diff --git a/.gitignore b/.gitignore
index 4c49bd7..ee79715 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1 +1,2 @@
 .env
+*.pyc
\ No newline at end of file
diff --git a/ai_intelligence_layer/models/__pycache__/input_models.cpython-313.pyc b/ai_intelligence_layer/models/__pycache__/input_models.cpython-313.pyc
index fb3dd05..7e69982 100644
Binary files a/ai_intelligence_layer/models/__pycache__/input_models.cpython-313.pyc and b/ai_intelligence_layer/models/__pycache__/input_models.cpython-313.pyc differ
diff --git a/ai_intelligence_layer/models/input_models.py b/ai_intelligence_layer/models/input_models.py
index ef0ba66..797dcbb 100644
--- a/ai_intelligence_layer/models/input_models.py
+++ b/ai_intelligence_layer/models/input_models.py
@@ -14,6 +14,8 @@ class EnrichedTelemetryWebhook(BaseModel):
     tire_cliff_risk: float = Field(..., ge=0.0, le=1.0, description="Probability of tire performance cliff (0..1)")
     optimal_pit_window: List[int] = Field(..., description="Recommended pit stop lap window [start, end]")
     performance_delta: float = Field(..., description="Lap time delta vs baseline (negative = slower)")
+    competitive_pressure: float = Field(..., ge=0.0, le=1.0, description="Competitive pressure from position and gaps (0..1)")
+    position_trend: Literal["gaining", "stable", "losing"] = Field(..., description="Position trend over recent laps")
 
 
 class RaceInfo(BaseModel):
@@ -32,6 +34,8 @@ class DriverState(BaseModel):
     current_tire_compound: Literal["soft", "medium", "hard", "intermediate", "wet"] = Field(..., description="Current tire compound")
     tire_age_laps: int = Field(..., ge=0, description="Laps on current tires")
     fuel_remaining_percent: float = Field(..., ge=0.0, le=100.0, description="Remaining fuel percentage")
+    gap_to_leader: Optional[float] = Field(default=0.0, description="Gap to race leader in seconds")
+    gap_to_ahead: Optional[float] = Field(default=0.0, description="Gap to car directly ahead in seconds")
 
 
 class Competitor(BaseModel):
diff --git a/ai_intelligence_layer/prompts/__pycache__/brainstorm_prompt.cpython-313.pyc b/ai_intelligence_layer/prompts/__pycache__/brainstorm_prompt.cpython-313.pyc
index be44da2..260ec82 100644
Binary files a/ai_intelligence_layer/prompts/__pycache__/brainstorm_prompt.cpython-313.pyc and b/ai_intelligence_layer/prompts/__pycache__/brainstorm_prompt.cpython-313.pyc differ
diff --git a/ai_intelligence_layer/prompts/brainstorm_prompt.py b/ai_intelligence_layer/prompts/brainstorm_prompt.py
index aeb66f3..d1c4ae9 100644
--- a/ai_intelligence_layer/prompts/brainstorm_prompt.py
+++ b/ai_intelligence_layer/prompts/brainstorm_prompt.py
@@ -17,15 +17,25 @@ def build_brainstorm_prompt_fast(
     latest = max(enriched_telemetry, key=lambda x: x.lap)
     pit_window = latest.optimal_pit_window
     
+    # Format position and competitive info
+    position = race_context.driver_state.current_position
+    gap_to_leader = race_context.driver_state.gap_to_leader
+    gap_to_ahead = race_context.driver_state.gap_to_ahead
+    comp_info = f"P{position}"
+    if gap_to_ahead > 0:
+        comp_info += f", {gap_to_ahead:.1f}s behind P{position-1}"
+    if gap_to_leader > 0 and position > 1:
+        comp_info += f", {gap_to_leader:.1f}s from leader"
+    
     if count == 1:
         # Ultra-fast mode: just generate 1 strategy
         return f"""Generate 1 F1 race strategy for {race_context.driver_state.driver_name} at {race_context.race_info.track_name}.
 
-CURRENT: Lap {race_context.race_info.current_lap}/{race_context.race_info.total_laps}, P{race_context.driver_state.current_position}, {race_context.driver_state.current_tire_compound} tires ({race_context.driver_state.tire_age_laps} laps old)
+CURRENT: Lap {race_context.race_info.current_lap}/{race_context.race_info.total_laps}, {comp_info}, {race_context.driver_state.current_tire_compound} tires ({race_context.driver_state.tire_age_laps} laps old)
 
-TELEMETRY: Tire deg rate {latest.tire_degradation_rate:.2f}, Cliff risk {latest.tire_cliff_risk:.2f}, Pace {latest.pace_trend}, Pit window laps {pit_window[0]}-{pit_window[1]}
+TELEMETRY: Tire deg {latest.tire_degradation_rate:.2f}, Cliff risk {latest.tire_cliff_risk:.2f}, Pace {latest.pace_trend}, Position trend {latest.position_trend}, Competitive pressure {latest.competitive_pressure:.2f}
 
-Generate 1 optimal strategy. Min 2 tire compounds required.
+Generate 1 optimal strategy considering competitive position. Min 2 tire compounds required.
 
 JSON: {{"strategies": [{{"strategy_id": 1, "strategy_name": "name", "stop_count": 1, "pit_laps": [32], "tire_sequence": ["medium", "hard"], "brief_description": "one sentence", "risk_level": "medium", "key_assumption": "main assumption"}}]}}"""
     
@@ -33,21 +43,25 @@ JSON: {{"strategies": [{{"strategy_id": 1, "strategy_name": "name", "stop_count"
         # Fast mode: 2-5 strategies with different approaches
         return f"""Generate {count} diverse F1 race strategies for {race_context.driver_state.driver_name} at {race_context.race_info.track_name}.
 
-CURRENT: Lap {race_context.race_info.current_lap}/{race_context.race_info.total_laps}, P{race_context.driver_state.current_position}, {race_context.driver_state.current_tire_compound} tires ({race_context.driver_state.tire_age_laps} laps old)
+CURRENT: Lap {race_context.race_info.current_lap}/{race_context.race_info.total_laps}, {comp_info}, {race_context.driver_state.current_tire_compound} tires ({race_context.driver_state.tire_age_laps} laps old)
 
-TELEMETRY: Tire deg {latest.tire_degradation_rate:.2f}, Cliff risk {latest.tire_cliff_risk:.2f}, Pace {latest.pace_trend}, Delta {latest.performance_delta:+.2f}s, Pit window {pit_window[0]}-{pit_window[1]}
+TELEMETRY: Tire deg {latest.tire_degradation_rate:.2f}, Cliff risk {latest.tire_cliff_risk:.2f}, Pace {latest.pace_trend}, Delta {latest.performance_delta:+.2f}s, Position trend {latest.position_trend}, Competitive pressure {latest.competitive_pressure:.2f}
 
-Generate {count} strategies: conservative (1-stop), standard (1-2 stop), aggressive (undercut). Min 2 tire compounds each.
+COMPETITIVE SITUATION: Gap to ahead {gap_to_ahead:.1f}s ({"DRS RANGE - attack opportunity!" if gap_to_ahead < 1.0 else "close battle" if gap_to_ahead < 3.0 else "need to push"})
+
+Generate {count} strategies balancing tire management with competitive pressure. Consider if aggressive undercut makes sense given gaps. Min 2 tire compounds each.
 
 JSON: {{"strategies": [{{"strategy_id": 1, "strategy_name": "Conservative Stay Out", "stop_count": 1, "pit_laps": [35], "tire_sequence": ["medium", "hard"], "brief_description": "extend current stint then hard tires to end", "risk_level": "low", "key_assumption": "tire cliff risk stays below 0.7"}}]}}"""
     
     return f"""Generate {count} F1 race strategies for {race_context.driver_state.driver_name} at {race_context.race_info.track_name}.
 
-CURRENT: Lap {race_context.race_info.current_lap}/{race_context.race_info.total_laps}, P{race_context.driver_state.current_position}, {race_context.driver_state.current_tire_compound} tires ({race_context.driver_state.tire_age_laps} laps old)
+CURRENT: Lap {race_context.race_info.current_lap}/{race_context.race_info.total_laps}, {comp_info}, {race_context.driver_state.current_tire_compound} tires ({race_context.driver_state.tire_age_laps} laps old)
 
-TELEMETRY: Tire deg rate {latest.tire_degradation_rate:.2f}, Cliff risk {latest.tire_cliff_risk:.2f}, Pace {latest.pace_trend}, Performance delta {latest.performance_delta:+.2f}s, Pit window laps {pit_window[0]}-{pit_window[1]}
+TELEMETRY: Tire deg {latest.tire_degradation_rate:.2f}, Cliff risk {latest.tire_cliff_risk:.2f}, Pace {latest.pace_trend}, Delta {latest.performance_delta:+.2f}s, Position trend {latest.position_trend}, Competitive pressure {latest.competitive_pressure:.2f}
 
-Generate {count} diverse strategies. Min 2 compounds.
+COMPETITIVE: Gap ahead {gap_to_ahead:.1f}s, Position trending {latest.position_trend}
+
+Generate {count} diverse strategies considering both tire management AND competitive positioning. Min 2 compounds.
 
 JSON: {{"strategies": [{{"strategy_id": 1, "strategy_name": "name", "stop_count": 1, "pit_laps": [32], "tire_sequence": ["medium", "hard"], "brief_description": "one sentence", "risk_level": "low|medium|high|critical", "key_assumption": "main assumption"}}]}}"""
 
@@ -78,7 +92,9 @@ def build_brainstorm_prompt(
             "pace_trend": t.pace_trend,
             "tire_cliff_risk": round(t.tire_cliff_risk, 3),
             "optimal_pit_window": t.optimal_pit_window,
-            "performance_delta": round(t.performance_delta, 2)
+            "performance_delta": round(t.performance_delta, 2),
+            "competitive_pressure": round(t.competitive_pressure, 3),
+            "position_trend": t.position_trend
         })
     
     # Format competitors
@@ -92,12 +108,14 @@ def build_brainstorm_prompt(
             "gap_seconds": round(c.gap_seconds, 1)
         })
     
-    prompt = f"""You are an expert F1 strategist. Generate 20 diverse race strategies based on lap-level telemetry.
+    prompt = f"""You are an expert F1 strategist. Generate 20 diverse race strategies based on lap-level telemetry AND competitive positioning.
 
 LAP-LEVEL TELEMETRY METRICS:
 - tire_degradation_rate: 0-1 (higher = worse tire wear)
 - tire_cliff_risk: 0-1 (probability of hitting tire cliff)
 - pace_trend: "improving", "stable", or "declining"
+- position_trend: "gaining", "stable", or "losing" positions
+- competitive_pressure: 0-1 (combined metric from position and gaps)
 - optimal_pit_window: [start_lap, end_lap] recommended pit range
 - performance_delta: seconds vs baseline (negative = slower)
 
