Category: Blog

Never fashioned myself the type to blog… but, that said, there are a lot of side-stories going on in the background of producing my price estimates. I want to do my best to not just present predictions, but to explain them. This is especially important, because I am certain there are flaws in my process and, to whatever extent possible, it is this sort of communication which forces me to think aloud about what those might be — or to create a window for you to catch them.

As such, it will be under this heading that you can find a variety of background info on how this project came about; what it seeks to accomplish; how the data is built; and how the data is turned into predictions. Plus, I throw a few thoughts into the mix about my experience with the hobby.

Happy reading,

–SCV

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Building the Data & Predictive Models Uncategorized

The Estimates & Challenges of Estimation

Summary — What Could Go Wrong?

  • This is a big data project (Price Guide found here): data points & estimates are not individually inspected; rather I collect auction data from a major eBay consigner and then use a statistical model to try to extrapolate the recent value of a card based on its past values, which are adjusted for price trends (in the broader market and for the particular player / subset) since the card was listed
  • Though always incomplete, I am always chipping away to develop strategies to reduce lost accuracy & errors:
    • Data points could be off due to mistakes in auction listings, which would hurt estimate accuracy
    • Data points could be off due to occurrences of “shill bidding” that can occur on auction platforms, which would hurt estimate accuracy
    • Data points could be off due to unknown, irregular, characteristics of a particular auction listing, that are not easily detected based on the data that my web scraper collects (e.g., if a card is graded by a service I do not know about, then I have no way to account for the effect of this premium service has on the card’s final sale price), which would hurt estimate accuracy
    • When certain players or subsets do not go for sale often, then the model cannot be precise in updating the price trends for their particular cards. Rather, an average market trend is used, which results in lost accuracy — if, for example, the player has fallen out of favor, or the subset was originally perked-up by a fad that has since died… then the values would be overstated if there were not many sales since the decline in price
    • Estimates will be off when a particular card has few recent data points & its price movements behave in an idiosyncratic matter; certain cards might abide their own logic in terms of price movements
    • There may well be other factors I cannot account for…

How the Estimates Work

Roughly, this website is a project, based out of academic curiosity, which tries to estimate the present value of a card based on data about cards from the past (realistically, the “present value” should be construed as the “recent value” of the card — card prices move around really fast these days, and I only update every 2-3 weeks). I “scrap” auction sales data off of eBay, for one particular seller: COMC.com. Because COMC is just one seller, that cannot possibly be selling every notable card with any sort of regularity, I must use a statistical model to try and estimate what their cards, sold in the past, would be worth in the present. 

Largely, this is done by adjusting the price of cards based on general trends in the market. To estimate the current value of a card that sold a month ago, not much adjustment will likely be needed. But, let us say that the card market has been crashing for the last year (which it has been, as I write this at the beginning of 2023); in such a case, if a particular card’s most recent sale was a year ago…. Well, then that card’s value should probably be adjusted downward in reflection of the downward slide of prices in the market. 

Of course, this is quite crude. By this logic, if the market is down 50% since the card’s last sale, then the card’s value gets “penalized” by half. But, a player that has long-term injury, or did terrible things in their non-sporting life, is probably down more than 50% — in contrast, a player that had a career year is probably not down the full 50% — heck, maybe they are even up, defying the broader market trend. 

To partially redress this, my model then adds more “individualized” adjustors for cards. So long as there are sufficient data points, then the statistical model will try to adjust for trends in the values of more nuance features about the card (such as the player or subset to which it belongs). So, maybe a particular Connor McDavid card only has one data point from a year ago… but some of his other cards are up over that time… well, then the model will put an upward pressure on the price for that card from a year ago, to try and better reflect the player’s price movements. 

The models try to do these sorts of adjustments based on the player, the set (in fact, I use the subset) and the year the card was produced. Note that the adjustments only occur if a minimum thresholds of card sales are made for the particular category: so, for example, I cannot adjust the Connor McDavid card’s value for the ‘player effect’ if there have only been a few of this player’s cards sold over the year (this is obviously a silly example — McDavid has lots of cards selling). So, for example, if I have not witnessed at least 5 price movements in my data set for a particular player, then I do not try to make an adjustment based on the specific player’s price trends. Likewise for the subset that cards are from. 

Let me finish this section by noting: I am not appraising cards one-by-one. Rather, I scrap data from Ebay, and then build a statistical model that will try to estimate what cards are worth based on a combination of what they sold for before and by consider what we might learn about this card’s value based on what has been happening to other cards values. This occurs through an automated process… Unsurprisingly, I am not checking all 100,000+ estimates individually. 

So… in terms of our estimates, what can go wrong? Well, I sense errors can come from two places. 

So… What Can Go Wrong?

So… in terms of our estimates, what can go wrong? Well, I sense errors can come from two places. 

1. The Data

First, I cannot presume the data I am collecting will be perfect, nor that I can perfect it. 

I use a web scraper to collect realized auction prices on eBay from the consignment service COMC.com (Ebay User: comc_consignment)

COMC does two things: they write a title for each auction and they identify in their description the “identity” of each card based on their internal-library-system (each distinct card gets its own “code” in the library). The title is designed for marketing purposed (to attract eyeballs), whereas the “identity” is for organizational purposes. My models use the “identity” feature, but it is important to note that choices COMC makes in the title can affect the auction results in ways that hurt my model’s accuracy. 

So, the title might read with a bunch of key words, like “2015 Upper Deck Young Guns Connor McDavid Rookie RC,” whereas the description will follow the internal logic of their library’s labeling system, e.g., “2015-16 Upper Deck – [Base] #201 – Young Guns – Connor McDavid”. 

