International Journal of Computer Networks and Applications

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Rethinking Audience Clustering in Sports Market using Gossip Protocol


Affiliations
1 Department of Computer Science and Engineering, National Institute of Technology, Srinagar, India
2 Department of Computer Science and Engineering, NIT Srinagar, India
 

Analytics and inferences have found their place in all the business domains varying from large-scale businesses with criticality to small scale business with less criticality. Sports are considered to be big business in its aspects like amount of money spent on it but in its other version like number of people associated with it, it is comparatively a small industry. Sports analytics have changed their dimension both in the manner they are thought about and number of participation from scientific society that grew over the years. Contribution from analytics is being looked from by sports management to enhance various industries associated to it. The authors realize that sports industry is a close, strongly connected group that is very similar in its behavior to a social network. The authors propose a graph theoretic model in context of sports analytics that presents preliminary study of using gossip protocol for sharing information among members of sports oriented social network.


Keywords

Clustering, Gossip Protocol, Sports, Social Network.
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  • Fry, Michael J., and Jeffrey W. Ohlmann. "Introduction to the special issue on analytics in sports, part I: General sports applications." (2012): 105-108.

  • Davenport, Thomas H. "Analytics in sports: The new science of winning." International Institute for Analytics 2 (2014): 1-28.

  • https://home.liebertpub.com/cfp/special-issue-on-sports-analytics/119/

  • James, Bill. The new Bill James historical baseball abstract. Simon and Schuster, 2010.

  • Lewis, Michael. Moneyball: The art of winning an unfair game. WW Norton & Company, 2004.

  • Lindsey, George R. "An investigation of strategies in baseball." Operations Research 11.4 (1963): 477-501.

  • https://en.wikipedia.org/wiki/MIT_Sloan_Sports_Analytics_Conference

  • Lucey P, Morgan S, Wiens J, Yue Y (2016) KDD workshop on large-scale sports analytics. http://www. large- scale- sports-analytics.org/ MathSport International (2017).

  • Davis J, van Haaren J, Kaytoue M, Zimmermann A (2013) Machine learning and data mining for sports analytics. https://dtai.cs.kuleuven.be/events/MLSA17/

  • Deason L (2006) ShotLink a statistical superstar. Accessed January 3, 2012, http://www.pgatour.com/story/9596346/.

  • Miller, Thomas W. Sports analytics and data science: winning the game with methods and models. FT Press, 2015.

  • Andrew Ball, “Should Teams Use Baseball America’s Rankings to Draft?” Beyond the Box Score website, Dec. 24, 2013, http://www.beyondtheboxscore.com/2013/12/24/5240610/should- teams-use-baseball-americas-rankings-to-draft

  • Winston, Wayne L. Mathletics: How gamblers, managers, and sports enthusiasts use mathematics in baseball, basketball, and football. Princeton University Press, 2012.

  • Ira Boudway, “Baseball Set for Data Deluge as Player Monitoring Goes Hi-Tech,” Bloomberg News, March 31, 2011.

  • Eddie Metz, “Saviormetrics,” ESPN the Magazine, April 14, 2013, http://espn.go.com/mlb/story/_/page/Mag15saviormetrics/oakland-brandon-mccarthy- writing-moneyball-next-chapter-reinventing-analytics-espn-magazine.

  • Stan Conte information from Molly Knight, “The Hurt Talker,” ESPN The Magazine, August 13, 2012, and Stan Conte panel presentation, “Staying on the Field: Injury Analytics,” MIT Sports Analytics Conference 2013

  • Catapult Sports case study: Tom Myslinski, Jacksonville Jaguars, Catapult Systems website, http://catapultsports.com/wp-content/uploads/2013/06/Case-study-Tom-Myslinski.pdf

  • Ameet Sachdev, “Baseball teams get dynamic with ticket pricing,” Chicago Tribune, May 12, 2013, http://articles.chicagotribune.com/2013-05-12/business/ct-biz-0512-stub-hub-- 20130512_1_stubhub-bleacher-ticket-ticket-reselling

  • Adam Rubin, “Mets introduce Sandy Alderson,” ESPNNewYork.com, October 30, 2010, http://sports.espn.go.com/newyork/mlb/news/story?id=5741492

