mickeyrosa.com

I often encounter gamblers who say things like “What’s $100? I often bet that in a single hand.” Or “Why be so tight with tips? $5 is nothing when I regularly have swings of $2000 a night.”

Though these statements sound plausible, for blackjack they are likely false. The real question isn’t the size of your swings, but the size of your expectation. If your expectation on that $100 bet is $1, then $5 is a lot. If you’re betting kelly, you have to make 9 more such bets (without tipping) just to get back to zero!

Here’s another way to put it. If you win $10000 in a night, don’t get too cocky. Ask yourself, what was your expectation for the play? If it was $500, then don’t be too surprised to lose back $9000 of it next time. That’s just average luck.

Counting cards is just like this. Your short term swings overwhelm your expectation. How much play do you need just to make one standard deviation of your result equal to your expectation? In the blackjack literature, this quantity is known as N0, and greatly depends on the quality of your game, but in almost all realistic cases, it’s a lot, at least a few thousand hands. In fact, if you’re not careful with game selection, it could easily approach 20000, a rather daunting amount. (Assuming 50 rounds/hour, 4*N0 is 1600 hours, an amount almost anyone would be hard-pressed to play in a year.)

If you can’t get N0 hands in in a year, then you really are just gambling, even with an edge. You can’t truly call yourself a professional, because you just won’t get into the long run in that year. Really you want to get at least 4*N0 hands, which would mean a loss for the year would be a 2 standard deviation result, about a 2.5% event on the negative side.

So what is the moral? Simulate your play. Keep your expenses low in comparison to your expectation. To play enough to get into the long run, you probably need to play higher quality games.

An additional fact of importance is that no matter what your risk of ruin, if you do not increase your bankroll, you will eventually be ruined. To take a practical example, if you choose a 5% risk of ruin, and cap your gambling bankroll at that (or, equivalently, always bet at that level), you will likely wipe out at some point in your gambling career.

It is impossible to make anything foolproof, because fools are so ingenious. - Unknown

Despite this, I’ll mention some common myths.

Myth: If player at third base misplays his hands, he will make you lose.

Related: Splitting 10’s causes everyone to lose.

Related: Changing the number of hands changes the “flow”. If you do this after a win, you will never get on a roll.

Reality: Let’s assume it is true. But It’s easy to imagine a situation that it’s false. Just pretend the dealer will pull 21 if you don’t split your tens (or change the number of hands, or violate basic strategy), and busts otherwise. Therefore it cannot be true, hence it’s false. Q.E.D.

Perhaps what people mean is that you are more likely to lose when this happens, not that it’s a guarantee. But unless you know something about the distribution of cards to come out, how can further randomizing the cards change the long term result?

Myth: You will lose if you don’t follow proper money management. That is, if you keep playing while you’re ahead, you’ll lose it back. And if you don’t press your bets while you’re winning, you won’t win enough to cover your losses later.

Reality: Money management is not “quitting while you’re ahead” or “pressing your bets when you’re winning”. Quitting (regardless of whether you’re ahead or behind) is the best policy if you’re playing a losing game. But if you’re playing a winning game, playing more is the best policy. As for pressing your bets while you’re winning, the only time I can imagine this to be useful advice at the blackjack table is if you somehow run into a positive game by accident.

Myth: You must keep your emotions in control in order to win.

Reality: The cards do not know whether you are angry, ecstatic, upset, laughing, or crying. As long as you’re making mathematically correct decisions, your emotional state is irrelevant. Now, if you get upset, you may be less likely to make correct decisions, so it makes some sense.

Myth: Conservation of luck. Your luck is limited. If you’ve been unlucky, you’ll be lucky soon, just to balance out your bad luck. Or conversely.

Reality: We haven’t noticed this. We have noticed the opposite. Some people seem luckier than others, but we attribute this to greater skills and preparation. In a global sense it is true. If you’re playing a zero-sum game, luck evens out between both players. But this is a useless observation.

Myth: It takes genius to count cards.

