STRIDD for nerds.
Seven interactive tools for runners who want the signal, not the noise. Compare Riegel against Cameron on race predictions. Plot acute-to-chronic workload ratios to stay in the sweet spot. See exactly how heat, altitude and elevation change your race time. Browse every equation we use, with citations. Built for coaches, data-curious athletes, and anyone who wants to understand why their plan works.
Race Lab: Riegel vs Cameron vs VDOT-derived predictions
The Race Lab compares three published race-time prediction models side-by-side. Riegel (1977) uses the formula T2 = T1 times (D2/D1) to the power 1.06 — a single exponent across all distances. Cameron (1998) uses a variable exponent that adjusts for the non-linear fatigue of longer races. VDOT-derived predictions scale pace zones from Jack Daniels' 1998 tables. All three agree for short distances (3K-10K). They diverge at the marathon: Riegel is typically 3-8 percent optimistic, Cameron is usually closer to observed finish times. Use all three as a triangulation rather than a single answer.
Training load: ACWR, chronic fitness, acute overreach
Acute-to-Chronic Workload Ratio (ACWR) compares your last 7 days of training load to your rolling 28-day average. The sweet spot is 0.8 to 1.3 — progressive overload with manageable risk. Ratios above 1.3 double injury risk. Below 0.8 indicates detraining. The metric was developed by Tim Gabbett in Australian team sports and validated in distance running. STRIDD's plan generator respects ACWR bounds by capping week-over-week volume increases at 10 percent.
Pace zones: why 5 zones, what each targets
The five Daniels pace zones (Easy, Marathon, Threshold, Interval, Repetition) each target a distinct physiological adaptation. Easy pace (60-79 percent VO2max) builds aerobic capacity with minimal fatigue. Marathon pace (78-88 percent) develops race-specific efficiency. Threshold (83-88 percent) raises the lactate clearance ceiling. Interval (97-100 percent) expands VO2max. Repetition (105-120 percent) develops neuromuscular speed. The zone boundaries correspond to physiological inflection points — they are not arbitrary.
Course factors: hills, heat, altitude, and their cumulative effect
Every 100 metres of elevation gain adds about 30 seconds per kilometre to running pace. Heat above 15 degrees Celsius costs about 0.3 percent in marathon pace per degree. Altitude above 1500 metres reduces VO2max by 1-2 percent per 300 metres of elevation. These factors compound — a Ladakh marathon at 3500m in September combines altitude, mild heat, and net elevation change. STRIDD's Course FX tool stacks all three to produce a realistic time adjustment.
Taper curves: turning fitness into freshness
A 2-3 week taper with 40-60 percent volume reduction, maintained intensity, and 20 percent reduced frequency produces optimal race performance — 0.5-3.0 percent improvement over fitness level, per Bosquet et al. (2007) meta-analysis. Under-taper leaves residual fatigue. Over-taper loses sharpness. The last hard workout should sit 4-5 days before race day. STRIDD's taper curves model these principles explicitly.
Formula vault: every equation STRIDD uses
Riegel race prediction (1977), Cameron race prediction (1998), Daniels VDOT (1998), Karvonen heart rate formula (1957), Maffetone MAF (1984), Minetti elevation cost (2002), Bosquet taper optimisation (2007), Gabbett ACWR (2016). Every training pace, volume target, and phase transition in STRIDD traces back to one of these peer-reviewed equations plus small empirical calibration constants. The Formula Vault tab shows each equation with its citation.
AI Coach: LLM-assisted training insights
STRIDD's AI Coach accepts GPX files from your watch (Garmin, Coros, Polar, Strava export) or manual training summaries. It analyses recent training load, race progression, and goal alignment, and produces a plain-English training insight. You can use a demo mode, bring your own Anthropic API key, or route through STRIDD's proxy. Your data never leaves your browser when using BYOK mode.
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