{"id":"W3030855848","doi":"10.1515/jqas-2019-0102","title":"Understanding draws in Elo rating algorithm","year":2020,"lang":"en","type":"article","venue":"Journal of Quantitative Analysis in Sports","topic":"Sports Analytics and Performance","field":"Economics, Econometrics and Finance","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Interpretation (philosophy); Probabilistic logic; Computer science; Algorithm; Outcome (game theory); Binary number; Artificial intelligence; Machine learning; Mathematics; Mathematical economics","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":[],"consensus_categories":[],"category_scores_codex":[0.001562981,0.0001284794,0.0008537547,0.001268367,0.00003947071,0.00005411423,0.0001632787,0.00005764902,0.0004261099],"category_scores_gemma":[0.0001456238,0.0001316746,0.0002981577,0.002394478,0.00003904272,0.0004014417,0.00002348382,0.0003115204,0.00001013678],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001789134,"about_ca_system_score_gemma":0.00003631585,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009677737,"about_ca_topic_score_gemma":0.000149078,"domain_scores_codex":[0.9978654,0.0000159388,0.001569086,0.0002221626,0.0001175977,0.0002097525],"domain_scores_gemma":[0.99832,0.00006769114,0.00135185,0.0001054315,0.00005992992,0.00009509279],"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":[0.00002339456,0.00005455407,0.9311986,0.00001038066,0.0002137503,0.0002487992,0.002574176,0.03973385,0.00000388661,0.02558159,0.000102088,0.0002549348],"study_design_scores_gemma":[0.0007743475,0.0001671244,0.3804487,0.00006644799,0.0001108829,0.000005824291,0.004243141,0.5986857,0.00001096598,0.01375743,0.001426809,0.000302558],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7674255,0.001818734,0.2260484,0.001247688,0.0001780502,0.00007568199,0.00001742763,0.000004723161,0.003183771],"genre_scores_gemma":[0.9936017,0.0005985342,0.005409978,0.0002767142,0.00006678768,6.727998e-7,0.000004139762,0.0000109702,0.000030526],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5589519,"threshold_uncertainty_score":0.5369534,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2096473000940852,"score_gpt":0.3018856033698305,"score_spread":0.09223830327574528,"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."}}