Reputation and the League Standing Effect: The Case of a Split Season in Minor League Baseball

e University of San Francisco

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Sport Management College of Arts and Sciences

3-30-2018

Reputation and the League Standing Eect: e

Case of a Split Season in Minor League Baseball

Nola Agha

University of San Francisco, nagha@usfca.edu

omas Rhoads

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Recommended Citation

Agha, Nola and Rhoads, 6omas, "Reputation and the League Standing E5ect: 6e Case of a Split Season in Minor League Baseball"

(2018). Sport Management. 23.

h7ps://repository.usfca.edu/sm/23

REPUTATION AND THE LEAGUE STANDING EFFECT: THE CASE OF A SPLIT

SEASON IN MINOR LEAGUE BASEBALL

Abstract

Split season league design resets standings at the midpoint of the season, thus allowing for two

periods in which a team can potentially achieve success in a single season. This context allows us

to test both the reputation of the first half winner and the league standing effect on demand.

Examination of game-level data from the 2010 Southern League reveals fans are unaffected by

measures of both team quality and league standing in the second half of the season. On the other

hand, the first half winners saw an 11% increase in attendance as a percent of stadium capacity,

suggesting that in the second half of the season winners matter more than winning. (JEL L22 and

L83)

Keywords: demand, minor league baseball, league standing effect, reputation, split season

Agha, N. & Rhoads, T. A. (2018). Reputation and the league standing effect: The case of a split

season in Minor League Baseball, Applied Economics, (50)41, 4447-4455.

https://doi.org/10.1080/00036846.2018.1456646

1. Introduction

Consistently attracting fans to the ballpark is, of course, a primary goal of any

professional baseball team. This strategy provides at least a few steady revenue streams, the most

obvious being ticket, concession, and merchandise sales in addition to crowds that positively

contribute to home field advantage and player development. For minor league baseball teams in

particular, a business model that is more reliant on fans coming to the stadium is critical for

success. Because other major sources of revenue that Major League Baseball teams can rely

on—television revenue and revenue sharing, for instance—are mostly nonexistent for the minor

leagues, there are sometimes alternative ways to create, enhance, and maintain fan demand for

the ballpark experience. Additional entertainment such as postgame fireworks, concerts, and

bobblehead giveaways are just a few of the more typical methods used to bring additional fans to

the minor league ballpark that may not be drawn by the quality of the baseball competition alone.

Much work has already focused on this aspect of minor league baseball attendance.

The literature shows that fans generally respond positively to team quality at all levels of

professional sports. However, the response is somewhat muted for minor league sports—Gitter

and Rhoads (2010) and Winfree and Fort (2008) found that average attendance increases only

about 2% for minor league baseball and hockey teams when teams see a 10% increase in

winning percentage. Focusing exclusively on minor league baseball, this result can be somewhat

troubling from a revenue generation perspective for at least a few reasons. First, team quality is

entirely a function of the Major League parent team. Minor league affiliates are meant to serve as

the player development grounds for the Major League team while also providing opportunities to

play in more distracting conditions in order to learn to focus and block out noise and heckling

from fans. This suggests that winning games is not as important as developing player talent for

the Major League team. Second, the minimal impact from the additional fans from winning

suggests a team at the AA level of minor league baseball would see attendance increase by about

90 fans per game, or by about 6,250 fans annually. Using the minor league baseball average cost

of about $65 for a family of four to attend a game, this points to additional revenue of a little

more than $1,400 per game, or approximately $100,000 per season that would be attributed to a

higher quality team.

While this figure is not insignificant, we must keep in mind that this

additional revenue stream is purely a function of the quality of the minor league team, which is

completely out of the control of the owners of that minor league team.

Given the above discussion, it should not be surprising that minor leagues cannot rely on

winning alone to maintain or increase attendance. Promotions and special events are standard for

minor league baseball—fireworks nights and bobblehead giveaways are typically the games with

the highest attendance during the season. But another way some minor leagues appear to have

tried to increase attendance is through a split season structure of regular season competition. In

those minor leagues with a split season, the teams making the playoffs are determined by

splitting the season into two halves to determine a first half and second half winner. The first half

winner is determined as the team with the best record at the midpoint of the season. Then, at the

midpoint of the season, the first half records are wiped clean and new second half standings are

generated. The team with the best record in the second half of the season is the second half

winner, and plays the first half winner in the playoffs. Usually, there are two divisions in a minor

league with a split season and the winners of each half of these divisions meet in a playoff. All

five leagues in the A level of minor league baseball use a split season format to determine

playoff teams while neither of the two AAA level leagues do. The AA level of minor league

See http://www.milb.com/news/article.jsp?ymd=20150615&content_id=130739074&fext=.jsp&vkey=pr_milb.

