{"id":"W2905907956","doi":"10.26483/ijarcs.v9i6.6337","title":"FOOTBALL MATCH WINNING TEAM PREDICTION USING MACHINE LEARNING","year":2018,"lang":"en","type":"article","venue":"International Journal of Advanced Research in Computer Science","topic":"Sports Analytics and Performance","field":"Economics, Econometrics and Finance","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Tournament; Football; Computer science; Competition (biology); Quarter (Canadian coin); Operations research; Artificial intelligence; Advertising; Law; Political science; History; Mathematics; Business","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004305645,0.00007404508,0.0001704245,0.001364769,0.0002002569,0.0002279591,0.0008950265,0.00002996081,0.00005871972],"category_scores_gemma":[0.0001473712,0.00007346876,0.00004920002,0.0008128394,0.0003800824,0.00105609,0.0002434018,0.0004794969,0.00002189255],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004116214,"about_ca_system_score_gemma":0.0001310139,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001095576,"about_ca_topic_score_gemma":0.00001170843,"domain_scores_codex":[0.9983478,0.00001796285,0.0006292309,0.0002505752,0.0004351161,0.0003192837],"domain_scores_gemma":[0.9984279,0.00005883341,0.0003735439,0.0001280301,0.0009134472,0.00009822455],"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":[0.0001412165,0.0001807966,0.5543265,0.00001259185,0.00005647487,0.00009403564,0.001422533,0.3958214,0.002301331,0.009834831,0.0001016916,0.03570665],"study_design_scores_gemma":[0.0004513384,0.000299869,0.02166209,0.0001374157,6.22721e-7,0.00009801439,0.00002876764,0.9624739,0.0005656094,0.00613285,0.008065825,0.00008374002],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8253379,0.0003723625,0.1702325,0.0004778139,0.002488943,0.00006181749,0.000007043935,0.000007164206,0.001014442],"genre_scores_gemma":[0.9776582,0.0001891799,0.02115359,0.00005734844,0.0008799733,6.574364e-7,7.880894e-7,0.000007599627,0.00005269781],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5666525,"threshold_uncertainty_score":0.2995969,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1014954190798626,"score_gpt":0.3712565632193323,"score_spread":0.2697611441394697,"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."}}