Posts tagged: linkedin

Retire Sooner by Ignoring Financial Advisors

By , May 20, 2010 3:06 pm

The recent market activity and what we saw in September 08 and March 09 is why people should not listen to any ‘investment professional’ who tells you to invest based on how old you are.  It’s ridiculous to apply the “average” growth rate of the market without considering where in the market cycle you are in.  If you invested during any peak, you are almost guaranteed NEVER to see growth if your portfolio without a significant amount of dollar cost averaging just to get you back to even.

Here’s VanGuard’s model portfolio mix and my portfolio mix for my 401K.  If my portfolio looked anything like their ‘model’ portfolio, I’d be, to put it meekly, royally screwed.

I’ve been in cash since the the 4Q of 2009 after being purely in individual stocks for most of the year (I was also completely in cash during 08-09 crash).  Don’t get my wrong, I’m not advocating staying out of the market, but timing is important and my portfolio mix will look a bit more like the model mix when stocks become cheap, and hopefully irrationally cheap, again.  Dow 8500 looks like a good place to start thinking about it.  What do you think?  Dow 8500 or Dow 11500 first?

How Bad is my Fantasy Baseball Team?

By , May 3, 2010 8:54 pm

I’m sitting in 9th place, 67 whopping points behind the league leader after the first month of the baseball season.  That’s pretty awful.  The question is, do I have any hope?  I decided to take a statistical approach to see if I should jump ship on my team, or try to hold through this miserable start.

The first thing I looked at was the historical monthly averages and standard deviations of the players on my team.  When I did this, I got a pretty ugly picture for April, but some hope for the future.

The green represents the categories where my players are performing better than their historical averages, while the red …. well, you know.  A lot of red on this chart … a lot.  Should I be selling low right now?

The data is telling us that April 2010 was a uncharacteristically bad for my team when compared to typical months in their career.  If statistics hold, I should expect a reasonable bounce back for nearly all my players for the rest of the year.  I should hold out for a little bit longer before making any significant panic moves, but I will certainly be looking for improvements across the board.  I’ll be keeping a close eye on riskier players like Ichiro (age) and Hamilton (injuries), and they will be the guys I’d look to move for a younger/healthier equal poor starting ‘star.’

What have I done so far?  I’ve moved Molina for the ‘no timeshare’ Miguel Olivio in Colorado, and I’ve gotten Lance Berkman back to take the spot of Curtis Granderson (who actually wasn’t that much of a drag on my team since I only play him against right-handers, check the splits for his career).   If Rich Harden can put it together, Grienke get’s some offensive help, and Dave Duncan has really fixed Brad Penny, I might have a fighting shot.

Your turn.  Who would you target right now in a trade, and who would you move off my team?

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How Mint.com Can Fix The Banking Industry

By , February 12, 2010 2:18 am

So you are unemployed, with no job prospects in sight and a healthy mortgage and family to worry about.  You are not alone as a staggering 17% of Americans are currently out of work (sorry, U.S. Bureau of Labor Statistics, this is a more realistic vision of the jobless rate, not the 10% that you are ‘reporting’ and will subsequently adjust a year from now when no one is paying attention).  Here’s Mint.com’s spin on the real unemployment rate in America:

Now while many Americans are finding it hard to earn a wage, there is a strange thing going on with the employed folks at our country’s largest banks.  They are getting bonuses, and not in a figurative sense of ‘hey, you have the bonus of keeping your job although your performance was a bit spotty abysmal over the past few years.’  No, they are paying themselves cold hard cash partly financed by the federal (read as American tax payer dollars) bank bail-out program.  Now I understand that a few of these banks have begun to repay their financial debt to the country, but the ‘lifestyle debt’ of long-term unemployment, mortgage foreclosures, small business bankruptcies, and retirement saving losses that these banks helped create are nowhere close to being recovered.  To turn a blind eye to anything beyond the reach of their balance sheets is just another example of the lack of fiscal and moral responsibility so prevalent in modern day banking.

In nearly any other industry, these poorly operated business would be purged in typical capitalist fashion during down business cycles; survival of the fittest, Darwinism in industry.  The main reason these industry Dodos survived was because the government had to intervene and lend mega amounts of money to them.  They were so big, and supported so many consumers and industries, that letting them fail would cause massive devastation to our economy (and the world’s economies).  In fact, 15 of the top 21 recipients of bailout funds were banks or bank subsidiaries whose survival has been contingent purely on their size rather than their abilities to operate, a fact ignored when decisions were made to pay out bonuses this year.

