May 7, 2013
As it has been six months since our last Open House event we feel it is time to throw open our doors once again for another evening with our customers. We would like to invite all of our customers to join us in our London offices on Thursday 30th May for pizza, drinks and conversation. Our Open house events have been an enjoyable success in the past and offer the chance for users to meet old and new members of the Smarkets team as well as other customers. Those attending will also be able share their own personal feedback regarding the website and we’d be happy to answer any questions you may wish to ask throughout the evening. Details for the event are as follows:
Date – Thursday 30th May 2013
Time – 6:30pm – 8:30pm
Address – Smarkets HQ,
Royal London House, 4th Floor North,
22-25 Finsbury Square,
RSVP – To confirm your attendance or if you have any queries please contact firstname.lastname@example.org
We all look forward to seeing you,
The Smarkets Team
March 8, 2013
People love getting something for nothing. The idea of gaining something for no outlay appeals to us subconsciously and nobody knows this better than the bookies. £10 Free Bet! £20 Sign Up Bonus! £50 Money Back! Sometimes you’d think that these guys are the most generous people around. But anyone who has ever placed a bet knows that a bookie hates nothing more than losing money. So how can they just dish out money for nothing to those willing to take it? Simple, they aren’t.
By appealing to our human side with shiny bonus baubles they are employing a variation of the old adage “the first hit is always free”. They have calculated that once they can get you through the door they will make that bonus back and plenty more. Sure, there might be one or two slippery customers who take the cash and run out as soon as it’s gone but they’re making more than enough from the ones who stay to be too worried about that. Besides when you can add a bunch of small print which requires betting through multiples and other obscure terms you can shut that down and really sink your claws into them.
Not convinced? Then here’s some data for you:
|Free Bet Promotion||n/a||£50.00||£50.00||£50.00||£30.00|
|Average Higher Smarkets Winnings (Based on price comparison)||13.13%||18.92%||19.62%||19.83%||14.91%|
|Ongoing Additional Profit on Smarkets @ £500 a month average winnings. 1 Month.||£65.65||£94.60||£98.12||£99.17||£74.53|
|Ongoing Additional Profit on Smarkets @ £500 a month average winnings. 1 Year.||£787.76||£1,135.24||£1,177.41||£1,190.09||£894.36|
 This is the point where using Smarkets becomes benefical over the other sites. Any winnings made above this point renders the free bonus moot. This assumes you retain all of the initial bonus.
|Average Higher Smarkets Winnings (Based on price comparison)||13.56%||17.60%||20.45%||16.34%||17.83%|
|Ongoing Additional Profit on Smarkets @ £500 a month average winnings. 1 Month.||£67.80||£88.02||£102.25||£81.69||£89.16|
|Ongoing Additional Profit on Smarkets @ £500 a month average winnings. 1 Year.||£813.61||£1,056.22||£1,226.99||£980.26||£1,069.97|
There’s a lot of information there to digest but here’s just a couple of the insights:
- While Coral offer you a free bet of £50 if you open an account, Smarkets’ better price means that on horse race winnings over £252 you will still be worse off at Coral – and that is despite our assumption that you will turn your £50 free bet into winnings of £50.
- If you won an average of £500 on horse races a month at Ladbrokes, you would lose out on additional monthly winnings at Smarkets of £98 – so even after 1 month you would be better off at Smarkets despite Ladbroke’s £50 free bet. Over the course of a year, your additional winnings at Smarkets would amount to £1,177.
- On football markets, the difference in winnings between Smarkets and others could be even more substantial at £1,227 a year.
So now what? Despite the message of this post so far we’re not here to tell you not to take these bonuses. If you have the willpower to take it and then get out you’d be a fool not to. Our aim here is twofold. Firstly to explain why, as part of our transparent goals, we won’t be offering bookie-like bonuses for Cheltenham or in the foreseeable future. Secondly to inform you again how much better off you are on Smarkets. With our industry low 2% commission, every single time you win you win more here. If you plan on winning now and for a long time to come then don’t pass that up for one quick hit.
February 21, 2013
We’re pleased to announce that we have successfully closed a series A funding round and raised $2.3 million in venture financing from T-Venture, a division of Deutsche Telekom, and our existing investors Passion Capital.
These long-awaited funds are going to allow us to accelerate technical and product development, and take Smarkets to the next level. As well as improving site stability and reliability, new features planned include a dedicated mobile app, price history, trading charts and new markets for users to trade on.