Herein, two issues (…that I can think of…) might arise. COMC might make a mistake in the title of the listing, resulting in the card going for less than it should. This would get carried into our estimates for the card’s value (which is not based on the eBay title — we do not detect if mistakes were made in the title). OR, the title might be fine, but COMC might mislabel the “bin” to which the card belongs in their library (again, this is not found in the auction’s title, but the auction’s description). The card may then sell for its market value, but our estimate will assign that value to the incorrect “identity.” 

Less technical, but two other issues might arise. First, market manipulations. There are definitely a lot of shill bidders out there, who might push up the value of a card, without any real intent to buy it. Obviously, this would result in our models having a data point that overstates the value and, thereby, our models would likely overstate the value. We do not have any great techniques, as of now, to identify shill bids in our models. Second, sometimes there are weird qualifiers for an auction listing that I struggle to incorporate into the models. For example, I try to remove cards that have been graded… but this is hard to do when I don’t know the grading service — I cannot write into my computer program a condition to remove such items, since I do not even know the grader name in the first place. There are also after-market autographs. Some people get cards signed by the player, but my models may not pick-up on the fact that this card, which is not normally autographed, has been autographed thereby increasing its value… I try to remove these sort of items, so they don’t affect estimates, but some may get through. 

2. The Statistical Modeling: Trying to Extrapolate Over Time

Okay, this should be obvious, but I will put it out there: the card market is super complex. There are so many individual cards. Many try to find ways to guess what prices movements will happen to what cards — but there is just way too much going on to capture every effect that happens. 

More specifically, there a few difficulties to elaborate upon…. 

First, we might have a card in our data base from a sale a long long time ago. Our model is going to try to predict today’s price based on assumptions about what price movements in other cards might tell us about this card. So, if we don’t have a recent sale of Connor McDavid’s Young Guns rookie card, then we might adjust the price of it upwards (or downwards) if his other cards, for which we do have recent values, have been going up (or down). So, here we have a difficulty: what if there is something particular about this card — some sort of outside appeal — that just makes it different from other cards. It might follow its own logic in pricing and, if so, then our model won’t capture that “X-factor” in the price trend. So, to the extent we have thin data on a card, and its price trends do their own things, distinct from cards that are similar to it, then we are “shit out of luck.”

Second, maybe you do feel — as I do — that most of the time there should be patterns whereby similar cards should have similar price movements. So, if a player is trending up, that should affect all their cards … or if a certain subset is becoming popular, then all players of that subset should be getting a little bit of a tailwind…. To the extent this is true, then my models should definitely be helping to extrapolate old auction data to today’s value. But, what if there is simply other stuff that I cannot fit into my models? I can put “player” into the model, and “subset” and the “year” (to capture potential effects distinct to, lets say, vintage cards)… but there are so many other things that I just cannot get into the data. Maybe Croatians are really starting to love hockey and players from that country are going up… Well, that is really hard to capture.

Note that I think many troubles pricing-out cards is a hybrid of the two effects above. Cards mostly do follow patterns based on their “vintage,” their player and the (sub)set to which they belong. Upwards or downwards pressures in prices for the categories of a card will likely be shared with other cards in said category. But, cards do have a hint of individuality. Trends do not affect all cards equally. Good times for a player will likely cause a boom for their limited run rookie cards, but only slightly create an uptick in their more mass produced inserts. The effect of playing 10% better might be a 10% boost in some of the players cards, but a 50% boost in others. Obviously, this sort of thing will be tough to capture, unless I have enough data on every individual cards — which, of course, often I don’t have that. Without enough data, I need to use the general price trend for the player… but the price trend due to the player is an average effect, that in fact varies based on other characteristics of those cards that feature the player. 

Third, sometimes the data is just to thin to adjust values for a card based on important criteria, such as the player. Let’s say a player is a star but then does something really terrible. Maybe the card value crashes and so people don’t auction his cards. Well, then, my models are going to struggle to update the value of that player’s cards, because there is no data to do so. This is, obviously, a challenge. If the declining value of a card means fewer listings of it, then that causes my models to lack data needed to update to the lower pricing.

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Challenges & Insights: Generating a Price Guide of Trading Card Values Uncategorized

Price Trends for Selected (High Volume) Cards

Recently I pulled a small subset of cards: the top-5 selling cards for each sports category (and the misc bin). So, that would be the top-5 selling cards (based on my dataset) in hockey, basketball, baseball, football and “the rest” (mostly, Marvel).

I haven’t much analysis — other than to say that, the cards are drawn from a dataset made up of a single consigner’s auctions (COMC). As such, the types of cards most likely to appear are skewed given the service’s price structure (e.g., it tends to cost $3.50 to 5.50 to list cards, so you won’t see high-volume cards that clear the market at a couple bucks).

So, without further ado…

Baseball Cards including Juan Soto; Ronald Acuna; Ken Griffey Jr; etc.

Perhaps most notably, while some cards boomed into July of 2020, all experienced steady declines thereafter.

Select baseball card price trends…

Basketball Cards including Michael Jordan; Kevin Durant; Ja Morant; etc.

Pay close attention to the scaling on this graph. . . the y-axis peaks around $350, and so the lower valued cards that look like they have a shallow decline actually have a pretty brutal decline. Otherwise, I leave it to your inspection…

Select basketball card price trends…

Football Cards including Patrick Mahomes; Justin Herbert; Barry Sanders; etc.

Perhaps more stability than the two graphics above… but, nonetheless, an uninspired environment. Curiously, I figured the hit to the market would be concentrated in the ultra-modern stuff, but looks like the Barry Sander’s card isn’t fairing so well either.

Select football card price trends…

Hockey Cards including Cale Makar; the Hughes Brothers; Kaprizov and Lafreniere

Once again, hockey — like football — displays some relative stability, yet still not much to write home about. Incredible climb for Cale Makar, plateauing soon thereafter. Lafreniere’s value seemingly tracking his slow start.