Accessed April 12, 2016.

baseball is unique in that two of the three leagues—the Southern League and the Texas

League—both have a split season format, while the Eastern League does not. This unique nature

of split season format at the AA level suggests leagues can attempt to optimize attendance

through the playoff and season structure.

This paper examines the impact of a split season on game-level attendance in the

Southern League for the 2010 season. We specifically focus on two possible reasons a split

season approach to league and playoff design could affect attendance. First, the somewhat

arbitrary resetting of the standings at the midpoint of the season means all teams are put in an

equal position for playoff consideration at the start of the second half of the season, regardless of

their performance in the first half of the season. Of course, the quality of the team is not likely to

change much, if at all, at the midpoint of the season. So while the relative success, or lack of it,

in the first half of the season is likely to carry over to the second half of the season, the reset

standings may give the fan a new sense of how their team compares to the rest of the league. We

test these ideas using Neale’s (1964) league standing effect. Second, because the split season

produces a first half winner in each of the two divisions in the Southern League, two teams are

assured of making the end-of-the season playoffs. For these teams, this designation as a playoff-

quality team can therefore send a signal to their fans of team quality for the entire second half of

the season. In other words, gaining a reputation as a playoff-caliber team may provide useful

information to the fan of absolute team quality that may not be easily revealed or readily

determined from the daily standings.

We get two primary results from our model. First, our results suggest Southern League

fans are not responsive to the games behind metric used to test the league standing effect.

Specifically, these fans are not sensitive to a daily indicator of team performance, relative team

quality, and end-of-season championship possibilities. Further, this suggests more broadly that

minor league baseball fans, unlike Major League Baseball fans, are mostly not concerned with

the uncertainty of outcome. However, our model does provide a second result showing an 11-

point increase in per game attendance as a percent of capacity in the second half of the season for

the first half winner that secured a spot in the postseason playoffs. These results together suggest

that while minor league baseball fans do not appear to be sensitive to relative team performance,

they do respond to a reputation signal of overall team quality.

The rest of this paper proceeds as follows. In the next section, we examine how the split

season league and playoff design fits into the literature. In section three, we introduce the data

and our model. Section four presents the results and in section five we discuss our results before

concluding in the final section.

2. Literature Review

Developing a more complete understanding of how baseball fans respond to certain

features of game, league and playoff design is perhaps the primary motivating factor for much of

the research concerning minor league baseball. Baseball demand estimation began with

Rottenberg (1956) and Noll (1974) and focused first on Major League Baseball before efforts

were made to estimate minor league baseball demand. In moving to estimate demand for minor

league baseball, Siegfried and Eisenberg (1980) opened opportunities for others to study things

such as the impact of promotions (Gifis and Sommers 2006), winning (Gitter and Rhoads 2010),

top prospects (Gitter and Rhoads 2011), stadium construction (Gitter and Rhoads 2014), parent

club quality, distance, and affiliation changes (Agha and Cobbs 2015), proximity to other

professional baseball teams (Rhoads 2015), team name changes (Agha, Goldman, and Dixon

2016), and a host of other factors (Anthony et al 2014).

The body of evidence documenting the impact on attendance at the minor league level is

getting deeper and broader, and provides a further check on the robustness of the research

examining demand at the major league level across sports. Minor league and major league live

sporting events are typically viewed as substitutes by fans in baseball (Agha et al. 2016; Gitter

and Rhoads 2010), hockey (Winfree and Fort 2008) and football (Fort and Quirk 1999),

suggesting that minor league and major league sports fans can behave in a somewhat similar and

predictable fashion. But there are some notable distinctions between minor league and major

league sports. Agha (2013) identifies a positive impact on local income levels from minor league

baseball teams not typically seen from Major League Baseball teams and Gitter and Rhoads

(2010) and Agha and Cobbs (2015) find that fans respond minimally to winning minor league

baseball teams in comparison to winning Major League Baseball teams. This suggests all

professional sports leagues can potentially provide a reasonable arena within which to test

economic theories, with some leagues possibly being better suited for testing than others.

