{"id":"W3120470644","doi":"","title":"Applications of Discrete Markov Chains to Baseball Analysis","year":2018,"lang":"en","type":"article","venue":"URSCA Proceedings","topic":"Sports Analytics and Performance","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"MacEwan University","funders":"","keywords":"Markov chain; Stochastic matrix; Sequence (biology); Mathematical economics; Statement (logic); State (computer science); Matrix (chemical analysis); Computer science; Transition (genetics); Field (mathematics); Markov process; Mathematics; Algorithm; Pure mathematics; Epistemology; Statistics; Philosophy","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003246321,0.0001056242,0.000317484,0.000538166,0.00008805143,0.00004463586,0.0002465015,0.00005149769,0.001386942],"category_scores_gemma":[0.0000233208,0.0001137155,0.0001345305,0.001479293,0.00006615702,0.0001236242,0.00005714245,0.00005481769,0.0002583483],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003307646,"about_ca_system_score_gemma":0.000008093764,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001608377,"about_ca_topic_score_gemma":0.00003585209,"domain_scores_codex":[0.9989832,4.284284e-7,0.0004291089,0.0003268965,0.00004593183,0.0002144419],"domain_scores_gemma":[0.9992946,0.000006344149,0.000262804,0.0001895284,0.0001381053,0.0001086197],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002376068,0.00008124247,0.5997626,0.00005441341,0.000380127,2.103183e-7,0.001351931,0.00003120569,0.00008688277,0.3872864,0.009049254,0.001892011],"study_design_scores_gemma":[0.0003022011,0.0002355318,0.2444116,0.00002063856,0.0001813145,0.000001343044,0.0002428416,0.04626771,0.0007427411,0.004103432,0.7029625,0.0005280891],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5672644,0.0003623365,0.06535956,0.002479524,0.0001757905,0.00066439,0.0003950153,0.00008559463,0.3632134],"genre_scores_gemma":[0.9910316,0.00004451768,0.001494047,0.0002976975,0.0002136947,0.00004540114,0.00001310831,0.00001394314,0.006845957],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6939133,"threshold_uncertainty_score":0.9995259,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01278666469870903,"score_gpt":0.2283699687595594,"score_spread":0.2155833040608504,"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."}}