We’re also excited to finally have the resources to implement some of the suggestions our customers have been making to us over the past 3 years. We have heard your voice, taken note of your continuous feedback and are thrilled to crack on with putting your suggestions into action.
On the managerial side, we are welcoming Randeep Wilkhu, Senior Investment Manager at T-Venture, and Robert Dighero, Angel Investor and Partner at Passion Capital, as board members. With the expertise of an established company like Deutsche Telekom behind us, we’re looking to find the right growth strategy for Smarkets to make a major impact on the competitive online betting industry. In the role of the industry challenger, we’re looking to disrupt the market by offering a pure exchange experience, characterised by industry-leading low commission, high liquidity and the best trading experience.
We are grateful to all our existing customers for helping us get this far. We don’t take your trust or patience lightly and much appreciate your loyalty, support and feedback to date, especially during the rough times. It’s been an amazing journey so far, and we’re looking forward to embarking on the next stretch of it together with you.
February 21, 2013
Last night, the Smarkets site was down for over eight hours on a busy weekday betting night. This is an unacceptable level of downtime; we’re massively sorry, and we owe our customers a thorough explanation.
The problem arose around 18:30 UTC on Wednesday night, when our system started having issues accepting bets. We traced the issue to our exchange backend and attempted to restart it, however this revealed another issue. Our custom exchange software, written in the Erlang programming language, uses the Mnesia database to store some of its data. Mnesia has a limit of 2GB on the size of a table, and one of our tables picked this moment to exceed the limit, resulting in data corruption.
We had a fix for this oversized table ready to deploy, and we attempted to release it on Wednesday morning, but this release was delayed until Thursday because of a minor issue with our deployment system. We weren’t monitoring the size of this table, and we were caught out.
The bulk of the downtime period was spent attempting to reconstruct and restore this database. Data about all bets placed on Smarkets is stored in multiple places within our system, as well as being continually backed up off-site, so no information was lost. However, reconstruction of the database was a painstaking process and we weren’t happy with it until after 03:00 this morning, when our team finally went to bed.
A few accounts are still missing recent settlements, and we’re working on restoring these as quickly as possible. If you have any questions about your account history, please contact support and we’ll get back to you as soon as possible.
The root cause of this issue was a lack of knowledge of our backend systems within our team. We have a talented technical team but we’ve historically been lacking the staff to provide the levels of performance and reliability we aspire to. Our recent round of investment, announced today, will allow us to expand the size of our team and redouble our efforts to make Smarkets a fast and stable exchange platform.
Once again I’d like to offer my sincere apologies for letting you down last night, and I hope that our transparency will go some way towards restoring your confidence in Smarkets.
February 14, 2013
Just over a year ago we made an announcement introducing a set of payment fees that would allow us to continue offering 2% commission to every customer in as fair a way possible.
Part of our decision was that we would revisit this structure and revise it in accordance with customer behaviours and requirements. The general consensus is that the solution we arrived at was a good one . Our users still enjoy free deposits and withdrawals via debit cards and bank transfers while understanding that certain other methods have high transaction costs that we can’t absorb while maintaining our industry-leading low commission.
There is always room for improvement, and in this case that room is regarding Moneybookers/Skrill fees. Our current structure grants users free deposits up to free £/€500 each month, followed by a charge of 2.5% on any transactions over that amount. We’ve come to notice that this isn’t satisfactory for our most loyal Moneybookers users.
Because of this we have made the decision to lower the charge for Moneybookers deposits to 2% and to remove the £/€500 monthly amnesty altogether. While we realise that this will impact some of our lower-volume customers, we believe that this is a fairer way of passing on the high transaction costs for this method. These changes will come into force on Monday 18th February 2013.
As stated earlier, you can still make a deposit at no cost using your debit card or bank transfer, and we will not change this. If you have any comments, please give us your feedback either in the comments below or via an email to email@example.com – we are always listening. Hopefully this time next year we’ll have continued our march against absurd deposit fees, and we’ll be able to announce an even fairer scheme.
January 9, 2013
In this post we’ll take a look at behavioural economics and decision making – in particular, we’ll discuss what causes an individual to participate in a particular gamble and how the observed behaviour may not map onto that predicted by ‘rational’ decision making criterion. These decision making criteria state that a rational gambler will enter a game if (and only if) the price of entry is less than the expected value of a winning result. This expected value can be calculated as the sum of the expected pay-offs for all the consequences. So a player should enter a market on Smarkets if the expected result is greater than that invested. However as shown below, this rule is not always followed.
