MakeoverMonday: Fastest Cars in the World — Speed, Power, and the Price of Extremes

MakeoverMonday

Python

Automotive

Data Viz

Mapping the relationship between top speed, horsepower, price, and exclusivity across the world’s 8 fastest production cars

Author

chokotto

Published

March 23, 2026

Overview

This week’s MakeoverMonday features the 8 fastest street-legal production cars in the world as of 2026. Rather than a simple speed ranking, we explore the trade-offs between speed, power, price, and exclusivity — treating each hypercar as a data point in a multi-dimensional performance space.

Data Source: RankRed — 8 Fastest Cars In The World (2026) via data.world
Scope: 8 street-legal production cars with manufacturer-claimed or verified top speeds
Angle: speed-price efficiency, power-to-speed ratio, ICE vs. electric, exclusivity paradox

Data

Show code

import pandas as pd
import numpy as np
import plotly.graph_objects as go
import plotly.express as px
from plotly.subplots import make_subplots

Show code

df = pd.DataFrame({
    "Car": [
        "Hennessey Venom F5",
        "Bugatti Chiron SS 300+",
        "SSC Tuatara",
        "Koenigsegg Jesko",
        "Koenigsegg Agera RS",
        "Hennessey Venom GT",
        "Bugatti Veyron SS",
        "Rimac C Two",
    ],
    "Top_Speed_mph": [310.7, 304.77, 300, 300, 277.87, 270.49, 267.85, 258],
    "HP": [1817, 1578, 1750, 1600, 1160, 1244, 1184, 1914],
    "Price_M": [1.8, 3.9, 1.7, 2.8, 2.1, 1.25, 2.0, 2.1],
    "Units": [24, 30, 100, 125, 25, 13, 30, 150],
    "Engine": [
        "V8 Twin-Turbo", "W16 Quad-Turbo", "V8 Twin-Turbo", "V8 Twin-Turbo",
        "V8 Twin-Turbo", "V8 Twin-Turbo", "W16 Quad-Turbo", "Electric",
    ],
    "Manufacturer": [
        "Hennessey", "Bugatti", "SSC", "Koenigsegg",
        "Koenigsegg", "Hennessey", "Bugatti", "Rimac",
    ],
})

df["Top_Speed_kmh"] = (df["Top_Speed_mph"] * 1.60934).round(1)
df["MPH_per_M"] = (df["Top_Speed_mph"] / df["Price_M"]).round(1)
df["HP_per_mph"] = (df["HP"] / df["Top_Speed_mph"]).round(2)
df["Powertrain"] = df["Engine"].apply(lambda x: "Electric" if x == "Electric" else "ICE")

print(f"Cars: {len(df)}")
print(f"Speed range: {df['Top_Speed_mph'].min()} - {df['Top_Speed_mph'].max()} mph")
print(f"Price range: ${df['Price_M'].min()}M - ${df['Price_M'].max()}M")

Cars: 8
Speed range: 258.0 - 310.7 mph
Price range: $1.25M - $3.9M

My Makeover

What I Changed

The original article presents a simple numbered list. This makeover restructures the data into a multi-dimensional comparison:

Hero — Speed vs. Price: reveals the efficiency frontier (which cars give you the most speed per dollar?)
Power Efficiency: how much horsepower each car needs to achieve its top speed
Exclusivity Map: the inverse relationship between production volume and performance tier

Key design decisions: bubble charts for multi-variable storytelling, colour-coded by powertrain (ICE vs. Electric), insight-driven titles.

Speed vs. Price: The Efficiency Frontier

Show code

colors = [ELECTRIC if p == "Electric" else ACCENT for p in df["Powertrain"]]

fig = go.Figure()

fig.add_trace(
    go.Scatter(
        x=df["Price_M"],
        y=df["Top_Speed_mph"],
        mode="markers+text",
        marker=dict(
            size=df["HP"] / 40,
            color=colors,
            opacity=0.85,
            line=dict(width=1.5, color="white"),
        ),
        text=df["Car"].str.split(" ").str[-1],
        textposition="top center",
        textfont=dict(size=10, color="#475569"),
        hovertemplate=(
            "<b>%{customdata[0]}</b><br>"
            "Speed: %{y:.1f} mph<br>"
            "Price: $%{x:.1f}M<br>"
            "HP: %{customdata[1]}<br>"
            "Engine: %{customdata[2]}"
            "<extra></extra>"
        ),
        customdata=df[["Car", "HP", "Engine"]].values,
    )
)

