{"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":12993937,"sourceType":"datasetVersion","datasetId":8224833}],"dockerImageVersionId":31089,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"*Competition Progress Analysis ā€¢ Week 3 of 8 ā€¢ September 2025*\n\n---\n\n## Executive Summary\n\n**Grand X-Ray Slam Division A** enters its third week with explosive growth and intensifying competition. The field has nearly doubled in size with significant improvements in top scores.\n\n| Metric | Week 2 (Day 11) | Week 3 (Day 18) | Growth |\n|--------|-----------------|-----------------|--------|\n| **Total Entrants** | 217 | 320 | +103 (+47.5%) |\n| **Active Participants** | 34 | 63 | +29 (+85.3%) |\n| **Competing Teams** | 34 | 61 | +27 (+79.4%) |\n| **Total Submissions** | 99 | 256 | +157 (+158.6%) |\n| **Competition Period** | 8 weeks (Day 18 of 50) | | |\n| **Deadline** | October 10, 2025 | | |\n\n","metadata":{}},{"cell_type":"markdown","source":"---\n## Setup & Data Loading","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":"2025-09-08T09:46:25.846455Z","iopub.execute_input":"2025-09-08T09:46:25.846781Z","iopub.status.idle":"2025-09-08T09:46:31.000397Z","shell.execute_reply.started":"2025-09-08T09:46:25.846757Z","shell.execute_reply":"2025-09-08T09:46:30.999295Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Load both weeks' data for comparison\ndf_w2 = pd.read_csv('/kaggle/input/division-a-leaderboard-w3/Leaderboard_A_W2.csv')\ndf_w3 = pd.read_csv('/kaggle/input/division-a-leaderboard-w3/Leaderboard_A_W3.csv')\n\nprint(f\"Week 2 leaderboard: {len(df_w2)} teams\")\nprint(f\"Week 3 leaderboard: {len(df_w3)} teams\")\nprint(f\"New teams this week: {len(df_w3) - len(df_w2)}\")\n\nprint(\"\\nšŸ“Š WEEK 3 TOP 5 TEAMS:\")\nprint(\"=\" * 60)\ntop_5 = df_w3.head()\nfor _, row in top_5.iterrows():\n print(f\"{row['Rank']:2d}. {row['TeamName'][:25]:<25} | Score: {row['Score']:.6f}\")\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-09-08T09:46:31.002087Z","iopub.execute_input":"2025-09-08T09:46:31.002698Z","iopub.status.idle":"2025-09-08T09:46:31.023069Z","shell.execute_reply.started":"2025-09-08T09:46:31.00267Z","shell.execute_reply":"2025-09-08T09:46:31.022207Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"---\n\n## Growth Analysis","metadata":{}},{"cell_type":"code","source":"# Week-over-week 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 'Growth': [103, 29, 27, 157],\n 'Growth %': [47.5, 85.3, 79.4, 158.6]\n}\n\nmetrics_df = pd.DataFrame(metrics_comparison)\n\nfig = make_subplots(\n rows=2, cols=2,\n subplot_titles=('Growth in Numbers', 'Growth Percentage', 'Submission Trends', 'Team Activity'),\n specs=[[{'type': 'bar'}, {'type': 'bar'}], \n [{'type': 'scatter'}, {'type': 'histogram'}]]\n)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-09-08T09:46:31.024077Z","iopub.execute_input":"2025-09-08T09:46:31.024316Z","iopub.status.idle":"2025-09-08T09:46:31.122446Z","shell.execute_reply.started":"2025-09-08T09:46:31.024295Z","shell.execute_reply":"2025-09-08T09:46:31.121448Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Growth in absolute numbers\nfig.add_trace(\n go.Bar(x=metrics_df['Metric'], y=metrics_df['Week 2'], name='Week 2', marker_color='lightblue'),\n row=1, col=1\n)\nfig.add_trace(\n go.Bar(x=metrics_df['Metric'], y=metrics_df['Week 3'], name='Week 3', marker_color='darkblue'),\n row=1, col=1\n)\n\n# Growth percentage\nfig.add_trace(\n go.Bar(x=metrics_df['Metric'], y=metrics_df['Growth %'], \n