"""One-shot builder for notebooks/06_race_plan.ipynb. Run with: uv run python notebooks/_build_06.py """ from __future__ import annotations import json from pathlib import Path def md(*lines: str) -> dict: return {"cell_type": "markdown", "metadata": {}, "source": _join(lines)} def code(*lines: str) -> dict: return { "cell_type": "code", "execution_count": None, "metadata": {}, "outputs": [], "source": _join(lines), } def _join(lines): text = "\n".join(lines) parts = text.split("\n") return [p + ("\n" if i < len(parts) - 1 else "") for i, p in enumerate(parts)] cells: list[dict] = [] # --------------------------------------------------------------------------- cells.append(md( "# 06 — Race plan & tracker", "", "**Three-race progression in 17 weeks:**", "", "| race | date | week | role |", "|---------------|--------------|------|---------------------------------------------|", "| **30K** | Sat 2026-06-13 | wk 4 | hard long run; race-day fuel/kit rehearsal |", "| **50K** | Sat 2026-07-25 | wk 10 | peak-fitness tune-up; race-pace calibration |", "| **50 mile** | Sat 2026-09-12 | wk 17 | A-race |", "", "Plan philosophy: the two earlier races *are* the tune-ups — no separate dress rehearsals needed. The 50K does double-duty as the biggest pre-race effort, leaving 7 weeks for one final back-to-back peak, taper, and race.", "", "Built on Ethan's proven 2023–2025 50K formula (~22 km/wk mean, ~29 km longest training run) scaled up for the 50-mile. Recorded Garmin volume **does not** include hikes, strength, or unrecorded efforts — adherence numbers below are a floor, not a ceiling.", )) # Imports + plan definition cells.append(code( "import sys; sys.path.insert(0, '..')", "import numpy as np", "import pandas as pd", "import matplotlib.pyplot as plt", "from openrun import open_conn, load_activities", "", "PLAN_START = pd.Timestamp('2026-05-18')", "RACES = {", " 'wk 4 — 30K': pd.Timestamp('2026-06-13'),", " 'wk 10 — 50K': pd.Timestamp('2026-07-25'),", " 'wk 17 — 50 MILE': pd.Timestamp('2026-09-12'),", "}", "RACE_DATE = RACES['wk 17 — 50 MILE']", "TODAY = pd.Timestamp.today().normalize()", "", "_rows = [", " # phase, kind, long_km, week_km, notes", " ('P1: base', 'build', 22, 30, 'rebuild frequency; long-run HR ≤ 145'),", " ('P1: base', 'build', 26, 38, 'add 1 trail/vert run; practice fueling'),", " ('P1: base', 'pre-race ease', 22, 35, 'short shakeouts late-week, legs fresh for Sat'),", " ('P1: 30K', '30K RACE', 30, 40, 'aerobic effort, race-day fuel + kit rehearsal'),", " ('P2: build', 'recovery', 18, 28, 'one week easy; trail/strength fine, no hard runs'),", " ('P2: build', 'build', 28, 45, ''),", " ('P2: build', 'build', 32, 55, 'fueling: 60–90 g carb/hr non-negotiable on long'),", " ('P2: build', 'peak before 50K', 35, 60, 'optional B2B: 28 Sat + 15 Sun'),", " ('P2: build', 'pre-race taper', 22, 42, 'cut volume ~30%, keep frequency'),", " ('P3: 50K', '50K RACE', 52, 60, 'controlled effort; this is the calibration run'),", " ('P4: recover', 'deep recovery', 15, 30, 'no hard efforts; walk-jog week'),", " ('P4: recover', 'easy rebuild', 25, 50, 'all runs by feel, HR < 150'),", " ('P5: peak', 'build', 35, 70, ''),", " ('P5: peak', 'B2B peak', 40, 80, 'BACK-TO-BACK: 40 km Sat + 22 km Sun, full race kit. The single most important week.'),", " ('P6: taper', 'taper', 28, 55, ''),", " ('P6: taper', 'deep taper', 18, 35, 'last meaningful long run'),", " ('P6: 50 MILE', '50 MILE RACE', 80, 90, 'shakeouts 5–6 km early-week; RACE Sat'),", "]", "PLAN = pd.DataFrame(_rows, columns=['phase','kind','long_run_km','weekly_km','notes'])", "PLAN.index = pd.date_range(PLAN_START, periods=len(PLAN), freq='W-MON')", "PLAN.index.name = 'week_start'", "PLAN['week_num'] = range(1, len(PLAN) + 1)", "PLAN['date_range'] = [f\"{d.strftime('%b %d')}–{(d + pd.Timedelta(days=6)).strftime('%b %d')}\" for d in PLAN.index]", "PLAN[['week_num','date_range','phase','kind','long_run_km','weekly_km','notes']]", )) # --------------------------------------------------------------------------- cells.append(md( "## Plan vs actual (tracker)", "", "Coloured `status` column scores each elapsed week against planned km:", "", "- **on track** — 85–115% of plan", "- **over** — > 115% *(usually fine; watch fatigue if two in a row)*", "- **under** — 50–85% *(recoverable)*", "- **missed** — < 50% *(adjust the plan; don't try to make it up next week)*", "- **—** — future week, not scored", "", "Race weeks (4 / 10 / 17) are bolded.", )) cells.append(code( "conn = open_conn()", "acts = load_activities(conn)", "runs = acts[acts['activity_type'].isin(['running','trail_running'])].copy()", "runs['week_start'] = runs['start_time_local'].dt.to_period('W-MON').dt.start_time", "", "weekly_actual = runs.groupby('week_start').agg(", " actual_km=('distance_km', 'sum'),", " longest_run_km=('distance_km', 'max'),", " n_runs=('activity_id', 'size'),", ").round(1)", "", "tracker = PLAN.join(weekly_actual, how='left')", "tracker['actual_km'] = tracker['actual_km'].fillna(0)", "tracker['longest_run_km'] = tracker['longest_run_km'].fillna(0)", "tracker['n_runs'] = tracker['n_runs'].fillna(0).astype(int)", "tracker['weekly_delta_km'] = (tracker['actual_km'] - tracker['weekly_km']).round(1)", "tracker['long_run_delta_km'] = (tracker['longest_run_km'] - tracker['long_run_km']).round(1)", "", "elapsed_mask = tracker.index <= TODAY", "ratio = tracker['actual_km'] / tracker['weekly_km']", "status = pd.Series('—', index=tracker.index)", "status[elapsed_mask & (ratio >= 0.85) & (ratio < 1.15)] = 'on track'", "status[elapsed_mask & (ratio >= 1.15)] = 'over'", "status[elapsed_mask & (ratio >= 0.50) & (ratio < 0.85)] = 'under'", "status[elapsed_mask & (ratio < 0.50)] = 'missed'", "tracker['status'] = status", "tracker['is_race'] = tracker['kind'].str.contains('RACE', na=False)", "", "view = tracker[['week_num', 'date_range', 'phase', 'kind',", " 'long_run_km', 'longest_run_km', 'long_run_delta_km',", " 'weekly_km', 'actual_km', 'weekly_delta_km',", " 'n_runs', 'status', 'notes']]", "", "_status_colors = {", " 'on track': 'background-color:#a8dadc',", " 'over': 'background-color:#fff3b0',", " 'under': 'background-color:#f4a261',", " 'missed': 'background-color:#e76f51;color:white',", " '—': 'color:#bbb',", "}", "", "def _color_status(v):", " return _status_colors.get(v, '')", "", "def _bold_race(row):", " return ['font-weight:bold' if tracker.loc[row.name, 'is_race'] else ''] * len(row)", "", "(view.style", " .map(_color_status, subset=['status'])", " .apply(_bold_race, axis=1))", )) # --------------------------------------------------------------------------- cells.append(md( "## Where are we?", )) cells.append(code( "elapsed = tracker[tracker.index <= TODAY]", "weeks_done = len(elapsed)", "weeks_left = len(tracker) - weeks_done", "", "print(f'Today: {TODAY.date()} Race day: {RACE_DATE.date()} ({(RACE_DATE - TODAY).days} days / ~{(RACE_DATE - TODAY).days // 7} weeks to A-race)')", "print(f'Plan progress: {weeks_done}/{len(tracker)} weeks elapsed, {weeks_left} remaining')", "print()", "print('Upcoming races:')", "for label, d in RACES.items():", " days = (d - TODAY).days", " marker = '✓ done' if days < 0 else f'in {days} d'", " print(f' {label:25s} {d.date()} ({marker})')", "", "print()", "if weeks_done == 0:", " nxt = tracker.iloc[0]", " print(f'Plan starts {tracker.index[0].date()} — first week:')", " print(f' wk 1 ({nxt.date_range}): {nxt.phase} — {nxt.kind}')", " print(f' target {nxt.weekly_km} km, long run {nxt.long_run_km} km')", " if nxt.notes: print(f' note: {nxt.notes}')", "else:", " cur = elapsed.iloc[-1]", " print(f'Current week (wk {int(cur.week_num)}, {cur.date_range}):')", " print(f' phase: {cur.phase} — {cur.kind}')", " print(f' target: {cur.weekly_km} km, long run {cur.long_run_km} km')", " print(f' actual: {cur.actual_km} km, longest {cur.longest_run_km} km ({cur.n_runs} runs)')", " print(f' status: {cur.status}')", " if cur.notes: print(f' note: {cur.notes}')", " if weeks_left > 0:", " nxt = tracker.iloc[weeks_done]", " print()", " print(f'Next up (wk {int(nxt.week_num)}, {nxt.date_range}):')", " print(f' {nxt.phase} — {nxt.kind}: {nxt.weekly_km} km, long {nxt.long_run_km} km')", " if nxt.notes: print(f' note: {nxt.notes}')", )) # --------------------------------------------------------------------------- cells.append(md( "## Weekly volume — planned vs actual", )) cells.append(code( "fig, ax = plt.subplots(figsize=(15, 5.5))", "x = np.arange(len(tracker))", "w = 0.4", "ax.bar(x - w/2, tracker['weekly_km'], width=w, color='#264653', label='planned', alpha=0.9)", "", "_bar_colors = {", " 'on track': '#2a9d8f',", " 'over': '#e9c46a',", " 'under': '#f4a261',", " 'missed': '#9b2226',", " '—': '#dcdcdc',", "}", "actual_colors = [_bar_colors.get(s, '#dcdcdc') for s in tracker['status']]", "ax.bar(x + w/2, tracker['actual_km'], width=w, color=actual_colors, label='actual', alpha=0.95)", "", "max_y = max(tracker['weekly_km'].max(), tracker['actual_km'].max()) * 1.22", "ax.set_ylim(0, max_y)", "", "# Phase shading + labels", "phase_groups = tracker.reset_index().groupby('phase', sort=False)", "for p, g in phase_groups:", " i0, i1 = int(g.index.min()), int(g.index.max())", " ax.axvspan(i0 - 0.5, i1 + 0.5, color='gray', alpha=0.05)", " ax.text((i0 + i1) / 2, max_y * 0.96, p, ha='center', fontsize=8.5, color='#444')", "", "# Race markers — star above the actual bar", "race_x = np.where(tracker['is_race'].to_numpy())[0]", "for rx in race_x:", " ax.scatter([rx], [max_y * 0.88], marker='*', s=220, color='#d62828', zorder=10)", " ax.text(rx, max_y * 0.82, tracker['kind'].iloc[rx].replace(' RACE',''),", " ha='center', fontsize=8.5, color='#d62828', fontweight='bold')", "", "today_x = sum(tracker.index <= TODAY) - 0.5", "if -0.5 <= today_x <= len(tracker) - 0.5:", " ax.axvline(today_x, color='red', ls=':', lw=1.5, label='today')", "", "ax.set_xticks(x)", "ax.set_xticklabels([f'wk{n}' for n in tracker['week_num']], fontsize=8)", "ax.set_ylabel('km / week')", "ax.set_title('Weekly volume')", "ax.legend(loc='upper