Complete Race Analysis

import tif1
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

# Configure plotting
tif1.plotting.setup_mpl(color_scheme="fastf1")
sns.set_style("darkgrid")

# Load race session
session = tif1.get_session(2024, "Abu Dhabi Grand Prix", "Race")
laps = session.laps

# Basic race statistics
print(f"Total laps: {laps['LapNumber'].max()}")
print(f"Drivers: {len(laps['Driver'].unique())}")
print(f"Total lap records: {len(laps)}")

# Race winner
final_lap = laps[laps["LapNumber"] == laps["LapNumber"].max()]
winner = final_lap.sort_values("Position").iloc[0]
print(f"\nRace Winner: {winner['Driver']} ({winner['Team']})")

# Fastest lap
fastest = laps.loc[laps["LapTime"].idxmin()]
print(f"Fastest Lap: {fastest['Driver']} - Lap {fastest['LapNumber']} - {fastest['LapTime']:.3f}s")

def plot_position_changes(laps):
    """Plot position changes for all drivers."""
    fig, ax = plt.subplots(figsize=(16, 10))

    colors = tif1.plotting.get_driver_color_mapping(session)

    for driver in laps["Driver"].unique():
        driver_laps = laps[laps["Driver"] == driver].sort_values("LapNumber")

        ax.plot(
            driver_laps["LapNumber"],
            driver_laps["Position"],
            label=driver,
            color=colors.get(driver, "#ffffff"),
            linewidth=2,
            marker="o",
            markersize=3
        )

    ax.set_xlabel("Lap Number", fontsize=12)
    ax.set_ylabel("Position", fontsize=12)
    ax.set_title("Race Position Changes", fontsize=16, fontweight="bold")
    ax.invert_yaxis()  # Position 1 at top
    ax.set_yticks(range(1, 21))
    ax.grid(True, alpha=0.3)
    ax.legend(bbox_to_anchor=(1.05, 1), loc="upper left", fontsize=10)

    plt.tight_layout()
    plt.show()

plot_position_changes(laps)

def clean_race_laps(laps):
    """Remove invalid laps for race pace analysis."""
    clean = laps.copy()

    # Remove pit laps
    clean = clean[clean["PitInTime"].isna() & clean["PitOutTime"].isna()]

    # Remove lap 1 (standing start)
    clean = clean[clean["LapNumber"] > 1]

    # Remove very slow laps (safety car, incidents)
    fastest = clean["LapTime"].min()
    clean = clean[clean["LapTime"] < fastest * 1.15]

    # Remove deleted laps
    clean = clean[~clean["Deleted"]]

    return clean

clean_laps = clean_race_laps(laps)

# Top 5 finishers
final_positions = laps[laps["LapNumber"] == laps["LapNumber"].max()].sort_values("Position")
top_5_drivers = final_positions.head(5)["Driver"].tolist()

# Box plot of lap times
fig, ax = plt.subplots(figsize=(12, 6))
top_5_laps = clean_laps[clean_laps["Driver"].isin(top_5_drivers)]

colors_list = [tif1.plotting.get_driver_color(d) for d in top_5_drivers]

bp = ax.boxplot(
    [top_5_laps[top_5_laps["Driver"] == d]["LapTime"] for d in top_5_drivers],
    labels=top_5_drivers,
    patch_artist=True,
    showmeans=True
)

for patch, color in zip(bp["boxes"], colors_list):
    patch.set_facecolor(color)
    patch.set_alpha(0.6)

ax.set_xlabel("Driver")
ax.set_ylabel("Lap Time (s)")
ax.set_title("Race Pace Distribution - Top 5 Finishers")
ax.grid(True, alpha=0.3, axis="y")
plt.tight_layout()
plt.show()

def plot_tire_strategy(laps):
    """Visualize tire strategy for all drivers."""
    fig, ax = plt.subplots(figsize=(16, 12))

    # Get final classification
    final_lap = laps[laps["LapNumber"] == laps["LapNumber"].max()]
    drivers_sorted = final_lap.sort_values("Position")["Driver"].tolist()

    compound_colors = tif1.plotting.get_compound_mapping()

    for idx, driver in enumerate(drivers_sorted):
        driver_laps = laps[laps["Driver"] == driver].sort_values("LapNumber")

        for stint in driver_laps["Stint"].unique():
            stint_laps = driver_laps[driver_laps["Stint"] == stint]
            compound = stint_laps["Compound"].iloc[0]

            start_lap = stint_laps["LapNumber"].min()
            end_lap = stint_laps["LapNumber"].max()

            ax.barh(
                y=idx,
                width=end_lap - start_lap + 1,
                left=start_lap,
                height=0.8,
                color=compound_colors.get(compound, "#888888"),
                edgecolor="white",
                linewidth=1
            )

    ax.set_yticks(range(len(drivers_sorted)))
    ax.set_yticklabels(drivers_sorted)
    ax.set_xlabel("Lap Number")
    ax.set_ylabel("Driver (by finish position)")
    ax.set_title("Tire Strategy - Full Race")
    ax.invert_yaxis()

