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- Thread starter Fishhead
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quantumleap said:Thanks much cincy_. I caught that part. I had meant to ask if he had any additional insight on significance that could help us out.

Well you can use tests of significance to determine whether you are a winning player, although I guess checking your bankroll is good enough for most.

Lets say you want to determine whether you are winning at a 53% rate after 1000 games. You expect to win 530 and lose 470 and the square of difference between both results (observed and expected) can be used in a chi square test with one degree of freedom.

Woody0 said:Well you can use tests of significance to determine whether you are a winning player, although I guess checking your bankroll is good enough for most.

Lets say you want to determine whether you are winning at a 53% rate after 1000 games. You expect to win 530 and lose 470 and the square of difference between both results (observed and expected) can be used in a chi square test with one degree of freedom.

Woody0:

I was thinking more short-term. What if you were picking 60% winners after 50 picks, or you were following someone with the same rate, or you were trying out a system. How would you know if it is a bad choice? Is it a bad system/capper if 6 losers are picked in a row? That's where you could test it using the numbers you mentioned.

http://www.ento.vt.edu/~sharov/PopEcol/tables/chisq.html

This means that getting 60% winners on this small sample will happen by chance simply because you got lucky.

If you get 60% winners from 100 then the chi square is 2+2 or 4 and is significant at the 5% level. This means that less than once in 20 times will such a result occur by chance if you are merely lucky and only picking at the 50% rate.

As I said before 6 losers in a row is significant for a 60% win rate but I feel the above chi square test which becomes marginally significant after 100 samples for a 60% capper is a better test.

Since few cappers can maintain a 60% win rate and since the sample size needed increases greatly as one tries to measure a smaller difference from 50% you can see that large samples are needed. In fact one NFL season becomes meaningless for ATS picks.

never chasing, not drinking when I gamble, using reduced juice books, studying stats and tendencies, and betting teams I've seen play (didn't bet any Sun Belt games in football, and only will bet 13 of the 30 conferences in basketball) have been keys to success for me...

Woody0 said:If you get 30/50 winners (60%) then your deviation from the random 50% (25:25) is 5. This deviation gives 5^2 or 25/25 for winners and 25/25 for losers or 1 + 1 = 2 with one degree of freedom. This is not significant at the 5% level.

http://www.ento.vt.edu/~sharov/PopEcol/tables/chisq.html

This means that getting 60% winners on this small sample will happen by chance simply because you got lucky.

Here is how I do it:

If you have 50 samples in a binomial distribution, and you're flipping a coin then your mean is 25 and your standard deviation is 3.5 (sqrt (.5*.5*50) = 3.5).

That means there is a 95% chance that you would flip anywhere from 18 to 32 heads.

If you do it with 100 coins, the mean is 50 and the stdev becomes 2.5, so you would flip anywhere from 45 to 55 heads. If you get 60 heads, there is a 99.9%+ chance that it wasn't just dumb luck.

The Kelly criterion is a money-management formula of passionate interest (and controversy) to card players,

So who was Kelly? How did he get a money-management formula named after him?<o></o>

<o></o>

John Larry Kelly, Jr. (1923-65), was born in Corsicana, Texas. He came of age during World War II and spent four years as a flyer for the Naval Air Force. A capable pilot, he survived a plane crash into the ocean. Kelly did undergraduate and graduate work at the University of Texas, Austin. His 1953 Ph.D. topic was an "Investigation of second order elastic properties of various materials." This led to work in the oil industry. As Kelly told the story, his employer, a successful wildcatter, would smell the soil and drill by instinct, ignoring Kelly's carefully prepared scientific recommendations. The oilman's hunches were so unerring that Kelly decided he was in the wrong line of work. He accepted a job offer from Bell Labs. <o></o>

Bell Labs, in Murray Hill, New Jersey, was one of the world's most prestigious scientific research centers. Kelly was barely 30 when he arrived. His Texas drawl set him apart (oddly, it seemed to grow deeper the more years he lived in New Jersey). So did his interest in guns. Kelly belonged to a gun club and counted a Magnum pistol among his prize possessions. Kelly was married to the former Myldred Parham. Myldred was herself a pilot and had been the executive officer of a MASH unit in India during the war. As a couple, the Kellys were ruthless tournament bridge players. They raised three children -- Patricia, Karen, and David -- in a suburban house in Berkeley Heights, New Jersey. <o></o>

Kelly was "a lot of fun, the life of the party," I was told. Another associate described him as a "wild man." One tale claims that Kelly once earned a reprimand by prankishly flying a plane under the George Washington bridge. In another story, Kelly was at a conference on Cape Cod where a new rocket-powered ejection seat for pilots was being shown. Kelly decided it would be interesting to see if the seat really worked. He and several others put the seat in the back of a convertible and drove around Cape Cod looking (unsuccessfully) for a suitable place to launch it. <o></o>

