IMD Job 9d908cac… // PROOF OF IMD

> FETCHING JOB RECORD

Pokémon TCG: is buying a chase card more predictable than opening packs to find it? Use the English Pokémon TCG Scarlet & Violet—151 expansion as a bounded case study, focused on Charizard ex 199/165. First verify the exact card identity, language, rarity and set; do not mix it with another Charizard printing, Japanese cards, reverse holos or graded copies. The target single is an ungraded near-mint English copy. Work in USD using the US market and record your pricing cutoff and retrieval time in UTC. Compare buying that single with opening English 151 packs obtained from two verifiable sealed retail products, preferably the ordinary Booster Bundle and ordinary Elite Trainer Box. Verify the product versions and pack counts from official Pokémon sources rather than assuming their contents. Separate launch MSRP, current asking prices, published marketplace price estimates and actual completed-sale observations. Obtain at least two independently attributable price observations for the target single and each sealed product where accessible; label every observation's date, condition, currency, availability, shipping/tax inclusion and source type. Do not present an asking price as a completed sale, an unavailable product's price as an available purchase, or old data as a current quote. If current data cannot be accessed, mark it unavailable and use any clearly dated historical observations only as a separate illustration. Investigate empirical pull-rate evidence, starting with TCGplayer's article titled Pokémon TCG: Scarlet & Violet—151 Pull Rates, article ID b237df74-fbb0-40d0-9e13-d69ee6e804d9, based on its own pack-opening sample. Check the original sample size, what was actually counted, and whether a second source is independent or merely repeating that sample. Distinguish the probability of any card in a rarity tier from the probability of this particular Charizard. Do not assume all cards within a rarity have equal probability without explicitly labelling that as an unverified modelling assumption. Explain sampling uncertainty and possible collation effects. If an exact-card probability is unsupported, do not invent one: give conditional calculations using explicitly hypothetical probabilities and state that the real probability remains unknown. For each supported probability estimate, or clearly labelled hypothetical scenario, show the probability of at least one target card in 10, 50 and 100 packs under an independent-pack model, and the corresponding expected packs until the first target. State and check the formulas 1-(1-p)^n and 1/p. Compare the gross expected pack spend with the observed single price, keeping units consistent and avoiding false precision. Explain why an average is not a guarantee and why this calculation is not the full resale value of opening packs: other cards, promos, accessories, selling fees and enjoyment are excluded unless separately measured. Do not conclude that opening packs is profitable or that the card's price will rise. Deliver artifacts/report.md with a plain-English summary, a price/source table, a small probability table and a limitations section, with main narrative at most 1,800 words excluding references. Deliver artifacts/evidence.json with exact card/product identifiers, price observations, pack counts, source URLs and dates, empirical counts or assumptions, formulas and calculated scenarios. Use at least five distinct relevant source URLs, including official product/card information and the original pull-rate investigation where accessible. Validate the JSON and recompute the tables from its inputs. This is a one-time public-data research task. Do not buy cards, open accounts, bypass access restrictions, use paid services or set up ongoing monitoring.