This article shows how to implement synthetic Asian handicap bets, and how to construct a Dutch book so that any arbitrage opportunities arising between these synthetic bets and actual bets can be locked in to generate profits, irrespective of the outcome of the match.

Expected return is of the order of 0.2% per match, so some degree of automation is vitally important to cover large numbers of matches, as is availability of sufficient liquidity to match bets.

Asian handicap bets: the basics

An Asian handicap bet is one in which a handicap is assigned to one of the competing teams. The handicap is always given when referring to the bet; for instance, Liverpool -1.5&-2.0 means that Liverpool have a handicap of -1.75 goals (and their opponents a handicap, or an advantage, of +1.75 goals).

This handicap can be a multiple of a half in order to ensure that the result of a bet is always a win or a loss. For instance, a handicap of -1.5 goals for the home team means that the home team has to win by at least two goals in order to win an Asian handicap bet. If it wins by one goal, draws, or loses then it loses the Asian handicap bet.

Handicaps can also be integer valued, in which case a draw result is possible. In this case a draw results in the bettor’s stake being returned, so the net profit on the match is always zero.

Lastly, handicaps can be given in terms of quarter goals. A handicap of 0.25 goals is regarded as two separate bets, with equal stakes assigned to bets of h=0.0 and 0.5.

Match handicaps are usually chosen so as to ensure that the odds of the Asian handicap bet are as close to 50/50 as possible. This ensures a more exciting betting experience for punters who may support a weaker team.

Replicating Asian handicap bets

One of the simplest Asian handicap bets is to give the home team a handicap of -0.5 goals. With this handicap, the home team wins the Asian handicap bet if it wins, and loses the Asian handicap bet if it draws or loses. The odds of a home win in the market odds market are therefore exactly the same as the odds of a home win in the Asian handicap market.

Since the payoff for these two strategies is the same, we can say that the market odds home win bet replicates an Asian handicap home win bet at h=-0.5. It is therefore possible, in principle, to look for cases where the market odds of backing a home win are greater than the Asian handicap odds (at h=-0.5) of laying a home win. If such a situation occurs, then both sides of the trade may be taken to lock in a profit. The same situation occurs if the market odds of a home win are less that the Asian handicap odds, but in this case the trades should be reversed.

This arbitrage strategy appears to be well known, and as a result very few cases are seen in which market odds and Asian handicap odds diverge as described. However, we show below that it is possible to replicate exactly any Asian handicap bet with a handicap of between -0.5 and +0.5 using at most two market odds bets. This range of handicaps covers more than 60% of matches in the UK Premier League.

Arbitrage strategies

Denote

• the back and lay odds of a home win as;  H_B, H_L

• the back and lay odds of an away win as;  A_B, A_L

• the back and lay odds of a draw as;  D_B, D_L

• the back and lay odds of an Asian handicap home win for the current handicap as;  H*_B, H*_L

• the back and lay odds of an Asian handicap away win for the current handicap as;  A*_B, A*_L

Home team handicap H=-¼

There are four cases to consider, each of which must be examined to search for arbitrage opportunities. The payoff matrices are as shown:

Home team handicap H=0

Here we use the fact that a combination of backing a home win and laying an away win results in a zero return for a draw, which is the same behaviour as an Asian handicap bet.

Again there are four cases to consider, each of which must be examined to search for arbitrage opportunities. The payoff matrices are as shown (note that they are identical to the case where H=-¼, except for the return from the Asian handicap bet when the match results in a draw):

Home team handicap H=+¼

This case is essentially symmetrical with the H=-¼ handicap.

Home team handicap H=+½

Home team handicap H=-½

This case is essentially symmetrical with the H=½ handicap.

Dutching an Asian handicap book

By considering the payoffs of all outcomes, we determine if a combination of bets exists that always generates a positive return, irrespective of the result of the match. The aim of this section is to determine how much should be put on each bet to ensure that the return of the strategy is the same. One way to address this problem is to regard it as a matrix equation in three unknowns. If the payoff matrix is denoted by M and the amounts of capital w1, w2, w3 to be placed on each bet are denoted by w, then the ‘constant return’ condition is given by

M_w = 1

where is the (constant) profit from the strategy and 1 is the unit vector.

The solution is

(1.2)

Consider the following payoff matrix:

In this case, the matrix equation to solve is

1.02	1	– 0.73	W1		1
– 1	1	– 0.50	W2	=	1
– 1	-3.40	1	W3		1

This forms a set of three equations in four unknowns; the fourth constraint is given by

W₁ + W₂ + W₃ = 1

where each

Solving and normalising the

W₁

to one gives

W = (0.3529, 0.0674, 0.5796)

which are the appropriate relative amounts to bet on each market in order to lock in a profit of 0.42% per dollar staked.

Discussion

Although fleeting, several opportunities per week of this type arise in UK Premier League football, and others probably occur in the European markets. The opportunities appear to last for periods between minutes and hours.

To take advantage of these requires appropriate technology to perform the above arbitrage calculations for each market, and availability of liquidity, particularly in Asian handicap markets.

The profits seen from this strategy are small in relation to those generated by others, but they are almost completely risk-free by design.

The post Arbitrage of Synthetic Asian Handicap Bets appeared first on Sports Trading Network.

Closing out a trade to lock in your profit is one of those basic techniques that everyone should understand, but is surprisingly hard to find explained. So here is a simple account.

Closing out a back trade

Suppose that you’ve backed a soccer team to win at odds of 10. They do well during the match and the odds drop to 5. The value of your bet has increased in value, but you’re worried that the match could go against you. How do you close, or hedge, the bet before the game ends to lock in your profit?

You can do this by putting on a lay bet against the same team that gives equal but opposite exposure to your first bet. How much should you lay to lock in the profit?

The trick here is to look at how much each of the two bets will make at the end of the match. Suppose that

–          the odds at which you made your initial back bet were ODDS1;

–          the odds at which you  made your subsequent bet were ODDS2, where ODDS2 < ODDS1;

–          you bet $1 on the initial back bet;

–          you bet $S on the subsequent lay bet, where we don’t know S yet.

If the team wins, profit from the back bet will be -1 + ODDS1, and the loss from the lay bet will be (1-ODDS2) * S. The total amount won will be (-1+ODDS) + (1-ODDS2) * S.

If the team draws or loses, profit from the back bet will be -1, and the profit from the lay bet will be 1 * S. Therefore the total amount won will be -1 + S.

To lock in a guaranteed profit, irrespective of the outcome of the match, set the two expressions for winning amounts whether you win or lose to be the same: in other words

(-1 + ODDS1) + (1-ODDS2) * S = -1 + S

The only unknown in this expression is S. A little algebra shows that

S = ODDS1 / ODDS2

and the profit is S-1 for each dollar staked.

Example: In the above case, the value of S is 10/5  = 2.  To lock in a profit of S-1 – 2-1 = $1, you should hedge by placing a $2 lay bet at odds of 5.

Closing out a lay trade

Consider the opposite case, where we have placed a lay bet at ODDS1, seen the odds decrease, and now want to place a back bet at ODDS2 to take profits. As before, set the amount you back to be S.

If the team loses, profit from the lay bet will be 1, and the loss from the back bet will be -1 * S. The total amount won will be 1-S.

If the team wins, loss from the lay bet will be 1-ODDS1, and the profit from the back bet will be (-1 + ODDS2) * S. The total amount won will be (1-ODDS1) + (-1 + ODDS2)*S.

Setting the two expressions to give the same outcome implies

(1-ODDS1) + (-1 + ODDS2) * S = 1 – S

which gives

S = ODSS1/ODDS2

and a profit of 1-S.

For example, suppose you initially placed a lay bet on the team at odds of 5, and the odds move to 10. In this case S is 5/10 = 0.5. To lock in a profit of 1 – 0.5 = $0.5, hedge by placing a back bet of $0.5 at odds of 10.

Conclusion

The one result to take away from this is that the amount to hedge is always ‘first odds divided by second odds’, whatever your initial bet.

Happy hedging!

The post An easy guide to closing out trades appeared first on Sports Trading Network.

So, is it really Doomsday on September 30th 2014 with all these big players pulling out of the UK market?

Or will we survive and make and find new and better ways to gamble in the future?

I believe it will be the latter. Let’s be honest and start with the facts:

1) There are very few natural monopolies that exist in this world. Gambling certainly isn’t one of them. Less competition for UK punters means a worse deal for UK punters. Some of the monster names pulling out of the UK (and let’s be clear, there will be more to come) are low-margin, very highly respected and loved sportsbooks.

2) HMRC don’t fully understand the industry, and neither do the Gambling Commission. Amazingly, of the contact that we’ve had with the GC over the years (the most significant being when we were on the wrong end of a match-fixing situation with a high profile snooker player, having done our dough with no chance of ever winning), I’ve never met anyone who has ANY experience on either side of the fence. They are mostly (or were) licensing experts, people familiar with legislation, and the like.

Is that representative of the whole of the GC? Probably not. However, there is a mixture needed. I don’t think the GC have come out well with recent problems (which have been blindingly obvious) at Betbutler and Canbet. Both of these situations have been handled poorly and punters have been and will be ripped off in avoidable situations. The regulator HAS to stop that – THAT should be the focus.

3) The broker model, which is now becoming much talked about, is a complex beast. Lots of people who aren’t familiar with it will be dipping their toes in the water thanks to some aggressive marketing – a couple of brokers seem to think it is their Christmas. We believe that they certainly haven’t discussed this at length with HMRC.

Here’s why – let’s look at the model:

Your money goes via bank wire or e-wallet to the broker/agent

That agent holds it in a (hopefully ringfenced, not that that offers any guarantees of worth) holding account

That agent opens for you what is essentially a sub-account of his/hers at your desired bookies. Dependent on your standing (and their credit checks are a mystery to us!) you can obtain leverage of up to 5 times on your deposit. This is a massive plus under the model because it keeps your credit risk down.

That agent facilitates your betting at all these books, soon to be non-accessible to UK residents, either via a platform like mollybet, sportmarket etc, or via skype/MSN (chat betting). Skype betting is for the heavy hitters who cannot fill their whole stakes even by taking a max bet across the board (or a series of max bets).

The agent takes 0.125% commission and the platform takes 0.125% commission win or lose, from the bookie – i.e. the bookie is paying the agent to bring them the liquidity, because the model works on liquidity (rather than opposing an outcome, almost always the favourite, in the traditional UK bookmaking model). The agent takes a 0.25% commission on the skype bets, which is more from the punter’s end arguably (on skype you ask for a match, and tell them your stake, they then give you a price which is fill or kill pretty quickly, obviously. You don’t know where the 0.25% is being shaved off because it’s a lot less transparent).

