{"id":"W2182961570","doi":"10.6339/jds.2004.02(1).142","title":"A Two-Stage Bayesian Model for Predicting Winners in Major League Baseball","year":2021,"lang":"en","type":"article","venue":"Journal of Data Science","topic":"Sports Analytics and Performance","field":"Economics, Econometrics and Finance","cited_by":43,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Korea Science and Engineering Foundation","keywords":"League; Markov chain Monte Carlo; Bayesian probability; Bayesian inference; Computer science; Econometrics; Field (mathematics); Markov chain; Inference; Artificial intelligence; Operations research; Machine learning; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.003090281,0.00007657336,0.0002569642,0.0003067024,0.0001109623,0.0001507452,0.0008867721,0.00002582091,0.0001069996],"category_scores_gemma":[0.0003865006,0.00007738193,0.00005456427,0.0005636302,0.000113646,0.002063097,0.0001816244,0.0001596936,0.000003730931],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008306821,"about_ca_system_score_gemma":0.0003504555,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005506722,"about_ca_topic_score_gemma":0.0001832306,"domain_scores_codex":[0.9985933,0.000003058674,0.0007177817,0.0003144714,0.0000995576,0.0002718539],"domain_scores_gemma":[0.9987273,0.00003644381,0.0005494088,0.0004612438,0.0001164433,0.0001091109],"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":[0.00009410277,0.0003772441,0.5565434,0.0001383105,0.00006313914,0.0001785436,0.001409378,0.3024364,0.001117902,0.1307053,0.003551424,0.003384825],"study_design_scores_gemma":[0.0005843013,0.000027319,0.003203453,0.00003651033,0.000004513225,0.00002058319,0.0001586805,0.9902189,0.00008116342,0.001094788,0.004471166,0.00009862907],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4706391,0.001603278,0.5171114,0.002117533,0.0009229393,0.0001785624,0.001420795,0.000008174851,0.005998178],"genre_scores_gemma":[0.9789901,0.0001870621,0.0197134,0.0002622072,0.0001211224,9.334387e-7,0.00001474107,0.000007786875,0.0007026966],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6877825,"threshold_uncertainty_score":0.3155544,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09328473143994379,"score_gpt":0.3027483287623079,"score_spread":0.2094635973223641,"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."}}