A sucker bet is a bet based on something other than expected return including:

• parlays: One bet ticket written with at least two wagers (all must win for the ticket to cash).
• teaser: A sucker wager that allows bettors to add and subtract points from posted odds.
• exotic: Any wager other than a straight bet or parlay (also referred to as a proposition or prop).

To calculate expected return, use the following formula: EXPECTED RETURN = POTENTIAL WINNING * PROBABILITY OF WINNING – POTENTIAL COST * PROBABILITY OF LOSING.

It is a bet where the odds of winning are significantly and clearly worse than the payout.

For example, the chances of correctly guessing the order of the final 3 cards of Faro is usually 1 in 4, yet the bet only pays 3:1. Taking insurance in Blackjack is also often considered a sucker bet.

Sucker bets are often created to offer higher payouts to offer the player a chance at “fast money” in exchange for a decreased chance of winning.

Sources

• Betting glossary
• Glossary of Mathematical Mistakes

This guide is licensed under the GNU Free Documentation License. It uses material from the Wikipedia.

Julian L. Simon and Paul Ehrlich entered in a famous wager in 1980, betting on a mutually agreed upon measure of resource scarcity over the decade leading up to 1990.

The wager

Simon had Ehrlich choose five of several commodity metals. Ehrlich chose 5 metals: copper, chrome, nickel, tin, and tungsten. Simon bet that their prices would go down. Ehrlich bet they would go up.

“The face-off occurred in the pages of Social Science Quarterly, where Simon challenged Ehrlich to put his money where his mouth was. In response to Ehrlich’s published claim that “If I were a gambler, I would take even money that England will not exist in the year 2000″ – a proposition Simon regarded as too silly to bother with – Simon countered with “a public offer to stake US$10,000 … on my belief that the cost of non-government-controlled raw materials (including grain and oil) will not rise in the long run.

You could name your own terms: select any raw material you wanted – copper, tin, whatever – and select any date in the future, “any date more than a year away,” and Simon would bet that the commodity’s price on that date would be lower than what it was at the time of the wager.” … Ehrlich and his colleagues picked five metals that they thought would undergo big price rises: chromium, copper, nickel, tin, and tungsten. Then, on paper, they bought $200 worth of each, for a total bet of $1,000, using the prices on September 29, 1980, as an index. They designated September 29, 1990, 10 years hence, as the payoff date. If the inflation-adjusted prices of the various metals rose in the interim, Simon would pay Ehrlich the combined difference; if the prices fell, Ehrlich et al. would pay Simon. … Between 1980 and 1990, the world’s population grew by more than 800 million, the largest increase in one decade in all of history. But by September 1990, without a single exception, the price of each of Ehrlich’s selected metals had fallen, and in some cases had dropped through the floor. Chrome, which had sold for $3.90 a pound in 1980, was down to $3.70 in 1990. Tin, which was $8.72 a pound in 1980, was down to $3.88 a decade later. [1]

As a result, in October 1990, Paul Ehrlich mailed Julian Simon a check for $576.07 to settle the wager in Simon’s favor.

Analysis of why Ehrlich lost

According to Paul Ehrlich’s website: “In 1980, Julian Simon repeatedly challenged environmental scientists to bet against him on trends in prices of commodities, asserting that humanity would never run out of anything. Paul and the other scientists knew that the five metals in the proposed wager were not critical indicators and said so at the time. They emphasized that the depletion of so-called renewable resources–environmental resources such as soils, forests, species diversity, and groundwater–is much more indicative of the deteriorating state of society’s life-support systems….Nonetheless, after consulting with many colleagues, Paul and Berkeley physicists John Harte and John Holdren accepted Simon’s challenge in late 1980…”

Julian Simon won because the price of three of the five metals went down in absolute terms and all five of the metals fell in price in inflation adjusted terms [2] with both tin and tungsten falling by more than half (inflation adjusted). So, per the terms of the wager, Ehrlich paid Simon the difference in price between the same quantity of metals in 1980 and 1990 (which was $576.07). The prices of all five metals increased between 1950 and 1975, but Ehrlich believes three of the five went down during the 1980s because of the price of oil doubling in 1979, and because of a worldwide recession in the early 1980s.

Yet, it is significant that “All of the former’s [Ehrlich's] grim predictions had been decisively overturned by events. Ehrlich was wrong about higher natural resource prices, about “famines of unbelievable proportions” occurring by 1975, about “hundreds of millions of people starving to death” in the 1970s and ’80s, about the world “entering a genuine age of scarcity.”