Select hockey card price trends…

Misc Cards including Magic; Stan Lee; Marcos Ambrose; Collin Morikawa; etc.

It was hard to pull a rounded sample here… I just wanted to pull the most frequently occurring cards, but ideally I would have covered golf; Pokemon; tennis; etc. Relatively flat trends, which I may need to look into more closely — suspiciously flat…

Select price trends for cards from my miscellaneous bin…
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Challenges & Insights: Generating a Price Guide of Trading Card Values Uncategorized

[A Rant] How the Sports Card Industry Stays Too Comfortable — Manufacturing Scarcity, Instead of Demand

I am all for the explosion of parallels. They’re fun. But it also feels like the sports card makers are trying to pull the wool over our eyes. Consider the “1 of 1.” It is unique, which presumably means it is of scarcity that drives-up the price. A particular product might not only have cards numbered “1 of 1,” but also cards out of 10 and 100 and 1000. . . And. . . that’s all great. It is good fun to collect these cards. But when the number of parallels explode from a modest baseline — with many, many low numbered parallels — are they truly scarce? If a card company makes ten slightly distinct “1 of 1” cards, shouldn’t we just view them as out of 10? 

A great example comes with printing plates. They are technically unique. In hockey, their prices have always seems low for such rarity. I am sure some of that is just idiosyncrasy — for whatever reason, they just haven’t “caught on.” But, some of that is probably because every “one of a kind” printing plate is actually a “1 of 4.” Why? Because its hard to imagine the differentiation of the “cyan vs. magenta vs. yellow vs. black” plates is sufficient for collectors to really “buy-in” to a notion of uniqueness. 

If someone really buys into the notion that a unique card deserves greater value, however small the “differentiation factor,” then ask why no one is pressuring Panini (or Upper Deck or Topps) to have a random worker end of the production line randomly put doodles onto cards passing-by. 

The Comfort Zone: Manufacturing Scarcity

Of course, card companies are doing exactly what we would expect them to: increase sales. They tried, tested and (ultimately) confirmed that their sales improve upon creating dozens of parallel categories. Honestly, that’s fair enough.

But, the method dominates. As I see it, they are sticking to what they know…. they are sticking to what are comfortable with. . . which is to say, “manufacturing scarcity.” For us collectors, we need them to get uncomfortable. They need to innovate and elbow-grease their way into new hearts and minds.

As of now, they run the production lines, they own the printing presses… they are well positioned to do tweaks to existing products that ups their attractiveness-level. But, be warned! there is a fundamental contradiction at play: to create more scarce cards, the company actually has to print more scarce cards — thus watering down the specialness of getting a rare card, because now there are many other rare cards that could be pulled. Getting a rare card these days in not a rare event!

Going Beyond the Supply-Side: Manufacturing Demand

So, the problem: manufacturing perceptions of scarcity to drive sales can only go so far. In the end, these many unique cards (that are individually rare, but not as a class) need homes. There is only so much cash floating amongst existing collectors to bid these prices up. With the stock of rare cards growing, the collector’s pool of dollars gets diluted. And, so, we come to the main point!

The major players in the trading card industry must seek to drive-up demand for trading cards amongst new household, rather than focusing on ways to convince pre-existing hobbyists that to keep buying more of the many (seemingly) rare chase cards. In short, they have to drive the substance of scarcity, which is the demand-side of the equation. 

This is inherently uncomfortable. Card companies, for instance, are about making cool cards, not public outreach. But public outreach is what they have to do. A major driver of growing the hobby would be bringing youth and young adults into the fold. (At risk of sounding cynical, they are tomorrows money-makers.) If young people don’t pick up collecting, then card values will fall in the long-term, as the aging class of current collectors … well…. to be blunt… die-off. 

The “How” of Raising Demand

I’m just an obnoxious “armchair industrialist.” How to pull this off is not obvious; it will likely take many many prongs. But, a couple of thoughts…

One obvious starting point is ensuring a variety of products that are at once affordable and offer good value: this category of cards, targeted towards new collectors, can’t be an excuse for junk product, because then they won’t be coming back. It has to be fun to collect. Another option is to do giveaways for students. Most schools and universities are okay with “light-touch advertising” insofar as that simply means giving away freebies.  Likewise for organized sports teams/leagues.

It also means actively advertising. Likely, it is wise to pull-in folks that are already primed — such as handing out cards to families at sports games (generally an indicator of disposable income).  It might also mean including non-card prizes that those “lukewarm” collectors would appreciate chasing. (Not unlike the Tim Hortons hockey card promotion that takes place in Canada — which gives away gift cards, vehicles, chances to meet athletes, etc.) 

But, this is just a couple ideas, off-the-top-of-the-head. It is the industry’s brainstorm on which the steady appreciation (pray, not depreciation!) of millions (if not billions) worth of collectibles depends!

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Challenges & Insights: Generating a Price Guide of Trading Card Values Uncategorized

Recent Price Trends in the Trading Cards Hobby

It’s been a roller-coster for the sports card industry (likewise for gaming cards). With the onset of COVID, card prices boomed. But, they have since cooled. With that said, not too many folks are trying to put down an exact number on how much prices went up (and how much, since, they have come down). So I will try to do a little of that today.

Average Price Movements by Type of Sport: Basketball, Baseball, Football and Hockey

I leveraged my database on card sale prices from COMC to come up with a few interesting findings. Using cards with 3 or more appearances in the data, I built an index of average card price movements; I controlled for the average price of individual cards, and just focused on tracking whether prices, on average, are going up or down (and by how much).

Let’s take a look at my first finding. Here, I just broke down price movements by broad category of sport: price fluctuations in baseball, basketball, football, hockey, and “all else” (which includes other sports — such as golf, tennis, soccer — but also non-sport — such as Magic, Pokemon, Marvel, Star Wars, etc.).