Being “Too Big To Fail” creates an imbalanced risk/reward structure because it allows banks to engage in short-term highly profitable businesses (CMOs, proprietary trading, hedge funds) with limited consideration for the additional risk (thanks to 3rd party capital rescue), which tends to be a long-term compounding problem that grows unfettered over many years.  It essentially allows them to share the risks over a larger capital base (theirs plus the American tax payer) during crisis, yet distribute profits accumulated from their activity that leads to the crisis to themselves (through short-term incentive structures like year-end bonuses on annual financial performance).

So what can you do (and how can Mint.com help) so that this does not happen again?  Well, it’s pretty simple, fire your (big) bank.  Firing your bank is basically saying “I will not allow you to get so big that you can act irresponsibly because you are not worried about going bankrupt.”  To do this, all you need to do is move your savings accounts to a smaller, more responsible bank.  This exponentially reduces the size of a big bank, because your deposits are significantly leveraged in the modern day banking system (see the section called “Effects on Money Supply“).  Moving $1,000 dollars out of your large bank could potentially reduce the bank’s asset size by $9,000, so a lot if these jabs to shift the deposit base can amount to a staggering change.

Mint.com allows customers the opportunity to do this in their “Ways to Save” section.  Mint could take this a step forward by giving higher visibility to responsible banks or discouraging consumers (think “Didn’t Screw the Economy” rating) from moving accounts to bailout banks.  They could even flat out not allow irresponsible banks on their site, a move that would certainly be damaging to new customer acquisition for these banks.  With information that is publicly available, along with the data they are collecting consumers relationships with banks, they could create a banking watchdog system that brings the same transparency to the banking industry as they have brought to personal finance.  These types of ideas, although not necessarily beneficial in the short-term, could provide a larger pool of financially healthy individuals transacting in more stable and responsible banking industry.

So are you going to move your accounts to smaller, more responsible banks?  Would you like to see companies like Mint impact the banking industry for the better?  Chime in on the comments section if it suits you … or don’t.  I’ll be checking in to see if you did or didn’t while QAing Jeff’s latest build of Wixity.  Fun.

Bill Belichick + Statistics = Usually Good Outcomes

By , November 16, 2009 3:48 pm

I’ve been a die hard Giants fan for as long as I can remember, and although I was technically alive and barely remember parts of the 86 season (and nothing from that Super Bowl), the 90 Super Bowl team made me love the G-Men .  They had me at Mark Ingram’s 3rd and 13 conversion.

Bill Belichick was the Giants’ defensive coordinator in that Super Bowl and designed a genius defensive game plan, predicated on the statistics of his defensive unit.  Knowing that Jim Kelly and the Buffalo Bills could rip apart the Giants’ secondary, he had his defensive linemen and linebackers give up yards on 1st and 2nd down.  He believed that this would dictate that Buffalo would run the ball, rather than pass in longer 3rd down situations, a place where the Giants were statistically strong in all season.

His gutsy calls are not relegated to just defense.  The 07 Patriots’ offense was a great example of not playing into defenses strengths (case in point, going to five WR sets against the top ranked Minnesota run defense), and taking gratuitous unsportsmanlike advantages of your strengths when the game was well out of hand.  Gratefully honor prevailed and the Giants laid the smack-down on the Patriots to win their 3rd Super Bowl and squash a rather presumptuous book before it made its way onto bookshelves.

Last night’s Patriots Colts game is just another way Bill Belichick makes football analysts (like Mike Francesca) and arm-chair quarterbacks look like idiots.  It was absolutely the right move to go for it on 4th and 2 and the odds were completely in his favor, as described on “Advanced NFL Stats:

“With 2:00 left and the Colts with only one timeout, a successful conversion wins the game for all practical purposes. A 4th and 2 conversion would be successful 60% of the time. Historically, in a situation with 2:00 left and needing a TD to either win or tie, teams get the TD 53% of the time from that field position. The total WP for the 4th down conversion attempt would therefore be:

(0.60 * 1) + (0.40 * (1-0.53)) = 0.79 WP

A punt from the 28 typically nets 38 yards, starting the Colts at their own 34. Teams historically get the TD 30% of the time in that situation. So the punt gives the Pats about a 0.70 WP.

Statistically, the better decision would be to go for it, and by a good amount.”

