Secondary Market Pricing Transparency in Collectible Trading Card Games

Abstract

The collectible trading card game (TCG) secondary market — part of a broader trading card market valued at over $50 billion annually — has undergone rapid vertical integration since 2022. Two interconnected corporate structures now control, respectively, the dominant grading services (Collectors Holdings: PSA, Beckett, SGC) and the two largest online marketplaces (eBay Inc.: eBay, TCGplayer), a major auction house (Goldin), and key pricing analytics tools (TeraPeak, Card Ladder). This consolidation has coincided with the restriction of previously available public APIs, but the problem runs deeper than access: the pricing tools that do exist face meaningful accuracy, data quality, and usability challenges that limit their utility for the individual buyer or seller who needs them most.

This research presents an independent, cross-platform pricing aggregation system that operates without privileged API access. The platform includes a distributed data collection infrastructure, a six-stage cloud database cascade for normalization and archival, a cross-platform product identity resolution pipeline spanning nine catalogs across multiple languages, and a public comparison frontend. Preliminary results from over 85,000 cataloged products—approximately 35,000 English-language TCG singles with active pricing data, plus catalog metadata across eight additional international and specialty catalogs—suggest significant price dispersion across platforms and conditions, with information asymmetry disproportionately affecting individual sellers at in-person trade events.

Research Questions

1. Price dispersion: How do identical collectible items vary in price across marketplace platforms, and what factors (platform fees, seller type, listing format) explain the variance?
2. Consolidation effects: Has vertical integration of grading, marketplace, and analytics services measurably affected price discovery and market efficiency since 2022?
3. Information asymmetry: To what extent does restricted access to historical transaction data (e.g., closed APIs, paywalled analytics) create information advantages for institutional sellers over individual consumers?
4. Cross-platform arbitrage: Do persistent price differentials exist between formal marketplaces and informal social commerce channels (Facebook Marketplace, Discord, local groups), and what do these reveal about market efficiency?
5. Condition-price sensitivity: How do grading standards and condition terminology inconsistencies across platforms affect pricing accuracy and consumer trust?

Background & Motivation

The collectible trading card market reached an estimated $46–52 billion in 2025 (varying by scope and methodology across market research sources), driven by sustained interest in Pokémon, Magic: The Gathering, and Yu-Gi-Oh! products. Despite this market size, pricing transparency remains limited compared to other secondary markets of similar scale (e.g., used automobiles, real estate). There is no standardized valuation guide, no centralized listing service, and no consolidated transaction record.

The Consolidation Problem

Since eBay's acquisition of TCGplayer in October 2022 ($295M), followed by Goldin Auctions in April 2024, and GameStop's unsolicited $56B bid for eBay in May 2026 (rejected by eBay's board as "neither credible nor attractive"), the market faces a significant concentration of control:

Critically, TCGplayer's public API was restricted shortly after the eBay acquisition, limiting third-party access to pricing data that was previously available. This creates a structural barrier to independent pricing tools and research.

Grading Industry Consolidation

The consolidation is quantifiable. In 2025, major authenticators graded over 26.6 million cards. An investor group led by entrepreneur Nat Turner, with backing from Steve Cohen (owner of the New York Mets), acquired PSA's parent company (Collectors Universe) in 2021, SGC in February 2024, and announced a definitive agreement to acquire Beckett in December 2025. Post-acquisition, PSA, SGC, and Beckett collectively represent approximately 80% of all grading volume under a single corporate entity (Collectors Holdings).

The effects are already measurable. Following the SGC acquisition, leadership departed, service prices reportedly increased approximately 20%, and turnaround times reportedly increased. In April 2026, a class action antitrust lawsuit (Rasmussen v. Collectors Holdings) was filed in the Central District of California alleging illegal monopolization through serial acquisitions. Separately, U.S. Congressman Pat Ryan formally requested an FTC investigation into whether Collectors Holdings engaged in anticompetitive behavior. A prior class action (Savoy v. Collectors Universe, 2020) alleged that PSA knowingly graded altered and trimmed cards.

Two notable independent grading companies remain outside the Collectors Holdings umbrella. CGC (Certified Guaranty Company), backed by Blackstone's Tactical Opportunities fund, expanded from comics into trading card grading in 2021 and grew 121% year-over-year in 2025 to capture 18.4% market share. TAG (Technical Authentication & Grading), a smaller independent using a patented photometric stereoscopic imaging system, has gained traction in the Pokémon community as collectors seek alternatives.

