Optimism Bias in Fans and Sports Reporters
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{"title"=>"Optimism bias in fans and sports reporters", "type"=>"journal", "authors"=>[{"first_name"=>"Bradley C.", "last_name"=>"Love", "scopus_author_id"=>"7102112661"}, {"first_name"=>"Łukasz", "last_name"=>"Kopeć", "scopus_author_id"=>"56736580500"}, {"first_name"=>"Olivia", "last_name"=>"Guest", "scopus_author_id"=>"55699015300"}], "year"=>2015, "source"=>"PLoS ONE", "identifiers"=>{"issn"=>"19326203", "doi"=>"10.1371/journal.pone.0137685", "sgr"=>"84944811044", "scopus"=>"2-s2.0-84944811044", "pmid"=>"26352146", "pui"=>"606519505"}, "id"=>"51ae5a89-fa9e-34ca-a59b-ec91c81a3539", "abstract"=>"People are optimistic about their prospects relative to others. However, existing studies can be difficult to interpret because outcomes are not zero-sum. For example, one person avoiding cancer does not necessitate that another person develops cancer. Ideally, optimism bias would be evaluated within a closed formal system to establish with certainty the extent of the bias and the associated environmental factors, such that optimism bias is demonstrated when a population is internally inconsistent. Accordingly, we asked NFL fans to predict how many games teams they liked and disliked would win in the 2015 season. Fans, like ESPN reporters assigned to cover a team, were overly optimistic about their team’s prospects. The opposite pattern was found for teams that fans disliked. Optimism may flourish because year-to-year team results are marked by auto-correlation and regression to the group mean (i.e., good teams stay good, but bad teams improve). [ABSTRACT FROM AUTHOR]", "link"=>"http://www.mendeley.com/research/optimism-bias-fans-sports-reporters", "reader_count"=>19, "reader_count_by_academic_status"=>{"Researcher"=>4, "Student > Doctoral Student"=>2, "Student > Ph. D. Student"=>4, "Student > Postgraduate"=>1, "Other"=>1, "Student > Master"=>2, "Student > Bachelor"=>3, "Lecturer"=>1, "Lecturer > Senior Lecturer"=>1}, "reader_count_by_user_role"=>{"Researcher"=>4, "Student > Doctoral Student"=>2, "Student > Ph. D. Student"=>4, "Student > Postgraduate"=>1, "Other"=>1, "Student > Master"=>2, "Student > Bachelor"=>3, "Lecturer"=>1, "Lecturer > Senior Lecturer"=>1}, "reader_count_by_subject_area"=>{"Unspecified"=>1, "Medicine and Dentistry"=>1, "Arts and Humanities"=>1, "Neuroscience"=>1, "Business, Management and Accounting"=>1, "Psychology"=>7, "Computer Science"=>3, "Decision Sciences"=>1, "Economics, Econometrics and Finance"=>1, "Immunology and Microbiology"=>1, "Engineering"=>1}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>1}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>1}, "Neuroscience"=>{"Neuroscience"=>1}, "Decision Sciences"=>{"Decision Sciences"=>1}, "Psychology"=>{"Psychology"=>7}, "Economics, Econometrics and Finance"=>{"Economics, Econometrics and Finance"=>1}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>1}, "Computer Science"=>{"Computer Science"=>3}, "Business, Management and Accounting"=>{"Business, Management and Accounting"=>1}, "Unspecified"=>{"Unspecified"=>1}, "Arts and Humanities"=>{"Arts and Humanities"=>1}}, "reader_count_by_country"=>{"Luxembourg"=>1}, "group_count"=>1}

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Relative Metric

{"start_date"=>"2015-01-01T00:00:00Z", "end_date"=>"2015-12-31T00:00:00Z", "subject_areas"=>[]}
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