About This Tool

This site ranks Texas scratch-off games using up-to-date prize data and gives you an at-a-glance sense of which games currently offer the most remaining value. It also includes a Monte Carlo simulator so you can experiment with expected outcomes over many “what if” scenarios.

How the Rankings Work

• Value Score — Our core ranking metric. Roughly, this is the remaining prize value per $ of ticket price. Higher = better remaining value for your dollar.
• Remaining % — The share of the game’s total original prize pool that is still unclaimed (0–100%). If a game started with $X in prizes and $Y remain, Remaining% = Y / X.
• Pool Remaining — Dollar value of all unclaimed prizes still on the street (sum across tiers).
• Ticket Price — Face value of one ticket for the game.
• Top Prize — The largest single prize for the game.
• Top Prizes Left / Initial — How many top-tier prizes remain vs. how many there were at launch.
• Approx. Tickets Left — An estimate of unsold/remaining tickets, inferred from prize pool vs. ticket price (or provided directly when available). Used in probability modeling.

We sometimes show additional derived metrics when available:

• Sell-through — An estimate of how much of the game has likely sold (higher = more sold). Approximated as 1 − Remaining% unless we have better telemetry.
• Value Score (Weighted) — A conservative variant that discounts the Value Score as games get older (more sold), so fresh games with lots of remaining prizes bubble up.
• Jackpot Weight — A quick signal for jackpot-chasers: (top prizes remaining ÷ initial top prizes) × Remaining%. Higher = relatively more top prizes left and decent overall availability.

What the Colors/Sorting Mean

The “Top 10” bar chart is ordered by Value Score. In the table, rows are ranked by the same metric by default. Use the toggle (if enabled) to switch to jackpot-focused ranking.

Monte Carlo Simulator

The simulator uses the remaining counts of each prize tier to approximate per-ticket win probabilities, then runs many simulated “trials” to estimate outcomes.

Inputs

• Game — The scratch-off to simulate. Prize tiers pull from our live data.
• Trials — How many independent scenarios to simulate. More trials = smoother averages (but slower).
• Tickets per trial — Number of tickets you “buy” in each trial (e.g., 5 tickets per day).
• Seed (optional) — A number to make results repeatable. Leave blank for “fresh” randomness each run.

Outputs

• Per-ticket jackpot hit rate (sim) — Estimated probability of hitting the top prize on a single ticket under the current remaining-prize model.
• Average net per trial — (Total prizes won − cost of tickets) averaged across all trials. This is often negative because, like most games, expected value is below $0 once you pay for the ticket.
• EV (all tiers) — Theoretical “expected value” per ticket using remaining counts: sum of (prize × probability) across every tier minus ticket price.

Important: This is a model. Real-world outcomes depend on where the remaining winning tickets are, game closures, claim processing lags, and retailer inventory. Past performance and current “remaining” snapshots are not guarantees of future results.

How to Use This

1. Use the rankings to shortlist games with strong Remaining% and Value Score.
2. If you’re chasing big wins, switch to the jackpot-focused view and check Top Prizes Left.
3. Open the Monte Carlo simulator, select a game, and try different “tickets per trial” to get a feel for variance.

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