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The Day Zero was Banned from Roulette Wheels – How times have changed!

On December 30, 1967, senior detectives from Scotland Yard sent owners of gambling clubs into a proverbial spin. Anyone operating a roulette wheel that contained the number zero would be prosecuted, they warned. From now on the whirl of numbers would all be reds and blacks – starting with the number one. This warning 50 years ago followed a judgment in the House of Lords, the country’s highest court of appeal at the time, that the green zero was illegal under gaming law. According to these so-called “law lords”, this was because the chances must be equally favourable to all players in the game.The Lords’ problem with the zero was that players betting on the ball landing on an individual number were being offered odds of 35/1 – put £1 on number 7 and if it came up you got £35 back plus your stake. But standard British roulette wheels have 37 numbers including zero, so the odds should have been 36/1. This discrepancy gave the house an edge of 2.7% – the proportion of times the ball would randomly fall into the zero slot. (Note that in the US and South America roulette wheels normally have both a zero and double zero, giving them a house edge of just over 5%). The British edge on roulette wheels was a small one, such that someone staking £10 on a spin would expect statistically to lose an average of 27 pence. But it’s a vital one. Without an edge on a game the operator would expect only to break even, and that’s before accounting for running costs. The Lords’ decision also looked like the back door to banning every other game with a house edge, such as blackjack and baccarat.

It had been illegal in the UK to organise and manage the playing of games of chance since the Gaming Act of 1845. The Betting and Gaming Act 1960 was the most substantive change to gambling regulation since then. As well as permitting the likes of betting shops and pub fruit machines, it opened the door to gambling halls – though only in a very restricted way.Designed to permit small-stakes play on bridge in members’ clubs, the act legalised gaming clubs so long as they took their money from membership fees and from charges to cover the cost of the gaming facilities. Casinos soon proliferated, however, and by the mid-1960s around a thousand had sprung up. Many introduced French-style roulette, with wheels that included a single zero, since the law had arguably not been clear as to whether the house could have an edge. The one variation thought necessary by some to comply with the legislation was that when the ball landed on zero the house and player split the stake, instead of it being kept by the house. Not only had the law liberalised gambling more than had been envisaged by the government of the day, many casinos had apparent ties to organised crime. London gaming quickly became notorious. Film star George Raft, a man once linked to such shady characters as Las Vegas mobster Benjamin “Bugsy” Siegel, was one of the more high-profile names associated with the scene.

When the Lords drew a line in the sand in 1967 by banning zeros in roulette, gaming bodies went into overdrive. One proposal designed to save the zero was to offer odds of 36/1 on individual numbers, and instead levy a playing charge on the players. The government was soon persuaded it needed to legislate again. In 1968 a new Gaming Act introduced a Gaming Board and strict measures to regulate and police gaming in Great Britain. New licensing rules, including a “fit and proper persons” test, pushed out the shady operators.

The one concession to the industry was that gaming clubs and casinos would be permitted to play roulette with a zero. Other games with a house edge, such as baccarat, blackjack and craps were also explicitly permitted. In an environment of regulated, licensed gaming establishments, the government was saying, a small edge was acceptable as a way of paying for costs and turning a profit. This came on the back of another reform that was vital for developing the industry that we see today. Following the legalisation of betting shops in 1960, the government began taxing their turnover in 1966. It was the first tax on betting since the one introduced in 1926 by then Chancellor of the Exchequer, Winston Churchill, in the days before cash bookmaking was legal and above board. “I am not looking for trouble. I am looking for revenue,” Churchill declared at the time. He didn’t see much of the latter and got a lot of the former: endless enforcement difficulties and opposition from lobby groups and in parliament. The tax was gone by 1930. Yet the 1966 tax stuck, and today the UK gambling landscape is much changed – not only because of the introduction of the National Lottery in 1994 but thanks also in large measure to two key pieces of modernising legislation. The first was the radical overhaul of betting taxation in 2001 and the other was the Gambling Act of 2005, both of which I was closely involved with as an adviser.

Instead of taxing betting turnover, now operators are taxed on their winnings (gross profits). Casinos, betting shops and online operators can advertise on radio and TV; players no longer need to be members of casinos to visit them; and online operators based overseas but active in the UK market must comply with UK licence requirements. Betting exchanges allow people to bet person-to-person, a Gambling Commission regulates betting and gaming, and electronic roulette with a zero is legally available in betting shops and casinos.

The industry as a whole has grown very significantly in size and employs a lot of people, and there is more evidence-based research and focus on the issue of gambling prevalence and problem gambling than ever before. The wheel has certainly turned a long way since that Lords decision in 1967, when the country was still trying to decide what kind of gambling system it wanted. The question that now divides opinion is how far the wheel has turned for the better.

 

References

Leighton Vaughan Williams, The Day Zero was banned from British roulette: How times have changed. Article in The Conversation. Link below:

https://theconversation.com/the-day-zero-was-banned-from-british-roulette-how-times-have-changed-89467

Basic photography – manual settings in a nutshell.

If you are content to point and shoot with an automatic camera, you will these days usually do just fine. But let’s say you are looking for a bit more, with some manual control over the settings. That’s where most amateur snappers tend to take fright. They think it’s all a bit technical. Actually, it’s not. It’s all essentially about ‘exposure’, which is basically the brightness or darkness of a photo, and this comes down to three settings – aperture, shutter speed and ISO.

The aperture is simply a set of blades which widen and narrow to control how much light enters the camera. Aperture sizes are measured by f-stops, with a high f-stop (say f/18 or f/22) corresponding to the aperture being quite small (less light entering the camera), and a low f-stop (say f/3.5 or f/5.6) meaning that the aperture is bigger (more light). The aperture also controls what is known as the depth of field, which is an indication of how much of the picture is sharp and how much is blurry. So if you want a figure in the foreground to be sharp and the background blurry, you would want a shallow depth of field (low f-stop, wide aperture). If you want the entire field sharp (for example, a mountain range) you are looking for a high f-stop (small aperture). In summary, a wide aperture (low f-stop, say f/5.6) gives you a brighter photo but a shallower depth-of-field, while a small aperture (high f-stop, say f/18) gives you a darker picture but more depth of field.

The shutter speed is simply a measure of how long the shutter is open, so a slow shutter speed (say 1/60 of a second) allows time for more light to enter, producing a brighter picture (but more blur if objects are in motion), and a fast shutter speed (say 1/800 of a second) produces a darker picture but less blur. In summary, a fast shutter speed (say 1/1000) produces a darker picture, but will be susceptible to less blur, while a slow shutter speed (say 1/80) produces a brighter picture but is susceptible to more blur.

The ISO controls the exposure a different way, thrugh software in the camera that makes it extra sensitive to light. In particular, a high ISO (say 1600) will produce a brighter picture than a low ISO (say 200). A high ISO is consistent with more digital noise in the picture, however, which tends to make the photo look a bit more grainy. In summary, you would select a higher ISO for a brighter photo, but that opens you up to a ‘noisier’ (more grainy) picture, while a lower ISO will give you a darker (but less grainy) picture.

