2005 Superbowl betting online

Innovation In Bookmaking

2005-06-14T07:31:00.000-07:00

Q & A with
Dalton Wagner, Founder V.O. Group, S.A.

As Founder and Proprietor of V.O. Group, S.A., Dalton Wagner has the reputation of ferreting out new markets and attacking while other companies sit on their hindquarters and watch. One market that has always eluded offshore bookmakers is the ‘guy who likes dealing with his credit bookmaker’. Typically this market has been untouchable for the offshore bookmaker, but NOT for Dalton and V.O. Group, S.A. We had an opportunity to catch up with Dalton Wagner on his last visit to Panama.

Vegas Offshore: Tell us about this new product that you are calling your Price Per Head Agency Relationship.
Wagner: As you know, one market that a post-up sportsbook has historically been unable to attack is the huge market of bettors that have been betting with their local guy for years. The reason this market exists is the personal relationship that an onshore bookie has with his clients. Sportsbooks have attempted to educate the player and lure him away from the local bookie, however we have taken another tact. We have partnered with local bookies around the world to make their offering better and more valuable.

Vegas Offshore: So instead of attacking the local guy, you actually approach him with your offer?
Wagner: Exactly. The fact is that the relationship is the last thread holding clients to a local guy. Yes, there is the fact that the local guy extends credit, but with bonuses and promotions we can overcome that offering. The key is the relationship.

Vegas Offshore: I’m confused, please explain.
Wagner: As you know, the onshore bookmaking market has lost steam to the offshore providers like V.O. Group, S.A. The reason is that the offerings that I can provide are far superior to those that any onshore bookmaker can offer. The PPH concept is based on allowing the onshore bookmaker to provide his clients my offerings on a purely anonymous basis via an online automated tool. Rather, by signing up with my PPH service, an onshore bookmaker can compete with all offshore sportsbooks on equal footing offering 24-hour wagering, 365 days a year. All the bookmaker does is use an online system to assign PINs and PASSWORDs and pay me a small fee per week for the service.

Vegas Offshore: Wait a minute. So, this means that a small time operator in Cleveland, Ohio can offer all the same things that V.O. Group, S.A. does without making the multi-million dollar investment?
Wagner: Now your catching on. For as little as $25 per head per week, a guy in Ohio can compete with the biggest sportsbooks in the world. And, better yet, never answer another wagering phone call again.

Vegas Offshore: OK, I get it. They use your automated tool to drive their clients to your software online, and they simply pick up the figures at the end of the week.
Wagner: Exactly correct with one exception. Their clients can bet on the phone via our call center staffed with 250 people OR bet online. Again, they have access to all of the offerings V.O. Group, S.A. provides including call center access, client services and technical support.

Vegas Offshore: Is the PPH product only based on sports wagering?
Wagner: Very good question. The answer is no. We actually have the ability to offer clients two (2) casino products including blackjack, baccarat, slots, 3 card poker, roulette, etc. And, clients may also bet on horses at over 350 tracks worldwide.

Vegas Offshore: Wow! That truly is incredible. So, a bookie that has one client can compete with the likes of MVPsportsbook.com on equal footing?
Wagner: Exactly correct. But, there are two keys to the success of the product launch. The first is being able to compete head to head with the biggest sportsbooks in the world. But, arguably the most important factor is that since the onshore bookie no longer has to answer phones or track plays, he can spend all of his time picking up new clients, NOT writing bets.

Vegas Offshore: I remember that in a prior interview you had stated that the key is not winning, but finding more losers.
Wagner: An embarrassing quote, but true. Amateur bookmakers think that the key is to beat the clients you have. That is a huge mistake. The key to getting rich as a bookmaker is finding a new client every single day of the year. The numbers take care of themselves, the key is to get more and more people playing. That is the TRUE BEAUTY of the PPH software and agency relationship.

Vegas Offshore: Do you have any success stories to tell?
Wagner: I have many, but will tell you one that emphasizes what the PPH program can do. I have a bookmaker that has been working out of San Antonio, Texas for 10 years. The guy has historically made $200,000 a year from his 25 or 30 players. He was introduced to our product in July 2004, and today he is writing over 185 clients. The ability to outsource answering calls and writing tickets to us has allowed him to grow his business 600% and his revenue over 1000%.

Vegas Offshore: Why did his revenue grow more than his business? Is it because your lines are sharper than his?
Wagner: There you go again. That is the obvious mistake that everyone makes. Again, don’t worry about beating the clients. Worry about getting more clients to play. But, to answer your question directly, the ability for his clients to play in the casino, play 24 hours a day, play games/halves/quarters, props, etc. is where the extra revenue comes from. Our lines might be sharper, but the advantage is more clients betting more often.

Vegas Offshore: How many bookmakers do you currently work with?
Wagner: We are writing business for over 250 bookmakers now. Some represent groups as small as 2 or 3; and others represent groups as large as 500. Again, remember the key is finding as many clients as possible. If you get to where you are writing 15 clients or more, it is almost impossible to lose for an extended period of time.

Vegas Offshore: So how does a bookmaker learn more about this?
Wagner: Simply go to any of the V.O. Group, S.A. sites to learn more. I suggest starting at www.BettorsNet.com or calling 1-877-512-1001 or emailing agents@bettorsnet.com to get more information.

State of the Offshore Gaming Industry

2005-06-13T09:19:00.000-07:00

Q & A with
Dalton Wagner, Founder V.O. Group, S.A.

As Founder and Proprietor of V.O. Group, S.A., Dalton Wagner has the reputation of making one-dollar work like three in online marketing. With formidable competition, Mr. Wagner founded V.O. Group, S.A. in 1998 and has quickly risen to be one of the largest offshore operators in the world with over 50,000 active clients. We had an opportunity to catch up with Dalton Wagner on his last visit to Antigua.

Vegas Offshore: This year has been interesting for V.O. Group, S.A. and the offshore industry as a whole (advertising changes, etc.). How do you feel about the industry at the current time?
Wagner: I feel the industry is as strong as it has ever been. We are still finding that our advertising is bringing us new clients at an ever-growing rate. Some competitors are complaining that the crimp on advertising is affecting the industry, but we are not seeing this trend. Consolidation within the industry has begun, and I am sure you will continue to see smaller competitors gobbled up, and medium to larger companies merging. The weaker will go by the wayside.

