FAQ: Collectibles Rating Portal
What is this website about?
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This portal provides free access to analytics, ratings, and reports for modern collectible assets like gaming memorabilia, Toys, and Cards. It combines advanced data gathering, machine learning, GenAI and a proprietary evaluation framework to help collectors and investors make informed decisions about the potential growth and value of collectibles.
Who can benefit from this platform?
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Collectors, investors, and retailers can benefit by gaining insights into market trends, evaluating investment potential, and optimizing inventory strategies.
What makes this platform different from other resources?
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Our proprietary Collectible Rating (CR) framework evaluates collectibles using a holistic approach, factoring in scarcity, market demand, popularity, and machine learning-powered price forecasts. Unlike fragmented resources, this portal centralizes data and provides actionable insights, including short- and long-term investment predictions.
What is the Collectible Rating (CR)?
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CR is a comprehensive score (1–10) that evaluates the quality, rarity, and growth potential of collectibles. It is calculated using a weighted system based on seven core criteria:
- Price Forecast (25%)
- Trend (15%)
- Market Demand (10%)
- Scarcity (10%)
- Popularity (15%)
- Maturity (10%)
- Sales Velocity (Liquidity) (15%)
The formula ensures a balanced assessment of each collectible’s strengths.
How are the scores calculated?
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Scores are calculated using specific methodologies for each criterion, such as machine learning models for price forecasts, ChatGPT for Popularity, Google Trends data for Trend, and sales data from platforms like eBay US for market demand. Each criterion is weighted to ensure balanced evaluation.
- Price Forecast: Machine learning models (e.g., Prophet, ARIMA) predict 5-year price trends. Scores are assigned based on the Compounded Annual Growth Rate (CAGR).
- Trend: Analyzes Google search trends for the collectible. Scores are percentile-based, ranking items by search volume.
- Market Demand: Based on annual sales volume, extracted from platforms like eBay US. Items with higher sales volumes score better.
- Scarcity: Measures the number of active listings on major marketplaces. Fewer listings indicate higher scarcity and better scores.
- Popularity: Evaluates franchise strength, cultural impact, and longevity. Uses a curated score allocated by GenAI systems like ChatGPT.
- Maturity: Reflects the collectible's age and potential for future appreciation. Scores range from 1–10 based on growth potential milestones.
- Sales Velocity: Measures how quickly items sell compared to inventory. Indicates ease of sale and liquidity.
How is the Price Forecast Score calculated?
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Description: The price forecast score is calculated using machine learning tools to project the price for each individual collectible wherever data is available. These projections are based on historical price trends and other relevant economic factors.
Data Sources:
- Data gathering from Websites with Historical Price Trends: Data is collected from websites/API such as pricecharting.com, pokedata.io, and other economic data sources like interest rates, GDP, Consumer Price Index (CPI), and inflation.
Machine Learning-based Price Projections: Machine learning models like Prophet, ARIMA, and SARIMA are used to project prices for collectibles based on available historical data.
Calculations:
- 5-Year Compounded Annual Growth Rate (CAGR): The projected price is used to calculate the CAGR over a 5-year period, using the formula:
CAGR = (Ending Value / Beginning Value) ^ (1 / Number of Years) - 1
- Assigning Scores Based on CAGR: Scores are assigned based on the CAGR, as follows:
- >= CAGR 20%: Score 10
- 15% – 19.99% CAGR: Score 9
- 10% – 14.99% CAGR: Score 8
- 7% – 9.99% CAGR: Score 7
- 5% – 6.99% CAGR: Score 6
- 3% – 4.99% CAGR: Score 5
- 1% – 2.99% CAGR: Score 4
- 0% – 0.99% CAGR: Score 3
- -1% – -0.01% CAGR: Score 2
- < -1% CAGR: Score 1
- Fallback Score: For collectibles without sufficient historical data to project sales, a default fallback score of 3 will be assigned. These items are considered high-risk due to the lack of data.
What is the Trend Score?
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Description: Reflects the general interest or awareness around the collectible, based on online search activity.
Data Sources: Google Trends data for the item's name or relevant keywords.
