{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.11.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":112899,"databundleVersionId":13449579,"sourceType":"competition"},{"sourceId":13064323,"sourceType":"datasetVersion","datasetId":8273356}],"dockerImageVersionId":31089,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# Grand X-Ray Slam Division A - Week 4 Progress Report\n\n*Competition Progress Analysis • Week 4 of 8 • September 2025*\n\n---\n\n## Executive Summary\n\n**Grand X-Ray Slam Division A** reaches the halfway point with steady growth and intensifying competition. As we hit the 50% milestone, the field continues expanding while the battle for top positions becomes increasingly fierce.\n\n| Metric | Week 3 (Day 18) | Week 4 (Day 25) | Growth |\n|--------|-----------------|-----------------|--------|\n| **Total Entrants** | 320 | 421 | +101 (+31.6%) |\n| **Active Participants** | 63 | 85 | +22 (+34.9%) |\n| **Competing Teams** | 61 | 81 | +20 (+32.8%) |\n| **Total Submissions** | 256 | 391 | +135 (+52.7%) |\n| **Competition Period** | 8 weeks (Day 25 of 50) | | |\n| **Deadline** | October 10, 2025 | | |\n\n🎯 **HALFWAY MILESTONE**: We've officially crossed the 50% completion mark, with exactly 25 days remaining until the final submission deadline.\n\n---","metadata":{}},{"cell_type":"code","source":"import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport warnings\nwarnings.filterwarnings('ignore')\n\n!pip install --upgrade plotly\n\nimport plotly.express as px\nimport plotly.graph_objects as go\nfrom plotly.subplots import make_subplots\nfrom datetime import datetime, timedelta\n\nplt.style.use('seaborn-v0_8-darkgrid')\nsns.set_palette(\"husl\")\n\nprint(\"Libraries loaded successfully!\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-09-15T09:38:07.313416Z","iopub.execute_input":"2025-09-15T09:38:07.313791Z","iopub.status.idle":"2025-09-15T09:38:43.16808Z","shell.execute_reply.started":"2025-09-15T09:38:07.313763Z","shell.execute_reply":"2025-09-15T09:38:43.166733Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Load all three weeks' data for comprehensive analysis\ndf_w2 = pd.read_csv('/kaggle/input/division-a-leaderboard-w4/Leaderboard_A_W2.csv')\ndf_w3 = pd.read_csv('/kaggle/input/division-a-leaderboard-w4/Leaderboard_A_W3.csv')\ndf_w4 = pd.read_csv('/kaggle/input/division-a-leaderboard-w4/Leaderboard_A_W4.csv')\n\nprint(f\"Week 2 leaderboard: {len(df_w2)} teams\")\nprint(f\"Week 3 leaderboard: {len(df_w3)} teams\")\nprint(f\"Week 4 leaderboard: {len(df_w4)} teams\")\nprint(f\"New teams this week: {len(df_w4) - len(df_w3)}\")\n\nprint(\"\\n🏆 WEEK 4 TOP 5 TEAMS:\")\nprint(\"=\" * 60)\ntop_5 = df_w4.head()\nfor _, row in top_5.iterrows():\n    print(f\"{row['Rank']:2d}. {row['TeamName'][:25]:<25} | Score: {row['Score']:.6f}\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-09-15T09:38:43.170357Z","iopub.execute_input":"2025-09-15T09:38:43.171244Z","iopub.status.idle":"2025-09-15T09:38:43.226552Z","shell.execute_reply.started":"2025-09-15T09:38:43.171211Z","shell.execute_reply":"2025-09-15T09:38:43.225577Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"---\n\n## 🚀 Three-Week Growth Trajectory\n\nThe competition shows a maturing growth pattern as we approach the halfway point. While growth rates are moderating from the explosive Week 2-3 surge, participation remains strong with healthy week-over-week increases.