The data begins in early-March, with basketball cards at the beginning of their downturn. Notably, football cards kept going up in price for a while yet, before taking a dive also. Curiously, hockey and baseball demonstrated the most stability — limits on the upside, but the downside also.

Price movements since early March, 2021.

The graph suggests basketball prices have roughly halved since their peak. Football managed to squeak-out another 30% before falling back to early-pandemic levels.

Price Movements by Price-Levels (Does “Top-Tier” Collectibles Hold Their Value Better?) {The Example of Basketball Cards}

We see the bubble for basketball cards deflating above.

When confronted with the challenge: “aren’t collectibles in a bubble?” many proponents of sports cards — particular, the new genre of “collectibles-as-investments” advisors — suggest that collectors should be okay if they focus on “the top of the market.” That is, buying high-end collectibles, rather than more low-to-mid-ranged product. We can theorize all day long about whether that ought to work or not… but we can also just dive into the data. So, taking the example of basketball cards (which have experienced the most notable deflationary trend amongst the major sports categories) here is my main finding… and some rather modest conclusions that I believe may be safely drawn. (Once again, I control for the mean value of each individual card in the dataset, then I track average movements in prices for each individual card.)

Basketball price fluctuations based on average value of a trading card in the dataset.

In the above, we see that the relatively expensive cards in the dataset (over $100) managed to keep their steam longer than the other price-brackets, but ultimately fell back down to their early-pandemic price-levels. In general, prices for basketball cards lost around 40% of their value for cards over $20 (they have lost around 50-60% of their value for cards under $20). While faring marginally better, an individual buying “high-end” product would not, on average, have fared well as an investor.

I don’t want to be a false-prophet, who promises much on thin evidence, so let me keep my conclusions to the obvious: even if your high-end investments might fare marginally better than your lower-end items, your immunity will still wane to downward market pressures. The bubble-bursting, it seems, will get to them too, in time.

Price Movements for New Releases: The Hype-Factor Appears all to Real {The Example of Hockey Cards, and Especially their E-Packs Format}

Finally, I split my dataset into “New Releases” and “Past Releases” to draw some modest conclusions about one final observation: it really seems that cards get a major boost just by virtue of being recent releases. But recent releases eventually become old news. So, what is the premium that collectors are paying to get the latest and greatest?

In the following, I look at hockey cards. The choice of hockey cards is intentional: my data is pulled from COMC, which has an agreement with Upper Deck E-Packs to allow cards to be transferred from E-Pack to the COMC platform for purpose of selling. As such, COMC is especially well-placed to list “hyped-recent-releases” rather quickly (relative the other sports).

Crucially, the trendiness demonstrate a serious premium to buy into a recent release. Products from the 2020/21 release years are shown to dive in value, whereas hockey cards from pre-2020 are shown to have held steady across the sample.

Hockey card price movements: the blue line represents recent releases, whereas the red line represents cards released prior to 2020, well-before any recorded auctions in the dataset.

The size of the penalty is especially notable. Recent releases have lost approximately half their value since the beginning of the time-line, whereas older releases have held steady, and even made some marginal gains (again, I am looking at hockey as the subset of cards).

Final Notes: Be Wary those Over-Confident

Yes, some folks have done well by investing in sports cards. And perhaps you are especially astute too. But be careful. Maybe the are a good investment… I am not so smart as to tell the future. But some proponents of sports card investing seemingly do claim privilege over knowledge of the future. Ultimately, I want to make one key suggestion: the most important piece of data is missing.

A will write a future, in-depth, article on this very point: if cards are to be a steady, long-term, investment, then I very much suspect they need to be moving into the hands of long-term collectors — not short term flippers who can only drive short-term speculative bubbles. So, what is the first priority question that the industry should be surveying through a reputable pollster: “How much money do you [the interviewee] spend annually on collectibles that you intend to hold onto for the long-term?” The second priority question: “Given your economic outlook in the near-future, how much do you intend to spend in this coming year on collectibles to hold for the long-term?”

Making the (big) assumption that sport card companies will keep their supply-levels relatively constant, it would be tremendously useful to estimate how many dollars in demand exist, with which to buy-up the existing stock.

Once again, I’m just sharing a quick thought. I’ll aim for a more intensive article on the subject in the near future.

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Building the Data & Predictive Models

The Statistical Model: Estimating Trading Card Prices

The workhorse of my project looks complex at first glance, but is — I swear — really quite simple. The key idea is this: even though I am collecting a lot of data on market prices, there is no way I can find data for every single sports card to ever exist… let alone find multiple datapoint for every card to ever exist — which is really what would be required if not for a trick up my sleeve (an obvious trick, granted). If we were simply stating market price of individual cards based on observed data of those exact cards, then we would need to collect lots of data (an impossible amount of data, I figure) to be confident that all the card prices we’ve observed aren’t too high or too low due to random flukes — a botched auction, a typo in the auction’s title, a quiet week on eBay, etc.

So… what is to be done? Well, I would suggest a statistical model where we say, “hey, cards that are pretty alike are probably worth similar amounts…” and, of course, in a very much opposite vein, “and if they are pretty different, we probably can’t learn too much about one’s value from the other!”

And here you might say, “well, duh!” Fair enough. But now let’s step it up with some specific with the jargony, pretentious, math equation below. I am using what is called a linear regression model with fixed-effects in order to build predictions of value for each card in my dataset of trading cards. Sometimes a card’s value is mostly predicted by its own sales data. Sometimes its value is mostly predicted by the value of other cards that are most similar to it… This method, then, should allow us to predict the value of cards for which we have not directly observed market data.

