{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0115235,0.001337297,0.002263237,0.002229506,0.0009904173,0.00280014,0.004578824,0.003568847,0.008001757],"category_scores_gemma":[0.02056615,0.001952392,0.00146244,0.002206312,0.001337952,0.00330529,0.00157545,0.003486095,0.001665506],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002338161,"about_ca_system_score_gemma":0.002514057,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05744515,"about_ca_topic_score_gemma":0.05471647,"domain_scores_codex":[0.9972161,0.001396452,0.0001365872,0.0005674571,0.0002482443,0.0004352039],"domain_scores_gemma":[0.983228,0.01355905,0.00120804,0.0004646574,0.0009552582,0.0005849727],"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":[0.0009508852,0.0004778069,0.03041966,0.0001304848,0.0002689376,0.0002819496,0.0004542434,0.8715872,0.0006595006,0.05301533,0.005577726,0.03617632],"study_design_scores_gemma":[0.00007345159,0.00005397902,0.002677073,0.00001957798,0.00003690332,0.0000279023,0.00004043709,0.9821293,0.00007685696,0.01430944,0.0005257505,0.00002920507],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3768713,0.00118661,0.6050221,0.003532742,0.0001682664,0.0005146361,0.005658652,0.0009362581,0.006109504],"genre_scores_gemma":[0.9054903,0.0008633287,0.07318306,0.0003511978,0.0002452883,0.0007575389,0.005772753,0.0001076899,0.01322889],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.05744515,"threshold_uncertainty_score":0.1142215,"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."}}