Fan Loyalty in Finnish Ice Hockey

Fan loyalty in Finnish Ice Hockey

1. Introduction

Sport has become more professional over the years. Sport managers view their teams, leagues as brands to be managed. A product or service is considered as a brand if the name, logo, sign or slogan increases the value of that product or service. The psychological aspect in the consumer’s mind, the brand image consists of all information and associations with a product or service. The quality of brand associations is determined by the favourability, uniqueness and strength. High levels of brand awareness and a positive brand image should increase among others greater consumer loyalty (Keller 1993).

Brand knowledge has two dimensions: brand awareness and brand image. Furthermore brand awareness can be classified into active (brand recall) and passive (brand recognition) awareness. (Keller 1993).

Commitment is the emotional or psychological attachment to a brand. A sports consumer is committed if he/she feels a deep and persistent attachment to his/her favourite team and resists conflicting information or experience and the future welfare of the team is important (Bauer, Sauer and Exler 2005). Committed fans are loyal (Bauer, Sauer and Schmitt 2005).

This study studies fan loyalty in Finnish men’s ice hockey during the regular season 2008-2009 using stochastic frontier analysis. Most teams in the highest ice hockey league are local monopolies but there are two teams in Helsinki which might be substitutes since the distance between their stadiums is less than 3 km. Anecdotal evidence suggests that the fans of these teams come from different districts partly due to different public transport to their stadiums. One is located near to a railway station that can be reached easily from the eastern parts of Helsinki while the other is easily reached through bus services from the western parts of Helsinki. Moreover, there is one team in the neighbouring city, Espoo, whose stadium is at a distance of about 13 km from the previous. In addition there are two teams in Tampere with a shared stadium. However, some teams are local monopolies, and some teams meet higher competition. Therefore brand loyalty or fan loyalty might differ according to competitive position and the aim of this research is to study the relationship between fan loyalty and competitive position of teams. Competitive position is defined here as the geographical distance between teams’ stadiums. Consumers or spectators can be either loyal to ice hockey or to a particular team. Those living in Helsinki or Tampere region where there are at least two teams and are only loyal to (highest league) ice hockey have the possibility not to remain loyal to a particular team.

Teams in the highest leagues generally get revenues not just gate revenues but also from merchandise sales, sales of broadcast rights and commercial sponsorships. Loyal fans use fan shits and scarves.

Broadcast rights are usually sold by the league association and the broadcast revenue is shared among the teams. Sponsorship revenue is associated with larger attendance which in turn is associated with larger market base, i.e. larger home town population. This in enables increased budgets to spend on playing and coaching that facilitates improved team performance (Buraimo, Forrest and Simmons 2007). However,

Bauer, Sauer and Exler (2005) show that among German soccer fans the success of the team is not the central driving force of a fan’s utility opinion.

Fan loyalty can be measured as permanency of successive years’ attendance (Winfree, McCluskey,

Mitterhammer and Fort 2004), mean match tickets per market size (Brandes, Franck and Theiler 2010) or as an efficiency score in stochastic frontier analysis (Depken 2000, 2001). Also direct surveys to get self- revealed levels of fan loyalty have been used. Wakefield and Sloan (1995) show that fan loyalty increases home game attendance. The novelty of this study is that a panel data of Finnish men’s highest league ice hockey attendance during the regular season 2008-2009 will be analysed using stochastic frontier analysis.

There were 406 games played during that season beginning in September 2008 and ending in March 2009.

The Finnish men’s highest league, labelled “SM-Liiga” was a closed league with 14 teams, so the last did not drop. The best 10 teams continued in play-off games and the champion (JYP) was known in Mid April.

During the regular season all teams had 29 home games with total 1997019 spectators, i.e. on average

4919 per game ranging from 8456 (Jokerit from Helsinki) to 3437 (SaiPa from Lappeenranta). 2. Fan loyalty or brand loyalty – stochastic frontier analysis -

Following Depken (2000, 2001), a panel of Finnish men’s highest league ice hockey for the regular season

2008 – 2009 is used in the estimation. A conventional Cobb-Douglas form to explain attendance is used:

As (1) - in which TSV* denote for time specific variables, like weekday dummy and climate conditions

(temperature) - is transformed by taking logs of both sides, we get

* In which C a constant term identical to all teams, the β j and γj are parameters to be estimated, and ε I = εi –

ln(λi) is the error term.