It didn’t work out for Bill last night, but the decision was sound, and in the long run, he’s going to come out on top more often than not.  It’s why he’s a great NFL coach, and we shouldn’t be convinced that our “conventional” wisdom is better than his statistical prowess.  Just be content knowing that he’s a prick and move on.

Predicting the World Series using Python

By , October 29, 2009 7:45 am

Last week, I’ve started to learn Python through a peer-to-peer learning session set up through nextNY.  The material that we’ve gone through has made learning programming very easy to wrap our heads around, and the environment of cooperative learning has been awesome.  I’m looking forward to being a Python ninja* pretty soon.

With four and half chapters of Python at my disposal, I wanted to put my skills to the test.  Since I’m a huge baseball fan, I thought I’d try my hand in simulating who would lose the World Series this year, a pillow-fight match-up between the New York Yankees and the Philadelphia Phillies.

The first thing to do was to crunch the numbers.  Crunching the numbers means exactly that, figuring out the probabilities of events occurring over a seven game series.  I incorporated things like Ryan Howard’s immense strike-out rate, Derek Jeter’s incredible lack of range at shortstop, and Brad Lidge’s ninth inning ERA.  I also made sure to incorporate correlations, or how related each variable is to each other.  Funny enough, the highest correlation I found was between having a runner on first base with less that two outs in the seventh inning onwards and Arod weakly grounding into a double-play.  Numbers never lie.

Now this got me a pretty good picture of who would lose the World Series, but I hadn’t taken into consideration the qualitative variables, the intangibles, the “Cole Hamels’ is a play-off pitcher” and the “Mariano is unhittable in the World Series” bullshit bullshit.  These are usually the ‘statistics’ that overzealous fans throw out (with no meaningful data except their distorted memories) as their defense to a player’s immortality.

The classic intangible lies on the shoulders’ of the Yankee captain, Derek Jeter, a ball player that seems to find himself at the right place at the right time in the postseason.  Yankee fans have constantly spouted his ‘greatness’, and refuse to admit that he was horribly out of position on the Jeremy Giambi play at the plate, and doesn’t even register as having the highest batting average in a World Series (that designation goes to Billy Hatcher who hit a sickening .750 for the Reds in 1990 in 12 ABs).  Heck, Jeter doesn’t even deserve the nickname “Mr. November” for his play in the 2001 World Series.  He had 1 HR, 1 RBI, and 2 runs scored in November, numbers that were almost matched by a pitcher for the Arizona Diamondbacks (1 RBI and 2 runs scored).  Oh, and that pitcher also won two potentially series ending games in two days that November with a 2.22 ERA, .96 WHIP, 8Ks in 8.1 innings.  Derek Jeter, I’d like you to meet the real “Mr. November,” Randy Johnson.

Okay, so I wrote my little Python program to capture all of this.  The stats, the pseudo-stats, the Phillie Phanatic’s rants, and the countless times we’ll hear “26 World Series rings.”  With so many probabilities and interactions, this program chugged along for two days, and finally, yesterday before the first pitch, I got the result:  Value Error: Let’s Go Mets.

*Looking forward to the day when ninja is not used in start-up world employment searches and reverts back to its original awesomeness of stealthy nighttime assassin.

The Pay-Off Matrix to Icing the Kicker

By , September 24, 2009 3:00 pm

First off, Eli Manning is a great quarterback whose number will probably never reflect how good he really is.  I’m not interested in the big arm of Jay Cutler, and the accuracy of Drew Brees, or the double threat QB nightmare in Philadelphia (McNabb and Vick).  Eli Manning flat out just knows how to win games (without the ego and attitude), and he’s getting better at it.

Aside from this, there were two things I took away from the Giants Cowboys game on Sunday night, played at the eighth and ninth modern wonders of the world (Jerry Jones words, not mine), the new Cowboys Stadium.  The first thing is that the Giant receivers are pretty good and I’ll specifically note the circus TD catch by Super Mario Manningham and the incredible juke Steve Smith put on before scoring.  In basketball,  the term is “broken ankles” and in competitive urban street dance, I believe it’s “you got served Orlando Scandrick.”

The second thing was how ridiculously popular it’s become to attempt to freeze the kicker by calling a time-out at the last possible half second so that the first attempt at a field goal doesn’t count.  My buddy Jeff brought up how silly the pay-off matrix looks like when coaches try to do this.  Essentially if the kicker makes the first attempt, then there’s no reason for the kicker to believe that he can’t make it again, since kicking FGs is a highly repeatable and high probability of success event (NFL average FGs made percentage was 84.5% last year).  If he misses, then he essentially has taken a practice shot, and now can adjust to better his outcome since the physical conditions of the kick hasn’t change.  Your hoping that the best case scenario happens twice, which if kicking FGs were independent, it would be something like a 2% probability of missing twice.