Vegas Offshore: What about the rumors of V.O. Group, S.A. being bought?
Wagner: I am glad you used the term 'rumors'. The fact is that in July 2004 three suitors approached V.O. Group, S.A. because of our record-breaking profit year. In two of the cases we did see 'value' in a merger/relationship. However, at the end of the day, we could not come to terms that were agreeable on both sides. At this time V.O. Group, S.A. stands independent. And, we are happy being independent, profitable and growing.

Vegas Offshore: It is rumored that Bet On Sports (PLC; BSS.L) was one of the suitors. Is that true?
Wagner: There were three suitors that approached V.O. Group, S.A. And, in all cases confidentiality agreements were signed. For this reason, I cannot confirm or deny who any of the potential suitors were. What I can tell you is that one was a major sportsbook player, one was a major casino player and two were PLCs. I doubt that helps.

Vegas Offshore: Well, if Bet On Sports was involved, I bet you are happy you weren't involved in their November 24, 2004 stock debacle.
Wagner: The Bet On Sports stock debacle was an over-reaction by the market in my opinion. In one day you saw their stock drop 50% in value. And, the reason was poor performance in a very short period. If you looked at the other publicly held offshore gaming companies, you saw the same thing, just not as drastic as with BSS.L. Simply put, I know the founder of Bet On Sports, I know the management of Bet On Sports, I know the staff at Bet On Sports and I would not hesitate to invest in Bet On Sports. They were, and still are, undervalued in my opinion. They are a great competitor and a great company. I wish I had some of their stock at the new adjusted price. I just don't see how you can lose.

Vegas Offshore: It is rare to hear the 'competition' speaking so highly of a major competitor. Why would you do so?
Wagner: Simple. Bet On Sports is a great company. And, I am sure they would tell you the same about V.O. Group, S.A. The fact is, that there are companies out there that make our industry stronger. And, ones that make it weaker. Bet On Sports and V.O.Group, S.A. strive to make our industry stronger and more legitimate on a daily basis (as do BetCris.com; BoDog.com; etc.). What kind of ambassador for the industry would I be if I downplayed a 'good' competitor like Bet On Sports for my own benefit?

Vegas Offshore: If Bet On Sports did approach you, would you consider a merger?
Wagner: I like being independent. But, simply put, NEVER say NEVER.

Vegas Offshore: What of the rumors that you are looking at Panama as a potential relocation site?
Wagner: Our operation currently has offices in Costa Rica, Antigua and Belize. In Costa Rica we currently have over 400 employees. With employment costs in Costa Rica on the rise, the burden of Caja and the ever-changing political climate, we have looked at several alternative locations. One of the most desirable is/was Panama. However, due to the massive failure of BetPanAm.com and the political debacle involving their past gaming commission, we have decided to stay put for the current time. I think it is very safe to say that our primary location will be Costa Rica for the next 5 years. Pending any crazy licensing or governmental changes.

Vegas Offshore: I have visited your office in Antigua, however had no idea that you employed 400 individuals in Costa Rica. Why do you need such a large staff?
Wagner: Our industry has a reputation for being lazy and getting by on a shoestring budget. Most sportsbooks, casinos, racebooks and poker rooms answer phones when it is convenient and buy computers when the ones they are operating blow-up. Most are simply embarrassing to the legitimate operators. To run an operation like ours, servicing 50,000 bettors, you need lots of space, a 1st rate phone system, fiber and satellite phone and Internet backups, IT professionals, accounting professionals, etc. V.O. Group, S.A. is currently located in 35,000 square feet of space, operating 24-hours a day, 365 days a year, and we are busting at the seems. At the current time, we are negotiating to buy the building next to us for future expansion. I think we will employ 600 people come this same time next year.

Vegas Offshore: I have noticed that you are still branching into other products. How has this strategy worked for V.O. Group, S.A.?
Wagner: In 1998 we started with our sports product. We then branched into the casino product. In 2001 and 2002 we opened several major race books and poker rooms. The fact is that we are profitable in all products. However, our core product, and hub for our advertising, is focused on the sports product. The strategy has worked, but we will not desert our core product. You can look for our re-launch of interactive betting in early 2005 and bingo in mid 2005.

Vegas Offshore: What other changes do you see in 2005 for V.O. Group, S.A.
Wagner: I think you will see V.O. Group, S.A. work more on our Internet interface to make it more gamer-friendly; you will see our company move into land-based gaming; and you will see us acquire two or three smaller competitors. Rather, you will see what you have always seen, expansion.

Vegas Offshore: What other changes do you see in 2005 for the industry as a whole?
Wagner: The strong will get stronger. The weak will get weaker. And, the acquisition race will be on.

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2005-03-02T10:51:00.000-08:00

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online Betting Pool Raises One Office's superbowl betting lines 2005 Philadelphia Eagles New England Patriots Stakes --

2005-02-02T09:21:00.000-08:00

I can't believe how the superbowl betting lines 2005 Philadelphia Eagles New England Patriots betting pool that I organized for friends has mushroomed! I set up a pool using super bowl bettingfor my friends but other office pools are raising the stakes by throwing their wagers into my pot.

Always the one nominated for organizing my office's social activities, I decided to try a high tech approach when I was delegated to coordinate the group's superbowl betting lines 2005 Philadelphia Eagles New England Patriots pool this year.

"Everyone wanted a superbowl betting lines 2005 Philadelphia Eagles New England Patriots office pool but no one wanted to organize it, and as usual it fell to me," said the overworked administrative assistant. "This year, I just got the whole thing set up online and sent everyone to super bowl betting to place their wagers and check up on the pool value."

super bowl bettingadds a high tech twist to traditional office pool betting. There are dozens of sports and celebrity betting pools on the site, and anyone with an interesting betting proposition can get a new pool set up by contacting the site's managers.

"We made it sort of a funny wager I guess," . "I don't know much about American football myself, but apparently one team is pretty much a shoe-in. So betting on who would win wasn't very interesting to some of the guys in the office. So that's what we made our betting pool wager: 'Will Fox Television commentators make even ONE reference to Janet Jackson's nipple during the superbowl betting lines 2005 Philadelphia Eagles New England Patriots broadcast?'"

"We've seen some pretty crazy wagering propositions, but I'm really got our interest," says an super bowl betting spokesperson. "I don't think I should have been so surprised when so many other visitors wanted a piece of my action."

