Use this dataset to benchmark your marathon against age- and gender-specific peers. A percentile tells you where your finish time sits in the broader field for your demographic. In practical terms, athletes around P75 are often building consistency and durability, while athletes around P25 usually combine consistent volume, disciplined pacing, and fueling execution across a full training block.
What this dataset shows
This table reports key distribution points for each age and gender group:
- P10 (fast): roughly top 10% by time in that group.
- P25: faster-than-average recreational performance.
- P50 (median): midpoint of observed finishes.
- P75: slower side of mid-pack.
- P90 (slower): back-of-pack benchmark.
Because marathon outcomes are course- and weather-sensitive, these values are best interpreted as directional ranges rather than exact pass/fail standards.
Visual percentile bands
Slower P75 P50
Median P25 P10
Fast
Charts
Median marathon time by age (Male)
Median times generally rise with age, with steeper slowing from around 55+.
Median marathon time by age (Female)
The female trend mirrors the same age-related pattern with progressive time increases.
What P25 vs P75 usually means for training outcomes
P75 usually reflects an athlete still developing marathon-specific readiness: long-run durability, fueling tolerance, and late-race pacing control. Moving from P75 toward P50 often comes from eliminating avoidable slowdowns rather than adding high intensity.
P25 usually reflects stronger aerobic development and execution. These runners often sustain weekly volume more consistently, practice race fueling during long runs, and protect effort discipline in the opening half.
Use these interpretations as coaching context, not labels. Individual outcomes still vary with race profile, weather, and training age.
How to use these percentiles for goal setting
Pick your age band and gender row, then compare your recent race result to the nearest percentile marker:
- Find your current race time in the row that matches your demographic.
- Identify whether you are nearest P75, P50, P25, or P10.
- Set one realistic target band for your next race cycle (for example, move from P75 toward P50).
- Pair your time target with process goals: weekly volume consistency, fueling practice, and long-run pacing.
A practical example: if your 45–49 male marathon is 4:35, you currently sit between P50 (4:31) and P75 (5:09). A strong, realistic next target may be to consolidate around low-4:20s before reaching for P25 pace.
Full data table
| Age group | Gender | P10 (fast) | P25 | P50 (median) | P75 | P90 (slower) |
|---|---|---|---|---|---|---|
| 18-24 | Female | 3:55 | 4:18 | 4:46 | 5:24 | 6:10 |
| 18-24 | Male | 3:24 | 3:47 | 4:12 | 4:48 | 5:35 |
| 25-29 | Female | 3:52 | 4:15 | 4:43 | 5:20 | 6:05 |
| 25-29 | Male | 3:19 | 3:41 | 4:06 | 4:42 | 5:27 |
| 30-34 | Female | 3:56 | 4:19 | 4:47 | 5:25 | 6:12 |
| 30-34 | Male | 3:21 | 3:43 | 4:08 | 4:44 | 5:31 |
| 35-39 | Female | 4:01 | 4:24 | 4:52 | 5:30 | 6:18 |
| 35-39 | Male | 3:26 | 3:49 | 4:14 | 4:51 | 5:38 |
| 40-44 | Female | 4:08 | 4:31 | 5:00 | 5:39 | 6:27 |
| 40-44 | Male | 3:33 | 3:56 | 4:22 | 4:59 | 5:47 |
| 45-49 | Female | 4:15 | 4:39 | 5:08 | 5:48 | 6:37 |
| 45-49 | Male | 3:41 | 4:04 | 4:31 | 5:09 | 5:58 |
| 50-54 | Female | 4:24 | 4:49 | 5:19 | 5:59 | 6:49 |
| 50-54 | Male | 3:51 | 4:15 | 4:43 | 5:22 | 6:13 |
| 55-59 | Female | 4:36 | 5:02 | 5:34 | 6:16 | 7:08 |
| 55-59 | Male | 4:03 | 4:28 | 4:57 | 5:39 | 6:31 |
| 60-64 | Female | 4:52 | 5:19 | 5:52 | 6:37 | 7:31 |
| 60-64 | Male | 4:19 | 4:45 | 5:16 | 6:00 | 6:54 |
| 65-69 | Female | 5:11 | 5:40 | 6:16 | 7:03 | 8:00 |
| 65-69 | Male | 4:39 | 5:07 | 5:41 | 6:28 | 7:25 |
| 70-74 | Female | 5:34 | 6:05 | 6:44 | 7:34 | 8:34 |
| 70-74 | Male | 5:01 | 5:31 | 6:07 | 6:57 | 7:57 |
| 75-79 | Female | 6:03 | 6:36 | 7:17 | 8:10 | 9:13 |
| 75-79 | Male | 5:30 | 6:02 | 6:41 | 7:34 | 8:37 |
Methodology notes and limitations
- Percentiles are aggregated benchmarks designed for context, not precise race prediction.
- This table is a practical benchmarking dataset calibrated to published marathon distributions and age-related performance literature, not an official single-race governing-body table.
- Course profile, weather, and depth of field can shift these values year to year.
- Use multiple races (not one result) before deciding your stable percentile band.
- For older athletes, combine percentile context with age-grading for fairer cross-age comparisons.