fig.update_layout(
    **THEME,
    title=make_title(
        "The most expensive car isn't the fastest — "
        "<b>Venom F5</b> leads at less than half the Chiron's price"
    ),
    xaxis=dict(
        title="Price ($M)",
        showgrid=True, gridcolor="#e2e8f0", zeroline=False,
        range=[0.8, 4.5],
    ),
    yaxis=dict(
        title="Top Speed (mph)",
        showgrid=True, gridcolor="#e2e8f0", zeroline=False,
        range=[250, 320],
    ),
    showlegend=False,
    height=520,
    margin=dict(t=100, b=140, l=80, r=60),
)

add_legend_note(
    fig,
    "Bubble size = horsepower  &#183;  "
    "<span style='color:#e63946'>&#9679;</span> ICE  "
    "<span style='color:#10b981'>&#9679;</span> Electric",
    y=-0.15,
)
add_source(fig, y=-0.24)
assert_no_title_overlap(fig)
fig.show()
fig.write_image("chart-1.png", width=1200, height=600, scale=2)

Power Efficiency: Horsepower per mph

Show code

df_sorted = df.sort_values("HP_per_mph").reset_index(drop=True)

bar_colors = [
    ELECTRIC if p == "Electric" else (ACCENT if i == len(df_sorted) - 1 else MUTED)
    for i, p in enumerate(df_sorted["Powertrain"])
]

fig2 = go.Figure()
fig2.add_trace(
    go.Bar(
        y=df_sorted["Car"],
        x=df_sorted["HP_per_mph"],
        orientation="h",
        marker_color=bar_colors,
        text=df_sorted["HP_per_mph"].apply(lambda v: f"{v:.1f}"),
        textposition="outside",
        cliponaxis=False,
        hovertemplate="%{y}<br>%{x:.2f} HP/mph<extra></extra>",
    )
)

fig2.update_layout(
    **THEME,
    title=make_title(
        "Rimac's electric powertrain delivers the most HP per mph — "
        "but not the most speed"
    ),
    xaxis=dict(
        title="HP per mph (lower = more efficient at converting power to speed)",
        showgrid=True, gridcolor="#e2e8f0", zeroline=False,
    ),
    yaxis=dict(title=""),
    showlegend=False,
    height=420,
    margin=dict(t=80, b=120, l=200, r=80),
)

add_source(fig2, y=-0.25)
assert_no_title_overlap(fig2)
fig2.show()

The Exclusivity Paradox: Rarity vs. Speed

Show code

df_excl = df.sort_values("Units").reset_index(drop=True)

fig3 = go.Figure()
fig3.add_trace(
    go.Scatter(
        x=df_excl["Units"],
        y=df_excl["Top_Speed_mph"],
        mode="markers+text",
        marker=dict(
            size=df_excl["Price_M"] * 12,
            color=[ELECTRIC if p == "Electric" else SECONDARY for p in df_excl["Powertrain"]],
            opacity=0.8,
            line=dict(width=1.5, color="white"),
        ),
        text=df_excl["Car"].str.replace(" ", "<br>", n=1),
        textposition="top center",
        textfont=dict(size=9, color="#475569"),
        hovertemplate=(
            "<b>%{customdata[0]}</b><br>"
            "Units: %{x}<br>"
            "Speed: %{y:.1f} mph<br>"
            "Price: $%{customdata[1]:.1f}M"
            "<extra></extra>"
        ),
        customdata=df_excl[["Car", "Price_M"]].values,
    )
)

fig3.update_layout(
    **THEME,
    title=make_title(
        "More exclusive &#8800; faster: the Venom GT (13 units) "
        "sits mid-pack while Rimac (150 units) trails in speed"
    ),
    xaxis=dict(
        title="Production Units",
        showgrid=True, gridcolor="#e2e8f0", zeroline=False,
        type="log",
    ),
    yaxis=dict(
        title="Top Speed (mph)",
        showgrid=True, gridcolor="#e2e8f0", zeroline=False,
        range=[250, 320],
    ),
    showlegend=False,
    height=480,
    margin=dict(t=100, b=120, l=80, r=60),
)

add_legend_note(fig3, "Bubble size = price ($M)  &#183;  Log scale on x-axis", y=-0.15)
add_source(fig3, y=-0.24)
assert_no_title_overlap(fig3)
fig3.show()

