The paper tests, in the context of an open sports league, whether greater success is achieved by clubs in markets with larger populations. The relationship is strong but, to a limited extent, mitigated by more clubs establishing in large markets.

B Buraimo

D Forrest

R Simmons

English

Research Papers in Economics

 
 
 
 
 
 
 
 
 
 
 
Lancaster University Management School 
Working Paper  
2005/047 
 
 
 
 
 
Freedom of entry, market size and competitive outcome: 
Evidence from English soccer 
 
 
 
 
 
Babatunde Buraimo, David Forrest and Rob Simmons  
 
 
The Department of Economics                        
Lancaster University Management School 
Lancaster LA1 4YX 
UK 
 
 
© Babatunde Buraimo, David Forrest and Rob Simmons  
All rights reserved. Short sections of text, not to exceed 
two paragraphs, may be quoted without explicit permission, 
provided that full acknowledgement is given. 
 
The LUMS Working Papers series can be accessed at http://www.lums.lancs.ac.uk/publications/ 
LUMS home page: http://www.lums.lancs.ac.uk/  
 
 
 
 
FREEDOM OF ENTRY, MARKET SIZE AND COMPETITIVE OUTCOME: 
EVIDENCE FROM ENGLISH SOCCER 
 
 
 
by 
 
 
Babatunde Buraimo 
(University of Central Lancashire) 
 
David Forrest
1  
(University of Salford) 
 
and 
 
Robert Simmons 
(Lancaster University) 
 
 
 
Abstract: the paper tests, in the context of an open sports league, whether greater 
success is achieved by clubs in markets with larger populations. The relationship is 
strong but, to a limited extent, mitigated by more clubs establishing in large markets. 
 
 
JEL classification: L83 
key words: soccer, market size, entry, competitive balance, success 
 
 
 
 
 
                                                 
1 corresponding author: David Forrest, Centre for the Study of Gambling, 
University of Salford, Salford M5 4WT, UK 
telephone: +44 161 295 3674 
fax: +44 161 295 2130 
e-mail: d.k.forrest@salford.ac.uk 
 
 1. Market size in sports economics 
 
Market size has been accorded central importance in the analysis of professional 
sports leagues. The two-team-league model of El-Hodiri and Quirk (1971), 
popularised in Quirk and Fort (1992) and still the framework for contemporary 
analysis, predicted that the larger market club would achieve a higher win-ratio. It had 
the incentive to hire more talent than the smaller club because greater success on the 
field could be more effectively converted to dollars where the customer base was 
bigger. In equilibrium, the superiority of the big club would be more marked, the 
wider the discrepancy in populations. Hence the perceived problem of competitive 
imbalance in sports leagues came to be identified in the economics literature with the 
dominance of big city clubs. 
 
Fort and Quirk (1997) argued in favour of reform of US major league sport to 
encourage new entry into large markets. Currently, franchises confer territorial 
monopoly. Otherwise, multiple franchises would presumably emerge in the largest 
metropoles, as in open European soccer leagues. With the market divided between 
competing clubs there would be more chance of even competition in the pursuit of 
championships.  
 
Here we aim to illuminate the debate by testing how closely playing success is linked 
to market size in practice. Further, we are able to test whether such a relationship 
exists in a long established open sports league that conforms to the European rather 
than American model of sport. This provides relevant evidence on how radically the 
breaking of territorial monopoly in America would in fact ameliorate competitive 
imbalance. 
 