\n\n---","metadata":{}},{"cell_type":"code","source":"# Three-week comparison metrics\nmetrics_comparison = {\n    'Metric': ['Total Entrants', 'Active Participants', 'Competing Teams', 'Total Submissions'],\n    'Week 2': [217, 34, 34, 99],\n    'Week 3': [320, 63, 61, 256], \n    'Week 4': [421, 85, 81, 391],\n    'W2-W3 Growth': [103, 29, 27, 157],\n    'W3-W4 Growth': [101, 22, 20, 135],\n    'W2-W3 Growth %': [47.5, 85.3, 79.4, 158.6],\n    'W3-W4 Growth %': [31.6, 34.9, 32.8, 52.7]\n}\n\nmetrics_df = pd.DataFrame(metrics_comparison)\n\n# Create comprehensive visualization\nfig = make_subplots(\n    rows=3, cols=2,\n    subplot_titles=('Weekly Progression', 'Growth Rate Trends', \n                   'Submission Acceleration', 'Team Expansion', \n                   'Activity Heatmap', 'Competition Maturity'),\n    specs=[[{'type': 'scatter'}, {'type': 'scatter'}],\n           [{'type': 'bar'}, {'type': 'scatter'}],\n           [{'type': 'histogram'}, {'type': 'scatter'}]]\n)\n\n# Weekly progression\nweeks = ['Week 2', 'Week 3', 'Week 4']\nfor i, metric in enumerate(['Total Entrants', 'Active Participants', 'Competing Teams', 'Total Submissions']):\n    values = [metrics_df.iloc[i]['Week 2'], metrics_df.iloc[i]['Week 3'], metrics_df.iloc[i]['Week 4']]\n    fig.add_trace(\n        go.Scatter(x=weeks, y=values, mode='lines+markers', name=metric, line=dict(width=3)),\n        row=1, col=1\n    )\n\n# Growth rate trends\ngrowth_weeks = ['W2-W3', 'W3-W4']\nfig.add_trace(\n    go.Scatter(x=growth_weeks, y=[158.6, 52.7], mode='lines+markers+text',\n               text=['158.6%', '52.7%'], textposition='top center',\n               name='Submission Growth %', line=dict(width=4, color='red')),\n    row=1, col=2\n)\n\n# Submission acceleration\nfig.add_trace(\n    go.Bar(x=weeks, y=[99, 256, 391], name='Weekly Submissions', \n           marker_color=['lightblue', 'blue', 'darkblue']),\n    row=2, col=1\n)\n\n# Team expansion over time\nfig.add_trace(\n    go.Scatter(x=weeks, y=[34, 61, 81], mode='lines+markers+text',\n               text=[34, 61, 81], textposition='top center',\n               name='Team Count', line=dict(width=5, color='green')),\n    row=2, col=2\n)\n\n# Current week activity distribution\nfig.add_trace(\n    go.Histogram(x=df_w4['SubmissionCount'], nbinsx=20, \n                 name='W4 Team Activity', marker_color='orange'),\n    row=3, col=1\n)\n\n# Competition maturity indicator\nmaturity_weeks = ['Week 2', 'Week 3', 'Week 4']\nintensity = [99/34, 256/61, 391/81]  # Submissions per team\nfig.add_trace(\n    go.Scatter(x=maturity_weeks, y=intensity, mode='lines+markers+text',\n               text=[f'{i:.1f}' for i in intensity], textposition='top center',\n               name='Subs/Team Ratio', line=dict(width=4, color='purple')),\n    row=3, col=2\n)\n\nfig.update_layout(\n    height=1000,\n    title_text=\"📊 Division A: Three-Week Evolution Analysis\",\n    showlegend=True\n)\n\nfig.show()\n\nprint(\"📈 GROWTH PATTERN ANALYSIS:\")\nprint(f\"   • Week 2-3: Explosive growth phase (+158.6% submissions)\")\nprint(f\"   • Week 3-4: Stabilizing growth phase (+52.7% submissions)\")\nprint(f\"   • Current intensity: {391/81:.1f} submissions per team\")\nprint(f\"   • Growth rate moderation indicates competition maturation\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-09-15T09:38:43.227696Z","iopub.execute_input":"2025-09-15T09:38:43.228134Z","iopub.status.idle":"2025-09-15T09:38:43.854273Z","shell.execute_reply.started":"2025-09-15T09:38:43.228101Z","shell.execute_reply":"2025-09-15T09:38:43.852967Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"---\n\n## 🏁 Leaderboard Dynamics & Position Changes\n\nWith 81 teams now competing, the leaderboard has become increasingly competitive. Let's analyze how teams have evolved across the three-week period and identify the biggest movers.