There is a mental element to missing a field goal, much like their is one to making one, but I don’t think that there is a strong relationship between kicking events.  In math world, it’s call the autocorrelation, which is the cross-correlation of a signal with itself.  In field goal kicking/inane time-out  world, the signal is the made/miss on a FG attempt.  It’s saying that if a kicker produces higher than average  success rate (made FG = 100% which is greater than NFL average 84.5%), then if the autocorrelation is high he’s more likely to make the next, and if he misses (made FG = 0%), then he’s more likely to miss the next attempt.  While I can see the autocorrelation being high on made FG attempts, I just don’t think it’s true on missed attempts (how many times have you seen a kicker miss even two in a row in the NFL from the exact same spot?)

If you really want to play a mental game with an opposing kicker, giving him a practice kick is hardly the answer.  I’d call time-out once the kicker got comfortable, but not where he could complete his routine by taking the kick.  Or try something like this legendary inbound play in a high-school basketball game, because it’s all about the element of surprise, and the last-second freeze play isn’t a surprise anymore.


Casual Gaming, Fierce Competition

By , September 3, 2009 12:36 pm

Every Sunday night, my friend Jimmy (www.jimmycahill.com) organizes a small, local Sunday Night Poker game.  And every week he tempts me to get my visa and make it out to Brooklyn to play with his buddies.  With our without me, the game goes on, and every Sunday night there is someone who gets to walk home with a little more cash in their pocket.

I think this is a fairly common thing to do amongst groups of friends.  I mean, I think we’ve seen this make it into every RoCo (Romantic Comedy) ever made, and boy do those movies know how to parody every day life.  But an interesting problem was brought up when we were looking over how to rank each player.

Now, as typical (fill in day of the week) Poker Nights go, there are some people that are there every game, and then there are some who can rarely be bothered to visit Brooklyn that frequently.  That leaves us with a bit of problem since games tend to vary in size, as well as vary in who is playing on a given night.  We couldn’t simply tally up how many first to last place finishes everyone has to accurately rank a players skill.

Also, since the number of players can vary, this means that the difficulty of winning a particular game changes as well (the more players there are, the more you have to defeat to be the winner).  This throws another wrinkle in the ranking systems since winning an eight player game is harder to do than winning a three player game.  And then, how do you rank the random friend who came once and ended up winning?  Should he live forever as the best poker player your game has ever seen?

I did a little research and I found a pretty good way to rank players based on the size of the game, how rich the buy-in is, and where people finish in the game.  This equation came from Tourney.com, and it does a pretty job of creating separation between who finishes first and who wears the asshole hat (college reference anyone?):

equation2In this equation, B represents the buy-in, so the higher the stakes, the more points you can accumulate.  Also, since E, which is the number of entrants, is also in the numerator, a player gets more points for winning larger games.  P represents what place you finish in, so the further down you place, the larger the denominator, and the less points you can get.  Pretty cool.

The only problem I have with this system is that it rewards players who play a lot, and doesn’t penalize players that lose a lot.  With the scoring system, a player that plays a whole lot games and finishes in last place in all of them can outrank a player who plays a few games and actually wins them.

A better system would be to figure out relative points given the size of the game, and its a fairly simple adjustment to the equation.  We simply subtract out the points for what the average finish would be.  Those that finish above average, get positive points, and those that finish below get negative points.  Since losing games does not penalize you to the same magnitude as winning games reward you, a player can actually make up ground very quickly by winning games.

Now if you still find yourself at the bottom of your rankings, you may just have to suck it up and admit to yourself that you are not very good at poker.  And if you really still want to keep playing, we play every Sunday night so just give me a call.  I can even sell you a pass to the pool on the roof.

Rediscover Discovery

By , August 10, 2009 10:07 am

Discovery seems to be one of those things that we don’t really spend a lot of time doing, but when it happens, it’s like our world could not exist as it once did. Whether it’s great discoveries of mankind (oh snap, the world is round) or small discoveries made by each of us everyday (Baoguette – Vietnamese sandwich shop on 25th and Lexington), the result is the same … totally mind changing.