So – what’s actually happening? Effectively the agent is taking the bet, and putting it on in their name. The end layer is the same person – IBC, Pinnacle, SBO, whatever. How are HMRC going to feel about this when they take the time to understand it? My take would be that they will say the agent is laying the bet, and then laying off. Thus any profits the agent makes from the bets it lays (in HMRC’s eyes, which are not actually profits, of course, remember they are like an exchange in that sense, they take commission whatever the outcome) would be taxable under the new regime.

If an agent was catering for mostly sharp punters, as some do, this won’t actually cause a problem in practice (although there is still a risk) – overall money will be flowing from asia, via the agent, to the sharp punters. No profit, no tax. There’s still the licensing costs to consider of course.

However if an agent who caters for sharps and arbers in equal volume at the moment attracts a lot of arbers (the sharps in our opinion are a lot thinner on the ground – read 50 sharps for 1000 arbers as a finger in the air guess), they will be overall losing accounts – so the agent becomes the conduit for the money to flow from the UK arber towards asia over time, since arbers will lose at asian books over time and win at the softer UK books.

There is the position that will cause problems for the agents who are aggressively touting for UK business, over the next 2-3 years, since these wheels will move slowly.

However, what we are asking ourselves is, do we want any financial exposure to a company/individual/organization that looks odds on to get a tax demand or a criminal charge levelled against them, whether they think they are safe in Malta or not? Our answer is a resounding no.

Serious players will be relocating abroad – perhaps only virtually, but that is another matter for another STN blog…..

The post The Impact of the Point of Consumption Tax on the UK Betting Market appeared first on Sports Trading Network.

Given a set of probabilities for the runners in a multi-player competition, how much should you bet on each runner to maximise your expected profits?

The standard reference paper for this topic was published by Isaacs over 60 years ago[1]. Unfortunately the presentation is not always easy to follow, so this article presents an alternative approach.

Suppose that a pool with runners has amounts

bet by oneself, and amounts

bet by the rest of the market. The true probabilities of the runners winning the race are

calculated from the previous form of the runners.

Let  Q  be the total proportion of the pool that is paid out. Then the optimal bet proportions are given by

where

where the summation notation on the top means ‘add all values of for which the expected return from runneris negative’, and the corresponding sum on the bottom means ‘add all values offor which the expected return from runneris positive’.

Worked example

Consider a race with 8 dogs. The win pool sizes and probabilities are as shown in the following table:

The total pool size is $711, the track take-out is 19%, and the rebate is 9%. We assume that the pool is will grow by 15% from the time of placing any bets to when final dividends are calculated, and we decide to use 40% of the recommended Isaacs stake to reduce volatility. What bet sizes give the highest return?

The first step is to work out which dogs have a positive expectation, once take-out and rebate are taken into account. These are dogs for whichThe total pool size is $711, the track take-out is 19%, and the rebate is 9%. We assume that the pool is will grow by 15% from the time of placing any bets to when final dividends are calculated, and we decide to use 40% of the recommended Isaacs stake to reduce volatility. What bet sizes give the highest return?

where, for dog i,

is the probability of the dog winning;

is the amount staked in the public pool on this dog winning;

is the total amount staked in the pool (i.e., the pool size);

T is the track take-out;

and is the track rebate. The results are:

In this instance, dogs 3, 4, 5 and 7 have positive expectations.

The next step is to form the expression

This is straightforward using the results of the earlier calculation. Note that the factor  Q  refers to the track take-out and does not include any rebate.

In this example,  has a value of sqrt (0.9*$413/ 1-(0.9*0.56)) = 23.77.

The last step is to calculate the size of the actual bets using

The value of this expression will only be positive for the dogs with positive expectation. Again using the values above, the amounts to be staked on dogs 3, 4, 5, 7 are ($6.38, $21.73, $22.95, $3.42).

The last step is to take the increase in the pool size and the reduction factor into account. We expect the pool size to increase in size by 15% before the race starts, so all bets are multiplied by 1.15. Also, we have decided to bet 40% of the recommended Isaacs stake, so all bets are also multiplied by 0.40. The resulting bet sizes are ($2.94, $10.00, $10.56, $1.57).

[1] Isaacs, R. (1953)“Optimal Horse Race Bets” American Mathematical Monthly Vol. 60, No.5, (May, 1953), pp. 310-315, (May, 1953), pp. 310-315

The post Optimal bets for dog and horse races appeared first on Sports Trading Network.

In fixed-odds betting, the concept of ‘odds’ generally refers to the price, the amount the punter gets back if the bet wins. However, there is another, more traditional, definition of odds – one that can assist our understanding of probability and betting.

In statistics, the odds in favour, sometimes known as just the odds, of an event is the ratio of the probability of the event occurring to the probability of the event not occuring. If p is the probability of an event occurring, then the odds for that event are given by:

For example, if the probability of event A occurring is 0.4, then the odds of event A are 2/3. Similarly, the odds against an event is the ratio of the probability of the event not occurring to the probability if the event occurring. If the odds of event A are odds(A) and the odds of event B are odds(B), then we define the odds-ratio of A to B as

The odds-ratio measures the relative probability of two events. If the odds ratio of A to B is 2, then we will say that event A is twice as likely as event B. To illustrate, consider the difference between 50% and 51%, and 98% and 99%. In absolute terms, the difference between the two numbers in each pair is 1%, but it’s clear that intuitively the difference between 98% and 99% is much greater than the difference between 51% and 50%. The odds-ratio of 51% to 50% is 1.04, but the odds-ratio of 99% to 98% is 2.02, thus showing that the difference is indeed much greater. Also, while we might instinctively want to say that 60% is twice as likely as 30% (because 60/30 = 2), there is no answer to the question of what is twice as likely as 60% using this definition. But using the odds-ratio, we can say that 75% is twice as likely as 60%, because OR(75%, 60%) = 2.

One application of the odds and odds-ratio is to the bookmaker’s distribution of the overround, a concept that represents the bookmaker’s profit margin. Consider an event in a betting market that can only have one winner from n selections with bookmaker prices o1, o2, …, on. The overround v is defined as the sum of the inverse of the odds:

The overround represents the amount the bookmaker would need to lay in order to make a payout of exactly 1 unit of currency, assuming the stakes are distributed in the correct proportions across the selections. To illustrate, for v = 1.081, if the bookmaker laid £10.81 in the correct proportions, then they would pay out £10.00 to the punters. Another important concept, related to the overround, is the margin m of a betting market, defined as the bookmaker’s profit as a percentage of the turnover, assuming the stakes are distributed in the correct proportions. The margin is related to the overround as follows:

How any bookmaker sets the price (relative to the true price) to make a profit obviously varies, but with the advent of automated models in betting, having an algorithmic method of distributing the overround is desirable.

Consider for simplicity a general two-way market, consisting of selections having true probabilities p and q = 1 − p, so that p + q = 1. When distributing the overround algorithmically, ideally we require functions f and g that act on p and q, respectively, such that f(p) +g(q) = v, where v is the overround. One such choice is f(p) = vp and g(q) = vq (then f(p) + g(q) = vp + vq = v, so the condition is satisfied). This method is in fact called proportional distribution, and represents simply dividing the true odds by the required overround. The method is well-known because of its simplicity, but produces final probabilities greater than one if the true probabilities are high enough. Another method is to add the overround equally between both probabilities, so that f(p) = p + 0.5(v − 1) and g(q) = q+0.5(v−1). Again the overround condition is satisfied. This method seems to be better than proportional distribution but again suffers from the problem of probabilities exceeding 1. Another method, which ensures the final probabilities are always between 0 and 1, involves the use of the odds and the odds-ratio.

Suppose that the overround functions f and g takes the true probabilities p and q to the new probabilities x and y, respectively. A sensible method of distributing the overround might require that the difference between p and x, and q and y, is in a sense ‘equal’. The method we introduce now will ensure this by requiring equal odds-ratio between each pair of old and new probabilities, in the sense that

Suppose the odds-ratios are equal to c. Then

If we set x + y = v, then we can solve for c, and hence get final expressions for x and y. The following table gives the true odds and the new odds using the above distribution method for a two-way market with margin 0.075.

Note that, of course, the overround is the same for every pair of odds. Y is the (punter’s) yield, or rate of return, which is the expected profit or loss on a punter’s bet as a proportion of the stake. If o are the bookmaker odds and ot are the true odds, then the yield is given by

So if a punter bets £1 at the price of 1.5 on something with true odds of 1.6, the yield is −0.06 or −6% and he will on average get back £0.94. Notably, using this method of distributing the overround, bets on shorter odds will generate a higher rate of return in comparison to bets on longer odds. Also, the odds-ratios of x to p and of y to q are equal, as is required, and they increase as the probabilities deviate from evens. This means that, for example, the difference between (1.08, 13.5) and (1.045, 8.05) is ‘bigger’
than the difference between (1.8, 2.25) and (1.68, 2.06) – even though the overrounds are equal – so that markets with shorter odds have poorer ‘value’ in comparison to markets with more balanced odds, if they have the same overround. To compensate for this, some bookmakers lower the overround progressively as probabilities reach the tails. In addition to distributing the overround, we can also use this method to remove the overround. Now let’s compile the same table for proportional distribution, again with margin 0.075.

Again, the overround for every pair of odds is the same. The bookmaker’s yield for every selection is precisely the margin; this is the key feature of proportional distribution. With proportional distribution, it’s clear that too much of the overround is being put on the favourite and not enough on the underdog, eventually giving rise to probabilities greater than 1. Looking at the odds-ratios can help us understand what is going on: we see that they increase as the odds shorten, turning negative as the odds become less than one; and decrease as the odds get longer, meaning the new odds become very close to the true odds.

If a bookmaker wants the odds to be such that any unit stake has the same rate of return no matter the odds, the only way to achieve this is by proportional distribution, but this is clearly a poor way to set the odds. As we have seen, equal odds-ratios is a better way to set the final odds, but (at least in the two-way case) necessarily has the feature that punters’ bets on shorter odds generate a higher rate of return than bets on larger odds; also, the yield strictly increases as the odds shorten. It appears likely that equal odds-ratio will give rise to this phenomenon regardless of the number of selections in the event. The feature that bets on shorter odds generate a higher rate of return than bets on longer odds is sometimes known as the favourite–longshot bias. It seems reasonable that the primary reason the favourite–longshot bias exists in fixed-odds betting markets is because of the way bookmakers set their odds: setting the odds in what a bookmaker might consider a sensible manner (and taking into account any factors that might influence the odds) will likely produce final odds that feature the longshot bias.

The post Fixed-Odds Betting and Traditional Odds appeared first on Sports Trading Network.

I would like to pose and try and answer the what, why and who questions about sports betting brokers. What are sports betting brokers, why do they exist and do we need them and finally who are the prominent brokers and who should be using them.

