Pit Stop Optimizer

Overview

The pit stop optimization system manages pit stop timing, tire compound selection, and race strategy during simulations. It includes dynamic strategy adaptation for weather changes, safety car periods, and undercut/overcut opportunities. Source: race_engine.py

Pit Stop Constants

PIT_LOSS

Time penalty for making a pit stop (pit lane time loss). Source: race_engine.py:20

PIT_LOSS = 22.0  # seconds lost in pit lane

PIT_LOSS

float

default:"22.0"

Fixed time cost in seconds for pit stop execution

This includes:

Pit lane speed limit penalty
Deceleration and acceleration time
Actual stop time (~3-4 seconds)
Pit entry/exit time loss

Usage Example:

from race_engine import PIT_LOSS

race_time = 1500.0  # seconds
pit_stops = 1
total_time = race_time + (pit_stops * PIT_LOSS)

print(f"Race time with {pit_stops} stop(s): {total_time:.1f}s")

Output:

Race time with 1 stop(s): 1522.0s

Race Strategy Presets

STRATEGIES

Predefined pit stop strategies for dry conditions. Source: race_engine.py:84

STRATEGIES = {
    "S-M":  {"start": "SOFT",   "pits": [18],       "compounds": ["SOFT",   "MEDIUM"]},
    "M-H":  {"start": "MEDIUM", "pits": [26],       "compounds": ["MEDIUM", "HARD"]},
    "H-M":  {"start": "HARD",   "pits": [34],       "compounds": ["HARD",   "MEDIUM"]},
    "S-M-H":{"start": "SOFT",   "pits": [15, 32],   "compounds": ["SOFT",   "MEDIUM", "HARD"]},
    "M-M":  {"start": "MEDIUM", "pits": [28],       "compounds": ["MEDIUM", "MEDIUM"]},
}

start

string

Starting tire compound

pits

list

List of planned pit stop lap numbers

compounds

list

Ordered list of tire compounds to use (includes starting tire)

Strategy Details:

Strategy	Type	Starting Tire	Pit Lap(s)	Compounds Used	Best For
S-M	One-stop	SOFT	18	Soft → Medium	Qualifying pace, track position
M-H	One-stop	MEDIUM	26	Medium → Hard	Balanced approach
H-M	One-stop	HARD	34	Hard → Medium	Long first stint
S-M-H	Two-stop	SOFT	15, 32	Soft → Med → Hard	Maximum pace
M-M	One-stop	MEDIUM	28	Medium → Medium	Consistent pace

WET_STRATEGY

Strategy preset for wet weather races. Source: race_engine.py:93

WET_STRATEGY = {
    "start": "WET", 
    "pits": [], 
    "compounds": ["WET"]
}

start

string

Starts on full wet tires

pits

list

Empty list - no planned stops (until track dries)

compounds

list

Only wet tires used

Note: Strategy adapts dynamically if weather changes during race.

Strategy Assignment

assign_strategies

Assigns racing strategies to all drivers based on weather and grid position. Source: race_engine.py:168

def assign_strategies(weather):
    """Assign strategies to all 20 drivers"""

weather

string

Starting weather condition: “DRY”, “LIGHT_RAIN”, or “HEAVY_RAIN”

assignments

dict

Dictionary mapping driver codes to strategy objects

Strategy Assignment Logic:

# Heavy rain
if weather == "HEAVY_RAIN":
    strategy = WET_STRATEGY
    
# Light rain
elif weather == "LIGHT_RAIN":
    strategy = {
        "start": "INTER",
        "pits": [26],
        "compounds": ["INTER", "MEDIUM"]
    }
    
# Dry conditions
else:
    # Top teams (P1-P6)
    if grid_pos <= 6:
        strategy = random.choice(["S-M", "M-H", "S-M-H"])
    
    # Midfield (P7-P12)
    elif grid_pos <= 12:
        strategy = random.choice(["M-H", "H-M", "M-M"])
    
    # Backmarkers (P13-P20)
    else:
        strategy = random.choice(["H-M", "M-H"])

Example:

from race_engine import assign_strategies

strategies = assign_strategies(weather="DRY")

for driver_code, strategy in strategies.items():
    print(f"{driver_code}: {strategy['start']} → {strategy['compounds']}")

Output:

VER: SOFT → ['SOFT', 'MEDIUM']
HAM: MEDIUM → ['MEDIUM', 'HARD']
LEC: SOFT → ['SOFT', 'MEDIUM', 'HARD']
...