The ‘St. Petersburg Paradox’, a game first proposed in 1713 by Nicolas Bernouli, is one such example. According to Bernouli, you must imagine you are standing in St. Petersburg Square when a man approaches you to play the following game:
I toss a coin until it comes up tails. When this happens the game ends, and you get paid depending on the number of times that the coin has landed on heads prior to landing on tails. You get paid £1 if the coin comes up heads on the first toss, £2 if the coin comes up heads on the second toss, £4 on the third toss, £8 on the fourth toss and so on. The amount that you stand to win as a player doubles for each successive heads result, and then ends when the coin lands on tails.
The question then, is how much are you willing to pay to play such a game as this?
We can calculate the expected value of the game as the sum of the expected pay-offs for all of the consequences. This is where the paradox occurs – the potential pay-off for this game is huge. In fact, it is infinite. Following on from the first coin toss, you have a 50% chance of winning £2, plus a 25% chance of winning £4, plus a 12.5% chance of winning £8 and so on. This continues such that if the game lasts for 30 throws then a player will stand to win £537 million, and £563 trillion on the 50th throw.
If we accept this criteria then we must therefore accept also that humans are being irrational in not paying large sums to play this game. Who wouldn’t want to risk playing this game given the huge payout potential? But recent studies have shown that most players are not willing to pay more than £6 in order to play. This doesn’t align with our initial statement that
It is suggested that the reason for this difference is in the way that individuals think about profits and loss. It is important to note that for the player paying £6 to enter this game the most likely outcome is that a tail will occur in the first two throws and the player will lose money. It is not surprising that a player will be unwilling to pay a lot to play a game where the chances are greater that they will lose more than the amount paid to enter. It is also important to note that humans have been suggested to discount the profits made from overly unlikely events and so the potential larger winnings from this game may not be taken into account when deciding how much to pay to play.
Another explanation that has been suggested is that no one can actually pay out the largest sums available in this game, and so we need to cap the maximum payouts. This can have a huge effect of the expected value of the game.
Lets say a player expects the stranger to only be able to payout a maximum of £100. The expected value of the game becomes just £4.28. Lets say the stranger is a very wealthy businessman with a maximum payout of £1,000,000. In this case the expected value becomes slightly higher but not by much – it’s still only £10.95. Finally if the game were capped at £8.5 Trillion (The GDP of the United States in 2007) then the expected value of the game would be just £14.13.
When the actual expected values are taken into account, it’s not difficult to see why players are reluctant to put down huge sums of money. They are, in fact, not being that irrational afterall.
December 10, 2012
Measuring risk is inherent in every decision we make. Usually we don’t even notice that we’re making a risk-assessment when, say, stepping into a lift or crossing the road, but we do so on a second-to-second basis all the time. In spite of the near unconscious level at which we process these decisions, the fact remains that for each of them our brains have made a complex analysis of the different variables of which we’re aware within a given situation and decided on a particular course. We’re astoundingly good at this, given we need to do it on a minute-by-minute basis every day of our lives, keeping track of huge numbers of minor details without really thinking. However, we are by our nature fallible and do often get things wrong.
Given how important the accurate estimation of risk is to both the financial or betting industries on both an institutional and individual level, it’s important to understand how this is accomplished. It’s also useful to consider the potential pitfalls of inaccurate assessment, as well as how such errors can be avoided.
How Do We Measure Risk?
As previously mentioned, most of us make nearly unconscious risk-assessments all the time in our heads. This usually works well for everyday situations, but we all have our own biases, fallacies, and irrational ways of reacting to events to which we can fall victim in decision-making. So when it comes to important decisions such as in what way to trade millions of dollars of shares or which medical treatment should get government funding, we need help maintaining objectivity and rationality. Using statistical models to analyse which is the best outcome as well as how likely negative outcomes are allows us to disregard our own biases and assess decisions objectively, thereby allowing us to make better decisions. Essentially, these models are prosthetics which augment our ability to make the right call in situations too complex for our normal faculties to handle effectively.