Reality: Card counting is not memorizing every card in the deck, or even keeping track of how many high cards, low cards, and medium cards there are. The base skill is just adding or subtracting 1 — literally, counting. The average fifth grader has the intellectual skills to count cards. If you’re 70 years old, you might not be up to that, but otherwise any adult of average intelligence is capable of doing it.

This does not mean that everyone of average intelligence or better would play a winning game of blackjack if they tried. There are other factors, namely discipline and honesty. You need discipline to stick to the system through thick and thin, and honesty with yourself to recognize if you’re not playing well.

Related myth: To win, you cannot make any mistakes if you count cards.

Reality: Some mistakes are extremely costly, but others are relatively inexpensive. You can make a fair number of inexpensive mistakes and still play a winning game. Inexpensive mistakes are things like occasionally over- or under-betting by one unit, or being off by the count by 1. Some misplays don’t cost much, and in fact if you count cards but don’t alter your strategy, your basic strategy will change somewhat.

Misconception: If you reduce your bets while you’re winning, you’ll come out ahead with greater likelihood, at no cost to your long term results.

Reality: This is a corollary to the conservation of luck myth. If this happens during a shoe when the count is rising, you’re costing yourself in the long run. Essentially, you’re leaving the table when the cards are in your favor, and coming back with lower bets. In the end your win will be far less than if you bet properly. Yes, you will decrease your chances of losing your win in the short term, but if that’s your concern, why not stop playing altogether?

Myth: My progressive system is guaranteed to win!

Reality: No betting system can change a losing game into a winning one.

Myth: You should always take even money.

Reality: Taking even money is the same as taking insurance. Unless you are keeping track of the cards, or your bet is a huge fraction of your net worth, this is a bad bet.

What other myths have I missed?

The benefits of a team are:

1. You can combine bankrolls, so each person can bet as if the entire bankroll is his.
2. You get into the long run faster.
3. Teammates help you train more effectively, improve morale, enable task specialization.

How do blackjack teams work when it comes to splitting the result?

Incentives matter. A lot. Any agreed-upon way to set things up can work for a while, but after a little while problems will crop up. Blackjack banks are an ideal laboratory for devising and testing compensation schemes.

Here are common problems encountered:

1. Many times, players are rewarded only when the bank wins money. This is analogous to the usual payment scheme for hedge fund managers, who get 20% of the win, and none of the loss. There are two problems with this:

Desertion effect: If the bank is losing badly, players have little financial incentive to play, since every dollar they make at that point goes to investors. Essentially, players have an option whose value has diminished since it’s so far out of the money. Hedge fund managers can swing for the fences with a high-variance strategy, but this is not an option for card counters.

Bandwagon effect: If the bank is winning a lot, players will flock to the tables trying to grab a piece of the overly generous pie. This increases the likelihood of burning themselves and the game out, in a replay of the tragedy of the commons. Player’s option is not only deep in the money, under some schemes it’s greater than the value of their play.

You could try to get players to invest, so that their interests would not be completely dominated by their view as a player. But new players, or ones without money, would have a problem. This can be done somewhat by paying players shares, so that when the bank is down, they have an investment.

If you pay players a fraction of their return as salary, you can ameliorate the desertion effect (but the bandwagon effect remains). In the scheme that MIT used from 1994 onwards, we paid players 25% of the CE of the game as salary.

It was a tiered payment scale. First, player pay at 25% of CE. Then investor return at the same amount. Then investment return of 18% (arbitrary). Any remainder was split 50-50 between investors and players. It worked fine when the returns were strong, but when the play diminished and the 18% became onerous, the bank sputtered. Also, the bandwagon effect was strong.

We tried the “infinite bank”: Break the bank after each trip, paying players on a free-roll basis. This requires good reporting and record-keeping, since the percentage paid to players will be quite sensitive to the statistics of the play.