Pit Stop Decision Logic

Pit Stop Trigger

Pit stops are triggered by strategic planning or opportunistic timing. Source: race_engine.py:272-281

# Planned pit stop
pitting = False
if car.pit_stops and lap == car.pit_stops[0]:
    pitting = True

# Opportunistic SC pit
elif sc_active and car.pit_stops and abs(lap - car.pit_stops[0]) <= 3:
    pitting = True
    car.events.append(f"L{lap}: Pit under Safety Car!")

Pit Stop Conditions:

Planned Stop: Current lap matches next planned pit lap
Safety Car Window: Within 3 laps of planned stop AND safety car is out
Weather Change: Automatic tire change if current tire unsuitable

Pit Stop Execution

When a pit stop is executed, the following occurs: Source: race_engine.py:283-298

if pitting:
    car.pit_stops.pop(0)  # Remove executed pit from queue
    car.compound_idx += 1
    next_cpd = car.compounds[min(car.compound_idx, len(car.compounds)-1)]
    
    # Handle wet/dry transitions
    if current_weather in ["LIGHT_RAIN","HEAVY_RAIN"] and next_cpd in ["SOFT","MEDIUM","HARD"]:
        next_cpd = "INTER"
    elif current_weather == "DRY" and next_cpd == "WET":
        next_cpd = "MEDIUM"
    
    car.tire = next_cpd
    car.tire_age = 0
    car.pit_count += 1
    car.pit_laps.append(lap)
    car.total_time += PIT_LOSS
    car.events.append(f"L{lap}: PIT → {next_cpd} tires (pit #{car.pit_count})")

Pit Stop Effects:

Time Penalty: +22 seconds to total race time
Tire Reset: New compound, tire age reset to 0
Strategy Update: Next compound from planned sequence
Weather Adaptation: Automatic compound override if conditions changed

CarState Attributes

Source: race_engine.py:97-128

pit_count

int

Total number of pit stops completed

pit_stops

list

Remaining planned pit stop lap numbers (consumed as pits occur)

pit_laps

list

List of laps on which pit stops were actually taken

tire

string

Current tire compound on the car

tire_age

int

Number of laps completed on current tires

compounds

list

Full list of compounds in the strategy

compound_idx

int

Index of current compound in the compounds list

Example:

# After 2 pit stops
car.pit_count = 2
car.pit_laps = [15, 32]
car.tire = "HARD"
car.tire_age = 5
car.compounds = ["SOFT", "MEDIUM", "HARD"]
car.compound_idx = 2

Complete Pit Strategy Examples

from race_engine import simulate_race

# Run simulation with seed for reproducibility
result = simulate_race(weather="DRY", circuit="STANDARD", seed=42)

# Analyze pit strategies
for driver_result in result['results'][:5]:
    print(f"\nP{driver_result['position']}: {driver_result['name']}")
    print(f"  Pit stops: {driver_result['pit_count']}")
    print(f"  Pit laps: {driver_result['pit_laps']}")
    print(f"  Tires used: {' → '.join(driver_result['tires_used'])}")
    print(f"  Total time: {driver_result['total_time']:.1f}s")
    print(f"  Fastest lap: {driver_result['fastest_lap']:.3f}s")

Race Result Analysis

Pit Stop Statistics

After a race simulation, detailed pit stop data is available in results. Result Fields:

pit_count

int

Total number of pit stops completed

pit_laps

list

List of lap numbers when pits occurred

tires_used

list

Chronological list of tire compounds used

Example Analysis:

result = simulate_race(weather="DRY", seed=42)

print("PIT STOP ANALYSIS")
print("="*60)

# Analyze winner's strategy
winner = result['results'][0]
print(f"\nWinner: {winner['name']}")
print(f"Strategy: {' → '.join(winner['tires_used'])}")
print(f"Pit stops: {winner['pit_count']} (laps {winner['pit_laps']})")

# Compare strategies
stop_counts = {}
for driver in result['results']:
    stops = driver['pit_count']
    stop_counts[stops] = stop_counts.get(stops, 0) + 1

print("\nStrategy Distribution:")
for stops, count in sorted(stop_counts.items()):
    print(f"  {stops}-stop: {count} drivers")

# Average pit lap
all_pit_laps = [lap for d in result['results'] for lap in d['pit_laps']]
avg_pit_lap = sum(all_pit_laps) / len(all_pit_laps) if all_pit_laps else 0
print(f"\nAverage first pit: Lap {avg_pit_lap:.1f}")

Output:

PIT STOP ANALYSIS
============================================================

Winner: Max Verstappen
Strategy: SOFT → MEDIUM
Pit stops: 1 (laps [18])

Strategy Distribution:
  0-stop: 2 drivers
  1-stop: 14 drivers
  2-stop: 4 drivers

Average first pit: Lap 22.3

Dynamic Strategy Adaptation

Weather-Based Adaptation

Strategies automatically adapt when weather changes mid-race. Source: race_engine.py:287-291

