Collect86 uses historical sales data to estimate the current market value of 1986 Fleer Basketball cards. This page explains our methodology for computing prices across different conditions, grades, and card attributes.
Our pricing model is built from publicly available sold listings. We compute base prices for raw and PSA-graded cards, then apply multipliers for autographs, qualifiers, alternative graders, and fractional grades. All calculations are market-adjusted to account for price fluctuations over time.
For ungraded (raw) cards, we estimate condition based on sale price. Since raw cards don't have official grades, we use price quantiles as a proxy for condition:
We use a 3-month window to establish outlier bounds (5th and 95th percentiles), then compute the median price from 1-month sales data clipped to those bounds. This prevents anomalous sales from skewing estimates while capturing recent market conditions.
For PSA-graded cards (integer grades 1-10), we compute prices directly from sales of cards with matching grades. Our methodology:
Autographed cards command a premium over non-autographed cards. We compute auto multipliers by comparing median prices of autographed vs. non-autographed sales for the same card and grade, with market adjustment to account for timing differences.
Key characteristics:
Formula: Autographed Price = Base Price × Auto Multiplier
PSA qualifiers (MK, MC, OC, PD, ST) indicate defects noted during grading. Cards with qualifiers sell for less than clean cards of the same grade. We compute qualifier discounts by comparing median prices of qualified vs. non-qualified sales.
Qualifier types:
Discounts vary by tier (MJ, HOF, Non-HOF) and grade. Higher grades typically have steeper discounts because a defect on a near-mint card is more damaging to value than on a lower-grade card.
Formula: Qualified Price = PSA Price × Qualifier Multiplier (where multiplier < 1.0)
We use PSA as the baseline for pricing because PSA-graded cards have significantly more sales data available, providing the most statistically reliable price estimates. For other graders, we compute adjustment multipliers by comparing median prices of each grader vs. PSA for matching cards and grades.
Supported graders:
These multipliers reflect market pricing differences between graders at each grade level. Higher grades typically show more variance between graders as collector preferences differ at the top of the grading scale.
Formula: Other Grader Price = PSA Price × Grader Multiplier
Fractional grades (1.5, 2.5, ... 9.5) are priced relative to their floor grade. We compute the premium by comparing PSA sales of fractional grades to their corresponding integer grades, then apply these multipliers to all graders that use half-grades.
Key characteristics:
Formula: N.5 Price = N Price × Fractional Multiplier (where multiplier > 1.0)
When comparing prices across different time periods (e.g., autographed sales from 6 months ago vs. non-autographed sales from last month), we apply market adjustments to normalize prices to a common date.
This is done by computing the price index change between the original sale date and the target date, then multiplying the sale price by this ratio. This ensures that our multiplier calculations reflect the actual premium or discount, not timing differences in market conditions.
Many calculations use tiered multipliers based on card value:
This tiering ensures that high-value cards (which have different market dynamics) are not skewed by lower-value cards, and vice versa.
To estimate the value of any card, we:
Example: PSA 9.5 MK Jordan Auto
Start with PSA 9 Jordan base price → Apply 9.5 fractional premium → Apply MK qualifier discount → Apply auto multiplier
Our pricing models are built from publicly available sold listings for 1986 Fleer Basketball cards. We use rolling windows of 1-3 months of sales data to ensure prices reflect current market conditions while having sufficient sample sizes for statistical reliability. Models are updated weekly.
While our models aim to provide accurate estimates, there are inherent limitations:
See our Disclaimer for important information about using these estimates.
Last updated: February 2026