Efficiency in Determining What to Grade

by James Jou

228227_b1ee41128edf2829acb3a535d016b31c4d02415f-204x300 Efficiency in Determining What to GradeTHE PROBLEM

When deciding on a book to grade, is there a good strategy on how to decide which book to grade? Assuming limited financial resources, is there an efficient way to weigh the cost of grading versus the resultant market value?


For an initial exploration of a possible strategy, the books that will be used will be books currently under a $400 FMV level for a CGC 9.8. The reason for this being that the cost of grading and slabbing is a much larger factor at those price levels. Additionally, the strategy is more geared towards scenarios where margins and profit are a larger factor. As in, getting a CGC 1.0 grade back for an Amazing Fantasy #15 represents $11,000 in value; it is a smaller relative cost compared to a book like Wolverine #1, where CGC 9.8 is $260 and CGC 9.6 is $130, the relative cost is much greater. Worth noting, yes indeed, people often slab books that they like with no intention of reselling, but here financial resources will be a limiting factor that motivates the need for efficiency; otherwise the whole thing is moot anyway.




Instead of choosing books to grade based only on a high FMV for the 9.8 grade, it could potentially be more profitable to choose a book with lower FMV, but with a tighter FMV spread across the grading range. In doing so, allow for a higher chance for a greater payout and reduced emphasis on the gradee’s (person who is sending the book) ability to grade a book. An advantage for someone who can grade on a range, rather than hit a 9.8 on the spot, or a 9.6, etc.

A method that would allow for this through probability theory; specifically, expected value. Without getting too deep into the weeds, the expected value allows for a calculation of a probability-weighted average. For the purposes of comic book grading, we will use an example gradee who is able to, with a higher degree of ability, determine a raw book’s grade within a range of 9.0 to 9.8, with even probability distribution with each grade of the range.




The books below occupy similar market values at each grade. The tables show the expected value for the respective ranges. For example, the expected value of Saga #1 from CGC 9.2-9.6 is $174.9.


652491_saga-1-197x300 Efficiency in Determining What to GradeSaga #1 (2012)

saga1-300x94 Efficiency in Determining What to Grade


144023_c6c465935f4f83730a37a8684eb12f37ba5f32a7-198x300 Efficiency in Determining What to GradeSandman #1 (1981)

sm1-300x94 Efficiency in Determining What to Grade


131913_9259cfda0cf4fb3f7607e5b41ebbeb9a836c68c3-197x300 Efficiency in Determining What to GradeIron Man #128 (1979)

im128-300x94 Efficiency in Determining What to Grade



  • Between the three books, despite having the lowest 9.8 FMV, Saga #1 has a tight enough FMV spread at 9.0 and above to provide a gradee with a low 9.0 to 9.8 grading accuracy with the highest expected value.
  • Shift it one grade in each direction. From 9.2 to 9.8, Sandman #1 has a better-expected value. From 9.0 to 9.6, Saga #1 remains better.
  • Despite Sandman #1 and Iron Man #128 having similar 9.8 FMVs, low grading proficiency offers greater expected value between the two books at more ranges for Sandman #1.
  • If a gradee can accurately tell between 9.0-9.4, Saga #1 offers the best expected value.
  • If a gradee can accurately tell between 9.4-9.8 or 9.6-9.8, Sandman #1 is the best.
  • If an Iron Man #128 can be spotted at a 9.8 at 100% accuracy, that scenario might be the only time when it is comparable to Sandman #1 and better than Saga #1.

Although the difference in market value for one book isn’t anything to write home about; it would start to add up when grading in the 10+.

Time permitting, anyone with a batch of books they’re having a hard time deciding what to grade?



“Follow your intuition and all will be well.” – Marie Kondo




Not yet a GoCollect subscriber? Subscribe today to get access to the comic book price guide!


You may also like

Leave a Reply