Sport analytics : a data-driven approach to sport business and management

목차 (Table of Contents)

• CONTENTS
• List of illustrations = xiii
• Notes on contributors = xvii
• Foreword = xix
• Acknowledgments = xxi
• Introduction / Gil Fried ; Ceyda Mumcu = 1
• PART Ⅰ Data 101 = 15
• 1 An introduction to analytics and data / Ceyda Mumcu = 17
• Introduction = 17
• Analytics and its importance in the sport industry = 17
• What is data? = 18
• Types of data = 19
• Some key statistical concepts = 20
• Data analysis = 22
• Case study : managing a youth soccer organization's data = 27
• Conclusion = 31
• References = 31
• 2 The data ecosystem / Gil Fried = 33
• How to get the best system = 34
• Compatibility = 35
• Processing data = 35
• Cost = 38
• Speed of delivery = 40
• System operation = 41
• Case study : data management for a professional team = 42
• Keep it simple = 45
• Conclusion = 46
• References = 46
• PART Ⅱ Analytics in functional areas = 47
• 3 The data game : analyzing our way to better sport performance / Maria Nibali = 49
• The world of professional sport : from big business to big data = 49
• The evolution of sport analytics = 50
• The driving forces of sport analytics = 50
• Sport technology = 52
• Case study : use of GPS to predict training loads in Professional Australian Football = 56
• Case study : analytics at the 28th Southeast Asian(SEA) Games 2015 = 65
• Machine learning = 72
• The future of sport : predictive analytics = 12
• Conclusion = 14
• References = 14
• 4 Strategic talent management analytics / Khadija Al Arkoubi = 76
• Introduction = 76
• What is Strategic Talent Management(STM)? = 76
• What is analytics? = 77
• Case study : can numbers tell the whole story about an employee = 78
• Applications of strategic talent management analytics = 82
• Case study : catching managerial issues using analytics = 84
• Implications of STMA for the twenty-first-century organization = 88
• References = 89
• 5 Analytics in sport marketing / Ceyda Mumcu = 91
• Development of market research in sport = 91
• Customer analytics in sport = 92
• Analytics in ticket pricing = 101
• Analytics in sport sponsorship = 103
• Case study : sponsorship evaluation = 104
• Case study : concessions project planning : soft drinks vs. beer provisions = 107
• Conclusion = 112
• References = 112
• 6 Analytics in digital marketing / Ceyda Mumcu = 115
• Introduction = 115
• Rise of digital media and its impact on marketing = 115
• Email marketing = 116
• Social media marketing = 118
• Case study : examination of a social media account = 121
• Web analytics = 125
• Conclusion = 127
• References = 128
• 7 Sport finance by the numbers / Peggy Keiper ; Dylan Williams = 130
• Introduction = 130
• Traditional financial analysis = 131
• Balance sheet = 132
• Income statement = 133
• Financial ratios = 133
• Beyond traditional financial analytics = 141
• Case study : building an effective program budget = 143
• Case study : using data to make intercollegiate athletic decisions = 151
• Conclusion = 154
• References = 154
• 8 Sport law by the numbers / Gil Fried = 157
• Data in the courtroom = 158
• Tort law = 160
• Case study : comparing foul ball safety = 163
• Risk management = 169
• Contract law = 170
• Antitrust law = 171
• Government regulations = 171
• Conclusion = 171
• References = 172
• 9 Manufacturing／production analytics / Gil Fried = 173
• Introduction = 173
• The need for forecasting = 175
• Technology changes = 177
• Manufacturing／production analytics = 179
• Case study : building a better product = 182
• Nike = 187
• Case study : producing a major sport broadcast = 189
• ESPN = 196
• Conclusion = 197
• References = 197
• 10 Event management by the numbers / Juline E. Mills = 198
• Introduction = 198
• From planning to design to management = 199
• Case study : personnel deployment = 205
• Conclusion = 209
• References = 210
• 11 Facility management analytics / Kimberly L. Mahoney = 213
• Introduction = 213
• Venue marketing = 213
• Operational systems = 215
• Food and beverage = 218
• Parking and transportation = 220
• Safety and overall event management = 221
• Case study : CMMS proving the results = 223
• Ticketing = 226
• Overall guest experience = 227
• Conclusion = 229
• References = 230
• 12 Putting it all together / Gil Fried = 232
• Why analytics? = 232
• So how do we become analytical? = 233
• Are the numbers telling the truth? = 234
• Problem framing = 240
• Review of prior conclusions(research phase) = 241
• Variable selection - developing the right model = 242
• Data collection = 243
• Data analysis = 244
• Presenting the results = 245
• Putting the pieces together in an example = 245
• Conclusion = 246
• References = 246
• Index = 247