We turn our focus now to Neale’s (1964) league standing effect, which posits that “the

closer the standings, and within any range of standings the more frequently the standings change,

the larger will be the gate receipts” (p. 3). Importantly, it must be noted that it should be possible

to apply and test the league standing effect in any professional sports league—including any

minor league baseball league like the Southern League—that maintains and reports league

standings and where there exists the potential for league standings or rank to change at any point

before, during or after any game throughout the season (Andreff and Scelles 2015). In fact, a

literature that emerged in the late 1980s and early 1990s began to focus more on the importance

of the dynamics of championship league standings and the possible effects of daily changes on

attendance instead of simply examining how end-of-season competitive balance was related to

attendance. Cairns (1987) highlighted championship and relegation contention, especially in the

second half of the season, in the Scottish Football League. Likewise, Borland (1987) controlled

for those teams within two games of the league leader in the championship race in determining

attendance in the Victorian Football League—an Australia Rules football league. While

championship significance and league position were tested separately by Jennett (1984) for the

Scottish Football League and by Dobson and Goddard (1992) for the English Football League,

their metrics were ultimately found problematic by Baimbridge, Cameron and Dawson (1996)

who studied championship and relegation significance in the English Premier League.

The problem with some of the previous models in controlling for championship

significance is that fans were assumed to use information only available at the end of the season

in order to make ex ante attendance decisions. Baimbridge, Cameron and Dawson (1996) work

around this by including a dummy variable for a top four position in the standings, suggesting

the team is in contention for the championship. Additionally, they included controls for whether

or not the team already secured a championship or relegation for the following season. While

none of these highlighted variables were found to be significant in describing match attendance,

they nevertheless point to the types of variables that should be included when modeling the

league standing effect in professional baseball. Specifically, baseball fans pay attention to the

standings and the closeness of those standings through the games behind metric. This metric is

reported on a daily basis and shows how many wins (games) behind the current first place team

any given baseball team in the league is. The games behind metric is reported in the standings

and is updated in the newspaper and on league websites after every game is completed and is

readily available for any fan to access.

We note that some previous studies used the games behind metric to test the uncertainty

of outcome hypothesis. The games behind metric provides information to baseball fans about the

relative quality of the baseball teams playing, making it possible to form an ex ante prediction

about the uncertainty of outcome. Knowles, Sherony and Haupert (1992) include the sum of the

games behind for both the home and visiting teams playing the game while Soebbing (2008)

includes just the games behind for the home team. While these two previous studies were

certainly not the first to examine the impact of games behind on attendance (see, for example,

Demmert 1973, Noll 1974, and Whitney 1988) they do highlight a very common technique used

to test the uncertainty of outcome hypothesis. And while even more complex measures of game

and league championship uncertainty and game importance exist, (see Tainsky and Winfree,

2010 and Lei and Humphreys 2013) they are not expected to be easily accessible or used readily

by fans to make a decision about attending a baseball game.

We suggest here that the games behind metric is perhaps a better test of the league

standing effect as it is likely the metric most commonly used by fans to assess both relative team

quality and the likely significance of each game in the end-of-season championship race. Two

recent papers explicitly test the league standing effect. In looking at Major League Baseball,

Humphreys and Zhou (2015) use a measure that is probably less intuitive or accessible to fans

than a games behind metric, while Andreff and Scelles (2015) use a metric for the French

football league that is not as comprehensive in describing the championship possibilities as a

standard games behind metric. These two papers provide mixed results of the presence of the

league standing effect.

We must emphasize that the Southern League’s split season—and other leagues similarly

structured—where league standings are reset at the midpoint of the season, appears to be

designed in order to benefit from a fan’s expected preference to attend a baseball game with a

more direct and immediate impact on the end-of-season championship race. To our knowledge,

split season minor league baseball has not been used as a test bed to examine the extent to which

the league standing effect exists. In fact, Medcalfe (2009) seems to be the only one to have used

split season minor league data in any work, but he examined team effort and not fan demand

resulting from the league standing effect. Thus, our research is expected to fill a gap in the

literature by testing the league standing effect by using split season data from the Southern

League of AA minor league baseball. Finally, we will additionally test the reputational effects

afforded to the first half winner in attracting fans to the ballpark. This feature of league design

has attracted little attention as it relates to fan demand, but reputation due to winning the

season’s first half is expected to provide critical information to the fan regarding relative team

quality and end-of-season championship possibilities (see Czarnitzki and Stadtmann 2002 and

Ertug and Castellucci 2013).

