How jet lag impairs Major League Baseball performance.

Laboratory studies have demonstrated that circadian clocks align physiology and behavior to 24-h environmental cycles. Examination of athletic performance has been used to discern the functions of these clocks in humans outside of controlled settings. Here, we examined the effects of jet lag, that is, travel that shifts the alignment of 24-h environmental cycles relative to the endogenous circadian clock, on specific performance metrics in Major League Baseball. Accounting for potential differences in home and away performance, travel direction, and team confounding variables, we observed that jet-lag effects were largely evident after eastward travel with very limited effects after westward travel, consistent with the >24-h period length of the human circadian clock. Surprisingly, we found that jet lag impaired major parameters of home-team offensive performance, for example, slugging percentage, but did not similarly affect away-team offensive performance. On the other hand, jet lag impacted both home and away defensive performance. Remarkably, the vast majority of these effects for both home and away teams could be explained by a single measure, home runs allowed. Rather than uniform effects, these results reveal surprisingly specific effects of circadian misalignment on athletic performance under natural conditions.

A flexible model for correlated medical costs, with application to medical expenditure panel survey data.

We propose a flexible model for correlated medical cost data with several appealing features. First, the mean function is partially linear. Second, the distributional form for the response is not specified. Third, the covariance structure of correlated medical costs has a semiparametric form. We use extended generalized estimating equations to simultaneously estimate all parameters of interest. B-splines are used to estimate unknown functions, and a modification to Akaike information criterion is proposed for selecting knots in spline bases. We apply the model to correlated medical costs in the Medical Expenditure Panel Survey dataset. Simulation studies are conducted to assess the performance of our method.