The art now is in combining these settings to give the best overall effect. For example, say you want to take a photo with some movement in it, so you decide to select a fast shutter speed (say 1/800). But the picture comes out darker than you’d like as a result. So you try to compensate by opening up the aperture (to say f/3.5), although this reduces the depth-of-field, blurring the background. Still, the blur doesn’t concern you too much as it’s what’s in the foreground that is the subject of the picture. It’s still a bit darker than you’d ideally like, though. Finally, you turn to the ISO setting and increase that to brighten the picture, while being careful to balance this with your desire to avoid too much digital noise.

By manually and independently setting the aperture, the shutter speed and the ISO, you now have the picture pretty much as close as possible to how you wish it to come out. The automatic mode will often do a good enough job, but using the manual settings allows you to have that bit more control over the final product. You can also use ‘aperture priority’ mode, where you set the aperture and the camera automatically sets the shutter speed. Or else use ‘shutter priority’ mode, where you set the shutter speed and the camera sorts out the f-stop.

That’s photography using manual settings in a nutshell. I hope it’s been of some help.

Election 2017: How well did the pollsters and pundits do?

When Theresa May announced on April 18 that she would call a snap general election, most commentators viewed the precise outcome of the vote as little more than a formality. The Conservatives were sailing more than 20% ahead of the Labour party in a number of opinion polls, and most expected them to be swept back into power with a hefty majority.
Even after a campaign blighted by manifesto problems and two terrorist attacks, the Conservatives were by election day still comfortably ahead in most polls and in the betting markets. According to the spread betting markets, they were heading for an overall majority north of 70 seats, while a number of forecasting methodologies projected that Jeremy Corbyn’s Labour could end up with fewer than 210.

In particular, an analysis of the favourite in each of the seats traded on the Betfair market gave the Tories 366 seats and Labour 208. The Predictwise betting aggregation site gave the Conservatives an 81% chance of securing an overall majority of seats, in line with the large sums of money trading on the Betfair exchange.

The PredictIt prediction market, meanwhile, estimated just a 15% chance that the Tories would secure 329 or fewer seats in the House of Commons (with 326 technically required for a majority), while the Oddschecker odds comparison site rated a “hung parliament” result an 11/2 chance (an implied probability of 15.4%). Only the Almanis crowd forecasting platform expressed any real doubt, putting the chance of a Conservative overall majority at a relatively paltry 62%.
In reality, the Conservative party lost more than a dozen seats net, ending up with 318 – eight short of a majority. Labour secured 262 seats, the Scottish National party 35, and the Liberal Democrats 12. Their projected vote shares are 42.4%, 40%, 3% and 7.9% respectively.
So did the opinion polls do any better than the betting markets? With the odd exception, no.

In their final published polls, ICM put the Tories on 46%, up 12% on Labour. ComRes predicted the Tories would score 44% with a 10-point lead. BMG Research was even further out, putting the Conservatives on 46% and a full 13% clear of Labour. YouGov put the Tories seven points clear of Labour (though their constituency-level model did a lot better), as did Opinium; Ipsos MORI and Panelbase had them eight points clear on 44%.

Other polls were at least in the ballpark. Kantar Public put the Tories 5% ahead of Labour, and SurveyMonkey (for the Sun) called the gap at 4%. Survation, the firm closest to the final result in their unpublished 2015 poll, this time put the Conservatives on 42% and Labour on 40%, very close to the actual result. Qriously (for Wired)was the only pollster to put Labour ahead, by three points.

According to the Chris Hanretty 2017 UK Parliamentary Election Forecast polling model, the Conservatives were heading for 366 seats, Labour 207, and the Liberal Democrats seven. Allowing for statistical uncertainty, the projection was of an “almost certain” overall majority for the Conservatives. The probability of a hung parliament was put at just 3%. All very bad misses.

Many others were wrong, too. The 2017 General Election Combined Forecast, which aggregates betting markets and polling models, forecast a Conservative majority of 66 seats. Other “expert” forecasts came from Britain Elects (Tories 356 seats, Labour 219 seats), Ashcroft (363, 217), Electoral Calculus (358, 218), Matt Singh (374, 207), Nigel Marriott (375, 202), Election Data (387, 186), Michael Thrasher (349, 215), Iain Dale (392, 163) and Andreas Murr and his colleagues (361, 236).
So what went wrong?

In the wake of the 2015 election, the Brexit referendum and Donald Trump’s victory, forecasters are getting used to fielding that question. But the answer isn’t that difficult: the problem is in quantifying the key factor in the common forecasting meltdown in advance. That factor is turnout, and notably relative turnout by different demographics.

In the Brexit referendum and 2016 US presidential election, turnout by poorer and less educated voters, especially outside urban areas, hit unprecedentedly high levels, as people who had never voted before (and may never vote again) came out in droves. In both cases, forecasters’ pre-vote turnout models had predicted that these voters wouldn’t show up in nearly the numbers they did.
In the 2017 election, it was turnout among the young in particular that rocketed. This time the factor was widely expected to matter, and indeed get-out-the-vote campaigns aimed at the young were based on it. But most polling models failed to properly account for it, and that meant their predictions were wrong.

Polling is a moving target, and the spoils go to those who are most adept at taking and changing aim. So will the lesson be learned for next time? Possibly. But next time, under-25s might not turn out in anything like the same numbers – or a different demographic altogether might surprise everyone. We might not have long to wait to find out.

 

References:

Leighton Vaughan Williams. Report card: How well did UK election forecasters perform this time?  Article in The Conversation. Link below:

https://theconversation.com/report-card-how-well-did-uk-election-forecasters-perform-this-time-79237

Managing and beating the line in betting markets: a primer

The ‘over-round’

In a two-horse race, if both horses have an equal chance of winning (objectively), and both are offered at evens, then the expected profit of the market-maker (and of the bettor) is zero, ignoring operating, information and transactions costs.

In a two-horse race, if both are offered at evens (regardless of the respective probabilities of victory of the two horses), then it would require a stake of £x (split equally between the two horses) to be sure of being returned that £x (a net profit of zero) whichever horse wins.  In this circumstance, the over-round of the bookmaker is said to be 100%, i.e. a notional profit margin of zero.

In practice, even if the notional profit margin is zero, the bookmaker is at a disadvantage if the horses are not equally matched, as a sophisticated bettor can take advantage by staking more than half on the horse with the greater chance of winning.

More generally, the over-round does not yield an accurate indicator of the bookmaker’s profit margin if bettors do not stake across all options in such a way as to ensure that their total stake of £x yields a certain return of £x, factored by the over-round.

For example, if the over-round is 120%, the notional margin to the bookmaker is 20%, and put simply bettors would have to stake £120 to ensure a return of £100.  Say, for instance, that both horses in a 2-horse race are being offered at 4 to 6.  Then the bettor would need to stake £60 on each (£120 in total) to be guaranteed a return of £100 (£40 plus the £60 stake returned) whichever horse won.  In such circumstances, the bookmaker is guaranteed at 20% profit, regardless of the outcome.