A sports betting broker is simply an intermediary between the sports bettor and the bookmaker. Normally they will either take a deposit from the client and hold it and allocate funds within their own large company accounts with one bookmaker or divided among several, or they will actually open one or multiple accounts at the bookmakers for you which you then deposit to and they then can bet on at your request. One provides you with the simplicity of one deposit and simplified banking in general and one allows you to make sure your funds are held with bookmakers you feel may have greater financial stability than your broker. Sports betting brokers are predominantly concerned with providing access to the large liquidity bookmakers in Asia (sometimes other places) and to a degree exchanges and tote pools where lower commission rates or indeed just legal entry can be provided for. The client will provide instructions invariably by phone or Skype (possibly other instant messaging services) or by submitting bets direct by third party online execution platform or API to the bookmakers. The broker will act as the execution bridge between the two parties.

This brings us to the question why do companies do this? What is in it for them and also why do bettors use them? Brokers always exist in areas where there are difficult to facilitate transactions without the necessary expertise. One such complex area is the Asian bookmaking scene. Asian bookmakers have become known for large limits and a willingness to take on smart bettors with a view to shaping their books using their bets as markers for where the market should be. There is a small group of absolute bookmaking powerhouses who will ultimately see a very large portion of Asia’s bets once they have filtered through to them. I say filtered because the man on the street across Asia is normally not betting direct with these bookmakers. Street corner bookies take bets from punters and then lay off their liabilities by betting with bigger bookies who in turn do the same and on and on until a large and well respected agent will pass the business on to the likes of SBO, IBC, Singbet and a whole host of others.

The bookmakers get to build profitable relationships with the top agents and often the big book as it is sometimes called which is largely made up of syndicate and top agent money is massively larger than the small book or online retail facing portion of the business. You will have seen Dafabet, 12Bet, 188 Bet and the like on odds comparison websites and these are the online retail branches of the massive Asian parent bookmakers. These allow these books access to some low limit mostly non smart money and also a recognisable brand for the purpose of lucrative football sponsorships and the like. The big book works on huge volume and tiny percentages and the small book works to bigger margins and smaller volume. Your experience of one of the smaller retail brands will not be the same as if you dealt with either a top agent or the big book direct. Brokers are often your only way in to the agent system and wherever there is a service to be provided there is money to be made.

The broker makes money via rebate on volume provided to the big books and not by charging the bettor and that is why this becomes so interesting to the aspiring pro punter. For the vast majority of successful bettors the biggest problem they face is getting their bets on and in the size they want. The Asian agent system accommodates smart bettors and in larger size providing you want to bet on the markets that the Asian books specialise in. In fact some brokers and Agents like to profile their customer base because the appetite for smart bets is so high as it allows you to move your odds faster and position your book before the others can move. Some agents and brokers do not even want to facilitate losing bettors as the market place is awash with dumb money but there is only so much smart money available and he who has the most smart money has the sharpest lines.

In a market where the lines are bet to such low margins even a small odds move can open up a sub 100% book on a given line. This in turn attracts passive smart money more commonly known as arbitrage money which means the book who is now best price on the opposite side to the smart money bet they just took will see bets from arbitrage that will once again balance the book. They capture a fraction of a percent, as they know the market will move their way and the slowest moving books are forced to lock in a fraction of a percent loss. The smarter you are the more they want you. So I am a smart bettor who can no longer bet with retail bookies and I want access to the agent system and the easiest way to do it is through a broker however the benefits do not or rather should not stop there.

While the ability to get large bets on at very low margins is of course its own reward a good broker can offer more than that. Firstly there is what is known in the financial industry as broker “colour”. This simply is a little chat about what is going on in the betting markets. Seen a lot of business in one market or another, have been seeing a lot of late money in Serie C recently, heard from a well connected guy that player x is not going to start etc. Now a lot of broker colour is just noise but a good broker can educate you about the market far beyond what your own research could achieve. They are also able to put you in contact with some very helpful people such as software developers, data providers, other pros, tax advisors and syndicates.

Brokers often are the centres of a spider web of well-informed smart people who bet on sports, this is the kind of yellow pages you have always wanted. Some brokers can offer you all this while others may just offer you a Skype address and some Skrill details, as the number of brokers grows it will pay more and more to shop around. One factor that is on the rise is the provision of an electronic platform to place bets with. To many the use of online chat feels at best antiquated and at worst highly unprofessional. Now we are seeing the rise of software that provides the odds comparison screens we love with direct bet placement. On top of that they will mostly show you the amount available to bet at that price which creates an exchange style order book.

It is worth noting though that often the screen does not show the volume that could be available if your broker made direct contact with an agent. The screen represents what is available on the bookmaker’s API stream and they are not willing to just let bots run complete riot all over their prices just yet hence the smaller limits often available. Exceptions to this are operators such as Pinnacle where what you see on the API is basically what you get. These pieces of software are improving all the time and I am sure increasingly complex order types and other features will be appearing.

This brings us to who are the prominent brokers and who should be using them and I will highlight a couple of important factors about brokers. There are often multiple brand names all leading to the same broker and if a name disappears there are no guarantees whether the broker closed or simply that rendition of it was closed. Regardless of which broker you use, you must do you due diligence and research the company. Ask other people, Google it, speak to someone on the phone at the broker, read reviews and above all be cautious. Please don’t just see a website and handover your Neteller/Skrill without a thorough investigation. I can happily say that www.Eastbridge-sb.com is part of the largest sports betting brokerage in the world with the best contacts and service but do not take it from me, contact them and ask the hard questions of them and find out for yourself. Finally who should be using them? Everyone should be using them who takes their betting seriously and bets on the markets they offer but unfortunately they are not viable for everyone. The bigger and smarter your bets the more you will get out of a broker and you must be prepared to be turned away by some if the total you bet per month does not meet their minimums.

You must have come to terms with the fact that you will no longer be betting with UK high street bookmakers and the fact that your first player yellow card system will not be facilitated. You will know that the future holds thousands of bets a year and all for 3% ROI but that should not be sniffed at as in real money terms that is quite nice thank very much. You will be recording every bet and checking the settlement of every bet. You will be ready to move on to the final stage of professional straight up betting. Low volume losers need not apply.

I am sure there are plenty of errors here and areas where I have misunderstood the finer detail and I hope that those more experienced pros will point them out so they can be corrected.

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The scope of horse racing betting was changed forever at the turn of the century with the advent of the exchanges, and that has now filtered through even more considerably in the offerings that the traditional bookmakers have started to offer in order to attempt to keep up.

Betfair always offered the opportunity to “cash out” long before the bookies caught on (it took them over a decade!), and also to cash out some or all of your stake, or to select the guaranteed profit available via the desirable “green screen”. More importantly though, it broke through into what was the forefront of the instant gratification betting that now is embodied on the internet via casinos and instant games, and in the bricks and mortar establishments via the much-maligned Fixed Odds Betting Terminals (FOBTs).

This is because Betfair had the foresight to allow you to bet during the race. No longer was it waiting for what you hoped was the best price, striking a wager, and sitting back to enjoy the next few minutes (hopefully!). Now you could change or reverse a position, and this blew the game wide open.

It didn’t take long before the sharpest started to use Betfair at the track – not only for making and hedging a book, but also taking advantage of the (sometimes extremely significant) time delays between so-called “live” pictures on television and the actual real-time activity taking place at the track. Reports of 7 or even 9 second delays in what are now referred to as the “old days” are widespread.

This created an anomaly which is rarely (if ever) recognised in industry publications. There became two ways, simplistically, in which you could get “good at the game”:

1) Speed of execution
2) Race-reading and keeping a cool head/control

Of course, the best of the best have both in spades. Interestingly, however, some in the top bracket (who, these days, pay significant sums in Betfair’s Premium Charge) tend to excel in either 1 or 2, but not both. A great race-reader can make money without the very fastest pictures. A great executor who can access the fastest feeds (perhaps eyes, rather than racetech!) does not need the best-of-the-best skillset at reading races – he or she can simply seek to be better than the average, and this is still enough to make a profit.

What may be frightening to some reading this article is that this practice has continued, unabated (other than by the premium charge, and by some providers being embarrassed into working on improving the speed of their picture delivery) for over a decade and a half now. Many average punters have no idea this sort of thing goes on (and indeed, it may well affect their decision as to whether to bet at all or not in running, although to suggest they make rational decisions when it comes to placing wagers is a stretch too far – hence why they are average punters!).

The world of betting and especially P2P betting continues to expand – and whilst Betfair currently maintains an unnatural monopoly position especially in the UK market, the world is seeing other solutions pop up and aggressively grab market share (Citibet, anyone?). The hope for all involved is that the middleman can take out less commission (whether that’s by the traditional methods or by the premium charge) – currently, strategies are needed to mitigate the premium charge (a topic for another article), and that can take the eye off the ball of attempting to build a best-in-class operation, around horse racing or any other sport.

I’m always interested in further discussions around the issues raised in this article and can be contacted on betfairdu@gmail.com. Thanks for reading!

The post Horse racing – and they’re off! appeared first on Sports Trading Network.

A bit of a departure from my usual favoured topics, but I have been inspired by a seeming lack of articles around this subject, which is relatively simple but often underplayed or underestimated.

The entire concept goes right back to the cornerstone of the high-margin business that high street bookmakers in the UK used to enjoy after their legalisation back in the 60s. Finally punters could combine events, across matches, leagues, countries and sports, in the hope of landing a massive payout.

The price at which these payouts came, however, was very high. Still to this day do we (very occasionally!) see the betting press run a story on a punter winning hundreds of thousands, or even a million, from a small stake. Why are these stories still so popular?

Many know that coupon bets or “acca” bets are so often a mugs bet. However, they are not quite as clear as they should be on why. The answer is relatively simple; think of it as the same effect as compound interest has.

If I borrow £100 at a rate of 10% over a 10 year term, and make no payments in the interim, but merely pay the entire sum back at the end, the interest accumulates on the interest. Thus, I accrue £10 of interest in the first year but £11 in the second year, £10 on the principal and £1 on the existing interest. This phenomenon, compound interest, means I accrue £159.37 interest over the term. If I only paid interest on the principal, I would have accrued £100. So, I have paid nearly 60% more thanks to compounding.

Happily, for the savers out there, the same phenomenon applies and this effect turned around the other way helps make mountains out of relatively small investments, if they are left to accrue for long enough. The same logic applies to an accumulator bet.

Imagine two punters – Average Joe and Sharp Steve. Joe picks 10 bets he thinks will win, 10 favourites which for the sake of argument all have a 10% edge to the house. He bets £10. His £10 is exposed to the edge, compounded, 10 times. On a £10 single he would lose £1 on average to the edge.

On a £10 accumulator he loses £6.51 on average to the house edge, so his money disappears 6.5 times as quickly. He faces (essentially) a 65% house edge – such a game is incredibly difficult to beat of course!

