{"id":"W4226127985","doi":"10.1007/978-3-030-98682-7_28","title":"Improving Dribbling, Passing, and Marking Actions in Soccer Simulation 2D Games Using Machine Learning","year":2022,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Sports Analytics and Performance","field":"Economics, Econometrics and Finance","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"Memorial University of Newfoundland; Dalhousie University","funders":"","keywords":"Computer science; League; Action (physics); Artificial intelligence; Matching (statistics); SAFER; Competition (biology); Machine learning; Human–computer interaction; Operations research; Computer security","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":[0.0004990698,0.001271351,0.001165087,0.0005471183,0.0003035392,0.0006968909,0.001275832,0.0009608365,0.004638828],"category_scores_gemma":[0.001565307,0.0005429703,0.0005074474,0.0004286285,0.0002666202,0.0008097354,0.0008978007,0.001182686,0.0009168836],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006540833,"about_ca_system_score_gemma":0.0009377577,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0131533,"about_ca_topic_score_gemma":0.01829099,"domain_scores_codex":[0.9997302,0.00004396218,0.0000134125,0.00009182879,0.00006140905,0.00005922119],"domain_scores_gemma":[0.9993941,0.0003371874,0.00004210991,0.00004124796,0.0001267817,0.00005869376],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005663206,0.0005088944,0.001896652,0.0001410416,0.00007012536,0.0000473549,0.00007736777,0.4514112,0.01134065,0.002002958,0.003477921,0.5284595],"study_design_scores_gemma":[0.00001128111,0.00006510637,0.0004563244,0.000005253914,0.00001162199,0.000008373038,0.000008920503,0.9966259,0.001749375,0.000743365,0.0003093015,0.000005184465],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2481622,0.001343556,0.734112,0.0002995877,0.0002508375,0.0001436556,0.0003763859,0.004698625,0.01061325],"genre_scores_gemma":[0.7888376,0.000380655,0.2008723,0.0001193054,0.00004863549,0.0001227557,0.0006376812,0.0002542792,0.00872694],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0131533,"threshold_uncertainty_score":0.0261535,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04349675614061881,"score_gpt":0.2542418091761838,"score_spread":0.210745053035565,"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."}}