diff --git a/fetch_data.ipynb b/fetch_data.ipynb
deleted file mode 100644
index 5eaaf14..0000000
--- a/fetch_data.ipynb
+++ /dev/null
@@ -1,653 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "id": "9a9714f8",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "logger      WARNING \tFailed to load schedule from FastF1 backend!\n",
-      "req            INFO \tUsing cached data for season_schedule\n",
-      "req            INFO \tUsing cached data for season_schedule\n",
-      "core           INFO \tLoading data for Italian Grand Prix - Race [v3.6.1]\n",
-      "req            INFO \tUsing cached data for session_info\n",
-      "req            INFO \tUsing cached data for driver_info\n",
-      "core           INFO \tLoading data for Italian Grand Prix - Race [v3.6.1]\n",
-      "req            INFO \tUsing cached data for session_info\n",
-      "req            INFO \tUsing cached data for driver_info\n",
-      "req            INFO \tUsing cached data for session_status_data\n",
-      "req            INFO \tUsing cached data for lap_count\n",
-      "req            INFO \tUsing cached data for track_status_data\n",
-      "req            INFO \tUsing cached data for session_status_data\n",
-      "req            INFO \tUsing cached data for lap_count\n",
-      "req            INFO \tUsing cached data for track_status_data\n",
-      "req            INFO \tUsing cached data for _extended_timing_data\n",
-      "req            INFO \tUsing cached data for timing_app_data\n",
-      "core           INFO \tProcessing timing data...\n",
-      "req            INFO \tUsing cached data for _extended_timing_data\n",
-      "req            INFO \tUsing cached data for timing_app_data\n",
-      "core           INFO \tProcessing timing data...\n",
-      "req            INFO \tUsing cached data for car_data\n",
-      "req            INFO \tUsing cached data for car_data\n",
-      "req            INFO \tUsing cached data for position_data\n",
-      "req            INFO \tUsing cached data for position_data\n",
-      "req            INFO \tUsing cached data for weather_data\n",
-      "req            INFO \tUsing cached data for race_control_messages\n",
-      "core        WARNING \tDriver 55 completed the race distance 06:14.695000 before the recorded end of the session.\n",
-      "core        WARNING \tDriver 4 completed the race distance 05:40.439000 before the recorded end of the session.\n",
-      "core        WARNING \tDriver 44 completed the race distance 05:48.209000 before the recorded end of the session.\n",
-      "core        WARNING \tDriver 16 completed the race distance 06:14.511000 before the recorded end of the session.\n",
-      "core        WARNING \tDriver 1 completed the race distance 06:25.888000 before the recorded end of the session.\n",
-      "req            INFO \tUsing cached data for weather_data\n",
-      "req            INFO \tUsing cached data for race_control_messages\n",
-      "core        WARNING \tDriver 55 completed the race distance 06:14.695000 before the recorded end of the session.\n",
-      "core        WARNING \tDriver 4 completed the race distance 05:40.439000 before the recorded end of the session.\n",
-      "core        WARNING \tDriver 44 completed the race distance 05:48.209000 before the recorded end of the session.\n",
-      "core        WARNING \tDriver 16 completed the race distance 06:14.511000 before the recorded end of the session.\n",
-      "core        WARNING \tDriver 1 completed the race distance 06:25.888000 before the recorded end of the session.\n",
-      "core        WARNING \tDriver 11 completed the race distance 06:19.824000 before the recorded end of the session.\n",
-      "core        WARNING \tDriver 11 completed the race distance 06:19.824000 before the recorded end of the session.\n",
-      "core        WARNING \tDriver 23 completed the race distance 05:40.782000 before the recorded end of the session.\n",
-      "core        WARNING \tDriver 23 completed the race distance 05:40.782000 before the recorded end of the session.\n",
-      "core        WARNING \tDriver 14 completed the race distance 05:39.594000 before the recorded end of the session.\n",
-      "core        WARNING \tDriver 63 completed the race distance 06:07.860000 before the recorded end of the session.\n",
-      "core        WARNING \tDriver 14 completed the race distance 05:39.594000 before the recorded end of the session.\n",
-      "core        WARNING \tDriver 63 completed the race distance 06:07.860000 before the recorded end of the session.\n",
-      "core           INFO \tFinished loading data for 20 drivers: ['55', '4', '44', '16', '1', '10', '81', '11', '40', '20', '23', '24', '27', '2', '14', '63', '77', '31', '22', '18']\n",
-      "core           INFO \tFinished loading data for 20 drivers: ['55', '4', '44', '16', '1', '10', '81', '11', '40', '20', '23', '24', '27', '2', '14', '63', '77', '31', '22', '18']\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Created dataframe with 16584 rows\n",
-      "Total laps in race: 51.0\n",
-      "Laps covered: 1.0 to 51.0\n",
-      "Tire life range: 1.0 to 33.0 laps\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>lap_number</th>\n",
-       "      <th>total_laps</th>\n",
-       "      <th>speed</th>\n",
-       "      <th>overall_time</th>\n",
-       "      <th>throttle</th>\n",
-       "      <th>brake</th>\n",
-       "      <th>tire_compound</th>\n",
-       "      <th>tire_life_laps</th>\n",
-       "      <th>track_temperature</th>\n",
-       "      <th>rainfall</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>51.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0 days 01:22:21.734000</td>\n",
-       "      <td>23.0</td>\n",
-       "      <td>False</td>\n",
-       "      <td>MEDIUM</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>42.5</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>51.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0 days 01:22:21.894000</td>\n",
-       "      <td>23.0</td>\n",
-       "      <td>False</td>\n",
-       "      <td>MEDIUM</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>42.5</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>51.0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>0 days 01:22:22.214000</td>\n",
-       "      <td>26.0</td>\n",
-       "      <td>False</td>\n",
-       "      <td>MEDIUM</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>42.5</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>51.0</td>\n",
-       "      <td>14.0</td>\n",
-       "      <td>0 days 01:22:22.494000</td>\n",
-       "      <td>24.0</td>\n",
-       "      <td>False</td>\n",
-       "      <td>MEDIUM</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>42.5</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>51.0</td>\n",
-       "      <td>24.0</td>\n",
-       "      <td>0 days 01:22:22.774000</td>\n",
-       "      <td>24.0</td>\n",
-       "      <td>False</td>\n",
-       "      <td>MEDIUM</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>42.5</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>51.0</td>\n",
-       "      <td>31.0</td>\n",
-       "      <td>0 days 01:22:22.974000</td>\n",
-       "      <td>26.0</td>\n",
-       "      <td>False</td>\n",
-       "      <td>MEDIUM</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>42.5</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>51.0</td>\n",
-       "      <td>38.0</td>\n",
-       "      <td>0 days 01:22:23.254000</td>\n",
-       "      <td>36.0</td>\n",
-       "      <td>False</td>\n",
-       "      <td>MEDIUM</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>42.5</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>51.0</td>\n",
-       "      <td>50.0</td>\n",
-       "      <td>0 days 01:22:23.494000</td>\n",
-       "      <td>41.0</td>\n",
-       "      <td>False</td>\n",
-       "      <td>MEDIUM</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>42.5</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>51.0</td>\n",
-       "      <td>58.0</td>\n",
-       "      <td>0 days 01:22:23.694000</td>\n",
-       "      <td>44.0</td>\n",
-       "      <td>False</td>\n",
-       "      <td>MEDIUM</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>42.5</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>51.0</td>\n",
-       "      <td>71.0</td>\n",
-       "      <td>0 days 01:22:23.974000</td>\n",
-       "      <td>55.0</td>\n",
-       "      <td>False</td>\n",
-       "      <td>MEDIUM</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>42.5</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   lap_number  total_laps  speed           overall_time  throttle  brake  \\\n",
-       "0         1.0        51.0    0.0 0 days 01:22:21.734000      23.0  False   \n",
-       "1         1.0        51.0    0.0 0 days 01:22:21.894000      23.0  False   \n",
-       "2         1.0        51.0    4.0 0 days 01:22:22.214000      26.0  False   \n",
-       "3         1.0        51.0   14.0 0 days 01:22:22.494000      24.0  False   \n",
-       "4         1.0        51.0   24.0 0 days 01:22:22.774000      24.0  False   \n",
-       "5         1.0        51.0   31.0 0 days 01:22:22.974000      26.0  False   \n",
-       "6         1.0        51.0   38.0 0 days 01:22:23.254000      36.0  False   \n",
-       "7         1.0        51.0   50.0 0 days 01:22:23.494000      41.0  False   \n",
-       "8         1.0        51.0   58.0 0 days 01:22:23.694000      44.0  False   \n",
-       "9         1.0        51.0   71.0 0 days 01:22:23.974000      55.0  False   \n",
-       "\n",
-       "  tire_compound  tire_life_laps  track_temperature  rainfall  \n",
-       "0        MEDIUM             1.0               42.5     False  \n",
-       "1        MEDIUM             1.0               42.5     False  \n",
-       "2        MEDIUM             1.0               42.5     False  \n",
-       "3        MEDIUM             1.0               42.5     False  \n",
-       "4        MEDIUM             1.0               42.5     False  \n",
-       "5        MEDIUM             1.0               42.5     False  \n",
-       "6        MEDIUM             1.0               42.5     False  \n",
-       "7        MEDIUM             1.0               42.5     False  \n",
-       "8        MEDIUM             1.0               42.5     False  \n",
-       "9        MEDIUM             1.0               42.5     False  "
-      ]
-     },
-     "execution_count": 44,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "\"\"\"\n",
-    "FastF1 Data Fetcher for HPC F1 AI Strategy System\n",
-    "\n",
-    "Downloads telemetry and race data from a specific F1 session to simulate\n",
-    "live telemetry streaming from a Raspberry Pi \"racecar\" to the HPC layer.\n",
-    "\n",
-    "Usage:\n",
-    "    python fetch_race_data.py --year 2024 --race \"Monaco\" --driver VER --output data/\n",
-    "\"\"\"\n",
-    "import fastf1\n",
-    "import pandas as pd\n",
-    "\n",
-    "# 1. Load the session\n",
-    "session = fastf1.get_session(2023, 'Monza', 'R')\n",
-    "session.load(telemetry=True, laps=True, weather=True)\n",
-    "\n",
-    "# 2. Pick the driver\n",
-    "driver_laps = session.laps.pick_drivers('ALO')\n",
-    "\n",
-    "# Get total number of laps in the race (maximum lap number from all drivers)\n",
-    "total_laps = session.laps['LapNumber'].max()\n",
-    "\n",
-    "# 3. Collect all telemetry data with lap information\n",
-    "telemetry_list = []\n",
-    "\n",
-    "for lap_idx in driver_laps.index:\n",
-    "    lap = driver_laps.loc[lap_idx]\n",
-    "    lap_number = lap['LapNumber']\n",
-    "    tire_compound = lap['Compound']\n",
-    "    tire_life = lap['TyreLife']  # Number of laps on current tires\n",
-    "    \n",
-    "    # Get telemetry for this lap\n",
-    "    car_data = lap.get_car_data()\n",
-    "    \n",
-    "    if car_data is not None and len(car_data) > 0:\n",
-    "        # Add lap number, tire compound, and tire life to each telemetry point\n",
-    "        car_data['LapNumber'] = lap_number\n",
-    "        car_data['Compound'] = tire_compound\n",
-    "        car_data['TyreLife'] = tire_life\n",
-    "        telemetry_list.append(car_data)\n",
-    "\n",
-    "# 4. Combine all telemetry data\n",
-    "all_telemetry = pd.concat(telemetry_list, ignore_index=True)\n",
-    "\n",
-    "# 5. Get weather data\n",
-    "weather = session.weather_data\n",
-    "\n",
-    "# 6. Merge telemetry with weather based on timestamp\n",
-    "# First, ensure both have SessionTime column\n",
-    "all_telemetry['SessionTime'] = pd.to_timedelta(all_telemetry['SessionTime'])\n",
-    "weather['SessionTime'] = pd.to_timedelta(weather['Time'])\n",
-    "\n",
-    "# Merge using merge_asof for time-based joining\n",
-    "all_telemetry = all_telemetry.sort_values('SessionTime')\n",
-    "weather = weather.sort_values('SessionTime')\n",
-    "\n",
-    "merged_data = pd.merge_asof(\n",
-    "    all_telemetry,\n",
-    "    weather[['SessionTime', 'TrackTemp', 'Rainfall']],\n",
-    "    on='SessionTime',\n",
-    "    direction='nearest'\n",
-    ")\n",
-    "\n",
-    "# 7. Create final dataframe with requested columns\n",
-    "final_df = pd.DataFrame({\n",
-    "    'lap_number': merged_data['LapNumber'],\n",
-    "    'total_laps': total_laps,  # Total laps in the race\n",
-    "    'speed': merged_data['Speed'],\n",
-    "    'overall_time': merged_data['SessionTime'],\n",
-    "    'throttle': merged_data['Throttle'],\n",
-    "    'brake': merged_data['Brake'],\n",
-    "    'tire_compound': merged_data['Compound'],\n",
-    "    'tire_life_laps': merged_data['TyreLife'],  # Number of laps on current tires\n",
-    "    'track_temperature': merged_data['TrackTemp'],\n",
-    "    'rainfall': merged_data['Rainfall']\n",
-    "})\n",
-    "\n",
-    "print(f\"Created dataframe with {len(final_df)} rows\")\n",
-    "print(f\"Total laps in race: {total_laps}\")\n",
-    "print(f\"Laps covered: {final_df['lap_number'].min()} to {final_df['lap_number'].max()}\")\n",
-    "print(f\"Tire life range: {final_df['tire_life_laps'].min()} to {final_df['tire_life_laps'].max()} laps\")\n",
-    "final_df.head(10)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "id": "45d27f05",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Dataframe Info:\n",
-      "Total telemetry points: 16584\n",
-      "\n",
-      "Column types:\n",
-      "lap_number                   float64\n",
-      "total_laps                   float64\n",
-      "speed                        float64\n",
-      "overall_time         timedelta64[ns]\n",
-      "throttle                     float64\n",
-      "brake                           bool\n",
-      "tire_compound                 object\n",
-      "tire_life_laps               float64\n",
-      "track_temperature            float64\n",
-      "rainfall                        bool\n",
-      "dtype: object\n",
-      "\n",
-      "Basic statistics:\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>lap_number</th>\n",
-       "      <th>total_laps</th>\n",
-       "      <th>speed</th>\n",
-       "      <th>overall_time</th>\n",
-       "      <th>throttle</th>\n",
-       "      <th>tire_life_laps</th>\n",
-       "      <th>track_temperature</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>count</th>\n",
-       "      <td>16584.000000</td>\n",
-       "      <td>16584.0</td>\n",
-       "      <td>16584.000000</td>\n",
-       "      <td>16584</td>\n",
-       "      <td>16584.000000</td>\n",
-       "      <td>16584.000000</td>\n",
-       "      <td>16584.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mean</th>\n",
-       "      <td>25.891341</td>\n",
-       "      <td>51.0</td>\n",
-       "      <td>235.570188</td>\n",
-       "      <td>0 days 01:59:34.577446394</td>\n",
-       "      <td>72.291546</td>\n",
-       "      <td>15.339243</td>\n",
-       "      <td>42.908816</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>std</th>\n",
-       "      <td>14.710977</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>76.948906</td>\n",
-       "      <td>0 days 00:21:30.065940875</td>\n",
-       "      <td>40.561237</td>\n",
-       "      <td>8.558018</td>\n",
-       "      <td>0.897756</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>min</th>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>51.0</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0 days 01:22:21.734000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>40.800000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25%</th>\n",
-       "      <td>13.000000</td>\n",
-       "      <td>51.0</td>\n",
-       "      <td>180.000000</td>\n",
-       "      <td>0 days 01:40:53.558000</td>\n",
-       "      <td>40.000000</td>\n",
-       "      <td>8.000000</td>\n",
-       "      <td>42.500000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50%</th>\n",
-       "      <td>26.000000</td>\n",
-       "      <td>51.0</td>\n",
-       "      <td>245.000000</td>\n",
-       "      <td>0 days 01:59:31.222000</td>\n",
-       "      <td>100.000000</td>\n",
-       "      <td>15.000000</td>\n",
-       "      <td>43.100000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>75%</th>\n",
-       "      <td>39.000000</td>\n",
-       "      <td>51.0</td>\n",
-       "      <td>309.000000</td>\n",
-       "      <td>0 days 02:18:13.365000</td>\n",
-       "      <td>100.000000</td>\n",
-       "      <td>21.000000</td>\n",
-       "      <td>43.600000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>max</th>\n",
-       "      <td>51.000000</td>\n",
-       "      <td>51.0</td>\n",
-       "      <td>351.000000</td>\n",
-       "      <td>0 days 02:36:49.228000</td>\n",
-       "      <td>100.000000</td>\n",
-       "      <td>33.000000</td>\n",
-       "      <td>44.400000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         lap_number  total_laps         speed               overall_time  \\\n",
-       "count  16584.000000     16584.0  16584.000000                      16584   \n",
-       "mean      25.891341        51.0    235.570188  0 days 01:59:34.577446394   \n",
-       "std       14.710977         0.0     76.948906  0 days 00:21:30.065940875   \n",
-       "min        1.000000        51.0      0.000000     0 days 01:22:21.734000   \n",
-       "25%       13.000000        51.0    180.000000     0 days 01:40:53.558000   \n",
-       "50%       26.000000        51.0    245.000000     0 days 01:59:31.222000   \n",
-       "75%       39.000000        51.0    309.000000     0 days 02:18:13.365000   \n",
-       "max       51.000000        51.0    351.000000     0 days 02:36:49.228000   \n",
-       "\n",
-       "           throttle  tire_life_laps  track_temperature  \n",
-       "count  16584.000000    16584.000000       16584.000000  \n",
-       "mean      72.291546       15.339243          42.908816  \n",
-       "std       40.561237        8.558018           0.897756  \n",
-       "min        0.000000        1.000000          40.800000  \n",
-       "25%       40.000000        8.000000          42.500000  \n",
-       "50%      100.000000       15.000000          43.100000  \n",
-       "75%      100.000000       21.000000          43.600000  \n",
-       "max      100.000000       33.000000          44.400000  "
-      ]
-     },
-     "execution_count": 45,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Display dataframe info and sample statistics\n",
-    "print(\"Dataframe Info:\")\n",
-    "print(f\"Total telemetry points: {len(final_df)}\")\n",
-    "print(f\"\\nColumn types:\")\n",
-    "print(final_df.dtypes)\n",
-    "print(f\"\\nBasic statistics:\")\n",
-    "final_df.describe()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "id": "2fbcd2f9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "final_df.to_csv(\"ALONSO_2023_MONZA_RACE.csv\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "id": "729fb12e",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Tire compound usage throughout the race:\n",
-      "    lap_number tire_compound  tire_life_laps\n",
-      "0          1.0        MEDIUM             1.0\n",
-      "1          2.0        MEDIUM             2.0\n",
-      "2          3.0        MEDIUM             3.0\n",
-      "3          4.0        MEDIUM             4.0\n",
-      "4          5.0        MEDIUM             5.0\n",
-      "5          6.0        MEDIUM             6.0\n",
-      "6          7.0        MEDIUM             7.0\n",
-      "7          8.0        MEDIUM             8.0\n",
-      "8          9.0        MEDIUM             9.0\n",
-      "9         10.0        MEDIUM            10.0\n",
-      "10        11.0        MEDIUM            11.0\n",
-      "11        12.0        MEDIUM            12.0\n",
-      "12        13.0        MEDIUM            13.0\n",
-      "13        14.0        MEDIUM            14.0\n",
-      "14        15.0        MEDIUM            15.0\n",
-      "15        16.0        MEDIUM            16.0\n",
-      "16        17.0        MEDIUM            17.0\n",
-      "17        18.0        MEDIUM            18.0\n",
-      "18        19.0        MEDIUM            19.0\n",
-      "19        20.0        MEDIUM            20.0\n",
-      "20        21.0        MEDIUM            21.0\n",
-      "21        22.0          HARD             4.0\n",
-      "22        23.0          HARD             5.0\n",
-      "23        24.0          HARD             6.0\n",
-      "24        25.0          HARD             7.0\n",
-      "25        26.0          HARD             8.0\n",
-      "26        27.0          HARD             9.0\n",
-      "27        28.0          HARD            10.0\n",
-      "28        29.0          HARD            11.0\n",
-      "29        30.0          HARD            12.0\n",
-      "30        31.0          HARD            13.0\n",
-      "31        32.0          HARD            14.0\n",
-      "32        33.0          HARD            15.0\n",
-      "33        34.0          HARD            16.0\n",
-      "34        35.0          HARD            17.0\n",
-      "35        36.0          HARD            18.0\n",
-      "36        37.0          HARD            19.0\n",
-      "37        38.0          HARD            20.0\n",
-      "38        39.0          HARD            21.0\n",
-      "39        40.0          HARD            22.0\n",
-      "40        41.0          HARD            23.0\n",
-      "41        42.0          HARD            24.0\n",
-      "42        43.0          HARD            25.0\n",
-      "43        44.0          HARD            26.0\n",
-      "44        45.0          HARD            27.0\n",
-      "45        46.0          HARD            28.0\n",
-      "46        47.0          HARD            29.0\n",
-      "47        48.0          HARD            30.0\n",
-      "48        49.0          HARD            31.0\n",
-      "49        50.0          HARD            32.0\n",
-      "50        51.0          HARD            33.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Show tire compound changes and stint information\n",
-    "print(\"Tire compound usage throughout the race:\")\n",
-    "tire_changes = final_df.groupby(['lap_number', 'tire_compound', 'tire_life_laps']).size().reset_index(name='count')\n",
-    "tire_changes = tire_changes.groupby(['lap_number', 'tire_compound', 'tire_life_laps']).first().reset_index()[['lap_number', 'tire_compound', 'tire_life_laps']]\n",
-    "print(tire_changes.drop_duplicates())\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d9ebc90c",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "base",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.13.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/fetch_lap_data.ipynb b/fetch_lap_data.ipynb
index 0077128..86e697f 100644
--- a/fetch_lap_data.ipynb
+++ b/fetch_lap_data.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 4,
    "id": "be0c6fbf",
    "metadata": {},
    "outputs": [],
@@ -31,7 +31,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 5,
