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El. knyga: Sport Analytics: A data-driven approach to sport business and management

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Edited by (University of New Haven, US), Edited by (University of New Haven, US)
  • Formatas: 278 pages
  • Išleidimo metai: 10-Nov-2016
  • Leidėjas: Routledge
  • ISBN-13: 9781317212881
Kitos knygos pagal šią temą:
Sport Analytics: A data-driven approach to sport business and managementDidesnis paveikslėlis
  • Formatas: 278 pages
  • Išleidimo metai: 10-Nov-2016
  • Leidėjas: Routledge
  • ISBN-13: 9781317212881
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The increasing availability of data has transformed the way sports are played, promoted and managed. This is the first textbook to explain how the big data revolution is having a profound influence across the sport industry, demonstrating how sport managers and business professionals can use analytical techniques to improve their professional practice.

While other sports analytics books have focused on player performance data, this book shows how analytics can be applied to every functional area of sport business, from marketing and event management to finance and legal services. Drawing on research that spans the entire sport industry, it explains how data is influencing the most important decisions, from ticket sales and human resources to risk management and facility operations. Each chapter contains real world examples, industry profiles and extended case studies which are complimented by a companion website full of useful learning resources.

Sport Analytics: A data-driven approach to sport business and management is an essential text for all sport management students and an invaluable reference for any sport management professional involved in operational research.
List of illustrations
xiii
Notes on contributors xvii
Foreword xix
Acknowledgments xxi
Introduction 1(14)
Gil Fried
Ceyda Mumcu
PART I Data 101
15(32)
1 An introduction to analytics and data
17(16)
Ceyda Mumcu
Introduction
17(1)
Analytics and its importance in the sport industry
17(1)
What is data?
18(1)
1 Data relevance -- what data is needed?
18(1)
2 Data source -- where can this data be obtained?
18(1)
3 Data quantity -- how much data is needed?
19(1)
4 Data quality -- how can the data be made more accurate and valuable for analysis?
19(1)
Types of data
19(1)
Some key statistical concepts
20(2)
Data analysis
22(5)
Regression analysis
22(1)
Logistic regression analysis
22(1)
Decision trees
23(1)
Time series analysis
24(2)
Cluster analysis
26(1)
Sentiment analysis
27(1)
Case study: managing a youth soccer organization's data
27(4)
Conclusion
31(1)
References
31(2)
2 The data ecosystem
33(14)
Gil Fried
How to get the best system
34(1)
Compatibility
35(1)
Processing data
35(3)
Cost
38(2)
Speed of delivery
40(1)
System operation
41(1)
Case study: data management for a professional team
42(3)
Keep it simple
45(1)
Conclusion
46(1)
References
46(1)
PART II Analytics in functional areas
47(200)
3 The data game: analyzing our way to better sport performance
49(27)
Maria Nibali
The world of professional sport: from big business to big data
49(1)
The evolution of sport analytics
50(1)
The driving forces of sport analytics
50(2)
Data management
50(2)
Sport technology
52(4)
Force plate technology
52(2)
Wearable technology
54(1)
"Smart" Clothing and Equipment
55(1)
Case study: use of GPS to predict training loads in Professional Australian Football
56(9)
Case study: analytics at the 28th Southeast Asian (SEA) Games 2015
65(7)
Machine learning
72(1)
The future of sport: predictive analytics
12(2)
Conclusion
14(1)
References
14(62)
4 Strategic talent management analytics
76(15)
Khadija Al Arkoubi
Introduction
76(1)
What is Strategic Talent Management (STM)?
76(1)
What is analytics?
77(1)
Case study: can numbers tell the whole story about an employee
78(4)
Applications of strategic talent management analytics
82(2)
Talent planning
82(1)
Talent engagement and retention
83(1)
Case study: catching managerial issues using analytics
84(4)
Implications of STMA for the twenty-first-century organization
88(1)
References
89(2)