Steve picks 10 outcomes where he estimates that the player edge in his favour is 10% on each bet. If he bets a £10 single, he would win £1 on average.

On a £10 accumulator he wins £15.94 on average (returning £25.94) – a 159% advantage.  Truly a crippling edge.

Of course, Steve has to factor in the probability of his outcomes to optimise such situations. If each bet he combines he is receiving 1000/1 on a true 900/1 shot, and he has to combine such bets, the odds are overwhelming that he will die before he ever cashes in. Using the Kelly Criterion to determine optimum stakes, the determined stake for almost any bankroll would be effectively zero.

A further benefit to Steve, however, is that the use of the accumulator offers him a smokescreen. Accumulators are rarely used by sharp punters – the variance that they introduce can offer an extremely rough ride – however, if used sparingly and wisely, they can also turn a comparatively small edge of 1-2% into a large one, through the “magic” of compounding advantage.

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Logistic Regression (Or the “Logit Model”) is a fundamental tool for modelling and predicting the outcome of 0/1 events. (Win or lose).  In this article, I’m going to take things a bit further and explain how the conditional logistic regression model is very applicable to a lot of contests where there can be more than two outcomes.  (i.e. a horse race.)

First, I’ll quickly review linear regression, move to a logistic regression, and then finally cover conditional logistic regression.  We’ll use a fictitious horse race for all our examples here.

Overview

All models start with some assumptions and beliefs about how the world works.  These assumptions will become important later, but we’re going to skip them for now.  At a basic level, we’re trying to predict something based on data.  The thing we’re trying to predict is formally called the “dependent variable”, and the data we’re using to make that prediction are called the “independent variables” or “factors”.  In traditional statistical notation, “Y” represents the dependent variable and “X” represents the factors.  So, we want to learn how Y is related to X.   More formally, we want to know how much of the variance in Y can be explained by the variance in X.  Variance is a VERY, VERY important concept, but beyond the scope of this article.  I’ll address it in the future.  Also, X can be a represent a single variable, or a matrix of hundreds of variables.  For this article, in the interest of simplicity, we’ll just use one.

Simple Linear Models

As the name implies, linear models assume that the relationship between Y and X is linear.  The notation we use is:

The B in this formula represents the weight of X. (Formally called the “coefficients”)  In simple terms, B represents, “How much does a change in X cause a change in Y.”  We then use some relatively simple math (built into Excel, R, Matlab, etc…) to solve for the best B in our model.  This is a common theme in all regression:  A model is defined, and then we use some mathematical or computing techniques to find the best weights for the model.  Often, different factors, transformations of data, or model structures are tried to find the one that best fits the empirical data.  One important note, and more of an advanced topic, is that no model fits the data perfectly.  What we are estimating is known as “BLUE”: Best Unbiased Linear Estimator.

The plot below illustrates this perfectly.  The red dots are the data and the black line is the best linear estimator.  Even someone with no math background can quickly see that this nicely represents the relationship between X and Y.  However, notice that the line doesn’t pass through many of the red points.  So, while the line represents the relationship well, it is actually wrong for any individual point.  (This is what the e at the end of the equation represents: the wrongness or “noise”.)  The amount of wrongness will become a very important factor in predictions of future events, and is something I’ll delve into in a future article.

Logistic Model

Linear models are fine when you want to predict something numeric and continuous such as speed, time, weight, etc.  However, they don’t work well when you want to look at phenomena that have a binary outcome such as: win/lose, live/die, complete/fail, etc.  With binary outcome events, what we are most interested in is the probability of an event happening given the data.  Something called an “inverse logit” can represent this relationship well.  I’m going to skip the formal derivation and math here, but a quick Google search will provide more than you want to know.  The form of logistic regression, using the same nomenclature as above is:

This will give us a smooth curve, demonstrating how the probability of Y happening is a function of X.  The plot below demonstrates how the logistic model fits the data.  Notice that some points are outside of the curve.  That is another example of “wrongness” that all models have.

Conditional Logistic Regression

Finally, we’re at the point of this article.  Hopefully, you have a general understanding of regression models by now.

One area I’ve studied a lot is that of horse racing.  I’ve modeled horse races using a number of advanced methods, but the fundamental structure remains the same.  What we ultimately want to know is the probability of a horse winning a race.  If the public has mispriced that horse, then we have a betting opportunity with positive expected value.

A subtle, but critical distinction needs to be made.  We don’t care about the “probability of the horse winning”; we care about the “probability of the horse winning THIS race”.  Of course, this is a horse race, so we have to estimate his probability of winning relative to all the other horses in the race.  That probability depends on all the other horse’s performances as well.  For example, if I race my neighbor down the street there is a 90% chance that I’ll win.  If I race Usain Bolt, there is a .00001% chance that I’ll win.  So, winning is relative to the other competitors.

This is where conditional logistic regression (CLR) comes in.  The “Conditional” part is that winning probabilities are relative to the competitors in the race.  Additionally, to follow the laws of probability, all probabilities for a race must sum to 1.0.

Transforming a list of any values, so that they sum to 1.0 is a trivial mathematical function, just divide them by the sum.  For example   1,2,3,4,5  – just divide each number by 10 and you get 0.067, 0.133, 0.200, 0.267, 0.333.   However, this will NOT let us learn the best factor weights.  To do that, we need a formal statistical model that we can fit use correct mathematical techniques.  The equation is:

Each horse has a “strength”, represented by the exponential of the linear function.  (The top half of the fraction) The strengths are then summed up over all the other strengths in the same race. (Bottom half of fraction)   Looking closely, it is easy to see that, this is similar to the toy example I provided above.  The tricky part is learning the weights.  There is no closed form analytical solution for this.  An iterative technique, often gradient descent, is used to find the best weights.  Some software packages will handle this well for basic models with a reasonable number of factors.  Fancier varieties of this model will require custom computer code to be written.  (I use C++ and GPU parallel computing to fit this general form with 186 factors over 40,000 races.)

Summary

While brief, this article demonstrated the rationale for both logistic regression and conditional logistic regression.  The goal was not to create working models, or explain model fitting procedures, but to give you a general understanding of the three models and when to apply them.  For events with a single possible outcome, use logistic regression.  For events with multiple possible outcomes, use conditional logistic regression.

In future articles, I’ll discuss variable screening, transformation, prediction variance, and a host of other tools needed to properly fit a predictive model.

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Whether you are a large well-funded syndicate supported by hundreds of highly skilled Quantitative Analysts and Data Modelers or if you are just a punter with an eye for a ‘value’ bet it seems that everyone shares the same struggle: ‘getting on’.

People looking in from outside the industry often find it hard to believe the notion of the bookmakers restricting you to a tiny bet, never mind full blown refusing to even look at the action you would love to send them. The sad fact is that getting real bets away with the vast majority of the UK books is a near-impossible task. Any hint of a chunky bet or a selection that moves a couple of points renders your account next to useless.

Sadly I can only see the liquidity in the UK getting tighter over the years to come. Bookmakers are investing more and more resources in trying to get ahead of the sophisticated proprietary modelers betting to the value they find from formulating their own more detailed pricing models. In the cross fire will be a lot of unsuspecting losing regular punters who have been auto restricted by an arbitrary computer system, with qualitative human decision making disappearing from the industry all together.

Thankfully we all know that there are options outside of betting with the household names to be able to get our bets on. Far away from the FTSE listed companies desperate to keep their shareholders happy with smooth FOBTs backed profits, lie a selection of other outfits that can fill the liquidity gap. I am talking about brokers, private layers, betting exchanges and a very small amount of online bookmakers with a higher appetite to take risk.

The best place for your action really does depend on the sort of business you are trying to get away. For instance if you are arbitrage trading then the vast majority of the online bookmakers wouldn’t be interested in just accepting your top price business, but there are other options available. If you are an API driven high execution trader then there are also numerous options to make sure you are maximizing your volume; same applies if you are looking to seed markets.

On the flip side if you are just a punter who likes to have a big bet but has been knocked back by the ‘traders decision’, then there are options where you will get treated with respect and also looked after to a level that the rest of the industry just cannot match up to.

Getting £500k+ on an NFL / MLB game and £1m+ on a top tier Soccer Asian Handicap are all very possible, you just need to look in the right places for those reliable robust firms who are willing and able to take the action.

If you need help finding new outs or are looking for introductions into specific bookmakers just get in touch and I will be happy to help you where I can.

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In an enticing all-British fight, longtime rivals James DeGale and Chris Eubank Jr will finally square off on February 23 at the O2 arena in London. Looking for value in the DeGale vs. Eubank Jr odds? Read on to inform your DeGale vs. Eubank Jr prediction.

A close look at the James DeGale vs. Chris Eubank Jr odds

The Money Line odds suggest this will be an extremely competitive fight. Chris Eubank Jr, despite being beaten by George Groves recently, is the favourite, and the odds suggest the younger man has around a 60% chance of victory.

The Total Rounds is set at 9.5, with over 9.5 rounds priced at 1.314*, which suggests there is a high probability that the fight will go the distance.

What does recent form suggest?

DeGale spent 2018 reviving his career as he bounced back from a surprise loss to American fringe contender Caleb Truax, by recapturing his IBF super-middleweight title in an instant rematch. He considers himself to have far too much ability and experience for Eubank Jr. – but the odds disagree.

Eubank Jr. was defeated by George Groves at the beginning of 2018 in the World Boxing Super Series semi-final, but after a third-round stoppage of JJ McDonagh in September, he will enter the fight off the back of a victory, which should rejuvenate his confidence.

Analysing the strengths and weaknesses

In boxing, the saying goes, “styles make fights”. Analysing both fighter’s strengths and weaknesses is significant to finding an edge in this bout. Eubank Jr. has come under heavy criticism for his losses against Billy Joe Saunders and George Groves and many experts feel that when the Brighton man is challenged by a boxer who has superior pure boxing ability than him, that he is not good enough to overcome the challenge.

DeGale is the better boxer, but for all his talent and technical ability, he doesn’t punch hard enough to prevent Eubank coming into range and letting his flurry of shots go. The one thing that dictated the pace of Eubank Jr.’s fight with Groves was his jab and punching power, not so much the movement. Eubank was unwilling to come in so consistently due to the power Groves possesses in the right hand, and DeGale doesn’t punch as hard as Groves, which means the probability is higher that he will have success in this fight.

Will size be a factor?

One factor that could become apparent once the first bell sounds is the size difference between the two fighters. Jr. is naturally the smaller man, and fighting a weight class up from his natural size. He is strong enough to compete but DeGale won’t need to worry about getting knocked out with one punch, as at 168lbs Eubank needs to overwhelm opponents rather than hit them with single shots to demand their respect. The former IBO champion has a ferocious work rate, but if DeGale can use his size advantage correctly, it will certainly give the former world champion an added edge in the fight.

• Read: Why is it important to analyse boxing styles?