    "id": "f757cb34",
    "metadata": {},
    "outputs": [
@@ -39,7 +39,6 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "req         WARNING \tDEFAULT CACHE ENABLED! (318.35 MB) /Users/adipu/Library/Caches/fastf1\n",
       "core           INFO \tLoading data for Italian Grand Prix - Race [v3.6.1]\n",
       "req            INFO \tUsing cached data for session_info\n",
       "req            INFO \tUsing cached data for driver_info\n",
@@ -95,7 +94,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 6,
    "id": "aa5e5d8f",
    "metadata": {},
    "outputs": [
@@ -136,6 +135,9 @@
        "      <th></th>\n",
        "      <th>lap_number</th>\n",
        "      <th>total_laps</th>\n",
+       "      <th>position</th>\n",
+       "      <th>gap_to_leader</th>\n",
+       "      <th>gap_to_ahead</th>\n",
        "      <th>lap_time</th>\n",
        "      <th>average_speed</th>\n",
        "      <th>max_speed</th>\n",
@@ -150,6 +152,9 @@
        "      <th>0</th>\n",
        "      <td>1</td>\n",
        "      <td>51</td>\n",
+       "      <td>11</td>\n",
+       "      <td>6.223</td>\n",
+       "      <td>0.620</td>\n",
        "      <td>0 days 00:01:33.340000</td>\n",
        "      <td>210.17</td>\n",
        "      <td>326.0</td>\n",
@@ -162,6 +167,9 @@
        "      <th>1</th>\n",
        "      <td>2</td>\n",
        "      <td>51</td>\n",
+       "      <td>11</td>\n",
+       "      <td>8.229</td>\n",
+       "      <td>0.712</td>\n",
        "      <td>0 days 00:01:28.012000</td>\n",
        "      <td>236.87</td>\n",
        "      <td>330.0</td>\n",
@@ -174,6 +182,9 @@
        "      <th>2</th>\n",
        "      <td>3</td>\n",
        "      <td>51</td>\n",
+       "      <td>11</td>\n",
+       "      <td>9.799</td>\n",
+       "      <td>0.898</td>\n",
        "      <td>0 days 00:01:27.546000</td>\n",
        "      <td>236.40</td>\n",
        "      <td>331.0</td>\n",
@@ -186,6 +197,9 @@
        "      <th>3</th>\n",
        "      <td>4</td>\n",
        "      <td>51</td>\n",
+       "      <td>11</td>\n",
+       "      <td>10.953</td>\n",
+       "      <td>0.919</td>\n",
        "      <td>0 days 00:01:27.221000</td>\n",
        "      <td>240.13</td>\n",
        "      <td>341.0</td>\n",
@@ -198,6 +212,9 @@
        "      <th>4</th>\n",
        "      <td>5</td>\n",
        "      <td>51</td>\n",
+       "      <td>11</td>\n",
+       "      <td>11.563</td>\n",
+       "      <td>0.917</td>\n",
        "      <td>0 days 00:01:27.033000</td>\n",
        "      <td>236.09</td>\n",
        "      <td>345.0</td>\n",
@@ -210,6 +227,9 @@
        "      <th>5</th>\n",
        "      <td>6</td>\n",
        "      <td>51</td>\n",
+       "      <td>11</td>\n",
+       "      <td>12.123</td>\n",
+       "      <td>0.923</td>\n",
        "      <td>0 days 00:01:27.175000</td>\n",
        "      <td>236.74</td>\n",
        "      <td>343.0</td>\n",
@@ -222,6 +242,9 @@
        "      <th>6</th>\n",
        "      <td>7</td>\n",
        "      <td>51</td>\n",
+       "      <td>11</td>\n",
+       "      <td>12.694</td>\n",
+       "      <td>0.387</td>\n",
        "      <td>0 days 00:01:26.929000</td>\n",
        "      <td>239.72</td>\n",
        "      <td>340.0</td>\n",
@@ -234,6 +257,9 @@
        "      <th>7</th>\n",
        "      <td>8</td>\n",
        "      <td>51</td>\n",
+       "      <td>10</td>\n",
+       "      <td>13.413</td>\n",
+       "      <td>2.760</td>\n",
        "      <td>0 days 00:01:26.943000</td>\n",
        "      <td>238.45</td>\n",
        "      <td>351.0</td>\n",
@@ -246,6 +272,9 @@
        "      <th>8</th>\n",
        "      <td>9</td>\n",
        "      <td>51</td>\n",
+       "      <td>10</td>\n",
+       "      <td>14.320</td>\n",
+       "      <td>3.387</td>\n",
        "      <td>0 days 00:01:27.383000</td>\n",
        "      <td>236.81</td>\n",
        "      <td>330.0</td>\n",
@@ -258,6 +287,9 @@
        "      <th>9</th>\n",
        "      <td>10</td>\n",
        "      <td>51</td>\n",
+       "      <td>10</td>\n",
+       "      <td>15.177</td>\n",
+       "      <td>3.760</td>\n",
        "      <td>0 days 00:01:27.368000</td>\n",
        "      <td>232.42</td>\n",
        "      <td>331.0</td>\n",
@@ -271,32 +303,44 @@
        "</div>"
       ],
       "text/plain": [
-       "   lap_number  total_laps               lap_time  average_speed  max_speed  \\\n",
-       "0           1          51 0 days 00:01:33.340000         210.17      326.0   \n",
-       "1           2          51 0 days 00:01:28.012000         236.87      330.0   \n",
-       "2           3          51 0 days 00:01:27.546000         236.40      331.0   \n",
-       "3           4          51 0 days 00:01:27.221000         240.13      341.0   \n",
-       "4           5          51 0 days 00:01:27.033000         236.09      345.0   \n",
-       "5           6          51 0 days 00:01:27.175000         236.74      343.0   \n",
-       "6           7          51 0 days 00:01:26.929000         239.72      340.0   \n",
-       "7           8          51 0 days 00:01:26.943000         238.45      351.0   \n",
-       "8           9          51 0 days 00:01:27.383000         236.81      330.0   \n",
-       "9          10          51 0 days 00:01:27.368000         232.42      331.0   \n",
+       "   lap_number  total_laps  position  gap_to_leader  gap_to_ahead  \\\n",
+       "0           1          51        11          6.223         0.620   \n",
+       "1           2          51        11          8.229         0.712   \n",
+       "2           3          51        11          9.799         0.898   \n",
+       "3           4          51        11         10.953         0.919   \n",
+       "4           5          51        11         11.563         0.917   \n",
+       "5           6          51        11         12.123         0.923   \n",
+       "6           7          51        11         12.694         0.387   \n",
+       "7           8          51        10         13.413         2.760   \n",
+       "8           9          51        10         14.320         3.387   \n",
+       "9          10          51        10         15.177         3.760   \n",
        "\n",
-       "  tire_compound  tire_life_laps  track_temperature  rainfall  \n",
-       "0        MEDIUM               1               42.5     False  \n",
-       "1        MEDIUM               2               42.5     False  \n",
-       "2        MEDIUM               3               43.2     False  \n",
-       "3        MEDIUM               4               43.2     False  \n",
-       "4        MEDIUM               5               43.1     False  \n",
-       "5        MEDIUM               6               43.3     False  \n",
-       "6        MEDIUM               7               43.6     False  \n",
-       "7        MEDIUM               8               43.6     False  \n",
-       "8        MEDIUM               9               43.6     False  \n",
-       "9        MEDIUM              10               43.9     False  "
+       "                lap_time  average_speed  max_speed tire_compound  \\\n",
+       "0 0 days 00:01:33.340000         210.17      326.0        MEDIUM   \n",
+       "1 0 days 00:01:28.012000         236.87      330.0        MEDIUM   \n",
+       "2 0 days 00:01:27.546000         236.40      331.0        MEDIUM   \n",
+       "3 0 days 00:01:27.221000         240.13      341.0        MEDIUM   \n",
+       "4 0 days 00:01:27.033000         236.09      345.0        MEDIUM   \n",
+       "5 0 days 00:01:27.175000         236.74      343.0        MEDIUM   \n",
+       "6 0 days 00:01:26.929000         239.72      340.0        MEDIUM   \n",
+       "7 0 days 00:01:26.943000         238.45      351.0        MEDIUM   \n",
+       "8 0 days 00:01:27.383000         236.81      330.0        MEDIUM   \n",
+       "9 0 days 00:01:27.368000         232.42      331.0        MEDIUM   \n",
+       "\n",
+       "   tire_life_laps  track_temperature  rainfall  \n",
+       "0               1               42.5     False  \n",
+       "1               2               42.5     False  \n",
+       "2               3               43.2     False  \n",
+       "3               4               43.2     False  \n",
+       "4               5               43.1     False  \n",
+       "5               6               43.3     False  \n",
+       "6               7               43.6     False  \n",
+       "7               8               43.6     False  \n",
+       "8               9               43.6     False  \n",
+       "9              10               43.9     False  "
       ]
      },
-     "execution_count": 3,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -306,7 +350,10 @@
     "weather = session.weather_data\n",
     "weather['SessionTime'] = pd.to_timedelta(weather['Time'])\n",
     "\n",
-    "# 4. Create lap-level data by aggregating telemetry\n",
+    "# 4. Get all laps for position calculation\n",
+    "all_laps = session.laps\n",
+    "\n",
+    "# 5. Create lap-level data by aggregating telemetry\n",
     "lap_data_list = []\n",
     "\n",
     "for lap_idx in driver_laps.index:\n",
@@ -332,6 +379,43 @@
     "        tire_compound = lap['Compound']\n",
     "        tire_life = lap['TyreLife']\n",
     "        \n",
+    "        # Get position data for this lap\n",
+    "        position = lap['Position']\n",
+    "        \n",
+    "        # Calculate gaps: get all drivers' data for this lap\n",
+    "        this_lap_all_drivers = all_laps[all_laps['LapNumber'] == lap_number].copy()\n",
+    "        \n",
+    "        # Sort by position to calculate gaps\n",
+    "        this_lap_all_drivers = this_lap_all_drivers.sort_values('Position')\n",
+    "        \n",
+    "        # Calculate cumulative race time for gap calculations\n",
+    "        gap_to_leader = 0.0\n",
+    "        gap_to_ahead = 0.0\n",
+    "        \n",
+    "        if pd.notna(position) and position > 1:\n",
+    "            # Get our driver's data\n",
+    "            our_data = this_lap_all_drivers[this_lap_all_drivers['Driver'] == 'ALO']\n",
+    "            \n",
+    "            if not our_data.empty:\n",
+    "                # Time at the end of this lap (cumulative)\n",
+    "                # Note: FastF1 provides 'Time' which is cumulative race time\n",
+    "                our_time = our_data['Time'].values[0]\n",
+    "                \n",
+    "                # Get leader's time (P1)\n",
+    "                leader_data = this_lap_all_drivers[this_lap_all_drivers['Position'] == 1]\n",
+    "                if not leader_data.empty:\n",
+    "                    leader_time = leader_data['Time'].values[0]\n",
+    "                    # Convert numpy.timedelta64 to seconds\n",
+    "                    gap_to_leader = (our_time - leader_time) / np.timedelta64(1, 's')\n",
+    "                \n",
+    "                # Get car ahead's time (Position - 1)\n",
+    "                ahead_position = position - 1\n",
+    "                ahead_data = this_lap_all_drivers[this_lap_all_drivers['Position'] == ahead_position]\n",
+    "                if not ahead_data.empty:\n",
+    "                    ahead_time = ahead_data['Time'].values[0]\n",
+    "                    # Convert numpy.timedelta64 to seconds\n",
+    "                    gap_to_ahead = (our_time - ahead_time) / np.timedelta64(1, 's')\n",
+    "        \n",
     "        # Get weather data for this lap (use lap start time)\n",
     "        lap_start_time = pd.to_timedelta(lap['LapStartTime'])\n",
     "        \n",
@@ -344,6 +428,9 @@
     "        lap_record = {\n",
     "            'lap_number': int(lap_number),\n",
     "            'total_laps': int(total_laps),\n",
+    "            'position': int(position) if pd.notna(position) else None,\n",
+    "            'gap_to_leader': round(gap_to_leader, 3) if gap_to_leader > 0 else 0.0,\n",
+    "            'gap_to_ahead': round(gap_to_ahead, 3) if gap_to_ahead > 0 else 0.0,\n",
     "            'lap_time': lap_time,\n",
     "            'average_speed': round(avg_speed, 2),\n",
     "            'max_speed': round(max_speed, 2),\n",
@@ -359,7 +446,7 @@
     "    if lap_number % 10 == 0:\n",
     "        print(f\"Processed lap {int(lap_number)}...\")\n",
     "\n",
-    "# 5. Create final dataframe\n",
+    "# 6. Create final dataframe\n",
     "laps_df = pd.DataFrame(lap_data_list)\n",
     "\n",
     "print(f\"\\n✓ Created lap-level dataframe with {len(laps_df)} laps\")\n",
@@ -369,7 +456,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 7,
    "id": "b1086b8d",
    "metadata": {},
    "outputs": [
@@ -383,6 +470,9 @@
       "Column types:\n",
       "lap_number                     int64\n",
       "total_laps                     int64\n",
+      "position                       int64\n",
+      "gap_to_leader                float64\n",
+      "gap_to_ahead                 float64\n",
       "lap_time             timedelta64[ns]\n",
       "average_speed                float64\n",
       "max_speed                    float64\n",
@@ -418,6 +508,9 @@
        "      <th></th>\n",
        "      <th>lap_number</th>\n",
        "      <th>total_laps</th>\n",
+       "      <th>position</th>\n",
+       "      <th>gap_to_leader</th>\n",
+       "      <th>gap_to_ahead</th>\n",
        "      <th>lap_time</th>\n",
        "      <th>average_speed</th>\n",
        "      <th>max_speed</th>\n",
@@ -430,6 +523,9 @@
        "      <th>count</th>\n",
        "      <td>51.000000</td>\n",
        "      <td>51.0</td>\n",
+       "      <td>51.000000</td>\n",
+       "      <td>51.000000</td>\n",
+       "      <td>51.000000</td>\n",
        "      <td>51</td>\n",
        "      <td>51.000000</td>\n",
        "      <td>51.000000</td>\n",
@@ -440,6 +536,9 @@
        "      <th>mean</th>\n",
        "      <td>26.000000</td>\n",
        "      <td>51.0</td>\n",
+       "      <td>9.803922</td>\n",
+       "      <td>29.418314</td>\n",
+       "      <td>2.889235</td>\n",
        "      <td>0 days 00:01:27.596803921</td>\n",
        "      <td>235.797059</td>\n",
        "      <td>333.686275</td>\n",
@@ -450,6 +549,9 @@
        "      <th>std</th>\n",
        "      <td>14.866069</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>1.058671</td>\n",
+       "      <td>13.842909</td>\n",
+       "      <td>1.707825</td>\n",
        "      <td>0 days 00:00:03.069690434</td>\n",
        "      <td>7.855085</td>\n",
        "      <td>4.921342</td>\n",
@@ -460,6 +562,9 @@
        "      <th>min</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>51.0</td>\n",
+       "      <td>6.000000</td>\n",
+       "      <td>6.223000</td>\n",
+       "      <td>0.387000</td>\n",
        "      <td>0 days 00:01:26.105000</td>\n",
        "      <td>191.140000</td>\n",
        "      <td>322.000000</td>\n",
@@ -470,6 +575,9 @@
        "      <th>25%</th>\n",
        "      <td>13.500000</td>\n",
        "      <td>51.0</td>\n",
+       "      <td>9.000000</td>\n",
+       "      <td>16.928500</td>\n",
+       "      <td>1.123000</td>\n",
        "      <td>0 days 00:01:26.715000</td>\n",
        "      <td>236.105000</td>\n",
        "      <td>331.000000</td>\n",
@@ -480,6 +588,9 @@
        "      <th>50%</th>\n",
        "      <td>26.000000</td>\n",
        "      <td>51.0</td>\n",
+       "      <td>10.000000</td>\n",
+       "      <td>29.113000</td>\n",
+       "      <td>2.799000</td>\n",
        "      <td>0 days 00:01:26.943000</td>\n",
        "      <td>237.130000</td>\n",
        "      <td>332.000000</td>\n",
@@ -490,6 +601,9 @@
        "      <th>75%</th>\n",
        "      <td>38.500000</td>\n",
        "      <td>51.0</td>\n",
+       "      <td>10.000000</td>\n",
+       "      <td>43.405500</td>\n",
+       "      <td>4.136500</td>\n",
        "      <td>0 days 00:01:27.328500</td>\n",
        "      <td>238.655000</td>\n",
        "      <td>334.000000</td>\n",
@@ -500,6 +614,9 @@
        "      <th>max</th>\n",
        "      <td>51.000000</td>\n",
        "      <td>51.0</td>\n",
+       "      <td>12.000000</td>\n",
+       "      <td>48.171000</td>\n",
+       "      <td>8.135000</td>\n",
        "      <td>0 days 00:01:47.272000</td>\n",
        "      <td>241.700000</td>\n",
        "      <td>351.000000</td>\n",
@@ -511,28 +628,38 @@
        "</div>"
       ],
       "text/plain": [
-       "       lap_number  total_laps                   lap_time  average_speed  \\\n",
-       "count   51.000000        51.0                         51      51.000000   \n",
-       "mean    26.000000        51.0  0 days 00:01:27.596803921     235.797059   \n",
-       "std     14.866069         0.0  0 days 00:00:03.069690434       7.855085   \n",
-       "min      1.000000        51.0     0 days 00:01:26.105000     191.140000   \n",
-       "25%     13.500000        51.0     0 days 00:01:26.715000     236.105000   \n",
-       "50%     26.000000        51.0     0 days 00:01:26.943000     237.130000   \n",
-       "75%     38.500000        51.0     0 days 00:01:27.328500     238.655000   \n",
-       "max     51.000000        51.0     0 days 00:01:47.272000     241.700000   \n",
+       "       lap_number  total_laps   position  gap_to_leader  gap_to_ahead  \\\n",
+       "count   51.000000        51.0  51.000000      51.000000     51.000000   \n",
+       "mean    26.000000        51.0   9.803922      29.418314      2.889235   \n",
+       "std     14.866069         0.0   1.058671      13.842909      1.707825   \n",
+       "min      1.000000        51.0   6.000000       6.223000      0.387000   \n",
+       "25%     13.500000        51.0   9.000000      16.928500      1.123000   \n",
+       "50%     26.000000        51.0  10.000000      29.113000      2.799000   \n",
+       "75%     38.500000        51.0  10.000000      43.405500      4.136500   \n",
+       "max     51.000000        51.0  12.000000      48.171000      8.135000   \n",
        "\n",
-       "        max_speed  tire_life_laps  track_temperature  \n",
-       "count   51.000000       51.000000          51.000000  \n",
-       "mean   333.686275       15.411765          42.898039  \n",
-       "std      4.921342        8.616673           0.876924  \n",
-       "min    322.000000        1.000000          40.800000  \n",
-       "25%    331.000000        8.500000          42.500000  \n",
-       "50%    332.000000       15.000000          43.100000  \n",
-       "75%    334.000000       21.000000          43.600000  \n",
-       "max    351.000000       33.000000          44.300000  "
+       "                        lap_time  average_speed   max_speed  tire_life_laps  \\\n",
+       "count                         51      51.000000   51.000000       51.000000   \n",
+       "mean   0 days 00:01:27.596803921     235.797059  333.686275       15.411765   \n",
+       "std    0 days 00:00:03.069690434       7.855085    4.921342        8.616673   \n",
+       "min       0 days 00:01:26.105000     191.140000  322.000000        1.000000   \n",
+       "25%       0 days 00:01:26.715000     236.105000  331.000000        8.500000   \n",
+       "50%       0 days 00:01:26.943000     237.130000  332.000000       15.000000   \n",
+       "75%       0 days 00:01:27.328500     238.655000  334.000000       21.000000   \n",
+       "max       0 days 00:01:47.272000     241.700000  351.000000       33.000000   \n",
+       "\n",
+       "       track_temperature  \n",
+       "count          51.000000  \n",
+       "mean           42.898039  \n",
+       "std             0.876924  \n",
+       "min            40.800000  \n",
+       "25%            42.500000  \n",
+       "50%            43.100000  \n",
+       "75%            43.600000  \n",
+       "max            44.300000  "
       ]
      },
-     "execution_count": 4,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -549,7 +676,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 8,
    "id": "b2a3b878",
    "metadata": {},
    "outputs": [
@@ -569,7 +696,55 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 9,
+   "id": "efcff166",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Position and Gap Analysis:\n",
+      "Starting position: P11\n",
+      "Final position: P9\n",
+      "Average gap to leader: 29.42s\n",
+      "Average gap to car ahead: 2.89s\n",
+      "\n",
+      "Position changes over race:\n",
+      "    lap_number  position  gap_to_leader  gap_to_ahead\n",
+      "0            1        11          6.223         0.620\n",
+      "1            2        11          8.229         0.712\n",
+      "2            3        11          9.799         0.898\n",
+      "3            4        11         10.953         0.919\n",
+      "4            5        11         11.563         0.917\n",
+      "5            6        11         12.123         0.923\n",
+      "6            7        11         12.694         0.387\n",
+      "7            8        10         13.413         2.760\n",
+      "8            9        10         14.320         3.387\n",
+      "9           10        10         15.177         3.760\n",
+      "10          11        10         15.829         3.984\n",
+      "11          12        10         16.760         4.129\n",
+      "12          13        10         17.031         3.995\n",
+      "13          14        10         17.666         4.076\n",
+      "14          15        10         17.366         0.469\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Display position and gap information\n",
+    "print(\"\\nPosition and Gap Analysis:\")\n",
+    "print(f\"Starting position: P{laps_df['position'].iloc[0]}\")\n",
+    "print(f\"Final position: P{laps_df['position'].iloc[-1]}\")\n",
+    "print(f\"Average gap to leader: {laps_df['gap_to_leader'].mean():.2f}s\")\n",
+    "print(f\"Average gap to car ahead: {laps_df['gap_to_ahead'].mean():.2f}s\")\n",
+    "print(f\"\\nPosition changes over race:\")\n",
+    "print(laps_df[['lap_number', 'position', 'gap_to_leader', 'gap_to_ahead']].head(15))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
    "id": "0202372e",
    "metadata": {},
    "outputs": [
diff --git a/hpcsim/__pycache__/adapter.cpython-313.pyc b/hpcsim/__pycache__/adapter.cpython-313.pyc
index e1e77f8..5257f73 100644
Binary files a/hpcsim/__pycache__/adapter.cpython-313.pyc and b/hpcsim/__pycache__/adapter.cpython-313.pyc differ
diff --git a/hpcsim/__pycache__/enrichment.cpython-313.pyc b/hpcsim/__pycache__/enrichment.cpython-313.pyc
index 3326379..219039c 100644
Binary files a/hpcsim/__pycache__/enrichment.cpython-313.pyc and b/hpcsim/__pycache__/enrichment.cpython-313.pyc differ
diff --git a/hpcsim/adapter.py b/hpcsim/adapter.py
index 881cfcf..197ba84 100644
--- a/hpcsim/adapter.py
+++ b/hpcsim/adapter.py
@@ -9,6 +9,9 @@ def normalize_telemetry(payload: Dict[str, Any]) -> Dict[str, Any]:
     Accepted aliases for lap-level data:
     - lap_number: lap, Lap, LapNumber, lap_number
     - total_laps: TotalLaps, total_laps
+    - position: position, Position, Pos
+    - gap_to_leader: gap_to_leader, GapToLeader, gap_leader
+    - gap_to_ahead: gap_to_ahead, GapToAhead, gap_ahead
     - lap_time: lap_time, LapTime, Time
     - average_speed: average_speed, avg_speed, AvgSpeed
     - max_speed: max_speed, MaxSpeed, max
@@ -22,6 +25,9 @@ def normalize_telemetry(payload: Dict[str, Any]) -> Dict[str, Any]:
     aliases = {
         "lap_number": ["lap_number", "lap", "Lap", "LapNumber"],
         "total_laps": ["total_laps", "TotalLaps"],
+        "position": ["position", "Position", "Pos"],
+        "gap_to_leader": ["gap_to_leader", "GapToLeader", "gap_leader"],
+        "gap_to_ahead": ["gap_to_ahead", "GapToAhead", "gap_ahead"],
         "lap_time": ["lap_time", "LapTime", "Time"],
         "average_speed": ["average_speed", "avg_speed", "AvgSpeed"],
         "max_speed": ["max_speed", "MaxSpeed", "max"],
@@ -53,6 +59,24 @@ def normalize_telemetry(payload: Dict[str, Any]) -> Dict[str, Any]:
     except (TypeError, ValueError):
         total_laps = 51
 