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NFL: SUPERBOWL XXXIX super bowl BETTING ODDS BEFORE PLAY-OFFS

2005-01-21T13:36:00.000-08:00

NFL: SUPERBOWL XXXIX BETTING ODDS BEFORE PLAY-OFFS
CLICK HERE FOR NFL BETTING MARKETS
Superbowl : Winner Monday, 31 January 2005 Show all bookmakers
Bookmaker Betting Exchange New England Patriots 13/8
Pittsburgh 5/2 2.85/1
Philadelphia Eagles 3/1
Atlanta Falcons 7/1 7.6/1
After investing the AFC and NFC Championship winner markets individually, we now an opportunity to look at the outright superbowl betting market. Although odds on to win the NFC Championship, Philadelphia Eagles are only third favourites behind the equally fancied New England Patriots and Pittsburgh Steelers. Both the AFC teams can be backed at a best price 3/1 to go the distance. The strength of the AFC conference is demonstrated by the fact fourth in the betting is Indianapolis, available at 8/1 with . The Colts are one of three AFC franchises quoted in the top four for the superbowl betting market. Apart from the above teams, only Atlanta are awarded a single digit quote by the majority of firms. That said it is possible to find 12/1 for the Falcons. For those who feel San Diego can continue their season of surprises and actually win superbowl betting, it is worth noting that Super Odds are way out of line with their 25/1 quote. The general price on offer for the Chargers is around the 14/1 mark. Of the NFC teams without the luxury of a bye week, Green Bay Packers are available at 25/1 and Seattle Seahawks at 33/1. The recommendation at the start of the season was Minnesota at 50/1 and that is still the price available. The Vikings though will need to show considerable improvement if they are to have any chance. Also available at the same price as Minnesota are New York Jets. Denver Broncos and St.Louis Rams are the 66/1 outsiders.