We construct these models by gathering data, whether through scientific enquiry or passive methods such as records of stocks traded, which are publicly accessible. When we have enough data, we can analyse it and draw conclusions to apply as needed. There are a huge number of statistical tools that allow us to do this, and while I’m no statistician, at their base level many are easy to understand. Take the Law of Large Numbers, for instance, which states that a large enough sampling of data from the same set will tend to converge around the “expected value” (the weighted average of all possible values for the variable being measured). In essence, it means that the more times we repeat the same test, the closer we come to knowing the truth. For instance, the expected value for rolling a standard 6-sided die is 3.5; the more times we roll the die, the more closely the average of the results found will converge on that number.
There are numerous other powerful tools we can bring to bear on problems of assessing risk, such as Bayesian Probability, and they are all used in the assembly of models used for just that purpose. These models, in turn, allow the creation of complex algorithms and computer programs used to trade on financial markets worldwide. In fact, the vast majority of trading on US stock exchanges is done by programs known as “algo-bots.” This is both hugely positive and introduces enormous potential flaws into the system upon which the entire global economy is now based, as without any human oversight, programs can cause huge problems when they encounter situations they’re unable to handle.
I’ve written about the problems associated with exclusively using theoretical models to make decisions before, but a good example of the problems which occur when things go wrong is the “Flash Crash” of 2010. On the 6th of May that year, at 2:32pm, a mutual fund’s algo-bot attempted to trade $4.1bn worth of a particular type of futures commodity, a huge sale by any standard, which combined with increased volatility in trading that day to trigger a selling spree by other automated programs in the same market. As selling worsened over the course of the next 10 minutes, the fever spread to the stock markets and caused the Dow Jones Industrial Average to tumble by 600 points in a matter of minutes. The fall was only stopped when an automated monitoring program kicked in and stopped trading for five seconds, alleviating the pressure of so many programs selling and allowing the markets to start recovering (which itself took only minutes). Five seconds may not seem like much time to you or I, but in the time-frame of High Frequency Trading and algo-bots it’s more than enough time for such a precipitous drop to begin to reverse itself. By 3:00pm most of those 600 points lost had been regained, marking the largest one-day swing in stock market history.
This incident, short and sharp, which caused the largest one-day swing in the Dow Jones’ history, was entirely automated and perfectly illustrates the problem with inaccurately modeling risk. After Black Monday in 1987, where a very similar problem occurred with automated trading programs, the risk of using the primitive trading programs of the time (which accounted for only 10% of trading back then) was considered too great without human oversight. Unfortunately, our current system is built around such algorithmically-derived programs, and so one of the few things we can do is build safeguards like the system which stopped trading during the Crash. It also highlights the issue of inadequate modeling in the basic design of the algorithms governing the bots’ behaviour. It isn’t possible to anticipate every single situation using a model derived from past data, but it’s definitely possible to include situations such as the Flash Crash -which lie at the far end of the normal distribution of probabilities- in that model.
Modeling risk when betting on live events is arguably less easy than doing so with pure data placed at something of a remove from the real world, such as the financial markets. That being said, the potential losses are more limited, you are for instance unable to lose $2.1bn betting on horses (though I’m sure there are those who have tried). If the financial markets represent the most complex man-made system ever made, then consider how complex it is to assess risk statistically on betting exchanges fundamentally reliant on real world situations with a near-infinite number of variables.
The 2012 Grand National is a prime example of such unaccounted-for risk. The favourite horse, Synchronised, which had won the Cheltenham Gold Cup only weeks before and looked on track to win, fell at a notorious jump and had to be put down. At a stroke, this unanticipated event dashed the hopes and stakes of those who’d joined the consensus on its ability to win, not taking into account the dangerousness of the course or the likelihood of any one horse falling in spreading their bets.
While the incident has served to further fuel an already fierce debate over the safety of National Hunt-style racing in Britain, it also reinforces the point that even with the best modeling, it’s impossible to anticipate the chance of a particular horse falling and dying from past performance (as it obviously only happens once). The best that can be done is to include the possibility of any horse falling in a model that informs your spread on an exchange or between different bookmakers. It provides evidence for the argument that it’s fundamentally more difficult to account for risks when modeling exchanges based more firmly on the real than, say, the commodities futures markets.
The failure of a statistical modeling of specific risks in instances like the one I describe above allows us to see the limits of such an approach. While it’s always good to know the overall likelihood of an event occurring and be able to change your approach to suit that probability, the fact remains that knowing about risks and being able to truly account for them are two different things. It’s always tempting to believe we can fully control events if we understand them, but despite our best efforts that remains an impossibility. The most we can achieve is to measure and account for as much risk as we can.