{"id":"W4300290678","doi":"10.48550/arxiv.1209.5026","title":"Estimating Player Contribution in Hockey with Regularized Logistic\\n Regression","year":2012,"lang":"","type":"preprint","venue":"arXiv (Cornell University)","topic":"Sports Analytics and Performance","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Booth University College","funders":"","keywords":"Statistic; Logistic regression; Ice hockey; Econometrics; League; Aggregate (composite); Simple (philosophy); Odds; Computer science; Metric (unit); Statistics; Mathematics; Economics; Machine learning; Operations management","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00132227,0.0006957385,0.001374119,0.000757653,0.0003506385,0.0001563674,0.0006916912,0.000776763,0.0008500804],"category_scores_gemma":[0.0001318506,0.0007793173,0.0002878646,0.0009525668,0.0002916152,0.0006377231,0.0005349964,0.001196238,0.0003292687],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008918879,"about_ca_system_score_gemma":0.0001607501,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001035627,"about_ca_topic_score_gemma":0.0002165542,"domain_scores_codex":[0.9962045,0.00005920185,0.001058349,0.001604266,0.00006525288,0.001008418],"domain_scores_gemma":[0.9961857,0.0001147742,0.001957636,0.001209558,0.0002012938,0.0003309972],"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":[0.0004859154,0.0002617502,0.3954006,0.0001561509,0.0001516594,0.0002378509,0.0002162195,0.4709906,0.000005389125,0.1318911,0.00005425825,0.0001485853],"study_design_scores_gemma":[0.002840366,0.0001258342,0.05117298,0.0006894345,0.0001389607,0.00001369441,0.00008510279,0.9291492,0.00002406373,0.01374209,0.0009358265,0.001082515],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8267617,0.0005511372,0.1681029,0.00008325039,0.001166864,0.0005954998,0.0001702042,0.00004332662,0.002525134],"genre_scores_gemma":[0.9942267,0.001051394,0.001118649,0.0000558327,0.0002721435,0.000003163063,0.0002126575,0.00005845666,0.003001024],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4581586,"threshold_uncertainty_score":0.9994658,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1005718271619352,"score_gpt":0.1918935728880231,"score_spread":0.09132174572608782,"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."}}