Notably, TCG and non-sport cards accounted for 16.8 million of the 26.6 million cards graded in 2025 — surpassing sports cards for the first time — underscoring the scale and commercial significance of the market this research addresses.

The Social Commerce Gap

We estimate that 15–25% of TCG secondary market transactions may occur through social commerce channels — primarily Facebook Marketplace, Facebook Groups, Discord servers, and local trading communities — based on direct observation at trade events and in online trading communities. These transactions are almost entirely absent from existing pricing datasets, creating a systematic blind spot in market analysis.

Facebook Marketplace is of particular interest because it represents the largest informal marketplace for collectibles, operates without the fee structures that inflate formal marketplace prices (typically 10-15% seller fees), and captures local/regional pricing signals absent from national platforms. Understanding how prices in these informal channels compare to formal marketplaces is essential for accurate market modeling.

Why Existing Tools Fall Short

Before describing the system we built, it is important to understand why it was necessary. Several pricing tools exist for the TCG secondary market, and each has significant limitations that we have encountered directly as market participants.

The Pricing Complexity Problem

Consider the Charizard from Pokémon's Legendary Collection. It exists in three variants: Holofoil, Reverse Holofoil, and a Deck Exclusive Non-Holo — each with substantially different market values. Most collectors don't know the Deck Exclusive exists, and pricing tools frequently conflate the three. Each variant then has five raw condition grades (NM, LP, MP, HP, DMG), and may also exist as a graded slab from PSA, CGC, or BGS at grades 1 through 10. A single card can occupy dozens of distinct price points, and a buyer or seller must identify the exact intersection of variant, condition, and grading status to arrive at an accurate value.

Compounding this, counterfeit cards introduce noise into marketplace transaction data. Sales of fake cards — which most buyers and many sellers cannot reliably identify — are recorded alongside legitimate transactions, distorting price averages. Pricing tools that ingest completed sales without filtering for authenticity incorporate noise from a counterfeit market that appears to have grown in recent years.

Platform-Specific Issues

• PriceCharting: Limited data filtering. In our testing, lot sales (multiple cards bundled) appeared to be counted as single-card sales in some instances, inflating or deflating averages. Graded and ungraded sales appeared conflated in multiple instances we examined. In our experience, price estimates can diverge 20–40% from recent comparable completed sales for cards with thin trading volume — precisely the category where accurate pricing matters most.
• Collectr: In our experience, inconsistent data accuracy including apparent product misidentification, incorrect variant mapping (e.g., Reverse Holofoil price attributed to standard Holofoil), and stale pricing data that in some cases persisted for weeks or months after market conditions have changed.
• Rare Candy: Reasonably accurate underlying data, but an interface that makes it difficult to quickly identify which variant of a card is being displayed. If a vendor at a trade show cannot find the correct product page in under 30 seconds, the tool does not meet the speed requirements of in-person pricing scenarios.
• PSA Price Guide: Limited to graded cards, which represent a minority of secondary market transactions involving raw cards. Uses its own catalog numbering that maps to no other platform, making cross-referencing manual and error-prone.
• Card Ladder: Historical price charts and population reports for graded cards, gated behind a $15–20/month subscription. Serves only the graded segment — a fraction of total secondary market volume — with no data on raw cards. Notably, Card Ladder is owned by Collectors Holdings, the same entity that controls PSA, SGC, and Beckett, making it another example of vertical integration in the pricing infrastructure rather than an independent alternative.
• Marketplace platforms (eBay, TCGplayer): The primary data sources, but difficult to use effectively as pricing tools. Finding the correct product requires navigating a search space that conflates products, variants, printings, foilings, conditions, and languages. Even for someone who knows exactly what to look for, pricing a single card across both platforms takes 3–5 minutes — impractical at a trade show with dozens to hundreds of cards to evaluate. For a hobbyist pricing a collection of even modest size (say, 500 cards), that represents 25–40 hours of manual work. By the time they finish, market prices have shifted.

Market Price Ambiguity

Nearly all pricing tools lack transparency in how "market price" is calculated. Some use rolling averages, others use lowest current listings, others use proprietary weighted formulas — and most do not disclose their methodology. When two tools report different prices for the same card, a user has no way to determine which is more accurate or why they differ.

Conflicts of Interest

Beyond technical limitations, the pricing tool landscape has structural conflicts of interest at multiple levels.

Subscription-gated public data. Several pricing services charge $3–50/month for access to data ultimately derived from publicly visible marketplace transactions, with limited transparency about what portion of subscription fees covers infrastructure versus profit margin.