In 1990, for his having promoted “greater public understanding of environmental problems,” Ehrlich received a MacArthur Foundation “genius” award.” [3]

“[Simon] always found it somewhat peculiar that neither the Science piece nor his public wager with Ehrlich nor anything else that he did, said, or wrote seemed to make much of a dent on the world at large. For some reason he could never comprehend, people were inclined to believe the very worst about anything and everything; they were immune to contrary evidence just as if they’d been medically vaccinated against the force of fact. Furthermore, there seemed to be a bizarre reverse-Cassandra effect operating in the universe: whereas the mythical Cassandra spoke the awful truth and was not believed, these days “experts” spoke awful falsehoods, and they were believed. Repeatedly being wrong actually seemed to be an advantage, conferring some sort of puzzling magic glow upon the speaker.” [4]

Simon offered to raise the wager to $20,000 and use any resources at any time that Ehrlich preferred, but the two were unable to reach an agreement on the terms of a second wager.

The proposed second wager

Understanding that Simon wanted to bet again, Ehrlich and climatologist Stephen Schneider counter-offered, challenging Simon to bet on 15 current trends, betting $1000 that each will get worse (as in the previous wager) over a ten year future period.

The trends they bet would continue to worsen were:

The three years 2002-2004 will on average be warmer than 1992-1994.
There will be more carbon dioxide in the atmosphere in 2004 than in 1994.
There will be more nitrous oxide in the atmosphere in 2004 than 1994.
The concentration of ozone in the lower atmosphere (the troposphere) will be greater than in 1994.
Emissions of the air pollutant sulfur dioxide in Asia will be significantly greater in 2004 than in 1994.
There will be less fertile cropland per person in 2004 than in 1994.
There will be less agricultural soil per person in 2004 than 1994.
There will be on average less rice and wheat grown per person in 2002-2004 than in 1992-1994.
In developing nations there will be less firewood available per person in 2004 than in 1994.
The remaining area of virgin tropical moist forests will be significantly smaller in 2004 than in 1994.
The oceanic fisheries harvest per person will continue its downward trend and thus in 2004 will be smaller than in 1994.
There will be fewer plant and animal species still extant in 2004 than in 1994.
More people will die of AIDS in 2004 than in 1994.
Between 1994 and 2004, sperm cell counts of human males will continue to decline and reproductive disorders will continue to increase.
The gap in wealth between the richest 10% of humanity and the poorest 10% will be greater in 2004 than in 1994.

Simon declined the bet, and used the following analogy to explain why he did so:

“Let me characterize their [Ehrlich and Schneider's] offer as follows. I predict, and this is for real, that the average performances in the next Olympics will be better than those in the last Olympics. On average, the performances have gotten better, Olympics to Olympics, for a variety of reasons. What Ehrlich and others says is that they don’t want to bet on athletic performances, they want to bet on the conditions of the track, or the weather, or the officials, or any other such indirect measure.”

External links

• Wired Magazine article on Julian Simon as Doomslayer and the wager
• Paul Ehrlich’s webpage on the two Simon bets
• http://www.overpopulation.com/simon_bet.html
• Overpopulation — The Population Explosion “Brownlash” and the wager
• “Julian Simon’s Bet With Paul Ehrlich” by Brian Carnell at Libertarian International website

This guide is licensed under the GNU Free Documentation License. It uses material from the Wikipedia.

A scientific wager is a wager whose outcome is settled by scientific method. They typically consist of an offer to pay a certain sum of money on the scientific proof or disproof of some currently uncertain statement. Some wagers have specific date restrictions for collection, but many are open. Wagers occasionally exert a powerful galvanizing effect on society and the scientific community.Notable scientists who have made scientific wagers include Stephen Hawking and Richard Feynman. Stanford Linear Accelerator has an open book containing about 35 bets in particle physics dating back to 1980; many are still unresolved.