+    position = pick("position", 10)
+    try:
+        position = int(position)
+    except (TypeError, ValueError):
+        position = 10
+
+    gap_to_leader = pick("gap_to_leader", 0.0)
+    try:
+        gap_to_leader = float(gap_to_leader)
+    except (TypeError, ValueError):
+        gap_to_leader = 0.0
+
+    gap_to_ahead = pick("gap_to_ahead", 0.0)
+    try:
+        gap_to_ahead = float(gap_to_ahead)
+    except (TypeError, ValueError):
+        gap_to_ahead = 0.0
+
     lap_time = pick("lap_time", None)
     if lap_time:
         out["lap_time"] = str(lap_time)
@@ -97,6 +121,9 @@ def normalize_telemetry(payload: Dict[str, Any]) -> Dict[str, Any]:
     out.update({
         "lap_number": lap_number,
         "total_laps": total_laps,
+        "position": position,
+        "gap_to_leader": gap_to_leader,
+        "gap_to_ahead": gap_to_ahead,
         "average_speed": average_speed,
         "max_speed": max_speed,
         "tire_compound": tire_compound,
diff --git a/hpcsim/enrichment.py b/hpcsim/enrichment.py
index 04885a1..0833c5b 100644
--- a/hpcsim/enrichment.py
+++ b/hpcsim/enrichment.py
@@ -10,6 +10,9 @@ import pandas as pd
 # {
 #   "lap_number": 27,
 #   "total_laps": 51,
+#   "position": 5,
+#   "gap_to_leader": 12.345,
+#   "gap_to_ahead": 2.456,
 #   "lap_time": "0 days 00:01:27.318000",
 #   "average_speed": 234.62,
 #   "max_speed": 333.0,
@@ -36,6 +39,8 @@ class EnricherState:
     """Maintains race state across laps for trend analysis."""
     lap_times: List[float] = field(default_factory=list)  # Recent lap times in seconds
     lap_speeds: List[float] = field(default_factory=list)  # Recent average speeds
+    positions: List[int] = field(default_factory=list)  # Recent positions
+    gaps_to_ahead: List[float] = field(default_factory=list)  # Recent gaps to car ahead
     current_tire_age: int = 0
     current_tire_compound: str = "medium"
     tire_stint_start_lap: int = 1
@@ -63,6 +68,9 @@ class Enricher:
         # Extract lap data
         lap_number = int(lap_data.get("lap_number", 0))
         total_laps = int(lap_data.get("total_laps", 51))
+        position = int(lap_data.get("position", 10))
+        gap_to_leader = float(lap_data.get("gap_to_leader", 0.0))
+        gap_to_ahead = float(lap_data.get("gap_to_ahead", 0.0))
         lap_time_str = lap_data.get("lap_time")
         average_speed = float(lap_data.get("average_speed", 0.0))
         max_speed = float(lap_data.get("max_speed", 0.0))
@@ -77,6 +85,8 @@ class Enricher:
         # Update state
         self.state.lap_times.append(lap_time_seconds)
         self.state.lap_speeds.append(average_speed)
+        self.state.positions.append(position)
+        self.state.gaps_to_ahead.append(gap_to_ahead)
         self.state.current_tire_age = tire_life_laps
         self.state.current_tire_compound = tire_compound
         self.state.total_laps = total_laps
@@ -85,6 +95,8 @@ class Enricher:
         if len(self.state.lap_times) > 10:
             self.state.lap_times = self.state.lap_times[-10:]
             self.state.lap_speeds = self.state.lap_speeds[-10:]
+            self.state.positions = self.state.positions[-10:]
+            self.state.gaps_to_ahead = self.state.gaps_to_ahead[-10:]
         