If one horse is offered at 4 to 6 and the other at 6 to 4, the bettor can guarantee a zero profit (and loss) by staking £60 at 4 to 6 and £40 at 6 to 4.  That way, a £100 return is guaranteed for a total stake of £100, regardless of the outcome.  Again, if the horse offered at 4 to 6 is actually a 4 to 7 chance, and bettors stake exclusively on this horse, their expected return is positive (although there is now a risk of losing the entire stake), and the expected return of the bookmaker is negative (though the actual return may be positive).

To summarize, the notional margin, as implied in the over-round, formally equates to the actual margin only if bettors stake proportionately more on the outcome offered at shorter odds.

 Creating an over-round

Take as an example the following odds offered about a binary proposition to players, where the odds-maker believes that the objective probability of X winning is 1 in 5 (0.2) and of Y winning is 4 in 5 (0.8).

Assuming an over-round of 100% (i.e. margin of zero), the odds-setter (taken here to be a bookmaker) would set the following odds:

Odds about X = 5.0 (4 to 1): Odds about Y = 1.25 (1 to 4).

Assume now that the odds-maker wishes to create an over-round of 108%.

In each case the odds offered should be cut, by 8 per cent in each case. So 8% of 5.0 = 0.4. Deducting 0.4 from 5.0 gives 4.6. 8% of 1.25 = 0.1. Deducting 0.1 from 1.25 gives 1.15.

So in the particular example, the odds offered would be as follows:

Odds about X = 4.6; Odds about Y = 1.15.

Assuming an equal amount bet (say £1,000) bet on both sides of the proposition (i.e. a total of £2,000, consisting of perhaps 200 people betting £10 each), the profit (loss) to the bookmaker would vary depending on the outcome.

If horse X wins, the bookmaker will pay out:

4.6x £1,000 = £4,600

Total amount staked (on X and Y) = £2,000.

Net profit to bookmaker if horse X wins = £2,000 – £4,600 = – £2,600

So if horse X wins, bookmaker loses £2,600.

If horse Y wins, the bookmaker will pay out:

1.15 x £1,000 = £1,150

Total amount staked (on X and Y) = £2,000

Net profit to bookmaker if horse Y wins = £2,000 – £1,150 = £850

Expected value of profit = expected value of profit from X + expected value of profit from Y = (-£2,600) x 0.2 + (£850) x 0.8 = -£520 + £680 = £160.

This is assuming that the implied probabilities in the odds are the correct probabilities, i.e. odds of 4/1 = probability of 1/5 (0.2); odds of 1/4 = probability of 4/5 (0.8).

Note also that £160 = 8% of total stake on X and Y (£2,000).

This all assumes, as observed, that the objective probabilities are correctly observed and that the amount staked on both sides of the proposition are equal.

Even if we assume that the objective probabilities are correctly observed then there is still substantial volatility of outcome (i.e. risk) for the bookmaker. If the objective probability is incorrectly observed, however, the outcome for the bookmaker may be worse, i.e. a systematic loss.

For example, assume the probability of horse X winning is actually 25%; assume probability of horse Y winning is 75%.

At the given odds levels, and assuming equal stakes across both propositions, we derive the following.

As above, if horse X wins, the bookmaker will pay out, as before:

4.6 x £1,000 = £4,600

Total amount staked (on X and Y) = £2,000.

Net profit to bookmaker if horse X wins = £2,000 – £4,600 = – £2,600

So if horse X wins, bookmaker loses £2,600.

If horse Y wins, the bookmaker will pay out, as before:

1.15 x £1,000 = £1,150

Total amount staked (on X and Y) = £2,000

Net profit to bookmaker if horse Y wins = £2,000 – £1,150 = £850

Expected value of profit = expected value of profit from X + expected value of profit from Y = (-£2,600) x 0.25 + (£850) x 0.75 = -£650 + £637.50 = -£12.50, i.e. a loss of £12.50.

Insofar as the objective probability of horse X winning is greater than 20%, the expected profit to the bookmaker will decline. At 24.65%, the profit (rounded to the nearest pound) can be shown to be equal to zero, and above that to turn negative.

Assume objective probability of horse X winning = 0.2465; objective probability of horse Y winning = 0.753.

Then, expected value of profit = expected value of profit from X + expected value of profit from Y = (-£2,600) x 0.2465 + (£850) x 0.7535 = -£640 + £640 = 0

To the extent that the objective probabilities are inaccurately estimated, therefore there is significant potential from the bookmaker’s point of view for a negative expected (as well as actual) profit.

Using the probabilities from the original example, the staking pattern from the bettor’s point of view that will lead to a unique expected loss (8% in this case) across both betting propositions is to bet more on the favourite and less on the longshot, in this case £1,600 and £400 respectively.

This leads to the following outcomes:

Profit to a £400 bet on horse X (if it wins) at 4.60 = £1,840

Profit to a £1,600 on horse Y (if it wins) at 1.15 = £1,840

Guaranteed profit by staking these sums on each horse from the bettor’s point of view = – £160, i.e. a net loss of 8% of total stake.

Insofar as bettors can be induced to bet in these proportions, the operator is guaranteed a profit regardless of the outcome. If the average bet size is the same for bets made on either side, then we need four times as many bettors on the favourite as the longshot to achieve this. Otherwise, the same outcome can be achieved if those who are backing the favourite bet four times as much in total as those backing the longshot.

Another way to manage risk in the face of unbalanced staking patterns is to move the odds so as to limit the maximum loss.

In order to reduce the maximum downside (i.e. when X wins) the bookmaker may move the odds in such a way as to attract money on one horse and away from the other horse. To do this, the odds about one horse may be lengthened and those about the other horse shortened before a negative downside is occurred to ether outcome. While such a strategy may reduce the exposure of the operator, the price may be paid in reduced profits.

Ultimately, line management from the operator’s point of view is about balancing risk and return, while maintaining an edge in favour of the ‘house’. From the bettor’s point of view, it is about exploiting opportunities which might arise where one (or more) of the odds making up that over-round are mispriced in the bettor’s favour, a possibility which can arise even when the over-round favours the ‘house.’

 

 

Three strikes of the clock: Betting on the man to be Pope since 1503.

The history of forecasting election outcomes for betting purposes is well-documented for open elections, such as presidential elections in the US, and for longer, though in less detail for the closed elections of the Pope.

In the former, it has been traced, according to contemporaries, to the election of George Washington and has existed in organized markets since the 1860s.

The first recorded example of betting on a papal election, however, can be traced much further back, to the papal conclave of September, 1503, at which time it was considered already an old practice.

The brokers in the Roman banking houses (sensali) who made books and offered odds on who would be elected, made Cardinal Francesco Piccolomini the 100/30 favourite, ahead of Cardinals Guiliano della Rovere (100/15) and Georges d’Amboise (the favourite if judged by the vocal support of the street crowds) at 100/13.

Although Piccolomini is thought to have trailed in the first round of voting with 4 votes to 13 for d’Amboise and 15 for della Rovere, Piccolomini apparently benefited from a switch of votes from d’Amboise to himself in subsequent voting, and duly became Pope Pius III.

The bookmakers were proved right.

