This box stays open while you check other tabs — fill it at your own pace.

Savant-style percentile bars — each bar shows where the player ranks on this board (hitters vs hitters, pitchers vs pitchers). Longer + hotter = higher percentile.

Paste player names, one per line. Plain names work; Name, POS is better. Card-number prefixes (BDC-15 etc.) are stripped. Positions with P/RHP/LHP import as pitchers. Duplicates get tagged with the product instead of re-added.

Hitters (0–99): OPS is the backbone — from .550 (0 pts) up to 1.050 (62 pts). Home runs add up to 12 pts (capped at 30), steals up to 10 pts (capped at 40), and plate discipline (BB% minus K%) up to 16 pts. Being 20 or younger at AA or above adds a 6-pt bonus.

Pitchers (0–99): ERA drives it — 5.50 (0 pts) down to 1.80 (50 pts). WHIP adds up to 20 pts (1.60→0.90), K/9 up to 22 pts (6→13), and BB/9 up to 8 pts (4.5→1.5). Being 21 or younger at AA or above adds 6 pts.

Level multiplier: the subtotal is scaled by where the stats were earned — MLB ×1.25, AAA ×1.05, AA ×1.00, A+ ×0.93, A ×0.86, Rookie/CPX ×0.80. MLB numbers deliberately outweigh minor-league numbers: once a player is called up, big-league production is what moves card value.

Small-sample guard: right after a promotion, a hot week can't spike the score. Until a player logs 30 at-bats (hitters) or 12 innings pitched (pitchers) at the new level, they keep being rated on their prior level's stats and that level's multiplier. The row shows an amber “rated on” tag and a progress note (e.g. 14/30 AB at MLB) while the guard is active; once the sample qualifies, the next refresh switches them to new-level stats automatically.

Outlook (the second score): Heat asks "how is he performing right now?" — Outlook asks "how likely is this card to be worth much more later?" It blends youth vs level (up to 26 pts — confirmed by our cohort study as the strongest predictor), tool pace (HR and SB per 600 PA, up to 22 pts; K/9 for pitchers), contact safety (up to 8 pts — deliberately light, as the study measured only a modest edge), trajectory (a promotion in the last 75 days, weighted by destination — +12 to MLB, +9 to AAA, +7 below — plus +6 for surging momentum, capped at 15), proximity to the majors (up to 12, peaking at AAA where the call-up pop is next), an +8 premium for SS/CF (the study measured nearly double the star rate), a historical-cohort anchor (up to 14 pts, driven by the measured regular-and-star rates of 2010-2019 players with the same profile), and a 15% pitcher discount for injury and attrition risk. The weights are calibrated to the cohort study rather than judgment. Tiers: 80+ Elite · 65+ Strong · 50+ Solid · 35+ Fringe · under 35 Long Shot.

Reading the two scores together: high Heat + high Outlook = hold, or buy the hype. Low Heat + high Outlook = the undervalued buy — young and toolsy but slumping; these rows get a purple value watch tag. High Heat + low Outlook = sell into strength (often an old-for-level player having a career month). Low both = avoid. And click any score chip — on a row or a dashboard — for a plain-language breakdown of exactly why that player earned that number, with a one-line definition under every component explaining what it measures and why it matters.

Player dashboards: click anywhere on a row to open that player's full dashboard — both scores with the complete Outlook point breakdown, a month-by-month trend chart (OPS for hitters, ERA for pitchers, tagged improving / steady / declining), season line, rank and movement, promotion/IL/momentum history, your logged comps, and every action button — including 📰 News, which opens recent articles, call-up chatter, and scouting coverage for that player in a new tab. The dashboard's own ⇩ Excel button downloads a one-page snapshot of that player — scores, Outlook breakdown, season line, monthly trend, history, and comps — plus a second Report tab carrying the full written analysis: the Heat and Outlook narratives with their point breakdowns and the promotion-readiness read. Improving-before-everyone-notices is the chart's whole purpose. Dashboards also show the player's next game — date, matchup, and venue in your local time, with a Gameday link to live pitch-by-pitch (and a green LIVE indicator when his team is mid-game) — pulled from the official schedule. The index rows carry a compact version too: once a player has been refreshed (which records his club), his next game and a Gameday link appear right under his name on the main board — with a green LIVE dot during games. A Confidence label (High = 250+ PA or 60+ IP, Medium = 120+/30+, Low = less) tells you how much sample sits behind the scores.