Speed Leaderboard: Ranked with Context

Show code

df_rank = df.sort_values("Top_Speed_mph", ascending=True).reset_index(drop=True)

rank_colors = []
for i, row in df_rank.iterrows():
    if row["Powertrain"] == "Electric":
        rank_colors.append(ELECTRIC)
    elif i >= len(df_rank) - 3:
        rank_colors.append(ACCENT)
    else:
        rank_colors.append(MUTED)

fig4 = go.Figure()
fig4.add_trace(
    go.Bar(
        y=df_rank["Car"],
        x=df_rank["Top_Speed_mph"],
        orientation="h",
        marker_color=rank_colors,
        text=[f"{s:.1f} mph  |  ${p:.1f}M  |  {h} HP"
              for s, p, h in zip(df_rank["Top_Speed_mph"], df_rank["Price_M"], df_rank["HP"])],
        textposition="outside",
        textfont=dict(size=10),
        cliponaxis=False,
        hovertemplate="%{y}<br>%{x:.1f} mph<extra></extra>",
    )
)

fig4.update_layout(
    **THEME,
    title=make_title(
        "Top speed leaderboard: ICE still dominates, but electric is closing in"
    ),
    xaxis=dict(
        title="Top Speed (mph)",
        showgrid=True, gridcolor="#e2e8f0", zeroline=False,
        range=[200, 380],
    ),
    yaxis=dict(title=""),
    showlegend=False,
    height=440,
    margin=dict(t=80, b=120, l=200, r=160),
)

add_legend_note(
    fig4,
    "<span style='color:#e63946'>&#9632;</span> Top 3  "
    "<span style='color:#10b981'>&#9632;</span> Electric  "
    "<span style='color:#94a3b8'>&#9632;</span> Others",
    y=-0.15,
)
add_source(fig4, y=-0.24)
assert_no_title_overlap(fig4)
fig4.show()

Key Takeaways

Price doesn’t buy speed linearly: The Bugatti Chiron SS 300+ costs $3.9M but is beaten by the Hennessey Venom F5 at $1.8M — the “efficiency frontier” of speed-per-dollar favours American engineering
Electric power is raw but not yet fastest: Rimac’s C Two has the highest HP (1,914) but ranks last in top speed — converting electric torque into sustained top speed remains an engineering frontier
Exclusivity is independent of performance: The rarest car (Venom GT, 13 units) sits mid-pack in speed, while the most produced (Rimac, 150 units) trails in top speed
The 300 mph barrier has fallen: Three cars now exceed 300 mph, all within the last few years — the next frontier appears to be 350 mph

This post is part of the MakeoverMonday weekly data visualization project.

Disclaimer

This analysis is for educational and practice purposes only. Speed figures are manufacturer-claimed or independently verified under controlled conditions. Prices and production numbers may vary by market and edition.