The next conclave for which we have the betting odds is that of December, 1521, in which odds were offered on no fewer than twenty cardinals.

Giulio de’Medici, the cousin of Leo X, was the betting favourite, at 100 to 25 (4/1), followed closely by Cardinal Alessandro Farnese at 100/20 (5/1), whose odds shortened to 100 to 40 (5/2) after a Roman mob plundered his house.

Though Farnese at one point came close to being elected Pope, he could not reach the required two-thirds of the vote, and ultimately the cardinals looked outside of the conclave, electing Adrian of Utrecht as Pope Adrian VI.

In the papal election of 1549-50, Cardinal Gianmaria del Monte (who was eventually elected Julius III) had opened in the betting as the 5/1 (against) favourite, but within three days Cardinal Reginald Pole had been established at odds of 4/1. On December 5, as balloting began, Pole was clear favourite at 100/95.

On that day, he received 26 of the 28 votes that would have given him the two-thirds majority required to elect him Pontiff. Although on the point of being made Pope by acclamation, Pole insisted on waiting until he won the formal two-thirds majority.

By the time that four additional French cardinals, opposed to Pole, arrived December 11, however, he was trading at 5/2, and a month later he was being offered at odds of 100/16. His chance had gone.

In the papal conclave of April, 1555, Gian Pietro Carafa stood a good chance of being elected pope, ranking among the top three papabile in the first ballot of the conclave. It is reported that brokers intentionally “spread the rumour that Naples [i.e. Carafa] had died”, in order to attract money on the other candidates. Carafa went on to be elected Pope.

The first 1590 conclave, in September, is the earliest in which reports of insider trading emerged, when two of the key influencers of votes in the conclave, Cardinals Montalto and Sforza secretly agreed to join forces in support of Niccolo Sfondrato.

It is reported that both made fortunes betting on him, at odds of 10/1 the day before he was elected as Pope Urban VII.

As the conclave opened, he had been trading at 100/11, compared to Giambattista Castagna, who was offered at 100/22.

During the second conclave of 1590, Cardinal Gabriele Paleotti at one point increased to an implied probability of 70 per cent in the betting: “Wednesday at the twenty-second hour rumour began to hold Paleotti as pope, and it went on increasing so that at the end of the morning, he had risen to 70 in the wagering.”  The odds were not reflected in the outcome. Giovanni Battista Castagna was elected Pope Urban VII.

In 1603, despite a papal bull ‘Cogit Nos’, by Pope Gregory XIV, issued on March 21, 1591, which imposed a penalty of excommunication for wagering on papal or cardinal elections, or length of the papal reign, 21 cardinals were quoted odds of winning by the bookmakers.

The favourite was Cesare Baronius, at 10/1. The closest he came to election, however, was gaining the support of 32 cardinal electors, nine short of the required tally. Ultimately, Alessandro de’Medici became Pope Leo XI.

This ban on papal betting was abrogated in 1918 by Pope Benedict XV’s reforms.

In relation to the papal conclave of 1878, a New York Times correspondent wrote that: “The death and advents of the Popes has always given rise to an excessive amount of gambling in the lottery, and today the people of Italy are in a state of excitement that is indescribable.” There is no available known record, however, of the odds offered on that election. Similarly, the papal conclaves of 1903 and 1922 also attracted a great deal of wagering interest, which was reported widely in the international press, though no known record remains of the odds offered.

Bookmaker odds in Milan are available, however, for the 1958 conclave, which show Cardinal Angelo Roncalli the 2/1 favourite, followed by Cardinals Agagianian and Ottaviani at 3/1, then Stefan Wyszynski and Giuseppe Siri at 4/ 1. The odds were justified when Cardinal Roncalli was elected Pope John XXIII.

For the first conclave of 1978, bookmakers in London were offering odds of 5/2 about Cardinal Sergio Pignedoli, 7/2 about Sebastian Baggio and Ugo Poletti and 4/1 about Carlo Benelli. The best odds about a non-Italian were 8/1 about Johannes Willebrands. Of these only Pignedoli showed any strength in the voting, unconfirmed reports of the voting indicating that he obtained about 18 votes in the first ballot, compared to about 23 for Albino Luciani and 25 for Giuseppe Siri. Ultimately, Cardinal Luciani was elected Pope John Paul I.

For the second conclave of 1978, following the death of Pope John Paul I, the Associated Press noted that:

“Once again, there is no odds-on favourite to be elected as the new pope of the Roman Catholic Church … mentioned most often are Corradi Ursi, Salvatore Pappalardo, Ugo Poletti, Giuseppe Siri, Giovanni Colombo, Giovanni Benelli and Antonio Poma… Non-Italian front-runners include Argentinian Eduardo Pironio, 57, and Dutchman Johannes Willebrands, 68.”

Cardinal Carol Wojtyla, archbishop of Krakow, was elected Pope John Paul II, after the eighth ballot.

In 2005, Cardinal Joseph Ratzinger opened in the betting at 12/1 with one major bookmaker.

At that point, another leading bookmaker made Cardinal Arinze favourite, with Archbishop Tettamanzi, Cardinal Ratzinger and Cardinal Hummes as the next in the betting.

After three ballots, Ratzinger was favourite on two out of the three online betting boards monitored by CNN, his shortest odds being 5/2. He was at that point in the conclave being offered at between 9/2 favourite and 11/2 second favourite.

By the last day of the conclave, Cardinal Ratzinger had shortened to a clear 3/1 favourite, closely followed by Carlo Martini at 100/30 and Jean-Marie Lustiger at 7/2.

By that point, Francis Arinze had dropped back to 8/1, the same price as Claudio Hummes (who was now in the top six in all three lists). He had opened at 12/1. At the same time, Jorge Bergoglio was trading at 12/1 and Angelo Scola at 25/1.

According to a newspaper report, “among those speculating about who the next pope will be, the big money – literally is on Joseph Ratzinger, who delivered a stirring homily at the late Pope’s funeral … As of yesterday, most gambling sites gave Ratzinger … the best odds, with a host of second-tier candidates not far behind.”

Side bets were available on the name of the next pope.

Benedict was the 3 to 1 favourite. John Paul was offered at 7 to 2. Pius at 6 to 1. Peter at 8 to 1. John at 10 to 1.

Joseph Ratzinger was elected Benedict XVI.

The first show of odds following the 2005 conclave for the successor to Benedict was: Angelo Scola 6-1; Christoph Schonborn 7-1; Oscar Maradiga 7-1; Jorge Bergoglio 9-1; Francis Arinze 10-1; Dionigi Tettamanzi 25-1.

In 2013, a survey of the so-called experts made Angelo Scola favourite, although the expert assessment and the betting odds diverged to some degree after that. A survey of Vatican watchers by YouTrend.It listed Timothy Dolan of the United States as the second most likely pope, followed by Cardinals Marc Ouellet, Odilo Scherer and Thomas O’Malley. Luis Tagle of the Phillipines was sixth was ranked sixth. Some of the bookmakers’ favourites, notably Cardinals Turkson and Bertone, did not appear on this experts’ list.

