Today’s article discusses the question what would have happened when **backing the underdog playing away from home** in the German Bundesliga?

Such a match was played in this league on 23/05/2015 between Moenchengladbach and Augsburg. The best bookmaker odds for the full-time 1×2 market at kick-off were: 1.57 Home; 5.00 Draw; 7.30 Away.

Moenchengladbach were the clear favourites at 1.57; Augsburg the rank outsiders. However, the men of Augsburg won the game, 1-3, defying their long odds.

**How regular do such things occur? Is it profitable to bet on outsiders?**

Here’s a screenshot from the ‘Backing by Odds’ tab in the simulation table for this league:

In the table above you can see that from a total of 306 matches during 2014-15, the away team won 79 times. *(Click on the table to enlarge it in a new browser tab)*.

79 of 306 is 25.8%, and this percentage shows that the away team won, on average, slightly better than once every four matches.

Looking at the profit/loss (P/L) summaries in the ‘Totals’ column, adding together the first six rows of odds clusters produces a loss of -2,564 units, based on a flat stake of 100 units per bet.

Essentially this means if the away team was priced as a clear favourite or close to the home team’s prices, they won less frequently than the probabilities indicated by their odds. The last of these first six cluster groups closes at away odds of 2.90.

Look at the second row of the table. The odds cluster between 1.66 (implied probability 60.2%) and 2.00 (implied probability 50%) contains 83 matches and, if the odds had been ‘fair’, 55.1% (60.2% + 50% / 2) of the away teams priced in this group should have won.

As you can see, this was not the case! Of 83 games in five seasons only 43 were away wins (51.8%).

Therefore, punters who regularly backed away favourites in the Bundesliga during 2010-15 surrendered ‘value’ in their bets to the bookmakers. When this happens, only one side of the deal wins in the long-run; invariably it isn’t the bettors!

Okay, **let’s take a look at the away underdogs…**

German Bundesliga – ‘Inflection Points’ tab – Five Seasons 2010-15

This screenshot shows a steep rising curve starting at odds of 4.40 and continuing until odds of 17.0.

Over five seasons, 462 matches fell into this group *(Moenchengladbach vs. Augsburg being one of them)*. The away underdog won 88 times = 19% hit rate!

In these odds clusters the away team won, on average, once in every five matches. The average betting odds were 6.40, representing a probability of 15.6%.

The curve shows, as well as the calculations (19%/15.6% = 121.7%), that the mathematical advantage was on the side of the gambler!

The P/L curve registered 653 units profit at the start of our selected segment and finished at 13,727 units. This is a difference of 13,074 units of profit located solely within the away odds cluster group from 4.40 to 17.0.

**Why does this advantage exist? How does it happen?**

Most bettors prefer betting on the more popular and ‘emotionally safer’ shorter-priced favourites, but please ask yourself the following two questions:

- How does a profit-oriented company
*(i.e. bookmaker)*set its prices? - Should the prices
*(odds)*for favourites rise or drop?

Both common sense and business acumen prevail in this situation:

* The market dynamics are the following:* The more bets expected to be placed on a particular outcome, the more bookmakers reduce their odds. Reducing odds mean that the bettor must risk more money (stake more) to achieve the same financial outcome. The punter therefore pays a ‘higher price’

Odds 2.0 → stake 50 = win 50

Odds 1.5 → stake 100 = win 50

Odds 1.25 → stake 200 = win 50

Falling odds means:

⇒ Rising stakes

⇒ Potential to lose more money

⇒ Lower percentage returns should the bet win!

**Although this relationship may seem paradoxical, falling odds means rising prices!**

* To reiterate:* Falling odds for an outcome is a clear indicator that this is a favourite.

Falling odds mean bookmakers are effectively raising the price for the product! The product itself does not change in the slightest (i.e. betting on the favourite), but it becomes more expensive to buy. The bettor has to risk more money in order to win the same amount. In this case, you do not get ‘more for your money’, but considerably less!

Let’s use a different example. A confectionery company launches a new chocolate bar, which becomes an instant success. Demand increases; the company naturally takes advantage of the situation by raising the price. You can certainly make the statement that if the price of the chocolate increases it is a ‘favourite’, but the product itself never changes – it’s still a 100g chocolate bar!