3. Data and Model

Demand for a professional sporting event is necessarily a function of the league standing

effect in addition to team and game quality. Neale’s (1964) observation that “progress towards a

championship or changes in the standings” can help determine demand for a sporting event and

suggests that in order to incorporate a split season league design, a demand model must allow for

the possibility of two halves in a season and the opportunity to identify the first half division

winners. Our demand model for split season minor league baseball below is unique in that it

includes split season flexibility in identifying the league standing effect for each half of the

season in addition to the standard inclusion of team and game quality measures that drive game

attendance:

Game Attendance = F (1

Half League Standing Effect, 2

Half League Standing

Effect, 1

Half Division Winner, Team Quality, Game Quality)

To test the league standing effect and the reputational effect of a split season first half

winner, we used individual home game observations from all 10 teams in the 2010 Southern

League season (n=693). Specifically, we utilized ordinary least squares (OLS) to estimate

= β

+ β

+ υ

+ ε

(1)

where y

is per game attendance as a percent of stadium capacity for team i in game j, similar to

Cebula, Toma, and Carmichael (2009). X

captures team quality and game quality, Z

contains

split season-related indicators, υ

are city fixed-effects, and ε

is a random disturbance. If the split

season format successfully results in two separate “seasons” then each half should be analyzed

separately thus we also estimate this model by removing Z

from equation (1) and replacing it

with a single indicator for the first half winner. We relied on the plentiful research on individual

game demand in minor league baseball to formulate our empirical specification (Anthony et al.

2014; Cebula et al. 2009; Howell, Klenosky, and McEvoy 2015; Paul, Toma, and Weinbach

2009; Paul and Weinbach 2013a; Paul and Weinbach 2013b; Siegfried and Eisenberg 1980)

where individual game demand is a function of team quality, game quality, and city-specific

features.

Team quality is captured through win percent, cumulative homeruns, and the number of

top prospects defined as any player ranked in the top 20 by Baseball America at the start of the

2010 season. Both win percent and cumulative homeruns are calculated for each game and are

re-set at the beginning of the second half due to the split season. We expect the coefficients on

both the number of top prospects (Gitter and Rhoads 2011) and the number of homeruns (Gitter

and Rhoads 2010; Siegfried & Eisenberg 1980) to be positive. Both Agha and Cobbs (2015) and

Gitter and Rhoads (2010) found the coefficient on win percent to be positive and significant in

AA leagues as a whole, but analysis of only the Southern League (Anthony et al. 2014; Paul and

Weinbach 2013a) found the coefficient on win percent to be insignificant. Game quality is

captured by dummy variables for opening day, doubleheader, day of the week, month, weather,

fireworks, and non-fireworks promotions. City fixed effects are included to capture constants

such as population, per capita income, preference for minor league baseball, and other

unobservable city specific features.

Relying on Neale’s (1964) claim that gate receipts derive from, “excitement in the daily

changes in the standings or…possibilities of changes in standings” (p. 3) we operationalize the

league standing effect as games behind. This common measure is widely distributed, easily

understood by local fans, and can signal both potential excitement for a game, and “progress

towards a championship” (Neale, 1964, p. 4). In a split season this progress occurs twice—once

halfway through the season and once at the end. Thus, games behind is re-set halfway through

the season. To be thorough, we test both games behind for the home team (Soebbing 2008) and

the sum of games behind for both home and visiting teams (Knowles et al. 1992). Furthermore,

we test for a possible reputational effect of the first half winner on second half demand with a

dummy variable. Table 1 summarizes the descriptive statistics for each of the variables.

To date, all demand modeling on minor league baseball has omitted measurement of a

split season league and analyzed a single season as if it had one championship. Thus we begin

with a single equation that captures team quality, game quality, and city-specific features. To

capture the unique structure of the split season league we include an indicator for first half

games, an interaction of this first half dummy and games behind, and an indicator for the first

half winners, of which there are two (one for each division). The full season empirical

specification is

AttendanceAsPctOfCapacity = β

+ β

FirstHalfWinner + β

FirstHalfDummy +

FirstHalfWinnerxFirstHalfDummy + β

TopProspects + β

WinPct + β

Homeruns +

GamesBehind + β

OpeningDay + β

Doubleheader + β

10-15

DayOfWeek + β

16-20

Month +

Temperature + β

Windspeed + β

Clear + β

Sunny + β

Cloudy + β

Overcast + β

Drizzle

+ β

Rain + β

Fireworks + β

NonFireworksPromotion + city fixed-effects + ε (2)

The empirical specification for separate first and second halves removed the first half

dummy and the interaction term and months were adjusted accordingly.

