Overview The tire degradation system models how tire performance deteriorates over race distance, affecting lap times and pit stop strategy. The system includes degradation rates, tire cliff effects, and compound-specific characteristics used in race simulations and ML predictions.
Sources: race_engine.py:22-42, feature_engineering_v2.py:26-30Tire Degradation Constants TIRE_DEG Degradation rate in seconds added per lap for each tire compound.
Source: race_engine.py:29TIRE_DEG = { "SOFT" : 0.085 , "MEDIUM" : 0.050 , "HARD" : 0.028 , "INTER" : 0.060 , "WET" : 0.035 , }
Soft compound: Highest degradation, fastest initial pace
Medium compound: Balanced degradation and pace
Hard compound: Lowest degradation, slowest initial pace
Intermediate: For light rain conditions
Full wet: For heavy rain, lower degradation due to water cooling
Usage Example: from race_engine import TIRE_DEG tire = "SOFT" laps_on_tire = 15 degradation = TIRE_DEG [tire] * laps_on_tire print ( f "After { laps_on_tire } laps on { tire } tires:" ) print ( f "Added lap time: + { degradation :.3f} s" )
Output: After 15 laps on SOFT tires: Added lap time: +1.275s
TIRE_PACE Base lap time offset (in seconds) compared to optimal pace for each compound.
Source: race_engine.py:22TIRE_PACE = { "SOFT" : 0.0 , "MEDIUM" : 0.4 , "HARD" : 0.8 , "INTER" : 1.5 , "WET" : 3.0 , }
+0.4s slower than soft compound
+0.8s slower than soft compound
+1.5s slower (in wet conditions)
+3.0s slower (in heavy rain)
Usage Example: from race_engine import TIRE_PACE base_lap_time = 90.0 # seconds tire = "MEDIUM" lap_time = base_lap_time + TIRE_PACE [tire] print ( f "Lap time on { tire } : { lap_time :.3f} s" )
Output: Lap time on MEDIUM: 90.400s
TIRE_CLIFF Lap number after which degradation accelerates exponentially.
Source: race_engine.py:36TIRE_CLIFF = { "SOFT" : 18 , "MEDIUM" : 28 , "HARD" : 40 , "INTER" : 22 , "WET" : 30 , }
Soft tires hit performance cliff after 18 laps
Medium tires hit performance cliff after 28 laps
Hard tires hit performance cliff after 40 laps
Intermediate tires hit cliff after 22 laps
Wet tires hit cliff after 30 laps
Cliff Effect: After reaching the cliff lap, degradation rate increases by 2.2x.
# From race_engine.py:147-148 cliff = TIRE_CLIFF .get(tire, 25 ) age_factor = tire_age if tire_age < cliff else cliff + (tire_age - cliff) * 2.2
Usage Example: from race_engine import TIRE_DEG , TIRE_CLIFF tire = "SOFT" base_deg = TIRE_DEG [tire] cliff_lap = TIRE_CLIFF [tire] # Before cliff (lap 15) lap_15_deg = base_deg * 15 print ( f "Lap 15 total degradation: + { lap_15_deg :.3f} s" ) # After cliff (lap 25) laps_past_cliff = 25 - cliff_lap age_factor = cliff_lap + (laps_past_cliff * 2.2 ) lap_25_deg = base_deg * age_factor print ( f "Lap 25 total degradation: + { lap_25_deg :.3f} s" )
Output: Lap 15 total degradation: +1.275s Lap 25 total degradation: +2.377s
Tire Degradation for Feature Engineering TIRE_DEGRADATION (V2) Simplified degradation rates used in machine learning feature engineering.
Source: feature_engineering_v2.py:26TIRE_DEGRADATION = { 'SOFT' : 0.08 , 'MEDIUM' : 0.05 , 'HARD' : 0.03 }
Soft compound degradation rate for ML features
Medium compound degradation rate for ML features
Hard compound degradation rate for ML features
Tire Strategy Features These features are computed in feature_engineering_v2.py for machine learning models.
Starting_Tire The tire compound used at race start.
One of: ‘SOFT’, ‘MEDIUM’, ‘HARD’
Distribution:
SOFT: 50% probability
MEDIUM: 40% probability
HARD: 10% probability
Tire_Degradation_Rate Expected degradation rate in seconds per lap based on starting tire.
Value from TIRE_DEGRADATION dict based on Starting_Tire
Example: starting_tire = "SOFT" degradation_rate = TIRE_DEGRADATION [starting_tire] # 0.08
Optimal_Pit_Lap Calculated optimal lap to pit based on tire degradation.
Lap number when tire loses approximately 1.6 seconds of pace
Formula: optimal_pit_lap = int ( 20 / expected_degradation)
Examples:
SOFT (0.08): Lap 250 → Pit around lap 20
MEDIUM (0.05): Lap 400 → Pit around lap 28
HARD (0.03): Lap 666 → Pit around lap 40
Source: feature_engineering_v2.py:81Tire_Advantage Normalized tire performance advantage at race start.