The last word here is that since the books have to be ‘balanced’ *(i.e. the payout of all three 1×2 bets combined needs to add up to around 100%)*, whilst the ‘prices’ for favourites are lowered to take advantage of the demand, on the opposite side, the odds for the underdogs rise.

This article is a step-by-step guide explaining how to compute the probability that, for example, * exactly* 4 out of 6 picks win, or how to calculate the likelihood that

To help your understanding of this topic you will need to comprehend the basics of football result probability calculations, which I explained in detail in the article **Calculation of Odds: Probability and Deviation**.

The following picks table contains 6 **value bets** including the **calculated probabilities** for each bet to win:

Of the 6 published picks, 4 won and made a profit of 19.9% on the 50.00 € betting bank. I will now attempt to explain the mathematics behind the above selections.

The calculation of the **probability that all 6 Picks will win** is relatively easy and requires no knowledge of difficult formulas. You simply multiply together the given probabilities, thus:

The result of **6.3%** is the probability that all 6 picks in the portfolio win.

Of course, the other end of the scale is that **all 6 picks will lose**. Again, this is a straight forward calculation: simply multiply the opposing probabilities to those used in the ‘win’ scenario, thus:

The result of **0.1973%** is the probability that all 6 picks lose.

- Probability that all 6 Picks win: 6.3%
- Probability that all 6 Picks lose: 0.1973%

If you divide 6.3% by 0.1973% the result is 31.93. This means the probability in this particular portfolio that all 6 picks win is almost 32 times higher than the probability that all 6 picks lose.

Practically speaking, there is a 32 times higher chance of winning all 6 bets and cashing 40.90 € profit than losing all 6 bets together with the entire 50.00 € starting bank.

- To win all 6 picks:
**15.9**(1 divided by 6.3%) - To lose all 6 picks:
**506.7**(1 divided by 0.1973%)

These odds express that on average all 6 selected bets should win once in every 16 rounds and only once every 507th round should a total loss of the portfolio occur.

A single season’s football league betting will usually comprise approximately 80 rounds of matches (midweek and weekend betting). This means that statistically a total loss may happen once every 6.3 years betting on a similar portfolio to the example above each time. Of course, it could happen more often as wins and losses have a nasty habit of not lining up as cleanly as statistical theory says they should. For example, 2 total losses could occur in the first 2.6 years and then no more for another 10 years.

Further interesting questions include what are the probabilities that exactly 5 of the selected 6 picks win, or at least 4 of the picks win, and following this, it is natural to ask whether it is viable to make long-term profit on this type of portfolio and if so, how much?

An easy starting point for assessing whether a portfolio is ‘worthwhile’ is by calculating the ‘expectancy’, in other words, how many of the picks are likely to win. This is simply the average of the win probabilities of the selected picks:

This value means that by betting on the above portfolio a success rate of 63.55% is ‘expected’, which would correspond to a hit rate of 4 from 6 picks (i.e. 6 [picks] times 63.55% = 3.81 [roughly 4 picks]). This means that on average this portfolio should usually bring around 4 successful picks. However, it is obviously necessary to check if the combination of 4 successful picks and 2 failed ones will produce a profit:

The above illustration shows that every combination of 4 picks from our 6-match portfolio would have returned a profit of between **7.02 €** and **16.71 €** depending upon the combination.

Please note that the average value (expectancy value) does not mean a 63.55% probability that exactly 4 picks will win every betting round. The average value indicates that if you bet on this type of 6-match portfolio often enough, an ‘average’ of 4 hits can be expected.

The method described below explains how to use our **HDA simulation tables** for recognising profitable 1×2 betting strategies and **building a portfolio** from a selection of major European leagues.

Profitable betting on football is about compiling successful portfolios and understanding the underlying market economics.

The following analysis portrays just one successful scheme in detail – Have fun learning about market behaviour and deriving a betting system from it!

It is impossible to predict with total accuracy the outcome of one particular match; however, it is possible to identify and use historical distributions of data to judge the future in general.

