Statistical methods for analysing discrete and categorical data recorded in performance analysis

In this paper, we identify appropriate statistical methods for analysing categorical differences in discrete variables or 'performance indicators' resulting from performance analysis. The random mechanisms associated with discrete events do not follow a normal distribution; that is, the normal distribution is a continuous not a discrete probability distribution. We propose appropriate statistical methods based on two key discrete probability distributions, the Poisson and binomial distributions. Two approaches are proposed and compared using examples from notational analysis. The first approach is based on the classic chi-square test of significance (both the goodness-of-fit test and the test of independence). The second approach adopts a more contemporary method based on log-linear and logit models fitted using the statistical software GLIM. Provided relatively simple one-way and two-way comparisons in categorical data are required, both of these approaches result in very similar conclusions. However, as soon as more complex models or higher-order comparisons are required, the approach based on log-linear and logit models is shown to be more effective. Indeed, when investigating those factors and categorical differences associated with binomial or binary response variables, such as the proportion of winners when attempting decisive shots in squash or the proportion of goals scored from all shots in association football, logit models become the only realistic method available. By applying log-linear and logit models to discrete events resulting from notational analysis, greater insight into the underlying mechanisms associated with sport performance can be achieved.     

Rethinking fat as a fuel for endurance exercise

Abstract

A key element contributing to deteriorating exercise capacity during physically demanding sport appears to be reduced carbohydrate availability coupled with an inability to effectively utilize alternative lipid fuel sources. Paradoxically, cognitive and physical decline associated with glycogen depletion occurs in the presence of an over-abundance of fuel stored as body fat that the athlete is apparently unable to access effectively. Current fuelling tactics that emphasize high-carbohydrate intakes before and during exercise inhibit fat utilization. The most efficient approach to accelerate the body’s ability to oxidize fat is to lower dietary carbohydrate intake to a level that results in nutritional ketosis (i.e., circulating ketone levels >0.5 mmol/L) while increasing fat intake for a period of several weeks. The coordinated set of metabolic adaptations that ensures proper interorgan fuel supply in the face of low-carbohydrate availability is referred to as keto-adaptation. Beyond simply providing a stable source of fuel for the brain, the major circulating ketone body, beta-hydroxybutyrate, has recently been shown to act as a signalling molecule capable of altering gene expression, eliciting complementary effects of keto-adaptation that could extend human physical and mental performance beyond current expectation. In this paper, we review these new findings and propose that the shift to fatty acids and ketones as primary fuels when dietary carbohydrate is restricted could be of benefit for some athletes.