Famous scientific wagers

• In 1959, Richard Feynman bet $1000 that no-one could construct a motor no bigger than 1/64 of an inch on a side. He lost the bet when Bill McLennan, using amateur radio skills, constructed such a motor. Feynman had never formalized the bet because he couldn’t define his terms sufficiently precisely, but paid up anyway; Feynman is also on record as saying that he was disappointed with the outcome because he had hoped his reward would stimulate some new fabrication technology, but McLellan’s motor used only existing techniques. Physicist Philip Ball, writing in Nature Materials, discusses this episode and concludes “Do we, like Feynman, always underestimate what our current technologies can achieve?”
• In 1975, cosmologist Stephen Hawking bet fellow cosmologist Kip Thorne a subscription to Penthouse magazine for Thorne against four years of Private Eye for him that a Cygnus X-1 would turn out not to be a black hole. It was, so Hawking lost. It has been said that Hawking hoped to lose the bet as so much of his own work depended upon the existence of black holes. For Hawking, then, the bet was a type of insurance.
• In 1997 Stephen Hawking and Kip Thorne made a bet with John Preskill on the ultimate resolution of the apparent contradiction between Hawking radiation resulting in loss of information, and a requirement of quantum mechanics that information cannot be destroyed. Hawking and Thorne bet that information must be lost in a black hole; Preskill bet that it must not. The formal wager is: “When an initial pure quantum state undergoes gravitational collapse to form a black hole, the final state at the end of black hole evaporation will always be a pure quantum state”. The stake is an encyclopedia of the winner’s choice, from which “information can be recovered at will”. Hawking conceded the bet in 2004. See also: Thorne Hawking Preskill bet
• In 1980, biologist Paul R. Ehrlich bet economist Julian Lincoln Simon that the price of a portfolio of $200 of each of five mineral commodities (copper, chromium, nickel, tin, and tungsten) would rise over the next 10 years. He lost: by 1990, the prices had fallen to $576. See also: Wager between Julian Simon and Paul Ehrlich.
• In 1870, Alfred Russel Wallace bet John Hampden, a believer in the flat-Earth theory, that he (Wallace) could prove the flat Earth hypothesis incorrect. The sum staked was £500 (then worth a great deal more than now). A test, involving a stretch of the Old Bedford Canal, in London, was agreed on: Wallace measured the curvature of the canal’s surface using two markers separated by about five kilometres and suspended at equal heights above the water’s surface. Using a telescope mounted 5km from one of the markers, Wallace established that the nearer one appeared to be the higher of the two. An independent referee agreed that this showed the Earth’s surface to curve away from the telescope, and so Wallace received his money. However, Hampden never accepted the result and made increasingly unpleasant threats to Wallace.
• In 1684, Christopher Wren announced that he would give a book worth 40 shillings to anyone who could deduce Kepler’s laws from the inverse-square law. Isaac Newton’s musings on this problem eventually grew into his Principia. However, Newton was too late to qualify for the book. Historian Alan Shapiro has stated that this episode was “undoubtedly one of the most crucial wagers in scientific history”.

See also

• The efforts of photographer Eadweard Muybridge to capture the motion of a galloping horse were not part of a wager, contrary to popular opinion.
• Pascal’s wager is not a wager in the sense used in this article.

This guide is licensed under the GNU Free Documentation License. It uses material from the Wikipedia.

In gambling, a proposition bet (also called a prop bet) is any bet made for a sum of money that involves a proposition. An example is the professional poker player, Howard “The Professor” Lederer, a vegetarian who wagered $10,000 with a fellow gambler that he could eat a cheeseburger. He did it and won the money. Golf is a very common game for prop bets.

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Theoretical challenges

Some academic research has focused on potential flaws with the prediction market concept. In particular, Dr. Charles F. Manksi of the Northwestern University Department of Economics published a paper in 2004, “Interpreting the Predictions of Prediction Markets”, [2] in which he attempts to show mathematically that under a wide range of assumptions the “predictions” of such markets do not closely correspond to the actual probability beliefs of the market participants unless the market probability is near either 0 or 1. Manski suggests that directly asking a group of participants to estimate probabilities may lead to better results. However, Steven Gjerstad (Purdue) in his paper “Risk Aversion, Beliefs, and Prediction Market Equilibrium” [3] has shown that prediction market prices are typically very close to the mean belief of market participants if the distribution of beliefs is smooth (as with a normal distribution, for example). Justin Wolfers (Wharton) and Eric Zitzewitz (Stanford) have obtained similar results, and also include some analysis of prediction market data, in their paper “Interpreting Prediction Market Prices as Probabilities” [4]. In practice, the prices of binary prediction markets have proven to be closely related to actual frequencies of event in the real world. Relevant data has been published in Pennock et al’s “The real power of artificial markets” [5] (Science, 2001) and Servan-Schreiber et al’s “Prediction Markets: Does Money Matter?” [6] (Electronic Markets, 2004).

