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Farm Team Shuffle: 
The Effects of Major League Affiliations in 
Minor League Baseball 
Nola Agha, University of San Francisco 
Joe Cobbs, Northern Kentucky University 
1
Minor League Baseball (MiLB) 
2 
• 19 leagues 
• 6-16 teams per 
league 
• Attendance gains 
24 of last 29 
seasons 
• 40+ million 
attendees (2010) 
• Shifting geographic 
trend in parent 
affiliation
Club Affiliation Decision 
• Major League Administrators 
o Cannibalize attendance? 
o Player/Administrator travel time 
o Administrative costs 
o Managerial oversight/ownership 
• Minor League Administrators 
o Attendance +/- 
o Fan identification 
o Brand association/equity 
3
Research Questions 
1. Does geographical proximity benefit 
the minor league team? 
2. Do quality features of the major 
league club benefit the minor league 
team? 
3. Does switching to a 
4 
better affiliation 
benefit the minor 
league team? 
4. Is there a switching 
cost?
Demand Theory in Baseball 
• Attendance = f[price, quality, 
substitutes, income] 
• MiLB: classifications not homogeneous 
(Agha, 2012; Branvold, Pan, & Gabert, 1997; Gitter & Rhoads, 2010) 
o Win percentage non-significant at AAA; 
5 
significant at AA 
• New MiLB stadium 
• MLB team within 100 miles (-) 
• New MLB stadium 
H1 
H2 
H3 
H4
Organizational Alliance Theory 
• Smaller firms align with larger firms to 
establish marketplace legitimacy 
(Sarkar, Echambadi, & Harrison, 2001) 
o Alliance strategy entails switching costs 
• Alliance partner characteristics 
(Castellucci & Ertug, 2010; Dyer & Singh, 1998) 
o Status: enhanced endorsement (Sarkar et 
6 
al., 2001) 
o Proximity: knowledge sharing, relational 
assets (Dyer & Singh, 1998)
Alliance-based Hypothesis 
• Alliance partner characteristics 
o Geographic distance (miles) 
o Status of MLB affiliate 
H5 
o Market size 
o Popularity (attendance) 
o Win percentage H6c 
7 
H6a 
H6b
Switching-based Hypothesis 
8 
• Switching cost 
o Negative effect on MiLB team demand 
• Attenuated by new partner 
characteristics 
o Geographic distance (miles) 
o Status of MLB affiliate 
o Market size 
o Popularity (attendance) 
o Win percentage 
H7 
H8 
H9a 
H9b 
H9c
9 
Data 
• 15 years: 1992-2006 
o AAA: American Association, International 
League, Pacific Coast League 
o AA: Eastern League, Southern League, 
Texas League
10 
Model 
yjt = β1Xjt + β2Zjt + υj + εjt 
yjt = natural log annual attendance 
β1 = vector of demand parameters 
Xjt = vector of demand variables 
β2 = vector of MLB club parameters 
Zjt = vector of MLB club variables 
υj = PMSA specific fixed-effect 
εjt = random disturbance
Results 
• Analysis 1: Do quality and distance to 
alliance partner matter? (yes) 
Variable AAA AA 
H1. 36% Win percent 0.216 ***0.364 
H2. 24% New MiLB Stadium ***0.215 0.075 
H3. -53%, -13% Number of MLB in PMSA ***-0.749 **-0.141 
H4. 6% New MLB Stadium **0.059 0.029 
Strike 94/95 0.006 0.057 
H5. 0.024% Affiliate Distance -0.00023258 ***0.0002 
H5. -0.00001% Affiliate Distance Squared 0.0000001 ***-0.0000001 
H6a. -0.000001% Affiliate Population **-0.00000001 0.00000001 
H6b. 43% Affiliate Win Percent **0.434 0.343 
H6c. -0.00001% Affiliate Attendance **-0.00000005 -0.00000002 
11 
***p<0.01, **p<0.05
Results 
• Analysis 2: Does switching to a better or 
closer affiliate matter? (no) 
• Is there a switching cost? (yes) 
Variable AAA AA 
H1. 42% Win percent 0.280 ***0.424 
H2. 22% New MiLB Stadium ***0.200 0.081 
H3. -51%, -13% Number of MLB in PMSA ***-0.711 **-0.134 
H4. 6% New MLB Stadium **0.060 0.031 
Strike 94/95 0.035 **0.075 
H7. -25% Affiliate Change Dummy -0.024 ***-0.293 
H8. Change to Closer Affiliate -0.129 0.059 
12 
H9a. 
