Sex Differences in Marathon Pacing: Analysis of 873,000 Berlin Marathon Runners Reveals Men are Twice as Likely to "Hit the Wall"

Cluster-scale analysis of 873,334 finishers of the Berlin Marathon (1999–2025), examining sex differences in pacing stability and the prevalence of catastrophic deceleration.

Status

R2 bundle handed off to Beat Knechtle on 2026-05-27 for SNAPP resubmission to Scientific Reports. Awaiting editorial decision. Frozen artifacts tagged v1.2-r2-resubmit (local; push pending). Triage and audits in _reviews/r2/ (workspace-only).

Authors

• Aldo Seffrin¹ — ORCID: 0000-0001-8229-8565
• Elias Villiger² — ORCID: 0000-0001-8371-1390
• Marília Santos Andrade³ — ORCID: 0000-0002-7004-4565
• Thomas Rosemann² — ORCID: 0000-0002-6436-6306
• Katja Weiss² — ORCID: 0000-0003-1247-6754
• Beat Knechtle²* — ORCID: 0000-0002-2412-9103

1 — Nova O2 Sports Science, São José dos Campos, Brazil 2 — Institute of Primary Care, University of Zurich, Zurich, Switzerland 3 — Department of Physiology, Federal University of São Paulo, Brazil

*Corresponding author

Key Finding

Among 873,334 finishers of the Berlin Marathon (1999–2025), male runners exhibited a twofold higher risk of "hitting the wall" — operationally defined as a ≥20% slowdown in the second half of the race relative to the first — compared with female runners (17.63% vs 9.66%; OR = 2.00, 95% CI 1.97–2.03). After adjustment for age and performance category, the disparity strengthened (adjusted OR = 3.88, 95% CI 3.81–3.94). The gap widened markedly among the fastest runners: in the sub-3h cohort, men were approximately six times more likely to experience catastrophic deceleration than women (1.42% vs 0.23%). Mean percentage slowdown was significantly greater in men across all five performance categories (10.73% ± 11.41% vs 8.34% ± 8.91%; p < 0.001).

Sample

• n = 873,334 finishers (men: 659,294, 75.5% — women: 214,040, 24.5%)
• Pacing-valid analytical cohort: n = 872,670 (excluding 664 finishers without valid half-marathon split data)
• Deduplicated sensitivity subset: n = 700,877 (first appearance per composite key of normalized name + age group)
• Source: Official BMW Berlin Marathon Results Archive
• Period: 27 editions, 1999–2025
• Inclusion: chip-timed finishers with valid net finish time and half-marathon split
• Exclusion: biologically implausible times (< 1:59:00) or beyond official cutoff (> 6:15:00); records missing critical pacing checkpoints. Total excluded: 7,445 records (0.85% of 880,779 raw entries)
• Age distribution: mature cohort; ~50% of the field aged 35–49

Data

Raw and processed datasets are hosted externally due to GitHub size limits.

• Zenodo deposit: 10.5281/zenodo.19342683
• Mirror (Google Drive): Datasets folder

Primary pipeline files

File	Format	Stage	Description
Dataset_Berlin_Marathon_1999-2025_original.csv	CSV	Raw	Web-scraped output (1999–2025)
Dataset_Berlin_Marathon_1999-2025.parquet	Parquet	Optimized	CSV converted for memory efficiency (Step 1 output)
Dataset_Berlin_Cleaned_Analysis_Ready.parquet	Parquet	Cleaned	Nulls removed, time strings parsed, outliers filtered (Step 2 output)
Dataset_Berlin_Features_Engineered.parquet	Parquet	Final	Adds pacing metrics (pct_slowdown, hit_wall); used by figures (Step 3 output)

Revision-round derivative files

File	Format	Description
wall_baseline_873k.parquet	Parquet	Analytical cohort (n = 873,334 finishers; raw checkpoints + age_group + sex + year), input to R1 notebooks
dedup_subset.parquet	Parquet	Deduplicated subset (n = 700,877; first appearance per composite key of normalized name + age group), produced by notebooks/r1_dedup_sensitivity.py and consumed by r1_logistic_age_controlled.py

Place all files in data/ to reproduce. The data/ directory is gitignored.

Analyses

Primary pipeline

Notebook	Content
notebooks/OTIMIZATION.ipynb	Step 1: memory optimization — convert raw CSV to Parquet (~60% size reduction)
notebooks/CLEANING.ipynb	Step 2: standardise sex encoding, parse HH:MM:SS times to seconds, apply physiological filters
notebooks/MAIN_ANALYSIS.ipynb	Step 3: feature engineering (pacing metrics, "wall" definition), statistical tests (Welch, Mann-Whitney U, Chi², Odds Ratios), figure generation

Run notebooks in the order above. Outputs feed each other.

Revision analyses

Additional analyses developed during peer review, with outputs written to notebooks/results/r1/ (R1 round) and notebooks/results/r2/ (R2 round).