The implied win probabilities in the Oddschecker display of best bookmaker odds on March 3rd were as follows: Scola, 23%; Turkson, 22%; Bertone, 16%; Ouellet, 12%; Bagnasco, 10%; Ravasi, 8%; Sandri, 7%; Erdo, 7%; Scherer, 6%; Schonborn, 6%; Maradiaga, 5%; Arinze, 5%; O’Malley, 4%; Tagle, 4%; Bergoglio, 4%; Dolan, 3%; Hummes, 3%; Grocholewski, 3%; Dziwisz, 3%; Carrera, 2%; Piacenza, 2%; Marini, 2%; Rylko, 2%; Sarah, 2%; Martino 2%. Note that the probabilities add up to more than 100 due to rounding and the in-built margin in the bookmakers’ odds.

A Washington Post analysis, published on March 11th, calculated the implied probabilities of the ‘frontrunners’ based on betting sites including the betting exchange, Betfair.

The results were: Scola, 19.9%; Scherer, 11.9%, Turkson, 9.7%; Bertone, 8.3%; Ouellet, 5%; Erdo, 4.9%; O’Malley, 3.8%; Schonborn, 3.7%; Ravasi, 3.4%; Tagle, 2.6%; Sandri, 2.5%; Dolan, 2.3%; Bagnasco, 2.3%.

On the morning of the final ballot, on March 13th, 2013, the Guardian newspaper Liveblog reported that: “Ladbrokes has Scola at 9/4, Scherer at 3/1 and Turkson at 6/1. Paddy Power has Scola at 11/4, Scherer at 7/2 and Turkson at 9/2.”

A post by Vatican Insider journalist Andrea Tornielli was also published ahead of the final ballot, stating that “The first casting of ballots, which will serve as a primary, will see votes merge towards the Archbishop of Milan, Angelo Scola, as well as the Canadian Marc Ouellet and the Brazilian Odilo Pedro Scherer. Some votes might also go to the Argentinian Jorge Mario Bergoglio and to other cardinals mentioned during the past few hours, such as the Sinhalese Malcolm Ranjith, the American Timothy Dolan and others. It remains to be seen if, among these nominations, there will be one able to garner at least two-thirds of the votes.”

Despite this level of detail, the same article declared that “From the moment cardinal electors entered the Santa Marta residence, they have not had any contact with the outside world and have to use protected paths that are constantly under surveillance, to get about. Every space they enter is monitored and blocked off from all forms of communication… All those who have to access the Holy See during the Conclave are bound to the strictest confidentiality.”

Then came the three strikes of the clock.

The first strike of the clock was a post by Vatican Insider journalist Giacomo Galeazzi, time-stamped on Vatican Insider Twitter at 8.24am that morning. It noted that there were only five candidates left in the running: Scola, Scherer, Bergoglio, Ouellet, Dolan.

The second strike of the clock was  a link to a post by Vatican Insider journalist Giacomo Galeazzi, time-stamped on Vatican Insider Twitter at 11.12am: “After the first negative scrutinies, lunch breaks and dinners in Santa Marta House, the cardinals’ residence during the conclave, become opportunities for informal discussions on disregarding candidates with weaker consensuses, to the advantage of the papabile who have obtained more votes so far (Scola, Bergoglio, Ouellet).”

So, by 11.12 am, according to Galeazzi, it was effectively down to three – Cardinals Scola, Bergoglio and Ouellet.

The third strike of the clock came at 11.57am, when the Guardian Liveblog reported that: “La Stampa’s Vatican Insider claims that most of the votes have been going to Cardinals Scola, Bergoglio and Ouellet. This morning it was claiming most of them were going to Scola, Scherer, Bergoglio, Ouellet and Dolan. But it’s hard to know where they can be getting this information from.”

So what was actually going on while the clock was striking once, twice, thrice? A post-election report, published in La Repubblica, claims that Scola received approximately 35 votes in the first vote, to 20 for Bergoglio and 15 for Ouellet. National Catholic Reporter also reports that there was some support for Scherer: “After two rounds of voting Wednesday morning, it had become clear that neither Scola nor Scherer were likely to cross the finish line and gain the 77 votes needed for election … The fourth ballot, the first of Wednesday afternoon, saw Bergoglio separate himself from the pack.”

So it appears that Galeazzi’s tweeted reports conformed broadly to what we now understand to have been the case. Somehow it seems he knew!!!

But the markets failed to respond except for a flicker towards Bergoglio on the exchanges after the Guardian Liveblog posted the niche Galeazzi tweets to their wider audience.

So, either the new information was not (for good or bad reason) sufficiently believed. Or it was for the most part overlooked by those trading on the exchanges. Or the market was not sufficiently liquid to make it possible to earn a significant return, so most sophisticated traders did not bother to participate.

Whatever the reason, the betting markets did not perform as well as might have been expected in responding to new public information, which subsequently turned out to be accurate, unless the reports were accurate by sheer chance and deserved to be disbelieved. After all, it was ‘Vatican Insider’ itself that declared how “All those who have to access the Holy See during the Conclave are bound to the strictest confidentiality.”

This cannot be explained either in terms of the fog of conflicting signals as there were no other credible sources issuing conflicting information.

So the ‘Galeazzi anomaly’, as I term it, turns into a mystery, partly because he seemed to know what he shouldn’t have known, but also because hardly anyone seemed to believe him. Giacomo Galeazzi shouted wolf, and there was a wolf! It is a lesson that some, in an efficient market, will now have learned.

 

Further Reading.

Vaughan Williams, L. and Paton, D., (2015), Forecasting the Outcome of Closed-Door Decisions: Evidence from 500 Years of Betting on Papal Conclaves, Journal of Forecasting, 34 (5), August, 391-404.

 

 

 

The Madness of Crowds, Polls and Experts

 

Since records began in 1868, no clear favourite for the White House has lost, except in the case of the 1948 election, when 8 to 1 longshot Harry Truman defeated his Republican rival, Thomas Dewey.

We can now add 2016 to that list, thanks to Donald Trump, who has beaten 1 to 5 favourite, Hillary Clinton, to take the presidency. In so doing, he also defied the polls, the experts and the wisdom of crowds.

I have been tracking various forecasting methodologies and prognosticators over the past few months, right up to election day, and can confirm that the rout of conventional wisdom was almost total.

Odds on

On the morning of the election, the best price available about Hillary Clinton was 2 to 7, equal to an implied win probability of about 78%. The spread betting markets made her a little over an 80% favourite, and gave her a head start over Trump of more than 80 electoral votes. The PredictIt prediction market assigned her a 79% chance of victory, and estimated her likely advantage as 323 electoral votes to 215 for Trump. Meanwhile, the Predictwise crowd wisdom platform assessed her chance of winning at a solid 89%, compared to 75% by the Hypermind prediction site.

The polling aggregation services fared no better. The RealClearPolitics and HuffPost Pollster polling averages gave Hillary Clinton a lead of between 3% and 6%. The FiveThirtyEight platform, which removes bias from polls based on their previous performance, gave her a popular vote lead on the day of 3.6% and an electoral vote advantage of 67 over Trump. Her chance of winning was assessed as 71.9% based on this polling.