Although it seemed mathematically elegant, it had a significant flaw: It hurt team morale, because each trip made no difference to the players who weren’t on it. The desertion and bandwagon effects are still there, just limited to a single trip. In particular, desertion manifests itself in why should a player go to the trouble to negotiate a loss rebate? Also, in the middle of a losing trip he may bail, instead of trying to dig out.

You can avoid these problems by scheduling mass attacks every trip, so that everyone is involved, and it’s unclear to the individual players how they stand. Play until either you win or get thrown out. This is what the Greeks did, and it worked fine until they burned out the games.

2. If you pay each player at the same rate, there is little incentive for a less skilled player to improve his skills. The difficulty is in measuring the contribution. How do you decide how much a BP gets vs a spotter? A GBP?

The “girlfriend” problem is an example. Players will recruit their girlfriends to play. Without a specific compensation scheme based on skills or contribution, by default they get an equal share. Yeah, I know, she was good for camouflage. Other teammates will likely suffer financially as a result.

For players in the same category, you adjust pay for each player based on the errors made during checkouts, and metrics based on effectiveness. Spotters, for example, could be paid according to: # of shoes spotted + 5*(# of shoes played). We never tried this. It may be that objective measurements cannot capture the full behavior and that players would game the system in perverse ways (including sabotaging their teammates, or being uncooperative, or keeping performance secrets to themselves). If so, perhaps a (mandatory?) bonus rewarded at the subjective discretion of the managers might do the trick.

In Blackjack Forum, Don Schlesinger wrote an article on the incremental value of each additional spotter. It makes various assumptions, but in any case, it’s a good starting point. The BP-spotter split can be computed using this as a basis, adjusting for specific conditions.

3. If you cover expenses (like airfare, hotel, car rental, legal costs), a player has no incentive to keep costs down.

Solution? Charge each party his proportional share of the expense. So if players get 40% of the win, charge them 40% of the expense. Do not aggregate this in the win figure; the statistics on the games are not necessarily accurate (due to fallible memory, unnoticed player mistakes, and modeling error), but expenses are.

4. If you don’t pay for management, you won’t get any. The result is minimal recruitment and training, no development of new games, poor record-keeping, long delays in pay, haphazard trip planning. In the long run, the demise of the team is likely. Accounting and training take by far the greatest number of hours, but they are relatively low value-added per unit time activities that can be automated to a great extent. Game development is a high value-added activity that is crucial in the long run. This is for at least 2 reasons. One, a single approach gets burned out after a while. Two, you can’t use the money once your bankroll exceeds a million or so. Blackjack, however, is not the only way to make money.

Solution? Come up with measurements for each of the things that are valued and pay for them. For example, accounting, trip planning, record-keeping, and general management are important, but their value is limited by the amount of play gotten. Perhaps 5-10% of the value of the play should be devoted to that. Recruitment? Each recruit’s shares in his first 3 months of play are matched by the investors to his recruiters. Training? 5% - each training session attended gets some credit. Game development? 5%, subject to games developed making money greater than the amount paid for them.

These percentages are ballpark estimates.

5. Spinoffs. Golden handcuffs via vesting or benefits can be used. Otherwise, personality conflicts and self-interest as investors will tend to break up the group.

6. Compensation for game development: pay developers dry shares on the game in question. If it makes money, so do they. Practically speaking, this can be difficult if accounting cannot distinguish between result due to counting or result due to technique #4, but an estimate can be made on the basis of random spot checks or simulations.

Intangibles like the camaraderie between teammates (which can lead to fruitful collaborations) are hard to measure, but they have real value. There are also longer-term results from play, such as comps, or return from movie or book deals, lawsuits. And results from research will take some time to work themselves into play return. Measuring the value of such assets is not easy, but a share-based scheme puts people into the long run and makes this measurement less important.

Creating objective measurements of performance is not easy, but can make things run more or less automatically. To head off a huge problem or conflict, everyone must agree in principle that there must be a measurement, and that this measurement can be reviewed and amended. If the measurement has inherent uncertainty, you can use utility analysis to set a CE for it.