Change to Affiliate with 
Higher Population -0.096 0.027 
H9b. 
Change to Affiliate with 
Higher Win Percent 0.002 0.132 
H9c. 
Change to Affiliate with 
Higher Attendance -0.144 0.134 
***p<0.01, **p<0.05
13 
Discussion 
• Consistent with demand theory 
o AAA fans more concerned with MLB 
affiliate success 
o MLB is substitute for MiLB 
• Alliance implications 
o AAA  status as decision criteria for 
affiliate decisions 
o AA  switching costs, proximity as 
decision criteria for affiliate decisions

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Agha, Cobbs; Minor League Baseball: Farm team shuffle, nassm 2012

  • 1. Farm Team Shuffle: The Effects of Major League Affiliations in Minor League Baseball Nola Agha, University of San Francisco Joe Cobbs, Northern Kentucky University 1
  • 2. Minor League Baseball (MiLB) 2 • 19 leagues • 6-16 teams per league • Attendance gains 24 of last 29 seasons • 40+ million attendees (2010) • Shifting geographic trend in parent affiliation
  • 3. Club Affiliation Decision • Major League Administrators o Cannibalize attendance? o Player/Administrator travel time o Administrative costs o Managerial oversight/ownership • Minor League Administrators o Attendance +/- o Fan identification o Brand association/equity 3
  • 4. Research Questions 1. Does geographical proximity benefit the minor league team? 2. Do quality features of the major league club benefit the minor league team? 3. Does switching to a 4 better affiliation benefit the minor league team? 4. Is there a switching cost?
  • 5. Demand Theory in Baseball • Attendance = f[price, quality, substitutes, income] • MiLB: classifications not homogeneous (Agha, 2012; Branvold, Pan, & Gabert, 1997; Gitter & Rhoads, 2010) o Win percentage non-significant at AAA; 5 significant at AA • New MiLB stadium • MLB team within 100 miles (-) • New MLB stadium H1 H2 H3 H4
  • 6. Organizational Alliance Theory • Smaller firms align with larger firms to establish marketplace legitimacy (Sarkar, Echambadi, & Harrison, 2001) o Alliance strategy entails switching costs • Alliance partner characteristics (Castellucci & Ertug, 2010; Dyer & Singh, 1998) o Status: enhanced endorsement (Sarkar et 6 al., 2001) o Proximity: knowledge sharing, relational assets (Dyer & Singh, 1998)
  • 7. Alliance-based Hypothesis • Alliance partner characteristics o Geographic distance (miles) o Status of MLB affiliate H5 o Market size o Popularity (attendance) o Win percentage H6c 7 H6a H6b
  • 8. Switching-based Hypothesis 8 • Switching cost o Negative effect on MiLB team demand • Attenuated by new partner characteristics o Geographic distance (miles) o Status of MLB affiliate o Market size o Popularity (attendance) o Win percentage H7 H8 H9a H9b H9c
  • 9. 9 Data • 15 years: 1992-2006 o AAA: American Association, International League, Pacific Coast League o AA: Eastern League, Southern League, Texas League
  • 10. 10 Model yjt = β1Xjt + β2Zjt + υj + εjt yjt = natural log annual attendance β1 = vector of demand parameters Xjt = vector of demand variables β2 = vector of MLB club parameters Zjt = vector of MLB club variables υj = PMSA specific fixed-effect εjt = random disturbance
  • 11. Results • Analysis 1: Do quality and distance to alliance partner matter? (yes) Variable AAA AA H1. 36% Win percent 0.216 ***0.364 H2. 24% New MiLB Stadium ***0.215 0.075 H3. -53%, -13% Number of MLB in PMSA ***-0.749 **-0.141 H4. 6% New MLB Stadium **0.059 0.029 Strike 94/95 0.006 0.057 H5. 0.024% Affiliate Distance -0.00023258 ***0.0002 H5. -0.00001% Affiliate Distance Squared 0.0000001 ***-0.0000001 H6a. -0.000001% Affiliate Population **-0.00000001 0.00000001 H6b. 43% Affiliate Win Percent **0.434 0.343 H6c. -0.00001% Affiliate Attendance **-0.00000005 -0.00000002 11 ***p<0.01, **p<0.05
  • 12. Results • Analysis 2: Does switching to a better or closer affiliate matter? (no) • Is there a switching cost? (yes) Variable AAA AA H1. 42% Win percent 0.280 ***0.424 H2. 22% New MiLB Stadium ***0.200 0.081 H3. -51%, -13% Number of MLB in PMSA ***-0.711 **-0.134 H4. 6% New MLB Stadium **0.060 0.031 Strike 94/95 0.035 **0.075 H7. -25% Affiliate Change Dummy -0.024 ***-0.293 H8. Change to Closer Affiliate -0.129 0.059 12 H9a. Change to Affiliate with Higher Population -0.096 0.027 H9b. Change to Affiliate with Higher Win Percent 0.002 0.132 H9c. Change to Affiliate with Higher Attendance -0.144 0.134 ***p<0.01, **p<0.05