Do keep in mind, the model’s estimates will only be as good as the data which feeds it. So, I do recommend checking-out my other blog post in this section concerning how I collect data…. Crucially, I stick to auction prices, rather than asking prices (which is to say, what something is worth is what someone is actually willing to pay for it), and I have strict criteria about which sellers enter the database… In any case, I digress. Jumping back, here is what the model looks in statistics speak (albeit, dumbed-down for my own sake):

And, in case your into the statistical programming side of things, this is what the model, running in R, looks like:

Alright then…. So…. In basic English, What is all that saying?

Well, pretty much this:

A hockey card’s market value is proportionate to some combination of: (1) the year the card was produced; (2) the player featured on the card; (3) the series and subset to which the card belongs; (4) the player’s relative “value-added” to a card (PRV)… vs. other players; (5) whether the card features (i) rookie status; (ii) an autograph; (iii) a patch from a jersey affixed to the card; (6) jersey memorabilia affixed to card; and (7) any other memorabilia affixed to the card (ill-defined). Moreover, note the * which stands for “interaction.” In other words, the model considers the effect of every possible combination of values being interacted. So, some subsets may create a lot of extra value for players with a high PRV whereas others might not generate any special extra value; as well, I interact PRV with the already crazy interaction of Rookie, Patch and Autograph… since various combinations of these items might generate extra special values, separate from measuring each of these alone. So… an awesome player like Gretzky might make a card be worth a little extra (or any other high-end player — who’d be represented with a very high PRV)… and an autograph might make a card worth a little extra… but together, I’d bet that’s worth a lot extra… its the interaction that’s really big here… not each element working in isolation.

And, so, that’s the basic idea. The model is not a dogma. It is bound for tinkering to reflect new findings, about what matters, in the data. Yet, that’s where we stand now.

Further updates to come.

Best,

SCV

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Building the Data & Predictive Models

Generating the Data: Setting Quality Control Criteria for Web-Scraping

The core data to this set: eBay auction prices, hosted by a select set of consigners… so as to control all the random crap that would otherwise flood my numbers with noise. In other words, by restricting sellers eligible for the dataset, we can control (somewhat) the variability in the sales prices due not to the card itself, but due to differences across sellers in terms of their: shipping costs; general sketchiness (e.g., feedback ratings; clarity of writing; formatting choices); detailedness of listings, quality of photos, etc. 

I pick a select list of sellers, then web-scraped data from their eBay sales history. I collect data about the card’s set, subset, player, team, rookie status, whether autographed and/or containing memorabilia. Sellers must start auctions at low prices (rather than operate, essentially, as a shop by listing the starting price as the price they want to get); they’re shipping and handling policies must be consistent. What someone is willing to pay for the card should be well-represented the final bid price. It should not be a reflection of other stuff: like variabilities in shipping prices. 

That, then, explains what data I am collecting and the minimum quality standards I enforce for data to be collected. On another page, you can check-out all the components that go into building my models to predicts trading card values. But, whether you look into that or not, the idea of this page is simple: take at look at what cards actually sell for, if you are going to jump into prediction. Don’t take people’s asking prices as a good guide. Also, in the above listing of criteria, I probably have exposed certain biases and beliefs systems about how value can be determined. I am theorizing. Don’t take my values fore-granted. Be critical, judge them. Ask if they are sensible relative your own experiences and/or research. A great place to kick off some research is by searching for your cards of interest on Ebay. Use the “sold” filter to see if my numbers map onto what you can find. 

In short, I recommend you don’t believe me blindly when I argue my predictions are best. Judge for yourself. Do my predictions match-up to reality? You decide. 

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Challenges & Insights: Generating a Price Guide of Trading Card Values Uncategorized

A Couple of Hard Truths on Estimates

I’ll be the first to say it — despite pouring thousands of hours into this project, some (many!) card prices totally evade me. Recently a Wayne Gretzky rookie card sold for $3.75 million. My model pegged it at $175,000. So, I missed by a margin of 20:1. Ouch!

So, a moment of self reflection is merited:

First off, this is an evolving project. I am three months in on data collection (using my new methodology…). So, I can cast about for some excuses there: lots of cards just don’t have enough price points.

Still, a margin of 20:1 is pretty brutal, even with serious data limitations. So what else is going on?

Well, secondly, I gotta say there is a major challenge in estimating card prices because there are, seemingly, to be two distinct “data generating processes” going on — with a very ill-defined line cutting off cards from being in one set versus the other.

Very loosely speaking, there are some cards that have everything going from them: for the vintage cards, you might think of the uber high grade cards (from PSA and Beckett) of the top players in any given sport — like Gretzky or Howe, Michael Jordan, Mickey Mantel, Jerry Rice or Joe Montana, etc. It is my belief that these cards obey Exponential laws… tiny upticks in a card’s rarity blows-up the value like crazy. It feels as if every “big dollar” collector wants these cards and will spend what it takes to get them. A PSA 10 isn’t just worth 10% or 50% more than a PSA 9. It might be worth 1000% more.

Then there are the cards of players who are extraordinary but fall just short of being superheroes. Let’s get real… these guys and girls are amazing… they represent the top 0.001% of sports talent in the world — but… they aren’t quite in the top 0.0000001%. And, because of that, their cards go up incrementally in value. A linear relationship might exist between their rookie card as a PSA 9 vs. PSA 10 — maybe one is worth $100 and the other $150. More, but not crazily so. Perhaps the RPA of a 20 goal scorer in hockey might be worth 50% more than a 30 goal scorer — which, of course, feels right… a player that producers 50% more should be represented by a card worth 50% more. (And, yet, this relation falls apart when we are talking about 50 goal scorers. Their value might be 500% that of the 30 goal scorer.)

What’s to be Done?

So… the problem having been stated… and a plausible theory explaining the puzzle having been presented… what can be done?

Well, first-off, anyone doing data science for purposes of prediction is going to have to make trade-offs between doing data-driven results vs. parametric modeling.