         # Set baseline (best lap time)
         if self._baseline_lap_time is None or lap_time_seconds < self._baseline_lap_time:
@@ -96,6 +108,8 @@ class Enricher:
         tire_cliff_risk = self._compute_tire_cliff_risk(tire_compound, tire_life_laps)
         pit_window = self._compute_optimal_pit_window(lap_number, total_laps, tire_life_laps, tire_compound)
         performance_delta = self._compute_performance_delta(lap_time_seconds)
+        competitive_pressure = self._compute_competitive_pressure(position, gap_to_ahead)
+        position_trend = self._compute_position_trend()
         
         # Build enriched telemetry
         enriched_telemetry = {
@@ -104,7 +118,9 @@ class Enricher:
             "pace_trend": pace_trend,
             "tire_cliff_risk": round(tire_cliff_risk, 3),
             "optimal_pit_window": pit_window,
-            "performance_delta": round(performance_delta, 2)
+            "performance_delta": round(performance_delta, 2),
+            "competitive_pressure": round(competitive_pressure, 3),
+            "position_trend": position_trend
         }
         
         # Build race context
@@ -118,10 +134,12 @@ class Enricher:
             },
             "driver_state": {
                 "driver_name": "Alonso",
-                "current_position": 5,  # Mock - could be passed in
+                "current_position": position,
                 "current_tire_compound": tire_compound,
                 "tire_age_laps": tire_life_laps,
-                "fuel_remaining_percent": self._estimate_fuel(lap_number, total_laps)
+                "fuel_remaining_percent": self._estimate_fuel(lap_number, total_laps),
+                "gap_to_leader": gap_to_leader,
+                "gap_to_ahead": gap_to_ahead
             }
         }
         
@@ -238,6 +256,56 @@ class Enricher:
         