\n\n---","metadata":{}},{"cell_type":"code","source":"# Track teams across all three weeks\nw2_teams = set(df_w2['TeamName'])\nw3_teams = set(df_w3['TeamName'])\nw4_teams = set(df_w4['TeamName'])\n\n# Find different team categories\ncontinuing_teams = w2_teams.intersection(w3_teams).intersection(w4_teams)\nw3_w4_continuing = w3_teams.intersection(w4_teams)\nnew_w4_teams = w4_teams - w3_teams\n\nprint(f\"📊 TEAM EVOLUTION BREAKDOWN:\")\nprint(f\"   • Teams competing all 3 weeks: {len(continuing_teams)}\")\nprint(f\"   • Teams from Week 3 continuing: {len(w3_w4_continuing)}\")\nprint(f\"   • Brand new teams in Week 4: {len(new_w4_teams)}\")\nprint(f\"   • Teams that stopped after Week 3: {len(w3_teams - w4_teams)}\")\n\n# Score improvements for Week 3-4 continuing teams\nimprovements = []\nfor team in w3_w4_continuing:\n    try:\n        w3_score = df_w3[df_w3['TeamName'] == team]['Score'].iloc[0]\n        w4_score = df_w4[df_w4['TeamName'] == team]['Score'].iloc[0]\n        w3_rank = df_w3[df_w3['TeamName'] == team]['Rank'].iloc[0]\n        w4_rank = df_w4[df_w4['TeamName'] == team]['Rank'].iloc[0]\n        \n        improvements.append({\n            'TeamName': team,\n            'W3_Score': w3_score,\n            'W4_Score': w4_score,\n            'Score_Improvement': w4_score - w3_score,\n            'W3_Rank': w3_rank,\n            'W4_Rank': w4_rank,\n            'Rank_Change': w3_rank - w4_rank  # Positive = improved rank\n        })\n    except:\n        continue\n\nimprovements_df = pd.DataFrame(improvements)\ntop_improvers = improvements_df.nlargest(5, 'Score_Improvement')\nbiggest_climbers = improvements_df.nlargest(5, 'Rank_Change')\n\nprint(f\"\\n🏆 TOP 5 SCORE IMPROVERS (Week 3 → Week 4):\")\nprint(\"=\" * 70)\nfor _, row in top_improvers.iterrows():\n    print(f\"{row['TeamName'][:20]:<20} | +{row['Score_Improvement']:.6f} | Rank: {row['W3_Rank']} → {row['W4_Rank']}\")\n\nprint(f\"\\n🚀 BIGGEST RANK CLIMBERS (Week 3 → Week 4):\")\nprint(\"=\" * 70)\nfor _, row in biggest_climbers.iterrows():\n    print(f\"{row['TeamName'][:20]:<20} | Rank: {row['W3_Rank']} → {row['W4_Rank']} (+{row['Rank_Change']} positions)\")\n\n# Visualization of score evolution\nfig = go.Figure()\n\n# Continuing teams progression\nfig.add_trace(go.Scatter(\n    x=improvements_df['W3_Score'],\n    y=improvements_df['W4_Score'],\n    mode='markers',\n    marker=dict(size=10, color='blue', opacity=0.7),\n    name='Continuing Teams',\n    text=improvements_df['TeamName'],\n    hovertemplate='<b>%{text}</b><br>Week 3: %{x:.6f}<br>Week 4: %{y:.6f}<extra></extra>'\n))\n\n# New teams\nnew_teams_data = df_w4[df_w4['TeamName'].isin(new_w4_teams)]\nfig.add_trace(go.Scatter(\n    x=[0.5] * len(new_teams_data),\n    y=new_teams_data['Score'],\n    mode='markers',\n    marker=dict(size=10, color='red', opacity=0.7),\n    name='New Teams (Week 4)',\n    text=new_teams_data['TeamName'],\n    hovertemplate='<b>%{text}</b><br>New Team<br>Score: %{y:.6f}<extra></extra>'\n))\n\n# Add improvement reference line\nfig.add_trace(go.Scatter(\n    x=[0.5, 1.0],\n    y=[0.5, 1.0],\n    mode='lines',\n    line=dict(dash='dash', color='gray'),\n    name='No Improvement Line',\n    showlegend=False\n))\n\nfig.update_layout(\n    title=\"🎯 Team Performance Evolution (Week 3 → Week 4)\",\n    xaxis_title=\"Week 3 Score\",\n    yaxis_title=\"Week 4 Score\",\n    height=600\n)\n\nfig.