Calculations:
- Extract Data: Obtain annual search volume for the collectible from Google Trends.
- Sort Data: Arrange the search volume in ascending order.
- Rank Items: Assign a rank to each item (1 = highest search volume).
- Calculate Percentile Rank: Use the formula:
Percentile Rank = ((Total Items - Rank of Item) / (Total Items - 1)) x 10
- Assign Score: Scale the percentile rank to a 10-point system.
What is the Market Demand Score?
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Description: Measures the total annual sales volume for each item.
Data Sources: Annual sales volume data from platforms like eBay.
Calculations:
- Extract Data: Collect eBay US sales volume per year, starting with 90-day historical data.
- Sort Data: Arrange sales data in descending order (highest sales first).
- Rank Items: Assign a rank to each item (1 = highest sales volume).
- Calculate Percentile Rank: Use the formula:
Percentile Rank = ((Total Items - Rank of Item) / (Total Items - 1)) x 10
- Assign Score: Scale the percentile rank to a 10-point system.
What is the Scarcity Score?
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Description: Evaluates the rarity of an item based on the number of active listings on major marketplaces.
Data Sources: Number of active listings for the item on eBay US.
Calculations:
- Extract Data: Count the number of active listings for the item.
- Sort Data: Arrange active listings in ascending order (fewer listings = higher rank).
- Rank Items: Assign a rank to each item (1 = fewest listings).
- Calculate Percentile Rank: Use the formula:
Percentile Rank = ((Total Items - Rank of Item) / (Total Items - 1)) x 10
- Assign Score: Scale the percentile rank to a 10-point system.
What is the Popularity Score?
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Description: Assesses the item's popularity based on franchise strength, cultural impact, and longevity.
Data Sources: ChatGPT-assigned scores based on criteria like franchise strength and cultural impact.
Calculations:
- Criteria: Franchise strength, cultural impact, longevity, and current market perception.
- Prompt: "Evaluate each collectible on a scale of 1-10 based on these criteria."
- Assign Score: Direct score allocation by ChatGPT or expert evaluation.
What is the Maturity Score?
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Description: Evaluates the growth potential of a collectible item based on its age. It quantifies the potential for future appreciation in value over the next 5-10 years. It is designed to reflect how an item is likely to grow in demand, taking into account its age and potential for cultural resurgence, much like how retro or "classic" items become more valuable over time.
Data Sources: Year of release data from pricecharting.com.
Calculations:
- Determine Age: Calculate the item's age as
Current Year - Release Year.
- Assign Score: Based on age ranges:
- 0–5 years: Score 3
- 6–10 years: Score 5
- 11–15 years: Score 8
- 16–20 years: Score 10
- 21–25 years: Score 8
- 26–30 years: Score 7
- 31–35 years: Score 5
- 36+ years: Score 4
What is the Sales Velocity (Sell-Through Rate)?
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Description: Measures how easily an item can be sold within the market. It considers the frequency of sales relative to the availability of the item. Higher liquidity indicates that the asset is in high demand and is likely to be sold quickly, while lower liquidity suggests that the asset may be harder to sell due to lower demand or higher availability in the market.
Data Sources:
- Annual Sales Volume: Extract eBay sales volume per year using eBay, starting with the available 90-day historical data and building on it.
- Number of Total Listings: Count how many total (active and sold) listings of that item are on eBay within the last year.
Calculations:
- Sales Velocity: Calculate using the formula:
Sales Velocity = Annual Sales Volume / Number of Total Listings
- Sort the Data: Arrange liquidity data in ascending order. If items have the same count, assign them the same ranking order.
- Rank of Item: The rank of an item is its position in the sorted list (1 = highest liquidity).
- Total Items: Total number of items in the dataset.
- Calculate Percentile Rank: Use the formula:
Score = ((Total Items - Rank Item) / (Total Items - 1)) x 10
- Scale the Score: Convert the calculated percentile rank to a 10-point scale.
What data sources are used?
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The platform aggregates data from reliable sources, including price trends from websites like PriceCharting, sales volumes from eBay, search trends from Google Trends, and ChatGPT for popularity insights.