Marketplace fees. Most pricing tools also operate their own marketplaces, collecting transaction fees (typically 8–15%) on every sale. This creates a direct financial incentive to drive users toward their marketplace rather than toward the most accurate price.

Vertical integration and data access gatekeeping. The platforms that generate the underlying transaction data are now consolidated under interconnected corporate entities that also control grading. The practical difficulty of accessing market data reinforces the gatekeeping: eBay's Marketplace Insights program requires an established developer account with demonstrated user volume. TCGplayer's API, publicly accessible prior to the eBay acquisition, now requires a partnership application not generally accessible to independent researchers. Meta's Content Library API requires academic or non-profit institutional affiliation and approval through an external review body, a process that can take months and excludes commercial market participants entirely. In each case, the resulting approval processes structurally favor large, established partners over independent researchers or small vendors.

Methodology

Data Collection

We collect publicly available transaction and listing data from multiple marketplace platforms using a distributed collection infrastructure. Data sources include:

Analysis Framework

Collected data is normalized into a unified schema that accounts for condition grading differences across platforms, variant/printing distinctions, and temporal price movements. Statistical analysis includes:

• Cross-platform price dispersion analysis (coefficient of variation per product)
• Time-series decomposition of pricing trends pre- and post-consolidation events
• Regression modeling of price determinants (condition, rarity, platform, seller type, listing age)
• Market efficiency metrics (bid-ask spread equivalents, price convergence rates)
• Geographic price variation analysis using Facebook Marketplace regional data

Infrastructure

The research platform operates on cloud infrastructure with a distributed architecture across multiple compute instances in two geographic regions. Data flows through a six-stage cloud database cascade:

Each stage uses always-free tier cloud database instances, distributed across multiple regions for fault tolerance. The entire stack operates at zero recurring cloud infrastructure cost — a deliberate architectural decision ensuring research continuity independent of funding status.

The frontend research tool (card-prices.com) provides free, real-time access to aggregated pricing comparisons. Consistent with this research's focus on pricing accessibility, the tool provides free access without requiring registration.

Data Coverage

The system catalogs products across multiple collectible card game ecosystems and languages, extending beyond the three major English-language TCGs:

This multi-catalog scope is necessary because the secondary market does not respect platform boundaries. A Japanese-exclusive promo may appear on an English-language marketplace; a Topps card from 1999 may be listed alongside modern TCG singles. Accurate cross-platform pricing requires a product identity graph spanning all of these catalogs.

Platform Coverage

This research surveys pricing infrastructure across five geographic regions, encompassing marketplaces, auction houses, price aggregators, and grading services. Data access is pursued through official API channels where available; web collection of publicly visible data is used only as a last resort. Note: Platform Coverage indicates the scope of the study. Currently active pricing collection covers TCGplayer. Catalog metadata has been compiled for Japanese markets. European, Chinese, and Korean platform integration is planned for Summer–Fall 2026 as data partnerships are established.

The geographic breadth of this study is deliberate. Pricing dynamics differ structurally across regions: the U.S. market relies on transaction-based marketplace pricing (TCGplayer Market Price), Europe uses trend-based aggregation (Cardmarket Trend Price), and Japan derives market reference prices from posted buy/sell lists at major retailers. Understanding these methodological differences is essential to evaluating pricing transparency globally, not just within a single market.

Preliminary Findings

With 85,000+ products cataloged across nine catalogs, seven condition grades, and five geographic regions, preliminary analysis reveals several patterns consistent with the information asymmetry hypothesis. We note that these results are preliminary and have not yet been subjected to formal statistical modeling — we present them to demonstrate the feasibility and initial utility of the platform.

• Price dispersion: Identical products show significant pricing inconsistency across conditions and platforms, confirming that a single "market price" is insufficient for informed transactions.
• Non-linear condition sensitivity: The price drop from Near Mint to Lightly Played varies substantially by card value tier — common cards show moderate depreciation, while high-value cards experience significantly steeper condition-based discounts. Simple pricing tools fail to capture these non-linear relationships.
• Social commerce gap: Based on direct observation at trade shows and in online trading communities, a substantial portion of secondary market transactions — potentially 15–25% — occur through social commerce channels that are entirely absent from existing pricing datasets.
• Single-source reliance at trade shows: Sellers at in-person events frequently reference a single platform's "market price," missing cross-platform disparities that can be significant for cards above $10. This single-source reliance is the direct, observable consequence of the tool inadequacies described above.