How we find things on the web, or rather how we think the best way to find things on the web has changed dramatically.  The initial thoughts were to build web portals, where anything and everything you wanted to know would be conveniently located on a single web destination.  Our one stop shop for discovery.  That quickly changed when consumers were not able to find the best information in one place, and instead scoured the web for sources they personally found valuable.  As content became easier to publish, resident experts were now making places on the web to get very specialized information, and we needed a new way to find things.

Hello Yahoo, I’d like to introduce you to Google.  Google – “We’ve got this great algorithm that scours the entire web and returns the best result. You can buy us for $1M.”  Yahoo – “Well that’s silly, why would we want people to leave our site?  We want people to stay on our site for as long as possible.  Carry on now, nothing to see here.”  (I’m still shocked that Yahoo would pass on this; if not for the not so obvious revenue model, but just to use Google’s search to discover great content to add to their own portal!)

So spurned by Yahoo, Google acquires a small company called AdSense and the rest is somewhat recent history.  Google really capitalized by making a better way for users to find things.  They served up what people were looking for better (PageRank) and linked it with people providing what users were searching for.  Awesome, so now people can find out where to get things that they are looking for.

Often discovery is more than just finding where to get what you are looking for.  Discovery is often a question of “what do I want,” versus “where do I get it.”  Search does a great job of solving the “where”question (Where is Boaguette located?), but it’s not so great for the “I could really go for something sweet & spicy, but not served in a sauce with rice.”   To solve that, we look to the experiences of others, and we count on their recommendations to discover new things.  Search will always have it’s place, but taking good ol’ word of mouth and placing that in a useful platform on the web will open up all kinds of new doors of discovery for everyone.

Recommendation models have come a long way, but there is still work to be done.  NetFlix recently ended their competition to improve their recommendation algorithm and will pay the winning team $1M.  There is a huge amount of value in this space, and companies are recognizing that more and more each day.  How do we capture that on the web?  A difficult problem to solve, but one with great rewards.

I’ll leave you with a quote from Greg Linden, the man behind Amazon’s recommendation system, that pretty much sums it up.  He said, “Whoever manages to change the nature of content display on the Web from a search problem to a recommender problem will reap tremendous rewards.”  It makes a lot of sense when you think about where you got your last great restaurant recommendation.

Blackjack, Basic Strategy, Battle of Wits – Part III

By , August 5, 2009 12:28 pm

Have you ever been on a blackjack table and accidentally hit a hard 14 with the dealer showing a 5 while playing basic strategy?  Replay it in your mind, you bust on the King, dealer makes his 21 on a 6, the entire table gives you the death stare, curses your first born, all while mumbling under their breath, “Never hit on a hard 14 with the dealer showing a 5 idiot. That King was the dealer’s bust card. We all would have won.”  Tough room.

First things first, those people have no idea what they are talking about.  There is no such thing as “that was the dealer’s bust card.”  The deck doesn’t know whether the dealer or the player is hitting or staying and the cards don’t change because of how someone plays their hand.  The probabilities that guide basic strategy haven’t been altered because someone does not make the optimal play and theoretically the dealer still has the same likelihood of busting (in practice though, since the deck has a fixed amount of cards, the distribution of remaining cards changes the underlying probabilities of basic strategy.  Card counting attempts to exploit this by identifying random deck distributions that happen to have a large amount of 10-value cards remaining).

The important thing to remember here is that basic strategy gives you the probabilistically best play given that the deck has a RANDOM DISTRIBUTION OF CARDS.  That means that if the deck is not random, basic strategy might not be the optimal play.  So what everyone should consider before baptize themselves in the holy waters of basic strategy is what it takes to make a deck random (and who controls what it takes to make a deck random).

To make a single deck random, the deck must be riffle shuffled about 7 times.  Since suit doesn’t matter in blackjack, and K, Q, J, and 10 hold the same value, you actually need to shuffle a single deck less, about 4 times, to make it random.  Most casino blackjack tables play with 6 or 8 decks at once which are shuffled together and played from a dealer’s shoe.  In order to randomize a shoe of 8 decks, it takes about 12 riffle shuffles.  Does your casino shuffle a shoe 12 times?  Probably not.  Most casinos shuffle a shoe 4 times, and that has some interesting implications when an entire table is playing basic strategy.