R1 round (first-round comments)

Notebook	Content
notebooks/r1_logistic_age_controlled.py	Multivariable logistic regression (sex + age + performance category + sex × age interaction)
notebooks/r1_dedup_sensitivity.py	Sensitivity analysis on deduplicated subset (composite key: normalized name + age group)
notebooks/r1_pacing_fine_grained.py	Fine-grained pacing metrics from 5 km splits (CV, inflection, late-deceleration, oscillation, km-30 gradient)
notebooks/r1_age_relative_quintile.py	Within-cohort sex × age-group quintile re-stratification
notebooks/r1_temporal_trend.py	27-year temporal trend (Mann-Kendall + linear regression)
notebooks/r1_threshold_severity.py	Threshold sensitivity (15%/20%/25%) and graded severity
notebooks/r1_perf_cat_prevalence.py	Per-category × sex prevalence with odds ratios
notebooks/_r1_common.py	Shared feature engineering, colour palette, save_results helper

R2 round (Reviewer 2 second-round diagnostics)

Notebook	Content
notebooks/r2_logistic_diagnostics.py	Logistic model diagnostics — Variance Inflation Factors, McFadden pseudo-R², AIC/BIC, decile calibration with Hosmer-Lemeshow, 10-fold cross-validated recalibration intercept and slope, comparison of linear / quadratic / natural-cubic-spline parameterisations of age (likelihood-ratio + ΔAIC), Pregibon dbeta influence summary, missing-data exclusion cascade. Verbatim summary reproduced in Appendix A of the R2 response letter.
notebooks/_r2_common.py	R2 shared helpers — compute_vif, mcfadden_r2, decile_calibration, hosmer_lemeshow, spline_basis_age (with sum-to-zero constraint avoiding rank-deficiency against the intercept), calibration_in_the_large_and_slope_cv (10-fold cross-validated, non-degenerate).

Build helpers

Notebook	Content
notebooks/generate_figures.py	Idempotent regeneration of Figures 1–5
notebooks/generate_tables.py	Idempotent regeneration of Table 2 (with crude OR + 95% CI columns added in R2) and Supplementary Tables S1–S6 (CSV → Markdown)

Figures

1. Figure 1 — Density (figures/Figure_1_Density.tiff) — kernel density estimation of percentage slowdown by sex; visualises the heavier right tail of male pacing failures
2. Figure 2 — Stratified prevalence (figures/Figure_2_Boxplot.tiff) — mean percentage slowdown by performance category × sex; the sex disparity persists across all five tiers
3. Figure 3 — Risk (figures/Figure_3_Risk_Plot.tiff) — bar plot of "hitting the wall" prevalence by sex with 95% CIs and odds ratio annotation
4. Figure 4 — Fine-grained pacing variability (figures/Figure_4_Pacing_Variability.tiff) — five 5 km-split-derived metrics (CV, inflection km, late-deceleration %, oscillation, km-30 gradient) by sex
5. Figure 5 — Temporal trend (figures/Figure_5_Temporal_Trend.tiff) — wall prevalence by sex across 27 editions (1999–2025) with Mann-Kendall and linear-regression annotations

Structure

.
├── README.md                # This file
├── LICENSE                  # MIT — copyright Nova O2
├── requirements.txt         # Python dependencies
├── notebooks/               # Reproducible analysis pipeline
│   ├── OTIMIZATION.ipynb
│   ├── CLEANING.ipynb
│   ├── MAIN_ANALYSIS.ipynb
│   ├── _r1_common.py
│   ├── r1_*.py              # R1 revision analyses
│   ├── _r2_common.py
│   ├── r2_logistic_diagnostics.py   # R2 revision diagnostics
│   ├── generate_figures.py
│   ├── generate_tables.py
│   └── results/
│       ├── r1/              # R1 tabular outputs (CSV + Markdown)
│       └── r2/              # R2 tabular outputs (CSV + Markdown + calibration TIFF)
└── figures/                 # Manuscript figures (TIFF + PNG, 300 DPI)

Build artefacts (manuscript/, Makefile, scripts/) and large datasets (data/) are maintained off-repo. Data is available via the Zenodo deposit linked above.

Tech

• Python: 3.10+
• Dataset encoding: UTF-8
• Separator: comma
• Decimal: period
• Key packages: Pandas, NumPy, SciPy, statsmodels, pymannkendall, Matplotlib, Seaborn

Citation

To be added upon acceptance. For now, please cite this repository:

Seffrin A., Villiger E., Andrade M.S., Rosemann T., Weiss K., Knechtle B. (2026). Sex Differences in Marathon Pacing: Analysis of 873,000 Berlin Marathon Runners Reveals Men are Twice as Likely to "Hit the Wall" [Code and figures]. GitHub. https://github.com/Nova-O2/berlin-marathon-wall

License

MIT — Copyright (c) 2026 Nova O2 Sports Science.