There is a need for a sophisticated approach to track athletic performance and to quantify factors affecting it in practical settings. Purpose: To demonstrate the application of mixed linear modeling for monitoring athletic performance. Methods: Elite sprint and middle-distance swimmers (three females and six males; aged 21-26 yr) performed 6-13 time trials in training and competition in the 9 wk before and including Olympic-qualifying trials, all in their specialty event. We included a double-blind, randomized, diet-controlled crossover intervention, in which the swimmers consumed caffeine (5 mg/kg body mass) or placebo. The swimmers also knowingly consumed varying doses of caffeine in some time trials. We used mixed linear modeling of log-transformed swim time to quantify effects on performance in training versus competition, in morning versus evening swims, and with use of caffeine. Predictor variables were coded as 0 or 1 to represent absence or presence, respectively, of each condition and were included as fixed effects. The date of each performance test was included as a continuous linear fixed effect and interacted with the random effect for the athlete to represent individual differences in linear trends in performance. Results: Most effects were clear, owing to the high reliability of performance times in training and competition (typical errors of 0.9% and 0.8%, respectively). Performance time improved linearly by 0.8% per 4 wk. The swimmers performed substantially better in evenings versus mornings and in competition versus training. A 100-mg dose of caffeine enhanced performance in training and competition by ~1.3%. There were substantial but unclear individual responses to training and caffeine (SD of 0.3% and 0.8%, respectively). Conclusions: Mixed linear modeling can be applied successfully to monitor factors affecting performance in a squad of elite athletes.
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