If you do not know what the term ‘distribution’ means, check out this article for an introduction:

**Goal Distribution Comparison – EPL, Bundesliga, Ligue 1, Eredivisie**

However, understanding distributions, odds calculation and probabilities is only the first step.

The next step is to understand the market economics. Just in case you missed them here are two articles describing how the bookmaker market works:-

**How do Bookmakers Tick? How & Why do They Set Their Odds as They do?**

**How Bookmakers’ Odds Match Public Opinion**

**The main message of these two articles:**

- Bookmakers set odds based on a mixture of statistical probabilities and public opinion. Effectively, their odds match public opinion.
- Bookmakers do not speculate (gamble). Their
**priority**is__balancing the books__.

Remember your basic economics lessons in school or college which were about **supply and demand**.

*Adapt this to the football betting market:* In which situations will bookmakers reduce their prices (odds), and which prices will increase as a result? Which bets are traditionally the most popular?

The fact is the majority of punters prefer betting on favourites up to odds of 2.5. Just look at online odds comparison sites which show the percentage distribution of bets on a certain outcome. It is frequently above 60% on the favourite *(independent from the offered odds)*, if not higher.

On the other hand, consumer demand for bets on the underdog is often much lower than the actual chance of it winning.

Bookmakers are aware of this market behaviour and try their best to predict trends, time the market, and choose the best outlets for their odds. Customer behaviour is well analysed and used to generate various marketing strategies aimed at balancing the books and boosting sales.

Therefore, for the bettor, it is safe to assume that many favourites will be under-priced to win, and draws and/or away wins will be over-priced to “make up and balance the book”.

For example, a traditionally strong team like Bayern Munich playing away will, of course, attract a good deal of punters betting on them to win rather than any weaker opponent playing at home. However, most punters are normally ignorant of the fact that even teams such as the mighty Bayern Munich win approximately just 50% of their away games.

In these game constellations bookmakers, simply by following market economics, have to reduce their prices for the (away) favourites massively and balance this by increasing the price of the less fancied home team.

From what has been explained in the previous chapter it should now be obvious that favourites are often under-priced to win, and draws and/or away are frequently over-priced. Therefore, it should be possible to find a workable strategy using this knowledge.

Now comes some maths… hang in there!

In the last five seasons, a total of 1,900 matches were played in the English Premier League (EPL), of which, 46.74% finished in a home win:

EPL: Full-time 1×2 distribution – Five seasons 2009-14

The home team was priced the favourite in 1,351 of these matches *(home odds lower than the away odds)*, and a total of 763 games did indeed end in a home win, equating to 56.48%.

EPL: Favourite home wins – Five seasons 2009-14

The balance of 549 matches saw the home team priced as the underdog *(home odds higher than the away odds)*. From these games, 125 finished in a home win for the underdog, equating to 22.77%.

EPL: Underdog home wins – Five seasons 2009-14

Now **convert** these favourite and underdog win percentages into odds:

Home wins (Favourite): 56.48% = 1.77 [European odds]

Home wins (Underdog): 22.77% = 4.40 [European odds]

The above two odds are “**inflection points**”, the points on a curve at which the curvature or concavity changes from plus to minus or, from minus to plus. *Translated into layman’s language…* the pivot points along the profit/loss curve where profits turn to losses or, where losses turn to profits.

However, these are purely the mathematical inflection points and do not take market forces into consideration.

Therefore, please do not start betting on every favourite at home priced below 1.77 in the EPL, or on every underdog playing at home priced above 4.40. *(Although following this simple strategy would have produced quite a profit!)*.

One of the phenomenons of probability is

In other words, the maxim of gambler’s ruin is that if you play long enough you will eventually go bankrupt and have to quit the game prematurely.

Collage of Shutterstock images; Foreground: wacpan, Background: Lisa S.

The truth is that in the world of sports betting, the common gambler has far less money than a bookmaker or casino, and there will inevitably be a time when he will simply be unable to continue playing and, of course, the house will not be giving credit.

“Long enough” may be a very long time though. It mainly depends on how much money the gambler starts with, how much he bets, and the odds of the game. Even with better than even odds, the gambler will eventually go bankrupt. But, this may take a very long time indeed.