The percentage split between investors and players is rather arbitrary, depending mostly upon the alternatives available to each, or perhaps philosophical arguments. MIT generally split it 50-50.

If recruitment and training, game development are not an issue because all the players are experienced and knowledgeable, there are simpler methods. If everyone is adequately bankrolled, you can simply split the result 1/n for each of the player-investors. This generally requires a strong underlying game, and limits that are generally far below what the group could bet without constraints. Hole-carders do this on a routine basis.

If a player is not so well-bankrolled, the player can take a free roll. You decide what long-run percentage of the result a player will receive. Then you gather statistics to compute the distribution of results. Assuming a normal distribution with an estimated mu and sigma, you can compute the percentage of win that corresponds to that long-run percentage. For most games this percentage settles to a particular value fairly quickly. But if varying conditions cause your edge and variance to also vary, gathering the statistics can become daunting.

A counting game is weak, about 1-2% edge to the players. This is close enough to zero that errors can cause some players to play a losing game. The more likely problem is that errors (everyone makes them) diminish the quality of the game significantly enough such that a much larger than expected bankroll to withstand swings is required — 800 unit downswings are not unheard of. Most players are under-bankrolled, and will tap out as a result. Proper money management is required to allow your edge to work for you.

Money management? What’s that? Basically it’s how much you bet when you have an edge. If you bet too much you’ll likely go broke. If you bet too little you are leaving money on the table. What’s the right amount? The short answer is to bet proportional to your edge, inversely proportional to your variance. If you bet an amount bankroll * edge / variance (full kelly), this is very aggressive, leaving little room for error. Due to inevitable errors, you’re better off betting a fraction of this. MIT chose to bet at 30% of full kelly. And this is for a mathematically solvable game. For investors in the markets, a much tougher game with many more unknowns, betting half as much as that is probably pretty reasonable.

Here’s my current thinking on this: Pay players 40% of the CE as shares, valued at the pre-trip bank level. Do not allow players to cash out more than half their shares after any bank. This puts some of their skin in the game for the long term. Pay out management shares on the same basis, under the following categories: 5% R&D, 5% accounting / record-keeping / trip planning / general management, 5% training, 5% recruitment. The remaining 40% goes to investors.

A share-based scheme makes it simple in theory to raise or reduce capital investment as needs require. Assuming that capital can be raised and disbursed quickly, play for a short period can easily be accommodated.

Issue dividends if and when capital on hand exceeds what can be used.

Actually, this scheme does not require that the bank ever be broken. This alone is a benefit, since we lost a fair amount of play as a result of reforming each bank and even stopping play in the middle of a trip. Also, a limit on the maximum shares a player can cash amounts to a vesting scheme. Perhaps no more than half of a player’s compensation could be cashed out every half year (or period that it takes to get into the long run i.e. ev > 2 sd), unless the amount remaining was under some value not worth worrying about anymore, say $100 or so.

This method is quite sensitive to the estimation of the value of the game. If you play a game whose value is very sensitive to conditions or mis-reporting, it would be a bad method. If you play a mix of games, some of which are more sensitive than others to these problems, it would be easy to pay some players far more than their play was worth. This could easily destroy a team.

Perhaps a better way would be to use Bayesian techniques to estimate the expectation and variance of a game. Some games have zero variance, other games have huge variance, some games are hard to estimate the value of. Under the Bayesian framework, you can account for all this. You state your prior, then as results come in you modify that prior, using that value as the basis of pay. It’s important to include all relevant parameters that may change the value in the Bayesian analysis. This may prove impractical under all conditions, the Achilles heel of the approach.

How can things go wrong?

The biggest problem is poor quality play. This includes choosing to play under poor conditions, modeling error, and carelessness with handling money. The next biggest problem is poor record-keeping and accounting. If a player’s game is materially different from what he reports, someone (most likely everyone else) is getting screwed. If you forget to record a transfer of 10K (could easily happen in the heat of battle, if a player transfers money surreptitiously in the middle of a session), the player who is 10K short can feel rather disturbed.

