Expected annual cost in the sample scenario
Using the default inputs, collision probability is 8% and the average collision loss is 4,500. If a plan’s collision deductible is 500, the modeled collision out‑of‑pocket per year is 0.08 × 500 = 40. Comprehensive is 5% with a 2,500 loss, so a 500 deductible adds 0.05 × 500 = 25. Premium plus these expected costs becomes a quick, comparable annual budget figure.
Coverage score from targets and weights
Each limit is scored as min(1, plan ÷ target). With targets set to 100k/300k liability and 50k property damage, a plan that matches those limits earns ratios of 1.00 for those parts. Deductibles are scored as min(1, target ÷ plan), so a 500 preferred deductible gives a perfect 1.00 when the plan deductible is 500 or lower. The weighted average is then scaled to a 0–100 score.
Deductible trade-offs measured in currency
At the same 8% collision probability, moving from a 1,000 deductible to a 250 deductible changes expected collision out‑of‑pocket from 0.08 × 1,000 = 80 to 0.08 × 250 = 20. That 60 difference is visible immediately, before considering premium. If your comprehensive probability is 5%, a 1,000 deductible adds 50 per year while a 250 deductible adds 12.5.
Liability and medical gaps can dominate outcomes
The calculator estimates liability out‑of‑pocket as max(0, loss − (BI per accident + property damage)). If liability loss is 450,000, and limits total 350,000, the gap is 100,000. With a 2% probability, the expected annual add‑on becomes 0.02 × 100,000 = 2,000, often larger than premium differences. Medical gaps work similarly when losses exceed PIP/MedPay.
Reading the value index and recommendation
Value index is (coverage score ÷ expected annual cost) × 1,000, so a 95 score and 1,185 expected cost yields about 80.2. The recommendation blends a cost score and coverage score using your chosen weights, such as 50/50. Adjust weights to test how price-sensitive your decision is. Re-run comparisons whenever premiums or driving conditions change.