{"id":"W2025559840","doi":"10.1198/tast.2010.09147","title":"Strategies for Pulling the Goalie in Hockey","year":2010,"lang":"en","type":"article","venue":"The American Statistician","topic":"Sports Analytics and Performance","field":"Economics, Econometrics and Finance","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Ice hockey; League; Markov chain; Computer science; Bayesian probability; Estimation; Econometrics; Markov chain Monte Carlo; Operations research; Simulation; Engineering; Machine learning; Artificial intelligence; Mathematics; Economics; Physical medicine and rehabilitation; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001726171,0.0004872397,0.0005099878,0.0004756001,0.0004778354,0.001192524,0.001268259,0.001017582,0.006407584],"category_scores_gemma":[0.0100888,0.0002927785,0.0003982019,0.0003578594,0.000564921,0.001258846,0.0008415555,0.0008936832,0.0007876705],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009770767,"about_ca_system_score_gemma":0.001252231,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008826665,"about_ca_topic_score_gemma":0.0208816,"domain_scores_codex":[0.9990846,0.0004423791,0.00004071291,0.0001695981,0.0001432333,0.000119458],"domain_scores_gemma":[0.9966403,0.002102035,0.0004340789,0.0002939912,0.0002007394,0.000328765],"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":[0.0008821447,0.0006168567,0.0556483,0.0001096167,0.0001796076,0.0001798255,0.0005074008,0.8682569,0.002801117,0.03678419,0.004353697,0.02968044],"study_design_scores_gemma":[0.0001165231,0.000471432,0.01303014,0.00003569318,0.00002950364,0.00008164057,0.0006522962,0.9573633,0.001513656,0.02265921,0.003996242,0.00005023048],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9072314,0.000065658,0.0783058,0.0004809442,0.00004052532,0.0002649618,0.001516888,0.0001612183,0.01193251],"genre_scores_gemma":[0.9815006,0.00006623485,0.01415145,0.00005475808,0.000005654585,0.0001393515,0.001032131,0.00002580387,0.003023994],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008826665,"threshold_uncertainty_score":0.0214355,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02623175888914076,"score_gpt":0.2618707400671466,"score_spread":0.2356389811780059,"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."}}