The 2026 Heidi Challenge Edition spanned five demanding marathons across Berlin and Brandenburg. The diverse routes—featuring extensive cobblestone sections, minimal asphalt, long stretches of natural trails, challenging root-strewn paths, and moderate elevation gains—made each stage a unique challenge in its own right.
Read moreThe Berlin city leg offered a great glimpse into the metropolis of Berlin.
Background and Objectives: Pacing behavior has been consistently associated with performance in long-distance running, yet its patterns across different race distances and population subgroups remain insufficiently understood. Therefore, this narrative review synthesizes and critically discusses available evidence on pacing in half-marathon and marathon running, considering both within- and between-race differences, with a particular focus on performance level, sex, and age. Materials and Methods: A structured, non-systematic search was conducted in PubMed, SPORTDiscus, Scopus, and Web of Science, with Google Scholar used as a supplementary source. Peer-reviewed English-language full-text studies reporting pacing-related outcomes were included, and findings from 37 studies published up to March 2026 were synthesized narratively. Results: Available observational evidence suggests differences in pacing profiles between race distances, with generally more even pacing in the half-marathon and more positive pacing in the marathon. Performance level showed the strongest association with pacing behavior: faster runners exhibited more stable pacing patterns, whereas slower runners showed greater variability and a more pronounced pace decline. Sex differences appeared more evident in marathon running, where women generally demonstrated more even pacing than men, whereas age-related differences were small and inconsistent. Comparisons between race distances suggest that pacing variability may increase with longer race duration and lower performance level. Conclusions: Overall, performance level showed the most consistent relationship with pacing behavior in long-distance running. In mass-participation events, observed pacing profiles may reflect fatigue-related changes and inaccurate pace selection rather than a deliberate strategy. More even or slightly negative pacing has generally been associated with more favorable performance outcomes, but this should be interpreted as an observed association rather than a direct prescriptive recommendation. The proposed practical reference points should be viewed as tentative, evidence-informed aids for interpreting pacing behavior according to performance level and race distance, while using sex and age as secondary contextual factors.
Read moreTo identify the best-performing nationalities and race locations for professional IRONMAN® triathletes, and to explore the best environmental conditions. We collected and analyzed records from 6,848 professional finishers representing 37 countries participating in 54 IRONMAN® race locations between 2002 and 2022. Information extracted includes athlete information (sex, country of origin, race location), performance (hh:mm:ss), and environmental conditions (i.e., water and air temperature). A non-linear machine learning predictive model was developed with race finish time as the predicted variable and sex, country of origin, event location, and the locations’ average air and water temperatures as predictors. Several model interpretability tools were used to assess the contribution of each predictor to the model’s predictions. All analyses, modeling, and visualizations were performed using Python. Among the top five, the fastest overall male race times were achieved by athletes from Ukraine (8.71±0.58 h), Portugal (8.79±0.41 h), Poland (8.94±0.68 h), Switzerland (8.98±0.48 h), and Estonia (9.00±0.67 h). The fastest overall female race times were recorded by athletes from Poland (9.37±0.35 h), Finland (9.53±0.64 h), Chile (9.70±0.33 h), Switzerland (9.71±0.55 h), and Denmark (9.78±0.59 h). The fastest overall race times were achieved at IRONMAN® Tallinn (9.01±0.67 h), and IRONMAN® Switzerland Thun (9.02±0.38 h). The model identified sex as the most important predictor, followed by the athlete’s country of origin. For air temperature, the model predicted the best race times within a range of 19 to 27 °C. For water temperature, the model favored warmer temperatures for faster race time predictions. The professional IRONMAN® performance is influenced by a combination of geographical and environmental factors. European countries and race locations were consistently associated with superior performances, while favorable environmental conditions were characterized by relatively warm water temperatures and moderate air temperatures.
Read morePhysician and endurance athlete