{"id":"W2567192494","doi":"10.1080/02664763.2016.1268106","title":"A functional data approach to model score difference process in professional basketball games","year":2016,"lang":"en","type":"article","venue":"Journal of Applied Statistics","topic":"Sports Analytics and Performance","field":"Economics, Econometrics and Finance","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China","keywords":"Basketball; Realization (probability); Process (computing); Computer science; Momentum (technical analysis); Econometrics; Statistics; Mathematics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008762484,0.001331193,0.001116516,0.002000452,0.0005247059,0.001673236,0.00256554,0.002291305,0.004222941],"category_scores_gemma":[0.02387109,0.0006438185,0.001626687,0.001552925,0.001854926,0.002804386,0.001885746,0.003078503,0.0005728539],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001881982,"about_ca_system_score_gemma":0.00161248,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01466079,"about_ca_topic_score_gemma":0.00936914,"domain_scores_codex":[0.9970359,0.001736633,0.0001504031,0.0005268919,0.000326956,0.0002233383],"domain_scores_gemma":[0.9880751,0.008820211,0.001225877,0.0005418031,0.001028713,0.0003082842],"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":[0.0001068554,0.0001707178,0.02236568,0.0002003248,0.0001582407,0.000384296,0.0005774677,0.4944316,0.0008969223,0.4536242,0.002053598,0.02502998],"study_design_scores_gemma":[0.00001085906,0.00005378318,0.001562398,0.00001920202,0.00001569504,0.00004363726,0.00007201172,0.9471129,0.0001149384,0.04979998,0.001175785,0.00001887564],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04258614,0.0002893407,0.9533043,0.000880414,0.00007131316,0.0001177337,0.0005261099,0.0001234637,0.002101124],"genre_scores_gemma":[0.8548973,0.0007091237,0.1341574,0.0003657473,0.000163258,0.0006930854,0.001324318,0.0000934851,0.007596325],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01466079,"threshold_uncertainty_score":0.04634094,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09927779556843992,"score_gpt":0.2682724620487213,"score_spread":0.1689946664802814,"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."}}