Let’s be positive. It’s true that, in his books, Mezrich took artistic liberties and made up some stuff. But in the end, he captured the spirit of what it was like to play on the MIT team pretty well. If you apply the “Is this a Hollywood moment?” criterion, you can filter out the fiction and get to most of the fact.

Let me list some amazing things that really did happen:

Jeff Ma (Kevin Lewis) really did date a Ram’s cheerleader. He also dated one of the grand-daughters of Yue-Kong Pao (a shipping magnate, the Onassis of Hong Kong).

There really is a detective agency that lists card counters as cheaters. Though James Grosjean’s lawsuit put them into Chapter 11 bankruptcy, they are still in business.

There truly was a team of blackjack players, primarily (but not exclusively) from MIT, that beat the casinos for millions. Much follows:

Blackjack is a mathematically beatable game. To beat it in practice for more than peanuts was not so easy, and required organization, training, and capital.

Counting cards is not the only legal way to beat blackjack.

High rolling players do get treated like royalty by casinos. Our players enjoyed experiences like an entire wedding hosted and paid for by Caesars Palace; helicopter rides to the casino; playing with and meeting sports legends like Michael Jordan; ringside seats at heavyweight title fights; penthouse parties with Hollywood celebrities; stock car racing. Too bad very little of this made it into the books.

It really feels like you’re James Bond, except the bad guys aren’t going to kill you if you screw up.

Going from MIT student to high roller and back again does feel a bit like donning a superhero’s outfit and taking it off again. It does wonders for your confidence.

We did sometimes bet thousands of dollars a hand, hitting the table maxes fairly often.

We were at the riot at MGM after Tyson-Holyfield II, and MGM did change their chips afterwards.

At least 2 MIT teams took the Mohegan for quite a bit during their opening weeks.

We did employ make-up artists.

We were evicted from rooms on occasion.

On at least 2 occasions, a disgruntled player betrayed us to either the casinos or to Griffin.

It pays to act like a high roller. People treat you better. Hosts in Las Vegas are unmatched for what they can do for people they value.

For instance, in 1990, Jon showed up at the Hilton without a reservation on the Friday before Comdex. Ordinary folks would never be able to get a room at that late date. Did that stop Jon? No.

He went to the front desk and asked for a room. Of course they told him none were available. Jon asked to speak with host. The desk clerk dialed the number and handed Jon the phone:

Jon: Hello, this is Jon Hirschtick, player card number xxxxxxxx.

Host: [clicking away...sees Jon's rating] … Yes sir! How may I help you?

Jon: I’m standing here at the front desk. I have $60000 in my briefcase. Now, one of two things is going to happen at the end of our conversation:

1. I’m getting a room here at the Hilton, RFB; or

2. I’m going to Caesars.

Host [stammering]: But… but… but, that’s not how it works.

Jon: Like I said, one of two things…

Host: I’ll be right down.

[A few minutes later]

Host: Good to meet you Mr. Hirschtick. [eyes Jon top to bottom. Jon is wearing a nice suit, carrying a leather briefcase, wearing a Rolex watch.] Claire, please check Mr. Hirschtick into a nice suite, authorization code xyz123.

Jon: Don’t you want to see what’s in my briefcase?

Host: No, that’s all right.

[Claire, desk clerk]: Yes, sir. [hands Jon the keys]

Jon: Thank you.

In the hubbub, Claire, the desk clerk, had neglected to take Jon’s credit card imprint. So not only did we have RFB (unlimited food and beverage), we could sign tips to the room! We could also make unlimited phone calls to anywhere in the world. We could charge stuff in the gift shop to the room as well.

We did not take full advantage of the situation, but we did make some generous tips. As a result, our room service waiters were very attentive. We asked one for a list of all the parties in the hotel. He came back later: Toshiba is in Penthouse suite xxxx; they’re serving shrimp on ice, fruit and cheese plates, champagne; Dell is in xx-xxx at 9a, with cheese and crackers; and so forth.