Performance multiplier: 1.0 (SOFT), 0.8 (MEDIUM), 0.6 (HARD)
Source: feature_engineering_v2.py:115tire_advantage = { 'SOFT' : 1.0 , 'MEDIUM' : 0.8 , 'HARD' : 0.6 }[starting_tire]
Lap Time Calculation base_lap_time Method Calculates the lap time for a car considering tire degradation and other factors.
Source: race_engine.py:130def base_lap_time ( self , lap , weather , sc_active ): """Calculate lap time for this lap""" base = 90.0 # base lap time in seconds # Car performance base -= ( self .car - 0.85 ) * 12.0 # Driver skill eff_skill = self .wet_skill if weather in [ "LIGHT_RAIN" , "HEAVY_RAIN" ] else self .skill base -= (eff_skill - 0.85 ) * 8.0 # Tire compound offset base += TIRE_PACE .get( self .tire, 0.8 ) # Tire degradation deg = TIRE_DEG .get( self .tire, 0.05 ) cliff = TIRE_CLIFF .get( self .tire, 25 ) age_factor = self .tire_age if self .tire_age < cliff else cliff + ( self .tire_age - cliff) * 2.2 base += deg * age_factor # Safety car if sc_active: base = max (base, 115.0 ) # Random variation (±0.4s) base += random.gauss( 0 , 0.4 ) return max (base, 82.0 )
Weather condition: “DRY”, “LIGHT_RAIN”, or “HEAVY_RAIN”
Whether safety car is deployed
Calculated lap time in seconds
Example: from race_engine import CarState car = CarState( "VER" , strategy, grid_pos = 1 , weather = "DRY" ) car.tire = "SOFT" car.tire_age = 12 lap_time = car.base_lap_time( lap = 12 , weather = "DRY" , sc_active = False ) print ( f "Lap { 12 } time: { lap_time :.3f} s (tire age: { car.tire_age } )" )
Output: Lap 12 time: 85.276s (tire age: 12)
Complete Usage Examples Tire Degradation Analysis
Optimal Strategy Calculator
Feature Engineering
from race_engine import TIRE_DEG , TIRE_PACE , TIRE_CLIFF def analyze_stint ( tire_compound , stint_laps ): """Analyze tire performance over a stint""" base_pace = TIRE_PACE [tire_compound] deg_rate = TIRE_DEG [tire_compound] cliff = TIRE_CLIFF [tire_compound] print ( f " \n { tire_compound } Tire Analysis ( { stint_laps } laps):" ) print ( f "Base pace offset: + { base_pace } s" ) print ( f "Degradation rate: { deg_rate } s/lap" ) print ( f "Performance cliff: Lap { cliff } \n " ) for lap in [ 1 , 10 , 15 , 20 , 25 ]: if lap > stint_laps: break if lap < cliff: age_factor = lap else : age_factor = cliff + (lap - cliff) * 2.2 total_deg = deg_rate * age_factor lap_time = 90.0 + base_pace + total_deg print ( f "Lap { lap :2d} : { lap_time :.3f} s (deg: + { total_deg :.3f} s)" ) # Compare compounds analyze_stint( "SOFT" , 20 ) analyze_stint( "MEDIUM" , 28 ) analyze_stint( "HARD" , 35 )
Tire Strategy Presets Predefined race strategies used in simulations.
Source: race_engine.py:84STRATEGIES = { "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" ]}, }
Compound Comparison
Stint Length
Time Loss
Tire Compound Trade-offs: Compound Base Pace Deg Rate Cliff Lap Best For SOFT 0.0s 0.085s/lap Lap 18 Qualifying, short stints MEDIUM +0.4s 0.050s/lap Lap 28 Balanced race strategy HARD +0.8s 0.028s/lap Lap 40 Long stints, one-stop INTER +1.5s 0.060s/lap Lap 22 Light rain WET +3.0s 0.035s/lap Lap 30 Heavy rain
Optimal Stint Lengths:
SOFT : 15-18 laps (pit before cliff)MEDIUM : 25-28 laps (maximize stint)HARD : 32-40 laps (one-stop strategy)INTER : 18-22 laps (drying track)WET : 25-30 laps (heavy rain) Pitting 2-3 laps before the cliff provides best overall pace. Degradation Over Race: 50-lap race, no pit stops:
SOFT: +7.6s (but massive cliff penalty)
MEDIUM: +5.8s
HARD: +4.2s
Including optimal pit stops (22s penalty):
SOFT → MEDIUM (1 stop): ~90.3s total
MEDIUM → HARD (1 stop): ~88.7s total
SOFT → MED → HARD (2 stops): ~93.5s total
Notes
Tire degradation is non-linear : accelerates after the cliff lap
Weather conditions significantly impact tire choice and degradation patterns
Safety car periods are ideal for pit stops (no time loss relative to field)
Undercut strategy : Pitting earlier to gain track position with fresh tiresOvercut strategy : Staying out longer to gain time while others pitReal F1 degradation varies by circuit (high/low severity tracks)