superbowl betting ISSUES RELATED TO

2005-01-13T07:38:00.000-08:00

ISSUES RELATED TO superbowl betting

SummaryThis paper looks at various issues that are of interest to the superbowl gambler. First, anexpression is obtained for the distribution of the final bankroll using fixed wagers witha specified initial bankroll. Second, fixed percentage wagers are considered where theKelly method is extended to the case of simultaneous bets placed at various odds; acomputational algorithm is presented to obtain the Kelly fractions. Finally, we considerthe problem of determining whether a betting system is profitable based on the historicalresults of bets placed at various odds.Key words: gambler’s ruin, Kelly method, optimal wagering, comparative inference, Gibbssampling, meta-analysis.1. IntroductionDespite its illegality in many jurisdictions, betting on the outcome of sporting eventsis a common activity. For example, Crist (1998) states that Americans illegally wager∗Robin Insley is Senior Lecturer, Lucia Mok is an MSc graduate and Tim Swartz is Professor, De-partment of Statistics and Actuarial Science, Simon Fraser University, Burnaby BC, Canada V5A1S6.Swartz’s work was partially supported by a grant from the Natural Sciences and Engineering ResearchCouncil of Canada. The authors thank an Associate Editor and two referees whose comments lead to animprovement in the manuscript.1--------------------------------------------------------------------------------Page 2 over $100 billion annually on professional and college superbowl. On the 1999 Superbowl(the championship game of the National Football League), approximately $87 millionwas wagered legally in Las Vegas superbowlbooks (Ordine, 2000). In Australia, commercialbetting on racing alone (horses and greyhounds) resulted in individual losses of $1.6billion in 1997-1998 (Productivity Commission, 1999).superbowl betting has been increasing over the years (Productivity Commission, 1999)and it is likely that the trend will continue as more government bodies are eager to sharein the huge profits that have and can be made. One particular avenue for growth is superbowlbetting via the internet (Haywood, 2000). Whereas such internet sites are illegal in theUnited States and in Canada, they are legal and operational in various Caribbean andLatin American countries and in Australia. Internet betting yields various murky legalissues such as the determination of where the wager is actually placed (e.g. in one’s homein North America where the activity is illegal or in the offshore country).In this paper, we put ethics and legal issues aside and take a look at various practicalproblems associated with superbowl betting. We are primarily concerned with issues involvingthe performance of wagering systems. To a lesser extent, the results are also applicableto financial investments corresponding to an investor who engages in numerous transac-tions. Many fundamental probabilistic results concerning optimal systems for favourablegames are reviewed in Thorp (1969). We are also interested in the problem of identifyingprofitable betting systems based on past data.In section 2, we provide an introduction to pointspread wagering where we emphasizethat it is theoretically possible to develop winning systems. betting terminology isexplained and the main objective of a bookmaker is discussed. In section 3, we obtaindistributional results for the gambler’s current bankroll after placing a finite number ofbets. This is done in the context of both ‘large’ and ‘small’ initial bankrolls. Although the2--------------------------------------------------------------------------------Page 3 results involve only binomial calculations and a simple extension of the classical drunk-ard’s walk problem, they are practical and we have not seen them recorded in the superbowlbetting literature. Moreover, much of the work concerning the gambler’s ruin and re-lated problems was developed long ago with an emphasis on challenging mathematics andapproximate solutions. Our approach is based on the realization that today many peoplehave a computer on their desk. In section 4, we turn to fixed percentage wagering whereeach bet is a fixed percentage of the current bankroll. We provide some theory and anumerical algorithm to obtain optimal fixed percentages in the context of simultaneousbets placed at various odds. The results in this section are extensions of the Kelly system(Kelly, 1956). We also give some distributional results concerning the final bankroll underfixed percentage wagering. In section 5, we consider the historical results of bets placed atvarious odds for a proposed betting system. The practical question arises as to whetherthe historical data provides evidence that the betting system is profitable. Profitabilityentails more than simply checking whether a profit is made in a given year; this couldoccur by chance. We are instead interested in whether there is long-term profitability ina system. The problem raises inferential issues for which different approaches yield con-flicting results. The major inferential problem that arises is the testing of a hypothesisΩ0versus a hypothesis Ω1where both Ω0and Ω1are ‘small’ sets relative to the entireparameter space. We propose a non-standard Bayesian approach to the problem whichcalculates ‘distances’ from Ω0and Ω1as measures of evidence in favour of each hypoth-esis. In section 6, we provide a concluding discussion with advice applicable to typicalsuperbowlbook scenarios where upper limits on wagering are imposed.2. A primer on betting the pointspreadThere are many types of wagers that can be placed on sporting events (McCune,1989). Perhaps the most common, is a wager placed against the pointspread. For ex-3--------------------------------------------------------------------------------Page 4 ample, consider a contest between a strong team (Team A) and a weak team (Team B).Whereas popular sentiment may overwhelmingly favour Team A to win, there is typicallyno concensus on the magnitude of the victory. To facilitate interest in wagering on sucha match, a posted line may appear asTeam A −l −110(1)Team B +l −110 .The line (1) is based on American odds and stipulates that a wager of $110 placed onTeam A returns the original $110 plus an additional $100 if Team A wins by more thanl points. Alternatively, a wager of $110 placed on Team B returns the original $110 plusan additional $100 if Team B wins or if Team B loses by less than l points. In the casewhere Team A wins by exactly l points, the original bets are returned. The quantity l isreferred to as the pointspread and is determined by the bookmaker, i.e. the individual ororganization that posts the line and collects the bets.We point out a few variations in the above example. First, different odds may be given.For example, American odds of -120 stipulate that a winning wager of $120 returns theoriginal $120 plus an additional $100. When the American odds are positive, this suggeststhat an event is less