The explanatory variables Xi used in this study are conventional and consistent with other studies (for a review, see Borland and MacDonald 2003 or Simmons 2006): home town population, visitor’s town population, distance between teams’ home stadiums, the winning percentage of the home team and of the visitor team, the game round, the unemployment rate. The time specific variables are weekday dummies and the outside temperature measured in Celsius. Due to the nature of these variables, they are not

* transformed by taking logs. The error term has two components ε I = εi – ln(λi) in which εi is the random error term that captures noise as well as team and time-specific unobserved heterogeneity (Greene

2005).The inefficiency term λi in the stochastic frontier is time invariant and team specific. Two possible distributions have frequently been used (see Greene 2008, 538): the absolute value of a normally distributed variable (“half-normal*) and an exponentially distributed variable. The distributions are asymmetric. However, the problem with stochastic frontier analysis is that the error term distribution assumption has its effects on the size of the fan loyalty. If the team specific term is fixed, one of the teams is considered strong (as 100 % strong) in the sense of fan loyalty. Fans are committed. The fan loyalty of the other teams is relative to the best-practise team(s) in the sample (cf. Last and Wetzel 2010). The fan loyalty estimates are sensitive to sample selection criteria and outliers. The fixed effects approach is distribution free and it allows for correlation between effects and time-specific regressors. The random effects approach maintains the original distributional assumption. With a half-normal model the least squares is unbiased and consistent and efficient among linear unbiased estimators while the maximum likelihood estimator is not linear but it is more efficient (Greene 2008, 539). The shortcoming with a random effects model is that it has stronger distribution assumptions that the effects are time invariant and uncorrelated with the explanatory variables in the model (Greene 2005). Furthermore, these models tend to overestimate the disloyalty. The assumption of time invariance is more problematic if the time series is long, however, in this study the sample consists of game attendance during the regular season 2008 – 2009, i.e. from September 2008 to March 2009.

As the disloyalty measure approaches 0, fans are more loyal, and the other explanatory variables, especially winning percentage and other time specific variables matter less. Teams with low levels of fan loyalty lose more spectators as the quality of games goes down. The climate conditions, i.e. the temperature and a worsening winning record are more relevant and the less enthusiast spectators do not attend. There is a wide sports economics literature that use frontier models but most of these focus on cost efficiency (for a good survey, see Barros and Garcia-del-Barrio 2008) or technical efficiency (Kahane 2005). The output typically is related to team performance, like winning percentage and the inputs are cost related, like wages or the number of coaches. Most of these study professional baseball or football (soccer) in USA or UK.

There are few studies with ice hockey data and even less using frontier analysis (Kahahe 2005). The pioneering attendance study of Noll (1974) has been very influential. Jones and Ferguson (1988) showed with NHL data that home town population, winning percentage and team related attributes like way to play, the number of stars in the team are important to explain attendance. The effect of population incomes was negative. However, Cocco and Jones (1997) show that the effect of incomes was positive as expected. Using frontier analysis Kahane (2005) shows that teams owned by corporations are more efficient than team owned by individuals.

3. Stylized facts: Finnish Ice Hockey

There were 14 teams playing in the highest men’s ice hockey league in Finland. Three of the teams were located in the metropolitan area of Helsinki, two from Tampere and the rest are local monopolies.

