The 17 Worst Cars That No One Buys According to Consumer Reports.

The 17 Worst Cars Calculator is an interactive web tool that ranks up to 17 vehicles by a computed “worstness” score—derived from user-supplied metrics (reliability, safety, fuel economy, maintenance cost, resale value, owner satisfaction, recalls) and adjustable weights—so owners, buyers, and researchers can quickly identify the vehicles most likely to cause headaches.

How to Use the 17 Worst Cars Calculator — a practical guide

When researching vehicles, simple headlines and clickbait lists often miss nuance. The 17 Worst Cars Calculator gives you a transparent, customizable way to combine objective metrics and personal priorities into a single comparative ranking. Below I’ll explain what the tool measures, how the scoring works, how to prepare data, and how to interpret outputs responsibly.

What the calculator measures (and why it matters)

The calculator accepts the following per-car inputs (all on a 0–10 scale where higher = worse):

• Reliability — frequency and severity of mechanical problems reported. (10 = very unreliable)
• Safety — crashworthiness and safety features (10 = very unsafe).
• Fuel economy — lower economy is worse (10 = worst MPG).
• Maintenance cost — average upkeep and repair expense (10 = very costly).
• Resale value — poor resale hurts ownership cost (10 = worst retention).
• Owner satisfaction — owner survey sentiments (10 = very dissatisfied).
• Recalls — scaled recall impact or frequency (0–10; higher means more recall concern).

Why these? They cover ownership pain points: safety, money, and reliability. The app multiplies each numeric metric by an adjustable weight (you control how important each metric is), sums them, and ranks cars by the final “worstness” score.

Step-by-step — preparing your data

1. Gather measured figures: Use manufacturer reports, owner surveys, recall databases (e.g., NHTSA), and reviews to estimate each metric per vehicle. For best accuracy, source each metric from a known authority (safety ratings from IIHS or NHTSA, reliability from long-term tests or consumer reports).
2. Scale to 0–10: Convert source numbers to the 0–10 scale the tool expects. For instance, if a car gets 2 out of 5 reliability stars, you might map that to a 6–8 on the “worse” scale depending on your mapping rules.
3. Format rows as CSV: Each line is CarName, Reliability, Safety, FuelEconomy, MaintenanceCost, Resale, OwnerSatisfaction, Recalls. Example:
   Budget B, 10, 9, 9, 10, 10, 9, 8
4. Paste into the text box or load the sample dataset to test.

Choosing weights — make the tool reflect your priorities

Weights determine how much each metric influences the final score. Default weights bias towards safety, reliability, maintenance costs, and recalls. If your priority is long-term ownership cost, increase Maintenance and Resale weights. If safety is non-negotiable, raise Safety weight. Use decimal weights for subtler tuning.

Running the calculation & interpreting outputs

• Click Calculate Worst 17. The calculator:
  • Parses your CSV rows,
  • Applies weights,
  • Computes a summed score per vehicle,
  • Sorts descending and displays the top 17 entries.
• Output includes:
  • An interactive horizontal bar chart (Plotly.js) showing scores,
  • A sortable table listing rank, raw metrics, and score,
  • CSV export of the results.

Interpretation tips:

• The tool gives relative ranks — a top-ranked “worst” vehicle may still be acceptable for some users depending on absolute metrics.
• Look at the component metrics: a car ranked high due to poor resale but strong safety might still be a smart buy for a short-term owner.
• Cross-check with authoritative sources (safety ratings, recall notices) to validate outliers.

Practical use-cases

• Used-car shoppers: Scan candidate cars you find on classifieds; highlight ones that appear frequently in the top 17 and investigate further.
• Content creators and reviewers: Build data-driven “worst of” lists for articles or videos.
• Fleet managers: Compare potential fleet choices by maintenance cost and reliability weighting.
• Researchers: Aggregate and visualize public datasets to spot patterns and problematic models.

Trust, accuracy, and best practices

• Be transparent about your data sources and mapping rules. Consider adding a short “Data & Methodology” section that links to the raw datasets used.
• Encourage users to validate critical decisions (e.g., buying a car) with official sources and inspections.
• Use the tool as a comparative filter—not a final judge.

External authority links (suggested)

• NHTSA: for official recall and safety notices.
• IIHS: for crash-test ratings and safety assessments.
• Consumer Reports or J.D. Power for reliability and owner satisfaction studies.

FAQ (Frequently Asked Questions)

Q1: What does a higher score mean?
A: Higher score = more likely to be “worse” according to the weighted metrics you supplied. The tool ranks by score descending.

Q2: Where should I get reliable metric inputs?
A: For safety and recalls, use NHTSA and IIHS. For reliability and owner satisfaction, consult long-term tests and consumer research organizations like Consumer Reports or J.D. Power.

Q3: Can I change the number of cars shown?
A: The tool is configured to show the top 17 by design, but the code is easy to modify—change rows.slice(0,17) to another number if you prefer.

Q4: Are defaults biased?
A: Defaults are intentionally balanced toward safety, reliability, maintenance, and recalls. Tweak weights to reflect your own priorities.

Q5: Is the tool authoritative legal or safety advice?
A: No. Use it as a comparative analytic tool. For legal or official safety decisions, consult the manufacturer, regulatory bodies, and certified inspection services.