Skills and talent will often make the bookmaker favour a fighter in their odds but this fight is the opposite. DeGale is the bigger and better-rounded fighter, but the odds sit in Eubank Jr.’s favour.

The reason for the edge is that despite these advantages, Degale is possibly damaged goods. The 33 year-old has encountered a lot of grueling 12-round bouts since he became champion and this is why the bookmakers are giving the advantage in favour of the younger, fresher man.

James DeGale vs. Chris Eubank Jr: Where is the value?

The key factor in this fight is how well each fighter can implement their style on their opponent. Eubank Jr. is facing a fighter in James DeGale who is more talented and naturally more gifted than him in boxing ability, so he will need to out-fight, out-brawl, and push DeGale to the limit where he breaks.

Eubank Jr. is more of a fighter than a boxer and the probability of him out-boxing DeGale is extremely low. DeGale brings a 25-2-1 record into the fight and has plenty of experience at the highest level, but the signs have been there in his last few performances that he is beginning to slow down as a fighter.

• Read: What should bettors consider before betting on a boxing match?

The Harlesden man has the better pedigree of the two, but boxing is often about timing and Eubank Jr could be getting DeGale just at the right time. DeGale often gets lazy in the championship rounds and he cannot afford to sit on the ropes like he has done in previous fights, especially against a warrior as persistent in their fighting style as Eubank Jr.

It is difficult to ignore Eubank having success at some point in the fight and bettors should expect this fight to have plenty of drama. For all his technical flaws, Eubank is extremely game, tough, and throws punches in high volume which has the potential to unsettle DeGale’s rhythm, but at the highest level of boxing you need more than that. His style makes him dangerous against anyone, it’s whether he can implement that style against the elite boxers in the division that remains the big question.

A sensible bet would be for the fight to go over 9.5 rounds at odds of 1.314*. Both have good chins, and lack one punch KO power at the highest level. The odds suggest the bookmakers are expecting this also, so the potential value pick has to be DeGale to win the fight on points.

About the author

Chris McCarthy studied Media and Sports Journalism at The University of Leeds before finding a career that united his adoration of social media and interest in sports betting. A lifelong boxing fan, Chris has written for numerous publications and produced content for several major fights. He was also the betting correspondent for Matchroom Sports Prizefighter series.

See more articles by Chris McCarth

The post James DeGale vs. Chris Eubank Jr betting preview appeared first on Sports Trading Network.

Pinnacle’s Betting Resources has previously compared and contrasted a number of different staking strategies. I’ve looked at the expected profitability and risks of ruin for these strategies. For my latest article I want to compare specifically the two most commonly used plans: level versus percentage staking. 

Level staking

With a level staking strategy all stakes are the same size, regardless of what your betting odds are. Some bettors find level staking too inflexible in as much as it takes no account of the probability of winning your bet – or rather the risk of you losing one.

Why, for instance, would you want to risk the same amount of capital on something that has half, or quarter, or an eighth the chance of something else happening? Doesn’t it make more sense to scale stakes so that they are proportional to the risk associated with the bet?

In the short term, such an argument has its merits; over the longer term, perhaps less so. Betting at longer odds means you are more at the mercy of statistical variance, or luck, both good and bad. More good luck can mean more profit. Unfortunately, the corollary is that more bad luck implies more loss.

However, the longer your betting history is, the smaller that variance becomes. Good and bad luck even out. Readers of my article last month may remember the simple formula I used to estimate the spread (or standard deviation,σ) of possible returns (%) betting n level stakes at ‘fair’ odds of o.

σ=√(o-1)/√n

Having four times the number of bets will half the statistical spread of possibilities. Betting longer odds increases the spread of possibilities, but it will still decrease with increasing number of bets. 400 bets at odds of 5, for example, will have the same spread of possibilities as 100 bets at odds of 2.

Betting the same stake for longer odds does imply a greater risk of capital loss on a bet by bet basis. But over the longer term you are not giving up potential profits by reducing those stakes (provided, of course, you are a bettor holding positive expected value).

Staking to win the same profit regardless of the odds means less profit will be contributed by the winning longer odds, simple by virtue of the fact they win less often. One might then wonder whether it is even worth bothering to bet longer odds at all.

Percentage staking

Percentage or proportional staking calculates stakes as a proportion of your current bankroll; hence they will increase as your bankroll grows after winning, decrease as it shrinks after losing. Advocates of one specific percentage staking plan, the Kelly criterion, argue that it is the most efficient way to grow a bankroll, although it can only achieve this by requiring a rather aggressive attitude towards risk management.

More generally its appeal lies in allowing a winning bettor to grow their bankroll faster than they could by simply betting level stakes. It’s also worth reminding ourselves that, in theory at least, we can’t ever go bust betting percentage stakes, as even if you lost every single bet, you are never committing the whole of your remaining bankroll, only a proportion of it.

Nevertheless, it is the interplay of losing and winning in sequence that throws up some rather interesting observations when comparing the performance of this money management strategy with level staking, as we shall see.

Distribution of profits for level versus percentage staking

Consider a betting history of 1,000 bets at odds of 2.00 where the bettor holds a 5% expected value (EV) that is the expectation of returning $105 for every $100 wagered. The histogram below shows the spread of profits for level stakes (5 units) and percentage stakes (5%) alike from a 10,000-run Monte Carlo simulation.

For level staking the spread of possible profits follows the typical bell-shaped normal distribution curve as we would expect. The average (and median) profit is 250 units, which is what we would expect after turning over 5,000 units holding a 5% advantage.

For percentage staking the shape of the distribution is markedly different, and heavily skewed towards the higher profitability end. Again, it’s probably not that surprising, since a lucky performance could see bankrolls and stakes grow exponentially.

I’ve stopped the chart at a profit of 7,000 units simply for clarity, but the largest profit made in the 10,000 runs was nearly 95,000 units. This skew has a significant influence on the average profit. Whilst the median is still 250 (implying about half are less and half are more profitable), the average is 1,120, weighted by a few very large profits that the Monte Carlo simulation delivered.

Look closely at the left-hand side of the histograms. You will see that there are more underachieving outcomes for percentage staking than for level staking. About 21% of them in this simulation were actually unprofitable, compared to only about 5% for level staking.

Distribution of yields for level versus percentage staking

Instead of comparing profits, let’s now compare yield percentage for the two staking plans. Clearly for a very profitable percentage staking history, the total turnover of stakes will be much greater.

One percentage staking profit, for example, saw a profit of 2,462 units (compared to 440 from level staking), but to achieve that 33,699 units were turned over (compared to 5,000 units for level staking). In fact, in this example the profit over turnover or yield was lower for percentage staking (6.85%) than it was for level staking (8.80%). Is that typical? The next chart shows how all yields were distributed for the full Monte Carlo simulation.

The average yield from level staking was 5.00%. Compare this to the average for percentage staking which was just half this at 2.51%. The chart also further illustrates how many more possible outcomes are unprofitable when betting percentage stakes compared to betting level stakes.

We can change the simulation parameters, for example different betting odds and different expected values (EV) held by the bettor.

For this article I chose 40 different EV / Odds pairs. To limit further the number of possible parameter combinations I only considered the percentage stake size equivalent to that dictated by full Kelly staking strategy, calculated by EV / Odds -1, where EV is expressed as a percentage.

For example, for the scenario already discussed (EV = 5%, Odds = 2.00), the Kelly percentage is 5% / (2.00 – 1). The percentage stakes are shown below for all 40 combinations. For the level stake scenarios, the magnitude of the percentage was used. Thus, for the EV = 3%, odds = 3.00 combination which implies 1.5% stakes sizes, level stakes of 1.5 units were used.

Percentage stakes sizes for different EV / odds pairs 

The next two tables compare the average yields achieved from the Monte Carlo simulations. For level stakes, the yields are in line with expectation, plus or minus a little bit of random noise which to reduce further would have strained my limited computational resources.

In contrast, the yields from percentage staking are generally about half those values. This was truly an unexpected and perhaps unintuitive finding, although the discussion which follows will reveal why it happens.

Average yield after 1,000 bets with levels stakes 

Average yield after 1,000 bets with percentage stakes 

Probability of unprofitability

Even sharp bettors holding profitable expected value face a non-zero probability of failing to make a profit over a specified betting history. Of course, the law of large numbers means that probability diminishes as their betting history gets longer. Nevertheless, it’s worth considering those probabilities for these simulated 1,000-bet histories for the purposes of comparting level and percentage staking.

The last two tables show the probability of each EV / Odds combination failing to return a profit based on the 10,000 simulation runs.

Again, as for average yield there will be a little bit of residual random noise, but the broader pattern is clear: you’re always more likely to fail to show a profit betting percentage stakes compared to betting level stakes, no matter what odds you bet or what EV you hold, and sometimes the magnitude of the difference is considerable.

Probability of not making a profit after 1,000 bets with level stakes 

Probability of not making a profit after 1,000 bets with percentage stakes

By way of example, a reasonably sharp handicapper (odds around 2.00) holding a 3% advantage over the bookmaker could expect to be showing losses after 1,000 3-unit bets about 1 in every 6 times. If, instead, they chose to bet 3% stakes, that would rise to nearly 1 in 3.

An explanation: the asymmetry of percentage losses and gains

Why does percentage staking appear to be inferior to level staking, at least in terms of expected yields and the ability to show a profit? The simple explanation is that it takes a bigger percentage growth to recover a previous loss.

Let’s consider the example of even-money betting. Losing a 5% stake drops a 100-unit bankroll to 95 units. To recover that takes a profit of 5/95 or 5.26%, but the percentage staking strategy would only advocate a next bet of 4.75 units and winning it at odds of 2.00 would return the bankroll 99.75. By contrast, the bankroll from level staking would be back at 100 units.

The problem is the same in reverse. Losing an even-money bet following a previous even-money winner will lose more absolute capital than was previously won. In this example, regardless of whether you win or lose first, your bankroll is going to finish on 99.75, less than what you started with, despite theoretically holding an expected value of 0% for this pair of bets.

• Read: Asymmetric information in sports betting

More generally, and regardless of the betting odds, when you lose it will take longer to recover; when you win it will take less time to regress.

Of course, in purely monetary terms a bettor holding proven profitable expected value will make more profit absolutely than their level stakes counterpart. That, after all, is the point of percentage staking.

Nevertheless, this exercise has been a useful reminder that as with anything in gambling, there is always a trade off to be had between risk and reward.

In return for a more aggressive acceleration of profits which percentage staking offers, one must accept a greater likelihood of doing considerably worse than expectation (and potentially losing money) simply because of the asymmetric nature of the distribution of possible outcomes.