left')", "fig.tight_layout()", )) # --------------------------------------------------------------------------- cells.append(md( "## Long-run progression", "", "Race weeks (red stars) replace the planned long run with the race itself. Note the staircase: 22 → 26 → 22 (pre-race ease) → **30K** → recover → 28 → 32 → 35 → 22 (pre-race ease) → **50K** → recover → 25 → 35 → **40 (B2B peak)** → 28 → 18 → **50 mile**.", )) cells.append(code( "fig, ax = plt.subplots(figsize=(15, 4.5))", "x = np.arange(len(tracker))", "ax.plot(x, tracker['long_run_km'], 'o-', color='#264653', lw=2, label='planned long run')", "", "el = np.asarray(tracker.index <= TODAY)", "if el.any():", " ax.scatter(x[el], tracker.loc[el, 'longest_run_km'].to_numpy(),", " s=80, color='#e76f51', zorder=5, label='actual longest of week')", "", "race_x = np.where(tracker['is_race'].to_numpy())[0]", "ax.scatter(race_x, tracker['long_run_km'].iloc[race_x].to_numpy(),", " marker='*', s=280, color='#d62828', zorder=10, label='race')", "", "today_x = sum(tracker.index <= TODAY) - 0.5", "if -0.5 <= today_x <= len(tracker) - 0.5:", " ax.axvline(today_x, color='red', ls=':', lw=1.5)", "", "ax.set_xticks(x)", "ax.set_xticklabels([f'wk{n}' for n in tracker['week_num']], fontsize=8)", "ax.set_ylabel('km')", "ax.set_title('Long-run progression — planned, actual, races')", "ax.legend(loc='upper left')", "fig.tight_layout()", )) # --------------------------------------------------------------------------- cells.append(md( "## Cumulative volume", "", "The forgiving lens — a missed week is recoverable as long as the cumulative line is within ~10% of plan. Chronic divergence for 3+ weeks is the signal to adjust the plan, not push harder.", )) cells.append(code( "tracker['cum_planned'] = tracker['weekly_km'].cumsum()", "actual_for_cum = tracker['actual_km'].where(tracker.index <= TODAY, other=np.nan)", "tracker['cum_actual'] = actual_for_cum.cumsum()", "", "fig, ax = plt.subplots(figsize=(15, 4.5))", "ax.plot(tracker.index, tracker['cum_planned'], color='#264653', lw=2, label='planned cumulative')", "ax.plot(tracker.index, tracker['cum_actual'], color='#2a9d8f', lw=2, label='actual cumulative')", "", "for label, d in RACES.items():", " if tracker.index.min() <= d <= tracker.index.max() + pd.Timedelta(days=7):", " ax.axvline(d, color='#d62828', alpha=0.5, lw=1)", " ax.text(d, ax.get_ylim()[1] * 0.02, label.split('—')[1].strip(),", " rotation=90, ha='right', va='bottom', fontsize=8, color='#d62828')", "", "if PLAN_START <= TODAY <= tracker.index.max() + pd.Timedelta(days=7):", " ax.axvline(TODAY, color='red', ls=':', lw=1.5, label='today')", "", "ax.set_ylabel('cumulative km')", "ax.set_title('Cumulative volume')", "ax.legend(loc='upper left')", "fig.autofmt_xdate()", "fig.tight_layout()", )) # --------------------------------------------------------------------------- cells.append(md( "## Adherence summary", )) cells.append(code( "elapsed_only = tracker[tracker.index <= TODAY]", "if len(elapsed_only) == 0:", " print('Plan has not started yet — no completed weeks to score.')", "else:", " counts = (elapsed_only['status']", " .value_counts()", " .reindex(['on track', 'over', 'under', 'missed'])", " .fillna(0).astype(int))", " print('Completed weeks by status:')", " print(counts.to_string())", " total_planned = elapsed_only['weekly_km'].sum()", " total_actual = elapsed_only['actual_km'].sum()", " print()", " print(f'Planned km through today: {total_planned:.0f}')", " print(f'Actual km through today: {total_actual:.0f}')", " print(f'Recorded adherence: {total_actual / total_planned * 100:.0f}% of plan')", " print()", " print('Off-watch training (hikes, strength, unrecorded runs) is not in this number.')", )) # --------------------------------------------------------------------------- cells.append(md( "## Projected fitness / fatigue / form (Banister PMC)", "", "Combine historical training-load (from `activities.training_load`) with a forecast built from the plan above to project **CTL / ATL / TSB** through race day. The shape matters more than the absolute values — a clean taper should land TSB in **+10 to +25** on Sept 12.", "", "Forecasting assumption: each plan week's km are converted to daily training-load by multiplying weekly km × the historical median TL/km from recent running, then distributing evenly Mon–Sun. The Banister EWMAs (τ=42 for CTL, τ=7 for ATL) smooth out the day-of-week pattern anyway.", )) cells.append(code( "from openrun import banister, daily_training_load_series", "", "# Historical daily load (running + trail) up to today", "hist = daily_training_load_series(conn)", "", "# TL/km conversion factor — median across the last 12 months of running", "recent_acts = pd.read_sql('''", " SELECT distance_m, training_load", " FROM activities", " WHERE activity_type IN ('running','trail_running')", " AND training_load IS NOT NULL", " AND date(start_time_local) >= date('now','-12 months')", " AND distance_m >= 2000", "''', conn)", "recent_acts['tl_per_km'] = recent_acts['training_load'] / (recent_acts['distance_m'] / 1000)", "tl_per_km = recent_acts['tl_per_km'].median()", "print(f'historical TL/km (last 12 mo, median): {tl_per_km:.1f}')", "", "# Build forecast: distribute weekly load across days.", "# For race weeks: race day gets most of the km, lead-in days are tapered shakeouts.", "# For training weeks: long run Sat gets ~40%, two mid-week runs ~20% each, rest distributed.", "# Race-day TL/km is empirically lower (~7) than training (~11) — long ultras spread out load.", "RACE_DAY_TL_PER_KM = 7.0 # observed from prior 50K races (mean ~7.5)", "", "race_day_set = set(RACES.values())", "forecast_rows = []", "for week_start, row in tracker.iterrows():", " week_days = [week_start + pd.Timedelta(days=i) for i in range(7)]", " is_race_week = any(d in race_day_set for d in week_days)", " if is_race_week:", " race_d = next(d for d in week_days if d in race_day_set)", " # Race itself: long_run_km on race day at race TL/km", " race_load = row['long_run_km'] * RACE_DAY_TL_PER_KM", " # Remaining km this week (shakeouts) at training TL/km, spread across 3 days", " rem_km = max(row['weekly_km'] - row['long_run_km'], 0)", " rem_load = rem_km * tl_per_km", " for d in week_days:", " if d == race_d:", " forecast_rows.append((d, race_load))", " elif d.weekday() in (0, 2, 4): # Mon/Wed/Fri shakeouts", " forecast_rows.append((d, rem_load / 3))", " else:", " forecast_rows.append((d, 0.0))", " else:", " # Training week: long run on Sat (40%), Tue+Thu mid-runs (20% each), Mon/Wed easy (10% each)", " weights = {0:0.10, 1:0.20, 2:0.10, 3:0.20, 4:0.00, 5:0.40, 6:0.00}", " total_load = row['weekly_km'] * tl_per_km", " for