◎ Value Board: the daily-read summary — buy-low value watch, sell-into-strength, biggest rank climbers and fallers, surging players, current IL, and (once you've logged comps on 6+ players) names priced below their Outlook. Every entry clicks through to the dashboard. The board's ⇩ Weekly report button generates an on-demand Buy / Sell / Watch spreadsheet with a written reason beside every name. Every report call is also logged to the Track record (button beside it): a timestamped predictions ledger graded by pre-registered rules — Buy/Watch hits need the comp up 10%+ within 45–150 days, Sell hits need it flat or down, entries come from your logged comps. Hit rates by call type build the public proof (or honest disproof) of the model over time; a meaningful record needs 30+ graded calls, and the ledger exports to Excel.

Promotion readiness: each dashboard estimates how ripe a player is for the next level by comparing his line to the typical run environment at his level (dominance), how much sample he's banked (orgs promote on proof, not hot weeks), momentum, and whether he's an old-for-level dominator — the profile teams push fastest. Labels run Developing → Building the case → Trending toward a move → Knocking on the door, and 70+ names surface on the Value Board. The readiness score itself remains a heuristic, but it now sits beside the real record: a VS PEERS block shows where the player's line lands among all 2010-2025 seasons at his level (historical percentile, 60,706 player-seasons), what happened to historical peers with his profile (measured promotion rates from the 2010-2024 record — in-season and within a year, with cohort sizes; e.g. young AA hitters +150 over league were promoted in-season 47% and within a year 92% (n=152), vs 7% and 36% below league — and the modern era moves even faster: 97% within a year in 2021-24 alone), and his live standing vs this season's qualified players at the level, fetched fresh: rank, league average, and percentile bars for each core stat (OPS, HR and SB pace, K%, BB%; ERA, WHIP, K/9, BB/9 for arms) — plus the promotion rate one dominance tier higher, so the next threshold is always visible. Below the live bars, the same stats repeat against the decade: each measured as distance from league and placed within the 60,706 harvested 2010-2025 player-seasons at that level, so era differences wash out. Two rulers, one scroll — this season's field, then ten years of it. "Promoted" in the historical record means establishing a qualifying sample at a higher level, so in-season rates run conservative.

MLB Odds (historical cohort): built from a decade of harvested MiLB history — 41,119 player-seasons, 2010–2019, careers now resolved — each dashboard shows what actually happened to players with this exact profile: same level, same age-vs-level bucket, same dominance over that year's league environment. Three rates appear with the cohort size: reached MLB, became a regular (1,500+ PA / 300+ IP), and star-length career (4,000+ PA / 900+ IP). Study headlines: age rules everything (3+ years young and dominant at AA: 79% became regulars, 48% stars — a cohort containing Trout, Altuve, and Bogaerts; old-for-level dominators at any level: under 13% regulars, ~1% stars, confirming the sell-into-strength signal empirically); SS/CF nearly doubles star rates versus corner bats (30% vs 16%). Small cohorts mean wide uncertainty; these are historical rates, not guarantees.