  • Information about Allardyce comes from regular in-person meetings during 2012-13 and 2013-14 season, an interview for this study by Al Sim (Feb 2014), and the following articles: Sam Allardyce – Barclays Premier League Profile http://espnfc.com/manager/_/id/29/samallardyce?cc=5739

  • “Scoring higher revenue with analytics,” SAS customer stories, http://www.sas.com/en_us/customers/orlando-magic.html

  • Information about the New England Patriots from an interview with Jessica Gelman and from Heather Fletcher, “Pats’ Pact: Fans Are Family,” Target Marketing, December 2011, http://www.targetmarketingmag.com/article/new-england-patriots-use-analytics-and-trigger- emails-retain-season-ticket-holders/

  • Kate Kaye, “How P&G Inspired Cleveland Indians to Offer Fewer Bobbleheads,” Ad Age, March 18, 2013, http://adage.com/article/datadriven-marketing/school-marketing-practice- cleveland-indians/240362/

  • Anton Troianovski, “Phone Firms Sell Data on Customers,” The Wall Street Journal, May 22, 2013, p. B1.

  • “NBA Launches New Tracking System to Capture Player Statistics,” CBS News, January 27, 2014, http://www.cbsnews.com/news/nba-launches-new-tracking-system-to-capture-player- statistics/

  • Rein, Irving, Ben Shields, and Adam Grossman. The Sports Strategist: Developing Leaders for a High-Performance Industry. Oxford University Press, USA, 2014.

  • Fort, Rodney, and James Quirk. "Optimal competitive balance in a season ticket league." Economic inquiry 49.2 (2011): 464-473.

  • Kesenne, Stefan. "Revenue sharing and owner profits in professional team sports." Journal of sports Economics 8.5 (2007): 519-529.

  • Rysman, Marc. "The economics of two-sided markets." Journal of Economic Perspectives 23.3 (2009): 125-43.

  • Wright, M. B. "50 years of OR in sport." Journal of the Operational Research Society 60.1 (2009): S161-S168.

  • Kahn, Lawrence M. "The sports business as a labor market laboratory." Journal of Economic Perspectives 14.3 (2000): 75-94.

  • Mullin, Bernard J., Stephen Hardy, and William Sutton. Sport Marketing 4th Edition. Human Kinetics, 2014.

  • https://nest.latrobe/the-impact-of-social-and-digital-media-on-sport/

  • https://www.clearinghouseforsport.gov.au/knowledge_base/organised_sport/sports_administration_and_management/social_media_and_sport

  • Fan, Weiguo, and Michael D. Gordon. "The power of social media analytics." Communications of the ACM 57.6 (2014): 74-81.

  • Stieglitz, Stefan, et al. "Social media analytics." Business & Information Systems Engineering 6.2 (2014): 89-96.

  • Shah, Devavrat. "Network gossip algorithms." Acoustics, Speech and Signal Processing, 2009. ICASSP 2009. IEEE International Conference on. IEEE, 2009.

  • S. Bornholdt and H. Georg Schuster, editors Handbook of graphs and networks, Wiley-VCH, 2003.

  • Haeupler, Bernhard, et al. "Discovery through gossip." Proceedings of the twenty-fourth annual ACM symposium on Parallelism in algorithms and architectures. ACM, 2012.

  • https://en.wikipedia.org/wiki/Gossip_protocol

  • O. Babaoglu and M. Jelasity, Self-* properties through gossiping, Philos Trans R Soc A 366 (2008), 3747–3757.

  • S. Boyd, A. Ghosh, B. Prabhakar, and D. Shah, Randomized gossip algorithms, IEEE Trans. on Infor. Theory 52 (2006), 2508–2530.

  • J.-Y. Chen and G. Pandurangan, Almost-optimal gossip-based aggregate computation, SIAM J Comput 41 (2012), 455–483.

  • A. Demers, D. Greene, C. Hauser, W. Irish, J. Larson, S. Shenker, H. Sturgis, D. Swinehart, and D. Terry, Epidemic algorithms for replicated database maintenance, In PODC, 1987, pp. 1–12.

  • R. M. Karp, C. Schindelhauer, S. Shenker, and B. Vöcking, Randomized rumor spreading, In FOCS, 2000, pp. 565–574.

