{"id":"W4396619687","doi":"10.3389/fpsyg.2024.1383084","title":"Estimating winning percentage of the fourth quarter in close NBA games using Bayesian logistic modeling","year":2024,"lang":"en","type":"article","venue":"Frontiers in Psychology","topic":"Sports Analytics and Performance","field":"Economics, Econometrics and Finance","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Quarter (Canadian coin); Offensive; Outcome (game theory); Statistics; Basketball; Pace; Psychology; Mathematics; Operations research; Mathematical economics","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":[0.003885189,0.0009927592,0.0009836832,0.002856583,0.0008040622,0.002154264,0.001635417,0.0009514767,0.004001234],"category_scores_gemma":[0.01589958,0.0006753287,0.001265905,0.001651071,0.0006459806,0.001577926,0.00177596,0.001576126,0.001447012],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001196662,"about_ca_system_score_gemma":0.001483364,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03148309,"about_ca_topic_score_gemma":0.03131057,"domain_scores_codex":[0.9980166,0.0005673929,0.0001224048,0.0006258867,0.0003689013,0.0002987305],"domain_scores_gemma":[0.9945883,0.003065243,0.00103471,0.0002518069,0.0006877593,0.0003721486],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006709567,0.00041372,0.8143415,0.000193489,0.0002324646,0.0005493445,0.001090228,0.1083578,0.001498202,0.007826923,0.002901444,0.06192388],"study_design_scores_gemma":[0.00002960806,0.0002082044,0.1665489,0.0001382989,0.0001026927,0.0003338169,0.001405824,0.8177763,0.0008143818,0.009500353,0.003042578,0.00009899802],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8562021,0.0004475426,0.1326253,0.0005672904,0.00007735988,0.0002887918,0.002768192,0.0003519677,0.006671355],"genre_scores_gemma":[0.9775109,0.0002230681,0.01614031,0.00006977883,0.00002348681,0.0001783927,0.00266843,0.00004451779,0.00314109],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03148309,"threshold_uncertainty_score":0.06259966,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04474487489766241,"score_gpt":0.2986664948956844,"score_spread":0.253921619998022,"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."}}