Shuffle Bored, Anyone?
by Arnold Snyder
(From Card Player, March 1993)
© Arnold Snyder1993
The non-random shuffle gurus are making the rounds again. Or, at least, there are some new nonrandom shuffle gurus making the rounds. Every few years for the past decade, some self-proclaimed genius starts hustling a blackjack system based on the fact that casino shuffles do not distribute the cards randomly. For a few hundred bucks, one of these brilliant system developers will sell you the inside scoop on how to play blackjack by following the “trends,” “clumps,” and “biases.”
This all started ten years ago with the TARGET system, an invention of Eddie Olson, later hawked by Jerry Patterson. Many variations have arisen since then, but the theory and playing methodology never really changes much.
Here’s how Swami Nonrandami’s logic goes:
First, it’s necessary to acknowledge that casino-style shuffles are less than perfect, and that the cards are not randomly distributed by these shuffles. No problem, since anyone who has played any length of time at casino blackjack tables can see that sloppy shuffles are easy to find. When new decks are brought in, it’s not unusual to see occasional cards being dealt in consecutive new-deck order. So we know the shuffles are imperfect.
Next, you must accept the fact that these nonrandom shuffles are affecting the decisions on the hands dealt. No problem again. If you happen to see a dealer hit his fourteen with a six of spades, right after you doubled down on your eleven vs. his four up — you having caught the spade five — then you will be a believer. Yes! Yes! Those nonrandom clumps are killing me!
Now, what if you had a system designed to play those clumpy games? A system that made rational assumptions about hitting and standing based on the severity of the clumps? Yes! Yes!
Finally, a blackjack system that takes into account the kinds of weird stuff we actually see in the casinos. It’s not a system based on some mathematician’s analysis of some computer programmer’s simulated billion hands of play. This is a reality-based system, and that’s the only kind of system that works in the real world.
Card counters are out there talking about advantages of 1%, and they don’t even realize that the casinos sometimes have a 10% advantage over them, based on the nonrandom shuffles. What’s worse is that the same counters don’t realize that they can get a 10% advantage over the casinos, courtesy of the same lousy shuffles.
Etc., etc., etc., etc., etc., etc. . . .
A lot of players buy this baloney, and to be honest, it sounds very legit. There’s only one thing I don’t like about Swami Nonrandomi’s “logic,” and that is that it cannot be demonstrated by computer simulation.
John Imming’s Real World Casino (RWC) software allows programmable, nonrandom, casino-style shuffles. The deck(s) begin in regulation new deck order, and the shuffle routines simulate actual riffles, strips, cuts and washes, as fine or as clumpy as you decide, even utilizing casino-style breaks into multiple shuffling segments if you so desire.
Here’s what I’ve found with the RWC software so far:
The biggest effect on the player’s expectation I could find comes from no shuffling whatsoever. Ironically, this is a player advantage, not a house advantage. I’ve tried Imming’s software with 1, 2, 4, 6 and 8 deck games, with both lay & pay, and pick & pay, dealing styles, and the player advantage rises by .70%-.75% if playing one-on-one with the dealer, regardless of the number of decks in play or the pick up style. Somehow, the play of the hands puts the cards into an order that favors the player.
Both Stanford Wong and John Gwynn had independently discovered this years earlier. Wong, in fact, ran a computer analysis to determine in what way the play of the hands ordered the discards differently from random, and he discovered that in the discard pile high cards do tend to clump with high cards, and low cards with low cards. We don’t know why this favors the player, but it does.
As multiple players are added to the table, this no-shuffle player advantage diminishes. For some reason, the first base side of the table retains the advantage, but the third base side loses it and then some.
Once you start adding any type of shuffle at all to the game, the (dis)advantages diminish, until the real world shuffle results are indistinguishable from random-number-generated shuffle results. The biggest effect I could find in a simulated casino game, utilizing what I figured to be the sloppiest shuffle you might realistically expect to find, was a couple tenths of a percent more or less than the normal basic strategy expectation.
My attempts at creating a sloppy shuffle which would have a greater effect than this were unsuccessful, even though the RWC software allows unlimited variations on lousy, inadequate shuffles.
So, where is this monstrous effect that Swami Nonrandami is crying about? I just don’t buy the explanation that it happens in a casino, but not in a computer. Why not? New deck order is new deck order, and nonrandom sloppiness is nonrandom sloppiness. There’s nothing magical about a lousy, lopsided riffle that a computer can’t simulate.
But there is one factor all the nonrandom shuffle gurus have in common. They all say: “Oh, by the way, you can’t simulate this effect on a computer.” Yet they spout all kinds of precise percentages, based on their “personal studies.”
I say, “Baloney.” Computers may not be able simulate everything under the sun, but card games are one of the things computers are very good at simulating, especially if what you’re looking for is the player’s expectation vs. a fixed house strategy. So take a hike, Swami. I don’t believe in gambling systems based on faith. If you can’t do the math, hit the path.
If you want information on legitimate professional gambling techniques for exploiting non-random shuffles in blackjack, see my book Blackbelt in Blackjack to get started. If after reading that, you decide you’re up to the challenge of actually learning to win with shuffle tracking, see The Blackjack Shuffle Tracker’s Cookbook: How Players Win (And Why They Lose) with Shuffle Tracking.
Send items of faith, hope and especially charity, to the Bishop. ♠