{"id":"W1539623756","doi":"10.1214/lnms/1196285406","title":"Forecasting NBA basketball playoff outcomes using the weighted likelihood","year":2004,"lang":"en","type":"book-chapter","venue":"Lecture notes-monograph series","topic":"Sports Analytics and Performance","field":"Economics, Econometrics and Finance","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Relevance (law); Basketball; Exploit; Computer science; Artificial intelligence; Outcome (game theory); Sample (material); Machine learning; Mathematics; Geography; Computer security; Mathematical economics; Political science","routes":{"ca_aff":true,"ca_fund":false,"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":[0.002697597,0.0006014435,0.000617589,0.00123171,0.0002568778,0.001111141,0.001008598,0.0008602869,0.001904231],"category_scores_gemma":[0.01336182,0.0004011283,0.0004676585,0.001097119,0.0003942395,0.001383019,0.0008002414,0.001131254,0.0006614774],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007946289,"about_ca_system_score_gemma":0.0005366034,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0293511,"about_ca_topic_score_gemma":0.0256552,"domain_scores_codex":[0.9994487,0.0003152368,0.00002513791,0.00008150978,0.00008462252,0.00004484126],"domain_scores_gemma":[0.9972446,0.00194441,0.0003715917,0.0001266267,0.0001934727,0.0001192985],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003949369,0.0001625177,0.09701703,0.00006809612,0.0001571788,0.0001687373,0.0001953291,0.764305,0.0007505108,0.01789794,0.007388888,0.1114939],"study_design_scores_gemma":[0.00001723281,0.000015908,0.007012931,0.000007505771,0.000009169866,0.000006536891,0.00002936597,0.9832332,0.000150372,0.009056377,0.0004531887,0.000008325524],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7122392,0.001017331,0.2735112,0.002785111,0.00009908804,0.0001106171,0.001991146,0.0007193877,0.007526928],"genre_scores_gemma":[0.9706058,0.0003365829,0.0243444,0.00006240543,0.0001008856,0.00005382312,0.001647455,0.00005219442,0.002796367],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0293511,"threshold_uncertainty_score":0.05836046,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03946619965846514,"score_gpt":0.2137969885284394,"score_spread":0.1743307888699743,"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."}}