name='Growth %', marker_color='green', showlegend=False),\n row=1, col=2\n)\n\n# Submission trends\nweeks = ['Week 2', 'Week 3']\nsubmissions = [99, 256]\nfig.add_trace(\n go.Scatter(x=weeks, y=submissions, mode='lines+markers', \n name='Submissions', line=dict(width=4), showlegend=False),\n row=2, col=1\n)\n\n# Team activity distribution\nfig.add_trace(\n go.Histogram(x=df_w3['SubmissionCount'], nbinsx=15, \n name='Team Activity', marker_color='orange', showlegend=False),\n row=2, col=2\n)\n\nfig.update_layout(\n height=800,\n title_text=\"šŸ“ˆ Week 2 ā†’ Week 3 Growth Analysis\",\n showlegend=True\n)\n\nfig.show()\n\nprint(\"šŸš€ GROWTH HIGHLIGHTS:\")\nprint(f\" ā€¢ Submissions surged by {metrics_df.loc[3, 'Growth %']:.1f}% - highest growth metric\")\nprint(f\" ā€¢ Active participants increased by {metrics_df.loc[1, 'Growth %']:.1f}%\")\nprint(f\" ā€¢ Competition intensity: {(256/61):.1f} submissions per team avg\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-09-08T09:46:31.124142Z","iopub.execute_input":"2025-09-08T09:46:31.124538Z","iopub.status.idle":"2025-09-08T09:46:31.484601Z","shell.execute_reply.started":"2025-09-08T09:46:31.124513Z","shell.execute_reply":"2025-09-08T09:46:31.483679Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"---\n\n## Leaderboard Evolution","metadata":{}},{"cell_type":"code","source":"# Merge dataframes to track position changes\ndf_w2['Week'] = 2\ndf_w3['Week'] = 3","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-09-08T09:46:31.485588Z","iopub.execute_input":"2025-09-08T09:46:31.485951Z","iopub.status.idle":"2025-09-08T09:46:31.491916Z","shell.execute_reply.started":"2025-09-08T09:46:31.485918Z","shell.execute_reply":"2025-09-08T09:46:31.490953Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Find teams that existed in both weeks\nw2_teams = set(df_w2['TeamName'])\nw3_teams = set(df_w3['TeamName'])\ncontinuing_teams = w2_teams.intersection(w3_teams)\nnew_teams = w3_teams - w2_teams\n\nprint(f\"šŸ“Š LEADERBOARD DYNAMICS:\")\nprint(f\" ā€¢ Continuing teams: {len(continuing_teams)}\")\nprint(f\" ā€¢ New teams this week: {len(new_teams)}\")\nprint(f\" ā€¢ Teams that stopped competing: {len(w2_teams - w3_teams)}\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-09-08T09:46:31.492801Z","iopub.execute_input":"2025-09-08T09:46:31.493057Z","iopub.status.idle":"2025-09-08T09:46:31.509841Z","shell.execute_reply.started":"2025-09-08T09:46:31.493036Z","shell.execute_reply":"2025-09-08T09:46:31.508957Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Score improvements for continuing teams\nimprovements = []\nfor team in continuing_teams:\n w2_score = df_w2[df_w2['TeamName'] == team]['Score'].iloc[0]\n w3_score = df_w3[df_w3['TeamName'] == team]['Score'].iloc[0]\n w2_rank = df_w2[df_w2['TeamName'] == team]['Rank'].iloc[0]\n w3_rank = df_w3[df_w3['TeamName'] == team]['Rank'].iloc[0]\n \n improvements.append({\n 'TeamName': team,\n 'W2_Score': w2_score,\n 'W3_Score': w3_score,\n 'Score_Improvement': w3_score - w2_score,\n 'W2_Rank': w2_rank,\n 'W3_Rank': w3_rank,\n 'Rank_Change': w2_rank - w3_rank # Positive = improved rank\n })\n\nimprovements_df = pd.DataFrame(improvements)\ntop_improvers = improvements_df.nlargest(5, 'Score_Improvement')\n\nprint(f\"\\nšŸ† TOP 5 SCORE IMPROVERS (Week 2 ā†’ Week 3):\")\nprint(\"=\" * 70)\nfor _, row in top_improvers.iterrows():\n print(f\"{row['TeamName'][:20]:<20} | +{row['Score_Improvement']:.6f} | Rank: {row['W2_Rank']} ā†’ {row['W3_Rank']}\")\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-09-08T09:46:31.510832Z","iopub.execute_input":"2025-09-08T09:46:31.511175Z","iopub.status.idle":"2025-09-08T09:46:31.578717Z","shell.execute_reply.started":"2025-09-08T09:46:31.511153Z","shell.execute_reply":"2025-09-08T09:46:31.577942Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Visualization of score improvements\nfig = go.Figure()\n\n# Continuing teams\nfig.add_trace(go.Scatter(\n x=improvements_df['W2_Score'],\n y=improvements_df['W3_Score'],\n mode='markers',\n marker=dict(size=8, color='blue', opacity=0.7),\n name='Continuing Teams',\n text=improvements_df['TeamName'],\n hovertemplate='%{text}
Week 2: %{x:.6f}
Week 3: %{y:.6f}'\n))","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-09-08T09:46:31.58017Z","iopub.execute_input":"2025-09-08T09:46:31.58057Z","iopub.status.idle":"2025-09-08T09:46:31.639342Z","shell.execute_reply.started":"2025-09-08T09:46:31.580541Z","shell.execute_reply":"2025-09-08T09:46:31.638427Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# New teams\nnew_teams_data = df_w3[df_w3['TeamName'].isin(new_teams)]\nfig.add_trace(go.Scatter(\n x=[0.5] * len(new_teams_data), # Placeholder x-value for new teams\n y=new_teams_data['Score'],\n mode='markers',\n marker=dict(size=8, color='red', opacity=0.7),\n name='New Teams',\n text=new_teams_data['TeamName'],\n hovertemplate='%{text}
New Team
Score: %{y:.6f}'\n))","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-09-08T09:46:31.640268Z","iopub.execute_input":"2025-09-08T09:46:31.640522Z","iopub.status.idle":"2025-09-08T09:46:31.695752Z","shell.execute_reply.started":"2025-09-08T09:46:31.640502Z","shell.execute_reply":"2025-09-08T09:46:31.694949Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Add diagonal line for reference (no improvement)\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=\"šŸŽÆ Team Performance Evolution (Week 2 ā†’ Week 3)\",\n xaxis_title=\"Week 2 Score\",\n yaxis_title=\"Week 3 Score\",\n height=600\n)\n\nfig.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-09-08T09:46:31.698267Z","iopub.execute_input":"2025-09-08T09:46:31.698524Z","iopub.status.idle":"2025-09-08T09:46:31.757266Z","shell.execute_reply.started":"2025-09-08T09:46:31.698505Z","shell.execute_reply":"2025-09-08T09:46:31.75624Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"---\n\n## Current Week 3 Standings","metadata":{}},{"cell_type":"code","source":"# Convert last submission date for analysis\ndf_w3['LastSubmissionDate'] = pd.to_datetime(df_w3['LastSubmissionDate'])\ndf_w3['DaysAgo'] = (datetime.now() - df_w3['LastSubmissionDate']).dt.days","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-09-08T09:46:31.758138Z","iopub.execute_input":"2025-09-08T09:46:31.758408Z","iopub.status.idle":"2025-09-08T09:46:31.768495Z","shell.execute_reply.started":"2025-09-08T09:46:31.758387Z","shell.execute_reply":"2025-09-08T09:46:31.767394Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Current standings visualization\nfig = make_subplots(\n rows=1, cols=2,\n subplot_titles=('Top 15 Teams Performance', 'Score Distribution'),\n specs=[[{'type': 'bar'}, {'type': 'histogram'}]]\n)\n\ntop_15 = df_w3.head(15)\nfig.add_trace(\n go.Bar(\n x=top_15['Score'],\n y=top_15['TeamName'],\n orientation='h',\n marker_color='lightcoral',\n text=[f\"#{rank}\" for rank in top_15['Rank']],\n textposition='inside'\n ),\n row=1, col=1\n)\n\nfig.add_trace(\n go.Histogram(\n x=df_w3['Score'],\n nbinsx=20,\n marker_color='skyblue',\n opacity=0.7\n ),\n row=1, col=2\n)\n\nfig.update_layout(\n height=600,\n title_text=\"šŸ“‹ Grand X-Ray Slam Division A - Week 3 Current Standings\",\n showlegend=False\n)\n\nfig.show()\n\nprint(f\"\\nšŸ“ˆ WEEK 3 PERFORMANCE METRICS:\")\nprint(f\" Leading Score: {df_w3['Score'].max():.6f}\")\nprint(f\" Average Score: {df_w3['Score'].mean():.6f}\")\nprint(f\" Score Range: {df_w3['Score'].min():.6f} - {df_w3['Score'].max():.6f}\")\nprint(f\" Most submissions: {df_w3['SubmissionCount'].max()} by {df_w3.loc[df_w3['SubmissionCount'].idxmax(), 'TeamName']}\")\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-09-08T09:46:31.769723Z","iopub.execute_input":"2025-09-08T09:46:31.770549Z","iopub.status.idle":"2025-09-08T09:46:31.858352Z","shell.execute_reply.started":"2025-09-08T09:46:31.770526Z","shell.execute_reply":"2025-09-08T09:46:31.857417Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"---\n\n## Activity Heatmap & Submission Patterns","metadata":{}},{"cell_type":"code","source":"# Recent activity analysis\nrecent_submissions = df_w3[df_w3['DaysAgo'] <= 2]\nactive_teams = len(recent_submissions)\n\nprint(f\"šŸ”„ WEEK 3 ACTIVITY HIGHLIGHTS:\")\nprint(\"=\" * 50)\nprint(f\" ā€¢ {active_teams} teams submitted in last 48h\")\nprint(f\" ā€¢ Leading team: {df_w3.iloc[0]['TeamName']}\")\nprint(f\" ā€¢ Current best score: {df_w3.iloc[0]['Score']:.6f}\")\nprint(f\" ā€¢ Average submissions per team: {df_w3['SubmissionCount'].mean():.1f}\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-09-08T09:46:31.859296Z","iopub.execute_input":"2025-09-08T09:46:31.859605Z","iopub.status.idle":"2025-09-08T09:46:31.86737Z","shell.execute_reply.started":"2025-09-08T09:46:31.859575Z","shell.execute_reply":"2025-09-08T09:46:31.86625Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Competition intensity calculation\nintensity_score = (df_w3['SubmissionCount'].sum() / len(df_w3)) * (active_teams / len(df_w3))\nprint(f\" ā€¢ Competition Intensity: {intensity_score:.2f}/1.0\")\n\n# Activity level categorization\ndf_w3['ActivityLevel'] = df_w3['SubmissionCount'].apply(\n lambda x: 'High (5+)' if x >= 5 else 'Medium (2-4)' if x >= 2 else 'Low (1)'\n)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-09-08T09:46:31.86821Z","iopub.execute_input":"2025-09-08T09:46:31.868455Z","iopub.status.idle":"2025-09-08T09:46:31.888536Z","shell.execute_reply.started":"2025-09-08T09:46:31.868436Z","shell.execute_reply":"2025-09-08T09:46:31.887491Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Team performance landscape\nfig = go.Figure()\n\nfig.add_trace(go.Scatter(\n x=list(range(1, len(df_w3)+1)),\n y=df_w3['Score'],\n mode='markers+lines',\n marker=dict(\n size=df_w3['SubmissionCount']*2,\n color=df_w3['SubmissionCount'],\n colorscale='viridis',\n showscale=True,\n colorbar=dict(title=\"Submissions\")\n ),\n line=dict(width=1, color='lightgray'),\n text=df_w3['TeamName'],\n hovertemplate='%{text}
Rank: %{x}
Score: %{y:.6f}
Submissions: %{marker.color}'\n))\n\nfig.update_layout(\n title='šŸ—ŗļø Team Performance Landscape (Bubble size = Submissions)',\n xaxis_title='Team Rank',\n yaxis_title='Score',\n height=500,\n hovermode='closest'\n)\n\nfig.show()\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-09-08T09:46:31.889486Z","iopub.execute_input":"2025-09-08T09:46:31.889759Z","iopub.status.idle":"2025-09-08T09:46:31.962285Z","shell.execute_reply.started":"2025-09-08T09:46:31.88974Z","shell.execute_reply":"2025-09-08T09:46:31.961326Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Activity distribution\nactivity_counts = df_w3['ActivityLevel'].value_counts()\nfig_activity = px.pie(values=activity_counts.values, names=activity_counts.index,\n title=\"Team Activity Distribution\",\n color_discrete_map={'High (5+)': 'red', 'Medium (2-4)': 'orange', 'Low (1)': 'lightblue'})\nfig_activity.