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-09-15T09:38:43.8554Z","iopub.execute_input":"2025-09-15T09:38:43.855763Z","iopub.status.idle":"2025-09-15T09:38:44.043231Z","shell.execute_reply.started":"2025-09-15T09:38:43.855695Z","shell.execute_reply":"2025-09-15T09:38:44.042043Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"---\n\n## 📋 Week 4 Current Standings & Performance Metrics\n\nThe competition field has expanded to 81 teams, creating the most competitive environment yet. Score distributions are tightening as teams optimize their approaches.\n\n---","metadata":{}},{"cell_type":"code","source":"# Convert last submission date for analysis\ndf_w4['LastSubmissionDate'] = pd.to_datetime(df_w4['LastSubmissionDate'])\ndf_w4['DaysAgo'] = (datetime.now() - df_w4['LastSubmissionDate']).dt.days\n\n# Current standings visualization\nfig = make_subplots(\n    rows=2, cols=2,\n    subplot_titles=('Top 20 Teams Performance', 'Score Distribution Evolution',\n                   'Submission Activity Levels', 'Performance Landscape'),\n    specs=[[{'type': 'bar'}, {'type': 'histogram'}],\n           [{'type': 'pie'}, {'type': 'scatter'}]]\n)\n\n# Top 20 teams\ntop_20 = df_w4.head(20)\nfig.add_trace(\n    go.Bar(\n        x=top_20['Score'],\n        y=top_20['TeamName'],\n        orientation='h',\n        marker_color='lightcoral',\n        text=[f\"#{rank}\" for rank in top_20['Rank']],\n        textposition='inside'\n    ),\n    row=1, col=1\n)\n\n# Score distribution comparison\nfig.add_trace(\n    go.Histogram(x=df_w2['Score'], nbinsx=20, name='Week 2', opacity=0.7, marker_color='lightblue'),\n    row=1, col=2\n)\nfig.add_trace(\n    go.Histogram(x=df_w3['Score'], nbinsx=20, name='Week 3', opacity=0.7, marker_color='blue'),\n    row=1, col=2\n)\nfig.add_trace(\n    go.Histogram(x=df_w4['Score'], nbinsx=20, name='Week 4', opacity=0.7, marker_color='darkblue'),\n    row=1, col=2\n)\n\n# Activity levels\ndf_w4['ActivityLevel'] = df_w4['SubmissionCount'].apply(\n    lambda x: 'High (7+)' if x >= 7 else 'Medium (3-6)' if x >= 3 else 'Low (1-2)'\n)\nactivity_counts = df_w4['ActivityLevel'].value_counts()\nfig.add_trace(\n    go.Pie(labels=activity_counts.index, values=activity_counts.values,\n           marker_colors=['red', 'orange', 'lightblue']),\n    row=2, col=1\n)\n\n# Performance landscape\nfig.add_trace(\n    go.Scatter(\n        x=list(range(1, len(df_w4)+1)),\n        y=df_w4['Score'],\n        mode='markers',\n        marker=dict(\n            size=df_w4['SubmissionCount']*1.5,\n            color=df_w4['SubmissionCount'],\n            colorscale='viridis',\n            showscale=True\n        ),\n        text=df_w4['TeamName'],\n        hovertemplate='<b>%{text}</b><br>Rank: %{x}<br>Score: %{y:.6f}<br>Submissions: %{marker.color}<extra></extra>'\n    ),\n    row=2, col=2\n)\n\nfig.update_layout(\n    height=900,\n    title_text=\"📈 Week 4 Comprehensive Performance Analysis\",\n    showlegend=True\n)\n\nfig.show()\n\nprint(f\"\\n📊 WEEK 4 PERFORMANCE METRICS:\")\nprint(f\"   • Leading Score: {df_w4['Score'].max():.6f}\")\nprint(f\"   • Average Score: {df_w4['Score'].mean():.6f}\")\nprint(f\"   • Score Range: {df_w4['Score'].min():.6f} - {df_w4['Score'].max():.6f}\")\nprint(f\"   • Most submissions: {df_w4['SubmissionCount'].max()} by {df_w4.loc[df_w4['SubmissionCount'].idxmax(), 'TeamName']}\")\nprint(f\"   • Teams above 0.90: {len(df_w4[df_w4['Score'] > 0.90])}\")\nprint(f\"   • Teams above 0.85: {len(df_w4[df_w4['Score'] > 0.85])}\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-09-15T09:38:44.045504Z","iopub.execute_input":"2025-09-15T09:38:44.045839Z","iopub.status.idle":"2025-09-15T09:38:44.155585Z","shell.execute_reply.started":"2025-09-15T09:38:44.045815Z","shell.execute_reply":"2025-09-15T09:38:44.154389Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"---\n\n## 🎯 Halfway Point Milestone: Competition at 50%\n\nWe've officially reached the competition's midpoint. This milestone offers a perfect opportunity to assess trends, predict outcomes, and analyze the competitive landscape as teams enter the second half.\n\n---","metadata":{}},{"cell_type":"code","source":"# Timeline data\ntimeline_data = {\n    'Phase': ['Competition Start', 'Week 1 Complete', 'Week 2 Complete', 'Week 3 Complete', 'Current (Week 4)', 'Week 4 Complete', 'Final Submission', 'Results'],\n    'Date': ['Aug 21', 'Aug 28', 'Sep 4', 'Sep 11', 'Sep 15', 'Sep 18', 'Oct 10', 'Oct 12'],\n    'Status': ['Complete', 'Complete', 'Complete', 'Complete', 'Active', 'Upcoming', 'Upcoming', 'Upcoming']\n}\n\ntimeline_df = pd.DataFrame(timeline_data)\n\nfig = go.Figure()\ncolors = {'Complete': 'green', 'Active': 'blue', 'Upcoming': 'gray'}\n\nfor status in timeline_df['Status'].unique():\n    mask = timeline_df['Status'] == status\n    fig.add_trace(go.Scatter(\n        x=timeline_df[mask]['Date'],\n        y=timeline_df[mask]['Phase'],\n        mode='markers',\n        marker=dict(size=15, color=colors[status]),\n        name=status,\n        text=timeline_df[mask]['Status'],\n        textposition='middle right'\n    ))\n\nfig.update_layout(\n    title='📅 Competition Timeline - Halfway Point Reached',\n    xaxis_title='Date',\n    height=300,\n    showlegend=True\n)\nfig.show()\n\n# Progress calculation\ntotal_days = 50\ndays_passed = 25  \ndays_left = total_days - days_passed \nprogress_pct = (days_passed / total_days) * 100\n\n# Progress bar with milestone marker\nfig_progress = go.Figure()\n\nfig_progress.add_trace(go.Bar(\n    x=[total_days],\n    y=[\"Competition Progress\"],\n    orientation=\"h\",\n    marker=dict(color=\"lightgray\"),\n    width=0.5,\n    showlegend=False\n))\n\nfig_progress.add_trace(go.Bar(\n    x=[days_passed],\n    y=[\"Competition Progress\"],\n    orientation=\"h\",\n    text=[f\"🎯 HALFWAY POINT: {progress_pct:.0f}% complete\"],\n    textposition=\"inside\",\n    marker=dict(color=\"gold\"),\n    width=0.5,\n    name=\"Days Passed\"\n))\n\n# Add milestone marker\nfig_progress.add_vline(x=25, line_dash=\"dash\", line_color=\"red\", \n                      annotation_text=\"HALFWAY MILESTONE\", annotation_position=\"top\")\n\nfig_progress.update_layout(\n    title=f\"🏁 Competition Progress: Day {days_passed} of {total_days} (HALFWAY POINT!)