d in week_days:", " forecast_rows.append((d, total_load * weights.get(d.weekday(), 0)))", "forecast = pd.Series(dict(forecast_rows))", "forecast.index.name = 'd'", "", "# Splice: historical actual until today, forecast from tomorrow onward", "combined = pd.concat([", " hist[hist.index <= TODAY],", " forecast[forecast.index > TODAY],", "]).sort_index()", "combined = combined[~combined.index.duplicated(keep='first')]", "", "pmc = banister(combined)", "print(f'PMC range: {pmc.index.min().date()} → {pmc.index.max().date()}')", )) cells.append(code( "fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(14, 7.5), sharex=True)", "", "# Fitness + fatigue", "ax1.plot(pmc.index, pmc['CTL'], color='#2a9d8f', lw=2.2, label='CTL (fitness, 42d)')", "ax1.plot(pmc.index, pmc['ATL'], color='#e76f51', lw=1.2, alpha=0.85, label='ATL (fatigue, 7d)')", "ax1.axvline(TODAY, color='red', ls=':', lw=1.5)", "ax1.text(TODAY, ax1.get_ylim()[1]*0.95, ' today ', color='red', fontsize=8, va='top')", "ax1.set_ylabel('training load')", "ax1.legend(loc='upper left')", "ax1.grid(alpha=0.3)", "ax1.set_title('Projected Performance Management Chart — historical + plan-based forecast')", "", "# Form (TSB)", "ax2.plot(pmc.index, pmc['TSB'], color='#264653', lw=1.5)", "ax2.axhspan(10, 25, color='#2a9d8f', alpha=0.12, label='race-ready (+10 to +25)')", "ax2.axhspan(-30, -10, color='#e9c46a', alpha=0.12, label='productive overload (−30 to −10)')", "ax2.axhline(0, color='gray', lw=0.6)", "ax2.axhline(-30, color='#e76f51', ls='--', lw=0.8)", "ax2.axvline(TODAY, color='red', ls=':', lw=1.5)", "", "# Annotate planned races", "for label, d in RACES.items():", " if d in pmc.index:", " tsb = pmc.loc[d, 'TSB']", " ax2.axvline(d, color='#d62828', alpha=0.55, lw=1)", " ax2.scatter([d], [tsb], color='#d62828', s=70, zorder=5)", " ax2.annotate(f\"{label.split('—')[1].strip()}\\nTSB={tsb:+.0f}\",", " xy=(d, tsb), xytext=(8, 12),", " textcoords='offset points', fontsize=8.5, color='#d62828',", " fontweight='bold')", "", "ax2.set_ylabel('TSB (form)')", "ax2.legend(loc='lower left', fontsize=9)", "ax2.grid(alpha=0.3)", "fig.autofmt_xdate()", "fig.tight_layout()", )) cells.append(code( "# Race-day TSB check", "print('Projected fitness/fatigue/form on each race day:')", "print()", "for label, d in RACES.items():", " if d not in pmc.index:", " print(f' {label}: outside PMC range'); continue", " row = pmc.loc[d]", " tsb = row['TSB']", " if tsb < -30: tag = 'severely fatigued — UNSAFE'", " elif tsb < -10: tag = 'productive overload — not a race-day state'", " elif tsb < 0: tag = 'balanced — slightly underrested'", " elif tsb < 10: tag = 'sharpening — taper short'", " elif tsb < 25: tag = 'fresh / peaked — IDEAL race-day window'", " else: tag = 'detrained — taper too long'", " print(f' {label} {d.date()}')", " print(f' CTL={row[\"CTL\"]:>5.1f} ATL={row[\"ATL\"]:>5.1f} TSB={tsb:>+5.1f} → {tag}')", " print()", "", "# Historical comparison — what TSB did prior 50K races land at?", "print('Historical comparison — TSB on prior 50K races:')", "race_history = pd.to_datetime(['2023-09-23','2024-09-21','2025-09-06','2025-09-20'])", "for rd in race_history:", " if rd in pmc.index:", " print(f' {rd.date()} CTL={pmc.loc[rd,\"CTL\"]:>5.1f} TSB={pmc.loc[rd,\"TSB\"]:+5.1f}')", )) cells.append(md( "**Reading the chart**", "", "- **CTL slope after