Perhaps the biggest failure of the night, however, was Sam Wang’s Princeton Election Consortium, which gave Clinton more than a 99% chance of victory. Still, it must be said that his topline figures (an electoral college advantage of 307 to 231 for Clinton, and 2.5% in the popular vote) were less far off than a number of the other forecasting methodologies.

The New York Times Upshot elections model, which bases its estimates on state and national polls, gave Clinton a 84% chance of victory, which they helpfully compared to the chance of an NFL kicker making a 38-yard field goal. About 16% of the time they miss. That was the same chance as Hillary Clinton losing, they suggested.

Talking heads

Expert opinion was also woefully off. One of the most high-profile providers of expert political opinion is the Sabato Crystal Ball, run by Larry Sabato of the University of Virginia’s Center for Politics. This service has a very good track record. Yet, in line with the polls and the markets, the Crystal Ball got it badly wrong this time. Its final prediction was a win for Hillary Clinton by 322 electoral votes to 216.

It is the PollyVote election forecasting service which provides perhaps the most broad-based expert opinion survey, however, calling on its own panel of political experts to periodically update its forecast of the likely two-way vote share of the main candidates. The final expert panel survey, conducted on the eve of the election, put Clinton 4.4% up over Trump (52.2% to 47.8%).

In attempting to estimate the final vote share tallies of the candidates, PollyVote provides not just the estimates of experts, but also evidence gathered from a range of other methodologies, including prediction markets, poll aggregators, econometric models, citizen forecasts and index models. The idea is that aggregating and combining the wisdom of each and taking an average should provide a better estimate than any in isolation. It is a methodology which has served well over the past three election cycles.

This time the methodology broke down as badly as any of the main forecasting methodologies in isolation. Taking them in turn, the prediction market indicator (based on the trading in the Iowa electronic markets) gave Hillary Clinton a lead of 54.6% to 45.4%. Using data from RealClearPolitics and HuffPost Pollster to construct its poll aggregation metric, it gave the lead to Clinton by 52% to 48%.

PollyVote also highlights the various econometric forecasting models available, which typically use variables such as growth, unemployment, incumbency, and so on, to provide an aggregated estimate. That estimate was, this time, quite successful, giving Clinton the advantage in the popular vote of 50.2% to 49.8%. Winning the popular vote is, however, not the same thing as winning the electoral college, as Democrats in particular have learned in recent years.

The final two methodologies used to make up the PollyVote forecast are index models, which use information about the candidates, and citizen forecasts, which ask people whom they expect to win. The index models this time gave Clinton the edge over trump by 53.5% to 46.5%, and the citizen forecasts by 52.2% to 47.8%. Combining all these methodologies together produced an estimated advantage for Clinton over Trump of 52.5% to 47.5%.

The bottom line, therefore, is that most of the tried and tested forecasting methodologies failed this time. Election 2016 truly demonstrated, on a grand scale, the madness of crowds, polls and experts.

Further Reading and Links

https://selectnetworks.net

Donald Trump has won the battle: will he win the war?

Donald Trump has been declared the Republican Party’s nominee for the presidency of the United States – and for once, not only by himself. This victory defies all the laws of political gravity.

The traditional Republican way is to elect the establishment’s chosen candidate, generally someone who has served the party faithfully and well – and preferably someone plausibly electable against the Democrats’ standard bearer. The nominee is expected to stick to mainstream conservative principles and to be broadly acceptable to those pulling the strings at Fox News.

Trump fails all these tests. And with his signature blend of populism, provocation and spectacle, he has driven the party into a schism, pitting conservative against conservative.

In the immediate wake of the Indiana result the audience of Fox news was treated to a downcast debate between the network’s two principal conservative voices, Bill O’Reilly and Charles Krauthammer. While O’Reilly tried to defend Trump as a misunderstood populist hero, Krauthammer declared himself implacably opposed to a man he declared was not a true conservative and who could not be trusted to defend conservative values, far less be entrusted with the nuclear codes.
The party shows no sign of being ready to unite behind Trump. The Hill, an influential political newspaper published in Washington DC, has even provided a list of Republicans who have declared on the record that they simply will not back him. The list is long, and includes some very influential conservative names.

These horrified “NeverTrumpers”, who’ve been pushing their own #NeverTrump hashtag, are all too aware that nominating “The Donald” would not only betray the party’s core principles, but possibly doom the GOP to electoral catastrophe. Disgusted conservatives might well decline to vote at all. That would contaminate Republican candidates across the country; the party would probably lose control of the Senate, and perhaps even of the House of Representatives.

So what exactly are Trump’s chances against Hillary Clinton? The Real Clear Politics average of the most recent half dozen polls has Clinton leading Trump by an average of 6.5% in a hypothetical (and now very likely) match-up.

Take out the poll by the Rasmussen firm, which has a very chequered history – not least projecting a Mitt Romney victory on the eve of the 2012 election – and Clinton leads by 8.2%.

The respected Sabato Crystal Ball project at the University of Virginia’s Center for Politics offers another perspective. This uses expert judgement on a state-by-state level to assess the likely number of electoral votes that would be won in a match-up between Clinton and Trump.

The best estimate offered, as of today, is a projected 347 votes for Hillary Clinton in the electoral college, with 191 going to Donald Trump. A total of 270 votes is required to win the presidency. By way of comparison, Barack Obama won 332 electoral votes in 2012 to 206 for Mitt Romney.

The betting and prediction markets tell a broadly similar tale.

Finally, let’s look to the PollyVote project, which combines evidence derived from polls, expert judgement and prediction markets, plus a few other indicators, to provide an overall forecast of the likely outcome in November. As of today, the PollyVote predicts the Democrats to obtain 53.3% of the two-party popular vote, compared to 46.7% for the Republicans.

Trump stands today at the top of the Republican tree. He has won the battle. He will find it much harder to win the war.

From the 1503 papal conclave to the 2015 Nobels: Forecasting Closed Door Decisions

When the Belarusian writer Svetlana Alexievich won the 2015 Nobel Prize for Literature, it was not unexpected. She was not only the clear favourite with the bookmakers but had traded as one of the leaders in the betting in the previous two years.

While firms lay odds on the literature and peace prizes, there are no betting lines available for the Nobel Prizes in physics, chemistry and medicine. Instead, there is an organised platform which seeks to predict winners based on research citations.

Betting: from Hollywood to the Vatican

2015 was a very good year for favourites in awards contests. The favourite in the betting won almost every single one of the 24 Oscar categories at the Academy Awards. This domination of the favourites has been documented in politics for nearly 150 years, ever since hot favourite Ulysses S Grant strolled to the US presidency in 1868. The favourite in the betting won almost every single presidential election held since, up until 2016.

But the Nobel Prize deliberations are quite different from a political election or even a Hollywood awards ceremony. Instead, they are a little more like a papal conclave, where the deliberations are secretive and there is no defined shortlist of nominees. Betting on papal conclaves has been formally recorded from as early as 1503. In that year, the brokers in the Roman banking houses who offered odds on who would be elected Pope made Cardinal Francesco Piccolomini the clear favourite. It was no surprise, therefore, when he went on to become Pope Pius III.

