Demand for ice hockey, the factors explaining attendance of ice hockey games in Finland Seppo Suominen 27 October 2015 HAAGA-HELIA University of Applied Sciences 1

 Nelson (1970): search vs. experience goods  search: consumer can value a product’s quality by inspection before buying it  experience: the consumer must consume the product to determine its quality  Most cultural events are experience goods: going to opera performance, sport events, movies 27 October 2015 HAAGA-HELIA University of Applied Sciences 2

 But some evidence about the quality can be get before consuming  Opera, movies: critics review in newspapers, word-of-mouth  Sport: past performance of the team (winning %)  Problem in opera: all tickets sold before actual performance  Problem in sports: visitor team changes  Problem in movies: moviegoers are young (who do not read newspapers) 27 October 2015 HAAGA-HELIA University of Applied Sciences 3

 The study focuses on season 2007 – 2008 ice hockey league games in Finland.  The aim is to explain factors affecting attendance.  There are 14 teams playing in the highest level in Finnish men’s ice hockey league.  The regular season 2007 – 2008 was a four-fold series – i.e. 52 games per team – with teams located in Helsinki (HIFK and Jokerit) played extra four mutual games, two at home stadium and two at visitor’s stadium. In addition to that the remaining 12 teams played extra four games in subdivisions of three teams. 27 October 2015 HAAGA-HELIA University of Applied Sciences 4

 Altogether each team played 28 home games and 28 games as visitor  During regular season the number of games was 14 x 28 = 392 and the total attendance was i.e per game. 27 October 2015 HAAGA-HELIA University of Applied Sciences 5

Factors affecting attendance  Demand base, i.e. home town population  positive impact (wide literature)  Ticket price  negative (usually inelastic)  Previous performance, i.e. winning percentage of home team  positive  Distance between home team and visitor’s home town  negative, i.e. ”local games” attract more people  Weekday effect: during weekends more  Temperature for outdoor sports, like football (soccer), baseball: when colder then less attendance 27 October 2015 HAAGA-HELIA University of Applied Sciences 6

 Some mixed evidence: incomes, beginning of season, unemployment rate 27 October 2015 HAAGA-HELIA University of Applied Sciences 7

 What is the impact of population of visitor’s town?  Or visitor’s winning percentage?  Winning percentage from the beginning of season or three last games (form guide)?  Ice hockey is played indoors: temperature? 27 October 2015 HAAGA-HELIA University of Applied Sciences 8

27 October variablemeasuresourceexpected sign interest towards the game, int jk home town population (logHPop) Statistics Finland+ visitor’s town population (logVPop) Statistics Finland+ distance between home town and visitor’s town (logDist) Stadium address distance: - home team’s points per game (logHPoin): if zero, then replaced by 0,01 own calclulations based on Jääkiekkokirja visitor’s points per game (logVPoin): if zero, then replaced by 0,01 own calclulations based on Jääkiekkokirja ? home team’s points from 3 last games (logHLast): if zero, then replaced by 0,01 own calclulations based on Jääkiekkokirja visitor’s points from 3 last games (logVLast): if zero, then replaced by 0,01 own calclulations based on Jääkiekkokirja ? regional unemployment rate (Unempl) round, λ(t – r j ) played games since the beginning of th season (logHGame) if zero, then replaced by 0,01 own calclulations based on Jääkiekkokirja ticket price, p jk ticket price (logPrice)Jääkiekkokirja temperature, temp it temperature at nearist observation site (temp) ? weekday, t jk weekday, three dummies TU (tuessay) TH thursday), SA (saturday) TU – TH – SA +

 The first regular season games were played in September 2007 and the last in March After that some teams continued their games in play-offs and the champion (Kärpät) were known in mid April  Roughly 31 % of the games were played on Saturdays, about 27 % on Thursdays and about 25 % on Tuesdays.  In addition to that a few games were played on Mondays ( 6 %) and Sundays (> 3 %). 27 October 2015 HAAGA-HELIA University of Applied Sciences 10

 A bit more often there were Monday games in bigger towns (r = 0,109) against teams from far away (r = 0,106).  There seems to have been more games on Fridays in bigger towns (r = 0,121) and that seems have been reducing Saturday games (r = -0,128).  Otherwise the weekday variables do not seem to correlate with other variables. 27 October 2015 HAAGA-HELIA University of Applied Sciences 11

 Stadia or halls have different price categories. During the season 2007 – 2008 e.g. the ticket price of Blues’s (Espoo) home games in club seats ( ) was normally 27€, in the second long side ( ) 24€, standing places (terraces) (212) 10€, gable seats lower ( ) 18€, normal seat upper ( ) 14€, disabled persons 14€, conscripts and students 10€ (normal seats upper, not Blues – HIFK, nor Blues – Jokerit), boxes for box owners 14€, and children under 7 years free if they were sitting on parent’s knees. Since Espoo and Helsinki are neighboring towns, the ticket prices for games against HIFK or Jokerit (both from Helsinki) were 2€ higher. These prices were valid only when the ticket is bought advance. When bought on entrance, there was 1€ increase. For empirical purposes the variation is very challenging and since there was no data concerning true distribution of seats taken, the empirical equivalent for price is usually the ticket price of best seat including local game excess fees. For Blues this price is 27€ or 29€ with HIFK or Jokerit as visitor team 27 October 2015 HAAGA-HELIA University of Applied Sciences 12

 The correlation matrix reveals the ticket price seems to have been higher in larger towns  Points per game from the beginning of the season (HPoint) and points from the last three games (Last3H) were also positively correlated.  Regional unemployment rate seems to have been higher in areas with smaller towns. 27 October 2015 HAAGA-HELIA University of Applied Sciences 13