  1In the US, experience appears to be consistent with the notion that size matters (a lot). 
For example, Sandy et al. (2004), generalising across the four major team sports,  
remark that “teams that consistently win have been those with access to either the 
largest markets, revenue sources that are not shared with other teams, or heavily 
subsidized facilities” (p172). But despite such generalisations, there has been no 
direct systematic quantification of the relationship between market size and success in 
American sport. Impediments include that the number of clubs in each sport is small 
(about 30) relative to the requirements of econometric testing and that it is hard to 
compare market size across clubs. For example, Schmidt and Berri (2001) suggest 
that, in the attendance demand literature, “a common proxy for size of a team’s 
market is the size of its metropolitan statistical area” (p158). They accordingly enter 
this in linear form in a baseball demand equation. But we regard this as a 
misspecification. If one club is located in an SMSA with twice the population of 
another, it cannot be considered as having double the market size. The bigger SMSA 
will cover a wider area and the mean travel cost for residents to reach the stadium will 
be higher, implying that the ticket demand curve will not pushed as much to the right 
as the population figures alone might suggest. SMSA population is therefore an 
inadequate proxy for market size. 
 
2. Evidence from England 
Our context for formal testing of the relationship between success and size is English 
soccer. This has three advantages. First, it has the world’s largest professional league 
structure: with 92 clubs, there is an adequate number of observations for meaningful 
estimation of the slope of the relationship. Second, it is an appropriately challenging 
environment in which to test the size hypothesis because it is an open league: teams 
  2play in four hierarchical divisions (with the best and worst performing teams moving 
up or down a tier at the end of each season) and the bottom two of the 92 are replaced 
each year by the two clubs at the top of the structure of ‘minor’ (semi professional and 
amateur) leagues. Third, the richness of UK Census micro data and the availability of 
suitable GIS software permits precise measurement of both market size and overlap of 
markets between clubs.  
 
In our regressions, the dependent variable is POSITIONit which reflects the ranking of 
club i at the end of season t, taking the value 92 for the champion club of the top tier 
and the value one for the bottom team in the fourth tier.
2 Our data derive from seasons 
1997-8 to 2003-4. We restrict analysis to three seasons either side of the 2001 Census 
so that population measures capturing the market size of each club are based on 
reasonably contemporary enumeration. Standard errors of coefficients are clustered on 
clubs because consecutive seasonal outcomes are not independent; for example, a club 
promoted to the top tier from POSITION  72 must finish in the range 73-92 the 
following year. 
 
Using micro Census data for 175,000 enumeration districts, and manipulating them 
with the aid of stadia Ordnance Survey map references and the MapInfo software 
package, we constructed and measured the characteristics of two concentric rings, 
defined by radial distance 0-5 and 5-10 miles, around each stadium. According to data 
employed in Forrest et al. (2002), the bulk of attendance at soccer games originates 
                                                 
2 An alternative measure used in some literature on English soccer is minus logit of league rank 
(-log(rank/(93 – rank)) which gives greater weight to rankings in the tails (Szymanski, 2000). Our 
results are robust to the use of this alternative measure. We present results from a linear ordering for 
greater ease of interpretation. 
  3within 10 miles of the ground while the division of the catchment area into two 
ensures rough homogeneity of travel costs from each zone. 
 
In Model 1 (Table 1), we regress POSITION on (log) population (millions) in the 
inner/outer zones of a club’s catchment area. Additional regressors are the proportion 
of the catchment area’s population aged over 64 (seniors are more likely to suffer 
mobility restrictions that make attendance difficult) and the number of years since the 
club first joined the League (support may build up over time because interest is passed 
between generations). In this specification, only inner zone catchment area is 
significant though it is strongly so. The point estimate is that a 100,000 population 
increase from the mean (0.434m.) is associated with an improvement of 2.66 places in 
POSITION. The implication is that the size of community in which a club is located is 
a factor in determining club ranking, just as the two team model predicted. While it 
may of course be that we are not observing equilibrium in the study period, the theory 
appears to hold true even where there are less rigid barriers to entry in heavily 
populated areas than in American sport. 
 
Does this mean that reducing entry barriers would not in fact weaken the size-success 
relationship and thereby improve competitive balance? Not necessarily. Model 2 is 
more revealing in that it explicitly includes a measure of competition from 
neighbouring clubs. Again using MapInfo, we constructed a variable, overlap. Each 
club was given a ten mile radial catchment area. Overlap is the proportion of the 
catchment area shared with another club. Where there is more than one neighbouring 
club, these intersections of population are aggregated: overlap may then exceed one. 
Indeed it often does. Arsenal generated the highest value of overlap, 7.88, reflecting 
  4the extent to which clubs have found it worthwhile to enter the literally crowded 
London market. 
 