Letting data drive your model can be great, especially because the estimates you produce are gonna be accurate — they are, after all, pretty much just an averaging of the prices that you observed for that particular card… BUT this method is only gonna let you predict for what you’ve already got. Obviously, that sorta sucks — if two cards are highly similar, why not use data from one to inform prediction of the other?

Letting theory drive your model can be great, especially because it lets you predict the value of cards that you do not have data for, but only if your theory (or theories) are… in reality… the primary one’s that drive results. Moreover, if you need multiple theories to explain what is going on with some subsets of cards, but not others, then your scope conditions need to be well-defined (which is one of the major challenges here)

So, in my case, I may have to sacrifice range for accuracy — to focus more on what I do have rather than extrapolation. As a theoretical sort of guy, this isn’t wholly satisfying, but the game’s not over…

IF I can think through how to parameterize my model to accurately divide cards into the exponential vs linear data generating process… THEN I am the playoff team that was down 3 games to 1, but who has suddenly tied the series back up.

Some of this I have already done. Some I am working on. Some is still yet to be realized. But, overall, I feel pretty good that’s where we’re headed.

In future posts, I’ll get into some of the gritty details of what’s driving my current models and how I intend to evolve them.

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How to Sell Your Sports Cards: Problems I’ve Faced Using the Ebay Interface

(1) The Problem: in several instances, tracking purchased through eBay seems to not be synched to their [eBay’s] employees’ review of undelivered item cases.

The Experience: having delivered on my end of the bargain — by purchasing a shipping label through eBay and then sending out a buyer’s cards in timely fashion — eBay customer service nonetheless sent me messages, seemingly implying, that I am accountable for a buyer not receiving an item… with eBay arguing that I have not uploaded shipping. And, yet, I purchased my shipping through them. It’s Odd.

In one case — of particular frustration — I explained this to a representative, only to get back a what appeared to be a canned response that reiterated eBay’s original claim against me, without responding to the updated information I provided. My request for the problem I experienced to be escalated (I stated that I wanted to help eBay address what was clearly not a one-off, but a system’s-level, programming-based, problem) to a higher-level of management was either ignored, or at least not updated, in the response.

The Upshot: primarily, the major cost was time spent needlessly on the horn with customer service. Being forced to make a case for myself, when the system already had all the information it needed. To be clear: at a certain point, an item has been stuck in the mail for so long, that the buyer deserves a refund… but, so far as I am concerned, that is what insurance is for. I would also suggest, that eBay really should make a stronger effort to keep anyone overly impatient at bay, by sending them, on my behalf, a note that the seller (me!) put the item in the mail within X hours of receiving payment… with a reminder to the buyer that there is tracking, and then offering an indication of where the item currently is at within the postal system.

(2) The Problem: Postal insurance purchased through eBay doesn’t seem to be synched to eBay’s resolution of undelivered item cases.

The Experience: After getting an undelivered item case — not only did I have the awkward experience / inconvenience of needing to tell eBay the tracking number (of the shipping label I bought from them!) — but eBay then held me accountable to cover for the buyers loss. But here’s the thing: while the buyer has lost and should be compensated, I have too have lost my card! Obviously, if I paid eBay for insurance with my purchase of postage, then I should be compensated too. This is the very point of insurance. Again, for emphasis: this is the point of insurance, for which I have paid, in buying a shipping label via eBay. Rather than than experiencing a streamlined system in place to deal with these (inevitable) occurrences, eBay seems to have placed many hoops between me and a fair outcome, leaving the burden on the seller to jump through hoops, despite all the information being readily available in eBay’s system (seeing as the shipping label and insurance were purchased through eBay’s system).

The Upshot: well, covering for the lost mail comes, at least initially, out of my pocket. But most weird, I have to fight for it not to come out of my pocket. The system isn’t automated to use the information it has. Instead, the burden is placed on me to defend myself, when all the information already exists within the system for a judgement to be made in my defense, in my absence.

(3) The Problem: eBay’s willingness to unilaterally withdraw cash directly from my bank account. 

The Experience: After using eBay’s international shipping labels and their standard envelop for trading cards under $20, I have been put in the awkward experience of eBay pulling money from my bank account to compensate a buyer for an undelivered item. Look, to some extent, I get it: if you’ve got a seller who is a bad actor — selling products with no intention to deliver — then, yeah, of course, eBay needs to be able to pull money from their account to compensate the buyers. But, first off, I have a long track record of not being that guy. And, second, the debiting of my account often occurred in the midst of me having defended and explained myself — and of having provided all the info eBay needs to process an insurance claim.

So, this problem is linked to the problems above. Yes, there are circumstances in which eBay should pull cash against a seller’s account. But given the above two points, this would not happen unnecessarily if only the system was streamlined to use the information it already has available to it.

The Upshot: First off, Inconvenience. Second off, some very tangible potential damages. I happened to have the money in my bank account to cover a pretty big sale that got a claim against it (understandably, the buyer wasn’t too happy with the item being stuck in the post for four + weeks). But what if I hadn’t? Most weeks, that bank account is just for receiving eBay payments, at which point I transfer the funds to other accounts. I had bought a shipping label through eBay with insurance, in order to avoid exactly this sort of circumstance! I went to the post office and waited in line to get a scan of the label. The post had my item in their possession and eBay had the proof (they sold me the postal label and tracking and insurance… after all… ). Yet, then the money got drawn from my account. What if I had reinvested the money … (or stupidly spent it on some sort of trinket, for all that matters)? … Why on earth would that be held against me? This could do some real harm for folks who’ve done everything right on their sale, but find the post not delivering on their end of the bargain. To suddenly have a bank account that could go in the red — its an upsetting thought, particularly when you’ve done what felt like due diligence to not have any major past sales come back to haunt you.