Since then the betting markets have had a mixed record of success in predicting the winner. For example, Cardinal Ratzinger was a warm favourite to be elected pope in 2005, and duly became Pope Benedict. The election of Cardinal Bergoglio as Pope Francis, on the other hand, came as more of a surprise to the markets.
Betting on processes that take place behind closed doors also happens outside the church. In 2009, crowdsourced fantasy league (or “prediction market”) FantasySCOTUS.net launched an attempt to peer behind the doors of the US Supreme Court, predicting its deliberations – a market still going strong today. The Supreme Court might be particularly suitable for a prediction market, in that not only is there a relatively small number of decision makers, but the universe of possible outcomes is also very limited. Predicting the Nobel Prize announcements might be expected to be somewhat more difficult.

So how do the betting companies compile their odds when it comes to the Nobels? Ladbrokes has said that, in the absence of information, the best way is consulting literary contacts and following relevant online discussions. This is despite the fact that it only takes about ₤50,000 in bets on the Nobel in literature, compared with a couple of million for a big football match.

Patchy record

How well have the markets performed to date? For the Sveriges Riksbank Prize in Economic Sciences, established in 1968 by Sweden’s central bank and considered an unofficial “Nobel”, the most ironic failure of a sort came in 2009, when the betting market offered by Ladbrokes had Eugene Fama, a pioneering exponent of the theory of efficient markets, as the solid 2 to 1 favourite. Assuming the market was truly efficient in respect of all relevant information, we might have expected him to be well up there among the top contenders. But the prize was shared by Elinor Ostrom and Oliver Williamson, both of whom were trading as 50 to 1 longshots before the announcement. Fama did go on to share the Nobel Prize four years later.

On the other hand, Harvard University had already set up its own dedicated economics prize prediction market, which did much better than Ladbrokes by making Oliver Williamson one of the favourites. In 2010, Peter Diamond shared the prize after having been listed as one of the favourites by Harvard.

Of the others in the top eight in 2010, Jean Tirole went on to win in 2014, Robert Shiller and Lars Peter Hansen in 2013. Thomas Sargent and Christopher Sims, who shared the 2011 prize, were among the favourites in the 2008 Harvard prediction market, which has since closed down.

Most of the market based predictions, however, focus on the Nobel Prizes for Literature and Peace. In 2014, French writer Patrick Modiano won the Literature Prize. Before the announcement, Modiano was trading as a reasonably well fancied joint fourth favourite. The previous year, Canadian Alice Munro was heavily backed into second favourite before claiming the prize. In 2011, Tomas Transtomer won the Literature Prize having been clear favourite in 2010.

The peace prize, which is awarded by a committee of five people who are chosen by the parliament of Norway, is slightly more complicated as awards are sometimes given to organisations rather than individuals. This also makes it less satisfying for potential market players. Still, the 2014 Nobel Peace prize was shared by Malala Yousafzai and Kailash Satyarthi. Malala had actually been backed to win in the previous year.
Malala Yousafzai was the bookmaker’s favourite the year before she actually won. There’s a pattern there.

The physics, chemistry and medicine prizes, on the other hand, have not really attracted market attention to date, probably because it is too niche for the regular player. Instead this role has been taken up by Thomson Reuters, which claims to have identified 37 Nobel Prize winners since 2002, on the basis of an analysis of scientific research citations within the Web of Science. As an interesting development, Thomson Reuters has also now established a People’s Choice Poll, more akin to the “wisdom of crowds” methodology of a prediction market. Scientific society Sigma Xi has a prediction contest that enables people to vote for their favourite.

2015 Nobels: the verdict

This outline of the past few years is pretty much par for the course in the history of Nobel predictions. Far from perfect, but not at all unimpressive. Interestingly, the market is often a better predictor of future Nobel laureates than for that particular year.

In 2015, although the market got the Literature Prize spot on, it had not predicted that the Tunisian National Dialogue Quartet would win the peace prize. So well done to those who placed a bet on “none of the above”. It was trading as close second favourite to Angela Merkel on the PredictIt prediction market before the announcement.

Thomson Reuters got the 2015 physics, chemistry and medicine prizes wrong. This year it also highlighted Richard Blundell, John List and Charles Manski as the leading candidates for the economics prize, making special note of the former, who also won their People’s Choice poll. There was no organised betting on economics this year. This year’s economics Nobel went to Angus Deaton. Deaton, currently Dwight D. Eisenhower Professor of Economics and International Affairs at Princeton (formerly of Cambridge and Bristol universities) for his analysis of consumption, poverty and welfare.

So what will the prediction industry look like in ten years? On current trends, it will have grown up a lot. The science of forecasting and the power of prediction markets are currently growing apace. Will there ever come a time, I wonder, when we don’t need to wait for the announcement, but instead just look to the odds? Maybe we should set up a prediction market to answer that question.

Reference:

Leighton Vaughan Williams and David Paton, ‘Forecasting the Outcome of Closed-Door Decisions: Evidence from 500 Years of betting on Papal Conclaves, Journal of Forecasting, 34 (5), August, 2015, pp. 391-404. http://onlinelibrary.wiley.com/doi/10.1002/for.2339/full

Beware the Ides of March! US Election Special

The Ides of March, or March 15, has long been associated with doom and destruction. In 44BC, confident populist Julius Caesar ignored a soothsayer’s warning and met his demise at the height of his adulation by an adoring public. It was also the day that Czar Nicholas II in 1917 formally abdicated his throne, and the day that Germany occupied Czechoslovakia in 1939. And now it’s the turn of the Republican Party.

This year’s Ides of March could prove pivotal for the US presidential race, as the primaries roll into five big states: Florida, Ohio, Illinois, North Carolina and Missouri. With firebrand insurgent Donald Trump still denying all the Republicans’ attempts to stop him, the day’s massive delegate haul threatens to put him firmly on the path to the nomination.

Much will depend on what happens in Florida and Ohio, the home states of Florida Senator Marco Rubio and the Governor of Ohio,  John Kasich. Kasich has pledged to withdraw from the contest if he loses Ohio, while Rubio has himself said that whoever wins Florida will be the nominee of the Republican Party. If he falls behind, he will be under enormous pressure to bow out.

This confronts Trump’s conservative rival, Ted Cruz, with a fiendish dilemma. He’s won a fair number of states, but to have a decent chance at winning the nomination, Cruz needs Kasich and especially Rubio to drop out. So Cruz wants them to do poorly. But if either or both lose their home state, it’s Trump, not Cruz, who’s most likely to grab their delegates – a hefty 99 in Florida and a chunky 66 in Ohio, all allocated on a winner-take-all basis.

On the other hand, if Rubio somehow rallies to win Florida, he’s very likely to stay in, as is Kasich if he wins Ohio. This puts Cruz and other anti-Trump forces in the awkward position of needing Rubio and Kasich both to trump Trump and to fall short.

