{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 06 \u2014 Race plan & tracker\n", "\n", "**Three-race progression in 17 weeks:**\n", "\n", "| race | date | week | role |\n", "|---------------|--------------|------|---------------------------------------------|\n", "| **30K** | Sat 2026-06-13 | wk 4 | hard long run; race-day fuel/kit rehearsal |\n", "| **50K** | Sat 2026-07-25 | wk 10 | peak-fitness tune-up; race-pace calibration |\n", "| **50 mile** | Sat 2026-09-12 | wk 17 | A-race |\n", "\n", "Plan philosophy: the two earlier races *are* the tune-ups \u2014 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.\n", "\n", "Built on Ethan's proven 2023\u20132025 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 \u2014 adherence numbers below are a floor, not a ceiling." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "from openrun import open_conn, load_activities\n", "\n", "PLAN_START = pd.Timestamp('2026-05-18')\n", "RACES = {\n", " 'wk 4 \u2014 30K': pd.Timestamp('2026-06-13'),\n", " 'wk 10 \u2014 50K': pd.Timestamp('2026-07-25'),\n", " 'wk 17 \u2014 50 MILE': pd.Timestamp('2026-09-12'),\n", "}\n", "RACE_DATE = RACES['wk 17 \u2014 50 MILE']\n", "TODAY = pd.Timestamp.today().normalize()\n", "\n", "_rows = [\n", " # phase, kind, long_km, week_km, notes\n", " ('P1: base', 'build', 22, 30, 'rebuild frequency; long-run HR \u2264 145'),\n", " ('P1: base', 'build', 26, 38, 'add 1 trail/vert run; practice fueling'),\n", " ('P1: base', 'pre-race ease', 22, 35, 'short shakeouts late-week, legs fresh for Sat'),\n", " ('P1: 30K', '30K RACE', 30, 40, 'aerobic effort, race-day fuel + kit rehearsal'),\n", " ('P2: build', 'recovery', 18, 28, 'one week easy; trail/strength fine, no hard runs'),\n", " ('P2: build', 'build', 28, 45, ''),\n", " ('P2: build', 'build', 32, 55, 'fueling: 60\u201390 g carb/hr non-negotiable on long'),\n", " ('P2: build', 'peak before 50K', 35, 60, 'optional B2B: 28 Sat + 15 Sun'),\n", " ('P2: build', 'pre-race taper', 22, 42, 'cut volume ~30%, keep frequency'),\n", " ('P3: 50K', '50K RACE', 52, 60, 'controlled effort; this is the calibration run'),\n", " ('P4: recover', 'deep recovery', 15, 30, 'no hard efforts; walk-jog week'),\n", " ('P4: recover', 'easy rebuild', 25, 50, 'all runs by feel, HR < 150'),\n", " ('P5: peak', 'build', 35, 70, ''),\n", " ('P5: peak', 'B2B peak', 40, 80, 'BACK-TO-BACK: 40 km Sat + 22 km Sun, full race kit. The single most important week.'),\n", " ('P6: taper', 'taper', 28, 55, ''),\n", " ('P6: taper', 'deep taper', 18, 35, 'last meaningful long run'),\n", " ('P6: 50 MILE', '50 MILE RACE', 80, 90, 'shakeouts 5\u20136 km early-week; RACE Sat'),\n", "]\n", "PLAN = pd.DataFrame(_rows, columns=['phase','kind','long_run_km','weekly_km','notes'])\n", "PLAN.index = pd.date_range(PLAN_START, periods=len(PLAN), freq='W-MON')\n", "PLAN.index.name = 'week_start'\n", "PLAN['week_num'] = range(1, len(PLAN) + 1)\n", "PLAN['date_range'] = [f\"{d.strftime('%b %d')}\u2013{(d + pd.Timedelta(days=6)).strftime('%b %d')}\" for d in PLAN.index]\n", "PLAN[['week_num','date_range','phase','kind','long_run_km','weekly_km','notes']]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plan vs actual (tracker)\n", "\n", "Coloured `status` column scores each elapsed week against planned km:\n", "\n", "- **on track** \u2014 85\u2013115% of plan\n", "- **over** \u2014 > 115% *(usually fine; watch fatigue if two in a row)*\n", "- **under** \u2014 50\u201385% *(recoverable)*\n", "- **missed** \u2014 < 50% *(adjust the plan; don't try to make it up next week)*\n", "- **\u2014** \u2014 future week, not scored\n", "\n", "Race weeks (4 / 10 / 17) are bolded." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "conn = open_conn()\n", "acts = load_activities(conn)\n", "runs = acts[acts['activity_type'].isin(['running','trail_running'])].copy()\n", "runs['week_start'] = runs['start_time_local'].dt.to_period('W-MON').dt.start_time\n", "\n", "weekly_actual = runs.groupby('week_start').agg(\n", " actual_km=('distance_km', 'sum'),\n", " longest_run_km=('distance_km', 'max'),\n", " n_runs=('activity_id', 'size'),\n", ").round(1)\n", "\n", "tracker = PLAN.join(weekly_actual, how='left')\n", "tracker['actual_km'] = tracker['actual_km'].fillna(0)\n", "tracker['longest_run_km'] = tracker['longest_run_km'].fillna(0)\n", "tracker['n_runs'] = tracker['n_runs'].fillna(0).astype(int)\n", "tracker['weekly_delta_km'] = (tracker['actual_km'] - tracker['weekly_km']).round(1)\n", "tracker['long_run_delta_km'] = (tracker['longest_run_km'] - tracker['long_run_km']).round(1)\n", "\n", "elapsed_mask = tracker.index <= TODAY\n", "ratio = tracker['actual_km'] / tracker['weekly_km']\n", "status = pd.Series('\u2014', index=tracker.index)\n", "status[elapsed_mask & (ratio >= 0.85) & (ratio < 1.15)] = 'on track'\n", "status[elapsed_mask & (ratio >= 1.15)] = 'over'\n", "status[elapsed_mask & (ratio >= 0.50) & (ratio < 0.85)] = 'under'\n", "status[elapsed_mask & (ratio < 0.50)] = 'missed'\n", "tracker['status'] = status\n", "tracker['is_race'] = tracker['kind'].str.contains('RACE', na=False)\n", "\n", "view = tracker[['week_num', 'date_range', 'phase', 'kind',\n", " 'long_run_km', 'longest_run_km', 'long_run_delta_km',\n", " 'weekly_km', 'actual_km', 'weekly_delta_km',\n", " 'n_runs', 'status', 'notes']]\n", "\n", "_status_colors = {\n", " 'on track': 'background-color:#a8dadc',\n", " 'over': 'background-color:#fff3b0',\n", " 'under': 'background-color:#f4a261',\n", " 'missed': 'background-color:#e76f51;color:white',\n", " '\u2014': 'color:#bbb',\n", "}\n", "\n", "def _color_status(v):\n", " return _status_colors.get(v, '')\n", "\n", "def _bold_race(row):\n", " return ['font-weight:bold' if tracker.loc[row.name, 'is_race'] else ''] * len(row)\n", "\n", "(view.style\n", " .map(_color_status, subset=['status'])\n", " .apply(_bold_race, axis=1))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Where are we?" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "elapsed = tracker[tracker.index <= TODAY]\n", "weeks_done = len(elapsed)\n", "weeks_left = len(tracker) - weeks_done\n", "\n", "print(f'Today: {TODAY.date()} Race day: {RACE_DATE.date()} ({(RACE_DATE - TODAY).days} days / ~{(RACE_DATE - TODAY).days // 7} weeks to A-race)')\n", "print(f'Plan progress: {weeks_done}/{len(tracker)} weeks elapsed, {weeks_left} remaining')\n", "print()\n", "print('Upcoming races:')\n", "for label, d in RACES.items():\n", " days = (d - TODAY).days\n", " marker = '\u2713 done' if days < 0 else f'in {days} d'\n", " print(f' {label:25s} {d.date()} ({marker})')\n", "\n", "print()\n", "if weeks_done == 0:\n", " nxt = tracker.iloc[0]\n", " print(f'Plan starts {tracker.index[0].date()} \u2014 first week:')\n", " print(f' wk 1 ({nxt.date_range}): {nxt.phase} \u2014 {nxt.kind}')\n", " print(f' target {nxt.weekly_km} km, long run {nxt.long_run_km} km')\n", " if nxt.notes: print(f' note: {nxt.notes}')\n", "else:\n", " cur = elapsed.iloc[-1]\n", " print(f'Current week (wk {int(cur.week_num)}, {cur.date_range}):')\n", " print(f' phase: {cur.phase} \u2014 {cur.kind}')\n", " print(f' target: {cur.weekly_km} km, long run {cur.long_run_km} km')\n", " print(f' actual: {cur.actual_km} km, longest {cur.longest_run_km} km ({cur.n_runs} runs)')\n", " print(f' status: {cur.status}')\n", " if cur.notes: print(f' note: {cur.notes}')\n", " if weeks_left > 0:\n", " nxt = tracker.iloc[weeks_done]\n", " print()\n", " print(f'Next up (wk {int(nxt.week_num)}, {nxt.date_range}):')\n", " print(f' {nxt.phase} \u2014 {nxt.kind}: {nxt.weekly_km} km, long {nxt.long_run_km} km')\n", " if nxt.notes: print(f' note: {nxt.notes}')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Weekly volume \u2014 planned vs actual" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fig, ax = plt.subplots(figsize=(15, 5.5))\n", "x = np.arange(len(tracker))\n", "w = 0.4\n", "ax.bar(x - w/2, tracker['weekly_km'], width=w, color='#264653', label='planned', alpha=0.9)\n", "\n", "_bar_colors = {\n", " 'on track': '#2a9d8f',\n", " 'over': '#e9c46a',\n", " 'under': '#f4a261',\n", " 'missed': '#9b2226',\n", " '\u2014': '#dcdcdc',\n", "}\n", "actual_colors = [_bar_colors.get(s, '#dcdcdc') for s in tracker['status']]\n", "ax.bar(x + w/2, tracker['actual_km'], width=w, color=actual_colors, label='actual', alpha=0.95)\n", "\n", "max_y = max(tracker['weekly_km'].max(), tracker['actual_km'].max()) * 1.22\n", "ax.set_ylim(0, max_y)\n", "\n", "# Phase shading + labels\n", "phase_groups = tracker.reset_index().groupby('phase', sort=False)\n", "for p, g in phase_groups:\n", " i0, i1 = int(g.index.min()), int(g.index.max())\n", " ax.axvspan(i0 - 0.5, i1 + 0.5, color='gray', alpha=0.05)\n", " ax.text((i0 + i1) / 2, max_y * 0.96, p, ha='center', fontsize=8.5, color='#444')\n", "\n", "# Race markers \u2014 star above the actual bar\n", "race_x = np.where(tracker['is_race'].to_numpy())[0]\n", "for rx in race_x:\n", " ax.scatter([rx], [max_y * 0.88], marker='*', s=220, color='#d62828', zorder=10)\n", " ax.text(rx, max_y * 0.82, tracker['kind'].iloc[rx].replace(' RACE',''),\n", " ha='center', fontsize=8.5, color='#d62828', fontweight='bold')\n", "\n", "today_x = sum(tracker.index <= TODAY) - 0.5\n", "if -0.5 <= today_x <= len(tracker) - 0.5:\n", " ax.axvline(today_x, color='red', ls=':', lw=1.5, label='today')\n", "\n", "ax.set_xticks(x)\n", "ax.set_xticklabels([f'wk{n}' for n in tracker['week_num']], fontsize=8)\n", "ax.set_ylabel('km / week')\n", "ax.set_title('Weekly volume')\n", "ax.legend(loc='upper left')\n", "fig.tight_layout()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Long-run progression\n", "\n", "Race weeks (red stars) replace the planned long run with the race itself. Note the staircase: 22 \u2192 26 \u2192 22 (pre-race ease) \u2192 **30K** \u2192 recover \u2192 28 \u2192 32 \u2192 35 \u2192 22 (pre-race ease) \u2192 **50K** \u2192 recover \u2192 25 \u2192 35 \u2192 **40 (B2B peak)** \u2192 28 \u2192 18 \u2192 **50 mile**." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fig, ax = plt.subplots(figsize=(15, 4.5))\n", "x = np.arange(len(tracker))\n", "ax.plot(x, tracker['long_run_km'], 'o-', color='#264653', lw=2, label='planned long run')\n", "\n", "el = np.asarray(tracker.index <= TODAY)\n", "if el.any():\n", " ax.scatter(x[el], tracker.loc[el, 'longest_run_km'].to_numpy(),\n", " s=80, color='#e76f51', zorder=5, label='actual longest of week')\n", "\n", "race_x = np.where(tracker['is_race'].to_numpy())[0]\n", "ax.scatter(race_x, tracker['long_run_km'].iloc[race_x].to_numpy(),\n", " marker='*', s=280, color='#d62828', zorder=10, label='race')\n", "\n", "today_x = sum(tracker.index <= TODAY) - 0.5\n", "if -0.5 <= today_x <= len(tracker) - 0.5:\n", " ax.axvline(today_x, color='red', ls=':', lw=1.5)\n", "\n", "ax.set_xticks(x)\n", "ax.set_xticklabels([f'wk{n}' for n in tracker['week_num']], fontsize=8)\n", "ax.set_ylabel('km')\n", "ax.set_title('Long-run progression \u2014 planned, actual, races')\n", "ax.legend(loc='upper left')\n", "fig.tight_layout()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Cumulative volume\n", "\n", "The forgiving lens \u2014 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." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "tracker['cum_planned'] = tracker['weekly_km'].cumsum()\n", "actual_for_cum = tracker['actual_km'].where(tracker.index <= TODAY, other=np.nan)\n", "tracker['cum_actual'] = actual_for_cum.cumsum()\n", "\n", "fig, ax = plt.subplots(figsize=(15, 4.5))\n", "ax.plot(tracker.index, tracker['cum_planned'], color='#264653', lw=2, label='planned cumulative')\n", "ax.plot(tracker.index, tracker['cum_actual'], color='#2a9d8f', lw=2, label='actual cumulative')\n", "\n", "for label, d in RACES.items():\n", " if tracker.index.min() <= d <= tracker.index.max() + pd.Timedelta(days=7):\n", " ax.axvline(d, color='#d62828', alpha=0.5, lw=1)\n", " ax.text(d, ax.get_ylim()[1] * 0.02, label.split('\u2014')[1].strip(),\n", " rotation=90, ha='right', va='bottom', fontsize=8, color='#d62828')\n", "\n", "if PLAN_START <= TODAY <= tracker.index.max() + pd.Timedelta(days=7):\n", " ax.axvline(TODAY, color='red', ls=':', lw=1.5, label='today')\n", "\n", "ax.set_ylabel('cumulative km')\n", "ax.set_title('Cumulative volume')\n", "ax.legend(loc='upper left')\n", "fig.autofmt_xdate()\n", "fig.tight_layout()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Adherence summary" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "elapsed_only = tracker[tracker.index <= TODAY]\n", "if len(elapsed_only) == 0:\n", " print('Plan has not started yet \u2014 no completed weeks to score.')\n", "else:\n", " counts = (elapsed_only['status']\n", " .value_counts()\n", " .reindex(['on track', 'over', 'under', 'missed'])\n", " .fillna(0).astype(int))\n", " print('Completed weeks by status:')\n", " print(counts.to_string())\n", " total_planned = elapsed_only['weekly_km'].sum()\n", " total_actual = elapsed_only['actual_km'].sum()\n", " print()\n", " print(f'Planned km through today: {total_planned:.0f}')\n", " print(f'Actual km through today: {total_actual:.0f}')\n", " print(f'Recorded adherence: {total_actual / total_planned * 100:.0f}% of plan')\n", " print()\n", " print('Off-watch training (hikes, strength, unrecorded runs) is not in this number.')