{"id":"W2809133880","doi":"10.1145/3208788.3208800","title":"Outcome prediction of DOTA2 using machine learning methods","year":2018,"lang":"en","type":"article","venue":"","topic":"Sports Analytics and Performance","field":"Economics, Econometrics and Finance","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Outcome (game theory); Machine learning; Normalization (sociology); Artificial intelligence; Table (database); Task (project management); Data mining; Engineering","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":[0.001678268,0.001221425,0.0007196379,0.003101415,0.0004493084,0.001159897,0.0007641747,0.0006210876,0.002498605],"category_scores_gemma":[0.004862643,0.0001717585,0.0005882268,0.001441665,0.0002250231,0.0007752104,0.0008023955,0.0008634001,0.0007777528],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008221854,"about_ca_system_score_gemma":0.0008219594,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01469313,"about_ca_topic_score_gemma":0.01244887,"domain_scores_codex":[0.9993413,0.0001507617,0.00005843409,0.00020624,0.0001410935,0.0001020725],"domain_scores_gemma":[0.9974352,0.001211655,0.0002881877,0.0001730785,0.0006491935,0.0002426898],"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":[0.001080507,0.001142227,0.4690074,0.00017065,0.0002562282,0.0004113297,0.0001693765,0.2490805,0.001769202,0.003040086,0.009077503,0.2647951],"study_design_scores_gemma":[0.00001299404,0.00008263987,0.02078956,0.000009379996,0.00001618384,0.00002671358,0.00006025029,0.9767036,0.0005953193,0.001113926,0.0005777986,0.00001168296],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8614433,0.0004585751,0.1274155,0.0004111058,0.00012002,0.0002220949,0.003797005,0.001031934,0.005100374],"genre_scores_gemma":[0.965705,0.0001219139,0.02645665,0.00003755801,0.00005025946,0.0001409929,0.0051839,0.00002584253,0.00227792],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01469313,"threshold_uncertainty_score":0.02921522,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1404159682735247,"score_gpt":0.3333290935459258,"score_spread":0.1929131252724011,"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."}}