The best outcome Cruz can hope for is for Rubio and Kasich to do just enough to win Florida and Ohio respectively, therefore denying Trump the winner-take-all delegates, but to do so badly elsewhere that they drop out anyway. Not impossible, but unlikely.

So where does that leave us?

Splitting the difference

Trump just needs to seize Ohio and Florida to put him in touching distance of the prize, but that’s a big task, especially in Ohio. Illinois and Missouri offer a combined total of 121 delegates. North Carolina’s 72 delegates are in play as well, but those are allocated on a proportional basis, so grabbing the gold isn’t quite as important there.

So if Trump picks up Florida, Ohio and does well in Illinois and/or Missouri, the fight for the Republican nomination could be all but over by Wednesday morning. But that outcome is far from pre-ordained.

Let’s say Trump loses either Ohio or (less likely) Florida, but not both. That puts his chance of clinching a majority of delegates before the convention in jeopardy, maybe Illinois and/or Missouri tipping the scale. But if he loses both Ohio and Florida, he’s extremely unlikely to win a majority of the delegates before the convention in July.

If that’s the case, anything could happen. If it is ultimately not possible to construct a winning coalition of delegates around any of the current four horsemen of the Republican Party’s political apocalypse, the party could even turn outwards, to anoint a different saviour. This would presumably be someone undamaged by the internecine warfare that would have brought the party to that impasse. That would now seem to rule out Mitt Romney, given his recent full-on personal attacks upon Donald Trump. Instead they are more likely to look to a unifier, though they would need to change the convention rules to do so.

They have called upon someone fresh in dire straits before. At the end of 2015, the party could find nobody to replace John Boehner when he suddenly stood down as speaker of the House of Representatives. Then they found someone who at first said he wasn’t interested, but later relented: Paul Ryan, Mitt Romney’s running mate in 2012.

Is this a likely outcome? Not at all. While chatter around a possible Ryan candidacy suddenly spiked as March 15 loomed, a fundraising group formed to “draft” him recently shut down after his aides disavowed its work.

It’s far more likely that Trump will emerge as the Republican nominee, followed by Cruz, then Kasich and Rubio. But if no one can garner a majority of delegates to win the first ballot at the convention, any number of scenarios could play out.

As the betting markets currently see things, by far the most electable against a Democratic opponent in the general election are John Kasich and Marco Rubio. Of these two, Kasich is rated by the markets as much more likely to win the nomination. If he scrapes a win in the Ohio primary and finally starts winning delegates, might he somehow emerge from the pack at a contested convention, perhaps with Rubio or even Cruz in tow as his running mate? We shall see.

Further Reading and Links

https://selectnetworks.net

Super Tuesday is today. Will it be the day that Trump effectively clinches the Republican nomination?

In the wake of Donald Trump’s blowout victory amid the bright lights of the Las Vegas strip, the money has been piling on the billionaire businessman from New York to sweep all aside on the way to a coronation at the Republican convention. But just how smart is this money?
After all, Trump was also favourite to win the Iowa caucuses, not only in the betting markets but also in the polls and the pundits’ conventional wisdom. In the event, he lost Iowa to Ted Cruz, the arch-conservative senator from Texas.
He also only narrowly bested Senator Marco Rubio of Florida, who was suddenly being talked about as the main contender for the nomination – heralding a volatile period that sent the markets haywire.
It seems absurd that Rubio could vault past Trump after placing behind him, but that’s down to a little thing called the expectations game. If you can deflate expectations and come third, that’s somehow seen as better than attracting high expectations and coming second. It may not do much for you in the Olympics, but it matters a lot in politics.
It worked for Rubio until a fateful pre-New Hampshire debate encounter with New Jersey governor, Chris Christie. In a calamity that’s been compared to a scene from The Stepford Wives where a suburban woman suffers a circuitry meltdown, revealing her to be a robot, Rubio haplessly began repeating the same scripted attack on Barack Obama over and over again.
He promptly plummeted in the betting markets’ estimation, and eventually finished a poor fifth in New Hampshire. In his concession speech, he admitted that he had malfunctioned during the debate, but promised that systems were now restored and that no further meltdown would occur.
He seems to have been true to his word, but the drubbing Trump dealt him in the subsequent South Carolina primary suggested that the damage had already been done. Even so, Rubio’s stock had shown remarkable resilience in the betting, vying pre-Nevada with Trump for the shortest odds in the nomination market.
Meanwhile, Cruz – who won Iowa against the odds – has lengthened to odds usually indicative of someone with no chance at all. Why so? After all, he only narrowly lost the runner-up slot in South Carolina to Rubio, and performed similarly in Nevada. He also has plenty of money on hand. Part of the reason is that he’s losing against Trump among his own base of arch-conservative religious evangelicals – and was unable to beat Rubio even in South Carolina, whose demographics should have made it prime Cruz territory.
The Cruz brand has also earned something of a reputation for dirty tricks. On the night of the Iowa caucuses his team spread rumours that rival candidate Ben Carson was bowing out of the race. Before South Carolina, the campaign clumsily photoshopped Rubio’s head onto someone else shaking hands with Obama at the White House. Most recently, they released a video in which subtitles over Rubio’s slightly garbled speech suggest he was mocking the Bible, when in fact he did the exact opposite.
So where can Cruz go? Home to Texas, where he’ll be hoping for a very strong showing when the state holds its primary on March 1. He’ll need to win big to change the betting markets’ mind: they put his odds of clinching the keys to the White House at as long as 150-to-1.
With Nevada out of the way and the Trump campaign in full swing, the markets see the future pretty clearly.
Trump is now trading at short odds on (2-to-7) to win the Republican nomination, while Rubio is currently available at a best price of about 5 to 1 against. That makes Trump better than a 3-in-4 favourite to become the Republican standard-bearer for the general election, with Rubio’s chances about 1-in-6 as we head into the latest phase of the campaign.
The odds have been shuffled on the Democratic side too. The “Bern” that Bernie Sanders was feeling after routing Hillary Clinton in New Hampshire was reduced to something of a fizzle in Nevada, where she beat him by a comfortable five points. Nevada’s caucuses resolve ties by means of a card draw. At one deadlocked caucus in the town of Pahrump, an ace was drawn for Clinton against a six for Sanders, an apt representation of the night he had.
Sanders now faces Clinton in South Carolina, where she’s the heavy favourite. She’s also now a best priced 1-to-20 to secure the nomination, as a slew of southern and western states expected to fall to her line up to vote in the next three weeks. She is currently the odds on favourite (4-to-7) to win the presidency, which gives her about a 63 per cent chance of going all the way. Meanwhile, Trump is trading at about 11-to-4 (a 27% chance) to win the big prize, and Rubio at 16 to 1. Of the rest, Bernie Sanders and Michael Bloomberg have more chance of winning the Presidency, as we head into the afternoon of Super Tuesday, than any of the other Republican candidates.
And so we wait for array of states large and small to make their decisions – and potentially scramble the odds once again. (4-to-5) to win the presidency.
And so they all march on to Super Tuesday, when an array of states large and small will make their decisions – and potentially scramble the odds once again.