About the author

Joseph Buchdahl  is a betting analyst who manages the website www.Football-Data.co.uk, providing historical results, match statistics and betting odds data. He is also the author of Fixed Odds Sports Betting: Statistical Forecasting & Risk Management (2003), How to Find a Black Cat in a Coal Cellar: The Truth about Sports Tipsters (2013) and Squares & Sharps, Suckers & Sharks: The Science, Psychology & Philosophy of Gambling (2016).

See more articles by Joseph Buchdahl

The post A comparison of level and percentage staking appeared first on Sports Trading Network.

• Sports Trading Network newest contributor  MLT @mylittletip shares his thoughts & insight to the biggest horse race of the year!

There are two ways to study horse racing. One is by the formbook: watching all the races and using your eyes and ears to form your own opinions and judgement. There are some incredibly gifted form students out there whose form bank memory is outstanding and the better ‘judges’ make a good living out of it by that method alone. Some use speed figures, handicap figures, pace analysis statistics and many more ways of battling it out with our old enemy, the bookmaker. The second way is computer modelling.

Personally, I like both ways as it gives a little more perspective; however racing data is a very powerful tool in the right hands. It never fails to amaze me how many people think they can outsmart a computer that can work out extremely accurately and in milliseconds the price of a horse to win a race, whereas a team of the smartest human brains couldn’t work it out as accurately in a year. So many people scoff at the idea but I believe that is purely out of ignorance as they are not grasping what computer modelling is and why it is so effective. In today’s world computer modelling is in virtually every other industry. That said, there is no right or wrong way; everyone is different but I’m sure that both camps will be backing the same horse in this year’s Grand National.

From the form book point of view, Tiger Rolls’ win in the Boyne Hurdle had me scratching my head because, while the number crunchers out there rated that run around a stone better than previous form, he could easily have been rated more. Bare in mind, Gordon Elliott would probably have left a little bit of work to be done with him that day and he was eased up over an inadequate trip, so you can see why I was perplexed as to why this had happened. However, this puzzle fell into place at Cheltenham where it was obvious, to even the many Elliott knockers, that this 9-year-old Godolphin cast off had indeed improved way over a stone. He decimated the field in the Cheltenham Cross Country Chase and drew clear in a manner you will rarely see again. Once again, as he eased to a facile victory, my head was telling me as he trotted past the line that not only had he barely had a race, but more importantly had the race gone on for another half a mile he could have won by a bus ride! I cannot ever recall anything as impressive and compelling going into a Grand National, but if there is anything that can stop him following up last year’s National win, the weight on his back will not be it.

We all know that the Grand National is a bit of a lottery and with horses brought down, unseating, falling, refusing etc etc you are always in the lap of the Gods as a punter and many a deserved winner has not completed the race through no fault of their own. This however is where the computer is impartial and assesses the risks attached with no emotion whatsoever. Everything can be quantified and the bottom line is what price the horse should be.

Tiger Roll may be small for a chaser but he has never fallen in 34 races and only has one unseating to his name. 4 pulled ups (two lame), 11 wins, 6 seconds, 3 thirds and 4 fourths. Now looking at Grand National statistics over the past 20 years there have been 14% unseat, 2% brought down, 23% fell, 2% refused, 20% pulled up and less than 1% for other reasons. Basically, there is a 62% chance that something will happen during the race but this needs to be adjusted slightly to each individual horse, as clearly the chance of Tiger Roll falling is less than most others. However some other stats stand, for example the chance of getting brought down remains as he nearly found out on the first circuit at Becher’s Brook last year, when it was only because of the superb, quick reactions by Davy Russell that prevented him from running into ‘I Just Know’ who fell in front of him. It was that close from being yet another ‘winner that wasn’t’ so the risks are real regardless of how well handicapped he might look.

But again, let’s remain impartial and work it out as best we can. I have revised what I see as the risks that lie in wait, with the aid of my trusty PC, to: 11% unseat, 2% brought down, 6% fell, 2% refused, 17% pulled up and less than 1% other reasons. That’s around 39% but, to air on the side of caution, I will build in an extra 10% on top so that’s an estimate of 43% chance that Tiger Roll won’t make it round for whatever reason.

Now we need to multiply that by the chance he wins the race, assuming he has finished. This takes more imagination, but going strictly on the weights of his last two runs, he would be a very short price. Whichever way you look at it, it’s nowhere near the current price and whilst you can argue he may be a bigger price on the day, that is not 100% to be the case, so I feel it would be prudent to stake a small wager on him now with ‘non-runner no bet’ and have another larger bet on him on the day, when the bookmakers will be actively trying to lay him to the general public. That way we can get some steady runs on the board now before smashing one out the ground on the day!

It is also important to consider that Tiger Roll is versatile on all types of ground. Last year’s Grand National was run on heavy ground, the Boyne win was yielding whilst Cheltenham was soft and he has also won twice on good ground. Whilst he finished tired last year, which was understandable on heavy going, he has clearly improved a whole chunk since then. At 9 years old, the ‘trends’ guys amongst you will be happy. In the last 20 years running of this showpiece, horses under 8 are 0/51 runners whilst over 11 are 1/81, clearly 8 to 11 is where you would pitch it given the choice. Also horses who last ran between 6 and 20 weeks prior to the Grand National are only 2 wins from 224 runners which, incidentally, will be approximately a third of this year’s field, so a recent run would statistically be preferential and with that Cheltenham run under his belt that follows last year’s pattern too.

Staking is always a critical part of gambling for long term profit and as there is a chance Tiger Roll’s price could drift, I would advise having 1/4 of your intended stake now (NRNB) and the remaining 3/4 on the day with B-O-G at 5/1 or bigger. At least that way should the price shorten further it’s not the end of the world should he win.

This write up has been done free of charge and after all it is just a tip on a Grand National favourite at the end of the day. Should he win the race, and you have not contributed before now, I would appreciate it if you could donate just a tiny amount of your winnings to either of my chosen charities: Young Minds or Clic Sargent (links on my Twitter bio page).

Many thanks for your continued loyal following and support.

MLT (MyLittleTip)

The post UK Biggest Horse Race of the Year ‘The Grand National 2019’ appeared first on Sports Trading Network.

In this Masters preview, we discuss the predictive content of both course history and experience at Augusta National using data from every edition of The Masters since 1995. We then provide some specific predictions from our model, and highlight a few examples where our odds disagree with the market.

How important is experience?

Apart from the first two playings of The Masters, only Fuzzy Zoeller in 1979 has managed to win in his first appearance at Augusta National. At the time of this writing, there are 17 rookies in this year’s field; should we discount their chances of donning the Green Jacket come Sunday due to a lack of experience?

Shown below is a plot of the average strokes gained (i.e. strokes better than the field per round) for golfers with differing levels of experience at The Masters:

What’s going on here? There is an obvious confounder: namely, the “baseline ability” of each golfer. Those players with little experience at Augusta tend to be worse golfers (because they are younger, or just have never qualified before), while golfers with a huge amount of experience tend to be past champions who are past their primes. (The overall average strokes gained in the plot is above 0 because players with fewer than 20 rounds played in that season are excluded.)

Applying the strokes gained statistical model

To properly assess the importance of experience to performance, we need to control for each golfer’s baseline ability. Using a statistical model, we are able to obtain estimates of each golfer’s expected performance at every playing of The Masters. For a rough intuition, think of a player’s expected performance as their average (adjusted) strokes gained at all tournaments throughout that year.

• Read: How to bet on golf

For example, heading into the first round of the 2018 Masters, our model’s expectation for Jordan Spieth’s performance was 0.8 strokes gained over the field. (If this number seems small, remember that The Masters is a strong field.) Ultimately Spieth’s 66 was about 7.4 strokes better than the field’s average – meaning Spieth performed 6.6 strokes above his expectation in that round.

This next plot includes expected performance alongside actual performance as a function of experience. Only the fitted curves are included for clarity – just as in the plot above, the averages by experience bounce around these curves. The difference between expected strokes gained and actual strokes gained is an estimate of the effect of experience on a golfer’s performance at The Masters.

The plot indicates that rookies perform slightly (~ 0.1 strokes per round) below what their baseline predictions would indicate, while golfers with more than five years of experience tend to perform slightly above their expectations.

Put another way, if two golfers have identical baseline abilities, but one has 10 years of experience at Augusta while the other has none, than we would expect the former golfer to score 0.2-0.3 strokes better per round than the latter.

Note that the experience effects must sum to zero (one golfer’s gain is another golfer’s loss); this is the case in the above plot as there are more golfers in the low-experience bins.

Assessing course history

Now, let’s briefly discuss course history. As in the experience analysis, to assess whether or not a player’s course history has predictive power we look at golfer performance relative to expectation. The question to ask is: does performing above expectation at a course in the past predict above-expectation performance in the future?

Not surprisingly, the predictive power of course history depends critically on the number of rounds in that history. Because most players’ course histories are comprised of less than 15 rounds, overall it will be true that course history has very little predictive power.

We have found that about 2-4% of past performance is carried over to future performance using all course histories (ignoring sample size). That is, if a player performed 1 stroke above expectation in the past at a given course, we would only predict them to perform 0.02-0.04 strokes above expectation in their next round at this course.

The Masters is unique in that it is played at the same course every year. Therefore, for some players, very long course histories can be constructed

However, there is no reason to group all course histories together. As the sample size increases, course history does become meaningfully predictive. For example, using histories of at least 30 rounds, we find that 20% of past course form is maintained in future rounds.

It’s important to note that as the sample size increases, the variance in course histories decreases quite dramatically (e.g. while it’s not rare to find a player performing 1 stroke above expectation for 4 rounds, it is rare to find one doing so for 30+ rounds).

The Masters is unique in that it is played at the same course every year. Therefore, for some players, very long course histories can be constructed.

The larger the course history sample, the more strongly we weight past performance at Augusta National: this ranges from just 2% for histories of four rounds or less, up to 30% for the longest histories.

The tables below display our model’s baseline predictions, as well as the adjustments for course history and experience that could be made to the baseline predictions, for a few different samples of players in this year’s field. Everything is in units of strokes gained relative to the PGA Tour average performance.

A few notes are in order. First, most (but not all) of these adjustments are small in magnitude. The adjustment for experience is small in all cases, and the adjustment for course history becomes meaningful (e.g. > 10%) only for larger sample sizes. The differences across players in this adjustment are relatively small compared to the differences across players in their baseline predictions.

Second, Phil Mickelson’s record at The Masters is truly remarkable. In 98 rounds at Augusta National, Phil has averaged 1 stroke per round above expectation. This is noteworthy because of the large sample size. He is just the 33rd ranked player in this field in terms of baseline predictions, but is the 23rd ranked player after the adjustments are made.

Third, despite having the best course history at Augusta National at +1.76 strokes per round, Jordan Spieth does not receive the biggest adjustment due to his relatively small sample size. In other words, it is likely that a significant part of those 1.76 strokes is simply a product of randomness.