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Projected fitness / fatigue / form (Banister PMC)\n", "\n", "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 \u2014 a clean taper should land TSB in **+10 to +25** on Sept 12.\n", "\n", "Forecasting assumption: each plan week's km are converted to daily training-load by multiplying weekly km \u00d7 the historical median TL/km from recent running, then distributing evenly Mon\u2013Sun. The Banister EWMAs (\u03c4=42 for CTL, \u03c4=7 for ATL) smooth out the day-of-week pattern anyway." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from openrun import banister, daily_training_load_series\n", "\n", "# Historical daily load (running + trail) up to today\n", "hist = daily_training_load_series(conn)\n", "\n", "# TL/km conversion factor \u2014 median across the last 12 months of running\n", "recent_acts = pd.read_sql('''\n", " SELECT distance_m, training_load\n", " FROM activities\n", " WHERE activity_type IN ('running','trail_running')\n", " AND training_load IS NOT NULL\n", " AND date(start_time_local) >= date('now','-12 months')\n", " AND distance_m >= 2000\n", "''', conn)\n", "recent_acts['tl_per_km'] = recent_acts['training_load'] / (recent_acts['distance_m'] / 1000)\n", "tl_per_km = recent_acts['tl_per_km'].median()\n", "print(f'historical TL/km (last 12 mo, median): {tl_per_km:.1f}')\n", "\n", "# Build forecast: distribute weekly load across days.\n", "# For race weeks: race day gets most of the km, lead-in days are tapered shakeouts.\n", "# For training weeks: long run Sat gets ~40%, two mid-week runs ~20% each, rest distributed.\n", "# Race-day TL/km is empirically lower (~7) than training (~11) \u2014 long ultras spread out load.\n", "RACE_DAY_TL_PER_KM = 7.0 # observed from prior 50K races (mean ~7.5)\n", "\n", "race_day_set = set(RACES.values())\n", "forecast_rows = []\n", "for week_start, row in tracker.iterrows():\n", " week_days = [week_start + pd.Timedelta(days=i) for i in range(7)]\n", " is_race_week = any(d in race_day_set for d in week_days)\n", " if is_race_week:\n", " race_d = next(d for d in week_days if d in race_day_set)\n", " # Race itself: long_run_km on race day at race TL/km\n", " race_load = row['long_run_km'] * RACE_DAY_TL_PER_KM\n", " # Remaining km this week (shakeouts) at training TL/km, spread across 3 days\n", " rem_km = max(row['weekly_km'] - row['long_run_km'], 0)\n", " rem_load = rem_km * tl_per_km\n", " for d in week_days:\n", " if d == race_d:\n", " forecast_rows.append((d, race_load))\n", " elif d.weekday() in (0, 2, 4): # Mon/Wed/Fri shakeouts\n", " forecast_rows.append((d, rem_load / 3))\n", " else:\n", " forecast_rows.append((d, 0.0))\n", " else:\n", " # Training week: long run on Sat (40%), Tue+Thu mid-runs (20% each), Mon/Wed easy (10% each)\n", " weights = {0:0.10, 1:0.20, 2:0.10, 3:0.20, 4:0.00, 5:0.40, 6:0.00}\n", " total_load = row['weekly_km'] * tl_per_km\n", " for d in week_days:\n", " forecast_rows.append((d, total_load * weights.get(d.weekday(), 0)))\n", "forecast = pd.Series(dict(forecast_rows))\n", "forecast.index.name = 'd'\n", "\n", "# Splice: historical actual until today, forecast from tomorrow onward\n", "combined = pd.concat([\n", " hist[hist.index <= TODAY],\n", " forecast[forecast.index > TODAY],\n", "]).sort_index()\n", "combined = combined[~combined.index.duplicated(keep='first')]\n", "\n", "pmc = banister(combined)\n", "print(f'PMC range: {pmc.index.min().date()} \u2192 {pmc.index.max().date()}')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(14, 7.5), sharex=True)\n", "\n", "# Fitness + fatigue\n", "ax1.plot(pmc.index, pmc['CTL'], color='#2a9d8f', lw=2.2, label='CTL (fitness, 42d)')\n", "ax1.plot(pmc.index, pmc['ATL'], color='#e76f51', lw=1.2, alpha=0.85, label='ATL (fatigue, 7d)')\n", "ax1.axvline(TODAY, color='red', ls=':', lw=1.5)\n", "ax1.text(TODAY, ax1.get_ylim()[1]*0.95, ' today ', color='red', fontsize=8, va='top')\n", "ax1.set_ylabel('training load')\n", "ax1.legend(loc='upper left')\n", "ax1.grid(alpha=0.3)\n", "ax1.set_title('Projected Performance Management Chart \u2014 historical + plan-based forecast')\n", "\n", "# Form (TSB)\n", "ax2.plot(pmc.index, pmc['TSB'], color='#264653', lw=1.5)\n", "ax2.axhspan(10, 25, color='#2a9d8f', alpha=0.12, label='race-ready (+10 to +25)')\n", "ax2.axhspan(-30, -10, color='#e9c46a', alpha=0.12, label='productive overload (\u221230 to \u221210)')\n", "ax2.axhline(0, color='gray', lw=0.6)\n", "ax2.axhline(-30, color='#e76f51', ls='--', lw=0.8)\n", "ax2.axvline(TODAY, color='red', ls=':', lw=1.5)\n", "\n", "# Annotate planned races\n", "for label, d in RACES.items():\n", " if d in pmc.index:\n", " tsb = pmc.loc[d, 'TSB']\n", " ax2.axvline(d, color='#d62828', alpha=0.55, lw=1)\n", " ax2.scatter([d], [tsb], color='#d62828', s=70, zorder=5)\n", " ax2.annotate(f\"{label.split('\u2014')[1].strip()}\\nTSB={tsb:+.0f}\",\n", " xy=(d, tsb), xytext=(8, 12),\n", " textcoords='offset points', fontsize=8.5, color='#d62828',\n", " fontweight='bold')\n", "\n", "ax2.set_ylabel('TSB (form)')\n", "ax2.legend(loc='lower left', fontsize=9)\n", "ax2.grid(alpha=0.3)\n", "fig.autofmt_xdate()\n", "fig.tight_layout()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Race-day TSB check\n", "print('Projected fitness/fatigue/form on each race day:')\n", "print()\n", "for label, d in RACES.items():\n", " if d not in pmc.index:\n", " print(f' {label}: outside PMC range'); continue\n", " row = pmc.loc[d]\n", " tsb = row['TSB']\n", " if tsb < -30: tag = 'severely fatigued \u2014 UNSAFE'\n", " elif tsb < -10: tag = 'productive overload \u2014 not a race-day state'\n", " elif tsb < 0: tag = 'balanced \u2014 slightly underrested'\n", " elif tsb < 10: tag = 'sharpening \u2014 taper short'\n", " elif tsb < 25: tag = 'fresh / peaked \u2014 IDEAL race-day window'\n", " else: tag = 'detrained \u2014 taper too long'\n", " print(f' {label} {d.date()}')\n", " print(f' CTL={row[\"CTL\"]:>5.1f} ATL={row[\"ATL\"]:>5.1f} TSB={tsb:>+5.1f} \u2192 {tag}')\n", " print()\n", "\n", "# Historical comparison \u2014 what TSB did prior 50K races land at?