Prediction markets also suffer from the same types of inaccuracy as other kinds of market, i.e. liquidity or other factors not intended to be measured are taken into account as risk factors by the market participants, distorting the market probabilities. There can also be direct attempts to manipulate such markets. In the Tradesports 2004 presidential markets there was an apparent manipulation effort (an anonymous trader sold short so many Bush 2004 presidential futures contracts that the price was driven to zero, implying a zero percent chance that Bush would win. The only rational purpose of such a trade would be an attempt to manipulate the market in a strategy called a “bear raid”. The manipulation effort failed, however, as the price of the contract rebounded rapidly to its previous level.) As more press attention is paid to prediction markets, it is likely that more groups will be motivated to manipulate them. However, in practice, such attempts at manipulation have always proven to be very short lived. In their forthcoming paper entitled “Information Aggregation and Manipulation in an Experimental Market” (2005) [7], Hanson, Oprea and Porter (George Mason U), show how attempts at market manipulation in fact end up increasing the accuracy of the market because they provide that much more profit incentive to bet against the manipulator.

Prediction markets may also be subject to speculative bubbles. For example in the year 2000 IEM presidential futures markets a flood of new traders in the final week of the election caused the market to gyrate wildly, making its “predictions” useless.

A common belief among economists and the financial community in general is that prediction markets based on play money cannot possibly generate credible predictions. However, the data collected so far disagrees. Pennock et al (Science, 2001) analyzed data from the Hollywood Stock Exchange and the Foresight Exchange and concluded that market prices predicted actual outcomes and/or outcome frequencies in the real world. Servan-Schreiber et al (Electronic Markets, 2004) compared an entire season’s worth of NFL predictions from NewsFutures’ play-money exchange to those of Tradesports, an equivalent real-money exchange based in Ireland. Both exchanges performed equally well. In this case, using real money did not lead to better predictions.

Some experimental systems are underway to provide data on alternatives to prediction markets that seek to avoid some of the theoretical pitfalls mentioned earlier. For example, polling firm TIPP Online has experimented with “national zeitgeist” questions which ask participants who they think will win rather than who they will vote for personally. This proved to be a more stable and accurate predictor in the 2004 US presidential race than traditional polls. Another experimental system is Owise which directly asks participants to estimate probabilities on a wide range of future events, and rewards accurate performance with status, titles, and small cash prizes. Owise functions as a hive mind or a kind of neural network in which each “neuron” is a human being whose predictions are assigned a weight based on past performance. In fact, this is not so different from what naturally happens in a prediction market where those who make good predictions do profit at the expense of those who make bad predictions, thus progressively increasing their relative influence on the market through how much money they can bring to bear to back up their predictions. There is currently not enough data and history to check how these alternatives will compare to prediction markets in terms of forecasting ability.

Commercial interest

• Hewlett-Packard pioneered applications in sales forecasting and now uses prediction markets in several business units. Mentioned in academic publications from HP Labs. Also mentioned in Newsweek [8] (October 2004)
• Corning, Eli Lilly, Abbott Labs, Siemens, Masterfoods, Arcelor and other global companies are listed [9] as NewsFutures customers.
• Intel mentioned in Harvard Business Review (April 2003) in relation to managing manufacturing capacity.
• Microsoft is piloting prediction markets internally.
• France Telecom’s Project Destiny has been in use since mid-2004, with very successful predictive behaviour.
• Google has confirmed that it uses a predictive market internally in its official blog [10].

This guide is licensed under the GNU Free Documentation License. It uses material from the Wikipedia.

Prediction markets are speculative (i.e., betting) markets created for the purpose of making predictions. Assets are created whose final cash value is tied to a particular event (e.g., will the next US president be a Republican) or parameter (e.g., total sales next quarter). The current market prices can then be interpreted as predictions of the probability of the event or the expected value of the parameter. Other names for prediction markets include information markets, decision markets, idea futures, and virtual markets.People who buy low and sell high are rewarded for improving the market prediction, while those who buy high and sell low are punished for degrading the market prediction. Evidence so far suggests that prediction markets are at least as accurate as other institutions predicting the same events with a similar pool of participants.