  • M. Jelasity, A. Montresor, and O. Babaoglu, T-man: Gossip-based fast overlay topology construction, Comput Netw 53 (2009), 2321–2339.

  • D. Kempe, A. Dobra, and J. Gehrke, Gossip-based computation of aggregate information, In FOCS, 2003, pp. 482–491.

  • D. Kempe, J. Kleinberg, and A. Demers, Spatial gossip and resource location protocols, In STOC, 2001, pp. 163–172.

  • D. Mosk-Aoyama and D. Shah, Computing separable functions via gossip, In PODC, 2006, pp. 113–122.

  • Shah, Devavrat. "Gossip algorithms." Foundations and Trends® in Networking 3.1 (2009): 1-125.

  • J. Augustine, G. Pandurangan, and P. Robinson, Fast byzantine agreement in dynamic networks, In PODC, 2013, pp. 74–83.

  • J. Augustine, A. Rahman Molla, E. Morsy, G. Pandurangan, P. Robinson, and E. Upfal, Storage and search in dynamic peer-to-peer networks, In SPAA, 2013, pp. 53–62.

  • J. Augustine, G. Pandurangan, P. Robinson, and E. Upfal, Towards robust and efficient computation in dynamic peer-to-peer networks., In SODA, 2012, pp. 551–569.

  • C. Avin, M. Koucký, and Z. Lotker, How to explore a fast-changing world (cover time of a simple random walk on evolving graphs), In ICALP (1), 2008, pp. 121–132.

  • A. E. F. Clementi, P. Crescenzi, C. Doerr, P. Fraigniaud, M. Isopi, A. Panconesi, F. Pasquale, and R. Silvestri, Rumor spreading in random evolving graphs, In ESA, 2013, pp. 325–336.

  • A. E. F. Clementi, C. Macci, A. Monti, F. Pasquale, and R. Silvestri, Flooding time in edge- markovian dynamic graphs, In PODC, 2008, pp. 213–222.

  • A. E. F. Clementi, R. Silvestri, and L. Trevisan, Information spreading in dynamic graphs, In PODC, 2012, pp. 37–46.

  • J.-Y. Chen and G. Pandurangan, Almost-optimal gossip-based aggregate computation, SIAM J Comput 41 (2012), 455–483

  • F. Chierichetti, S. Lattanzi, and A. Panconesi, Almost tight bounds on rumor spreading and conductance, In STOC, 2010, pp. 399–408.

  • B. Doerr, T. Friedrich, and T. Sauerwald, Quasi-random rumor spreading, In SODA, 2008, pp. 773–781.

  • G. Giakkoupis, Tight bounds for rumor spreading in graphs of a given conductance, In STACS, 2011, pp. 57–68.

  • A. D. Sarma, A. R. Molla, and G. Pandurangan, Fast distributed computation in dynamic networks via random walks, In DISC, 2012, pp. 136–150.

  • R. M. Karp, C. Schindelhauer, S. Shenker, and B. Vöcking, Randomized rumor spreading, In FOCS, 2000, pp. 565–574.

  • Brefeld, Ulf, and Albrecht Zimmermann. "Guest editorial: Special issue on sports analytics." Data Mining and Knowledge Discovery 31.6 (2017): 1577-1579.


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  • Rethinking Audience Clustering in Sports Market using Gossip Protocol

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Authors

Asif Ali Banka
Department of Computer Science and Engineering, National Institute of Technology, Srinagar, India
Roohie Naaz
Department of Computer Science and Engineering, NIT Srinagar, India

Abstract


Analytics and inferences have found their place in all the business domains varying from large-scale businesses with criticality to small scale business with less criticality. Sports are considered to be big business in its aspects like amount of money spent on it but in its other version like number of people associated with it, it is comparatively a small industry. Sports analytics have changed their dimension both in the manner they are thought about and number of participation from scientific society that grew over the years. Contribution from analytics is being looked from by sports management to enhance various industries associated to it. The authors realize that sports industry is a close, strongly connected group that is very similar in its behavior to a social network. The authors propose a graph theoretic model in context of sports analytics that presents preliminary study of using gossip protocol for sharing information among members of sports oriented social network.


Keywords


Clustering, Gossip Protocol, Sports, Social Network.

References