Team Home game average Variation of home game Home town Distance to attendance, regular season attendance: min – max population 1st the nearest 2008-2009, n = 29 (std), coefficient of September team, km variation 2008 Blues 4651 3922 – 5722 (476.2) 240275 12.6 0.102 (Espoo) HIFK 6324 5005 – 8200 (933.9) 571887 2.6 0.148 (Helsinki) HPK 3780 3205 – 5360 (525.9) 65941 75.5 0.139 (Hämeenlinna) Ilves 5672 4197 – 7800 (936.9) 208657 0 0.165 (Tampere) Jokerit 8463 6283 – 13464 (1672.5) 571887 2.6 0.198 (Helsinki) JYP 4016 3531 – 4180 (192.3) 127186 147.2 0.048 (Jyväskylä) KalPa 4599 3703 – 5225 (429.1) 91601 148.9 0.093 (Kuopio) Kärpät 5741 4909 – 6614 (472.4) 132726 289.2 0.082 (Oulu) Lukko 3708 3019 – 5400 (567.8) 39757 50.0 0.153 (Rauma) Pelicans 4081 3422 – 4910 (470.8) 99816 75.5 0.115 (Lahti) SaiPa 3437 2843 – 4847 (457.0) 70267 152.5 0.133 (Lappeenranta ) Tappara 5138 3718 – 7800 (998.4) 208657 0 0.194 (Tampere) TPS 5139 3831 – 6813 (901.1) 175279 87.4 0.175 (Turku) Ässät 4110 3001 – 6472 (723.0) 76355 50.0 0.176 (Pori) Table 1: Average attendance statistics Note: Ilves and Tappara from Tampere are using a common stadium, whereas HIFK and Jokerit from Helsinki have separate stadiums. Distance is measured from the team’s stadium to the nearest.

The Coefficient of Variation of the attendance variable and the distance to the next nearest team are

negatively correlated (ρ = -0.669) which may indicate that loyalty is positively associated with the

competitive position, i.e. local monopolies have more loyal fans. However, since team performance among

others has been shown to have an impact on attendance, a stochastic frontier analysis is needed to obtain

more certain view about this proposition. Most of the games have been played on Tuesdays (28.6 %), on

Thursdays (26.1%) and on Saturdays (33%), some games on Fridays (32 games, i.e. 7,9%) and the rest on

Mondays (9), Wednesdays (5) and Sundays (4). The correlations of weekday dummies with the other

variables are negligible except that during Saturdays the attendance is bigger. The teams from Helsinki

(HIFK and Jokerit) have had the biggest attendance but it has been declining during the last years (see

Appendix 1). Only two teams were able to increase average attendance during the regular season 2008 –

2009: HPK by 15 % and KalPa by 36 %. The spectator number of TPS (-14 %) and Tappara (-10 %) decreased

Mean Std Att Cap Price Dist Temp Unempl HomePop VisPop PPGH PPGV FGH FGV 4918.6 1500.9 1 0.71 0.29 -0.09 -0.06 -0.47 0.75 0.09 0.08 -0.01 0.10 -0.08 7066.6 2595.0 1 0.23 -0.08 0.07 -0.65 0.70 -0.04 -0.04 0.00 0.02 -0.05 28.2 4.1 1 -0.29 0.11 -0.52 0.52 0.11 0.14 -0.05 0.02 -0.05 253.2 155.6 1 -0.08 0.21 -0.14 -0.13 0.09 0.10 0.05 0.09 3.0 6.3 1 -0.44 0.07 -0.02 -0.09 -0.08 -0.10 -0.07 8.4 2.2 1 -0.71 0.05 -0.05 0.03 -0.00 0.07 192187 166893 1 -0.07 0.12 -0.00 0.07 -0.05 192198 166861 1 -0.02 0.16 -0.03 0.06 1.47 0.44 1 0.14 0.52 0.10 1.48 0.43 1 0.07 0.44 4.39 2.44 1 0.04 4.31 2.50 1 29.5 16.8 : Variables, means, standard deviations and correlation matrix. : Att = home game attendance, Cap = Capacity of Stadium, Dist = distance between home team’s stadium and visitor’s stadium (km), Unempl = province monthly unemployment rate, HomePop = home town population in the beginning of the month, VisPop = visitor’s town population in the beginning of the month, PPGH = points per game, home team, PPGV = points per game, visitor, FGH = form guide (3 last games), home team, FGV = form guide, visitor, HomeG = leg. Price is the ticket price of the best plain seats, not box seats, it is overrated since most of the seats are cheaper. n = 406