Comparing model odds vs. market odds

Finally, let’s finish with a discussion of our model’s odds as they compare to the market odds. A cursory look at odds from a few popular books makes it clear that the market is heavily weighting course history and, perhaps to a lesser degree, experience at Augusta National.

For example, Jordan Spieth is priced as around the 8th to 10th favorite by most books. According to our model, to reach the top 10 players in this field Spieth’s ability needs to be increased by nearly a full stroke per round on top of the positive adjustment we have already given him. Unless you believe Spieth will continue to outperform his expectation by well more than a stroke per round at Augusta National, the market is overvaluing him this week.

Conversely, Gary Woodland and Patrick Cantlay are examples of players our model likes relative to the market. Followers of our work know that this tends to be the case every week, but this week the gap seems especially pronounced as both Woodland and Cantlay have poor histories at Augusta National.

• Golf betting strategy: Success = talent + luck.

In the experience analysis, we found that rookies at The Masters do not underperform their expectation by much. It’s clear in the market odds that rookies are being penalized (even after accounting for the fact that the rookies have lower baseline abilities).

Two rookies that our model likes the most relative to the market are Aaron Wise and Keith Mitchell. In general, taking a positive stance on rookies seems like a good idea this week given how the market is pricing them.

To conclude, the overall takeaway should be to not get swept away by narratives surrounding The Masters. We have shown that the years of experience a player has at Augusta has little predictive power for their future performance. However, how a golfer has performed historically at Augusta National does warrant some attention if they have a long enough history.

As always, it will be exciting to see how everything plays out at this year’s edition of The Masters.

 

About the author

Data Golf’s Matt and Will Courchene are brothers who founded Data Golf, a website that uses data and statistical analysis to provide unique insights into the sport of golf. They write about how to use statistics to better understand performance in golf. Both brothers are lifelong golfers and fans of the sport.

See more articles by Data Golf

The post 2019 US Masters predictions appeared first on Sports Trading Network.

What is a bubble?

A bubble occurs when an asset is valued in excess of its intrinsic value. In betting terms the intrinsic value of a bet is the true probability of the event occurring.

When placing a value bet a bettor is looking for an event where the “intrinsic value” probability of the event occurring is more likely than that implied by the odds.

Finding value in a bubble: Subprime mortgages

Michael Lewis’ “The Big Short” tells the story behind the US Subprime Mortgage Crisis focusing on the few who saw the crash coming and managed to short, or bet against, the housing market.

According to Lewis there were only around 20 parties in the World to do so. One such firm was investment firm Cornwall Capital.

“Cornwall seeks highly asymmetric investments, in which the upside potential significantly exceeds the downside risk. The firm has produced an average annual compounded net return of 40 percent (52 percent gross).”

Cornwall Capital’s Jamie Mai outlines their approach to trading as seeking situations where they had “conviction that the odds were substantially mispriced, providing us positive expected value”.

The necessity of shorting for market efficiency

So why were Cornwall one of the only parties to bet against a clear bubble with such high expected returns available? After all, Pinnacle’s odds show us how efficient betting markets are (and shorting is just a variant of betting). Surely the market should have corrected itself?

The issue here was the inaccessibility of the shorting option. Unlike a betting market, where staking is easily accessible to all parties, betting against the housing market was convoluted.

“Basic finance theory says that if there’s no way to invest and profit from an asset’s decline, the price is determined by the most optimistic buyer”

In essence, those optimistic about the housing market could easily bet on a continued rise by buying houses or mortgage debt bonds. The only easily accessible option for those wishing to bet against the market was simply to sell housing (although even sceptics need a place to live) and not buy bonds.

The Bitcoin bubble is another such example where the market price was set by optimists, causing over-valuation and an eventual crash. Optimists could buy Bitcoin whereas, at least initially, the only option for sceptics was to not hold Bitcoin themselves.

Interestingly, in a sports betting context we are looking for the opposite: the price to be set by the most pessimistic seller. The price needs to be “wrong” without the liquidity to correct it.

In this interview with datacamp Pinnacle’s head of trading Marco Blume talks about using Pinnacle’s customers as an “army of consultants” who can predict outcomes better than Pinnacle’s traders:

“I try to really separate for them [traders], you think you might know something because you’re sitting on this side of the table, but if you could, you would sit on the other side of the table maybe.”

With such a sharp clientele setting the lines how could a betting “bubble” ever exist?

Have we ever seen a sports betting bubble? Mayweather vs. McGregor

Whilst the betting market is usually rational there is perhaps evidence of some very rare bubble-like behaviour. The clearest example of this that comes to mind is the Mayweather vs. McGregor boxing match.

Take a look at this graphic posted to the Pinnacle twitter account prior to the fight:

Recent Mayweather opponents

Fighter	Achievements
Maidana	WBA Welterweight champion
Cotto	WBA light middleweight champion
Berto	Two-time former welterweight world champion
Guerrero	Multiple-time world champion in two weight classes
McGregor	Never boxed professionally

Pinnacle’s odds on the morning of the fight implied Mayweather (1.196) had an 83.6% chance of winning the bout. In contrast, he was as low as 1.02 with some bookmakers, or a 98% implied probability, to beat WBA interim welterweight title holder Berto prior to that fight.

In fact according to probabilities implied by betting odds, of Mayweather’s opponents since 2010, only Manny Paquiao and Canelo Alvarez had a better chance of beating Mayweather than the untested McGregor, and even Alvarez only narrowly so.

Favourite-longshot bias does not adequately explain this behaviour since Mayweather offered what looked to be a clear value bet, yet sharp customers still did not come close to correcting the market (Mayweather’s true odds to win the fight should proabably have been in the region of 1.01).

Arguably the entire bookmaker margin and more was placed on the side of the Irishman, allowing Mayweather to offer strongly positive expected value bet.

Perhaps the barrier here was the sheer volume of money needed to counteract the payout on such a highly backed outsider. Value bettors simply could not wager the volume required to correct the inefficiency.

This was truly a market dominated by the most optimistic buyer. Ordinarily the sharper price-sensitive customer would have gone some way to correct this but, not unlike the housing and Bitcoin bubbles, the Mayweather backers were easily outweighed by optimistic McGregor bettors.

What lessons can be learned from the two sides of market efficiency?

When markets don’t have equal flows of information represented by money staked, they can get out of sync and inefficiencies appear.

Mayweather vs. Mcgregor style inefficiencies, where over-optimistic backers distort the market, are incredibly rare since sharp money usually dictates the lines. As bettors we are usually looking for odds where the oddsmaker has been pessimistic about the chance of an event occurring.

It is perhaps more practical to look for situations where bookmakers have miscalculated odds initially, and there is not the two-way money flow needed to correct the market.

Perhaps anchoring bias could ensure that bettors have not looked too deeply into the reasons behind this and the price has not yet been corrected. As Marco says, traders are not necessarily more informed than bettors – so finding isolated cases where markets have not been able to correct themselves could offer value.

 

About the author

Luke Petty studied Economics at The University of Exeter before embarking on a career that combines two of his greatest interests, writing and sports betting. Despite a keen interest in soccer, he is open to exploring any sport where value might be present. He particularly enjoys analysing markets from different perspectives to try and find a profitable edge.

See more articles by Luke Pett

The post What can sports bettors learn from economic bubbles? appeared first on Sports Trading Network.

Following on from of his successful Grand National article we are delighted to publish this exclusive content from @mylittletip.

Why 97.3% of people should NOT do multiples.

Let’s start with the basics. Toss a coin and there will be a 50% chance of heads and 50% chance of tails. Over a long period of time the number of heads and tails will level out. Assuming you have single bets if you take Evens (2.0 decimal price) you will break level, take 10/11 (1.9) you will lose, take 11/10 (2.1) you will win. If you grasp that read on!

Now using a 6-sided dice, each number has a 16.67% chance or ‘5/1’ (6.0 in decimal odds). In a nut shell bookmakers will try and offer you 4/1 on a dice throw thus making themselves a long-term profit at your expense. Even worse an example is a multiple where a treble taking 4/1 each dice throw pays 124/1 when the true odds are 215/1. That is why most punters lose and should never do multiple bets. A casual gambler is happy to take 4/1 as he wants a bet and/or a winner. However, a professional gambler is looking for bigger than 5/1 in his quest to make long term profit. Offer a pro gambler three x 6/1 chances on a dice throw and they will try their hardest to get a treble on as well as the true odds which are 215/1 yet they are trying to get 342/1 for a 59% mark up! A pro gambler will ALWAYS maximise an opportunity.

However, there are bets whereby you can actually take 5/1 on the throw of a dice and make a long-term profit, even though on the face of it there is no edge to the bookmaker or the gambler. One such bet is the Lucky 15/31/63 (Lucky 15 consists of 4 singles (ABCD), 6 doubles (AB, AC, AD, BC, BD, CD), 4 trebles (ABC, ABD, ACD, BCD) and one accumulator (ABCD). The reason being is that most bookmakers (not all you need to check first) offer double the odds for one winner (some even treble) and if all selections are successful a 10% bonus (some 25%) on the whole return, not just the accumulator. In this instance, guessing 4 correct ‘dice throws’, the chances are actually 1295/1 but the price they are offering you is 1424.6/1 on the 4fold with the 10% bonus alone so although this will only statistically occur one in 1296 throws, it is a marginal gain and with normal ‘luck’ you will be in front over a long period, but, in the very long run, you will be way in front. Add to that the 10% on the four Trebles (215/1 you get 236.6/1), the six Doubles (35/1 becomes 38.6/1) and the four Singles (5/1 becomes 5.6/1). Also add to that the double the odds on one winner which will happen on the majority of the Lucky 15 bets (again a small % extra but more marginal gain for the punter) so you can see that although it’s a very tiny edge, you have turned it around from breaking level had you done singles. Remember, this is for LONG TERM PROFIT and there will be many lows along the way with the majority of Lucky 15s showing a loss obviously.

Now let’s look at it IF you can get 6/1 for the dice throw. The accumulator odds are 1295/1 on 4 dice throws but you are getting paid 2400/1 which becomes 2640/1 with the 10% all correct Lucky 15 bonus (remember the actual odds are 1295/1, a 103.7% mark up!!!), the Trebles are 343/1 (now 376/1, true price 215/1, a 74.5% mark-up), doubles 48/1 (now 53/1, true price 35/1) and singles 6/1 (6.7/1, true price 5/1).
Even the worst mathematicians amongst you can see as clear as day that the Lucky 15 is miles better value than singles for those who can get 6/1, as the mark ups are massively favoured to the punter, compared to the small margin of a single. Yes, this is only when all four wins, but remember, that is irrelevant as the long-term profit is what makes a professional gambler good as they have brilliant bank roll management and are immune to the ups and downs of winning and losing on a daily basis.