⚾ Statcast quality (MLB players): drop Baseball Savant leaderboard CSVs (Expected Statistics and Exit Velocity & Barrels, batters and pitchers) into the Statcast import and every matched MLB player gains a quality panel — xwOBA, hard-hit rate, barrel rate, and exit velocity, barrels per PA, max exit velocity, and sweet-spot rate (xERA, xwOBA-against, and contact allowed for pitchers) shown as percentiles against the whole league, headed by an expected-vs-actual table (BA/xBA, SLG/xSLG, wOBA/xwOBA with the gaps colored). When expected production runs 30+ wOBA points ahead of actual (0.60+ runs of ERA for pitchers), the row earns an x-quality ▲ tag and the player surfaces on the Value Board's buy-the-dip list: the surface line undersells the contact, which is where post-call-up cards get mispriced. For MLB players with Statcast on file, Outlook's tool components are replaced by contact-quality and expected-production percentiles — underlying quality instead of surface stats, exactly where surface stats lie the most. Matching is by MLB ID. Two ways to feed it: re-download the Savant CSVs weekly with your price files (manual, always works), or save a Cloudflare Worker URL in the Statcast import and the site refreshes quality data by itself every visit (12-hour throttle), falling back to manual drops if the worker is ever unreachable.

Rookie cohort (MLB players): a second harvested study — every 2010-2017 MLB debut with careers now resolved — gives first-year players the peer context percentiles can't: rookies who debuted at the same age, in a similar-length debut, performing similarly vs league, and what became of them (everyday player: 1,500+ PA / 300+ IP; star-length: 4,000+ PA / 900+ IP). It appears as a dashboard panel and fills the Outlook cohort component for MLB players (which previously sat neutral). Headline findings: debut age rules — 22-and-under debuts became everyday players at 73% and stars at 44%, versus 26% and 8% for 25-plus — and early results mislead: Trout and Judge both debuted below league. Age and stage carry the weight by design.

Movement, momentum & injuries: each refresh snapshots the rankings first, so every row shows how many spots a player climbed or fell (green ▲ / red ▼ under the rank number). An L30 surging tag means their last-30-day line is running well ahead of their season line at the same level (cooling = well behind it) — momentum often moves card prices before season stats catch up. A red IL tag means the transaction log shows them currently on the injured list, the fastest way card prices fall.

Transaction log: the ⇄ Transactions button opens a day-by-day log of every official move involving your tracked players — promotions, demotions, call-ups, IL placements, activations, and trades — over the last 1 to 90 days, searchable by player and filterable by move type. It's the fastest way to answer "what happened while I was away." The log's own ⇩ Excel button downloads exactly what you're viewing — date window, move filter, and search all applied — as its own styled spreadsheet.

Tiers: 80+ Scorching · 65+ Hot · 50+ Warm · 35+ Cool · under 35 Cold.

Promotions: refresh walks each player's official MLB assignment sequence chronologically across the current and prior season — only a genuine arrival at a higher level counts, so same-level paperwork re-assignments can't shift the date, and late-last-season promotions land correctly on backfilled price charts — and stamps the date of their most recent upward move (call-up or promotion) as a green ↑ tag — backfilled automatically, even for moves that happened before you started tracking. The 📅 button sets or clears a date manually, and manual dates are never overwritten.

Card comps: the 🔍 button opens the player's recent eBay sold listings (real prices and volume, newest first). The dashboard adds a 🛒 Buy on eBay button that opens live for-sale listings for the player, lowest total price first — research and purchase, one click apart. All comp figures follow one convention: the player's base 1st Bowman Chrome Autograph, ungraded market value (his base card where no auto exists). On SportsCardsPro, autos live on their own "…Autographs" set pages — download those CSVs; their non-parallel rows are the base autos, so the importer reads them natively. Drop auto sets first in a session so the auto wins as each player's canonical card. Once a player is priced via CSV, hovering his row price shows the exact set being tracked, and his dashboard headers name it too. If you ever need to switch a player to a different card (say his auto set turns out to live under a different name, or releases later), his dashboard's Reset price history button wipes his stream so the next import starts it fresh on the right card. You'll rarely need it, because the convention is enforced automatically: an Autographs set supersedes a base-card stream, and an earlier-year set supersedes a later one — the 1st Bowman is the earliest — with the old history replaced and the stream restarted on the truer card (the toast reports how many re-anchored). Prices from any other set than a player's tracked card are simply ignored, so drop order can't pollute a stream. The master sync follows the same rule across devices: when a player's tracked card changes, his history is replaced, never mixed — but only when the master's stream is newer than the device's own. A stale master can never revert fresh local imports; publish after importing and the direction always points the right way. The $ button logs the latest sale of their key card — log a comp each time you check and the row shows the price with a ▲/▼ trend versus your previous log — logging again the same day updates that day's point instead of stacking a duplicate. $ Import comps in the top bar logs a whole pasted list at once (Name, price per line) — the fastest way is asking Claude to research current sold prices for your top players and pasting its list straight in. The import box also accepts a SportsCardsPro set CSV directly — drop the file in and base-card (ungraded) prices auto-fill for every matched roster player; re-download the same set weekly so each player's comp tracks the same card. Dropping a CSV stages prices; clicking Import comps saves them — and the footer's priced count updating is your save receipt. Dated lines (e.g. a history backfill) sort into chronological place automatically; if backfilling from another price source, scale its history to the stream's current level first — sources measure price at systematically different levels, and an unscaled splice creates a phantom jump at the seam. Per-sale history exports (Date,Price CSVs, e.g. from CardHedge) can be dropped straight into the import: the player is matched from the filename, sales collapse to weekly medians, and the history anchors to the stream where the two overlap in time — so older points backfill and newer points move the current price.