Late Sunday night, we called room service for delivery at 8am Monday. Comdex would start at 9am, and we wanted to be fed before we got to our booth. I called.

John: I’d like to order some oatmeal with bananas, pitcher of orange juice, fruit plate, etc…

Room service: When would you like that delivered?

John: 8 am

Room service: Sorry, we can only deliver at 6 or 11. No other time slots are available.

John: I still want it at 8 am.

Room service: Sir, that’s impossible.

John: Tell you what, you just tell the room service waiters that we’ll triple our usual tip if they get it here at 8.

Room service: They can’t do that.

John: Just tell them [hang up]

…3 minutes later, phone rings.

Room service: Your breakfast will be there at 8.

Here’s an interesting article from Monday’s USA Today.

What do people think of these excerpts in particular?

Bill Kaplan ran the real life MIT blackjack team that won millions of dollars from casinos… 

and

We required our blackjack players to put some money in the bank so that they understood it from the investors’ perspective.

Starting from the part where Ben loses control at the Red Rock and loses 200K, the movie takes off on a tangent that has no resemblance to reality. Our players were far too disciplined to even think of doing something like that. As I see it, that entire scene is a plot device to end the movie - create a conflict between Campbell and Rosa that leads up to the switcheroo finale.

Speaking of Rosa, no MIT professor lead any team. One professor played for us a bit, his wife acted as BP on a number of trips, and his daughter later played for us and even had a college project featuring the team. I found my thesis advisor from a card counter catcher in Atlantic City who asked me if I knew “Nez”. I had no idea. Then I spotted a book on poker in the MIT Coop bookstore, by MIT professor Nesmith Ankeny.

Another plot device is the scholarship interview. Never happened. It’s a clever technique to tell the story, but because of it, it makes the ending too predictable. You know that Campbell never gets to keep what he made — otherwise, why would he be applying?

The movie exaggerates the wins and losses by at least a factor of 5 or 6. When you count cards, any single session result is mostly luck. Our biggest session win was probably around 120K. But what was the expectation? Unless a dealer made a huge mistake on coloring up a player (exchanging lower denomination chips for higher), even under the best of conditions a single session would not make more than 10K.

We did not pay our players after a single night’s play. If we did, what would happen when we lost? How is a student going to cough up his share of a 40K loss? Doesn’t work like that. I’ll dedicate another post to how things really work, if people express interest.

Players also practically never tipped. I gasped in the scene that Ben tipped the cashier $100. Two reasons we didn’t tip: 1. Our edge was small enough that it would be noticeable to our bottom line (tipping too little draws more contempt than not tipping at all); 2. It almost never buys you anything.

The Fisher character (also Asian in real life) was so distorted as to be unrecognizable. Mike Aponte was a great BP, extremely disciplined, well-mannered, a gentle giant. Fisher is moody, irresponsible, just generally an ass. I wonder whether bad blood between Jeff Ma and Mike Aponte had something to with that.

I’ve already mentioned Cole as an anachronistic character. If such a person existed today, he would be a huge legal liability for any casino or organization associated with him.

“Variable change” is a term the movie throws around for the concept of “conditional probability”. This baffles me. Why should they change the standard term to something that sounds like a technique in calculus (change of variable)? It just leads to confusion and cognitive dissonance for anyone who knows probability.

The classroom training could have been better done; 10 seconds spent explaining exactly what to do could sell a lot of DVDs. Players spent about 100 hours learning basic and counting, and about 15 minutes going through the code words for the count. This disparity shows how accurate the movie is in its depictions even when it’s not totally fiction.

Drake Bennett’s Boston Globe story reveals that Ben Mezrich’s supposed “non-fiction” stories are really a lot of fiction, and provides a plausible explanation: Ben is just too lazy to do the work.

Drake talked to more people in 5 days than Ben did for both books. Bravo Drake!