today** is what the plan *promises*. A flat or declining CTL means the plan isn't building fitness — usually because volume isn't ramping fast enough (or you've already peaked).", "- **ATL spikes** before each race week are expected — that's the race effort hitting the 7-day window. The taper then lets ATL bleed off faster than CTL.", "- **TSB on race day** is the actionable number. If a race lands below +10, the plan's taper into that race is too short; if above +25, too long.", "- The two earlier races (30K, 50K) are mid-build B-races; **TSB at those races doesn't need to be in the +10–25 sweet spot** — slightly negative is fine, since they're training stimuli, not peak performances.", "- The **50-mile (wk 17)** is the A-race; TSB here is the one that matters. Adjust the wk 14–17 plan rows if it's outside +10 to +25.", )) # --------------------------------------------------------------------------- cells.append(md( "## Race-specific notes", "", "### Wk 4 — 30K (June 13)", "", "- **Role:** longest pre-race long run + first dress rehearsal. Don't taper hard; this is training.", "- **Effort:** controlled aerobic, target avg HR < 155. Negative split is the win.", "- **Rehearse:** pack, shoes, nutrition cadence, salt. Whatever fails here gets fixed before the 50K.", "- **Recovery:** one easy week (wk 5) is enough.", "", "### Wk 10 — 50K (July 25)", "", "- **Role:** real race, but also the calibration run for the 50-mile. Pace it at projected 50-mile effort + 5–10 bpm.", "- **Effort:** controlled. Goal is to finish strong, not PR.", "- **Calibration:** the average HR you can hold for this distance comfortably ≈ your 50-mile ceiling. Note the number. Use it Sep 12.", "- **Recovery:** 2 weeks before resuming hard training (wk 11 deep recovery, wk 12 easy rebuild).", "", "### Wk 17 — 50 MILE (September 12)", "", "- **Pacing rule:** the average HR from the 50K is the *ceiling*, not the target. Start 5–10 bpm below it.", "- **Fueling:** 60–90 g carb/hr from minute 30. Don't skip aid stations.", "- **Walking strategy:** walk every climb from the start. The race is won in the final 20 km by people who walked the first 20 climbs.", "- **Drop-bag essentials:** chafe-prevention, headlamp/spare batteries if late finish, dry socks at midway.", )) cells.append(md( "## Key sessions to nail", "", "If everything else slips, these workouts move finish probability most:", "", "- **Wk 4 — 30K race.** Validates fueling and kit. A messy 30K is a 50K-fix-it list.", "- **Wk 8 — peak long before 50K** (35 km, optionally B2B 28+15). Last big training stimulus before the calibration race.", "- **Wk 10 — 50K race.** The calibration. Pace data here drives 50-mile pacing.", "- **Wk 14 — B2B peak (40 + 22).** The single most important week. Running on tired legs is the 50-mile race in miniature.", "- **Wk 17 — race week.** Don't add miles. Stay healthy. Show up.", )) # --------------------------------------------------------------------------- notebook = { "cells": cells, "metadata": { "kernelspec": {"display_name": ".venv", "language": "python", "name": "python3"}, "language_info": {"name": "python"}, }, "nbformat": 4, "nbformat_minor": 5, } out = Path(__file__).parent / "06_race_plan.ipynb" out.write_text(json.dumps(notebook, indent=1)) print(f"wrote {out} ({out.stat().st_size:,} bytes, {len(cells)} cells)")