Please note that we are talking here about a **“fair” game**; e.g. each bet with **zero value**. The practice of bookmakers and betting sites to offer **odds with an overround** in their favour makes this outcome just much quicker.

To make the dilemma of **gambler’s ruin** a little easier to understand imagine **coin flipping** with a friend. You each have a finite number of pennies (**n _{1}** for yourself and

Now, flip one of the pennies *(either player)*. Each player has a 50% probability of winning (head or tail). If it’s a head you win a penny and if it’s a tail you surrender a penny to your friend. Repeat the process until one of you has all the pennies.

If this process is repeated indefinitely, the probability that one of you will eventually lose all his pennies is 100%. In fact, the chances **P _{1}** and

Now let’s populate these equations with real numbers:

The above example is based on both players starting with the same amount of pennies (100 each). In other words, you and your friend have both an exact probability of 50% to end up with all of the pennies after many, many coin flips. This means that after an unknown number of coin flips either you or your friend will finish banking all the pennies. At the start, your chances are equal, and it is impossible to say who may win.

However, if one of you has many more pennies than the other, say you start with 100, and your friend with 10,000, then your chance of finishing with all of the pennies (yours as well as your friend’s) is as low as 1%, whilst your friend’s chances are 99% to win this unequal match.

To visualize the gambler’s ruin problem further, here is an overview of the probabilities of finishing with **N** amount of pennies.

Player 1 starts with * 5 pennies*. Player 2 has an

The top row shows the number of flips. The left hand column shows player 1’s current amount of money. The numbers in the table are probabilities *(click on the image to enlarge; opens in a new tab)*:

**Reading the table (examples):**

After the first flip player 1 has a 50/50 chance of ending up either with 4 pennies *(i.e. he lost the first coin flip)*, or with 6 pennies *(i.e. he won the first coin flip)*.

In 10.4% of the trials player 1 will be broke (penniless) after the tenth flip of the coin. This means that every 10th experiment of this nature player 1 will have been forced to give up after the 10th flip of the coin due to a run of “bad luck” whilst player 2 is not affected by “bad luck” purely because he has plenty of coins to sit through and survive any such spell.

82.04% of the players will still be in the game after coin flip 15. However, 17.96% of the gamblers will already have retired due to exhausted funds.

You can **download the above table** including all of its formulas, should you wish to experiment with different probabilities:
**EXCEL SPREADSHEET PROBABILITY TABLE – STARTING WITH 5 PENNIES**

In return for this freebie we would appreciate if you could share this article or give us a ‘thumbs-up’ with a ‘love’ or ‘like’ via Twitter or Facebook or any other social network site

Of course, you will now probably surmise that player 1 started with only 5 pennies, and by staking 1 penny each bet he was risking 20% of his starting bank on each coin flip, which is way too much. Player 1 should ideally have started with a much larger pile of pennies, and risked a far smaller percentage of his bank with each coin flip.

Anyway, eventually the same thing will always happen, albeit just much more slowly. Player 1 will still go broke sooner or later, * if* player 2 has an

Go to the next page, to see some more examples and illustrations…

We will also investigate the proposed stop-loss point at the end of the calendar year, and try to project what practical uses the theory behind this system may have in a different arena.

As we have seen, this is a progressive system with each stake individually calculated to claw-back all previous losses and to collect a net £100 win when the desired correct score line arrives.

In this way, the staking plan becomes exponential as we saw in the **first of our three articles**.

Taking the same staking system into its second season and, still assuming that each bet can be placed at odds of 11.0, the table for match rounds 24-46 is as follows:

*NB. The new season begins on the above staking plan table for non premier league teams, but for the top-flight teams, match 20 on the original staking plan table represents the first game of the 2012-13 season (as Premier League teams play only 19 home games in a season).*

You can see on the table above how quickly the stakes grow from game to game for teams continually failing to register the elusive 2-0 home win score line.

At some stage it will become difficult to get stakes of this size placed with any one market and therefore the system may have to rely on split stakes placed with more than one bookmaker or betting exchange to achieve full coverage of each match.

Looking ahead, staking may become more and more tricky in order to force this football betting system to its final conclusion; pursuing this course is dependent on having a very large betting bank and having the **desired result arrive before bankruptcy**.