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How to Sell Your Sports Cards: Shipping & Handling

Let me start by saying this: shipping has always been my biggest headache. Its not just time consuming — but has become an obsession of mine to figure-out: high transaction costs block out a vast majority of cards from being sellable (at least online).

In my own eBay selling, I offered free shipping & handling. Still not sure if that has ever been a good idea. But, it meant I learnt a lot. Especially about trying to ship cheap. So here are a few pieces of advice:

Most buyers, in my experience, hate paying for shipping, even if it means begrudgingly accepting their card being sent in a Plain White Envelop (PWE). Traditionaly, this means putting the card in a top-loader, folding it between a piece of paper (the eBay invoice), dropping it into a envelop (albeit, some will say: spring for semi-rigid cardboard mailers) and then throw on a stamp. Additionally, if you are paranoid, like me, about the letter getting destroyed in USPS’s big-ass machines…. then buy the non-machinable stamp from the post office and write/stamp “non-machinable” on the letter. Now, just drop it at the post-office.

In all honesty, I lifted this method from a blogger named waxpackhero, whose got tons of helpful advice about the hobby. He also writes better than me. So, check him out online at this link… in particular, his entries on shipping are really, really, informative. Just a gem of an article. He also has tons of advice about using different platforms — from COMC.com, StarStock, eBay.com and SportsLots.com (the hobby’s unsung hero, in my estimation).

Qualifiers About Doing PWE

A important qualifier though. Be clear on eBay that you are doing PWE. Not everyone likes them, and it would be wrong to try and be tricky about it. In general, you should offer the option of buyers paying an upgrade free to receive their cards via bubble mailer with tracking — more on this later — Be conscious that your low-value-cards may be worth a lot (at least sentimentally) to someone else. (“One man’s trash…”) So, give folks an option to pay an upgrade fee.

Finally, be very careful to note: PWEs traditionally send without tracking. eBay now has a “standard rate envelop” for shipping within the USA that comes with tracking, though my experience of its reliability has been spotty at best. I often do PWEs to Canada, which means taking a risk — going without tracking. (Blimey, I hope eBay figures out something to improve shipping to Canada soon). Once in a while, the card doesn’t make it. So — if you are going to do PWE — you gotta do the risk analysis. Weigh your costs and benefits, and the risks associated with items in each category. Ask yourself, given an X% chance of the cards being lost, am I okay to cover a refund on it?

Generally, on more valuable cards, I spring for more expensive shipping options, in order to get that tracking and for the luxury of being able to use a bubble envelop. I stiffen the bubble mailer with cardboard too — for safe measure.

Note that selling between the USA and Canada, or the USA and Europe, gets expensive. This has enticed me to offer PWE even at higher values when shipping to Europe. But, again, note the risk involved. You may just have to go the tough love route, and limit what you ship to other countries — or just be really upfront about the surcharges you need to levy to justify selling to customers across the pond.

As is usual of me — I’ve been a little too theoretical… a little too bird’s eye viewed… But, all in good time. I’ll try to post more about shipping methods in the future. In the mean time, I highly recommend checking out the resources linked to above.

Best,

— SCV

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How to Sell Your Sports Cards: the Major Platforms

I will have to revisit this post frequently for edits, because the hobby is rapidly evolving. Platforms are popping up everywhere. Platforms are expanding to do things they’ve never done before. I know some better than others… and, for yet others, nothing at all.

Still, for anyone who has been collecting, and now wants to consider selling, let me share some helpful resources. I will begin with my own thoughts… but I will also be sure to share links the resources cited and to the thoughts of others much cleverer and well-spoken than I.

Background — Auctions Forever (At Least in Theory)

All that said, I began selling cards a long time ago on eBay. This is your “go-to” if you want to use the “auction-house” selling format. Personally, I have a fondness for auctions — albeit, perhaps not for reasons of rationality. In short, they are great because they are ballsy; and they are high exposure. Normally, I do not celebrate exposure and balls within the same sentence…

… But in this case, it works. If you want to know the market price of something… then figure out what someone is actually willing to pay for it… not what someone is willing to ask for it… which may well be unrealistic. (FYI, auction results from eBay are also public for 90 days… so this makes for a valuable research tool to price-out your collection, even if you do not intend to use eBay itself as the platform to sell your collectibles.)

eBay’s perks, then, are sorta clear: you get a platform to sell your cards. They also have a lot of traffic. The costs? Most obviously, eBays fees: they are a quasi-monopolist that is sure as hell gonna take their pound of flesh… But, perhaps even more expensive (insofar as you want to place a value on your time): well, you have just bought yourself a low paying job (heck, this is true even if you are getting your cards for free). By logging-onto Ebay, you score yourself the mountain of work that is selling: runs to the local office supplies shop; postal mishaps; customer grievances; and, most dreaded of all, trying to get eBay’s customer service to be helpful.

eBay, a beautiful thing in theory, does sometimes hit the wall of cold-hard-reality. I can only speak to my own personal experience, which is one of frustration. But, even for me, that does not mean calling it quits. There are other options: consignment.

Consignment Services on eBay

That brings me to a second set of platforms — a general category I will call eBay consigners. These folks will spare you from having to deal with the headaches of eBay. They will, of course, take a cut for sparing you the trouble… but, I gotta say, many consigners take a relatively small cut compared to what eBay takes as a transaction fee…

Hence, what’s so interesting, is that if you shop around, you may find that you actually save money through consignment, all-the-while sparing yourself the work of listing yourself. How can this be so? Well, in my best estimation, it is because most consigners are big time sellers who get to throw their size around as leverage in negotiation with eBay. Put simply, the big guys pay lower commission fees to eBay than you or I. Currently, I am paying a consigner 16%, which is quite low given that he is doing a ton of work to list items and I would’ve been stuck paying eBay 12.5% if left to my own devices. Consigners being able to get a lower rate from Ebay leaves them some room to take a cut, while still leaving you right around what eBay would have charged you for listing direct.