It bothers me, however, that Bill Kaplan has claimed that he is the basis for the Micky Rosa character, and that Ben is apparently backing him up! Jeff joined years after Bill left the team. How could Jeff possibly have told Ben about him? What are these Harvard guys up to? [Correction: Jeff in fact did meet Bill during SI. He went to a couple practices, but never really started playing until 1994, after Bill's involvement ended.]

I suppose that the Bennett article is a bit too negative. For example,

‘I don’t even know if you want to call the things in there exaggerations, because they’re so exaggerated they’re basically untrue,’ said John Chang, an MIT graduate and one of the inspirations for the character Micky Rosa, who in the book is the team’s founder and leader.  

If “the things in there” refers to getting beaten up, swallowing a chip, having strippers cash out, or intimating that Micky had something to do with robbing the team, I agree. Otherwise I do not feel that much else is distorted beyond reason — perhaps people can remind me otherwise. If Ben makes Micky Rosa a professor to emphasize his authority, or changes his appearance to anonymize him, that’s ok. If Ben describes a non-existent Chinatown casino to describe the tension of the checkout, that’s ok too. Although many details were distorted, much of the essential flavor of the team was captured.

What will be the effect of 21? I expect it will encourage people to play blackjack. Unfortunately, almost everyone will lose more than what they would have had they never seen the movie.

History bears me out. In 1962 Ed Thorp came out with Beat the Dealer, the first book on card counting. It was a best-seller. The casinos initially reacted with panic, instituting awful rules. But when no one played, they brought back the old rules. Surprisingly, they won more than ever before. It turns out that Thorp’s book was the best thing that ever happened to the game, for the casinos.

How can this be? How can casinos offer a beatable game and come out ahead? Learning to count in a casino environment isn’t easy, and almost no one (perhaps no one at all) can do it by reading a book on the plane going to Vegas. This is not to say that learning to do it is impossible or even very difficult. It’s just that you can’t become proficient in a few hours reading a book about it. (If you’re not proficient, your act will suffer and you’ll probably get barred even if you manage to play a winning game on paper.) It would be like swimming a mile by reading about swimming, when in your whole life you’d never done more than soak in your bathtub. You’d probably drown if you tried.

Intelligence is not much help. I’ve seen MIT students (not teammates) make fools of themselves at the tables. Sad to say, they were belligerent, cocky, and ignorant know-it-alls.

One problem is that few people seriously consider gambling a money-making activity. The very word connotes blowing money, not earning it. People go to casinos to “have a good time” — meaning studying how to play well is the last thing on their minds.

A secondary effect for professionals is that it will create an atmosphere of paranoia in casino management. In the economic downturn we’re experiencing, casino profits have dropped significantly. Casinos will of course blame card counters. Likely Griffin will be resurrected from the grave from all the casinos trying to protect their coffers from being raided further. How foolish, how predictable.

Reports of quick barrings and false arrests have begun to trickle in. Are we back to the pre-Imperial Palace judgement days?

So, exactly why do I claim to be the basis of the Mickey (or Micky) Rosa character? As a friend of mine put it, a) Micky Rosa was a real asshole, and b) John Chang is supposed to be the basis for the Micky Rosa character, so therefore c) John Chang must be an asshole in real life.

Ow.

It’s the difference between the character in the movie versus the book. Mickey Rosa in 21 is basically a completely fictional character. From what you see on screen, no one (or anyone) could really claim to be him. The Micky Rosa character in Bringing Down the House is a different matter. Physically I have no resemblence to his description, but many stories, though distorted, are ones I’m intimately familiar with because I lived through them.

Interestingly, the Kianna Lam character is least distorted. She also provides the strongest link to Micky Rosa [BDTH, p. 250]:

The latest rumor was that Micky and Kianna were living together in Micky’s apartment, running a handful of new teams out of MIT.    

As my Blackjack Forum interview indicates, Kianna was helping me move when Jeff called to talk about softball. Kianna picked up the phone because I was selling my household goods and thought a buyer was calling. Jeff immediately recognized her distinctive voice, which was a great surprise since she lived across the country — what was she doing in my apartment?

That’s how that “rumor” ended up in the book.