         return self._baseline_lap_time - current_lap_time  # Negative if slower
     
+    def _compute_competitive_pressure(self, position: int, gap_to_ahead: float) -> float:
+        """
+        Calculate competitive pressure based on position and gap to car ahead.
+        Returns 0-1 (0 = no pressure, 1 = extreme pressure).
+        
+        High pressure scenarios:
+        - Close gap to car ahead (potential overtake opportunity)
+        - Poor position (need to push harder)
+        """
+        # Position pressure: worse position = higher pressure
+        position_pressure = min(1.0, (position - 1) / 15.0)  # Normalize to 0-1
+        
+        # Gap pressure: smaller gap = higher pressure (opportunity to attack)
+        if gap_to_ahead <= 0.0:
+            gap_pressure = 0.0  # Leading or no gap data
+        elif gap_to_ahead < 1.0:
+            gap_pressure = 1.0  # Very close - DRS range
+        elif gap_to_ahead < 3.0:
+            gap_pressure = 0.7  # Close - push to close gap
+        elif gap_to_ahead < 10.0:
+            gap_pressure = 0.3  # Moderate gap
+        else:
+            gap_pressure = 0.1  # Large gap - low pressure
+        
+        # Combined pressure (weighted average)
+        return round((position_pressure * 0.4 + gap_pressure * 0.6), 3)
+    
+    def _compute_position_trend(self) -> str:
+        """
+        Analyze recent positions to determine trend.
+        Returns: "gaining", "stable", or "losing"
+        """
+        if len(self.state.positions) < 3:
+            return "stable"
+        
+        recent_positions = self.state.positions[-5:]  # Last 5 laps
+        
+        # Calculate trend (lower position number = better)
+        avg_first_half = sum(recent_positions[:len(recent_positions)//2]) / max(1, len(recent_positions)//2)
+        avg_second_half = sum(recent_positions[len(recent_positions)//2:]) / max(1, len(recent_positions) - len(recent_positions)//2)
+        
+        diff = avg_first_half - avg_second_half  # Positive if gaining positions
+        
+        if diff > 0.5:  # Position number decreased = gained positions
+            return "gaining"
+        elif diff < -0.5:  # Position number increased = lost positions
+            return "losing"
+        else:
+            return "stable"
+    
     def _estimate_fuel(self, current_lap: int, total_laps: int) -> float:
         """Estimate remaining fuel percentage based on lap progression."""
         return max(0.0, 100.0 * (1.0 - (current_lap / total_laps)))
diff --git a/scripts/ALONSO_2023_MONZA_LAPS.csv b/scripts/ALONSO_2023_MONZA_LAPS.csv
index 853d0c4..779b33d 100644
--- a/scripts/ALONSO_2023_MONZA_LAPS.csv
+++ b/scripts/ALONSO_2023_MONZA_LAPS.csv
@@ -1,52 +1,52 @@
-lap_number,total_laps,lap_time,average_speed,max_speed,tire_compound,tire_life_laps,track_temperature,rainfall
-1,51,0 days 00:01:33.340000,210.17,326.0,MEDIUM,1,42.5,False
-2,51,0 days 00:01:28.012000,236.87,330.0,MEDIUM,2,42.5,False
-3,51,0 days 00:01:27.546000,236.4,331.0,MEDIUM,3,43.2,False
-4,51,0 days 00:01:27.221000,240.13,341.0,MEDIUM,4,43.2,False
-5,51,0 days 00:01:27.033000,236.09,345.0,MEDIUM,5,43.1,False
-6,51,0 days 00:01:27.175000,236.74,343.0,MEDIUM,6,43.3,False
-7,51,0 days 00:01:26.929000,239.72,340.0,MEDIUM,7,43.6,False
-8,51,0 days 00:01:26.943000,238.45,351.0,MEDIUM,8,43.6,False
-9,51,0 days 00:01:27.383000,236.81,330.0,MEDIUM,9,43.6,False
-10,51,0 days 00:01:27.368000,232.42,331.0,MEDIUM,10,43.9,False
-11,51,0 days 00:01:27.343000,237.18,331.0,MEDIUM,11,43.9,False
-12,51,0 days 00:01:27.339000,236.76,332.0,MEDIUM,12,43.3,False
-13,51,0 days 00:01:27.158000,235.47,330.0,MEDIUM,13,43.1,False
-14,51,0 days 00:01:27.607000,235.97,332.0,MEDIUM,14,43.7,False
-15,51,0 days 00:01:26.929000,237.04,332.0,MEDIUM,15,43.7,False
-16,51,0 days 00:01:27.164000,238.88,333.0,MEDIUM,16,43.9,False
-17,51,0 days 00:01:27.156000,235.35,333.0,MEDIUM,17,43.7,False
-18,51,0 days 00:01:27.275000,238.66,333.0,MEDIUM,18,43.5,False
-19,51,0 days 00:01:27.318000,234.62,333.0,MEDIUM,19,43.6,False
-20,51,0 days 00:01:28.284000,235.31,333.0,MEDIUM,20,44.3,False
-21,51,0 days 00:01:32.377000,224.11,332.0,MEDIUM,21,44.3,False
-22,51,0 days 00:01:47.272000,191.14,322.0,HARD,4,43.7,False
-23,51,0 days 00:01:26.849000,236.5,329.0,HARD,5,43.7,False
-24,51,0 days 00:01:26.949000,238.49,331.0,HARD,6,43.5,False
-25,51,0 days 00:01:26.899000,237.13,331.0,HARD,7,43.5,False
-26,51,0 days 00:01:26.792000,238.18,331.0,HARD,8,43.4,False
-27,51,0 days 00:01:26.666000,238.7,331.0,HARD,9,43.4,False
-28,51,0 days 00:01:26.723000,234.46,331.0,HARD,10,43.3,False
-29,51,0 days 00:01:26.997000,236.12,332.0,HARD,11,42.9,False
-30,51,0 days 00:01:26.915000,238.87,333.0,HARD,12,42.5,False
-31,51,0 days 00:01:26.848000,240.21,335.0,HARD,13,42.8,False
-32,51,0 days 00:01:26.747000,235.57,332.0,HARD,14,42.6,False
-33,51,0 days 00:01:26.769000,238.33,332.0,HARD,15,42.4,False
-34,51,0 days 00:01:26.563000,236.92,332.0,HARD,16,42.5,False
-35,51,0 days 00:01:26.517000,239.63,333.0,HARD,17,42.6,False
-36,51,0 days 00:01:26.457000,237.1,332.0,HARD,18,42.5,False
-37,51,0 days 00:01:26.380000,238.39,333.0,HARD,19,42.5,False
-38,51,0 days 00:01:26.574000,237.75,333.0,HARD,20,42.8,False
-39,51,0 days 00:01:26.707000,238.65,332.0,HARD,21,42.8,False
-40,51,0 days 00:01:26.610000,235.8,333.0,HARD,22,42.8,False
-41,51,0 days 00:01:26.815000,236.82,334.0,HARD,23,42.9,False
-42,51,0 days 00:01:26.403000,239.47,332.0,HARD,24,42.5,False
-43,51,0 days 00:01:26.105000,240.15,334.0,HARD,25,42.5,False
-44,51,0 days 00:01:26.155000,241.7,332.0,HARD,26,41.7,False
-45,51,0 days 00:01:26.277000,241.25,333.0,HARD,27,41.7,False
-46,51,0 days 00:01:26.453000,238.02,335.0,HARD,28,41.8,False
-47,51,0 days 00:01:26.912000,236.31,336.0,HARD,29,41.2,False
-48,51,0 days 00:01:27.183000,237.42,340.0,HARD,30,41.2,False
-49,51,0 days 00:01:27.245000,238.99,335.0,HARD,31,41.0,False
-50,51,0 days 00:01:27.360000,237.3,342.0,HARD,32,40.8,False
-51,51,0 days 00:01:27.395000,237.13,345.0,HARD,33,40.8,False
+lap_number,total_laps,position,gap_to_leader,gap_to_ahead,lap_time,average_speed,max_speed,tire_compound,tire_life_laps,track_temperature,rainfall
+1,51,11,6.223,0.62,0 days 00:01:33.340000,210.17,326.0,MEDIUM,1,42.5,False
+2,51,11,8.229,0.712,0 days 00:01:28.012000,236.87,330.0,MEDIUM,2,42.5,False
+3,51,11,9.799,0.898,0 days 00:01:27.546000,236.4,331.0,MEDIUM,3,43.2,False
+4,51,11,10.953,0.919,0 days 00:01:27.221000,240.13,341.0,MEDIUM,4,43.2,False
+5,51,11,11.563,0.917,0 days 00:01:27.033000,236.09,345.0,MEDIUM,5,43.1,False
+6,51,11,12.123,0.923,0 days 00:01:27.175000,236.74,343.0,MEDIUM,6,43.3,False
+7,51,11,12.694,0.387,0 days 00:01:26.929000,239.72,340.0,MEDIUM,7,43.6,False
+8,51,10,13.413,2.76,0 days 00:01:26.943000,238.45,351.0,MEDIUM,8,43.6,False
+9,51,10,14.32,3.387,0 days 00:01:27.383000,236.81,330.0,MEDIUM,9,43.6,False
+10,51,10,15.177,3.76,0 days 00:01:27.368000,232.42,331.0,MEDIUM,10,43.9,False
+11,51,10,15.829,3.984,0 days 00:01:27.343000,237.18,331.0,MEDIUM,11,43.9,False
+12,51,10,16.76,4.129,0 days 00:01:27.339000,236.76,332.0,MEDIUM,12,43.3,False
+13,51,10,17.031,3.995,0 days 00:01:27.158000,235.47,330.0,MEDIUM,13,43.1,False
+14,51,10,17.666,4.076,0 days 00:01:27.607000,235.97,332.0,MEDIUM,14,43.7,False
+15,51,10,17.366,0.469,0 days 00:01:26.929000,237.04,332.0,MEDIUM,15,43.7,False
+16,51,9,17.812,2.844,0 days 00:01:27.164000,238.88,333.0,MEDIUM,16,43.9,False
+17,51,9,18.235,2.415,0 days 00:01:27.156000,235.35,333.0,MEDIUM,17,43.7,False
+18,51,9,19.237,2.411,0 days 00:01:27.275000,238.66,333.0,MEDIUM,18,43.5,False
+19,51,9,20.117,2.28,0 days 00:01:27.318000,234.62,333.0,MEDIUM,19,43.6,False
+20,51,7,17.552,2.734,0 days 00:01:28.284000,235.31,333.0,MEDIUM,20,44.3,False
+21,51,6,16.826,4.269,0 days 00:01:32.377000,224.11,332.0,MEDIUM,21,44.3,False
+22,51,12,29.113,8.135,0 days 00:01:47.272000,191.14,322.0,HARD,4,43.7,False
+23,51,12,27.412,4.623,0 days 00:01:26.849000,236.5,329.0,HARD,5,43.7,False
+24,51,11,27.05,4.191,0 days 00:01:26.949000,238.49,331.0,HARD,6,43.5,False
+25,51,11,27.953,4.679,0 days 00:01:26.899000,237.13,331.0,HARD,7,43.5,False
+26,51,10,28.959,5.184,0 days 00:01:26.792000,238.18,331.0,HARD,8,43.4,False
+27,51,10,30.124,5.602,0 days 00:01:26.666000,238.7,331.0,HARD,9,43.4,False
+28,51,9,31.413,5.714,0 days 00:01:26.723000,234.46,331.0,HARD,10,43.3,False
+29,51,10,32.939,1.141,0 days 00:01:26.997000,236.12,332.0,HARD,11,42.9,False
+30,51,10,34.456,1.716,0 days 00:01:26.915000,238.87,333.0,HARD,12,42.5,False
+31,51,10,35.802,2.16,0 days 00:01:26.848000,240.21,335.0,HARD,13,42.8,False
+32,51,10,37.139,2.287,0 days 00:01:26.747000,235.57,332.0,HARD,14,42.6,False
+33,51,10,38.668,2.472,0 days 00:01:26.769000,238.33,332.0,HARD,15,42.4,False
+34,51,10,39.548,2.799,0 days 00:01:26.563000,236.92,332.0,HARD,16,42.5,False
+35,51,10,40.25,3.3,0 days 00:01:26.517000,239.63,333.0,HARD,17,42.6,False
+36,51,10,41.109,3.552,0 days 00:01:26.457000,237.1,332.0,HARD,18,42.5,False
+37,51,10,41.806,3.933,0 days 00:01:26.380000,238.39,333.0,HARD,19,42.5,False
+38,51,10,42.795,4.233,0 days 00:01:26.574000,237.75,333.0,HARD,20,42.8,False
+39,51,10,44.016,4.691,0 days 00:01:26.707000,238.65,332.0,HARD,21,42.8,False
+40,51,10,44.725,4.643,0 days 00:01:26.610000,235.8,333.0,HARD,22,42.8,False
+41,51,9,45.761,3.957,0 days 00:01:26.815000,236.82,334.0,HARD,23,42.9,False
+42,51,9,46.252,4.144,0 days 00:01:26.403000,239.47,332.0,HARD,24,42.5,False
+43,51,9,46.363,4.156,0 days 00:01:26.105000,240.15,334.0,HARD,25,42.5,False
+44,51,9,46.767,3.159,0 days 00:01:26.155000,241.7,332.0,HARD,26,41.7,False
+45,51,9,47.128,2.193,0 days 00:01:26.277000,241.25,333.0,HARD,27,41.7,False
+46,51,9,47.396,1.629,0 days 00:01:26.453000,238.02,335.0,HARD,28,41.8,False
+47,51,9,48.171,0.655,0 days 00:01:26.912000,236.31,336.0,HARD,29,41.2,False
+48,51,9,48.119,1.105,0 days 00:01:27.183000,237.42,340.0,HARD,30,41.2,False
+49,51,9,47.457,0.689,0 days 00:01:27.245000,238.99,335.0,HARD,31,41.0,False
+50,51,9,46.424,0.875,0 days 00:01:27.360000,237.3,342.0,HARD,32,40.8,False
+51,51,9,46.294,0.845,0 days 00:01:27.395000,237.13,345.0,HARD,33,40.8,False
diff --git a/scripts/ALONSO_2023_MONZA_RACE.csv b/scripts/ALONSO_2023_MONZA_RACE.csv
deleted file mode 100644
index 43a7223..0000000
--- a/scripts/ALONSO_2023_MONZA_RACE.csv
+++ /dev/null
@@ -1,16585 +0,0 @@
-,lap_number,total_laps,speed,overall_time,throttle,brake,tire_compound,tire_life_laps,track_temperature,rainfall
-0,1.0,51.0,0.0,0 days 01:22:21.734000,23.0,False,MEDIUM,1.0,42.5,False
-1,1.0,51.0,0.0,0 days 01:22:21.894000,23.0,False,MEDIUM,1.0,42.5,False
-2,1.0,51.0,4.0,0 days 01:22:22.214000,26.0,False,MEDIUM,1.0,42.5,False
-3,1.0,51.0,14.0,0 days 01:22:22.494000,24.0,False,MEDIUM,1.0,42.5,False
-4,1.0,51.0,24.0,0 days 01:22:22.774000,24.0,False,MEDIUM,1.0,42.5,False
-5,1.0,51.0,31.0,0 days 01:22:22.974000,26.0,False,MEDIUM,1.0,42.5,False
-6,1.0,51.0,38.0,0 days 01:22:23.254000,36.0,False,MEDIUM,1.0,42.5,False
-7,1.0,51.0,50.0,0 days 01:22:23.494000,41.0,False,MEDIUM,1.0,42.5,False
-8,1.0,51.0,58.0,0 days 01:22:23.694000,44.0,False,MEDIUM,1.0,42.5,False
-9,1.0,51.0,71.0,0 days 01:22:23.974000,55.0,False,MEDIUM,1.0,42.5,False
-10,1.0,51.0,88.0,0 days 01:22:24.414000,64.0,False,MEDIUM,1.0,42.5,False
-11,1.0,51.0,100.0,0 days 01:22:24.734000,69.0,False,MEDIUM,1.0,42.5,False
-12,1.0,51.0,109.0,0 days 01:22:24.974000,78.0,False,MEDIUM,1.0,42.5,False
-13,1.0,51.0,124.0,0 days 01:22:25.254000,97.0,False,MEDIUM,1.0,42.5,False
-14,1.0,51.0,133.0,0 days 01:22:25.414000,100.0,False,MEDIUM,1.0,42.5,False
-15,1.0,51.0,139.0,0 days 01:22:25.654000,100.0,False,MEDIUM,1.0,42.5,False
-16,1.0,51.0,153.0,0 days 01:22:25.894000,100.0,False,MEDIUM,1.0,42.5,False
-17,1.0,51.0,170.0,0 days 01:22:26.253000,100.0,False,MEDIUM,1.0,42.5,False
-18,1.0,51.0,182.0,0 days 01:22:26.613000,100.0,False,MEDIUM,1.0,42.5,False
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-6931,22.0,51.0,80.0,0 days 01:53:24.660000,73.0,False,HARD,4.0,43.7,False