Public examples include TradeSports, the Iowa Electronic Markets, NewsFutures, Hollywood Stock Exchange and HedgeStreet. One of the oldest and most famous is the University of Iowa’s Iowa Electronic Market. Since 1988, it has predicted the results of American presidential elections more accurately than traditional polls 75 percent of the time. The Hollywood Stock Exchange, a virtual market game established in 1996, in which players buy and sell prediction shares of movies, actors, directors, and film-related options, correctly predicted 35 of 2005′s 40 big-category Oscar nominees and 7 out of 8 top category winners. HedgeStreet, designated in 2004 as a market and regulated by the Commodity Futures Trading Commission, enables internet traders to speculate on economic events.

These markets actually have a long and colorful lineage. Betting on elections was common in the U.S. until at least the 1940s, with formal markets existing on Wall Street in the months leading up to the race. Newspapers reported market conditions to give a sense of the closeness of the contest in this period prior to scientific polling. The markets involved thousands of participants, had millions of dollars in volume in current terms, and had remarkable predictive accuracy. See Paul Rhode and Koleman Strumpf (2004) [1] for additional details.

In July 2003, the U.S. Department of Defense publicized a Policy Analysis Market and on their website speculated that additional topics for markets might include terrorist attacks. A critical backlash quickly denounced the program as a “terrorism futures market” and the Pentagon hastily cancelled the program.

Prediction markets were championed in James Surowiecki’s 2004 book The Wisdom of Crowds.

Prediction markets are rapidly becoming useful decision support tools for corporations. Several major companies in the US and in Europe are current users of internal prediction markets.

Commercial interest

• Hewlett-Packard pioneered applications in sales forecasting and now uses prediction markets in several business units. Mentioned in academic publications from HP Labs. Also mentioned in Newsweek [8] (October 2004)
• Corning, Eli Lilly, Abbott Labs, Siemens, Masterfoods, Arcelor and other global companies are listed [9] as NewsFutures customers.
• Intel mentioned in Harvard Business Review (April 2003) in relation to managing manufacturing capacity.
• Microsoft is piloting prediction markets internally.
• France Telecom’s Project Destiny has been in use since mid-2004, with very successful predictive behaviour.
• Google has confirmed that it uses a predictive market internally in its official blog [10].

Links

Real-money prediction exchanges (a.k.a. “event-driven futures exchanges” in the U.S., “betting exchanges” in the U.K.):

• BetFair – U.K., E.U.
• TradeSports – Ireland, E.U.
• Intrade – Ireland, E.U.
• Iowa Electronic Markets – U.S.A. (no-action letter from CFTC)
• HedgeStreet – U.S.A. (regulated by CFTC)
• UBC Election Stock Market – Canada
• Betdaq – Ireland, E.U.
• SpreadFair – U.K., E.U. (regulated by UK’s Financial Services Authority)

Play-money prediction exchanges:

• Foresight Exchange Canada
• NewsFutures U.S.A. + E.U.
• Inkling U.S.A.
• CrowdIQ U.S.A.
• PublicGyan India
• Yahoo! Tech Buzz Game U.S.A.
• Hollywood Stock Exchange U.S.A.
• Washington Stock Exchange U.S.A.
• World Economic Forum’s Global Risks Prediction Market (powered and web-hosted by NewsFutures) Switzerland
• CENIMAR: Central Intelligence Market U.S.A.

News articles and opinion pieces:

• The Chris. F. Masse 2005 Awards — Prediction Markets & Decision Markets — by Chris. F. Masse – 2006-03-06
• Wisdom of the crowd – Nature – 2005-11-17
• Market Magic – CFO Magazine – 2005-11-01
• Place your bets! – TIME Magazine – 2005-10-16
• Chris. F. Masse 2004 Year-End Awards – (Honorary awards to best and worst in prediction markets) – 2004-12-31
• Listen to the People Who Got the Election Right. – Prediction markets are brutally honest and uncannily accurate. – 2004-11-28

Academic research papers:

• The Informed Press Favored the Policy Analysis Market – by Robin Hanson - PDF file – 2005-05-05
• Interpreting Prediction Market Prices as Probabilities – Justin Wolfers and Eric Zitzewitz – PDF file – 2005-02-01
• Five Open Questions About Prediction Markets – by Justin Wolfers and Eric Zitzewitz – PDF file – 2005-01-21
• Risk Aversion, Beliefs, and Prediction Market Equilibrium – by Steve Gjerstad – PDF file – 2005-01-00
• The Iowa Electronic Market: Lessons Learned and Answers Yearned – by Joyce E. Berg and Thomas A. Rietz – PDF file – 2005-01-00
• Analysis of 2004 Political Futures Markets – by Ken Allen, Kevin Daniels, Darby Kopp and Brian Murdock – Dartmouth U. – MS Word .DOC file – 2004-11-09
• Prediction Markets: Does Money Matter? – by Emile Servan-Schreiber, Justin Wolfers, David Pennock & Brian Galebach – PDF file – Fall 2004.
• Prediction Markets – by Justin Wolfers and Eric Zitzewitz – PDF file – 2004-05-00
• Interpreting the Predictions of Prediction Markets – by Charles F. Manski – PDF file – 2004-02-00
• Historical Prediction Markets: Wagering on Presidential Elections – by Paul W. Rhode and Koleman S. Strumpf – PDF file – 2003-11-10