  • 13. 13 Discussion • Consistent with demand theory o AAA fans more concerned with MLB affiliate success o MLB is substitute for MiLB • Alliance implications o AAA  status as decision criteria for affiliate decisions o AA  switching costs, proximity as decision criteria for affiliate decisions

Editor's Notes

  1. Higher attendance than NBA, NFL or NHL
  2. Differing perspectives on affiliation decision We take MINOR LEAGUE perspective Concern for ATTENDANCE
  3. 1. Does geographical proximity to a parent club benefit the minor league team? 2. Do performance features of the MLB parent club such as quality or success benefit the minor league team? [could also frame this in terms of status – depends how we frame status in the lit review] 3. Does switching to a higher status major league affiliate benefit the minor league team? 4. Is there a switching cost associated with such a change?
  4. Quality (demand function) = win%, stadium, servicescape Substitutes = MLB team and MLB quality
  5. Not all affiliation changes generate uniform effect
  6. American Association disbanded in 1997 and its teams were dispersed to the PCL and IL
  7. PMSA fixed-effects (υj) to control for all time-invariant characteristics that are specific to a city Time trend variable captures city-invariant characteristics specific to year
  8. [Interpretation:  if the estimated coefficient is 0.05 that means that a one unit increase in x will generate a 5% increase in y.] H1: (MiLB) team win percentage (+): 3.6% increase in AA attendance as it moves from a .500 record to .600 record AA  significant H2: New (MiLB) stadium (+): A new AAA stadium is associated with a 21.5% (p < 0.001) increase in attendance AAA  significant H3: Local MLB competition (-): MLB teams in the same PMSA decrease MiLB attendance AAA, AA  significant H4: Local MLB stadium (+): a new MLB stadium in the same PMSA increases AAA attendance (people priced out of new MLB ballpark? Or excess demand?) AAA  significant H5: Affiliate distance (+): AA teams experience a 2.4% increase in attendance for every 100 miles further they move from their parent club. AA  significant H6a: Affiliate population (+): AAA clubs experience a significant 1% decrease in attendance for every 1,000,000 person increase in the MLB parent club’s population AAA  significant H6b: Affiliate win percent (+): 4.3% increase in AAA attendance as its MLB parent moves from a .500 record to .600 record AAA  significant H6c: Affiliate attendance (+): AAA clubs experience a significant, 1% decrease in attendance for every 100,000 person increase in the MLB parent club’s attendance (mean MLB attendance=2.4 mil) AAA  significant
  9. [Interpretation:  if the estimated coefficient is 0.05 that means that a one unit increase in x will generate a 5% increase in y.] Results H7:Switching cost to affiliation change: AA teams realized a 25% (p < 0.01) decrease in attendance the season after changing affiliations AA  significant H8: Switching to a closer affiliate  insignificant H9: Switching to a Higher Status affiliate [a] population, [b] attendance, [c]win %  all insignificant Controls – all the same as equation 1 H1: (MiLB) team win percentage (+): 4.2% increase in AA attendance as it moves from a .500 record to .600 record AA  significant H2: New (MiLB) stadium (+): A new AAA stadium is associated with a 22% (p < 0.001) increase in attendance AAA  significant H3: Local MLB competition (-): MLB teams in the same PMSA decrease MiLB attendance AAA, AA  significant H4: Local MLB stadium (+): a new MLB stadium in the same PMSA increases AAA attendance (people priced out of new MLB ballpark? Or excess demand?) AAA  significant
  10. There IS a switching cost in AA which is not attenuated by any partner characteristics AAA benefit most by affiliating with a winning MLB team in a smaller market AA benefit most by NOT switching and being located further from parent club