4. Results

We used OLS to estimate both the full and half season models. A Breusch-Pagan / Cook-

Weisberg test for heteroskedasticity indicated the need for robust standard errors in the full

season (χ

= 36.39, p < 0.001), first half (χ

= 13.93, p < 0.001), and second half (χ

= 32.17, p =

0.08) regressions. Variance inflation factors under 10 indicate multicollinearity is not a problem

in the first half and second half regressions.

To determine whether the data should be pooled into full season or regressed by halves of

the season, we tested for the equality of coefficients with a Hausman test using seemingly

unrelated regressions. The results indicate we can reject the equality of the common coefficients

between the full season and first half (χ

= 87.25, p < 0.0001) and between the full season and the

second half (χ

= 104.34, p < 0.0001).

Overall, our results in Table 2 are consistent with previous research on per game

attendance. As expected, we find attendance as a percent of capacity increases with promotions,

good weather, opening day, and Thursday, Friday, and Saturday games. Additionally, rain tends

to decrease attendance as a percent of capacity. Coefficients on team quality, measured as top

prospects and homeruns, were insignificant and line up with other estimations of demand for

Southern League baseball (Anthony et al. 2014; Paul and Weinbach 2013a). On the other hand,

team quality measured as win percent was significant in the first half but not the second half or

full season models. Table 2 further indicates the coefficient on the home team games behind

metric is insignificant in all cases, and a separate analysis found the coefficient on the sum of

games behind for the home and away teams was similarly insignificant (p > 0.4) with no change

in any of the other variable estimates.

Finally, first half winners are associated with an 11-point

gain in attendance as a percent of stadium capacity in the second half of the season.

5. Discussion

Neale’s (1964) league standing effect proposes that close standings, actual changes in

standings, or the possibility of changes in standings generate excitement in fans who then

convert that excitement into gate revenues. In theory, the split season league design attempts to

maximize this benefit for minor league baseball teams by providing more than just the usual one

period for a team to achieve success in the regular season. By providing a chance for a team to

either be the first half or second half winner during two distinct periods of the regular season,

league standings have more opportunity to be close and potentially matter more. Our results

These results are available upon request.

indicate that fans of AA Southern League baseball are not motivated to attend games due to any

measure of league standing in either half of the season. In fact, team win percent is the only

measure of team quality that affects demand in the first half of the season. When the second half

of the season begins, winning ceases to matter altogether. Instead, the first half winner benefits

from its reputation as a winner—attendance as a percent of stadium capacity increases 11% in

the second half of the season for the first half winner. These results are consistent with

Czarnitzki and Stadtmann (2002) who similarly found significant reputational effects that

outweighed measures of league position and Rindova et al (2005) who found prominence can be

more powerful than the ability to produce quality output. In short, in the split season minor

league baseball context, gaining a reputation as a winner becomes more important than actually

winning.

If fans are generally uninterested in the sporting performance of a minor league team

focused on developing player talent, Neale’s Fourth Estate Benefit might explain why first half

winners see an 11 point increase in attendance in the second half of the season. He suggests the

“reporter-newspaper-printer-distributor complex” (Neale 1964, p. 3) is incentivized to tout the

success of the first half winner. A minor league baseball team that is the first half winner and has

an active marketing department thus appears to have a strong incentive to directly promote the

quality of their playoff-caliber team. This not only drives revenue to the firm, but can also

meaningfully signal to the fans that a team has a reputation as a winner.

Bounded rationality provides an alternate explanation to the notion that fans respond to first

half winners but not to winning. While fans could benefit from using the games behind metric in

making a decision of whether or not to attend a baseball game, the cost of making that decision

may simply be too high. The level of information about the quality of the team provided from

being credentialed as a first half winner is likely enough to offset any cost of acquiring that

information. Southern League baseball fans thus appear to exhibit bounded rationality in their

decisions to attend baseball games.

