{"id":"W1991773164","doi":"10.1287/inte.1110.0612","title":"Quantifying the Contribution of NHL Player Types to Team Performance","year":2012,"lang":"en","type":"article","venue":"INFORMS Journal on Applied Analytics","topic":"Sports Analytics and Performance","field":"Economics, Econometrics and Finance","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Salary; League; Cluster analysis; Value (mathematics); Computer science; Artificial intelligence; Machine learning; Economics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001387319,0.0001618793,0.0003664054,0.0002849734,0.0002379912,0.00009128054,0.000289573,0.0000845841,0.000231827],"category_scores_gemma":[0.00009195422,0.0001118488,0.0001303044,0.0003891821,0.00004655518,0.0002772817,0.00004516194,0.0003592793,0.0006164851],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001057697,"about_ca_system_score_gemma":0.00002827829,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007721765,"about_ca_topic_score_gemma":0.000003899263,"domain_scores_codex":[0.9984522,0.000001939929,0.0008837451,0.0001104044,0.0001296788,0.0004219661],"domain_scores_gemma":[0.9987002,0.00006817619,0.000689552,0.0002771972,0.00009023465,0.0001746241],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001181699,0.0001875779,0.5001909,0.00002935203,0.000181212,7.783127e-7,0.001000308,0.02399131,0.00006107754,0.4665974,0.003423728,0.004218216],"study_design_scores_gemma":[0.001422772,0.0005120753,0.2541327,0.0001180685,0.00007882997,0.0000623416,0.0004698309,0.05776618,0.002966546,0.002429926,0.6792177,0.0008230855],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9441438,0.0004045662,0.003928822,0.0004304937,0.0006459628,0.0002182551,0.00003171743,0.00001616364,0.05018024],"genre_scores_gemma":[0.9977813,0.0004842655,0.0001415233,0.0008634761,0.0003718143,0.000004230346,0.000005577882,0.0000156334,0.0003321445],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6757939,"threshold_uncertainty_score":0.7923875,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04719951990944064,"score_gpt":0.2540716229958695,"score_spread":0.2068721030864288,"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."}}