The home town population is positively correlated with the attendance, the capacity of the stadium and

the ticket price and negatively with the province unemployment rate. These variables are also associated in other respects. The key variable or the cause is the town population. The two alternative team performance variables (for the home team: points per game, PPGH and the form guide, FGH, or the corresponding variables for the visitor: PPGV and FGV) are correlated. The temperature is negatively correlated with the leg, i.e. in September when the season begins the temperature is higher than in March when the regular season ends. The unemployment rate was increasing during the season and the variables are positively correlated.

4. Estimation and results The model is estimated first with OLS because of comparability and then with MLE assuming that the inefficiency term is distributed half-normal.

Variable OLS OLS OLS OLS OLS OLS OLS OLS LnCap 0.360 *** 0.270*** 0.345*** 0.264*** 0.349*** 0.263*** 0.330*** 0.254*** (0.035) (0.032) (0,036) (0.033) (0.035) (0.032) (0.035) (0.033) LnPrice -0.018 -0.173** 0.003 -0.138** -0.009 -0.162** 0.008 -0.129** (0.062) (0.058) (0.064) (0.059) (0.063) (0.058) (0.064) (0.059) LnDist -0.032*** -0.032*** -0.031*** -0.031*** -0.033*** -0.034*** -0.032*** -0.033*** (0.006) (0.006) (0.006) (0.006) (0.006) (0.006) (0.006) (0.007) Temp -0.003 -0.006** -0.005** -0.007*** -0.003 -0.006*** -0.004* -0.007*** (0.002) (0,002) (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) LnUnempl 0.274*** 0.247*** 0.273*** 0.243*** (0.049) (0.050) (0.049) (0.050) LnHomePop 0.233*** 0.201*** 0.234*** 0.205*** 0.236*** 0.202*** 0.238*** 0.207*** (0.016) (0.010) (0.016) (0.016) (0.016) (0.015) (0.016) (0.015) LnVisPop 0.032*** 0.037*** 0.034*** 0.038*** 0.030** 0.034*** 0.031** 0.036*** (0.009) (0,010) (0.010) (0.010) (0.094) (0.010) (0.010) (0.010) LnPPGH 0.027** 0.018 0.034** 0.025* (0.012) (0,012) (0.012) (0.012) LnPPGV -0.040** -0.042** -0.042** -0.043** (0,013) (0,013) (0.013) (0.013) LnFGH 0.004 0.002 0.005 0.004 (0.004) (0.004) (0.004) (0.004) LnFGV -0.008* -0.008* -0.008* -0.008* (0.004) (0.004) (0.004) (0.004) LnHomeG -0.031 -0.018 -0.042** -0.028* -0.028* -0.022 -0.034** -0.028* (0.016) (0,017) (0.016) (0.017) (0.014) (0.014) (0.014) (0.014) Tuesday -0.120*** -0.111*** -0.117*** -0.109*** (0.017) (0.017) (0.017) (0.017) Thursday -0.121*** -0.116*** -0.118*** -0.111*** (0.017) (0,018) (0.017) (0.018) Saturday 0.113*** 0.111*** 0.109*** 0.106*** (0,015) (0.016) (0.015) (0.016) Constant 1.97*** 4,15*** 1.99*** 3.96*** 2.02*** 4.21*** 2.08*** 4.023*** (0.482) (0.291) (0.490) (0.293) (0.483) (0.293) (0.492) (0.294) R2 0.747 0.728 0.737 0.721 0.743 0.724 0.733 0.717 LogL (Χ2) 238.97 (570.14) 223.63 (539.46) 230.68 (553.54) 218.52 (529.23) 236.17 (564.47) 220.74 (533.68) 227.37 (546.94) 215.52 (523.23)

All the variables, except the day of the week and the temperature, are logarithmic. ***, **, * denote 1%,5%,10% significance Table 3: OLS results, dependent variable is log(Attendance), n = 406 The OLS estimated price elasticity is negative only if the unemployment variable is not enclosed.