 Model 2 is more precisely determined than Model 1 because of the inclusion of a 
measure of competition. Holding this and other variables constant, we now predict an 
increase in a club’s performance when population increases in either inner or outer 
zones of the catchment area. Adding 100,000 population to the respective means 
boosts predicted club performance by 3.78 and 1.05 places respectively. These are 
larger impacts than in Model 1 because we hold the degree of competition constant 
whereas in fact the number of clubs will be greater in densely populated areas. 
Overlap attracts a strong negative coefficient, signifying that competition indeed 
mitigates the advantage of population size. For example, suppose population in both 
inner and outer zones increases by 100,000 (from mean values) but another club exists 
in the same location as the subject club. All of the beneficial impact of the higher 
population is then cancelled out. 
 
Model 2 demonstrates, then, that permitting freedom of entry to large population 
markets weakens the relationship between size and outcomes. But Model 1 shows that 
there is insufficient response in the spatial distribution of clubs to eliminate the 
importance of market size altogether. Market size remains important across the 
League structure. Of course, in terms of the identity of the Champion team, market 
size appears decisive. In only one of the last ten seasons has a Champion emerged 
from outside the London and Manchester conurbations and that club benefited from 
substantial subsidy from a benefactor. A clue to why there is insufficient entry is 
found in the club age variable. Incumbent teams have an advantage over new entrants 
  5because fans are reluctant to change allegiance given that much of their utility derives 
from the feeling of identity with the club they follow. We would predict that, for 
example, entry into the New York baseball market would indeed weaken the resource 
base of the Yankees. But any entry would be limited by the difficulty of weaning fans 
away from their beloved team. Territorial monopoly power would not be eroded 
completely and deregulation would still leave big city teams with disproportionate 
resources and playing success 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  6References 
El-Hodiri , M. and J. Quirk, 1971, An economic analysis of a professional sports 
league, Journal of Political Economy, 79, 1302-1319. 
Forrest, D., Simmons, R. and P. Feehan, 2002, A spatial cross-sectional analysis of 
the elasticity of demand for soccer, Scottish Journal of Political Economy, 49, 336-
355. 
Fort, R.D and J. Quirk, 1997, Introducing a competitive economic environment into 
professional sports, in: W. Hendricks, ed., Advances in economics of sports, Vol. 2 
(JAI Press, Greenwich, CT). 
Quirk, J. and R.D. Fort, 1992, Pay dirt: The business of professional team sports 
(Princeton University Press, Princeton, NJ). 
Sandy, R., Sloane, P. J. and M. S. Rosentraub, 2004, The economics of sport: an 
international perspective, (Palgrave Macmillan, Basingstoke).   
Schmidt, M.B. and D. J. Berri, 2001, Competitive balance and attendance: the case of 
Major League Baseball, Journal of Sports Economics, 2, 145-167.   
Szymanski, S, 2000, A market test for discrimination in the English soccer leagues, 
Journal of Political Economy, 108, 590-603. 
 
  7Table 1: Regression Results 
Dependent variable- POSITION 
    Model  1          Model  2 
                                      coeff.    |t|                          coeff.    |t| 
log inner zone population         11.54            2.39                       16.40         3.30 
log outer zone population     1.35             0.49                         7.63         2.43 
proportion 65+            -106.60             1.23                   -176.25         2.17 
years of membership                  0.26             4.10                         0.20         3.07 
overlap                                                                                          -5.61         3.33 
constant                                     55.74             4.19                      94.64         7.27 
 
 
R
2                                                 .38                                                  .43 
 
number of observations: 644 
clusters: 98 
 
 
  
 
 
  8

Freedom of entry, market size and competitive outcome: evidence from English soccer

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Freedom of entry, market size and competitive outcome: evidence from English soccer

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