Reputation as a playoff-caliber team thus seems to matter for fans in this setting where

information acquisition is costly and can lead directly to an additional revenue stream for the

first half winner. An increase in attendance of 11.4% of stadium capacity leads to an additional

884 fans per game for the average Southern League team. With the average AA baseball ticket

price around $7.00, this suggests the first half winner in the Southern League can increase ticket

revenue in the second half of the season by more than $200,000. Concessions and ancillary

purchases at the stadium can be expected to add to the bump in revenue the first half winner

could receive.

These results have interesting implications for demand modeling. First, they indicate that

leagues utilizing a split season design have unique demand characteristics by half and should be

estimated as such. This will be a challenge to future researchers when analyzing classifications

like AA that have both a split and non-split season format among the different leagues. Second,

while full season analysis finds significant effects of win percent in AA leagues (Agha and

Cobbs 2015; Gitter and Rhoads 2010) game-level analysis does not. This difference could stem

from the split season first half winner driving some of the results or from the differences between

split season and non-split season leagues.

The results of our research also have implications beyond baseball in some settings where

reputation as a winner matters and information acquisition is not costless. An Academy Award

nomination, for example, provides a strong reputational signal about a film’s quality to a

potential consumer. Box-office revenues increase with nominations (Nelson et al 2001),

suggesting that movie release dates can be a function of award schedules. But our results further

suggest that more awards in the entertainment industry may provide more opportunities for films

and television shows to gain a reputation for high quality. This allows consumers to gain

information about the quality of a movie or television show with relatively low acquisition costs

and can lead to higher revenues for production studios as more consumers watch films and

television shows considered the best. Also, consider the U.S. political landscape and the state-

level presidential primary contests that occur every four years. Primary candidates place a lot of

emphasis on winning the early races—New Hampshire, Iowa, and South Carolina, for instance—

with the expectation that an early win can provide momentum for future primary contests in

other states. An early win can send a signal to a future voter in another state about quality of the

candidate in a way that suggests reputation as a winner matters to voters much like reputation as

a playoff-caliber team matters to Southern League baseball fans.

Although a split season design allows standings to reset at the midpoint of the season, the

reality is that team quality changes little, if at all, at this point. That observation, coupled with

our results, nevertheless raises important questions about league design. For example, what

would happen to Southern League attendance if there was no split season or what would happen

to the Eastern League (currently no split season) if a split season was implemented? Similarly,

would MLB benefit from a split season? We encourage future researchers to examine more years

and more leagues to determine the robustness of our results. Finally, future research should also

attempt to more accurately determine those quality metrics that matter to minor league baseball

fans.

6. Conclusion

The minor league baseball business model centers on drawing fans to the ballpark

primarily with savvy marketing and promotions. In alignment with many minor league baseball

executives who claim the business is about “family entertainment” (Johnson 1995; Pietschmann

2010), the results of this analysis indicate the quality of the team and the closeness of the

championship race—that is, the league standing effect—generally do not motivate fans of

Southern League baseball to attend games. This holds true despite a split season league design

that doubles the opportunities for fans to see their team achieve success. In contrast, winning the

first half is comparable to having a fireworks night every night for the second half of the

season—a truly meaningful result for minor league managers and marketers.

Acknowledgements

The authors wish to thank Jeff Gurney for sharing his Southern League data.

References

Agha, N. (2013). The economic impact of stadiums and teams: The case of Minor League

Baseball. Journal of Sports Economics, 14(3), 227-252.

Agha, N., & Cobbs, J. (2015). Is the grass greener? Switching costs and geographic proximity in

the high status affiliations of professional baseball. Managerial and Decision

Economics, DOI: 10.1002/mde.2741

Agha, N., Goldman, M., & Dixon, J. C. (2016). Rebranding: The value of team name changes,

European Sport Management Quarterly, 16(5), 673-693.

Andreff, W., & Scelles, N. (2015). Walter C. Neale 50 years after: Beyond competitive balance,

the league standing effect tested with French football data. Journal of Sports Economics,

16(8), 819-834.

Anthony, T., Kahn, T., Madison, B., Paul, R. J., & Weinbach, A. (2014). Similarities in fan

preferences for minor league baseball across the American southeast. Journal of

Economics and Finance, 38(1), 150-163.

Baimbridge, M., Cameron, S. & Dawson, P. (1996). Satellite television and the demand for

football: A whole new ball game? Scottish Journal of Political Economy, 43(3), 317.

Borland, J. (1987). The demand for Australian Rules Football. Economic Record, 63(182), 220.