5 Analytics in sport marketing
91(24)
Ceyda Mumcu
Development of market research in sport
91(1)
Customer analytics in sport
92(9)
Customer value estimation
93(2)
Propensity models
95(1)
Segmentation
95(2)
Customer profiling
97(1)
Customer acquisition
97(2)
Campaign evaluations
99(2)
Analytics in ticket pricing
101(2)
Analytics in sport sponsorship
103(1)
Case study: sponsorship evaluation
104(3)
Case study: concessions project planning: soft drinks vs. beer provisions
107(5)
Conclusion
112(1)
References
112(3)
6 Analytics in digital marketing
115(15)
Ceyda Mumcu
Introduction
115(1)
Rise of digital media and its impact on marketing
115(1)
Email marketing
116(2)
Social media marketing
118(3)
Case study: examination of a social media account
121(4)
Web analytics
125(2)
Conclusion
127(1)
References
128(2)
7 Sport finance by the numbers
130(27)
Peggy Keiper
Dylan Williams
Introduction
130(1)
Traditional financial analysis
131(1)
Balance sheet
132(1)
Income statement
133(1)
Financial ratios
133(8)
Liquidity
134(1)
Asset management
135(2)
Leverage
137(1)
Profitability
138(1)
Market value
139(2)
Beyond traditional financial analytics
141(2)
Profit maximization
141(2)
Case study: building an effective program budget
143(8)
The balanced scorecard
149(2)
Case study: using data to make intercollegiate athletic decisions
151(3)
Valuation
153(1)
Conclusion
154(1)
References
154(3)
8 Sport law by the numbers
157(16)
Gil Fried
Data in the courtroom
158(2)
Tort law
160(3)
Case study: comparing foul ball safety
163(6)
Risk management
169(1)
Contract law
170(1)
Antitrust law
171(1)
Government regulations
171(1)
Conclusion
171(1)
References
172(1)
9 Manufacturing/production analytics
173(25)
Gil Fried
Introduction
173(2)
The need for forecasting
175(2)
Technology changes
177(2)
Manufacturing/production analytics
179(3)
Case study: building a better product
182(5)
Nike
187(2)
Case study: producing a major sport broadcast
189(7)
ESPN
196(1)
Conclusion
197(1)
References
197(1)
10 Event management by the numbers
198(15)
Juline E. Mills
Introduction
198(1)
From planning to design to management
199(6)
Case study: personnel deployment
205(4)
Conclusion
209(1)
References
210(3)
11 Facility management analytics
213(19)
Kimberly L. Mahoney
Introduction
213(1)
Venue marketing
213(2)
Operational systems
215(3)
Food and beverage
218(2)
Parking and transportation
220(1)
Safety and overall event management
221(2)
Case study: CMMS proving the results
223(3)
Ticketing
226(1)
Overall guest experience
227(2)
Conclusion
229(1)
References
230(2)
12 Putting it all together
232(15)
Gil Fried
Why analytics?
232(1)
So how do we become analytical?
233(1)
Are the numbers telling the truth?
234(6)
Problem framing
240(1)
Needs assessment
240(1)
Implementation evaluation
241(1)
Outcome/impact assessment
241(1)
Cost--benefit analysis
241(1)
Utilization focused evaluation
241(1)
Logic model
241(1)
Data-driven decision-making
241(1)
Review of prior conclusions (research phase)
241(1)
Variable selection -- developing the right model
242(1)
Data collection
243(1)
Data analysis
244(1)
Presenting the results
245(1)
Putting the pieces together in an example
245(1)
Problem framing
245(1)
Review of prior findings
245(1)
Variable selection
245(1)
Data collection
246(1)
Data analysis
246(1)
Presenting the results
246(1)
Conclusion
246(1)
References
246(1)
Index 247
Gil Fried is Professor and Chair of the Sport Management Department in the College of Business at the University of New Haven, USA. He has been a sport management practitioner for over 30 years and a faculty member for over 20 years. He is the author of ten books, including some of the worlds most widely used sport management textbooks, such as Sport Finance (with DeSchriver and Mondello) and Managing Sport Facilities.

Ceyda Mumcu is an Assistant Professor of Sport Management at the University of New Haven, USA. Her primary focus is on sport marketing and international sport management, and she has interests in quantitative research methods and the use of analytics. Before becoming an academic, she was a professional basketball player for eight years in the Turkish Womens Basketball League.
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