Kelly was a chain-smoker. Even in the family's home movies, Kelly is puffing away as he watches the children in the pool. Daughter Karen Kelly recalls that "Not only did we have guns and rifles in our house, but my father also had equipment to make bullets. He used to entertain people with shooting bullets with plastic or gummy inserts into a stone wall in the house. My mom said it was annoying to get them out."<o></o>

One of Kelly's best friends at Bell Labs was a fellow Texan, Ben Logan. Each morning, Kelly and Logan would make coffee, then go into Logan's office. Kelly would immediately put his feet up on the chalk rim of the blackboard and light up a cigarette. Faced with a difficult problem, Kelly would think a moment, take another drag, and say something showing the most amazing insight. Many rated Kelly the smartest person at Bell Labs next to Claude Shannon himself. <o></o>

Shannon was in a class by himself. He had single-handedly created the abstract theory of communication called information theory. Shannon presciently realized that computers could express numbers, words, pictures, audio, and video as strings of digital 1s and 0s. Information theory underlies the Internet and today's wired, and wireless, world. <o></o>

At Bell Labs, Kelly was working on data compression schemes for the still-young medium of television. This brought him into Shannon's new field. Kelly made an ingenious connection between information theory, gambling -- and television. <o></o>

On June 7, 1955, American television debuted a new quiz showcalled

Thinking about this convinced Kelly that a gambler with "inside information" could use some of Shannon's equations to achieve the highest possible return on his capital. Shannon was intrigued by this application and urged Kelly to publish his finding. Kelly's article appeared (under the opaque title "A New Interpretation of Information Rate") in a 1956 issue of the

Kelly wryly presented his idea as a system for betting on fixed horse races. A "gambler with a private wire" gets advance word of the races' outcomes. The natural impulse is to bet everything you've got on the horse that's supposed to win. But when the gambler adopts this policy, he is sure to lose everything on the first bum tip. Alternatively, the gambler could play it safe and bet a minimal amount on each tip. This squanders the considerable advantage the inside tips supply.<o></o>

In Kelly's analysis, the smart gambler should be interested in "compound return" on capital. He showed that the same math Shannon used in his theory of noisy communications channels applies to the gambler. The gambler's optimal policy is to maximize the expected logarithm of wealth. Though an aggressive policy, this offers important downside protection. Since log(0) is negative infinity, the ideal Kelly gambler never accepts even a small risk of losing everything.<o></o>

You don't even have to know what a logarithm is to use the so-called Kelly formula. You should wager this fraction of your bankroll on a favorable bet: <o></o>

In the Kelly formula,

Example: The tote board odds for Seabiscuit are 5:1. Odds are a fraction -- 5:1 means 5/1 or 5. The 5 is all you need.<o></o>

The tips convince you that Seabiscuit actually has a 1 in 3 chance of winning. Then by betting $100 on Seabiscuit you stand a 1/3 chance of ending up with $600. On the average, that is worth $200, a net profit of $100. The edge is the $100 profit divided by the $100 wager, or simply 1. <o></o>

The Kelly formula,

This version of the formula does not take into account the effect of one's own bet on the odds. It has the virtue of being easy to remember and applicable to other forms of gambling like blackjack. By always making the Kelly bet, you increase your bankroll faster than with any system. That's the good news. The bad news is that it's a rough ride. Downward plunges of wealth are frequent and steep. This can be rectified through diversification (as in team play in blackjack, or at a hedge fund, where the manager makes many simultaneous "bets" with low correlation). For the lone player betting on a single hand or horse, the Kelly formula demands guts and patience -- hence the controversy. Many have found the "half Kelly" strategy to be a good compromise. You bet half of

<o></o>

Kelly had originally titled his article "Information Theory and Gambling." That bothered some AT&T executives, as did his mention of a "private wire." Throughout the twentieth century, AT&T had leased wires to organized crime figures who ran "wire services" reporting racetrack results to bookies. Even in the 1950s, bookies were still big customers. The executives feared the press might conclude from Kelly's article that Bell Labs was doing work to benefit illegal gamblers. They pressured Kelly to change the title of his paper to "A New Interpretation of Information Rate." <o></o>

In fact, the executives didn't have much to worry about. Virtually no one took much note of the article when it first appeared. The practical application of the Kelly criterion began in the early 1960s, after MIT student Ed Thorp told Shannon about his card-counting system for blackjack. Shannon referred him to Kelly's article. Thorp used it to compute optimal bets in blackjack and later in the securities markets. It was Thorp's success as hedge fund manger that made Wall Street start to take notice of the Kelly criterion. <o></o>

<o></o>

Kelly died tragically young, of a brain hemorrhage at the age of 41. He was by then the head of Bell Labs' information coding and programming department and the author of several patents. Kelly has one further claim to fame. In 1961 Kelly and colleague Carol Lochbaum demonstrated a new voice synthesis system by making a recording of their machine singing the song "Daisy Bell," better known as "Bicycle Built for Two." <o></o>

It was the latter that inspired the death scene of the computer HAL in Stanley Kubrick's film

<o></o>

with a 55% long-term win pct....over the course of 1,000 plays, a bettor can expect a +/- 3% swing in win expectations...

meaning, that same bettor will win anywhere between 52 and 58% over 1,000 plays...

so few people realize how many actual plays it takes to fully realize an expected win pct.

people think they can play just a couple hundred plays and hit, say, 55% and they automatically assume that they really have a 55% win expectation...

it just doesn't work out that way. it takes THOUSANDS of plays to fully realize a long-term advantage. not just a a couple hundred.

