{"id":"W2287096358","doi":"10.1080/00949655.2015.1136629","title":"Optimal lineups in Twenty20 cricket","year":2016,"lang":"en","type":"article","venue":"Journal of Statistical Computation and Simulation","topic":"Sports Analytics and Performance","field":"Economics, Econometrics and Finance","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Cricket; Mathematics; Selection (genetic algorithm); Simulated annealing; Operations research; Statistics; Artificial intelligence; Mathematical optimization; Computer science","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":[0.001836845,0.0007613414,0.001351306,0.0008246817,0.0006389207,0.001484911,0.001191183,0.001526062,0.004648888],"category_scores_gemma":[0.006460563,0.0008840212,0.0006060959,0.0006220363,0.001157283,0.001291026,0.001128435,0.0009493093,0.0004332995],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001299815,"about_ca_system_score_gemma":0.001236943,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007159527,"about_ca_topic_score_gemma":0.007782933,"domain_scores_codex":[0.9992686,0.0003218052,0.00002258384,0.0001815424,0.0000660897,0.0001393381],"domain_scores_gemma":[0.9978279,0.00125917,0.000223137,0.0001360273,0.0001337666,0.0004198973],"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":[0.0005143083,0.0001763167,0.006751107,0.00006187431,0.00009309239,0.0001504112,0.00009305005,0.9765794,0.0007447299,0.005911577,0.0006616763,0.008262385],"study_design_scores_gemma":[0.00006755741,0.0002223851,0.003800588,0.00002563354,0.00002263844,0.00004557886,0.0002122382,0.9853274,0.000818204,0.008720172,0.0007047697,0.00003281382],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.940973,0.0002897447,0.05473361,0.0001551139,0.00001583117,0.00006425206,0.0001835174,0.0001301687,0.003454839],"genre_scores_gemma":[0.9707702,0.00007748538,0.02642396,0.0000649977,0.000005583876,0.00006141306,0.0004711378,0.0000711695,0.002054015],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007159527,"threshold_uncertainty_score":0.0155521,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03511956244643052,"score_gpt":0.2843340527956573,"score_spread":0.2492144903492268,"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."}}