# Wet weather adaptation
if current_weather in ["LIGHT_RAIN","HEAVY_RAIN"] and next_cpd in ["SOFT","MEDIUM","HARD"]:
    next_cpd = "INTER"

# Dry weather adaptation
elif current_weather == "DRY" and next_cpd == "WET":
    next_cpd = "MEDIUM"

Adaptation Rules:

Planned Tire	Weather Change	Actual Tire
SOFT/MED/HARD	→ LIGHT_RAIN	INTER
SOFT/MED/HARD	→ HEAVY_RAIN	INTER
WET	→ DRY	MEDIUM
INTER	→ DRY	Unchanged*

*INTER can handle drying conditions for several laps

Strategy Comparison

One-Stop vs Two-Stop
Tire Selection
Safety Car Impact

One-Stop Strategy:

Single pit stop (~22s penalty)
Longer stints with more degradation
Simpler execution, less risky
Best when tire deg is low

Two-Stop Strategy:

Two pit stops (~44s penalty)
Shorter stints, fresher tires
More complex, traffic risk
Best when tire deg is high

Typical Time Comparison (50 laps):

One-stop: Base + 22s + high deg
Two-stop: Base + 44s + low deg
Breakeven depends on deg rate

Starting Tire Impact:

Start	Pros	Cons	Best Grid Pos
SOFT	Fastest start, gain positions	Early pit required	P1-P5
MEDIUM	Balanced, longer stint	Slower start pace	P6-P12
HARD	Long stint, late pit	Very slow start	P13-P20

Track position > tire advantage for midfield teams.

Pit Strategy Optimization Tips

Undercut vs Overcut

Undercut (Pit Earlier):

Pit before competitor to gain time on fresh tires
Requires ~2-3s lap time advantage with fresh tires
Works best when traffic is low
Risk: Gives up track position temporarily

Overcut (Pit Later):

Stay out longer while competitor pits
Gain time while running in clean air
Can work if your tires hold up
Risk: May lose too much time on old tires

Factors to Consider

Tire Degradation: Higher deg → earlier pit more beneficial
Traffic: Heavy traffic → overcut more attractive
Safety Car Probability: High SC chance → delay pit if possible
Weather Forecast: Rain expected → delay switching to slicks
Gap to Competitor: Large gap → stick to planned strategy

Notes

Pit stop timing is one of the most critical strategic decisions in F1
Real-world pit stops average 2.5-4.0 seconds (not including pit lane time)
Optimal strategy depends heavily on circuit characteristics and tire degradation severity
Virtual Safety Car (VSC) also provides ~6s time saving for pitting
Teams run thousands of race simulations to optimize pit strategy
Undercuts typically gain 1-2 positions if executed within 3-lap window

Machine Learning

Data Processing

Simulation

Web API

Overview

Pit Stop Constants

PIT_LOSS

Race Strategy Presets

STRATEGIES

WET_STRATEGY

Strategy Assignment

assign_strategies

Pit Stop Decision Logic

Pit Stop Trigger

Pit Stop Execution

CarState Attributes

Complete Pit Strategy Examples

Race Result Analysis

Pit Stop Statistics

Dynamic Strategy Adaptation

Weather-Based Adaptation

Strategy Comparison

Pit Strategy Optimization Tips

Undercut vs Overcut

Factors to Consider

Notes

Build docs developers (and LLMs) love

Machine Learning

Data Processing

Simulation

Web API

​Overview

​Pit Stop Constants

​PIT_LOSS

​Race Strategy Presets

​STRATEGIES

​WET_STRATEGY

​Strategy Assignment

​assign_strategies

​Pit Stop Decision Logic

​Pit Stop Trigger

​Pit Stop Execution

​CarState Attributes

​Pit-Related Attributes

​Complete Pit Strategy Examples

​Race Result Analysis

​Pit Stop Statistics

​Dynamic Strategy Adaptation

​Weather-Based Adaptation

​Strategy Comparison

​Pit Strategy Optimization Tips

​Undercut vs Overcut

​Factors to Consider

​Notes

Build docs developers (and LLMs) love

Overview

Pit Stop Constants

PIT_LOSS

Race Strategy Presets

STRATEGIES

WET_STRATEGY

Strategy Assignment

assign_strategies

Pit Stop Decision Logic

Pit Stop Trigger

Pit Stop Execution

CarState Attributes

Pit-Related Attributes

Complete Pit Strategy Examples

Race Result Analysis

Pit Stop Statistics

Dynamic Strategy Adaptation

Weather-Based Adaptation

Strategy Comparison

Pit Strategy Optimization Tips

Undercut vs Overcut

Factors to Consider

Notes