--- title: "MakeoverMonday: Fastest Cars in the World — Speed, Power, and the Price of Extremes" description: "Mapping the relationship between top speed, horsepower, price, and exclusivity across the world's 8 fastest production cars" date: "2026-03-23" x-posted: false author: "chokotto" categories: - MakeoverMonday - Python - Automotive - Data Viz image: "thumbnail.svg" code-fold: true code-tools: true code-summary: "Show code" twitter-card: card-type: summary_large_image image: "thumbnail.png" title: "MakeoverMonday: Fastest Cars in the World" description: "Speed, power, and the price of extremes across the world's 8 fastest production cars" --- ## Overview This week's MakeoverMonday features the **8 fastest street-legal production cars in the world** as of 2026. Rather than a simple speed ranking, we explore the **trade-offs between speed, power, price, and exclusivity** — treating each hypercar as a data point in a multi-dimensional performance space. - **Data Source**: [RankRed — 8 Fastest Cars In The World (2026)](https://www.rankred.com/fastest-cars-in-the-world/) via [data.world](https://data.world/makeovermonday/8-fastest-cars-in-the-world-as-of-2026) - **Scope**: 8 street-legal production cars with manufacturer-claimed or verified top speeds - **Angle**: speed-price efficiency, power-to-speed ratio, ICE vs. electric, exclusivity paradox ## Data ```{python} #| label: load-packages #| message: false import pandas as pd import numpy as np import plotly.graph_objects as go import plotly.express as px from plotly.subplots import make_subplots ``` ```{python} #| label: load-data #| message: false df = pd.DataFrame({ "Car": [ "Hennessey Venom F5", "Bugatti Chiron SS 300+", "SSC Tuatara", "Koenigsegg Jesko", "Koenigsegg Agera RS", "Hennessey Venom GT", "Bugatti Veyron SS", "Rimac C Two", ], "Top_Speed_mph": [310.7, 304.77, 300, 300, 277.87, 270.49, 267.85, 258], "HP": [1817, 1578, 1750, 1600, 1160, 1244, 1184, 1914], "Price_M": [1.8, 3.9, 1.7, 2.8, 2.1, 1.25, 2.0, 2.1], "Units": [24, 30, 100, 125, 25, 13, 30, 150], "Engine": [ "V8 Twin-Turbo", "W16 Quad-Turbo", "V8 Twin-Turbo", "V8 Twin-Turbo", "V8 Twin-Turbo", "V8 Twin-Turbo", "W16 Quad-Turbo", "Electric", ], "Manufacturer": [ "Hennessey", "Bugatti", "SSC", "Koenigsegg", "Koenigsegg", "Hennessey", "Bugatti", "Rimac", ], }) df["Top_Speed_kmh"] = (df["Top_Speed_mph"] * 1.60934).round(1) df["MPH_per_M"] = (df["Top_Speed_mph"] / df["Price_M"]).round(1) df["HP_per_mph"] = (df["HP"] / df["Top_Speed_mph"]).round(2) df["Powertrain"] = df["Engine"].apply(lambda x: "Electric" if x == "Electric" else "ICE") print(f"Cars: {len(df)}") print(f"Speed range: {df['Top_Speed_mph'].min()} - {df['Top_Speed_mph'].max()} mph") print(f"Price range: ${df['Price_M'].min()}M - ${df['Price_M'].max()}M") ``` ```{python} #| label: shared-style #| include: false NOTE_TEXT = "Note: Street-legal production cars only" SOURCE_TEXT = "Source: RankRed / data.world | © 2026 chokotto" _NOTE_THRESHOLD = 50 def _build_source_note(note=NOTE_TEXT, source=SOURCE_TEXT): if len(note) <= _NOTE_THRESHOLD: return f"{note} | {source}" return f"{note} {source}" SOURCE_NOTE = _build_source_note() THEME = dict( template="plotly_white", font=dict(family="sans-serif", size=13, color="#1e293b"), paper_bgcolor="white", plot_bgcolor="#f8fafc", ) ACCENT = "#e63946" SECONDARY = "#3b82f6" ELECTRIC = "#10b981" MUTED = "#94a3b8" def make_title(text): return dict(text=text, font=dict(size=15, color="#1e293b"), x=0, xanchor="left") def add_legend_note(fig, text, y=-0.13): """Place a legend/subtitle annotation BELOW the chart (never above). Avoids overlap with bars/titles that occurs at y>1.""" fig.add_annotation( text=text, xref="paper", yref="paper", x=0, y=y, showarrow=False, font=dict(size=11, color="#64748b"), align="left", yanchor="top", ) def