{"id":"W4393138778","doi":"10.1093/jrsssa/qnae027","title":"What does rally length tell us about player characteristics in tennis?","year":2024,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series A (Statistics in Society)","topic":"Sports Analytics and Performance","field":"Economics, Econometrics and Finance","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia; Simon Fraser University","funders":"Canadian Statistical Sciences Institute; Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Data science","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001429513,0.0003216886,0.0008242172,0.00007062076,0.0001708576,0.0007168853,0.0005179677,0.0002422568,0.0007026294],"category_scores_gemma":[0.0002490077,0.0002327798,0.0004814062,0.0004732939,0.0004043872,0.0005394185,0.0001593359,0.00120098,0.00003539288],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000473843,"about_ca_system_score_gemma":0.0001748762,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001214047,"about_ca_topic_score_gemma":0.0001166205,"domain_scores_codex":[0.9969062,0.00003306827,0.001852113,0.0003810058,0.0002505768,0.0005769879],"domain_scores_gemma":[0.998324,0.0004710109,0.0006463202,0.0002850865,0.0001202348,0.0001533808],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.000133115,0.0004325,0.305672,0.001037731,0.0006220373,0.0003336651,0.008572547,0.002662099,0.00001083065,0.6077706,0.06793931,0.00481356],"study_design_scores_gemma":[0.001015709,0.0002290888,0.4953039,0.0007644765,0.00008929482,0.00003857936,0.001920502,0.1963267,0.00001231792,0.08101231,0.2225369,0.0007502881],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6529756,0.03249104,0.2615193,0.008644033,0.02829788,0.001431035,0.01114368,0.0001155898,0.003381918],"genre_scores_gemma":[0.9435982,0.018578,0.03061478,0.001391191,0.0007888381,0.00001303577,0.00004051834,0.00009308242,0.00488242],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5267583,"threshold_uncertainty_score":0.9492486,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01151480224061685,"score_gpt":0.2292542825988946,"score_spread":0.2177394803582778,"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."}}