Cairns, J. A. (1987). Evaluating changes in league structure. The reorganization of the Scottish

Football League. Applied Economics, 19(2), 259-275.

Cebula, R. J., Toma, M., & Carmichael, J. (2009). Attendance and promotions in minor league

baseball: The Carolina League. Applied Economics, 41(25), 3209-3214.

Czarnitzki, D., & Stadtmann, G. (2002). Uncertainty of outcome versus reputation: Empirical

evidence for the First German Football Division. Empirical Economics, 27(1), 101-112.

Demmert, H. G. (1973). The economics of professional team sports: Lexington, Mass.,

Lexington Books [1973].

Dobson, S. M., & Goddard, J. A. (1992). The demand for standing and seated viewing

accommodation in the English Football League. Applied Economics, 24(10), 1155.

Ertug, G., & Castellucci, F. (2013). Getting what you need: How reputation and status affect

team performance, hiring, and salaries in the NBA. Academy of Management Journal,

56(2), 407-431.

Fort, R & Quirk, J. (1999). The college football industry. In Fizel, J., Gustafson, E., & Hadley,

L. (Eds.). Sports economics: Current research: Westport, CT: Praeger.

Gifis, L. S., & Sommers, P. M. (2006). Promotions and attendance in Minor League

Baseball. Atlantic Economic Journal, 34(4), 513-514.

Gitter, S. R., & Rhoads, T. A. (2010). Determinants of Minor League Baseball

attendance. Journal of Sports Economics, 11(6), 614-628.

Gitter, S. R., & Rhoads, T. A. (2011). Top prospects and Minor League Baseball

attendance. Journal of Sports Economics, 12(3), 341-351.

Gitter, S. R., & Rhoads, T. A. (2014). Stadium construction and Minor League Baseball

attendance. Contemporary Economic Policy, 32(1), 144-154.

Howell, S. M., Klenosky, D. B., & McEvoy, C. D. (2015). Weather, timing, and promotions in

Minor League Baseball: An examination of attendance in the International

League. Journal of Applied Sport Management, 7(2).

Humphreys, B. R., & Zhou, L. (2015). The Louis-Schmelling Paradox and the league standing

effect reconsidered. Journal of Sports Economics, 16(8), 835-852.

Jennett, N. I. (1984). Attendances, uncertainty of outcome and policy in Scottish League

Football. Scottish Journal of Political Economy, 31(2), 176-198.

Johnson, A. T. (1995). Minor league baseball and local economic development. Urbana and

Chicago: University of Illinois Press.

Knowles, G., Sherony, K., & Haupert, M. (1992). The demand for Major League Baseball: A

test of the uncertainty of outcome hypothesis. American Economist, 36(2), 72-80.

Lei, X. & Humphreys, B. (2013). Game importance as a dimension of uncertainty of outcome.

Journal of Quantitative Analysis in Sports, 9(1), 25-36.

Medcalfe, S. (2009). Incentives and league structure in Minor League Baseball. Journal of Sport

Management, 23, 119-141.

Neale, W. C. (1964). The peculiar economics of professional sports: a contribution to the theory

of the firm in sporting competition and in market competition. Quarterly Journal of

Economics, 78, 1-14.

Nelson, R., Donihue, M., Waldman, D., & Wheaton, C. (2001). What’s an Oscar Worth?

Economic Inquiry, 39, 1–16.

Noll, R. G. (1974). Attendance and price setting. In R. G. Noll (Ed.), Government and the sports

business (pp. 115-157). Washington, DC: Brookings Institution.

Paul, R. J., Toma, M., & Weinbach, A. P. (2008). The minor league experience: What drives

attendance at South Atlantic League baseball games? Coastal Business Journal, 8(1),

70-84.

Paul, R. J., & Weinbach, A. P. (2013a). Fireworks saturation and attendance in minor league

baseball. International Journal of Sport Finance, 8(4), 312.

Paul, R. J., & Weinbach, A. P. (2013b). The Yankee effect in Minor League Baseball. New York

Economic Review, 44(1), 32-42.

Pietschmann, R. J. (2010, March 30). Diamond Mines. Departures, Retrieved from

http://www.departures.com/lifestyle/consuming-passions/diamond-mines

Rhoads, T. (2015). The call up to the majors: A proximity-based approach to the economics of

Minor League Baseball. New York, NY: Springer.