likely. For example, American odds of +140 stipulate that a winningwager of $100 returns the original $100 plus an additional $140. Second, wagers canbe made in multiples or fractions of the amounts discussed. Third, a nearly equivalentexpression of (1) is based on European odds and appears asTeam A −l 1.91(2)Team B +l 1.91 .4--------------------------------------------------------------------------------Page 5 Here, a winning wager of x dollars returns x(1.91) dollars. In the case of a $110 wager,the return is $110(1.91) = $210.10 which is nearly equivalent to the $110 + $100 = $210situation described above.Having discussed the betting procedure, it is important to understand the objective ofthe bookmaker. In (2), suppose that a total of y dollars is wagered on Team A and a totalof y dollars is wagered on Team B. In this case, the bookmaker collects 2y dollars, andgiven that a winner is decided, the bookmaker pays out y(1.91) dollars. The bookmakerhas made a profit of y(0.09) dollars regardless of the winning team and the percentageprofit (vigorish) is calculated as y(0.09)/(2y) → 4.5%. Thus, in the case of the line in(2), the ‘safe’ strategy for the bookmaker is to attempt to select the pointspread l soas to balance the total bets placed on Team A and on Team B for this guarantees aprofit. It is this fact that suggests opportunities for the gambler since the bookmakeris not trying to achieve an optimal line from the point of view of prediction. Rather,the bookmaker is trying to assess public opinion (i.e. determine the pointspread l) so asto balance the bets. A gambler then ‘simply’ needs to (a) have more insight on realitythan the rest of the betting public and (b) win often enough to overcome the vigorish.Unlike most mechanical games (e.g. roulette), it is evident that the possibility existsto develop winning strategies when betting on the outcome of sporting events. Stern(1998) suggests that bookmakers are good at setting lines in the sense that actual winningpercentages closely resemble the probabilities implied by the lines. For example, he claimsthat it is reasonable to approximate the outcome of a National Football League game usingthe normal distribution with mean equal to the point spread and standard deviation equalto 13.5.A simple heuristic for wagering is to develop a procedure for constructing pointspreads.When one’s personal pointspread differs sufficiently from the pointspread in the posted5--------------------------------------------------------------------------------Page 6 line, this signals a condition to wager. The size of the wager may depend on the magnitudeof the departure. We note in passing that the stated heuristic of comparing one’s personalpointspread to a bookmaker’s pointspread is an application of subjective probability. Infact, it is difficult to imagine how such an application can be reconciled in terms of afrequentist notion of probability.Finally, we mention two other types of wagers commonly available in the same matchinvolving Team A and Team B. First, odds are usually posted for Team A winning outright(i.e. without a pointspread). Odds are also posted for Team B winning outright. Thispair of odds is known as the moneyline. Second, there is usually a total t associated withthe match together with under and over odds. An under wager wins if fewer than t totalpoints are scored in the match and an over wager wins if more than t total points arescored in the match. If exactly t total points are scored in the match, the wagers arerefunded.3. Fixed wagersIn fixed wagering, without loss of generality, a gambler bets 1 unit on the outcome ofeach match. Given a betting system with probability 0 <> 1 in each of the matches, we are interested in the bankroll Bmthat is realized after placing m bets.Consider first the simplest situation where the gambler has an initial bankroll B0exceeding m (i.e. the gambler bets within his means and is prepared to lose each of the mbets). In this case, given m independent bets, it is straightforward to write the binomialprobability for the bankroll BmasPr (Bm= B0+ j(θ − 1) − (m − j)) = (mj) pj(1 − p)m−jj = 0, . . ., m.(3)6--------------------------------------------------------------------------------Page 7 From the binomial distribution, we have E(Bm) = B0+m(θp−1) and var(Bm) = θ2mp(1−p). The normal approximation to the binomial then suggestsBmd= N B0+ m(θp − 1), θ2mp(1 − p)from which probabilities may be calculated. For example, the probability of realizing aprofit after m bets is Pr(Bm> B0) ≈ Φ m(θp − 1)/ θ2mp(1 − p) . We might also usea continuity correction in the calculation of Pr(Bm> B0).In practice, a gambler does not know the probability p of choosing winners. Althoughp may be estimated from historical data, perhaps the most useful applications of (3) andsubsequent formulae contained in this paper are sensitivity analyses. A gambler mayinvestigate the range of p necessary to produce worthwhile profits. A gambler may alsoinvestigate the size of the loss if the system yields values of p unable to overcome thevigorish. For example, with ‘even’ odds such as those in (2), the gambler might be willingto assume a worst case scenario of p = 0.5 for this corresponds to no knowledge whatsoever(i.e. choosing Team A or Team B strictly by the flip of a coin).We now turn to the more complicated situation where the gambler has an initialbankroll of B0, where 0 < i =" 1," i =" 1," i =" 1," j =" 0," j=" Pr" bi=" B0+">i−B0θ0j ≤i−B0θwhere Q0,0= 1 and Q−1,k= Qk,−1= 0 for all k. This forms a tree structure where Qj,i−jis the jth entry along the ith row from the top of the tree. In this case, we constructthe tree beginning with the top element Q0,0and work downward along rows where thelast row {Qj,m−j: j = 0, . . ., m} gives the probabilities of interest. We note that theprobability of ruin is available from the final row via Pr(ruin) = 1 −mj=0Qj,m−j.Whereas much of the early work corresponding to the drunkard’s walk concerneditself with approximate solutions and detailed mathematics, we note that the approachdescribed here is based on ready access to a computing environment; the computationalapproach is fast and involves straightforward programming. For example, with θ = 1.91,B0= 5, p = 0.56 and m = 500, we obtain Pr(ruin) = 0.428 and this was obtained nearlyinstantaneously running Fortran on a SUN workstation.4. Fixed percentage wagersIn simple fixed percentage wagering, a gambler bets a fraction 0 ≤ f ≤ 1 of thebankroll on the outcome of a single match. When the outcome of the match is determined,the gambler resumes fixed percentage wagering on the new balance. Assuming the infinitedivisibility of money, one of the immediate attractions of fixed percentage wagering is that8--------------------------------------------------------------------------------Page 9 the gambler never goes into debt.In the context of information theory, Kelly (1956) provided a neat result that is oftenquoted but misused in betting circles. Given a betting system with probability 0