27 October 2015 HAAGA-HELIA University of Applied Sciences 14 Model 1Model 2Model 3 variablecoefficientstdt-valueP [|T|>t]coefficientstdt-valueP [|T|>t]coefficientstdt-valueP [|T|>t] constant4,7850,40311,870,0004,5960,40611,310,0004,8760,41811,680,000 LogPrice-0,2720,110-2,280,013-0,2650,111-2,390,017-0,3240,112-2,910,004 LogHPop0,3550,02812,840,0000,3600,02812,890,0000,3650,02812,890,000 LogVPop0,0470,0123,930,0000,0480,0133,960,0000,0440,0123,500,000 LogDist-0,0370,009-4,170,000-0,0380,009-4,220,000-0,0360,009-3,980,000 LogHGame-0,0320,013-2,440,015-0,0320,013-2,410,016-0,0260,011-2,440,015 LogHPoin0,0830,0174,790,0000,08460,0174,870,000 LogVPoin-0,0580,016-3,570,000-0,0580,016-3,520,000 LogHLast0,0140,0062,470,014 LogVLast-0,0030,005-0,620,537 LogUnempl0,0830,0601,390,1660,0870,0611,440,1510,0710,0621,150,253 Temp-0,0050,002-2,000,046-0,0050,002-2,020,044-0,0040,002-1,830,068 TU-0,1110,023-4,880,000-0,1060,023-4,580,000 TH-0,1220,022-5,500,000-0,1140,023-5,040,000 SA0,1160,0205,750,000 N = 392R 2 = 0,677 F = 75,34 χ 2 = 453,62 DW = 2,00R 2 = 0,672 F = 80,97 χ 2 = 446,68 DW = 2,03R 2 = 0,661 F = 70,53 χ 2 = 436,08 DW = 1,96

27 October 2015 HAAGA-HELIA University of Applied Sciences 15 Model 4Model 5Model 6 variablecoefficientstdt-valueP [|T|>t]coefficientstdt-valueP [|T|>t]coefficientstdt-valueP [|T|>t] constant4,7060,42011,200,0005,2100,26319,780,0005,0390,26519,020,000 LogPrice-0,3180,112-2,830,005-0,2650,110-2,420,016-0,2580,111-2,330,020 LogHPop0,3690,02912,920,0000,3320,02215,010,0000,3360,02215,040,000 LogVPop0,0440,0133,540,0000,0470,0123,930,0000,0480,0123,960,000 LogDist-0,0370,009-4,020,000-0,0360,009-4,060,000-0,0370,009-4,110,000 LogHGame-0,0260,011-2,390,017-0,0330,013-2,510,012-0,0330,013-2,490,013 LogHPoin0,0830,0174,800,0000,0850,0174,870,000 LogVPoin-0,0580,016-3,530,000-0,0580,016-3,490,001 LogHLast0,0150,0062,700,007 LogVLast-0,0030,005-0,600,549 LogUnempl0,0730,0621,170,243 Temp-0,0040,002-1,850,065-0,0050,002-2,310,021-0,0050,002-2,330,020 TU-0,1100,023-4,850,000 TH-0,1220,022-5,500,000 SA0,1090,0215,290,0000,1150,0205,710,000 N = 392R 2 = 0, 657 F = 75,96 χ 2 = 429,83 DW = 1,98R 2 = 0, 676 F = 82,48 χ 2 = 451,64 DW = 2,00R 2 = 0,671 F = 89,49 χ 2 = 444,56 DW = 2,03

27 October 2015 HAAGA-HELIA University of Applied Sciences 16 Model 7Model 8 variablecoefficientstdt-valueP [|T|>t]coefficientstdt-valueP [|T|>t] constant5,2400,27119,280,0005,0800,27318,620,000 LogPrice-0,3180,111-2,850,004-0,3110,112-2,780,006 LogHPop0,3450,02215,440,0000,3480,02315,460,000 LogVPop0,0440,0123,500,0000,0440,0133,540,001 LogDist-0,0360,009-3,900,000-0,0360,009-3,930,000 LogHGame-0,0270,011-2,540,009-0,0270,011-2,500,013 LogHPoin LogVPoin LogHLast0,0140,0052,600,0100,0160,0062,830,005 LogVLast-0,0030,005-0,570,570-0,0030,005-0,5500,583 LogUnempl Temp-0,0050,002-2,120,035-0,0050,002-2,140,033 TU-0,1060,023-4,550,000 TH-0,1140,023-5,040,000 SA0,1080,0215,270,000 N = 392R 2 = 0,661 F = 77,39 χ 2 = 434,73 DW = 1,96R 2 = 0,657 F = 84,17 χ 2 = 428,43 DW = 1,98

Conclusions  Both population of the home team and the visitor have a statistically significant effect on attendance as well as distance between home town and visitor’s town  Local games have a bigger attendance than other games  The demand is not elastic with respect to ticket price  Negative income elasticity (spectators are young?)  Success of both the home team and the visitor has an effect: home team’s success with positive and visitor’s with negative coefficient 27 October 2015 HAAGA-HELIA University of Applied Sciences 17

 The number of plays already played has a negative effect  Weekday effect is important: the attendance is bigger during Saturdays  The day temperature has an effect: the colder, the bigger attendance  The unemployment rate has no effect, and also the success factor of the last three games does not seem to explain as well as the success factor of all games played 27 October 2015 HAAGA-HELIA University of Applied Sciences 18