/or leagues that are investigating the use of either team expansion or the construction of new facilities to increase spectator attendance. Originality/value – This is the first study to investigate the relationship between expansion and new stadium construction in MLS over multiple years. The results indicate that MLS’s decision to use team expansion and the construction of soccer-specific stadiums has been beneficial with respect to spectator attendance.

Although the concept of rivalry is widely recognized as a contributing factor to consumer demand for sporting events, who constitutes a rival and to what degree rivalry influences attendance remains vague. Previous demand models consistently included rivalry as an explanatory variable but represented rivalry in inconsistent ways that often violated rivalry's core properties (i.e., non-exclusive, continuous in scale, and bidirectional). This study reviews past specifications for rivalry and tests multiple rivalry variables, including a 100-point allocation measure that conforms to rivalry's core properties, in attendance demand models for both Major League Soccer and the National Hockey League. Results across models generally favor the 100-point measure to represent the special attention fans give to certain opponents. This fan-derived rivalry representation offers researchers, marketers, event managers, and sponsors a more complete picture of rivalry as related to demand estimation for purposes such as promotional planning, game scheduling, and event security protocol.

DeSchriver and Jensen (2003) and Gerrard, Parent, and Slack (2007) have performed valuation studies of North American sport venue naming rights agreements, focusing on professional sport team stadia. No empirical research exists that solely examines the values of collegiate sport venue naming rights. This study attempts to fill that void, utilizing a hedonic pricing model. Naming rights agreements for 44 venues in which an NCAA Division I football or basketball team was the primary tenant were identified. Based on the literature, a final model consisting of eight explanatory variables predicted nearly 58% of the variance in collegiate sport venue naming rights values. Four of those variables were statistically significant: (a) facility type, (b) average attendance, (c) presence of multiple tenants, and (d) household income. These results differed considerably from earlier work with professional sport facilities.

In late 2011, at a time when other leagues such as the National Football League and the National Basketball Association had engaged in work stoppages, Major League Baseball owners and the MLB Players Association harmoniously agreed on a new fiveyear collective bargaining agreement. This article focuses on the reasons why MLB as an industry has maintained labor peace after decades of work stoppages. The primary aspects of the new MLB CBA, such as changes to the revenue sharing system, competitive balance tax, salary arbitration, and the amateur draft are addressed. The manner in which these economic mechanisms affect areas such as competitive balance will be analyzed. Lastly, a comparison was undertaken of the collective bargaining environment in MLB versus other professional sports leagues and other non-sports industries.

There are two generally accepted ways of plotting the aggregate demand (AD) and aggregate supply (AS) curves in the goods market. One puts the price level on the vertical axis (the P - y approach); the other plots the real interest rate on the vertical axis (the r - y approach). This paper develops the theoretical connections between these two approaches that permit one to tell a coherent dynamic story with the AD-AS model and also explores the conditions under which one approach or the other yields greater insight into the working of the model.

As financial and sustainability pressures placed upon collegiate athletic programs grow, it is important to understand all revenue generation areas, which include luxury suites. However, while suite finances are readily available on American professional sports, the opposite appears true for collegiate sports. As the first empirical investigation on the pricing of college suites, this study aimed to contribute to the limited literature on luxury suites and help better understand the luxury suite market. Multiple regression analyses were used to develop two significant models that estimated collegiate football luxury suite price using 16 explanatory variables. The results explained between 65% and 68% of the variation in suite price, highlighted the uniqueness of the college football suite market, and indicated that Conference Affliation, Suite Capacity, County Income, Tickets Included, College Basketball Competition, and Winning Percentage were positively related to suite price, while County Population had a significant negative impact.

The purpose of this study was to analyze the contribution of selected determinants in explaining the variation in spectator attendance at NCAA Division II football games. The variation was explained through the use of an economic demand model that was constructed through a multiple regression process. The findings of this study may aid Division II athletic administrators who are attempting to increase revenues attracting additional spectators to football contests. Data were collected regarding spectator attendance during the 1994 season from 95 of the 140 institutions (67.86%) that competed in Division II football. The results revealed that several factors significantly contributed to explaining the variation in game-specific spectator attendance. These factors included winning percentage, the presence of promotional activity and the price of a general admission ticket. In addition, the size of the local population and the number of full-time enrolled students of the institution hosting the contest significantly affected spectator attendance. The results also showed that factors such as the age of the stadium, weather, and the regional population surrounding the home institution did not significantly affect spectator attendance. Overall, 10 of the 22 determinants were found to be significant determinants. The final economic demand model explained 48.89 percent of the variation in NCAA Division II spectator attendance for games played during, the 1994 season.