Market value context: once 25+ cards are priced, each priced player's dashboard shows where his price sits against a fair-value curve fit to this board (log price vs Outlook, with a ±1 SD band and a similar-Outlook peer percentile). It is a mispricing screen, not a forecast — the honest claim is "cards with this profile trade around $X–$Y and his sits Z% under the curve," and the ledger tests whether such discounts close. After the backtest validated the residual as the strongest signal in the system (+148% vs +63% on cheap-vs-rich halves), it is now first-class: sort the board by % vs curve, the Value Board ranks the biggest discounts, and the Weekly report leads with them (rich-for-score cards lead the trim side). The 📰 Email edition button builds a send-ready weekly newsletter — the gap list, hot bats and arms of the week from live 7-day lines, and a transaction watch, every name deep-linked to his profile (a #p= link opens that player directly). Open the downloaded file, select-all, copy, paste into an email, send; automated subscriber delivery joins the Cloudflare Worker once deployed.

👁 Watchlist & 💼 Portfolio: on any player's dashboard, Watch follows him and Add to portfolio records cards you own (price paid and quantity, in lots) — his dashboard then shows Your Position with live P/L. Variations are welcome — PSA 10s, colored refractors, numbered parallels: describe the card when adding the lot and it is pegged as a multiplier of the tracked base card (calibrated from the value you give at entry), so it rides the player's market automatically. The honest limitation: variation-specific drift (pop reports, grading-premium cycles, color manias) is not captured — use each lot's mark link to re-calibrate against a fresh comp whenever you check one. The base 1st Bowman auto (raw) remains the analytical index all variations mark against. The 👁 My Collection page gathers everyone you follow — ★ owned, 👁 watched, or held in the portfolio — with verdict chips, prices, positions, and next games in one daily check-in, with a portfolio total up top; filter the main board by Watchlist or Portfolio too. Scaling the roster: importing a full checklist (e.g. all 2024 1st Bowman players) is supported and the board stays fast at 500+ players — the one linear cost is the full ⟳ Refresh, which grows with roster size. The playbook: bulk-import per product wave, run one full Refresh after a big import, then use the org and product filters with ⟳ Filtered to update just the slice you're working (one org, one product year, your watchlist) between full passes. Filter dropdowns build themselves from the roster, so new products and orgs appear automatically. The weekly email scans active players (Heat 35+, priced, or yours) to stay quick at any roster size.

House style: everything we publish should be understandable by a young collector reading it for the first time — short sentences, plain words, the answer before the evidence. 📌 The event log: the site records every promotion, IL move, activation, and organization change per player automatically, and the dashboard's Log event action pins anything else — awards, Futures Game selections, trades — with a date. Pinned events mark the price chart (gold dots) and feed the next round of studies: awards, Futures, trades, and injury-length effects publish as their data grows.

