{"id":"W3179134135","doi":"10.1038/s41598-021-93533-w","title":"Bayesian analysis of home advantage in North American professional sports before and during COVID-19","year":2021,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Sports Analytics and Performance","field":"Economics, Econometrics and Finance","cited_by":56,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Overdispersion; League; Football; Negative binomial distribution; Ice hockey; Basketball; Professional sport; American football; Coronavirus disease 2019 (COVID-19); Pandemic; Bayesian probability; Demographic economics; Marketing; Business; Economics; Medicine; Statistics; Geography; Mathematics; Poisson distribution; Physical medicine and rehabilitation","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005518033,0.0003719472,0.0009882574,0.001017857,0.0004770476,0.001351372,0.001049131,0.001057432,0.005955669],"category_scores_gemma":[0.01648019,0.0003889348,0.0008131376,0.0007595566,0.0009172156,0.001026998,0.001129815,0.001588205,0.0005475648],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001008928,"about_ca_system_score_gemma":0.000918313,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05379084,"about_ca_topic_score_gemma":0.03700904,"domain_scores_codex":[0.998516,0.0006783286,0.0000400269,0.0003816603,0.0001410029,0.0002428944],"domain_scores_gemma":[0.9866084,0.009629167,0.001738652,0.0006570101,0.0006849597,0.0006818298],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001141814,0.0006037105,0.5710377,0.0001510741,0.0007358624,0.0005982964,0.001400935,0.2617996,0.002269935,0.07783038,0.007235222,0.07519551],"study_design_scores_gemma":[0.00006331209,0.0002360966,0.310636,0.00006392266,0.0001689849,0.00009236767,0.000663685,0.6529892,0.0003743753,0.03112691,0.003505435,0.00007958082],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.956777,0.0004995427,0.03717135,0.001008496,0.00002445596,0.00004559147,0.0008357624,0.00009280618,0.003545075],"genre_scores_gemma":[0.9935693,0.0001954357,0.002454546,0.00006926487,0.00002624719,0.00003711726,0.0007525571,0.00001830038,0.002877204],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05379084,"threshold_uncertainty_score":0.1069555,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01122071059512424,"score_gpt":0.2461568609268074,"score_spread":0.2349361503316832,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}