    # Legend
    from matplotlib.patches import Patch
    legend_elements = [
        Patch(facecolor=compound_colors["SOFT"], label="Soft"),
        Patch(facecolor=compound_colors["MEDIUM"], label="Medium"),
        Patch(facecolor=compound_colors["HARD"], label="Hard"),
    ]
    ax.legend(handles=legend_elements, loc="upper right")

    plt.tight_layout()
    plt.show()

plot_tire_strategy(laps)

def analyze_stint_degradation(laps, driver, stint):
    """Analyze tire degradation for a specific stint."""
    stint_laps = laps[
        (laps["Driver"] == driver) &
        (laps["Stint"] == stint) &
        (laps["PitInTime"].isna())
    ].copy()

    if len(stint_laps) < 3:
        return None

    # Linear regression
    from scipy import stats
    slope, intercept, r_value, _, _ = stats.linregress(
        stint_laps["TyreLife"],
        stint_laps["LapTime"]
    )

    return {
        "driver": driver,
        "stint": stint,
        "compound": stint_laps["Compound"].iloc[0],
        "laps": len(stint_laps),
        "degradation": slope,
        "r_squared": r_value ** 2,
        "avg_time": stint_laps["LapTime"].mean(),
    }

# Analyze all stints for top 3
results = []
for driver in top_5_drivers[:3]:
    driver_laps = laps[laps["Driver"] == driver]
    for stint in driver_laps["Stint"].unique():
        result = analyze_stint_degradation(laps, driver, stint)
        if result:
            results.append(result)

# Display results
import pandas as pd
df_results = pd.DataFrame(results)
print("\nStint Degradation Analysis:")
print(df_results.to_string(index=False))

def calculate_gap_to_leader(laps):
    """Calculate gap to leader for each lap."""
    gaps = []

    for lap_num in laps["LapNumber"].unique():
        lap_data = laps[laps["LapNumber"] == lap_num].copy()

        # Find leader
        leader = lap_data[lap_data["Position"] == 1]
        if len(leader) == 0:
            continue

        leader_time = leader["SessionTime"].iloc[0]

        # Calculate gaps
        for _, row in lap_data.iterrows():
            gap = row["SessionTime"] - leader_time
            gaps.append({
                "LapNumber": lap_num,
                "Driver": row["Driver"],
                "Position": row["Position"],
                "GapToLeader": gap,
            })

    return pd.DataFrame(gaps)

gaps_df = calculate_gap_to_leader(laps)

# Plot gap evolution for top 5
fig, ax = plt.subplots(figsize=(16, 8))

colors = tif1.plotting.get_driver_color_mapping(session)

for driver in top_5_drivers:
    driver_gaps = gaps_df[gaps_df["Driver"] == driver]
    ax.plot(
        driver_gaps["LapNumber"],
        driver_gaps["GapToLeader"],
        label=driver,
        color=colors.get(driver, "#ffffff"),
        linewidth=2
    )

ax.set_xlabel("Lap Number")
ax.set_ylabel("Gap to Leader (s)")
ax.set_title("Gap to Leader - Top 5 Finishers")
ax.legend()
ax.grid(True, alpha=0.3)
plt.tight_layout()
plt.show()

def analyze_pit_stops(laps):
    """Extract and analyze pit stop data."""
    pit_stops = []

    for driver in laps["Driver"].unique():
        driver_laps = laps[laps["Driver"] == driver].sort_values("LapNumber")