The reason I use this example is that professional gamblers are great at spotting value and often looking for 6/1 on a 5/1 chance and as you can see when multiplied out over numerous dice throws, over numerous years, you will be way more in front than if you had just bet on singles. The normal ‘punter’ however say “Yeah, but you get one winner and you lose” or “Yeah but horse racing is random so you can’t attribute a price”…well sadly there is no helping this type of punter as they can’t think past one bet on a day to day basis, with horrific bank roll management and non-existent staking plans with knee jerk reactions to a few losers etc as they have no concept of statistics, mathematics, probability or profitability. More importantly that type of punter has no idea what computer modelling is! They are just desperately bad gamblers and sadly bookmakers love to exploit them with multiple bets to maximise their profits whilst heavily restricting/banning pro gamblers from doing them.

I honestly feel for them not being able to grasp it. I do however find it abhorrent when these losing gamblers then try to sell ‘tips’ as they are so bad in understanding long term profitability that they lead the unsuspecting betting public down a doomed long-term path, often taking 4/1 singles on 5/1 pokes. This type of punter/tipster is why the bookmakers love to promote the Lucky 15 in betting shops and online. The dice can land on any number, so the daily result is utterly irrelevant in making long term profit IF you have a controlled staking plan. It should be easy to grasp but for some reason only statisticians and pro gamblers take the long term view and bookmakers will continue to make millions from poor tipsters/gamblers sadly, especially on bets such as Lucky 15’s because finding 6/1 a 5/1 chance takes extreme skill and a talent that 97.3% do not have.

So the next time a tipster or punter tell you that a multiple bet with bonuses is a ‘mug’ bet you should be sporting a very wry grin on your face knowing for a fact they are actually one of the 97.3% of losing gamblers as they have just inadvertently shown you their losing hand. They will brag to winning thousands gambling but they cannot even work out the simplest of maths puzzles and will try to hoodwink you with ‘bull’. This type of losing gambler should NEVER bet but if they do, they should stick to singles as the mark up is less than multiple bets for them and therefore their losses will be less in the long run.

These are not my opinions, just facts that will be a bitter pill for some to swallow. Meanwhile, in the real world, the rich get richer at both ends of the spectrum, 2.7% of punters and bookmakers.

Many thanks for your continued loyal following and support.

MLT (MyLittleTip)

The post The Lucky 15 appeared first on Sports Trading Network.

Betting consensus numbers, or bet splits, are some of the most popular and highly-circulated pieces of betting content on the internet. However, there are limited benefits in using this kind of information when deciding which outcome to bet on for an event. Read on to find out why the kind of betting data you use is important.

Betting consensus numbers are certainly interesting and can often give us insight into what a sample of bettors think or what influences their decisions. Unfortunately, if you’re using it to try and identify a value bet, the data isn’t as useful as you might think and can often lead bettors to make poor decisions. Here is a look at the downside and what bettors should focus their energy on instead.

What is the betting consensus?

The betting consensus is a breakdown of all the bets on an event expressed in a percentage form to show which team has more action. For example, if 100 bettors place a bet on a match, and 60 bets are on Team A and 40 bets are on Team B, the consensus report will read Team A 60% – Team B 40%.

• Read: All you need to know to beat the bookies

Bettors will often look for large splits in the consensus and fade the higher percentage believing that the majority of bettors will be wrong in their prediction.

Why is it popular?

As the reach of sports betting increases into mainstream media, betting consensus numbers are becoming more popular than ever. Many bettors believe that the bookmaker never loses, and the appeal of being able to side with bookmakers on a decision instils many with confidence in their bets.

The willingness of an audience to latch on to these percentages has made consensus percentages a staple talking point across all content levels.

What is the downside?

I encourage bettors to avoid acting on the information derived from betting consensus numbers alone at all cost for a few main reasons:

Empty work doesn’t pay

Sports betting is bettor vs. bettor. Whoever has the most information has the biggest advantage. Studying betting consensus numbers is empty work. Even if the consensus numbers are accurate, none of the effort put in to decipher them can be carried over to the next day. Consensus numbers apply only to present events and are not predictive in any way.

Misaligned intent

Bettors like consensus numbers because they believe it aligns their need to win with the bookmaker. The problem is that no consensus percentage will express the true need of a bookmaker and seldom will it reflect the financial decision that matters to them.

• What can sports bettors learn from economic bubbles?

In my half-decade working as a bookmaker, rarely did pre-game straight bets account for more than 30% of the full handle on an event. Recent gaming surveys for 2018 show that some bookmakers earn up to 80% of their revenue off in-play bets.

An unknown source

“Fade the public” is unfortunately a popular term in sports betting. Many bettors will blindly oppose any large consensus percentage and believe their contrarian approach is due to pay off long term.

The big problem here is that the betting population which makes up the percentage is completely unknown. Betting percentages on the same event differ from bookmaker to bookmaker as do the clientele betting. Bettors opposing consensus percentages are blindly betting against an unknown popular and doing so without any context.

Limited opportunity

If bettors are only looking at events in which they estimate the house has a large liability, they limit their opportunities. Sports betting markets are extremely competitive and finding an edge is critical for success. If the potential to find an edge is limited to a small number of the total events per day, the bettor is at a huge disadvantage against the rest of the market.

What can bettors look at instead?

Every minute spent studying teams and odds should be done in a way that knowledge can compound over time. Rather than trying to find a value bet just from consensus percentages, bettors should focus their energy into understanding how bettors in the market interact with each team.

Studying margins, and seeing which teams move in price at which bookmaker, and how the movement affects the other bookmakers in the market, can provide valuable insight that can be used in the future.

Bettors should always be asking questions like;

• Which teams move early?
• Which teams move late?
• Is this a set-up, or a true position?
• Is this a low margin move, or recreational move?

Once answers to these questions become automatic, time wasted on consensus percentages will become clear.

About the author

Adam Chernoff left home in Canada at the age of 18 on a one-way ticket to the Dominican Republic. He had $2,000 in his pocket and wanted to see how long he could last betting on sports. It has been ten years, and he is still going. Over the past decade, Adam has worked his way up through betting companies in The Bahamas and Colombia. His articles are a product of a unique perspective and experience.

See more articles by Adam Chernoff

The post Why you need to look at betting market data in more detail appeared first on Sports Trading Network.

I think that it is important for bettors to compare sports betting with everyday life experiences. While many of us will never think about being sommeliers, an interesting documentary sparked my interest and led me to look at sports betting in a different way.

Blind tasting

Occasionally when the betting markets are slow, I turn my mind off and watch something on Netflix. I recently found the documentary “Somm”.

The movie was about four men who attempted to pass the master sommelier exam. The master exam is considered to be among the most difficult tests in the world to pass.

Those who attempt to pass spend years obsessing, studying and learning everything there is to know about wine from its history, to geography and of course, its taste.

• Read: Do you know how difficult betting is?

The blind tasting is the final part of the exam where participants will taste six different unlabelled glasses of wine and try to trace the origin and vintage by putting the wine into different categories using sight, smell, and taste.

When one exam taker on the movie exclaimed that he noted an aroma of “fresh tennis balls in a can” and “fresh rubber hose” from one of the glasses of white wine, I nearly vomited at the tone of condescending snobbery.

But as the process repeated and future sommeliers explained their methods, I could not help but make the connection to sports betting.

Reading a market

An essential part of my betting process is to read a market. With limited context I evaluate market prices and use numerous signals and signs to begin to tell a story about the market.

When odds change, determining if the move was based on a large number of bets or a few small bets for large amounts can be useful.

Sommeliers look for viscosity, volume, body, bitterness, and tannin to trace the origin of wine. I look for early movement, resistance, time stamps, existing precedence, bookmaker margins and where bettors are placing their bets to trace the origin of bets and identify value.

My goal with every market I read is to provide as much supporting information to build a profile of a market. The profile puts a game in a certain market situation that helps to determine the best time to place a bet.

Existing precedence

Making note of any existing precedence is important. Many bettors will only note the odds in previous matchups of teams or common opponents.

I look further to determine what the market conditions were like for those prior games. Where did the odds open? Where did the odds close? What other indicators were present?

The more information that can be gathered on how bettors made their decisions in similar situations, the more likelihood there is of determining if the same behaviour will repeat.

Building profiles

Once existing precedence is established I build a profile. These are examples of common indicators I look for when walking back a market.

• Read: The Green Lumber Fallacy

Determining if these indicators are present will allow me to evaluate the market conditions and make my wager at the best time. I search for all indicators by looking at the market as a whole, and not just at one bookmaker. These factors include:

MarginsI always begin by calculating margins. It is extremely important to know which bookmakers are offering the lowest margin price and which are offering the highest margin price. Low margin bookmakers typically cater to professionals while high margin bookmakers service recreational gamblers.

Using any odds comparison website will show all market movements with time stamps.

Early movement and stabilityEarly movement refers to changes in the odds within 5-10 minutes after the market is open. Sharp bookmakers like Pinnacle welcome winners and will encourage influential money to enter the market early to shape the best price.

If a bettor can identify bookmaker’s margins, note early line movement and spot stability, then volume moves or influential moves become very clear.

Identifying early movement can be a good indicator of where market makers were short and needed to adjust.

Early movement can often times be a cause of market manipulation where influential bettors intentionally bet on a team to move the odds to create a better price for the opponent. Looking for the first sign of stability in the market which is represented by no line movement for an extended period of time will confirm, or invalidate early line movement.

Volume move or influential movementWhen odds change, determining if the move was based on a large number of bets or a few small bets for large amounts can be useful.

Volume moves are represented by small but consistent odds changes. Influential moves are indicated by large, quick changes in price. Looking at time stamps in comparison with odds movement is an easy way to distinguish volume moves from influential moves.

Leading sourceMaking note of which bookmaker in the industry changes their price first is important.

Often times bookmakers will adjust their prices in relation to what competing operators have available.

Since these price changes are not based off of money, volume moves can often be mistaken for a leading source move.

Late movementOdds changes that occur within 30 minutes of the event start time can be as telling as early movement. Monitoring the accuracy of late and early movement per sport is important as the accuracy of the closing price may be inflated by recreational money betting late just to have money on the game.

The above examples are just a handful of many indicators I use in reading markets. If a bettor can identify bookmaker’s margins, note early line movement and spot stability, then volume moves or influential moves become very clear.

With that baseline established, looking at the leading source can identify the true position and project market movement. Putting this process in practice will lead to better market entry with wagers and clearer reads of betting markets.

 

About the author

Adam left home in Canada at the age of 18 on a one-way ticket to the Dominican Republic. He had $2,000 in his pocket and wanted to see how long he could last betting on sports. It has been ten years, and he is still going. Over the past decade, Adam has worked his way up through betting companies in The Bahamas and Colombia. His articles are a product of a unique perspective and experience.

See more articles by Adam Chernoff

The post Walking back betting markets appeared first on Sports Trading Network.