Public Race Data
Enhanced athlete results built from publicly available data.
Race managed by
New York Road Runners
Daniel Menendez
Bib: 4167 •M •Age: 17
Floral Park, NY
Race Timeline and Course Information
Your journey through the course with elevation, pacing, and checkpoint details
⏱️ Race Timeline
Watch your race unfold from start to finish
Splits & Pacing
Detailed breakdown of your race performance at each timing point
Checkpoint Splits
| Checkpoint | Distance | Time of Day | Time | Avg Pace | Segment Time | Segment Pace | Diff |
|---|---|---|---|---|---|---|---|
5K | 3.11 mi | 07:20:49 | 20:49 | 6:43 | 20:49 | 6:43 | - |
10K | 6.21 mi | 07:40:34 | 40:34 | 6:32 | 19:45 | 6:22 | -0:21 |
15K | 9.32 mi | 08:00:27 | 1:00:27 | 6:30 | 19:53 | 6:24 | +0:02 |
20K | 12.43 mi | 08:19:57 | 1:19:57 | 6:27 | 19:30 | 6:17 | -0:07 |
FINISH | 13.11 mi | 08:27:16 | 1:27:16 | 6:40 | 7:19 | 10:45 | +4:28 |
Checkpoint Splits Visualizations
Explore your race data through different visual perspectives. Scroll to discover each visualization.
📈Pace Line
Pace Progression
Track your pace changes throughout the race. Dot colors mark your quickest to easiest segments, relative to your own pace.
Fastest Pace
6:17 /mi
Average Pace
6:39 /mi
Slowest Pace
10:45 /mi
📊Segments
Segment Time Comparison
Bar length shows time spent in each segment. Colors mark your faster to slower segments, relative to your own pace.
Time at Faster Pace
0:00
0% of race
Time at Steady Pace
1:19:57
92% of race
Time at Slower Pace
7:19
8% of race
Average Pace: 6:39 /mi
🎨Heatmap
Pace Heatmap
Where you sped up and eased off relative to your own pace across the race. Colors are relative to your own range, not to other runners.
Click or hover over segments to see details
✨Sparklines
Checkpoint Splits with Pace Trends
Enhanced split table showing pace trends through mini sparkline visualizations.
Overall Pace Trend
| Checkpoint | Distance | Time of Day | Time | Avg Pace | Segment | Pace | Trend | Rank |
|---|---|---|---|---|---|---|---|---|
5K | 3.11 mi | 07:20:49 | 20:49 | 6:43 | 20:49 | 6:43 | #1587 | |
10K | 6.21 mi | 07:40:34 | 40:34 | 6:32 | 19:45 | 6:22 | #1265 | |
15K | 9.32 mi | 08:00:27 | 1:00:27 | 6:30 | 19:53 | 6:24 | #1172 | |
20K | 12.43 mi | 08:19:57 | 1:19:57 | 6:27 | 19:30 | 6:17 | #1040 | |
FINISH | 13.11 mi | 08:27:16 | 1:27:16 | 6:40 | 7:19 | 10:45 | #1207 |
Rankings
Your position at each checkpoint and how you ranked in each segment
Checkpoint and Segment Rankings
| Checkpoint | Distance | Seg. Dist | Overall | Males | M1-19 | Seg. Overall | Seg. Males | Seg. M1-19 |
|---|---|---|---|---|---|---|---|---|
| 5K | 3.11 mi | 3.11 mi | 1587 | 1360 | 11 | 997(Top 3%) | 903(Top 6%) | 7(Top 7%) |
| 10K | 6.21 mi | 3.10 mi | 1265+322 | 1113+247 | 9+2 | 966(Top 3%) | 871(Top 6%) | 7(Top 7%) |
| 15K | 9.32 mi | 3.11 mi | 1172+93 | 1046+67 | 7+2 | 997(Top 3%) | 903(Top 6%) | 7(Top 7%) |
| 20K | 12.43 mi | 3.11 mi | 1040+132 | 935+111 | 7 | 747(Top 2%) | 682(Top 5%) | 6(Top 6%) |
| FINISH | 13.11 mi | 0.68 mi | 1207-167 | 1036-101 | 7 | 2091(Top 7%) | 1702(Top 11%) | 10(Top 10%) |
Segment Performance Analysis
10K→15K
0.0 - 3.1 mi
Overall
997
Males
903
M1-19
7
5K→10K
3.1 - 6.2 mi
Overall
966
Males
871
M1-19
7
10K→15K
6.2 - 9.3 mi
Overall
997
Males
903
M1-19
7
15K→20K
9.3 - 12.4 mi
Overall
747
Males
682
M1-19
6
20K→FINISH
12.4 - 13.1 mi
Overall
2091
Males
1702
M1-19
10
Passing
Athletes you passed and who passed you throughout the race
Passing Analysis
| Segment | Athletes Passed | Passed By | Net Change | Start Rank | End Rank |
|---|---|---|---|---|---|
| Start → 5K | - | - | - | - | #1587 |
| 5K → 10K | 322* | 0 | +322 | #1587 | #1265 |
| 10K → 15K | 103 | 10 | +93 | #1265 | #1172 |
| 15K → 20K | 137 | 5 | +132 | #1172 | #1040 |
| 20K → FINISH | 61 | 200 | -167 | #1040 | #1207 |
| TOTALS | 623* | 215 | +380 |
* Includes athletes who did not complete a segment (DNF/DNQ).
Passing Analysis Visualizations
Explore your passing analysis through 5 different visualizations. Scroll to discover each one.
📈Position Graph
🎨Heatmap Timeline
🔄Passing Circle
🌊Position Flow
🏃Race Lanes
AI Key Insights
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