Limitations

• Single-researcher scope: This research is conducted by a single investigator, which may introduce observation bias in qualitative assessments. Quantitative findings, once published, will be reproducible from the underlying dataset.
• English/Pokémon skew: Active pricing collection currently covers English-language TCG products, with Pokémon representing the majority of the dataset. Magic: The Gathering and Yu-Gi-Oh! coverage is expanding. International and non-English pricing data is at the catalog metadata stage.
• Missing eBay completed-sale data: eBay API access for completed sales has not yet been secured, limiting cross-platform price comparison to listing data rather than realized transaction prices.
• Social commerce data gap: The 15–25% social commerce estimate is based on qualitative observation and has not been empirically validated. Facebook Marketplace and Discord pricing data collection is planned but not yet implemented.
• Free-tier infrastructure constraints: The zero-cost infrastructure introduces dependency on continued availability of cloud provider free tiers. While data is distributed across multiple regions and providers, free-tier services carry no SLA guarantees.
• No independent validation: The comparison tool's output has not been independently validated against ground-truth pricing data. Cross-validation against platform-reported prices is planned.

Barriers to Independent Research

The primary technical challenge is not building collection infrastructure — it is gaining access to data at all. We identify six systemic barriers that impede independent pricing research in collectible secondary markets:

1. API closure following consolidation. Platforms restrict or eliminate public API access to pricing data shortly after corporate acquisition, transforming straightforward data integration into significantly harder unstructured data extraction.
2. Infrastructure discrimination. Cloud-hosted research environments face systematic access barriers via WAFs that classify cloud provider IP ranges as suspicious, regardless of request volume or intent.
3. Bot detection collateral. Anti-bot systems designed to prevent credential stuffing and inventory manipulation do not distinguish between malicious automation and low-volume academic data collection, creating unintended barriers to research.
4. Ephemeral data with no archival mandate. Listings disappear when sold, historical pricing is controlled by the platform operator, and no centralized transaction record exists. Any gap in collection is permanent data loss.
5. The API access Catch-22. Platform API programs require applicants to demonstrate existing scale that independent researchers cannot achieve without the data the API would provide.
6. Cost and expertise barriers. The tools required to aggregate, normalize, and compare pricing data across platforms are prohibitively expensive or technically complex for the average buyer or seller. The result is a two-tiered market: institutional participants with access to consolidated data, and individuals making decisions based on incomplete, single-platform signals.

Data Access & Ethics

This research collects only publicly available data — listing prices, completed sale prices, and product metadata visible to any marketplace user without authentication. No private user data, personal information, or non-public transaction details are collected or stored.

• Data collection accesses only publicly visible information without authentication, consistent with established federal case law (hiQ v. LinkedIn, Meta v. Bright Data)
• Seller identities are anonymized in all analyses and publications
• Aggregated data is made publicly available through the card-prices.com research tool to promote market transparency
• No personally identifiable information (PII) is collected from any platform
• Data collection implements adaptive rate limiting with jittered delays to minimize load on platform infrastructure
• This research does not currently involve human subjects data; IRB review will be sought if future data collection phases involve user-generated content that may require it
• The lead researcher has completed the CITI Program Responsible Conduct of Research certification

European Data Protection

Where this research involves data from European platforms, we are mindful of obligations under the General Data Protection Regulation (GDPR). Pricing data collected from European marketplaces is limited to publicly visible product listings and aggregate market prices. Seller-identifying information is not collected or stored. Where official API access is available, data processing will be governed by the platform's data use agreement. We will conduct a Data Protection Impact Assessment (DPIA) prior to any systematic collection from European sources and will comply with data subject rights under GDPR Articles 15–22.

Legal Basis for Public Data Collection

The legality of collecting publicly visible web data has been addressed in multiple federal cases. In hiQ Labs, Inc. v. LinkedIn Corp. (9th Cir. 2022), the Ninth Circuit held that accessing publicly available data does not violate the Computer Fraud and Abuse Act (CFAA). In Meta Platforms, Inc. v. Bright Data Ltd. (N.D. Cal. 2024), the court ruled that platform Terms of Service applied only to logged-in users and did not bind researchers collecting publicly visible data without authentication. Despite this emerging legal consensus, platforms continue to erect technical barriers that disproportionately affect independent researchers.