\n", "print('Historical comparison \u2014 TSB on prior 50K races:')\n", "race_history = pd.to_datetime(['2023-09-23','2024-09-21','2025-09-06','2025-09-20'])\n", "for rd in race_history:\n", " if rd in pmc.index:\n", " print(f' {rd.date()} CTL={pmc.loc[rd,\"CTL\"]:>5.1f} TSB={pmc.loc[rd,\"TSB\"]:+5.1f}')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Reading the chart**\n", "\n", "- **CTL slope after today** is what the plan *promises*. A flat or declining CTL means the plan isn't building fitness \u2014 usually because volume isn't ramping fast enough (or you've already peaked).\n", "- **ATL spikes** before each race week are expected \u2014 that's the race effort hitting the 7-day window. The taper then lets ATL bleed off faster than CTL.\n", "- **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.\n", "- The two earlier races (30K, 50K) are mid-build B-races; **TSB at those races doesn't need to be in the +10\u201325 sweet spot** \u2014 slightly negative is fine, since they're training stimuli, not peak performances.\n", "- The **50-mile (wk 17)** is the A-race; TSB here is the one that matters. Adjust the wk 14\u201317 plan rows if it's outside +10 to +25." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Race-specific notes\n", "\n", "### Wk 4 \u2014 30K (June 13)\n", "\n", "- **Role:** longest pre-race long run + first dress rehearsal. Don't taper hard; this is training.\n", "- **Effort:** controlled aerobic, target avg HR < 155. Negative split is the win.\n", "- **Rehearse:** pack, shoes, nutrition cadence, salt. Whatever fails here gets fixed before the 50K.\n", "- **Recovery:** one easy week (wk 5) is enough.\n", "\n", "### Wk 10 \u2014 50K (July 25)\n", "\n", "- **Role:** real race, but also the calibration run for the 50-mile. Pace it at projected 50-mile effort + 5\u201310 bpm.\n", "- **Effort:** controlled. Goal is to finish strong, not PR.\n", "- **Calibration:** the average HR you can hold for this distance comfortably \u2248 your 50-mile ceiling. Note the number. Use it Sep 12.\n", "- **Recovery:** 2 weeks before resuming hard training (wk 11 deep recovery, wk 12 easy rebuild).\n", "\n", "### Wk 17 \u2014 50 MILE (September 12)\n", "\n", "- **Pacing rule:** the average HR from the 50K is the *ceiling*, not the target. Start 5\u201310 bpm below it.\n", "- **Fueling:** 60\u201390 g carb/hr from minute 30. Don't skip aid stations.\n", "- **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.\n", "- **Drop-bag essentials:** chafe-prevention, headlamp/spare batteries if late finish, dry socks at midway." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Key sessions to nail\n", "\n", "If everything else slips, these workouts move finish probability most:\n", "\n", "- **Wk 4 \u2014 30K race.** Validates fueling and kit. A messy 30K is a 50K-fix-it list.\n", "- **Wk 8 \u2014 peak long before 50K** (35 km, optionally B2B 28+15). Last big training stimulus before the calibration race.\n", "- **Wk 10 \u2014 50K race.** The calibration. Pace data here drives 50-mile pacing.\n", "- **Wk 14 \u2014 B2B peak (40 + 22).** The single most important week. Running on tired legs is the 50-mile race in miniature.\n", "- **Wk 17 \u2014 race week.** Don't add miles. Stay healthy. Show up." ] } ], "metadata": { "kernelspec": { "display_name": ".venv", "language": "python", "name": "python3" }, "language_info": { "name": "python" } }, "nbformat": 4, "nbformat_minor": 5 }