1, Kelly showed that the ‘optimal’betting fraction isf∗=pθ − 1θ − 1(4)provided that p > 1/θ. For example, consider a system that historically picks winners 54%of the time (i.e. p = 0.54) with the standard European odds payout of θ = 1.91. In thiscase, the optimal betting fraction is 3.45% of the bankroll. The Kelly criteria is optimalfrom several points of view; for example, it maximizes the exponential rate of growth andit provides the minimal expected time to reach a preassigned balance (Breiman, 1961).Breiman (1961) investigated the properties of betting systems and considered a moregeneral wagering scenario than the simple situation discussed above. However, Breiman’swork did not provide the derivation of solutions nor sharp statements concerning theuniqueness of solutions under the general framework.We are concerned with restricted problems that are of real interest to the superbowl bettor.For example, it is likely that a superbowl bettor would like to bet on several matches in asingle day. If the bettor uses the Kelly fraction (4) on n such matches where nf∗> 1,then the total amount to bet would exceed the current bankroll. The problem then is todetermine fixed percentages that satisfy some optimality.We therefore consider the situation where on day j = 1, . . ., m, the gambler wishes toplace njiwagers on matches with European odds θiand where the probability of pickingwinners is pi, i = 1, . . ., k. For example, it is possible that a gambler has a system forbetting the pointspread and another system for betting totals (i.e. k = 2). The question9--------------------------------------------------------------------------------Page 10 arises as to what are the optimal betting fractions f∗j1, . . ., f∗jkon day j where njiwagersare placed with a fraction fjiof the bankroll, i = 1, . . .k? Given an initial bankroll B0,the bankroll at the completion of day j isBj=nj1xj1=0· · ·njkxjk=0(1 −ki=1njifji)Bj−1+ki=1xjiθifjiBj−1∆ji(5)where ∆ji= I(Xji= xji), i = 1, . . ., k, in which the random variable Xjidenotes thenumber of winning wagers of type i on day j. Therefore, only one term in the product(5) is not equal to 1. Note also that the first term in the outer parentheses is the balanceof the bankroll not bet in a given day and we requireki=1njifji< i =" 1," g =" G(fj1," andg =" Enj1xj1="0·" njkxjk="0∆jilog" ki="1njifji+ki="1xjiθifji="nj1xj1="0·" njkxjk="0ki="1njixjipxjii(1" ki="1fji(xjiθi−" k =" 3," nj1=" 2," nj2=" 3," nj3=" 3," p1=" 0.545," p2=" 0.565," p3=" 0.585" 1=" θ2="" 3=" 1.91." j1=" 0.0416," j2=" 0.0811" j3=" 0.1215.The" asbm=" (1" ki="1nmifmi)Bm−1+ki="1XmiθifmiBm−1="" b0mj="1(1" ki="1fji(Xjiθi−" xjid=" Bi(nji," j =" 1," i =" 1," m =" 162,k" pi=" 0.56," i=" 1.91" nji=" 1" ji=" 0.0761." b0=" 100.Figure" ki="1E(njifji)(piθi−" pb=" 1"> 0 → pA> 1/θA.Similarly, we should bet on Team B if pB> 1/θB. Recall that pAand pBare unknownand the inequality 1/θA+ 1/θB> 1 is due to the vigorish.Now we first test the profitability of a betting system in a restricted context. Supposethat we have the results of n historical matches where the proposed betting system wouldhave bet on Team A with European odds as in (9). Suppose further that x of the n betsare winning bets. Then we test for a profitable system by considering H0: pA≤ 1/θAversus H1: pA> 1/θA. The corresponding P-value is given by the binomial probabilityPr(X ≥ x) =ni=x(ni) (1/θA)i(1 − 1/θA)n−i(10)13--------------------------------------------------------------------------------Page 14 where a small P-value indicates evidence of a profitable system. This is a simple procedureto determine whether historical data provides evidence of long-term profitability. Forexample, if there are x = 60 winning wagers out of n = 100 matches with standard payoutθA= 1.91, then pA= 1/θA= 0.524 and we have mild evidence (P = 0.076) of a profitablesystem. Remarkably, tests such as these are rarely (perhaps never) discussed in the myriadof betting books available at the Gamblers Book Shop (www.gamblersbook.com), a keysource for information on betting.The situation becomes more complex when bets are placed at various odds. Wehave collected data arising from the 2000 Major League Baseball season for which aproposed betting system was established. Ignoring some of the details, personal oddswere constructed corresponding to Team A defeating Team B. These odds were based onlogistic regression using the pitchers’ earned run averages, overall team batting averagesand team winning percentages as covariates. When the personal odds differed from theposted odds at the website (www.) by more than 10%, this signaleda condition to wager.Table 1 gives the data and P-values for groups of bets placed at 24 differents sets ofodds. The P-values are obtained using (10). We observe that only one of the results aresignificant. The problem now is to determine whether the overall system is profitable.A standard approach is based on Fisher’s test (D’Agostino & Stephens, 1986 p. 357).Following the notation above, let H0k(H1k) denote the hypothesis that the kth type ofbet is unprofitable (profitable) and let qkdenote the corresponding P-value, k = 1, . . ., 24.Fisher’s test considers the overall null hypothesis Ω0=24k=1H0kversus the alternativehypothesis that at least one of the H0kis false. Applying Fisher’s test to the data in Table1, we obtain the overall P-value Pr(χ248> −224k=1log qk) = Pr(χ248> 52.767) = 0.295which does not allow us to reject the overall null hypothesis Ω0that all of the component14--------------------------------------------------------------------------------Page 15 types of bets are unprofitable.The approach above is not quite right. With respect to the data in Table 1, there is notenough evidence to reject Ω0, and even if there was, this does not imply that the bettingsystem is profitable. We define a profitable betting system as one for which all of theH1kare true. Therefore we should instead test Ω0versus the alternative Ω1=24k=1H1kand exclude regions of the parameter space that do not belong in Ω0∪ Ω1. We excludesuch regions because the betting system uses the same betting criteria regardless of theodds and it is therefore logical that if it is an unprofitable (profitable) betting system,then it should be an unprofitable (profitable) system for all component types of bets. Weemphasize that the relevant problem is to test a hypothesis Ω0which is a small set whereall component bets are unprofitable versus a hypothesis Ω1which is a small set where allcomponent bets are profitable.To test Ω0versus Ω1in a classical framework, it is natural to reject Ω0based on largevalues of the generalized likelihood ratio statisticΛ =supΩ0∪Ω1L(x)supΩ0L(x)where L(x) is the corresponding product probability mass function based on the data x.The exact discrete distribution of Λ is beyond reach since the sample space is so large.Also, we cannot appeal to a large sample χ2distribution for 2 log Λ since dim(Ω0) =dim(Ω1) which means that we have ‘zero’ degrees of freedom. In addition, the supremumsin Λ occur on the boundaries of the parameter space and this invalidates necessary largesample assumptions.We consider now a Bayesian approach to testing Ω0versus Ω1. The Bayesian modelcontains a little more structure due to the necessity of prior distributions. Given theEuropean odds (9), we assume that over many matches the odds are set such that bets15--------------------------------------------------------------------------------Page 16 Table 1: Betting results using a proposed betting system; θAis the posted odds forTeam A, θBis the posted odds for Team B, x is the number of wins by betting on TeamA and n is the total number of corresponding matches. The P-value is given and anasterisk indicates significance at level 0.05.