🔗 Link MLB ID: the roster directory matches players by name, and baseball has many repeated names — if a player's stats look wrong or empty (a Juan Sanchez problem), his dashboard's Link MLB ID action searches MLB's index and lets you pick the right person, or accepts a pasted mlb.com / milb.com URL. A manually linked ID is authoritative and is never overwritten by name matching. Positions fill themselves: if a player is added without one, refresh pulls his official position from MLB and — critically — corrects hitter-vs-pitcher typing before fetching stats, so bare name lists import cleanly, and ages flow from official MLB birthdates on every refresh (players added by name get their real age automatically — youth scoring and cohort anchors depend on it). One calibration born of a good argument: the pitcher discount (×0.85) prices minor-league attrition risk, so it is waived for arms who have already proven it at MLB (60+ IP with an ERA of 3.30 or better) — a pitcher mid-Cy-Young-season has paid the risk the discount charges for. The waiver appears as a line in his Outlook breakdown. The hitter mirror: MLB hitters without a Statcast row are scored on actual production (OPS) plus power pace, not just tools — a near-1.000-OPS season now earns what it is. (With Statcast loaded, the xwOBA branch already does this, and better — keep the Savant CSVs current.) And the third graduation rule: for arrived MLB players past the promotion window, Trajectory becomes role security — full-season playing-time pace (560 PA / 170 IP = full credit), since an everyday role is the organization's daily vote of belief. Bench bats and shuttle arms earn proportionally less; recent call-ups keep the promotion credit, which is larger. Player photos are official MLB headshots served from MLB's public photo CDN — the same posture as the stats feed the site runs on. Card artwork is deliberately not embedded: card designs are Topps/Bowman intellectual property, and scans on marketplaces carry their own rights — publicly viewable is not publicly reusable, and snipping marketplace photos takes both the seller's photograph and the underlying artwork — the workaround that works is scanning cards you own: photograph your copy, drop the file into a cards/ folder inside your deploy folder, deploy, and use the dashboard's 🖼️ Card image action to point at it (e.g. cards/bazzana.jpg). Self-scans for identification and commentary are the hobby-standard posture; your scans ride the master file so visitors see them too. This data is private to your browser — it never rides the master file to other visitors (use Backup/Restore to move it between your own devices). Once the Cloudflare Worker is live, the same list powers a directed newsfeed and email alerts for just your players.

THE INVESTMENT THESIS (codified from the backtests): 1. Demand evidence — never pay hype prices for players without a qualifying pro record; every such card in the backtest lost (−34% to −71%). 2. Buy the gap, not the name — the market already charges ~7% per Outlook point, so raw quality is priced in; the edge is quality trading under the curve (cheap half +148% with 100% winners vs +63% rich). Target buy = −20% vs curve; deep value = −1 SD; both printed on every priced dashboard, the Value Board, the Targets sheet, and the newsletter. 3. Trim the rich — and rotate, never liquidate: +40% over the curve, old-for-level dominators (under 13% became regulars), and results-ahead-of-quality Statcast profiles are the trim list — but the proceeds belong in the gap list, not in cash. Tested on 88 Bowman Draft 2024 cards from July 2025: selling the rich half to cash returned +11% while the class rose +66%; rotating those sells into the cheap half returned +100%, beating holding everything (+66%). The signal ranks cards against each other; it is not a market-timing call. 4. Size by value, not fame — one expensive fully-priced card dragged a whole naive portfolio from +40% to +30%. 5. Buy dips, not spikes — weekly moves partially reverse (−0.24), so a hot week is a worse entry than a quiet one on a strong profile. 6. Never buy at release — measured on 46 release-anchored histories on this board, the median card sat at 42% of its first-sales price within 30 days, 35% by 60, and 34% at six months, with only 2-3 cards ever above water at any horizon; even the 75th-percentile outcome halved. Seasoned cards (bought 6+ months post-release) gained a median +30% over the following year with two-thirds winners (n=105). Release prices carry maximum hype at the moment of minimum evidence. 7. Sell the debut, don't buy it — across 10 MLB call-ups, the median card popped +12% in week one, then fell to 76% of pre-call price by day 30 (3/10 winners) and stayed depressed through 90 days; minor-league promotions, by contrast, are a mild tailwind (+9% at 7 days, +7% at 30, n=101). The debut pop is a roughly day-3-to-10 selling window, not an entry signal — unless you enter early on purpose: buying ~30 days pre-call and selling into the debut pop returned a median +34% (7/10 winners) across the same ten call-ups, while holding that entry through day 30 flipped it to −10%. The Call-Up Flip is a recognized trade (the Value Board surfaces AAA players at the 85th+ readiness percentile), and its sell rule is the entire edge. One year of evidence, not a law of nature: the ledger grades every one of these rules forward. Every board row carries an action pill (BUY / NEAR / HOLD / SELL / AVOID) with the target range in its tooltip — click it to open the full case. The 📊 Research page presents all house findings as visual cards with their charts, sample sizes, and caveats. The thesis renders itself on every dashboard as THE BOTTOM LINE — a plain-English verdict card up top (BUY ZONE / NEAR TARGET / FAIRLY PRICED / RICH — TRIM / NO RECORD) with the profile, the price vs fair value and targets, and the cohort odds in two or three sentences, so a first-time visitor gets the answer before the evidence. Two empirical notes from our price-history study: these cards barely move together (mean weekly co-movement +0.12), meaning prices respond to each player's own news — the best possible regime for fundamentals-based screening; and weekly moves partially reverse (mean autocorrelation −0.24), so chasing a single hot week is empirically poor timing, while buying fundamentally strong dips is supported.