These variables are highly negatively correlated and therefore the OLS estimates for the price variable without the unemployment variable are more plausible. The home town population coefficient is positive as expected and roughly 5 - 6 times higher than the visitor’s town population coefficient. The distance between the towns is significantly negative. The temperature matters even though the coefficient is tiny. The team performance measured from the beginning of the season is more suitable than the form guide which measures the performance of the last three games. Spectators can easily observe the points per game variable through tracking statistics that are shown in newspapers. During the season the spectator number diminishes since the leg variable (HomeG) is negative. The difference between Tuesday and Thursday games is significant with the Saturday games.

Variable OLS SF,MLE, fixed SF, MLE, random LnCap 0.264*** 0.264*** 0.279 (0.033) (0.032) (2.92) LnPrice -0.138** -0.138** 0.325 (0.059) (0.058) (2.63) LnDist -0.031*** -0.031*** -0.041 (0.006) (0.006) (0.032) Temp -0.007*** -0.007*** -0.003 (0.002) (0.002) (0.024) LnHomePop 0.205*** 0.205*** 0.143 (0.016) (0.015) (1.004) LnVisPop 0.038*** 0.038*** 0.018 (0.010) (0.010) (0.019) LnPPGH 0.025* 0.025* -0.025 (0.012) (0.012) (0.057) LnPPGV -0.043** -0.043** -0.013 (0.013) (0.013) (0.073) LnHomeG -0.028* -0.028* 0.003 (0.017) (0.016) (0.280) Saturday 0.111*** 0.111*** 0.107** (0.016) (0.015) (0.048) Constant 3.96*** 3.96 3.47 (0.293) (4.63) (21.2)

σ 0.141 0.310 R2 0.721 LogL 218.52 218.52 293.01

Table 4: estimation results, dependent variable is log(Attendance), n = 406 The stochastic frontier model (Table 4) without panel data assumption (fixed effects) yields almost

totally similar results as OLS. The similarity of the OLS and MLE estimates is not surprising since

both methods generate consistent estimates. With fixed effects model the inefficiency of the team

is relative to the best. With random effects model the inefficiency score is E[u|e]. The inefficiency

scores of the teams are listed in table 5 below.

Team Inefficiency, Fixed Inefficiency, random effects model effects model Blues 0,295 0.392 HIFK 0.172 0.306 HPK 0.199 0.294 Ilves 0.156 0.203 Jokerit 0.069 0.134 JYP 0.174 0.286 KalPa 0.062 0.119 Kärpät 0 0.015 Lukko 0.089 0.286 Pelicans 0.161 0.291 SaiPa 0.283 0.334 Tappara 0.209 0.318 TPS 0.278 0.376 Ässät 0.205 0.308

Table 5: inefficiency scores of teams.

The inefficiency scores are positively correlated (ρ = 0.907), even the random effects model is

unsatisfactory. These inefficiency scores and the coefficient of variation presented in table 1

measuring the variation of teams’ attendance figures are associated with the distance measure also

presented in table 1.

Inefficiency. Fixed Inefficiency. coefficient of distance effects model random effects variation model Inefficiency. Fixed 1 0.907 0.178 -0.371 effects model Inefficiency. 1 0.213 -0.509 random effects model coefficient of 1 -0.669 variation distance 1

Table 6: Correlation matrix of selected variables

The correlation coefficients reveal that fan loyalty measured as inefficiency scores or attendance’s

coefficient of variation is associated with the distance, i.e. competitive position of the team: the

bigger the distance, the bigger fan loyalty.

5. Conclusions

The purpose of this paper was to consider the relationship between fan loyalty and the competitive

position of the men’s highest league ice hockey teams in Finland using stochastic frontier approach.

Fan loyalty was measured conversely as inefficiency score of the stochastic frontier model

explaining attendance of games. This approach is useful because it reveals that there are

differences in fan loyalty and its relation with the competitive position is plausible.

