LAUSR

Abstract Data on the likelihood of becoming a professional athlete are abundant and readily available, yet athletes consistently overestimate their chances of achieving the top levels of career success. Research is needed to examine whether athletes and others update their career expectations when seeing new information. In this study, minor league baseball players created a career tree estimating their probabilities of moving through the minor league system and then read personalized trees built by a C5.0 machine learning algorithm. After seeing the C5.0 trees, many players updated their expectations consistent with updating theory, especially when reevaluating their chances of being out of the system; however, there was evidence of asymmetric updating. Some acted opposite to what Bayesian reasoning would suggest. Analysis of the interview data reveals three themes that explain asymmetric and contrary updating. Players believed optimism is necessary for their baseball career, they neglected their reference group, and they saw information as possessing affective qualities. Using these three themes caused athletes to ignore some information and, occasionally, circumvent the updating process altogether.