Tying price to fundamentals, looking back: the dashboard price chart is time-scaled across the full history with promotion and IL markers drawn on the price line, so event pops and slides are visible at a glance; a month-by-month table pairs the player's monthly OPS/ERA with his monthly median card price and its change (with a small-sample association note); and the Value Board's After Promotion panel measures, from your own price data, what promotions actually did to prices at +7 and +30 days — per player and as a running median that sharpens with every backfill. The chart itself is clickable: tap any point and a detail card shows that price, its move vs the prior point, what the price did over the next ~30 days, any promotion or IL within two weeks, and — fetched live from MLB's records — the player's actual line from the 14 days leading into that price, so every spike or slide can be interrogated against the performance underneath it. A Price Statistics panel adds the card's own profile: 52-week range, distance from the high, total return, weekly volatility with a typical 4-week range, max drawdown, and the biggest weekly moves (each clickable to investigate). Bulk imports log one comp per player per day — re-importing another set the same day keeps the first (canonical) price, so multi-set uploads stay clean. Once a player has two or more dated comps, his dashboard charts the price history alongside the list. Comps ride along into Excel exports.

Updating stats: ⟳ Refresh all stats pulls every player's current season line straight from MLB's official Stats API — no typing, no pasting. It also updates each player's level, stamps promotion dates, and stores MLB IDs so later runs match instantly. The ⟳ button on a single row refreshes just that player. Players that can't be matched get a note explaining why (usually spelling — match the name to MLB.com and refresh again).

Row buttons: ⟳ refresh this player · ☆ mark as owned · 🔍 recent eBay sold listings · $ log a comp price · 📅 set promotion date · ✕ remove player. Player names are clickable too — they open that player's MiLB.com stats page in a new tab (or Baseball Savant once they're in the majors). Names link after the player's first refresh, which is what stores their MLB ID.

Your collection: tap the star on any row to mark players whose cards you own — they get a gold ★ by their name, and the ★ My collection filter in the top bar shows just your inventory, ranked and sortable like everything else. Owned players carry a starred column in Excel exports.

