Introduction In the realm of competitive athletics, numerous variables have been examined for predictive utility with respect to player selection/development and outcomes on the field. Notwithstanding important advances, the current predictors only account for a modest amount of variance in outcomes of relevance in the National Football League (NFL). Objective The primary objective of this study was to investigate the predictive validity of a new measure of athletic intelligence, the Athletic Intelligence Quotient (AIQ), which is based on the empirically supported Cattell-Horn-Carroll (CHC) Theory of Intelligence. The predictive validity of the AIQ was determined in relation to performance metrics from 146 NFL players across several seasons. Results Hierarchical regression analyses indicate that specific AIQ factors accounted for a statistically significant increase in the explanation of variance beyond the current level of evaluation for several performance metrics (e.g., career approximate value; sacks, tackles, rushing yards). Further, specific factors of the AIQ are related to position specific statistics, offering the possibility that performance prediction can be focused in for the specific skills required by a given position. Discussion Given the recent impact of analytics in professional sports, and the significant findings noted in the current investigation, the authors discuss the potential importance of the AIQ in the selection and coaching processes.
The focus on quantifiable data in sport performance has led to incremental advantages in baseball and has played an important role in the development of new hitting, pitching, fielding, and coaching strategies. Recently, researchers and team representatives have considered the impact of additional factors in baseball, including cognitive functioning. In this study, predictive validity for the Athletic Intelligence Quotient (AIQ) was examined vis-à-vis performance outcomes in professional baseball. Specifically, AIQ scores were obtained from 149 Minor League Baseball (MiLB) players prior to the 2014 baseball season and their subsequent performance was assessed through traditional and newly emphasized baseball statistics. Using hierarchical multiple regression, it was demonstrated that the AIQ predicted statistically significant relationships with hitting and pitching statistics, after controlling for other variables. Given the recent impact of analytics in professional sports, the potential importance of the AIQ in the selection and coaching process was discussed.
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