add_source(fig, y=-0.20): fig.add_annotation( text=SOURCE_NOTE, xref="paper", yref="paper", x=0, y=y, showarrow=False, font=dict(size=10, color="#94a3b8", style="italic"), align="left", yanchor="top", ) def assert_no_title_overlap(fig): layout = fig.layout height = layout.height or 450 margin_t = layout.margin.t if layout.margin.t is not None else 80 plot_top = 1.0 - margin_t / height for ann in fig.layout.annotations: if ann.yref == "paper" and ann.y is not None and ann.y > plot_top: required_t = int((1.0 - ann.y + 0.06) * height) + 30 fig.update_layout(margin=dict(t=required_t)) margin_t = required_t plot_top = 1.0 - margin_t / height title_y = None if fig.layout.title and fig.layout.title.y is not None: title_y = fig.layout.title.y elif fig.layout.title and fig.layout.title.text: title_y = 0.98 if title_y is not None and title_y > plot_top: required_t = int((1.0 - title_y + 0.06) * height) + 30 fig.update_layout(margin=dict(t=required_t)) ``` ## My Makeover ### What I Changed The original article presents a simple numbered list. This makeover restructures the data into a **multi-dimensional comparison**: 1. **Hero — Speed vs. Price**: reveals the efficiency frontier (which cars give you the most speed per dollar?) 2. **Power Efficiency**: how much horsepower each car needs to achieve its top speed 3. **Exclusivity Map**: the inverse relationship between production volume and performance tier Key design decisions: bubble charts for multi-variable storytelling, colour-coded by powertrain (ICE vs. Electric), insight-driven titles. ### Speed vs. Price: The Efficiency Frontier ```{python} #| label: hero-speed-price #| fig-width: 10 #| fig-height: 6 colors = [ELECTRIC if p == "Electric" else ACCENT for p in df["Powertrain"]] fig = go.Figure() fig.add_trace( go.Scatter( x=df["Price_M"], y=df["Top_Speed_mph"], mode="markers+text", marker=dict( size=df["HP"] / 40, color=colors, opacity=0.85, line=dict(width=1.5, color="white"), ), text=df["Car"].str.split(" ").str[-1], textposition="top center", textfont=dict(size=10, color="#475569"), hovertemplate=( "%{customdata[0]} " "Speed: %{y:.1f} mph " "Price: $%{x:.1f}M " "HP: %{customdata[1]} " "Engine: %{customdata[2]}" "<extra></extra>" ), customdata=df[["Car", "HP", "Engine"]].values, ) ) fig.update_layout( **THEME, title=make_title( "The most expensive car isn't the fastest — " "Venom F5 leads at less than half the Chiron's price" ), xaxis=dict( title="Price ($M)", showgrid=True, gridcolor="#e2e8f0", zeroline=False, range=[0.8, 4.5], ), yaxis=dict( title="Top Speed (mph)", showgrid=True, gridcolor="#e2e8f0", zeroline=False, range=[250, 320], ), showlegend=False, height=520, margin=dict(t=100, b=140, l=80, r=60), ) add_legend_note( fig, "Bubble size = horsepower · " "● ICE " "● Electric", y=-0.15, ) add_source(fig, y=-0.24) assert_no_title_overlap(fig) fig.show() fig.write_image("chart-1.png", width=1200, height=600, scale=2) ``` ### Power Efficiency: Horsepower per mph ```{python} #| label: power-efficiency #| fig-width: 10 #| fig-height: 5 df_sorted = df.sort_values("HP_per_mph").reset_index(drop=True) bar_colors = [ ELECTRIC if p == "Electric" else (ACCENT if i == len(df_sorted) - 1 else MUTED) for i, p in enumerate(df_sorted["Powertrain"]) ] fig2 = go.Figure() fig2.add_trace( go.Bar( y=df_sorted["Car"], x=df_sorted["HP_per_mph"], orientation="h", marker_color=bar_colors, text=df_sorted["HP_per_mph"].apply(lambda v: f"{v:.1f}"), textposition="outside", cliponaxis=False, hovertemplate="%{y} %{x:.2f} HP/mph<extra></extra>", ) ) fig2.update_layout( **THEME, title=make_title( "Rimac's electric powertrain delivers the most HP per mph — " "but not the most speed" ), xaxis=dict( title="HP per mph (lower = more efficient at converting power to speed)", showgrid=True, gridcolor="#e2e8f0", zeroline=False, ), yaxis=dict(title=""), showlegend=False, height=420, margin=dict(t=80, b=120, l=200, r=80), ) add_source(fig2, y=-0.25) assert_no_title_overlap(fig2) fig2.show() ``` ### The