θAθBxn P-value2.800 1.455 12 32 0.4822.700 1.498 11 25 0.2992.650 1.541 10 23 0.3572.600 1.5568 18 0.3842.550 1.571 13 34 0.6102.500 1.588 17 35 0.1932.450 1.606 13 38 0.8402.400 1.625 44 89 0.0842.350 1.645 19 43 0.4722.300 1.666 26 56 0.3762.250 1.690 28 59 0.3672.200 1.714 39 65 0.013∗2.150 1.740 22 43 0.3222.100 1.770 23 41 0.1762.050 1.800 26 47 0.2262.000 1.833 20 39 0.5001.910 1.9109 17 0.5781.833 2.000 12 20 0.3991.800 2.05047 0.6201.770 2.100 29 51 0.5381.740 2.15046 0.4921.690 2.25033 0.2071.645 2.35035 0.6961.625 2.40035 0.70916--------------------------------------------------------------------------------Page 17 on Team A and Team B are equally attractive (i.e. they yield the same expected return).Therefore, based on a unit bet,(θA− 1)pA+ (−1)(1 − pA) = (θB− 1)(1 − pA) + (−1)pA→ pA= θB/(θA+ θB)where θB/(θA+θB) represents the expected probability that Team A wins and the expec-tation is taken over many matches. In any particular match, the true probability pAmaybe something different than θB/(θA+ θB). Let Xkdenote the number of winning bets oftype k out of nkwagers and let p(0)k= θB/(θA+ θB) for the kth type of bet, k = 1, . . ., N.For the data in Table 1, N = 24. This suggests the hierarchical model(Xk pk)d= Bi(nk, pk)k = 1, . . ., N(pk m)d= β mp(0)k, m(1 − p(0)k)k = 1, . . ., Nmd= U(l, ∞)where l = maxk1/p(0)k, 1/(1 − p(0)k)(11)where the pkare conditionally independent. The beta prior for pkis reasonable from thepoint of view that pkis constrained to the interval (0, 1) and E(pk m) = p(0)kas arguedpreviously. The hyperparameter m controls the variance of the pk, and we assign a flatimproper prior for m. We include the lower limit on m as this forces concave densities forp1, . . ., pN.The hierarchical model (11) induces a (N +1) dimensional posterior distribution givenby (p, m x). Inference based on (p, m x) is straightforward using the Gibbs samplingalgorithm as the full conditional distributions of the pkare beta distributions and mcan be generated in closed form from its full conditional distribution via inversion. Toobtain the posterior probabilities of Ω0and Ω1, we simply calculate the proportion of thegenerated ps that fall into the two respective sets. For the data in Table 1, it turns out17--------------------------------------------------------------------------------Page 18 that both of these probabilities are essentially zero.We are therefore faced with the problem of assessing two hypotheses Ω0and Ω1, oneof which we believe to be true, when both hypotheses correspond to very improbable sets.This is a general problem of inference which goes beyond the application considered here.Our approach which borrows on ideas from Swartz (1999) and Evans et al. (1997) is togenerate p from the Gibbs sampling algorithm and then calculate its Euclidean distanceD0from Ω0and its Euclidean distance D1from Ω1. The smaller (larger) the quantityD0/D1, the more evidence p provides in favour of the hypothesis Ω0(Ω1). We thereforeconsider posterior probabilities Pr(D0/D1≤ t x) for different values of t. To put morereliance on the data we also calculate the Bayes factorBFD=Pr(D0/D1≤ t x)Pr(D0/D1> t x)Pr(D0/D1> t)Pr(D0/D1≤ t)where small (large) values give evidence in favour of Ω1(Ω0). There is one difficulty withthe calculation of BFDand this concerns the improper prior for m. With an improperprior, we are unable to generate from the joint prior distribution of (p, m) to obtainPr(D0/D1≤ t) and Pr(D0/D1> t). For the data in Table 1, we therefore set m = 2.98which is the posterior mean of m estimated from the output of the Gibbs sampling algo-rithm, and we then simulate the pks from the beta priors with this value of m. Table 2shows BFD, the posterior odds of D0/D1≤ t and the prior odds of D0/D1≤ t correspond-ing to the data in Table 1. As expected, we see that both the posterior odds and priorodds are increasing in t. However, the prior odds are larger and increase more rapidlythan the posterior odds. This causes the Bayes factor BFDto be small and approach zeroas t increases. We also observe that BFDhas its largest value when t is around 1.Now if you have confidence that the prior is realistic, then it is often argued thatinference should be based solely on the posterior as Bayes Theorem provides the recipe18--------------------------------------------------------------------------------Page 19 Table 2: BFD, the posterior odds of D0/D1≤ t, the prior odds of D0/D1≤ t for selectedvalues of t using the data in Table 1.tBFDposterior odds prior odds0.6 0.0090.0010.1140.7 0.0300.0080.2710.8 0.0350.0180.5220.9 0.0460.0400.8591.0 0.0510.0811.5911.2 0.0480.2034.2081.4 0.0360.41011.5001.6 0.0250.63125.3161.8 0.0140.99670.4292.0 0.0121.532124.0002.5 0.0063.219499.0003.0 0.0005.410∞4.0 0.00013.925∞6.0 0.00070.429∞8.0 0.000165.667∞19--------------------------------------------------------------------------------Page 20 for combining information from the data (i.e. the likelihood) and information from theprior. In this case, the posterior odds from Table 2 clearly suggest that the bettingsystem is profitable based on the data in Table 1. Note that using the posterior odds fort = 1.0 in Table 2, we obtain Pr(D0< b0=" $500" x1=" $400," x2=" $200," bygi="∂G∂fji="nj1xj1="0·" njkxjk="0ki="1njixjipxjii(1" ki="1fji(xjiθi−" andgi1i2="∂2G∂fji1∂fji2="nj1xj1="0·" njkxjk="0ki="1njixjipxjii(1" ki="1fji(xjiθi−" i2=" 1," halfspaceki="1njifji<"> 1/θi, i = 1, . . ., k, we obtain(a) Gi> 0 when fji= 0, i = 1, . . ., k,(b) Gi1i2< i2=" 1," aski="1njifji→"> 0.The shape and smoothness of G yields a simple algorithm which is guaranteed to findf∗j1, . . ., f∗jk. We begin by initializing an interior point fji= 1/ki=12nji, i = 1, . . ., k. Toobtain the root of G1along the first coordinate direction, bisection is carried out using thelower starting value f(l)j1= 0 and the upper starting value f(u)j1which intersects the planeki=1njifji= 1. After the first coordinate is updated, the procedure is repeated along thecoordinate directions 2, . . ., k. The loop in this sequential procedure is repeated until themovement in the point is sufficiently small. The maximum has then been obtained. Theproposed algorithm is a special case of Gauss-Seidel iteration (Thisted 1988, page 187)where success is based on the recognition that every step of bisection results in a moveup the hill and that there are no saddle points or minima of the function G.ReferencesBreiman, L. (1961). Optimal betting systems for favorable games. Proceedings of the FourthBerkeley Symposium on Mathematical Statistics and Probability, Jerzy Neyman, editor,65-78.Crist, S. (1998). All bets are off. superbowl Illustrated, 88 (3), 82-92.22--------------------------------------------------------------------------------Page 23 D’Agostino, R.B. & Stephens, M.A. (1986). Goodness-of-Fit Techniques, Marcel Dekker, NewYork.Evans, M., Gilula, Z., Guttman, I. & Swartz, T.B. (1997). Bayesian analysis of stochasti-cally ordered distributions of categorical variables. Journal of the American StatisticalAssociation, 92, 208-214.Feller, W. (1968). An Introduction to Probability Theory and its Applications, Volume 1, ThirdEdition, John Wiley and Sons, Inc.Haywood, H. (2000). BeatWebCasinos.Com: The Shrewd Player’s Guide to Internet betting,RGE Publishing, Oakland, California.Kelly, J.L. (1956). A new interpretation of information rate. Bell System Technical Journal,35, 917-926.McCune, B. (1989). Education of a superbowl Bettor, McCune superbowl Investments, Las Vegas,Nevada.Mok, L. (2001). Testing whether a betting system is profitable. MSc project, Simon FraserUniversity, Department of Statistics and Actuarial Science.Ordine, B. (2000). Super bowl Sunday means lots of action in Las Vegas. Seattle Times,January 23.Productivity Commission (1999). Australia’s betting industries (Report No 10), PC InquiryReport, December, H.S. (1998). How accurate are the posted odds?. In the column A Statistician Readsthe superbowl Pages, Chance, 10 (4), 17-21.Swartz, T.B. (1999). Nonparametric goodness-of-fit. Communications in Statistics: Theoryand Methods, 28, 2821-2841.23--------------------------------------------------------------------------------Page 24 Thisted, R.A. (1988). Elements of Statistical Computing: Numerical Computation, Chapmanand Hall, New York.Thorp, E.O. (1969). Optimal betting systems for favorable games. Review of the Interna-tional Statistical Institute, 37, 273-293.24--------------------------------------------------------------------------------Page 25 02000400060008000100000200400600Figure 1: Histogram of the final bankroll based on 1000 simulationsfinal bankrollfrequency25--------------------------------------------------------------------------------Page 26 24680204060Figure 2: Histogram of the logarithm of thefinal bankroll based on 1000 simulationslog(final bankroll)frequency26

