{"id":"W2791249238","doi":"10.5121/csit.2018.80310","title":"Predicting Players' Performance in One Day International Cricket Matches Using Machine Learning","year":2018,"lang":"en","type":"article","venue":"","topic":"Sports Analytics and Performance","field":"Economics, Econometrics and Finance","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"Laurentian University","funders":"","keywords":"Computer science; Cricket; Naive Bayes classifier; Artificial intelligence; Random forest; Machine learning; Decision tree; Support vector machine; Classifier (UML); Selection (genetic algorithm); Operations research; Mathematics","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":[0.0007236962,0.0001140325,0.0002161392,0.0003012826,0.0001300716,0.00007613879,0.0001932387,0.00006097896,0.00211552],"category_scores_gemma":[0.00005598176,0.0001280121,0.00004140731,0.0002218307,0.00005087261,0.0003670226,0.00008160042,0.0001947963,0.0002285778],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009631563,"about_ca_system_score_gemma":0.00001314621,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001181318,"about_ca_topic_score_gemma":0.000270929,"domain_scores_codex":[0.9989204,0.000004622645,0.0004960896,0.000276139,0.00005083649,0.0002519067],"domain_scores_gemma":[0.999539,0.00002213189,0.0002164099,0.0001425903,0.00003523163,0.00004461678],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001141571,0.00003396902,0.9910125,0.000009426972,0.00002131345,9.213321e-7,0.0004951065,0.002513601,0.00001994763,0.005448559,0.00001894177,0.0004143507],"study_design_scores_gemma":[0.0002902646,0.00004213527,0.186882,0.00002833855,0.000002121714,0.000003101271,0.00006406754,0.8014238,0.0001822447,0.0002401951,0.01069084,0.0001508658],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9503189,0.0001485049,0.002395377,0.0001430827,0.0004808595,0.00006292497,0.00001446178,0.00003305958,0.04640278],"genre_scores_gemma":[0.9954969,0.0002155713,0.001321794,0.0001653114,0.0003432351,0.000002956129,0.00001642867,0.00001828965,0.002419542],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8041305,"threshold_uncertainty_score":0.9987967,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05675344110294021,"score_gpt":0.2343163236641138,"score_spread":0.1775628825611736,"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."}}