Christian said:i remember seeing a study once that showed....

with a 55% long-term win pct....over the course of 1,000 plays, a bettor can expect a +/- 3% swing in win expectations...

meaning, that same bettor will win anywhere between 52 and 58% over 1,000 plays...

so few people realize how many actual plays it takes to fully realize an expected win pct.

people think they can play just a couple hundred plays and hit, say, 55% and they automatically assume that they really have a 55% win expectation...

it just doesn't work out that way. it takes THOUSANDS of plays to fully realize a long-term advantage. not just a a couple hundred.

Fishhead said:Put 100 marbles in a can........60 black and 40 red(representing 60% winners).

Remove a marble, take note by writing down what color it was and REPLACE, shake-up, and repeat the process.

Do this many, many times.

You may be amazed at how many streaks of RED you will have greater than four.............and this is picking 60% winners..........something even the best of the best professional sportsbettors cannot achieve longterm.

lol...recently I went about 70-30 with a new strategy. Statistics says this is 95% likely to be >60% over the long term, and 99.5% likely to be greater than 55% over the long term (aka classicly profitable)

I rationalized this in a similar method to the marbles...to put the chances into perspective I decided to flip a coin and see if I got heads. I'd have to flip it 4 heads in a row to be roughly 95%.

So I start flipping...heads...heads...heads...heads, a total of 11 heads in a row the very first time I sit down to try this. The coin wasn't misweighted, I flipped some more to check.

These kind of real world examples have provided a lot of perspective to me, and I'd recommend trying them to keep yourself grounded when you need it.

Salain said:lol...recently I went about 70-30 with a new strategy. Statistics says this is 95% likely to be >60% over the long term, and 99.5% likely to be greater than 55% over the long term (aka classicly profitable)

I rationalized this in a similar method to the marbles...to put the chances into perspective I decided to flip a coin and see if I got heads. I'd have to flip it 4 heads in a row to be roughly 95%.

So I start flipping...heads...heads...heads...heads, a total of 11 heads in a row the very first time I sit down to try this. The coin wasn't misweighted, I flipped some more to check.

These kind of real world examples have provided a lot of perspective to me, and I'd recommend trying them to keep yourself grounded when you need it.

You just answered my next question, that being "What if you had a smaller sample size at a higher percentage, say 70%? What is the probability that the system is eventually viable at 55%?" Thanks.

Woody0 said:

http://www.ento.vt.edu/~sharov/PopEcol/tables/chisq.html

This means that getting 60% winners on this small sample will happen by chance simply because you got lucky.

If you get 60% winners from 100 then the chi square is 2+2 or 4 and is significant at the 5% level. This means that less than once in 20 times will such a result occur by chance if you are merely lucky and only picking at the 50% rate.

As I said before 6 losers in a row is significant for a 60% win rate but I feel the above chi square test which becomes marginally significant after 100 samples for a 60% capper is a better test.

Since few cappers can maintain a 60% win rate and since the sample size needed increases greatly as one tries to measure a smaller difference from 50% you can see that large samples are needed. In fact one NFL season becomes meaningless for ATS picks.

How do you feel about regression testing? That's my term for going back through a sample size to discover patterns that occur at a particular percentage (not to be confused with software regression testing). Let's say I buy a database of all NBA games played so far this season. Then I test for certain conditions such as spreads greater than +10 for home teams and I find that these situations hit greater than 55% of the time.

Is this valid modeling so that I can expect to blindly bet situations such as this? Or better yet, bet selectively by either applying additional conditions or just using my own gut feel.

I do believe there are situations in sports that fit this criteria. If one could find these situations I believe you would be successful.

quantumleap said:How do you feel about regression testing? That's my term for going back through a sample size to discover patterns that occur at a particular percentage (not to be confused with software regression testing). Let's say I buy a database of all NBA games played so far this season. Then I test for certain conditions such as spreads greater than +10 for home teams and I find that these situations hit greater than 55% of the time.

Is this valid modeling so that I can expect to blindly bet situations such as this? Or better yet, bet selectively by either applying additional conditions or just using my own gut feel.

I do believe there are situations in sports that fit this criteria. If one could find these situations I believe you would be successful.

You betcha there are!!!

Some of which have never produced a losing record in any year for 20+ years.

When you find one, suggest you do not tell ANYBODY.