The random effects model is unsatisfactory since the coefficients of the variables are not significant

and therefore inefficient. The fixed effects model is more plausible since it captures both the

relevant explanatory variables for attendance and the inefficiency scores. The estimated

coefficients of the explanatory variables are in line with those reported in the previous literature.

Since the team loyalty scores seem to be correlated with the distance measure, the fans are more

committed to ice hockey and not to a particular team. The brand of ice hockey is stronger than the

brand of an individual team. This is consistent with the results of Bauer, Sauer and Exler (2005) who

show that non-product-related attributes (e.g. logo and club colours, club culture and tradition, stadium and regional provenance) are more important for fan loyalty than product-related

attributes like players, success, general team performance.

The coefficient of variation of the teams’ attendance number and the distance measure are more

negatively correlated than the inefficiency scores obtained through the stochastic frontier models

but still the used approach is suitable to explain fan loyalty. However, a larger data is needed to

confirm the validity of the results.

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Source of the statistical data: official statistics (www.stat.fi) or The Finnish National Hockey League (www.sm-liiga.fi). Estimation made with NLogit 4.0 (www.limdep.com)

HIFK TPS Ilves Tappara Kärpät Blues Ässät JYP SaiPa HPK Pelicans KalPa 6821 6444 5867 5866 5791 5111 4183 3808 3724 3555 3455 3302 6629 6441 5660 5619 5697 4763 4391 3351 3439 3556 4042 3120 6573 5979 5914 5712 6055 4838 4235 4055 3558 3282 4253 3388 6324 5139 5672 5138 5741 4651 4120 4016 3437 3780 4081 4599

9.7 % 9.2 % 8.4 % 8.4 % 8.3 % 7.3 % 6.0 % 5.4 % 5.3 % 5.1 % 4.9 % 4.7 % 9.6 % 9.3 % 8.2 % 8.1 % 8.2 % 6.9 % 6.3 % 4.8 % 5.0 % 5.1 % 5.8 % 4.5 % 9.4 % 8.5 % 8.4 % 8.1 % 8.6 % 6.9 % 6.0 % 5.8 % 5.1 % 4.7 % 6.1 % 4.8 % 9.2 % 7.5 % 8.2 % 7.5 % 8.3 % 6.8 % 6.0 % 5.8 % 5.0 % 5.5 % 5.9 % 6.7 %

-2.8 % 0.0 % -3.5 % -4.2 % -1.6 % -6.8 % 5.0 % -12.0 % -7.7 % 0.0 % 17.0 % -5.5 %

-0.8 % -7.2 % 4.5 % 1.7 % 6.3 % 1.6 % -3.6 % 21.0 % 3.5 % -7.7 % 5.2 % 8.6 %

-3.8 % -14.0 % -4.1 % -10.0 % -5.2 % -3.9 % -2.7 % -1.0 % -3.4 % 15.2 % -4.0 % 35.7 %

1. 3. 2. 2. 3. 1. 2. 3. 3. 2. 1. 1. 3. 3. 2. 1. 1. 3. 3. 1. 2. 2. 1. 3. Appendix 1: Average attendance. home games. regular seasons. Source: www.sm-liiga.fi

Fan loyalty in Finnish Ice Hockey

Seppo Suominen Haaga-Helia University of Applied Sciences Malmi campus, Hietakummuntie 1 A FIN-00700 Helsinki, Finland e-mail: [email protected]

The study studies fan loyalty in Finnish men’s ice hockey during the regular season 2008-2009 using stochastic frontier analysis. Fan loyalty is measured as inefficiency score of the stochastic frontier model explaining games’ attendance. There were 14 teams playing in the highest men’s ice hockey league in

Finland with 406 games, i.e. all teams had 29 games at home stadium and 29 games as visitor. The error term of the stochastic frontier model has two components and the other of these can be considered as the inefficiency term or inversely as the fan loyalty term. The fan loyalty measure is reasonable and negatively correlated with the distance between home stadium and the nearest stadium of the other team. The distance is a proxy for local provenance or monopoly position. The fixed effects model is plausible and the fan loyalty terms are reasonable while the random effects model is not efficient.