-6932,22.0,51.0,80.0,0 days 01:53:24.980000,72.0,False,HARD,4.0,43.7,False
-6933,22.0,51.0,80.0,0 days 01:53:25.140000,72.0,False,HARD,4.0,43.7,False
-6934,22.0,51.0,80.0,0 days 01:53:25.580000,74.0,False,HARD,4.0,43.7,False
-6935,22.0,51.0,80.0,0 days 01:53:25.740000,73.0,False,HARD,4.0,43.7,False
-6936,22.0,51.0,80.0,0 days 01:53:26.060000,72.0,False,HARD,4.0,43.7,False
-6937,22.0,51.0,80.0,0 days 01:53:26.420000,71.0,False,HARD,4.0,43.7,False
-6938,22.0,51.0,80.0,0 days 01:53:26.580000,71.0,False,HARD,4.0,43.7,False
-6939,22.0,51.0,80.0,0 days 01:53:27.020000,70.0,False,HARD,4.0,43.7,False
-6940,22.0,51.0,80.0,0 days 01:53:27.220000,69.0,False,HARD,4.0,43.7,False
-6941,22.0,51.0,80.0,0 days 01:53:27.420000,69.0,False,HARD,4.0,43.7,False
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-6943,22.0,51.0,82.0,0 days 01:53:27.860000,47.0,False,HARD,4.0,43.7,False
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-6947,22.0,51.0,121.0,0 days 01:53:28.821000,73.0,False,HARD,4.0,43.7,False
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-6960,22.0,51.0,242.0,0 days 01:53:32.220000,100.0,False,HARD,4.0,43.7,False
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-6987,22.0,51.0,65.0,0 days 01:53:40.700000,0.0,False,HARD,4.0,43.7,False
-6988,22.0,51.0,64.0,0 days 01:53:40.940000,13.0,False,HARD,4.0,43.7,False
-6989,22.0,51.0,65.0,0 days 01:53:41.260000,25.0,False,HARD,4.0,43.7,False
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-7019,22.0,51.0,280.0,0 days 01:53:49.140000,100.0,False,HARD,4.0,43.7,False
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-13422,42.0,51.0,96.0,0 days 02:22:37.185000,52.0,False,HARD,24.0,42.5,False
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-13434,42.0,51.0,223.0,0 days 02:22:40.506000,100.0,False,HARD,24.0,42.5,False
-13435,42.0,51.0,226.0,0 days 02:22:40.666000,100.0,False,HARD,24.0,42.5,False
-13436,42.0,51.0,229.0,0 days 02:22:40.866000,100.0,False,HARD,24.0,42.5,False
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-13438,42.0,51.0,246.0,0 days 02:22:41.426000,100.0,False,HARD,24.0,42.5,False
-13439,42.0,51.0,251.0,0 days 02:22:41.706000,100.0,False,HARD,24.0,42.5,False
-13440,42.0,51.0,259.0,0 days 02:22:42.026000,100.0,False,HARD,24.0,42.5,False
-13441,42.0,51.0,265.0,0 days 02:22:42.305000,100.0,False,HARD,24.0,42.5,False
-13442,42.0,51.0,270.0,0 days 02:22:42.625000,99.0,False,HARD,24.0,42.5,False
-13443,42.0,51.0,273.0,0 days 02:22:42.785000,99.0,False,HARD,24.0,42.5,False
-13444,42.0,51.0,275.0,0 days 02:22:42.945000,100.0,False,HARD,24.0,42.5,False
-13445,42.0,51.0,278.0,0 days 02:22:43.145000,99.0,False,HARD,24.0,42.5,False
-13446,42.0,51.0,280.0,0 days 02:22:43.425000,100.0,False,HARD,24.0,42.5,False
-13447,42.0,51.0,283.0,0 days 02:22:43.705000,100.0,False,HARD,24.0,42.5,False
-13448,42.0,51.0,285.0,0 days 02:22:43.865000,99.0,False,HARD,24.0,42.5,False
-13449,42.0,51.0,286.0,0 days 02:22:44.145000,100.0,False,HARD,24.0,42.5,False
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-13451,42.0,51.0,292.0,0 days 02:22:44.545000,100.0,False,HARD,24.0,42.5,False
-13452,42.0,51.0,297.0,0 days 02:22:44.786000,100.0,False,HARD,24.0,42.5,False
-13453,42.0,51.0,298.0,0 days 02:22:45.026000,100.0,False,HARD,24.0,42.5,False
-13454,42.0,51.0,300.0,0 days 02:22:45.266000,100.0,False,HARD,24.0,42.5,False
-13455,42.0,51.0,302.0,0 days 02:22:45.586000,100.0,False,HARD,24.0,42.5,False
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-13457,42.0,51.0,307.0,0 days 02:22:46.146000,100.0,False,HARD,24.0,42.5,False
-13458,42.0,51.0,308.0,0 days 02:22:46.306000,100.0,False,HARD,24.0,42.5,False
-13459,42.0,51.0,308.0,0 days 02:22:46.466000,100.0,False,HARD,24.0,42.5,False
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-13461,42.0,51.0,311.0,0 days 02:22:47.026000,100.0,False,HARD,24.0,42.5,False
-13462,42.0,51.0,313.0,0 days 02:22:47.306000,100.0,False,HARD,24.0,42.5,False
-13463,42.0,51.0,315.0,0 days 02:22:47.506000,100.0,False,HARD,24.0,42.5,False
-13464,42.0,51.0,316.0,0 days 02:22:47.866000,100.0,False,HARD,24.0,42.5,False
-13465,42.0,51.0,317.0,0 days 02:22:48.106000,100.0,False,HARD,24.0,42.5,False
-13466,42.0,51.0,318.0,0 days 02:22:48.506000,100.0,False,HARD,24.0,42.5,False
-13467,42.0,51.0,318.0,0 days 02:22:48.786000,100.0,False,HARD,24.0,42.5,False
-13468,42.0,51.0,318.0,0 days 02:22:48.985000,100.0,False,HARD,24.0,42.5,False
-13469,42.0,51.0,317.0,0 days 02:22:49.265000,100.0,False,HARD,24.0,42.5,False
-13470,42.0,51.0,317.0,0 days 02:22:49.545000,100.0,False,HARD,24.0,42.5,False
-13471,42.0,51.0,316.0,0 days 02:22:49.745000,100.0,False,HARD,24.0,42.5,False
-13472,42.0,51.0,315.0,0 days 02:22:49.905000,100.0,False,HARD,24.0,42.5,False
-13473,42.0,51.0,315.0,0 days 02:22:50.105000,100.0,False,HARD,24.0,42.5,False
-13474,42.0,51.0,315.0,0 days 02:22:50.345000,100.0,False,HARD,24.0,42.5,False
-13475,42.0,51.0,313.0,0 days 02:22:50.585000,100.0,False,HARD,24.0,42.5,False
-13476,42.0,51.0,298.0,0 days 02:22:50.945000,0.0,False,HARD,24.0,42.5,False
-13477,42.0,51.0,273.0,0 days 02:22:51.145000,0.0,True,HARD,24.0,42.5,False
-13478,42.0,51.0,224.0,0 days 02:22:51.546000,0.0,True,HARD,24.0,42.5,False
-13479,42.0,51.0,174.0,0 days 02:22:51.986000,0.0,True,HARD,24.0,42.5,False
-13480,42.0,51.0,157.0,0 days 02:22:52.186000,0.0,True,HARD,24.0,42.5,False
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-13483,42.0,51.0,113.0,0 days 02:22:53.106000,0.0,True,HARD,24.0,42.5,False
-13484,42.0,51.0,105.0,0 days 02:22:53.426000,0.0,True,HARD,24.0,42.5,False
-13485,42.0,51.0,98.0,0 days 02:22:53.786000,0.0,False,HARD,24.0,42.5,False
-13486,42.0,51.0,95.0,0 days 02:22:54.266000,16.0,False,HARD,24.0,42.5,False
-13487,42.0,51.0,98.0,0 days 02:22:54.426000,32.0,False,HARD,24.0,42.5,False
-13488,42.0,51.0,101.0,0 days 02:22:54.586000,42.0,False,HARD,24.0,42.5,False
-13489,42.0,51.0,107.0,0 days 02:22:54.746000,45.0,False,HARD,24.0,42.5,False
-13490,42.0,51.0,111.0,0 days 02:22:55.106000,23.0,False,HARD,24.0,42.5,False
-13491,42.0,51.0,112.0,0 days 02:22:55.306000,27.0,False,HARD,24.0,42.5,False
-13492,42.0,51.0,111.0,0 days 02:22:55.466000,35.0,False,HARD,24.0,42.5,False
-13493,42.0,51.0,119.0,0 days 02:22:55.865000,46.0,False,HARD,24.0,42.5,False
-13494,42.0,51.0,128.0,0 days 02:22:56.105000,54.0,False,HARD,24.0,42.5,False
-13495,42.0,51.0,144.0,0 days 02:22:56.585000,74.0,False,HARD,24.0,42.5,False
-13496,42.0,51.0,160.0,0 days 02:22:56.985000,86.0,False,HARD,24.0,42.5,False
-13497,42.0,51.0,165.0,0 days 02:22:57.145000,100.0,False,HARD,24.0,42.5,False
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-13500,42.0,51.0,194.0,0 days 02:22:57.785000,100.0,False,HARD,24.0,42.5,False
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-13503,42.0,51.0,220.0,0 days 02:22:58.626000,100.0,False,HARD,24.0,42.5,False
-13504,42.0,51.0,226.0,0 days 02:22:58.826000,100.0,False,HARD,24.0,42.5,False
-13505,42.0,51.0,229.0,0 days 02:22:58.986000,100.0,False,HARD,24.0,42.5,False
-13506,42.0,51.0,234.0,0 days 02:22:59.146000,100.0,False,HARD,24.0,42.5,False
-13507,42.0,51.0,243.0,0 days 02:22:59.506000,100.0,False,HARD,24.0,42.5,False
-13508,42.0,51.0,244.0,0 days 02:22:59.666000,100.0,False,HARD,24.0,42.5,False
-13509,42.0,51.0,247.0,0 days 02:22:59.866000,100.0,False,HARD,24.0,42.5,False
-13510,42.0,51.0,248.0,0 days 02:23:00.026000,100.0,False,HARD,24.0,42.5,False
-13511,42.0,51.0,251.0,0 days 02:23:00.346000,0.0,True,HARD,24.0,42.5,False
-13512,42.0,51.0,220.0,0 days 02:23:00.666000,0.0,True,HARD,24.0,42.5,False
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-13514,42.0,51.0,193.0,0 days 02:23:01.106000,0.0,True,HARD,24.0,42.5,False
-13515,42.0,51.0,183.0,0 days 02:23:01.386000,0.0,True,HARD,24.0,42.5,False
-13516,42.0,51.0,165.0,0 days 02:23:02.386000,5.0,False,HARD,24.0,42.5,False
-13517,42.0,51.0,160.0,0 days 02:23:02.665000,44.0,False,HARD,24.0,42.5,False
-13518,42.0,51.0,162.0,0 days 02:23:02.945000,60.0,False,HARD,24.0,42.5,False
-13519,42.0,51.0,171.0,0 days 02:23:03.225000,68.0,False,HARD,24.0,42.5,False
-13520,42.0,51.0,182.0,0 days 02:23:03.585000,100.0,False,HARD,24.0,42.5,False
-13521,42.0,51.0,187.0,0 days 02:23:03.905000,100.0,False,HARD,24.0,42.5,False
-13522,42.0,51.0,194.0,0 days 02:23:04.105000,100.0,False,HARD,24.0,42.5,False
-13523,42.0,51.0,202.0,0 days 02:23:04.385000,100.0,False,HARD,24.0,42.5,False
-13524,42.0,51.0,212.0,0 days 02:23:04.866000,100.0,False,HARD,24.0,42.5,False
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-13526,42.0,51.0,222.0,0 days 02:23:05.386000,100.0,False,HARD,24.0,42.5,False
-13527,42.0,51.0,227.0,0 days 02:23:05.706000,100.0,False,HARD,24.0,42.5,False
-13528,42.0,51.0,232.0,0 days 02:23:05.906000,100.0,False,HARD,24.0,42.5,False
-13529,42.0,51.0,233.0,0 days 02:23:06.066000,100.0,False,HARD,24.0,42.5,False
-13530,42.0,51.0,238.0,0 days 02:23:06.426000,100.0,False,HARD,24.0,42.5,False
-13531,42.0,51.0,234.0,0 days 02:23:06.626000,100.0,True,HARD,24.0,42.5,False
-13532,42.0,51.0,203.0,0 days 02:23:06.906000,0.0,True,HARD,24.0,42.5,False
-13533,42.0,51.0,203.0,0 days 02:23:07.066000,0.0,True,HARD,24.0,42.5,False
-13534,42.0,51.0,173.0,0 days 02:23:07.386000,0.0,True,HARD,24.0,42.5,False
-13535,42.0,51.0,165.0,0 days 02:23:07.746000,0.0,True,HARD,24.0,42.5,False
-13536,42.0,51.0,154.0,0 days 02:23:08.146000,0.0,False,HARD,24.0,42.5,False
-13537,42.0,51.0,153.0,0 days 02:23:08.346000,23.0,False,HARD,24.0,42.5,False
-13538,42.0,51.0,151.0,0 days 02:23:08.546000,48.0,False,HARD,24.0,42.5,False
-13539,42.0,51.0,152.0,0 days 02:23:08.706000,51.0,False,HARD,24.0,42.5,False
-13540,42.0,51.0,156.0,0 days 02:23:08.946000,60.0,False,HARD,24.0,42.5,False
-13541,42.0,51.0,158.0,0 days 02:23:09.106000,67.0,False,HARD,24.0,42.5,False
-13542,42.0,51.0,164.0,0 days 02:23:09.305000,89.0,False,HARD,24.0,42.5,False
-13543,42.0,51.0,173.0,0 days 02:23:09.505000,100.0,False,HARD,24.0,42.5,False
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-13545,42.0,51.0,189.0,0 days 02:23:09.905000,100.0,False,HARD,24.0,42.5,False
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-13547,42.0,51.0,196.0,0 days 02:23:10.305000,100.0,False,HARD,24.0,42.5,False
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-13549,42.0,51.0,216.0,0 days 02:23:10.705000,100.0,False,HARD,24.0,42.5,False
-13550,42.0,51.0,224.0,0 days 02:23:11.065000,100.0,False,HARD,24.0,42.5,False
-13551,42.0,51.0,238.0,0 days 02:23:11.506000,100.0,False,HARD,24.0,42.5,False
-13552,42.0,51.0,247.0,0 days 02:23:11.826000,100.0,False,HARD,24.0,42.5,False
-13553,42.0,51.0,252.0,0 days 02:23:12.066000,100.0,False,HARD,24.0,42.5,False
-13554,42.0,51.0,258.0,0 days 02:23:12.306000,100.0,False,HARD,24.0,42.5,False
-13555,42.0,51.0,260.0,0 days 02:23:12.466000,100.0,False,HARD,24.0,42.5,False
-13556,42.0,51.0,265.0,0 days 02:23:12.706000,100.0,False,HARD,24.0,42.5,False
-13557,42.0,51.0,269.0,0 days 02:23:12.946000,100.0,False,HARD,24.0,42.5,False
-13558,42.0,51.0,274.0,0 days 02:23:13.266000,100.0,False,HARD,24.0,42.5,False
-13559,42.0,51.0,275.0,0 days 02:23:13.466000,98.0,False,HARD,24.0,42.5,False
-13560,42.0,51.0,279.0,0 days 02:23:13.706000,100.0,False,HARD,24.0,42.5,False
-13561,42.0,51.0,283.0,0 days 02:23:13.906000,100.0,False,HARD,24.0,42.5,False
-13562,42.0,51.0,283.0,0 days 02:23:14.066000,100.0,False,HARD,24.0,42.5,False
-13563,42.0,51.0,288.0,0 days 02:23:14.346000,100.0,False,HARD,24.0,42.5,False
-13564,42.0,51.0,293.0,0 days 02:23:14.786000,100.0,False,HARD,24.0,42.5,False
-13565,42.0,51.0,297.0,0 days 02:23:15.146000,100.0,False,HARD,24.0,42.5,False
-13566,42.0,51.0,297.0,0 days 02:23:15.306000,100.0,False,HARD,24.0,42.5,False
-13567,42.0,51.0,299.0,0 days 02:23:15.546000,100.0,False,HARD,24.0,42.5,False
-13568,42.0,51.0,302.0,0 days 02:23:15.786000,100.0,False,HARD,24.0,42.5,False
-13569,42.0,51.0,306.0,0 days 02:23:16.145000,100.0,False,HARD,24.0,42.5,False
-13570,42.0,51.0,309.0,0 days 02:23:16.385000,100.0,False,HARD,24.0,42.5,False
-13571,42.0,51.0,310.0,0 days 02:23:16.705000,100.0,False,HARD,24.0,42.5,False
-13572,42.0,51.0,313.0,0 days 02:23:16.905000,100.0,False,HARD,24.0,42.5,False