Exclusivity Paradox: Rarity vs. Speed ```{python} #| label: exclusivity #| fig-width: 10 #| fig-height: 5 df_excl = df.sort_values("Units").reset_index(drop=True) fig3 = go.Figure() fig3.add_trace( go.Scatter( x=df_excl["Units"], y=df_excl["Top_Speed_mph"], mode="markers+text", marker=dict( size=df_excl["Price_M"] * 12, color=[ELECTRIC if p == "Electric" else SECONDARY for p in df_excl["Powertrain"]], opacity=0.8, line=dict(width=1.5, color="white"), ), text=df_excl["Car"].str.replace(" ", " ", n=1), textposition="top center", textfont=dict(size=9, color="#475569"), hovertemplate=( "%{customdata[0]} " "Units: %{x} " "Speed: %{y:.1f} mph " "Price: $%{customdata[1]:.1f}M" "<extra></extra>" ), customdata=df_excl[["Car", "Price_M"]].values, ) ) fig3.update_layout( **THEME, title=make_title( "More exclusive ≠ faster: the Venom GT (13 units) " "sits mid-pack while Rimac (150 units) trails in speed" ), xaxis=dict( title="Production Units", showgrid=True, gridcolor="#e2e8f0", zeroline=False, type="log", ), yaxis=dict( title="Top Speed (mph)", showgrid=True, gridcolor="#e2e8f0", zeroline=False, range=[250, 320], ), showlegend=False, height=480, margin=dict(t=100, b=120, l=80, r=60), ) add_legend_note(fig3, "Bubble size = price ($M) · Log scale on x-axis", y=-0.15) add_source(fig3, y=-0.24) assert_no_title_overlap(fig3) fig3.show() ``` ### Speed Leaderboard: Ranked with Context ```{python} #| label: leaderboard #| fig-width: 10 #| fig-height: 5 df_rank = df.sort_values("Top_Speed_mph", ascending=True).reset_index(drop=True) rank_colors = [] for i, row in df_rank.iterrows(): if row["Powertrain"] == "Electric": rank_colors.append(ELECTRIC) elif i >= len(df_rank) - 3: rank_colors.append(ACCENT) else: rank_colors.append(MUTED) fig4 = go.Figure() fig4.add_trace( go.Bar( y=df_rank["Car"], x=df_rank["Top_Speed_mph"], orientation="h", marker_color=rank_colors, text=[f"{s:.1f} mph | ${p:.1f}M | {h} HP" for s, p, h in zip(df_rank["Top_Speed_mph"], df_rank["Price_M"], df_rank["HP"])], textposition="outside", textfont=dict(size=10), cliponaxis=False, hovertemplate="%{y} %{x:.1f} mph<extra></extra>", ) ) fig4.update_layout( **THEME, title=make_title( "Top speed leaderboard: ICE still dominates, but electric is closing in" ), xaxis=dict( title="Top Speed (mph)", showgrid=True, gridcolor="#e2e8f0", zeroline=False, range=[200, 380], ), yaxis=dict(title=""), showlegend=False, height=440, margin=dict(t=80, b=120, l=200, r=160), ) add_legend_note( fig4, "■ Top 3 " "■ Electric " "■ Others", y=-0.15, ) add_source(fig4, y=-0.24) assert_no_title_overlap(fig4) fig4.show() ``` ## Key Takeaways 1. **Price doesn't buy speed linearly**: The Bugatti Chiron SS 300+ costs $3.9M but is beaten by the Hennessey Venom F5 at $1.8M — the "efficiency frontier" of speed-per-dollar favours American engineering 2. **Electric power is raw but not yet fastest**: Rimac's C Two has the highest HP (1,914) but ranks last in top speed — converting electric torque into sustained top speed remains an engineering frontier 3. **Exclusivity is independent of performance**: The rarest car (Venom GT, 13 units) sits mid-pack in speed, while the most produced (Rimac, 150 units) trails in top speed 4. **The 300 mph barrier has fallen**: Three cars now exceed 300 mph, all within the last few years — the next frontier appears to be 350 mph *** _This post is part of the [MakeoverMonday](https://www.makeovermonday.co.uk/) weekly data visualization project._ :::{.callout-caution collapse="false" appearance="minimal" icon="false"} ## Disclaimer ::: {style="font-size: 0.85em; color: #64748b; line-height: 1.6;"} This analysis is for educational and practice purposes only. Speed figures are manufacturer-claimed or independently verified under controlled conditions. Prices and production numbers may vary by market and edition. ::: :::