Comparing the performance of us college football teams in the web and on the field

ABSTRACT

In previous research it has been shown that link-based web page metrics can be used to predict experts' assessment of quality. We are interested in a related question: do expert rankings of real-world entities correlate with search engine (SE) rankings of corresponding web resources? To answer this question we compared rankings of college football teams in the US with rankings of their associated web resources. We looked at the weekly polls released by the Associated Press (AP) and USA Today Coaches Poll. Both rank the top 25 teams according to the aggregated expertise of sports writers and college football coaches. For the entire 2008 season (8/2008 { 1/2009), we compared the ranking of teams (top 10 and top 25) according to the polls with the rankings of one to eight URLs associated with each team in Google, Live Search and Yahoo. We found moderate to high correlations between the final rankings of 2007 and the SE ranking in mid 2008 but the correlation between the polls and the SEs steadily decreased as the season went on. We believe this is because the rankings in the web graph (as reported via SEs) have "inertia" and do not rapidly fluctuate as do the teams' on the field fortunes.

REFERENCES

• 1: Brian Amento , Loren Terveen , Will Hill, Does “authority” mean quality? predicting expert quality ratings of Web documents, Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval, p.296-303, July 24-28, 2000, Athens, Greece [doi>10.1145/345508.345603]
• 2: Lars Backstrom , Jon Kleinberg , Ravi Kumar , Jasmine Novak, Spatial variation in search engine queries, Proceeding of the 17th international conference on World Wide Web, April 21-25, 2008, Beijing, China [doi>10.1145/1367497.1367546]
• 3: Ricardo A. Baeza-Yates , Felipe Saint-Jean , Carlos Castillo, Web Structure, Dynamics and Page Quality, Proceedings of the 9th International Symposium on String Processing and Information Retrieval, p.117-130, September 11-13, 2002
• 4: Krishna Bharat , George A. Mihaila, When experts agree: using non-affiliated experts to rank popular topics, ACM Transactions on Information Systems (TOIS), v.20 n.1, p.47-58, January 2002 [doi>10.1145/503104.503107]
• 5: Robert G. Capra , Christopher A. Lee , Gary Marchionini , Terrell Russell , Chirag Shah , Fred Stutzman, Selection and context scoping for digital video collections: an investigation of youtube and blogs, Proceedings of the 8th ACM/IEEE-CS joint conference on Digital libraries, June 16-20, 2008, Pittsburgh PA, PA, USA [doi>10.1145/1378889.1378925]
• 6: Junghoo Cho , Sourashis Roy, Impact of search engines on page popularity, Proceedings of the 13th international conference on World Wide Web, May 17-20, 2004, New York, NY, USA [doi>10.1145/988672.988676]
• 7: Junghoo Cho , Sourashis Roy , Robert E. Adams, Page quality: in search of an unbiased web ranking, Proceedings of the 2005 ACM SIGMOD international conference on Management of data, June 14-16, 2005, Baltimore, Maryland [doi>10.1145/1066157.1066220]
• 8: Martin Klein , Olena Hunsicker , Michael L. Nelson, Correlation of music charts and search engine rankings, Proceedings of the 9th ACM/IEEE-CS joint conference on Digital libraries, June 15-19, 2009, Austin, TX, USA [doi>10.1145/1555400.1555490]
• 9: Jon M. Kleinberg, Authoritative sources in a hyperlinked environment, Journal of the ACM (JACM), v.46 n.5, p.604-632, Sept. 1999 [doi>10.1145/324133.324140]
• 10: Ee-Peng Lim , Ba-Quy Vuong , Hady Wirawan Lauw , Aixin Sun, Measuring Qualities of Articles Contributed by Online Communities, Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence, p.81-87, December 18-22, 2006 [doi>10.1109/WI.2006.115]
• 11: Frank McCown , Michael L. Nelson, Agreeing to disagree: search engines and their public interfaces, Proceedings of the 7th ACM/IEEE-CS joint conference on Digital libraries, June 18-23, 2007, Vancouver, BC, Canada [doi>10.1145/1255175.1255237]
• 12: M. L. Nelson, M. Klein, and M. Magudamudi. Correlation of expert and search engine rankings. CoRR, abs/0809.2851, 2008.
• 13: Sandeep Pandey , Sourashis Roy , Christopher Olston , Junghoo Cho , Soumen Chakrabarti, Shuffling a stacked deck: the case for partially randomized ranking of search engine results, Proceedings of the 31st international conference on Very large data bases, August 30-September 02, 2005, Trondheim, Norway
• 14: Soo Young Rieh, Judgement of information quality and cognitive authority in the Web, Journal of the American Society for Information Science and Technology, v.53 n.2, p.145-161, January 15, 2002 [doi>10.1002/asi.10017.abs]
• 15: S. Y. Rieh and N. J. Belkin. Understanding Judgment of Information Quality and Cognitive Authority in the WWW. In American Society for Information Science and Technology Annual Meeting, pages 24--29, 1998.
• 16: Rong Tang , Kwong Bor Ng , Tomek Strzalkowski , Paul B. Kantor, Automatically predicting information quality in news documents, Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology: companion volume of the Proceedings of HLT-NAACL 2003--short papers, p.97-99, May 27-June 01, 2003, Edmonton, Canada [doi>10.3115/1073483.1073516]
• 17: T. Upstill, N. Craswell, and D. Hawking. Predicting fame and fortune: Pagerank or indegree? In Proceedings of the Australasian Document Computing Symposium, ADCS2003, pages 31--40, Canberra, Australia, December 2003.

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval

General Terms:
Design, Measurement, Performance

Keywords:
correlation, ranking, real world objects, search engines