{"id":"W4206933438","doi":"10.1002/cjs.11684","title":"Zero‐inflated Poisson model with clustered regression coefficients: Application to heterogeneity learning of field goal attempts of professional basketball players","year":2022,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Sports Analytics and Performance","field":"Economics, Econometrics and Finance","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Basketball; Poisson regression; Field (mathematics); Poisson distribution; Zero-inflated model; Regression analysis; Econometrics; Zero (linguistics); Computer science; Regression; Statistics; Mathematics; Psychology; Geography; Population","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.01061549,0.0008217401,0.001625893,0.00175469,0.000790393,0.001800118,0.004594611,0.002219894,0.002708885],"category_scores_gemma":[0.03121449,0.0006886894,0.002008659,0.001464054,0.001630186,0.002009744,0.001680003,0.003254845,0.0004481712],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002138655,"about_ca_system_score_gemma":0.001401095,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03468429,"about_ca_topic_score_gemma":0.02149436,"domain_scores_codex":[0.99743,0.001315266,0.0001080806,0.0006834682,0.0002063951,0.0002567025],"domain_scores_gemma":[0.9696968,0.02501433,0.002017052,0.001126458,0.001528792,0.0006166083],"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":[0.000334565,0.0002920104,0.070108,0.0001383131,0.0002746029,0.000486552,0.0008442855,0.8216779,0.0008490506,0.06605805,0.003127902,0.03580877],"study_design_scores_gemma":[0.00002078327,0.0000327374,0.002793557,0.00001448208,0.00002985562,0.00002758398,0.00008355993,0.9833916,0.0001438592,0.0130815,0.000356453,0.00002409445],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4526532,0.0007872679,0.5403211,0.002005747,0.000103467,0.0002063669,0.001237489,0.0005406034,0.002144885],"genre_scores_gemma":[0.9586841,0.0002706743,0.03560579,0.0003041236,0.00009244666,0.000157626,0.00129499,0.00007829372,0.003511991],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03468429,"threshold_uncertainty_score":0.06896484,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01886589454987327,"score_gpt":0.2286775191284378,"score_spread":0.2098116245785645,"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."}}