-13573,42.0,51.0,314.0,0 days 02:23:17.145000,100.0,False,HARD,24.0,42.5,False
-13574,42.0,51.0,315.0,0 days 02:23:17.345000,100.0,False,HARD,24.0,42.5,False
-13575,42.0,51.0,317.0,0 days 02:23:17.625000,100.0,False,HARD,24.0,42.5,False
-13576,42.0,51.0,317.0,0 days 02:23:17.866000,100.0,False,HARD,24.0,42.5,False
-13577,42.0,51.0,318.0,0 days 02:23:18.226000,100.0,False,HARD,24.0,42.5,False
-13578,42.0,51.0,318.0,0 days 02:23:18.506000,99.0,False,HARD,24.0,42.5,False
-13579,42.0,51.0,317.0,0 days 02:23:18.746000,100.0,False,HARD,24.0,42.5,False
-13580,42.0,51.0,316.0,0 days 02:23:19.186000,100.0,False,HARD,24.0,42.5,False
-13581,42.0,51.0,316.0,0 days 02:23:19.346000,100.0,False,HARD,24.0,42.5,False
-13582,42.0,51.0,315.0,0 days 02:23:19.586000,100.0,False,HARD,24.0,42.5,False
-13583,42.0,51.0,314.0,0 days 02:23:19.946000,100.0,False,HARD,24.0,42.5,False
-13584,42.0,51.0,312.0,0 days 02:23:20.386000,100.0,False,HARD,24.0,42.5,False
-13585,42.0,51.0,311.0,0 days 02:23:20.746000,100.0,False,HARD,24.0,42.5,False
-13586,42.0,51.0,311.0,0 days 02:23:20.946000,100.0,True,HARD,24.0,42.5,False
-13587,42.0,51.0,311.0,0 days 02:23:21.146000,100.0,True,HARD,24.0,42.5,False
-13588,42.0,51.0,273.0,0 days 02:23:21.306000,0.0,True,HARD,24.0,42.5,False
-13589,42.0,51.0,179.0,0 days 02:23:22.226000,0.0,True,HARD,24.0,42.5,False
-13590,42.0,51.0,165.0,0 days 02:23:22.585000,0.0,True,HARD,24.0,42.5,False
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-13642,42.0,51.0,320.0,0 days 02:23:36.425000,100.0,False,HARD,24.0,42.5,False
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-13687,42.0,51.0,308.0,0 days 02:23:47.866000,100.0,False,HARD,24.0,42.5,False
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-13851,43.0,51.0,222.0,0 days 02:24:31.706000,100.0,False,HARD,25.0,42.5,False
-13852,43.0,51.0,227.0,0 days 02:24:31.986000,100.0,False,HARD,25.0,42.5,False
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-13856,43.0,51.0,191.0,0 days 02:24:33.386000,0.0,True,HARD,25.0,42.5,False
-13857,43.0,51.0,179.0,0 days 02:24:33.586000,0.0,True,HARD,25.0,42.5,False
-13858,43.0,51.0,171.0,0 days 02:24:33.786000,0.0,True,HARD,25.0,42.5,False
-13859,43.0,51.0,164.0,0 days 02:24:34.026000,1.0,True,HARD,25.0,42.5,False
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-13862,43.0,51.0,154.0,0 days 02:24:34.866000,55.0,False,HARD,25.0,42.5,False
-13863,43.0,51.0,158.0,0 days 02:24:35.146000,62.0,False,HARD,25.0,42.5,False
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-13873,43.0,51.0,250.0,0 days 02:24:38.226000,100.0,False,HARD,25.0,42.5,False
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-13883,43.0,51.0,287.0,0 days 02:24:40.626000,100.0,False,HARD,25.0,42.5,False
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-13897,43.0,51.0,316.0,0 days 02:24:44.546000,100.0,False,HARD,25.0,42.5,False
-13898,43.0,51.0,315.0,0 days 02:24:44.986000,100.0,False,HARD,25.0,42.5,False
-13899,43.0,51.0,316.0,0 days 02:24:45.266000,100.0,False,HARD,25.0,42.5,False
-13900,43.0,51.0,315.0,0 days 02:24:45.506000,100.0,False,HARD,25.0,42.5,False
-13901,43.0,51.0,315.0,0 days 02:24:45.706000,100.0,False,HARD,25.0,42.5,False
-13902,43.0,51.0,314.0,0 days 02:24:45.946000,100.0,False,HARD,25.0,42.5,False
-13903,43.0,51.0,313.0,0 days 02:24:46.146000,100.0,False,HARD,25.0,42.5,False
-13904,43.0,51.0,312.0,0 days 02:24:46.466000,99.0,False,HARD,25.0,42.5,False
-13905,43.0,51.0,311.0,0 days 02:24:46.746000,100.0,False,HARD,25.0,42.5,False
-13906,43.0,51.0,311.0,0 days 02:24:47.066000,100.0,False,HARD,25.0,42.5,False
-13907,43.0,51.0,306.0,0 days 02:24:47.346000,0.0,True,HARD,25.0,42.5,False
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-13909,43.0,51.0,224.0,0 days 02:24:47.906000,0.0,True,HARD,25.0,42.5,False
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-13916,43.0,51.0,170.0,0 days 02:24:49.706000,73.0,False,HARD,25.0,42.5,False
-13917,43.0,51.0,171.0,0 days 02:24:49.867000,86.0,False,HARD,25.0,42.5,False
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diff --git a/scripts/simulate_pi_stream.py b/scripts/simulate_pi_stream.py
deleted file mode 100644
index c10477a..0000000
--- a/scripts/simulate_pi_stream.py
+++ /dev/null
@@ -1,182 +0,0 @@
-"""
-Raspberry Pi Telemetry Stream Simulator - Lap-Level Data
-
-Reads the ALONSO_2023_MONZA_LAPS.csv file lap by lap and simulates
-live telemetry streaming from a Raspberry Pi sensor.
-Sends data to the HPC simulation layer via HTTP POST at fixed 
-1-minute intervals between laps.
-
-Usage:
-    python simulate_pi_stream.py
-    python simulate_pi_stream.py --interval 30  # 30 seconds between laps
-"""
-
-import argparse
-import time
-import sys
-from pathlib import Path
-from typing import Dict, Any
-import pandas as pd
-import requests
-
-
-def load_lap_csv(filepath: Path) -> pd.DataFrame:
-    """Load lap-level telemetry data from CSV file."""
-    df = pd.read_csv(filepath)
-    
-    # Convert lap_time to timedelta if it's not already
-    if 'lap_time' in df.columns and df['lap_time'].dtype == 'object':
-        df['lap_time'] = pd.to_timedelta(df['lap_time'])
-    
-    print(f"✓ Loaded {len(df)} laps from {filepath}")
-    print(f"   Laps: {df['lap_number'].min():.0f} → {df['lap_number'].max():.0f}")
-    
-    return df
-
-
-def lap_to_json(row: pd.Series) -> Dict[str, Any]:
-    """Convert a lap DataFrame row to a JSON-compatible dictionary."""
-    data = {
-        'lap_number': int(row['lap_number']) if pd.notna(row['lap_number']) else None,
-        'total_laps': int(row['total_laps']) if pd.notna(row['total_laps']) else None,
-        'lap_time': str(row['lap_time']) if pd.notna(row['lap_time']) else None,
-        'average_speed': float(row['average_speed']) if pd.notna(row['average_speed']) else 0.0,
-        'max_speed': float(row['max_speed']) if pd.notna(row['max_speed']) else 0.0,
-        'tire_compound': str(row['tire_compound']) if pd.notna(row['tire_compound']) else 'UNKNOWN',
-        'tire_life_laps': int(row['tire_life_laps']) if pd.notna(row['tire_life_laps']) else 0,
-        'track_temperature': float(row['track_temperature']) if pd.notna(row['track_temperature']) else 0.0,
-        'rainfall': bool(row['rainfall'])
-    }
-    return data
-
-
-def simulate_stream(
-    df: pd.DataFrame,
-    endpoint: str,
-    interval: int = 60,
-    start_lap: int = 1,
-    end_lap: int = None
-):
-    """
-    Simulate live telemetry streaming with fixed interval between laps.
-    
-    Args:
-        df: DataFrame with lap-level telemetry data
-        endpoint: HPC API endpoint URL
-        interval: Fixed interval in seconds between laps (default: 60 seconds)
-        start_lap: Starting lap number
-        end_lap: Ending lap number (None = all laps)
-    """
-    # Filter by lap range
-    filtered_df = df[df['lap_number'] >= start_lap].copy()
-    if end_lap:
-        filtered_df = filtered_df[filtered_df['lap_number'] <= end_lap].copy()
-    
-    if len(filtered_df) == 0:
-        print("❌ No laps in specified lap range")
-        return
-    
-    # Reset index for easier iteration
-    filtered_df = filtered_df.reset_index(drop=True)
-    
-    print(f"\n🏁 Starting lap-level telemetry stream simulation")
-    print(f"   Endpoint: {endpoint}")
-    print(f"   Laps: {start_lap} → {end_lap or 'end'}")
-    print(f"   Interval: {interval} seconds between laps")
-    print(f"   Total laps: {len(filtered_df)}")
-    print(f"   Est. duration: {len(filtered_df) * interval / 60:.1f} minutes\n")
-    
-    sent_count = 0
-    error_count = 0
-    
-    try:
-        for i in range(len(filtered_df)):
-            row = filtered_df.iloc[i]
-            lap_num = int(row['lap_number'])
-            
-            # Convert lap to JSON
-            lap_data = lap_to_json(row)
-            
-            # Send lap data
-            try:
-                response = requests.post(
-                    endpoint,
-                    json=lap_data,
-                    timeout=5.0
-                )
-                
-                if response.status_code == 200:
-                    sent_count += 1
-                    progress = (i + 1) / len(filtered_df) * 100
-                    
-                    # Print lap info
-                    print(f"  📡 Lap {lap_num}/{int(row['total_laps'])}: "
-                          f"Avg Speed: {row['average_speed']:.1f} km/h, "
-                          f"Tire: {row['tire_compound']} (age: {int(row['tire_life_laps'])} laps) "
-                          f"[{progress:.0f}%]")
-                    
-                    # Show response if it contains strategies
-                    try:
-                        response_data = response.json()
-                        if 'strategies_generated' in response_data:
-                            print(f"       ✓ Generated {response_data['strategies_generated']} strategies")
-                    except:
-                        pass
-                else:
-                    error_count += 1
-                    print(f"  ⚠ Lap {lap_num}: HTTP {response.status_code}: {response.text[:100]}")
-                    
-            except requests.RequestException as e:
-                error_count += 1
-                print(f"  ⚠ Lap {lap_num}: Connection error: {str(e)[:100]}")
-            
-            # Sleep for fixed interval before next lap (except for last lap)
-            if i < len(filtered_df) - 1:
-                time.sleep(interval)
-        
-        print(f"\n✅ Stream complete!")
-        print(f"   Sent: {sent_count} laps")
-        print(f"   Errors: {error_count}")
-        
-    except KeyboardInterrupt:
-        print(f"\n⏸ Stream interrupted by user")
-        print(f"   Sent: {sent_count}/{len(filtered_df)} laps")
-
-
-def main():
-    parser = argparse.ArgumentParser(
-        description="Simulate Raspberry Pi lap-level telemetry streaming"
-    )
-    parser.add_argument("--endpoint", type=str, default="http://localhost:8000/ingest/telemetry",
-                        help="HPC API endpoint (default: http://localhost:8000/ingest/telemetry)")
-    parser.add_argument("--interval", type=int, default=60, 
-                        help="Fixed interval in seconds between laps (default: 60)")
-    parser.add_argument("--start-lap", type=int, default=1, help="Starting lap number")
-    parser.add_argument("--end-lap", type=int, default=None, help="Ending lap number")
-    
-    args = parser.parse_args()
-    
-    try:
-        # Hardcoded CSV file location in the same folder as this script
-        script_dir = Path(__file__).parent
-        data_path = script_dir / "ALONSO_2023_MONZA_LAPS.csv"
-        
-        df = load_lap_csv(data_path)
-        simulate_stream(
-            df,
-            args.endpoint,
-            args.interval,
-            args.start_lap,
-            args.end_lap
-        )
-    except FileNotFoundError:
-        print(f"❌ File not found: {data_path}")
-        print(f"   Make sure ALONSO_2023_MONZA_LAPS.csv is in the scripts/ folder")
-        sys.exit(1)
-    except Exception as e:
-        print(f"❌ Error: {e}")
-        raise
-
-
-if __name__ == "__main__":
-    main()
\ No newline at end of file
diff --git a/scripts/simulate_pi_websocket.py b/scripts/simulate_pi_websocket.py
index 36a23c4..3861d57 100644
--- a/scripts/simulate_pi_websocket.py
+++ b/scripts/simulate_pi_websocket.py
@@ -66,6 +66,9 @@ class PiSimulator:
         return {
             "lap_number": int(row["lap_number"]),
             "total_laps": int(row["total_laps"]),
+            "position": int(row["position"]) if pd.notna(row["position"]) else 10,
+            "gap_to_leader": float(row["gap_to_leader"]) if pd.notna(row["gap_to_leader"]) else 0.0,
+            "gap_to_ahead": float(row["gap_to_ahead"]) if pd.notna(row["gap_to_ahead"]) else 0.0,
             "lap_time": str(row["lap_time"]),
             "average_speed": float(row["average_speed"]),
             "max_speed": float(row["max_speed"]),