We’ve just released our 2008 Presidential Election Map. The map is based on markets for all 50 states. In addition to the page there is a map widget (displayed above). The widget goes into feed readers and also dynamically updates on a page refresh. Click on the widget above to access the code snippet.

It has been a dramatic week for the election markets with Obama surging back from around 50% to the low 60′s. The Hubdub market has generally kept in line with the UK bookmaker and betting exchange odds. Roll on the debates!

Doing a simple analysis of some the key events in the 2008 Presidential Elections against prices on Intrade shows that on discrete events there is a clear relationship between prices and news flow. However over longer periods the relationship is not always clear.

On the 4th of March CBS announced the results of a straw poll conducted at the conservative PAC convention in Washington DC. They picked Romney as their favourite. Romney’s price on Intrade lifted immediately where it stayed for about a week.

On the 11th of April the Fred Thompson revealed on Fox News and ABC Radio that he had been diagnosed with non-Hodgkin’s lymphoma nearly three years prior. The New York Times and other publications picked up the story the next day. Looking at his price chart shows he opened on the 12th of April at 19 but then closed at 15. The next day he opened at 11.2 but then closed at 17, as the story died down.

In both these cases, the news stories the media considered to be the important ones correspond with the news flow that traders thought was important.

However, the most interesting market movement of the year must be the Obama August slide. On the first of August Obama opened on Intrade at 35.5 but by the 24th of that month he had slide to 17.2. He continued sliding hitting a rock bottom of 10.7 on the 14th of October.

The question is what was the news flow on Obama from the 1st of August to the 24th of August? Analysing the news articles in the New York Times suggests a disconnect between what was reported and how the market was reacting. Obama started August badly with a bungled comment on use of nuclear weapons.

Additionally, his continued line that stabilisation of Iraq had been a ‘complete failure’ may also have cost him some points.

However in sum these news items don’t seem to correlate with an 18 point slide. This could lead us to two possible conclusions:

1. The New York Times didn’t report the most market sensitive news affecting Obama in August
2. Obama was over-sold in August and his price did not reflect his true value

Cross-posted from the Hubdub blog.

In the paper the authors show that while when you compare IEM with raw polls, IEM outperforms the polls however when you manipulate the poll data the polls are more accurate. The generate the manipulation the authors looked at poll data from elections from 1952 onwards which show that over time the early leader tends to lose that lead. They then used that relationship to manipulate polls for elections from 1988 onwards and compared the result with the IEM forecasts. The manipulated polls showed a higher level of forecast accuracy.

I think this is an interesting piece of research but it is a stretch to use this to claim that polls are more accurate than prediction markets. The fundamental problem is that when newspapers (or anyone else for that matter) quotes polls, they don’t refine them using historical data, they quote the actual poll result. If anything the authors have shown a small bias in IEM that one would now expect to get traded out (like the longshot/favorite bias in betting markets or the January effect in financial markets).

Fundamentally, the thing to note is that while polls make prediction markets more accurate, the converse does not hold.

The Flutter story itself is well documented (for example here) so I won’t repeat it, apart from to note that it was one great rollercoaster ride. What I did want to write about was functionality of letting users create their own markets or as we called it freeform betting. The original flutter concept was that we would create a mass market platform that would allow users to bet with anyone on anything. Note flutter wasn’t an exchange, each bet had one and only one counter party. The primary focus was on the social element of betting with other people not on value or the trading experience you might get on an exchange such as Betfair. When we initially launched the site we started by letting users only bet on markets we had created with a focus on sport and entertainment (we coincided with the first Big Brother which was a big betting market in the UK).

However the plan was always to quickly roll out functionality that would let users create their own bets. We soft launched that functionality as a tool for customer acquisition whereby visitors who arrived at the home page where offered the opportunity to create a bet and then email it to a friend. We would pony up £5 (about $10) for each side. When the event closed we would then email the two participants who would agree who won the bet (with an escalation process if they didn’t agree). Sounds great, but in practise even though it was free money it just didn’t take off the way we expected. Why not?

In 1999 the UK government commissioned a report into the prevalence of gambling in the UK population (you can read it here). One of their findings was that only 11% of the UK population had a bet with a friend in the past year and only 4% had a bet with a friend in the past week. These 4% were our target audience; this wasn’t a mass market audience. To make it even worse, in 2000 broadband penetration was paltry and even when people where on-line they were still not comfortable putting in their credit cards. So when you multiply the 4% by the percentage of people online times the percentage of online users ready to get their credit card out you end up with a very small user base.

But what if we had stuck with it (clinging on during the downturn), what sort of market could you achieve today with much more internet savvy audience and higher broadband penetration? Well the UK has a over 18 population of about 50 million; 4% of which is about 2 million punters who bet with friends every week. The report also tells us that the average number of bets per week was 1.4 and we can infer an average bet size of about £10. Therefore we have 2.8 million bets per week, £28 million in weekly turnover and an annual market of about £1.4 billion. Ooooh , now it gets interesting. But wait, that’s turnover, at a 5% commission this market would be worth £70 million to us if we could get everyone to bet through us. However, that is obviously very unlikely. Nearly all personal bets happen face-to-face in a transaction that goes something like this:

• Punter A “That US soccer team are a bunch of numpties, they have no chance”.
• Punter B “They will still cream your Scotland team”
• Punter A “You’re on – a tenner?”
• Punter B “Done”
• Then they shake hands.

Notice they don’t then run to their PC and type in the details of the bet. They have another pint and then watch the game. Sometimes bets don’t get collected but generally that isn’t a big issue. Now, there is a potential market with regards bets between friends who are remote from each other. Generally, the same exchange that happens above gets transacted by email, text or phone. Now what percentage of 2.8 million bets per week are remote bets? Well the prevalence survey helps us again. It found that the place bets were struck broke down as follows:

1. Place of work (25%)
2. In the pub (25%)
3. At sports ground (19%)
4. At home (17%)
5. At friends home (16%)
6. Elsewhere (9%)

Now if we assume that you are very unlikely to have a remote bet in the pub, at the sports ground or in someone else’s home. Also let’s assume that 20% of bets at work and 10% of bets at home are remote bets (which feels very optimistic). Therefore we can estimate that only about 7% of those 2.8 million bets per week are remote bets meaning the annual potential commission would be £5m. Worse still, to achieve that revenue we would need to sign up 2 million users which equates to £2.50 per user per annum in revenue. You need a very low customer acquisition and service cost to make that business work. You could argue that you will only target people who only bet with friends online but there is no evidence that people like that exist. Betting is a social activity and is technology independent.

Despite this, there was a boom of person-to-person betting sites in 2000, to name but two others, Betswap and Betmart, both of which came up with a very similar model to flutter totally independently. Even late last year a new site, Gottabet, launched. Having worked at Flutter, looking at Gottabet was like watching Star Wars Episode 1, the same idea but more modern technology (no Jar Jar Binks thankfully). Which leads me to my final question, why do internet entrepreneurs and investors seem to love this market? I think it comes down to the simple fact that internet entrepreneurs and investors tend to be young, competitive internet-savvy members of that 4%. And while it makes sense to build a product that you yourself would like, it is always important to make sure that you aren’t too atypical of the rest of the population!

For Gottabet I see if they can develop a bet creation process that is as simple as a text message or an email then they could take a chunk of the remote private betting market but it will be a challenge. Additionally, they could focus on the other part of their proposition which is challenge bets for play money. If they can become the ubiquitous challenge bet widget (which they can then market to the US) then they could be in with a shot at an advertising funded model.

This post has been cross posted from my blog at Big Hand Small Map.