Should You Buy 5 Scratch-Offs at One Store or Spread Across 5 Stores?

It's one of the oldest debates among scratch-off players: Is it better to buy all your tickets at one store, or spread them across different locations?

Some players swear by their "lucky store." Others insist that spreading out gives you access to different packs and better overall chances. Both sides have passionate defenders — but neither side has ever run the actual numbers.

We did. We used real payout data from 200+ New York lottery retailers and ran 100,000 Monte Carlo simulations for each strategy. Here's what we found.

The Data: How Much Do NY Stores Actually Vary?

Before we can simulate anything, we need to understand how different stores actually are. We pulled 30-day payout data from the NY Open Data API for 200 high-volume lottery retailers across the state.

Store Payout Distribution (30-Day Window)
Based on 200 NY lottery retailers with >$5,000 in monthly sales
Lowest store
58.6%
25th percentile
70.6%
Median
76.2%
75th percentile
81.5%
Highest store
125.7%

The spread is enormous. The lowest-payout store returned 58.6 cents on the dollar, while the highest returned $1.26 for every dollar sold (that store had recent big winners). The standard deviation across all stores is 9.9 percentage points — that's not a rounding error, it's a meaningful difference in outcomes.

A store paying out at 80% versus 60% means you're getting back $4 more on every $20 you spend there. Over a year of weekly play, that's $200+.

Why Do Stores Have Different Payout Rates?

This isn't about "lucky" stores. There are structural reasons why one retailer pays better than another in any given month:

The Simulation: 100,000 Trials Each

We modeled two strategies, each buying $50 worth of $10 scratch-off tickets:

  1. Strategy A — Concentrate: Buy all 5 tickets at one randomly selected store
  2. Strategy B — Diversify: Buy 1 ticket at each of 5 randomly selected stores

We used the real distribution of store payout rates (adjusted to the statewide average of ~62%) and ran 100,000 independent trials for each strategy. Each ticket was modeled as a binary outcome based on the store's payout probability.

🔬 Methodology Note
This is a Monte Carlo simulation — a statistical technique that runs thousands of random trials to estimate probability distributions. It's the same approach used in financial risk analysis, weather forecasting, and engineering reliability testing. Our 100,000 trials per strategy give us very tight confidence intervals.

The Results

Metric 5 at 1 Store 1 at 5 Stores
Total cost $50 $50
Average return $31.00 $30.99
Average profit/loss −$19.00 −$19.01
Return volatility (stdev) $11.68 $10.88
Break even or better 12.0% 9.3%
Total wipeout ($0 back) 1.2% 0.8%

The expected value is virtually identical

The average return is $31.00 vs. $30.99 on a $50 spend. A difference of one penny across 100,000 trials. If you're choosing stores at random, the strategy literally doesn't matter for long-run expected outcomes.

But the variance tells a different story

The concentrate strategy has ~7% higher volatility ($11.68 vs. $10.88 standard deviation). In practical terms:

Think of it like investing: concentrating is high-beta, diversifying is low-beta. Same expected return, different risk profile.

The Plot Twist: What If You Pick Your Store Wisely?

Here's where it gets interesting. The simulation above assumes random store selection. But what if you use data to pick a top-performing store?

We re-ran the simulation with a new matchup:

Metric 5 at Top Store 1 at 5 Average
Average return $41.26 $31.13
Average profit/loss −$8.74 −$18.87
Edge per $50 spent +$10.13

The Verdict

If you're picking stores randomly, it doesn't matter. Same expected return either way. You're choosing between slightly wilder swings (concentrate) and slightly smoother losses (diversify).

But if you use data to pick your store? Concentrating 5 tickets at a top-rated store cuts your expected loss nearly in half — from $18.87 to $8.74 per $50 spent. That's a $10.13 edge, and it compounds every time you play.

The store matters more than the strategy. Where you buy is the variable that actually moves the needle.

How to Find a Top-Performing Store

Our Store Finder ranks every NY lottery retailer using a Smart Score that combines nine real-time factors:

The balanced profile weights these factors using a machine-learning model that retrains nightly against actual next-day outcomes. It's not a crystal ball, but it measurably outperforms random store selection.

Important Caveats

We're data nerds, not dream sellers. Here's what this analysis doesn't mean:

  1. No strategy makes scratch-offs profitable. Even the best store still has a negative expected value. The statewide average payout is ~62%, meaning you lose ~38 cents per dollar long-term. Data helps you lose less, not win.
  2. Past payout doesn't guarantee future payout. A store's 30-day win rate reflects who happened to buy tickets there. Consistency scores help filter out noise, but variance is real.
  3. Pack theory has limits. While ticket packs have fixed winners, packs are reshuffled randomly. Consecutive tickets from the same roll are still independent draws. There's no "due" theory that holds up.
  4. This is aggregate data. NY Open Data gives us total scratch-off dollars settled and paid per store — not a per-game breakdown. A store's high payout could come from one lucky $30 ticket buyer, not the $5 game you're playing.

Bottom Line

The classic "one store vs. five stores" question has a boring answer when you're choosing randomly: it's a coin flip.

But the real question — "does it matter which store?" — has a much more interesting answer: yes, a lot. The variance between stores is real, measurable, and large enough to cut your expected losses in half if you choose wisely.

So don't debate whether to concentrate or diversify. Debate which store to concentrate at.

Find your top-rated store

Our Store Finder scores 11,000+ NY retailers in real-time using 9 data-backed factors.

Open Store Finder →

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Data sourced from nylottery.ny.gov and data.ny.gov. Updated daily. For entertainment and informational purposes only. Please play responsibly.

AP
Alex P.
Lead Data Analyst at ScratchOffsNY

Alex builds the Smart Score model and analyzes scratch-off data daily using official NY Lottery prize reports and open data APIs. All rankings are based on math, not gut feeling. Learn about our methodology.