\",\n    barmode='overlay',\n    xaxis=dict(range=[0, total_days], title=\"Days\"),\n    yaxis=dict(showticklabels=False),\n    height=200\n)\n\nfig_progress.show()\n\nprint(f\"🏁 HALFWAY MILESTONE ACHIEVED:\")\nprint(f\"   • Days completed: {days_passed} of {total_days}\")\nprint(f\"   • Days remaining: {days_left} days\")\nprint(f\"   • Time remaining: {(days_left/7):.1f} weeks\")\nprint(f\"   • Competition intensity: Peak performance phase begins\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-09-15T09:38:44.156543Z","iopub.execute_input":"2025-09-15T09:38:44.15703Z","iopub.status.idle":"2025-09-15T09:38:44.272811Z","shell.execute_reply.started":"2025-09-15T09:38:44.157004Z","shell.execute_reply":"2025-09-15T09:38:44.271873Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Advanced performance insights\nprint(f\"🔍 ADVANCED WEEK 4 INSIGHTS:\")\nprint(\"=\" * 50)\n\n# Score progression analysis across all weeks\nw2_top_score = df_w2['Score'].max()\nw3_top_score = df_w3['Score'].max() \nw4_top_score = df_w4['Score'].max()\n\nw2_w3_improvement = w3_top_score - w2_top_score\nw3_w4_improvement = w4_top_score - w3_top_score\n\nprint(f\"   • Week 2-3 top score improvement: +{w2_w3_improvement:.6f}\")\nprint(f\"   • Week 3-4 top score improvement: +{w3_w4_improvement:.6f}\")\nprint(f\"   • Current leading score: {w4_top_score:.6f}\")\n\n# Submission efficiency analysis\ndf_w4['Efficiency'] = df_w4['Score'] / df_w4['SubmissionCount']\ntop_efficient = df_w4.nlargest(5, 'Efficiency')\n\nprint(f\"\\n🎯 MOST EFFICIENT TEAMS (Score per Submission):\")\nfor _, row in top_efficient.iterrows():\n    print(f\"   {row['TeamName'][:20]:<20} | {row['Efficiency']:.6f} | ({row['Score']:.6f}/{row['SubmissionCount']} subs)\")\n\n# Competition intensity trends\navg_scores = [df_w2['Score'].mean(), df_w3['Score'].mean(), df_w4['Score'].mean()]\nprint(f\"\\n📊 COMPETITION TRENDS:\")\nprint(f\"   • Average score progression: {avg_scores[0]:.6f} → {avg_scores[1]:.6f} → {avg_scores[2]:.6f}\")\nprint(f\"   • Score improvement rate is {'accelerating' if avg_scores[2]-avg_scores[1] > avg_scores[1]-avg_scores[0] else 'decelerating'}\")\nprint(f\"   • Top 10 average: {df_w4.head(10)['Score'].mean():.6f}\")\nprint(f\"   • Score standard deviation: {df_w4['Score'].std():.6f}\")\n\n# Recent activity analysis\nrecent_submissions = df_w4[df_w4['DaysAgo'] <= 2]\nactive_teams = len(recent_submissions)\n\nprint(f\"\\n🔥 RECENT ACTIVITY (Last 48h):\")\nprint(f\"   • Teams with recent submissions: {active_teams}\")\nprint(f\"   • Activity rate: {(active_teams/len(df_w4)*100):.1f}% of teams\")\nprint(f\"   • Current competition intensity: {(391/81):.1f} submissions per team\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-09-15T09:38:44.274081Z","iopub.execute_input":"2025-09-15T09:38:44.274469Z","iopub.status.idle":"2025-09-15T09:38:44.297074Z","shell.execute_reply.started":"2025-09-15T09:38:44.27443Z","shell.execute_reply":"2025-09-15T09:38:44.295675Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"print(f\"🔮 WEEK 5 PREDICTIONS & SECOND HALF OUTLOOK:\")\nprint(\"=\" * 60)\n\n# Growth rate calculations with trend analysis\nentrant_growth_w3_w4 = (421 - 320) / 320\nteam_growth_w3_w4 = (81 - 61) / 61\nsubmission_growth_w3_w4 = (391 - 256) / 256\n\n# Compare with previous week growth rates\nentrant_growth_w2_w3 = (320 - 217) / 217\nteam_growth_w2_w3 = (61 - 34) / 34\nsubmission_growth_w2_w3 = (256 - 99) / 99\n\nprint(f\"📈 GROWTH RATE EVOLUTION:\")\nprint(f\"   • Entrant growth: W2-W3: {entrant_growth_w2_w3:.1%} → W3-W4: {entrant_growth_w3_w4:.1%}\")\nprint(f\"   • Team growth: W2-W3: {team_growth_w2_w3:.1%} → W3-W4: {team_growth_w3_w4:.1%}\")\nprint(f\"   • Submission growth: W2-W3: {submission_growth_w2_w3:.1%} → W3-W4: {submission_growth_w3_w4:.1%}\")\n\n# Project Week 5 numbers with refined modeling\nprojected_entrants = int(421 * (1 + entrant_growth_w3_w4 * 0.8))\nprojected_teams = int(81 * (1 + team_growth_w3_w4 * 0.7))\nprojected_submissions = int(391 * (1 + submission_growth_w3_w4 * 0.9))  # Higher factor as teams push harder\n\nprint(f\"\\n🎯 PROJECTED WEEK 5 NUMBERS:\")\nprint(f\"   • Estimated entrants: {projected_entrants:,}\")\nprint(f\"   • Estimated teams: {projected_teams}\")\nprint(f\"   • Estimated submissions: {projected_submissions}\")\n\nprint(f\"\\n🏁 SECOND HALF PHASE PREDICTIONS:\")\nprint(f\"   • Competition intensity: PEAK performance phase\")\nprint(f\"   • Score improvements: Expect smaller, harder-fought gains\")\nprint(f\"   • Team strategies: Ensemble methods and fine-tuning focus\")\nprint(f\"   • New entrants: Likely to face steeper learning curve\")\n\nprint(f\"\\n🎲 KEY MILESTONES TO WATCH:\")\nprint(f\"   • Target: Breaking {w4_top_score:.6f} barrier\")\nprint(f\"   • Trend: Cross-validation optimization surge expected\")\nprint(f\"   • Focus: Quality over quantity in submissions\")\nprint(f\"   • Watch: Top 15 teams' submission frequency patterns\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-09-15T09:38:44.298399Z","iopub.execute_input":"2025-09-15T09:38:44.299268Z","iopub.status.idle":"2025-09-15T09:38:44.330019Z","shell.execute_reply.started":"2025-09-15T09:38:44.299224Z","shell.execute_reply":"2025-09-15T09:38:44.32837Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"---\n\n## 🏆 Summary: Competition at the Halfway Mark\n\nWeek 4 marks a pivotal moment in Grand X-Ray Slam Division A. Having reached the exact halfway point, the competition shows signs of maturation while maintaining strong growth momentum. With 81 teams now competing and 391 total submissions, the field has become increasingly competitive.\n\n**Key Insights:**\n\n**🚀 Growth Evolution**: The explosive growth of Weeks 2-3 (+158.6% submissions) has stabilized into steady expansion (+52.7% this week), indicating the competition is entering its mature phase.\n\n**🎯 Competitive Intensity**: At 4.8 submissions per team, participants are optimizing their approaches rather than simply increasing volume, suggesting a focus shift from exploration to exploitation.\n\n**📊 Score Progression**: The tightening score distributions and smaller week-over-week improvements indicate teams are approaching performance plateaus, making every gain more valuable.\n\n**🔄 Team Dynamics**: With 20 new teams joining this week and strong retention from previous weeks, the competition maintains healthy participant diversity.\n\n**Looking Ahead**: The second half promises intensified competition as teams leverage refined strategies, ensemble methods, and optimized validation techniques. Every submission will count more as the margin for improvement narrows.\n\n**Next Update:** Week 5 Progress Report (September 22, 2025)\n\n---\n\n**🔥 Good luck to all 81 teams as we enter the second half! The race to the top is about to get even more intense! 🔥**\n\n---\n\n## Resources & Competition Links\n\n**Competition Resources:**\n- [Competition Homepage](https://www.kaggle.com/competitions/grand-xray-slam-division-a) \n- [Discussion Forum](https://www.kaggle.com/competitions/grand-xray-slam-division-a/discussion?sort=hotness)\n- [Week 3 Progress Report](https://www.kaggle.com/code/guntasdhanjal/division-a-week-3-158-submission-surge)\n\n**Related Competitions:**\n- [Grand X-Ray Slam Division B](https://www.kaggle.com/competitions/grand-xray-slam-division-b)","metadata":{}}]}