So let’s take a look at what happens to a shoe when the entire table is playing basic strategy.  The first thing is that anyone that has a strong hand on their first two cards (17+) is instructed to stay, and their cards remain on the table until the deal is over.  Players with weak hands play out their hands, and if they bust, the cards are removed from the table and placed in the discard shoe.  This begins to create layers of cards in the shoe; clusters of low cards placed on the shoe first, followed by clusters of high cards that were left on the table.  Since most casinos do not shuffle the shoe enough times, these layers loosely exist in the new shoe, and are further propagated when the entire table plays basic strategy (some people attempt to exploit this by using a technique called cluster counting).

Clustering of cards creates decks that are not random, which is one of the critical assumptions that basic strategy is built on.  This creates opportunities for dealers to win/push more hands than basic strategy predicts.  During a high cluster deal, a dealer is likely to have high cards to push, or even beat, the tables “strong” 19s and 20s.  In the case where low card clusters are being dealt, a dealer will likely have a low up-card, a situation where basic strategy dictates to hit against until about 14.  The thing is that since it’s a low cluster deck, the dealer  has a better chance to make a hand!  The player also has a better chance to make a hand, but basic strategy actually advises them not to try.  INCONCIEVABLE!

Basic strategy is still by far the best way to reduce the house odds, but since decks are not completely random, there is certainly room for improvements in game play.  For example, in high cluster deck situations, it may be worthwhile to split face cards, while in low cluster situations, taking another card to try to make a better hand may be your best bet.  Playing this way may add a little bit more excitement to the rule-based approach of basic strategy as you’d be trying to exploit the rest of the table playing the basic strategy system.  And if it pisses anyone off at your table, just turn to them and say “You fell victim to one of the classic blunders!  The most famous is never get involved in a land war in Asia, but only slightly less well-known is this: never play basic strategy against a dealer when deck isn’t random!”  I’d get a kick out of that if I heard that on a blackjack table.

Blackjack, Basic Strategy, Battle of Wits – Part II

By , July 20, 2009 11:06 am

A little over a week ago I started to talk about the strange (to me) occurrence of blackjack dealers encouraging their opponents to play basic strategy.  A few people have commented that this could have to do with the casinos playing on human psychology; folks who think they have a “system” will play at the casino longer.  This could allow the casinos to maintain a revved up atmosphere longer, and perhaps more sinisterly, give them an opportunity to pump us full with free liquor, which we all know hardly impairs judgment especially in a city with so little stimulation like Las Vegas.  All plausible thoughts, but what if the casinos have figured out a way to game basic strategy?  Inconceivable!

The foundations of basic strategy are built around a few key assumptions, but the two in particular that I’ll focus on in this post are:

  1. The deck is completely random
  2. The dealer’s face down card is a 10-value card (10, J, Q, K)

Basic strategy gives a player the probabilistically optimal play for every combination of the player’s cards and the single card the dealer is showing.  It assumes the dealers face down card has a value of 10 (assumption No. 2) and bases all hit/stand/double down/split decisions off of that.  Here is what a basic strategy chart would look like for hard player hands:

Basic Strategy

This is not to say that the reason why we assume a 10 value card is because that is the most probable value in a deck (16 out of 52 cards), a popular misconception.  The expected value of a card chosen at random is actually closer to 7, however working under that assumption does not yield the most optimal play.  This is primarily because both the player and the dealer have the option to draw more than one card until they reach 21.  If this wasn’t the case, the strategy would look like something like this (I did not work out the actual math behind this chart, this is more of a rough estimate):

Basic Strategy - Adjusted

This is very different from the optimal strategy, which is a testament to how brilliant basic strategy is.  As part of research for one of the most famous books on basic strategy, “Beat the Dealer,” Ed Thorpe tested its concepts on MIT computers and found it accurate to a couple of hundredths of a percentage point.  For their genius, the “Four Horsemen” of blackjack, the inventors of basic strategy, were recently inducted into the Blackjack Hall of Fame back in January 2008.

Now I don’t think that there is a great fatal flaw in basic strategy.  It is well grounded in probability theory and the strategic assumption on evaluating the dealers hand is clearly too legit to quit.  However, there is one teeny tiny detail that basic strategy depends on, and it’s that detail that rests squarely in control of the casinos.  That detail I’m referring to is the random deck, and I’ll get into my thoughts around how casinos shuffle decks and the implications on basic strategy next week.

Until then, check out the Blue launch of Wixity.  You’ll be able to browse and search for summer events in New York City.  We have been working very hard on it, and would love to hear everyone’s feedback.  If you are interested in more of the features, you can be invited to our our private beta by emailing me at rathan.haran@wixity.com.

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