Research Timeline

Expected Contributions

• Empirical evidence of pricing effects from marketplace consolidation in collectible markets — to our knowledge, the first peer-reviewed examination of cross-platform price aggregation in TCG secondary markets
• Open-source pricing tool providing free, transparent market data to consumers and researchers — demonstrating that a cost-recovery model for pricing data is technically feasible
• Cross-platform pricing dataset bridging formal and social commerce channels, capturing the estimated 15–25% of transactions currently absent from all existing pricing tools
• Policy implications for antitrust considerations in vertically integrated marketplace-grading-analytics entities, informed by the ongoing Rasmussen v. Collectors Holdings litigation and FTC inquiry
• Methodology for studying pricing transparency in niche secondary markets with restricted data access — a replicable framework for other markets facing similar consolidation
• Barrier documentation identifying six systemic obstacles to independent market research, informing platform policy and academic data access advocacy

Selected References

The following is a selection of key references informing this research. A full bibliography accompanies the formal paper.

• G. A. Akerlof, "The Market for 'Lemons': Quality Uncertainty and the Market Mechanism," The Quarterly Journal of Economics, vol. 84, no. 3, pp. 488–500, 1970.
• S. D. Levitt and C. Syverson, "Market Distortions When Agents Are Better Informed: The Value of Information in Real Estate Transactions," The Review of Economics and Statistics, vol. 90, no. 4, pp. 599–611, 2008.
• J. Heck et al., "Sales Characteristics of Pokémon Trading Cards: A Prospective One-Year Field Study," PLoS One, vol. 21, no. 3, Mar. 2026.
• hiQ Labs, Inc. v. LinkedIn Corp., 31 F.4th 1180 (9th Cir. 2022).
• Meta Platforms, Inc. v. Bright Data Ltd., Case No. 3:23-cv-00077-EMC (N.D. Cal., Jan. 2024).
• Rasmussen v. Collectors Holdings, Inc., Case No. 8:26-cv-00897 (C.D. Cal., Apr. 2026).
• "Major Authenticators Graded More Than 26 Million Cards in 2025," cllct.com, Jan. 2026.
• E. M. Salonia, "Magic the Gathering: Economic Analysis of the Market of a Collectible Card Game," Working Paper, Jun. 2017.

Affiliation & Contact

This is an independent research initiative led by Eric Schnelker, an Operations Analyst for US Transportation Command (TRANSCOM) at Scott Air Force Base, Illinois. Eric previously served as a Computer Scientist at Tinker Air Force Base, Oklahoma, where he graduated from the Palace Acquire Program — the Air Force's civilian acquisition development program. Eric holds B.S. degrees in Computer Science, Cybersecurity, and Mathematics from The University of Tulsa, with minors in Data Science and Music. He is currently pursuing a Master of Science in Electrical and Computer Engineering (Software Engineering) at Purdue University's Elmore Family School of Electrical and Computer Engineering. The research infrastructure is independently operated, drawing on the lead researcher's direct experience as a collector and vendor in the secondary market to inform data collection methodology and analysis.

Lead Researcher: Eric Schnelker
Position: Operations Analyst, US TRANSCOM · Scott AFB, IL
Program: MSECE in Software Engineering, Purdue University
Email: eschnelk@purdue.edu
ORCID: 0009-0003-1288-9433
Research Platform: game-enthusiasts.com

Cite This Research

@misc{schnelker2026tcg,
  author = {Schnelker, Eric},
  title = {Secondary Market Pricing Transparency in Collectible Trading Card Games},
  year = {2026},
  note = {In preparation for IEEE submission},
  url = {https://card-research.org},
  institution = {Purdue University, Elmore Family School of ECE}
}

Disclosures

Government disclaimer: This research was conducted entirely on personal time using personal resources. The views expressed do not represent the Department of Defense, the Department of the Air Force, US Transportation Command, or any other government entity. No government funding, equipment, or classified information was used.

Researcher background: The lead researcher actively participates in the secondary market as a buyer and vendor at trade events. This direct market participation informs research methodology and motivates the work — the pricing problems described in this research were observed firsthand. The comparison tool built as part of this research does provide a data advantage for pricing decisions; the researcher's response to that advantage was to make it publicly accessible and free. The tool uses the same methodology and displays the same data for all users, requires no account, and generates no revenue.

Litigation: The lead researcher is not a party to, consultant for, or data provider to any pending litigation or regulatory inquiry referenced on this page. References to legal proceedings are included solely as evidence of the market conditions this research investigates.

Acknowledgments

This research is conducted independently. We acknowledge the use of Claude (Anthropic, Inc.) as an AI coding assistant for routine software engineering tasks such as boilerplate code generation, syntax debugging, and deployment scripting. All research design, system architecture, database schema design, distributed pipeline engineering, data interpretation, analysis methodology, and written content are the sole work of the lead researcher.