I don’t want to advertise any one consigner. So, I will just say there are many good ones… some meh… but do your homework — read up on their rates — maybe do a trial-run with your lesser-prized-possessions to see who does their job the best. A google search of “eBay sport card consignment services” should get you well on your way.

COMC… Great in Theory…

Finally, I will break my earlier rule and mention a couple consigners in particular. These are bigger players who partially operate on eBay, but also allow you to have your own (digitized) “card shop” on their own websites. First, check-out-my-cards, better known as COMC, offers a really interesting service. So many cards in our hobby are impossible to transact, because the shipping fees are greater than the value of the cards. COMC has found a nice trick around that.

COMC will consign your cards for a 50 cent fee (as of 2021) upon you shipping-out your cards in bulk to their massive warehouse. Buyers can then accumulate cards from everything at the one location, so as to save on shipping by purchasing many-many cards all at once to be shipped.

What is so amazing about this service (drawbacks in a moment)… is that they do the hard work of categorizing, digitizing (picturing), listing and, ultimately, doing the payment processing and shipping of your trading cards. Its a lot of work. And, yes, they charge a flat fee per card put into their system. They also take their pound of flesh through an additional percent commission on final sale value (and, yet another, percent penalty if you remove money from their ecosystem). Yet, so far, despite all the fees, I have found that I save money selling certain cards (generally in the $2-20 range) by operating through them rather than eBay. Why? Well, in addition to saving me time by doing work for me, they avoid the huge mess that is postage fees.

Normally, a low value card is bought and now must be mailed. But COMC lets you build an inventory (as buyer or seller)– meaning you can pay a combined shipping rate for a mountain of cards (whenever you’ve decided you’ve built-up enough cards to now claim). There is a major economies of scale to this system. Shipping one card, or shipping 60, it costs about the same. May as well build-up 60. It’s a smart system.

COMC does have its draw-backs. I will have to save the full experience for another post, but for the time being, let me just note: they do a lot of work for you, but you still have to research if the platform will be useful for getting a decent value back on your card. Sometimes I send in cards that must be worth 2-3 bucks on eBay, only to find-out they are selling for a quarter (this seems especially true of card series for which e-packs are sold — but more on that another time). For some series, the market is just flooded. So, I’d recommend doing a quick spot check of what things look like for your cards before shipping them in. As such, on some cards, if you send them in you might lose money on the COMC transaction fees alone.

I should also mention, they seem to be having some difficulties with their workflow. Recently (as of 2021), their ingestion period of new cards has been quite long — with their deadline estimates frequently changing. Shipment requests have been slow too. Not only are they estimating 6 weeks for shipping, but I have been extended well over a month past that deadline. I’ve heard worse stories yet through the grapevine. So, the service may be best for those especially patient.

Lastly, COMC will consign your cards on eBay. Its an easy process to turn them over from their site to eBay. So that is nice. But, I do worry their poor reputation (i.e., feedback rating) of late may be impacting the prices received at auction. Ultimately, if buyers hate a seller, they do not buy from that seller. And if that seller is managing your portfolio, its hard to imagine they are going to realize full value. So, once again, be warned. Watch their feedback ratings — presumably they will get this figured-out. If ratings improve, then I’d imagine buyers will forgive the past and solid prices will then be realized at auction.

All that is just to say, do your research. COMC has some advantages, but it is not a panacea. COMC works well to minimize transaction fees for some cards, but not all cards.

(Final Note: as implied earlier, if you use COMC, you will be holding your cards in their warehouse. They will hold the cards in their own possession, unless you decide at a future date to not sell them and call them back. Obviously, for hobbyists who enjoy flipping through cards, this could be a bummer.)

The Humble but Steadfast, Substance over Style, Workhorse of the Hobby: Sportlots.com

I will mention a lesser known site: sportcardlots.com. They’ve an interesting system going on. Lowest transaction fees, it seems to me, of all the existing platforms listed so far. The website is humble. Positively 1990s. And, yet, massively functional. They allow for you to do an auction format, and also a simple electronic card shop format — whereby you enter your cards into their system and name a price. Unlike COMC, you do not send in your cards to be held in their possession until sold — this does mean you are doing the hard labour of listing. However, they do try to replicate COMC’s smart shipping system — whereby buyers can request cards be sent to their headquarters … so as to be combined into lower (per-unit) cost parcels. As a seller, this allows you to ship cards purchased by multiple buyers all at once, also getting down your per-card shipping cost.

The Stock Market of Sports Cards — StarStock

Lastly, a new site called StarStock is doing some interesting work. They are not yet selling cards for all sports, nor for all series within the sports they do cover. My experience is limited here, as their range has historically been limited to sports that I don’t normally collect (being a hockey guy myself — they just opened markets for hockey in 2021).

They are fashioning themselves the stock market of rookie cards (yes, that’s right, they are sticking — at the time of writing — just to consigning your rookie cards). It is pretty clever. Like COMC, you send in your items to a warehouse… which enables lower per-unit shipping costs. Also like COMC, they will get their pound of flesh via a small upfront processing fee and a later transaction fee. But, unlike COMC, there mechanism of exchange is slightly different — looking more like a stock market than a store front. Sellers list the price at which they will sell. Buyers list the price at which they will buy. In moments of numbers overlapping, the market clears and cards will automatically be exchanged between parties.

Where this all leaves us?

Each mode of selling will have its advantages and setbacks. What matters is sorting out your cards into piles for each format, as will minimize transaction costs (which, I would argue, should include a price on your time… at least for the parts of selling that you find a headache).

This review is meant as a bird’s eye overview, but in future weeks I will try to bulk-up on more pointed advice — both of my own, and by sharing links to others who’ve done better than me by these services… and, thus, I should have much more of a wealth of experiences to share.