superbowl betting superbowl betting sheets betting lines 2005 superbowl odds betting lines superbowl odds NY Jets Pittsburgh Steelers St. Louis Rams

2005-01-12T08:21:00.000-08:00

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Superbowl Betting Lines

2005-01-11T16:24:00.000-08:00

Super Bowl Betting Lines

Understanding What They Really Mean

Super Bowl betting lines may be one of the most misunderstood elements of the
betting game. An important part of Super Bowl betting is to understand the job
of the odds makers. Their job is not to pick the winners and losers for the
games. Their job is to balance the bets so that the same amount is wagered
on each side. Read on for complete details and to gain a better understanding
of how the point spread is determined.

Insider Betting Secrets Revealed:

How the Sportsbooks Determine Super Bowl Betting Lines!

Successful Super Bowl handicapping starts with throwing out those old strategies
and systems that, on the surface, seem like they should work, but in reality
never do. When most sports gamblers set out to make pro Super Bowl picks, they
call upon all manners of NFL statistics as well as their knowledge of the sport
to try to determine which team has the best chance to cover the spread. Seems
logical, right?

Wrong!

This is how most people go about handicapping games, and almost everyone loses.
To make enough winning NFL picks to beat the sportsbooks' lines over the long
run, you must begin by analyzing the line on a given game to match the perspective
of the odds makers.

This can be accomplished by looking backwards to determine why the line on
a given game involving 2 teams has been set at a particular number, adjusted
to a particular number, or maintained at a particular number. How did the line
originate? Why is the line the way it is?

By analyzing the line in reverse, you'll be able to do two important things
for increasing the success of your NFL Super Bowl betting picks:

1) You'll be able to shift your focus away from using statistics, sports knowledge,
and other unprofitable techniques to pick which team you think will cover the
point spread.

2) You'll be able to redirect your focus towards using indications in the
NFL line on a given game to identify which team the odds makers think will
cover.

Looking for indications in the sportsbooks' lines and point spreads is your
best bet for picking which team the odds makers and insiders think will cover
the spread in a given game.

The key is to understand that the sportsbooks' Super Bowl betting lines and
Super Bowl betting point spreads are the odds makers' instruments for dividing
the monetary betting action in half for a given game. In other words, the very
existence of betting lines and point spreads gives a sportsbook an element
of control over how the population as a whole decides to bet in a given game.

To start, the line on a game is not the odds makers' assessment of what the
difference in final score will be. It is their assessment of what particular
Super Bowl odds line number will draw even action from the combination of sports
gamblers.

Odds makers are masters at using Super Bowl-betting lines and point spreads
to keep betting action divided in half. By making adjustments in a given line
or point spread odds makers can sway large numbers of sports gamblers who have
not yet made a decision on which team to bet on in a game to place their bet
on the team that has "lesser action." Ask yourself, how often have
you been undecided on a game with a 3-point spread only to make your decision
after the line moved down to 2 1/2 or up to 3 1/2? The movement in the line
was the book's effort to balance betting action, and often times it can have
a direct result on your betting decision.

Of course, when Super Bowl betting lines and point spreads are moved, it can
also sway sports gamblers who have already placed a bet on a game to "put
down" additional action on that game; or even reverse their direction
and bet the other way to try to "sandwich the game" and hit both
sides. But as far as the odds makers are concerned, keeping the betting action
split at each odds line number (dollar number) or point spread is the key.
Doing so allows the sportsbooks to make their juice.

By controlling the Super Bowl betting odds and Super Bowl betting point spreads,
the bookmakers have an amazing amount of control over who bets what amount,
and at what point in time they bet that amount, thus enabling them to keep
the action divided in half. However, before odds makers can even begin to make
adjustments to a particular betting line to keep action divided in half, they
must choose a starting point or "opening line" for the game.

When creating opening lines, the incentive for a sportsbook's odds maker is
to choose Super Bowl lines that will do a fast, efficient job of splitting action
in half. Doing so guarantees that they can make the most juice.

If an opening line doesn't draw even action, the sooner the line can be adjusted
to draw even action, the more juice the sportsbook can guarantee for itself.
Simply put, the more a line needs to be adjusted to keep betting action even,
the more overall risk the sportsbook is exposed to, and the lower the profit
they stand to make. This is because the sportsbook can get stuck with uneven
betting action for any given NFL odds line or NFL point spread number, which
cuts into profits.

Therefore, you can see that the odds makers would be very interested in knowing
what specific Super Bowl betting point spread number or Super Bowl betting odds
line number would draw even betting action for a given game before having to
release the opening line for that game to the public!

But before odds makers could know what an opening line on a given game would
need to be set at to draw even action, odds makers would need to know which
team sports gamblers planned to put their money on in advance of that game!
And for a variety of different NFL odds line numbers. This brings us to the
odds makers' greatest strength when it comes to using betting lines and point
spreads to divide betting action in half.

The odds makers' greatest strength for dividing betting action in half is
based on the fact that most sports gamblers make their betting decisions by
relying on some level of information they have collected about the match up.
To cope, odds makers have developed techniques to allow them to measure the
level of information that prospective sports gamblers know about a given game,
and odds makers can look at this information before having to release the opening
line for that game.

One method odds makers use to measure the information level known to sports
gamblers about a game is to release an exclusive "unrefined" test
line for select knowledgeable and well respected gamblers or "insiders" to
bet into.

For inside gamblers who have access to these "unrefined" test lines,
it's like having access to free money. Because if the betting line ends up
being far off from the test line, the game can be sandwiched by insiders for
a potential double hit.

But it's well worth it for the odds makers to give these insiders the sandwich
opportunity. Because by allowing select insiders an exclusive opportunity to
place bets against early test lines, odds makers get a chance to determine
whether or not the insiders are betting the same side in a game, whether insiders
are split, and how strongly insiders feel about their selection in terms of
how much they are betting.

Collecting this type of information about how insiders are evaluating their
selection on a given game helps the odds makers make an assessment of what
the opening line will need to be set at in order to generate even betting action
from the combination of inside sports gamblers and general public sports gamblers
on a given game.

Of course odds makers also study general public betting patterns. But as a
rule they are more concerned with measuring insider betting interest because
insiders place bigger bets (which are harder to balance), and because insiders
can at any moment have access to more relevant information about a given game
than anyone else.

For example, insiders may know:

What types of strategies the teams plan to use

Whether or not the teams are emotionally ready to play

Who is officiating the game and what affect it can have on the teams' playing
styles

Whether the starting quarterback has a blister on his thumb

Insiders can be in possession of so much pertinent info (a.k.a. inside info/inside
reads) that never gets disseminated to the public prior to the start of the
game that most people simply wouldn't believe it. The level of information
that insiders possess is obviously the driving point for their bets. And since
odds makers are providing insiders with an early test line to bet into so that
they can measure the level of insider interest in a game before creating the
opening line for the sole purpose of balancing action, sportsbooks' Super Bowl
odds and point spreads very often reflect the level of information that is
known to insiders about a given game. This is the golden egg of NFL Super Bowl
handicapping.

So now you know the golden egg of Super Bowl betting handicapping. It's not
the teams that matter. It's not the stats that matter. It's the line on the
game that matters.

super bowl betting

2005-01-11T07:22:00.000-08:00

super bowl betting