As we look at any given set and its overall value, I regularly discuss the variance that exists between boxes. While a booster box's expected value shows the averaged value that you'll get over a multitude of boxes, it can often be misleading — especially with top-heavy sets that exceed normal deviation. Expected value is a great appraisal for several dozen boxes, but what about when we're looking at only a few boxes? The actual return on investment can be considerably different from the expected value. This is where box variance is an extremely important value to keep in mind. I've talked about this many times, but it's never had a real representation on our tables. This week we're going to fix that, and add a value that can help illustrate the variance.
Quantifying the Variance: Hit-Over-Miss
We know there are boxes that hit all the great cards, and boxes that miss all the great cards, and plenty of boxes in-between. Trying to quantify the variance in a meaningful and understandable metric is quite difficult, especially if we try to stick with traditional variation metrics. Standard deviation, quartiles, and interquartiles can often be more unintelligible than they are helpful, especially to anyone unfamiliar with them.
It is, specifically, the two ends of the hit-and-miss spectrum that we need to look at, and the difference that lies between them. This will show us the full range of variation that's possible between boxes. A spread value can illustrate this, but it lacks context unless we also display the hit and miss values that it represents. Instead, if we calculate the set's hit value and the set's miss value, we can represent how much variance exists by dividing them. This will give us an easy to understand value, that is quickly quantifiable even without the spectrum's full values for reference. A hit-over-miss value of 300%, for instance, tells us quickly that a really good box has three times the value of a bad box. Context of how good or bad this value is can be compared just as we do with other metrics. A set's rare slot average, for example, is only considered good or bad based on how it fares compared to other sets. Likewise, a set is considered "consistent" or "inconsistent" as it compares to others, and hit-over-miss is a direct metric we can use for this comparison.
Calculating the Good and Bad
What represents a "good" or "bad" box can be subjective, but a quick dive into the rational numbers gives us some good guidelines. While there are always extreme outliers, what we want to look at are the practical and realistic boxes that we could actually expect to see. Boxes with a dozen foil mythics might exist somewhere out there, but we could hardly expect to ever see such a thing. With this in mind, I've calculated the hit and miss values with a subtle - but financially solid - change: A box's hit value is determined by calculating the expected value of a box which specifically contains the set's top three mythics and top three rares. The miss box value is the expected value of a box that does not contain any of these top cards. For sets with Masterpiece potential, the hit value receives only the average Masterpiece value, and the miss value thus receives no Masterpiece value. These changes are far from overly-dramatic, but the price difference they represent gives us a good indication of where real boxes will land.
I originally started writing this as an MMI Special that was completely separate from our normal weekly edition, but as I wrote, it become increasingly clear that the weekly tables were a necessary component of the discussion. I doubt anyone wants to go over the same tables twice, so this replaces our weekly edition. I'll be taking a good look at this statistic for each set, so we can get a better feel for what it represents, and illustrate the differences it has between sets.
If you're tired of hearing about box variance, don't worry - I won't be doing this again. As our first week with the hit-over-miss values, I thought it was important to cover this for every set to see exactly where we stand. Next week we'll return to our regularly scheduled commentary.