Rindova, V. P., Williamson, I. O., Petkova, A. P., & Sever, J. M. (2005). Being good or being

known: An empirical examination of the dimensions, antecedents, and consequences of

organizational reputation. Academy of Management Journal, 48(6), 1033-1049.

Rottenberg, S. (1956). The baseball players' labor market. Journal of Political Economy, 64,

242-258.

Siegfried, J. J., & Eisenberg, J. D. (1980). The demand for Minor League Baseball. Atlantic

Economic Journal, 8(2), 59-69.

Soebbing, B. P. (2008). Competitive balance and attendance in Major League Baseball: An

empirical test of the uncertainty of outcome hypothesis. International Journal of Sport

Finance, 3(2), 119-126.

Tainsky, S., & Winfree, J. A. (2010). Short-run demand and uncertainty of outcome in Major

League Baseball. Review of Industrial Organization, 37(3), 197-214.

Whitney, J. D. (1988). Winning games versus winning championships: The economics of fan

interest and team performance. Economic Inquiry, 26(4), 703-724.

Winfree, J. A., & Fort, R. (2008). Fan substitution and the 2004-05 NHL lockout. Journal of

Sports Economics, 9(4), 425-434.

Table 1. Descriptive statistics of 2010 Southern League home games

Variable Mean Std. Dev. Min Max

Dependent variable

Attendance as a percent of capacity 0.445

0.252

.043

1.332

Split Season Measures

First half winner dummy 0.101

0.302

First half dummy 0.498

0.500

Games behind by half x First half dummy 2.081

3.484

16.5

Team Quality

Number of top prospects 0.127

0.333

Win percent by half 0.496

0.142

Cumulative homeruns by half 10.909

7.912

37.0

Game Quality

Games behind, home team, by half 3.958

3.904

16.5

Games behind, sum of both teams, by half 7.851

5.717

23.5

Opening day dummy 0.014

0.119

Doubleheader 0.091

0.288

Sunday 0.141

0.349

Tuesday 0.104

0.305

Wednesday 0.143

0.350

Thursday 0.162

0.368

Friday 0.154

0.362

Saturday 0.157

0.364

April 0.159

0.366

May 0.203

0.403

July 0.189

0.392

August 0.206

0.405

September 0.045

0.207

Temperature 84.156

8.567

104

Wind speed 6.929

4.303

Clear 0.253

0.435

Sunny 0.059

0.236

Cloudy 0.175

0.380

Overcast 0.066

0.249

Drizzle 0.009

0.093

Rain 0.017

0.131

Fireworks 0.182

0.386

Non-fireworks promotions 0.691

0.462

Table 2. Demand estimation on attendance as a percent of capacity in the Southern League,

2010

Full Season First Half Second Half

β β β

First half winner dummy 0.0552

0.1144*

First half dummy -0.0259

Games behind by half x First

half dummy -0.0018

Number of top prospects -0.0008 -0.0774 -0.0169

Win percent by half 0.0188 0.1708* -0.1237

Cumulative homeruns by

half 0.0026 0.0030 -0.0024

Games behind, home team,

by half 0.0011 -0.0013 -0.0001

Opening day dummy 0.1843 0.1312 0.0000

Doubleheader -0.0276 -0.0362 -0.0193

Sunday 0.0190 -0.0296 0.0561

Tuesday 0.0192 0.0556 -0.0153

Wednesday 0.0372 0.0902* -0.0186

Thursday 0.0665** 0.0763* 0.0551*

Friday 0.1898*** 0.2265*** 0.1642***

Saturday 0.2515*** 0.2932*** 0.2213***

April 0.0682 0.1084*

May 0.0479* 0.0647*

July 0.0297

0.0532

August -0.0554

0.0205

September -0.0606

0.0465

Temperature 0.0001 0.0011 0.0014

Wind speed -0.0009 -0.0018 0.0018

Clear 0.0157 -0.0176 0.0450*

Sunny 0.0719* 0.0979* 0.0514

Cloudy -.0392* -0.0420 -0.0184

Overcast -0.0228 -0.0322 0.0199

Drizzle -0.0468 -0.1660*** -0.0560

Rain -0.1344*** -0.2109*** -0.1126*

Fireworks 0.1447*** 0.1119*** 0.1629***

Non-fireworks promotions 0.0671*** 0.1244*** 0.0275

Observations 683

340

343

0.6288

0.6202

0.7230

Note: Fixed effects suppressed; *p < 0.05; **p < 0.01; ***p < 0.001