Managing the roster: + Add player adds one name; ⇮ Bulk import takes a pasted checklist (one name per line — card-number prefixes are stripped, duplicates get product-tagged instead of re-added). ⇩ Excel downloads a dated, ranked snapshot of everything, including comps and promotion dates. Filters, sorting, and a live search (player name, organization, or minor-league club) are in the top bar. The rank column always shows each player's overall heat rank — filter to one team or your collection and the numbers keep their true place on the full board. The View toggle swaps each row's stat columns between traditional stats and value signals: rank move, L30 line, momentum, Outlook−Heat gap, latest comp and its trend, confidence, and promotion date.

Your data: everything saves automatically in this browser on this computer. It does not sync between devices or people — take an Excel export with you, or re-refresh on a new machine. Reset to starter list restores the built-in roster and clears your changes. When the site's built-in roster gains new players in an update, they merge into your saved data automatically on your next visit — your stats, stars, comps, and removals are untouched. Do data work in one tab at a time — tabs now sync automatically when one saves, but close leftover tabs after site updates, since a tab running old code can still overwrite newer data. The footer's ⇩ Backup downloads your entire dataset — players, comps, collection, and track record — as a single file, and ⇮ Restore brings it back in any browser on any device. Back up after big imports.

Master dataset: if a master-data.json file is published alongside the site, it becomes the baseline everyone builds on. Brand-new visitors inherit it wholesale — players, stats, comps, promotion dates (collection stars stay personal). Returning users get a sync on every visit: the master adds players they lack, fills fields they've never set, merges in new price days (their own same-day prices always win), and carries the master's manual 📅 promotion corrections over local auto-stamps — but never overwrites a user's own manual edits. To publish a master: click ⇩ Backup, rename the file to master-data.json, and deploy it in the same folder as index.html. Keep both files in one local folder and always deploy the folder together — deploying index.html alone removes the master file (the site still works; the baseline just reverts to the built-in roster). The footer shows your data home — the exact web address this data lives under — plus how many players carry prices and the last price date. Storage is per-address: www and non-www are different homes, so if prices ever look missing, check that the footer's home matches where you imported (and set a primary domain in your host so every variant redirects to one address). Site updates never touch stored data. Price history is append-protected: every save re-checks storage and preserves price days the saving tab doesn't have, so a leftover stale tab can no longer erase prices — only explicit actions (a card supersede, Reset price history, Clear today's comps, or Restore) remove price days.

Workflow tip: sort by Heat, look for a hot score with a fresh green ↑ MLB tag, hit 🔍 to see if the market has priced it in, and log the comp. Strong performance plus a flat comp trend is the window collectors look for. Heat measures on-field production — prices also move on hype, autograph supply, and grading, so treat it as a signal, not a guarantee.

Today's signals across your board. Click any player for their dashboard.

Every Weekly-report call is logged with its date, scores, and entry comp, then graded by pre-registered rules: Buy/Watch hit = comp up 10%+ within 45–150 days; Sell hit = comp flat or down. Entry = your latest logged comp at call time (or one logged within 7 days after). Rules were fixed before results — that's the credibility.

Official MLB moves for your tracked players — promotions, demotions, call-ups, injured list, activations, and trades.

Drop Baseball Savant leaderboard CSVs — Expected Statistics and Exit Velocity & Barrels, batters and/or pitchers, any or all at once. Rows match your MLB-level players by MLB ID (name as fallback); percentiles are computed against the full imported leaderboard. Re-download and re-drop weekly alongside your price CSVs.

With a worker saved, Statcast refreshes itself every visit (12h throttle). Manual drops below always work as fallback.

build r24 · 2026-08-15 · the morning prospect market opens

One player per line: Name, price — optional date third (Name, 24.99, 2026-06-28; otherwise today is used). Names match your roster automatically, accents ignored. Each line logs a comp, so pasting a list weekly builds every player's price trend with zero per-player typing. Works with any source: your own eBay checks, a SportsCardsPro/PriceCharting export, or a comps list you ask Claude to research for you. Use plain numbers (no thousands separators). Convention: all prices track each player's base 1st Bowman Chrome Autograph, ungraded — download the Autographs set pages from SportsCardsPro (their non-parallel rows ARE the base autos). Drop auto-set CSVs first; base sets after act as a fallback for players without an auto.