This guide is licensed under the GNU Free Documentation License. It uses material from the Wikipedia.

Video: What is a prediction market?

A parlay is a single bet that links together two or more individual wagers and is dependent on all of those wagers winning together. The benefit of the parlay is that there are much higher payoffs than placing each individual bet separately since the difficulty of hitting it is much higher. If any of the bets in the parlay loses, the entire parlay loses.For example: Joe placed a three-team NFL parlay on the Lions, Bears and Bengals. If any one of those teams fail to cover the spread, Joe loses his parlay bet. But if all three teams beat the spread, Joe gets paid $600 for every $100 bet.

Links

• About.com definition
• Parlay Odds Calculator – Calculate the payout of a potential parlay.

This guide is licensed under the GNU Free Documentation License. It uses material from the Wikipedia.

Betting odds slang is a series of words used to describe particular common fractional odds.

• Evens – Levels, Scotch
• 2/1 – Bottle
• 3/1 – Carpet, Gimmel
• 4/1 – Rouf
• 5/1 – Hand
• 5/2 – Face
• 6/1 – X’s
• 7/1 – Nevs
• 8/1 – T.H.
• 9/1 – Enin
• 10/1 – Cockle, Net
• 11/10 – Tips
• 33/1 – Double Carpet
• 100/30 – Burlington Bertie

In probability theory and statistics the odds in favor of an event or a proposition are the quantity p / (1 − p), where p is the probability of the event or proposition. The logarithm of the odds is the logit of the probability.Odds have long been the standard way of representing probability used by bookmakers, though the method of presenting odds varies by location.

Taking an event with a 1 in 5 probability of occurring (i.e. 0.2 or 20%), then the odds are 0.2 / (1 − 0.2) = 0.2 / 0.8 = 0.25. If you bet 1 at fair odds and the event occurred, you would receive back 4 plus your original 1 stake. This would be presented in fractional odds of 4 to 1 against (written as 4 : 1 or 4/1), in decimal odds as 5.0 to include the returned stake, in craps payout as 5 for 1, and in moneyline odds as +400 representing the gain from a 100 stake.

By contrast, for an event with a 4 in 5 probability of occurring (i.e. 0.8 or 80%), then the odds are 0.8 / (1 − 0.8) = 4. If you bet 4 at fair odds and the event occurred, you would receive back 1 plus your original 4 stake. This would be presented in fractional odds of 4 to 1 on (written as 1 : 4 or 1/4), in decimal odds as 1.25 to include the returned stake, in craps as 5 for 4, and in moneyline odds as −400 representing the stake necessary to gain 100.

The odds are a ratio of probabilities; an odds ratio is a ratio of odds, that is, a ratio of ratios of probabilities. Odds-ratios are often used in analysis of clinical trials. While they have useful mathematical properties, they can produce counter-intuitive results: in the example above an 80% probability is four times the chance of a 20% probability but the odds are 16 times higher.

Web resources

• Notes + Practice Problems to Learn probability and odds at “FutureAccountant.com”

This guide is licensed under the GNU Free Documentation License. It uses material from the Wikipedia.

HTML clipboardFixed Odds Betting Terminals (FOBTs) are computerised machines normally found in betting shops which allow players to bet on the outcome of various games and events with Fixed Odds. They were introduced to UK shops in 2002, shortly after the abolition of the Betting Tax in October 2001.The most commonly played game is Roulette. The minimum bet per spin is £1 and the maximum is £100. Chips can be as small as 20 pence. The maximum amount that can be won on any spin is £500.

Other games include Spoof, Bingo, Virtual Racing and Triple Disc.

Shops are allowed a maximum of four such terminals, although since this number also includes fruit machines, many shops have fewer than four.

This guide is licensed under the GNU Free Documentation License. It uses material from the Wikipedia.