{"id":"W1930997193","doi":"10.5750/jpm.v2i1.433","title":"THE IMPACT OF SENTIMENT ON POINT SPREADS IN THE COLLEGE FOOTBALL WAGERING MARKET","year":2012,"lang":"en","type":"article","venue":"The Journal of Prediction Markets","topic":"Sports Analytics and Performance","field":"Economics, Econometrics and Finance","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Football; Arbitrage; Exploit; Financial economics; Efficient-market hypothesis; Point (geometry); Economics; Stock market; Computer science; History; Political science; Law; Computer security","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001235544,0.0002548237,0.0003087102,0.0005417985,0.0004385085,0.002023076,0.0002138739,0.0006779882,0.005497363],"category_scores_gemma":[0.01090175,0.0001336219,0.0002119092,0.0003375751,0.0005750278,0.001289696,0.0007162147,0.00107529,0.0004954556],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005257512,"about_ca_system_score_gemma":0.0001804792,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004128391,"about_ca_topic_score_gemma":0.003596324,"domain_scores_codex":[0.9996462,0.0001297671,0.00001592902,0.00004528624,0.00009074925,0.00007198025],"domain_scores_gemma":[0.992278,0.003284153,0.002640562,0.0002183566,0.0004876057,0.001091411],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002981594,0.0009489534,0.8664328,0.00009586466,0.0002748171,0.001433683,0.002027423,0.0106295,0.01973403,0.01543316,0.002839689,0.07716862],"study_design_scores_gemma":[0.0000498462,0.000397816,0.9687677,0.00001857667,0.00006286683,0.0001129118,0.001136383,0.02071938,0.001109351,0.006770732,0.0008113797,0.00004307095],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9932879,0.0001084002,0.0003863177,0.0002985539,0.00001053242,0.000006429679,0.00004151473,0.000006067004,0.005854415],"genre_scores_gemma":[0.9989672,0.00005233341,0.00006501629,0.00003767645,0.00001769116,0.000001451883,0.00002274703,0.000002926407,0.0008329045],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005497363,"threshold_uncertainty_score":0.01839054,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01936040462171883,"score_gpt":0.2397499542335801,"score_spread":0.2203895496118613,"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."}}