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-09-08T09:46:31.963528Z","iopub.execute_input":"2025-09-08T09:46:31.963867Z","iopub.status.idle":"2025-09-08T09:46:32.118402Z","shell.execute_reply.started":"2025-09-08T09:46:31.963837Z","shell.execute_reply":"2025-09-08T09:46:32.117456Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"---\n\n## Timeline & Progress Update","metadata":{}},{"cell_type":"code","source":"# Timeline data\ntimeline_data = {\n 'Phase': ['Competition Start', 'Week 1 Complete', 'Week 2 Complete', 'Current (Week 3)', 'Week 3 Complete', 'Final Submission', 'Results'],\n 'Date': ['Aug 21', 'Aug 28', 'Sep 4', 'Sep 8', 'Sep 11', 'Oct 10', 'Oct 12'],\n 'Status': ['Complete', 'Complete', 'Complete', 'Active', 'Upcoming', 'Upcoming', 'Upcoming']\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='šŸ“… Competition Timeline',\n xaxis_title='Date',\n height=300,\n showlegend=True\n)\nfig.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-09-08T09:46:32.119303Z","iopub.execute_input":"2025-09-08T09:46:32.119683Z","iopub.status.idle":"2025-09-08T09:46:32.186254Z","shell.execute_reply.started":"2025-09-08T09:46:32.119655Z","shell.execute_reply":"2025-09-08T09:46:32.185071Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Progress calculation\ntotal_days = 50 # Aug 21 to Oct 10\ndays_passed = 18 # Aug 21 to Sep 8\ndays_left = total_days - days_passed \nprogress_pct = (days_passed / total_days) * 100\n\n# Progress bar\nfig_progress = go.Figure(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\"{progress_pct:.1f}% complete\"],\n textposition=\"inside\",\n marker=dict(color=\"green\"),\n width=0.5,\n name=\"Days Passed\"\n))\n\nfig_progress.update_layout(\n title=f\"Competition Progress (Day {days_passed} of {total_days})\",\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\"ā° REMAINING TIME:\")\nprint(f\" ā€¢ Days until deadline: {days_left} days\")\nprint(f\" ā€¢ Competition progress: {progress_pct:.1f}% complete\")\nprint(f\" ā€¢ Time remaining: {(days_left/7):.1f} weeks\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-09-08T09:46:32.187662Z","iopub.execute_input":"2025-09-08T09:46:32.188081Z","iopub.status.idle":"2025-09-08T09:46:32.244682Z","shell.execute_reply.started":"2025-09-08T09:46:32.18805Z","shell.execute_reply":"2025-09-08T09:46:32.243861Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"---\n\n## Performance Insights & Analysis","metadata":{}},{"cell_type":"code","source":"# Score progression analysis\nprint(f\"šŸ” PERFORMANCE INSIGHTS:\")\nprint(\"=\" * 50)\n\n# Compare week 2 vs week 3 top scores\nw2_top_score = df_w2['Score'].max()\nw3_top_score = df_w3['Score'].max()\nscore_improvement = w3_top_score - w2_top_score\n\nprint(f\" ā€¢ Top score improvement: +{score_improvement:.6f} ({(score_improvement/w2_top_score*100):.2f}%)\")\nprint(f\" ā€¢ Week 2 best: {w2_top_score:.6f}\")\nprint(f\" ā€¢ Week 3 best: {w3_top_score:.6f}\")\n\n# Submission efficiency analysis\ndf_w3['Efficiency'] = df_w3['Score'] / df_w3['SubmissionCount']\ntop_efficient = df_w3.nlargest(5, 'Efficiency')\n\nprint(f\"\\nšŸŽÆ 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 trends\navg_score_w2 = df_w2['Score'].mean()\navg_score_w3 = df_w3['Score'].mean()\navg_improvement = avg_score_w3 - avg_score_w2\n\nprint(f\"\\nšŸ“Š OVERALL TRENDS:\")\nprint(f\" ā€¢ Average score improvement: +{avg_improvement:.6f}\")\nprint(f\" ā€¢ Score spread (std): {df_w3['Score'].std():.6f}\")\nprint(f\" ā€¢ Teams above 0.90: {len(df_w3[df_w3['Score'] > 0.90])}\")\nprint(f\" ā€¢ Teams above 0.85: {len(df_w3[df_w3['Score'] > 0.85])}\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-09-08T09:46:32.245632Z","iopub.execute_input":"2025-09-08T09:46:32.24592Z","iopub.status.idle":"2025-09-08T09:46:32.263286Z","shell.execute_reply.started":"2025-09-08T09:46:32.24587Z","shell.execute_reply":"2025-09-08T09:46:32.262461Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"---\n\n## Week 4 Predictions & What's Next\n","metadata":{}},{"cell_type":"code","source":"print(f\"šŸ”® WEEK 4 PREDICTIONS:\")\nprint(\"=\" * 50)\n\n# Growth rate calculations\nentrant_growth_rate = (320 - 217) / 217\nteam_growth_rate = (61 - 34) / 34\nsubmission_growth_rate = (256 - 99) / 99\n\n# Project Week 4 numbers\nprojected_entrants = int(320 * (1 + entrant_growth_rate * 0.7)) # Assuming growth slows\nprojected_teams = int(61 * (1 + team_growth_rate * 0.6))\nprojected_submissions = int(256 * (1 + submission_growth_rate * 0.5))\n\nprint(f\"šŸ“ˆ PROJECTED WEEK 4 NUMBERS:\")\nprint(f\" ā€¢ Estimated entrants: {projected_entrants:,}\")\nprint(f\" ā€¢ Estimated teams: {projected_teams}\")\nprint(f\" ā€¢ Estimated submissions: {projected_submissions}\")\n\nprint(f\"\\nšŸŽÆ KEY AREAS TO WATCH:\")\nprint(f\" ā€¢ Score plateau detection: Will teams break {w3_top_score:.6f}?\")\nprint(f\" ā€¢ Ensemble methods: Expected surge in advanced techniques\")\nprint(f\" ā€¢ Late-entry performance: How new teams adapt quickly\")\nprint(f\" ā€¢ Submission efficiency: Quality vs quantity strategies\")\n\nprint(f\"\\nšŸš€ UPCOMING MILESTONES:\")\nprint(f\" ā€¢ Target: Break 0.95 barrier\")\nprint(f\" ā€¢ Watch: Top 10 teams' submission patterns\")\nprint(f\" ā€¢ Focus: Cross-validation optimization phase\")\nprint(f\" ā€¢ Trend: Expected ensemble model implementations\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-09-08T09:46:32.264107Z","iopub.execute_input":"2025-09-08T09:46:32.26433Z","iopub.status.idle":"2025-09-08T09:46:32.284575Z","shell.execute_reply.started":"2025-09-08T09:46:32.264312Z","shell.execute_reply":"2025-09-08T09:46:32.283293Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"---\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 2 Progress Report](https://www.kaggle.com/code/guntasdhanjal/x-ray-slam-division-a-week-2-battle-report)\n\n**Related Competitions:**\n- [Grand X-Ray Slam Division B](https://www.kaggle.com/competitions/grand-xray-slam-division-b)\n\n---\n\n## Summary\n\nWeek 3 has shown remarkable growth across all metrics, with submission volume increasing by 158.6% and nearly doubling the number of active teams. The competition is entering a critical phase where efficiency and advanced techniques will likely separate the leaders from the pack.\n\n**Key Takeaways:**\n- Competition intensity is at an all-time high\n- Score improvements are becoming more incremental, suggesting plateau approaches\n- New teams are entering with competitive scores, indicating knowledge sharing\n- The race for the top is tightening with smaller score gaps\n\n**Next Update:** Week 4 Progress Report (September 15, 2025)\n\n---\n\n**Good luck to all 61 teams! The race is heating up! šŸ”„**","metadata":{}}]}