{"id":"W2966915277","doi":"10.1177/1527002519867367","title":"The Impact of Variable Pricing, Dynamic Pricing, and Sponsored Secondary Markets in Major League Baseball","year":2019,"lang":"en","type":"article","venue":"Journal of Sports Economics","topic":"Sports Analytics and Performance","field":"Economics, Econometrics and Finance","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Ticket; League; Dynamic pricing; Revenue; Value (mathematics); Variable (mathematics); Economics; Microeconomics; Pricing strategies; Panel data; Business; Marketing; Econometrics; Finance; Computer science","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00239299,0.0001884447,0.0007304762,0.0004145958,0.0000565484,0.0000721876,0.0002691785,0.0001077363,0.0009439992],"category_scores_gemma":[0.00004167535,0.0001556733,0.0002126278,0.0001616593,0.00006522882,0.0003551949,0.00005751599,0.0003351629,0.00001020442],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002510257,"about_ca_system_score_gemma":0.0001937818,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003050414,"about_ca_topic_score_gemma":0.0000453998,"domain_scores_codex":[0.9978253,0.000008391987,0.001593611,0.0002264933,0.00003932977,0.0003068722],"domain_scores_gemma":[0.9973246,0.00009906938,0.002084318,0.0003281308,0.00006045403,0.0001034115],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002587739,0.00006952582,0.9803312,0.00004686378,0.000134152,0.00001335455,0.0001715547,0.009077432,0.00001654592,0.007729932,0.0003216461,0.001829029],"study_design_scores_gemma":[0.001180554,0.000166748,0.9411897,0.00008200952,0.00001254724,0.00009380551,0.00009282651,0.04552767,0.000013184,0.005255418,0.006178129,0.000207381],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9880261,0.002416951,0.00005301258,0.00009561505,0.0004441892,0.0001714712,0.00004497497,0.000002773698,0.008744865],"genre_scores_gemma":[0.9925078,0.005416983,0.0003199107,0.00004942262,0.00005514069,0.00000107451,0.00000402292,0.00002749344,0.001618218],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03914146,"threshold_uncertainty_score":0.9999692,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006291562358965268,"score_gpt":0.2044861577764028,"score_spread":0.1981945954174375,"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."}}