        # Find pit laps (where PitInTime is not null)
        pit_laps = driver_laps[driver_laps["PitInTime"].notna()]

        for _, pit_lap in pit_laps.iterrows():
            pit_stops.append({
                "Driver": driver,
                "Lap": pit_lap["LapNumber"],
                "PitInTime": pit_lap["PitInTime"],
                "PitOutTime": pit_lap["PitOutTime"],
                "Duration": pit_lap["PitOutTime"] - pit_lap["PitInTime"] if pd.notna(pit_lap["PitOutTime"]) else None,
                "Stint": pit_lap["Stint"],
            })

    return pd.DataFrame(pit_stops)

pit_stops_df = analyze_pit_stops(laps)

if len(pit_stops_df) > 0:
    print("\nPit Stop Summary:")
    print(f"Total pit stops: {len(pit_stops_df)}")
    print(f"Average duration: {pit_stops_df['Duration'].mean():.2f}s")
    print(f"Fastest stop: {pit_stops_df['Duration'].min():.2f}s")
    print(f"Slowest stop: {pit_stops_df['Duration'].max():.2f}s")

    # Plot pit stop timing
    fig, ax = plt.subplots(figsize=(14, 8))

    colors = tif1.plotting.get_driver_color_mapping(session)

    for driver in top_5_drivers:
        driver_stops = pit_stops_df[pit_stops_df["Driver"] == driver]
        if len(driver_stops) > 0:
            ax.scatter(
                driver_stops["Lap"],
                [driver] * len(driver_stops),
                s=200,
                color=colors.get(driver, "#ffffff"),
                edgecolor="white",
                linewidth=2,
                zorder=3
            )

    ax.set_xlabel("Lap Number")
    ax.set_ylabel("Driver")
    ax.set_title("Pit Stop Timing - Top 5 Finishers")
    ax.grid(True, alpha=0.3, axis="x")
    plt.tight_layout()
    plt.show()

def compare_race_pace(laps, drivers, compound="MEDIUM"):
    """Compare race pace for specific drivers on same compound."""
    fig, ax = plt.subplots(figsize=(14, 6))

    colors = tif1.plotting.get_driver_color_mapping(session)

    for driver in drivers:
        driver_laps = clean_laps[
            (clean_laps["Driver"] == driver) &
            (clean_laps["Compound"] == compound)
        ].copy()

        if len(driver_laps) == 0:
            continue

        # Plot lap times vs tire life
        ax.scatter(
            driver_laps["TyreLife"],
            driver_laps["LapTime"],
            color=colors.get(driver, "#ffffff"),
            label=driver,
            alpha=0.6,
            s=50
        )

        # Add trend line
        if len(driver_laps) >= 3:
            from scipy import stats
            slope, intercept, _, _, _ = stats.linregress(
                driver_laps["TyreLife"],
                driver_laps["LapTime"]
            )
            x_trend = range(int(driver_laps["TyreLife"].min()), int(driver_laps["TyreLife"].max()) + 1)
            y_trend = [slope * x + intercept for x in x_trend]
            ax.plot(x_trend, y_trend, color=colors.get(driver, "#ffffff"), linestyle="--", alpha=0.8)

    ax.set_xlabel("Tire Life (laps)")
    ax.set_ylabel("Lap Time (s)")
    ax.set_title(f"Race Pace Comparison - {compound} Compound")
    ax.legend()
    ax.grid(True, alpha=0.3)
    plt.tight_layout()
    plt.show()

compare_race_pace(laps, top_5_drivers[:3], compound="MEDIUM")

def generate_race_report(session, laps):
    """Generate a comprehensive race report."""
    print("=" * 60)
    print(f"RACE REPORT: {session.event['EventName']} {session.year}")
    print("=" * 60)

    # Winner
    final_lap = laps[laps["LapNumber"] == laps["LapNumber"].max()]
    winner = final_lap.sort_values("Position").iloc[0]
    print(f"\n🏆 Winner: {winner['Driver']} ({winner['Team']})")

    # Fastest lap
    fastest = laps.loc[laps["LapTime"].idxmin()]
    print(f"⚡ Fastest Lap: {fastest['Driver']} - Lap {fastest['LapNumber']} - {fastest['LapTime']:.3f}s")

    # Race statistics
    print(f"\n📊 Race Statistics:")
    print(f"   Total Laps: {laps['LapNumber'].max()}")
    print(f"   Classified Finishers: {len(final_lap)}")

    # Pit stops
    pit_stops = laps[laps["PitInTime"].notna()]
    print(f"   Total Pit Stops: {len(pit_stops)}")

    # Top 5
    print(f"\n🏁 Top 5 Finishers:")
    top_5 = final_lap.sort_values("Position").head(5)
    for _, driver in top_5.iterrows():
        print(f"   {int(driver['Position'])}. {driver['Driver']} ({driver['Team']})")

    print("\n" + "=" * 60)

generate_race_report(session, laps)