After describing the **Football Roulette – Correct Score Betting** system, it is time to reveal the results of our first paper test.

30 teams were picked according to a popular selection process:

- 8 from the English Premier League (top 8 from the previous season)
- 7 from the English Championship (the 3 relegated sides from the Premier League plus the top 4 sides that didn’t get promoted into the Premier League)
- 7 from English League One (the 3 relegated sides from the Championship plus the top 4 sides that didn’t get promoted into the Championship)
- 8 from English League Two (the 4 relegated sides from League One plus the top 4 sides that didn’t get promoted into League One)

Our chosen score, was a **2-0** home win.

The system sounded good enough to put to the test and the following tables show the results from the 2011/2012 English League season (click on each table to open in a new tab and then place mouse pointer over the table and use magnifier to enlarge):

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Claimed by many to be very successful, this simple football correct score betting system is based on the statistical likelihood of a certain event occurring.

In casino roulette, there is a well-known method where the player waits until a colour (red or black) has not come up ‘X’ number of times before betting on it to arrive.

Image: Slavoljub Pantelic (Shutterstock)

He starts with a small amount, say £1, and if his chosen colour does not win, he doubles his stake in the following round, continuing in this fashion until finally it does win.

As soon as this happens he stops, cashes his money, and starts the game afresh.

The only limitations with this system are the minimum and maximum stakes employed by the casinos.

If the minimum stake is for example 100 units, the gambler must double his stakes with every losing round meaning 200, followed by 400, then 800, 1,600, etc.

However, casinos also have maximum stake limits which, can finally lead to the point where the player can only bet the maximum leaving him with an uncomfortable choice of haemhorraging more cash with each losing bet or cutting his losses and running.

The principle behind this particular system is easily transferrable to football betting and there are various betting related blogs recommending its usage in the high odds **correct score** market.

The strategy is to bet continually on the same score, progressively increasing stakes, until eventually the desired correct score arrives.

One of the main advantages of this system is that there is literally no maximum stake (stakes can be split with different bookmakers if necessary). The only limit is your own bank reserves.

Of course, this correct score betting strategy is also reliant on utmost discipline in sticking to the rules of the system.

This article will explore whether there is any viability in the Football Roulette Correct Score System together with our observations, improvement suggestions and advice we feel it is necessary to impart.

In football correct score betting, the odds are substantially more attractive than the traditional ‘evens’ offered by casinos betting on ‘red’ or ‘black’ in roulette.

Odds are as high as **11.0** (and sometimes higher depending upon the relative strength of the teams involved) for common full-time scores such as 2-0 or 2-1, and can be found for virtually every football match (whether using bookmakers or betting exchanges).

Advocates of the Football Roulette Correct Score System advise that it is best started at the beginning of a new football season and the main objective is to win £100 from each selected team (or whatever your fixed monetary objective is).

- For example, in order to win a possible £3,000, 30 teams should be selected.
- Select only those teams likely to win one home game 1-0, 2-0 or 2-1 during the targeted season.

**An example we have seen selects the 30 teams as follows:**

- Eight from the English Premier League (the top eight from the previous season)
- Seven in the English Championship (the three relegated sides from the Premier League plus the top four sides that didn’t win promotion from the Championship)
- Seven in English League One (the three relegated sides from the Championship plus the top four sides that didn’t win promotion from League One)
- Eight in English League Two (the four relegated sides from League One plus the top four sides that didn’t win promotion from League Two)

*Congratulations to those ‘mad’ scientists at the International School of Management (ISM) in Frankfurt and the German Sports University in Cologne whose predictions we followed throughout Euro 2012.*

Image: Pikoso.kz (Shutterstock)

*Those of you who also followed suit reaped the benefits of their statistical predictions realising a yield of between 19.7% and 25.5% depending upon which style of staking plan you employed (either fixed stake or fixed risk/win).*

However, some of their prophecies did not materialise…

For example, co-hosts Poland did not make the semi-finals.

There were plenty of goals in the England v. Sweden match but not a huge goal difference for England and in the end they struggled to win at all.

Alas, the scientists’ beloved Germany did not get a chance to dethrone the defending champions in the final despite saying, “chance, luck and statistics are favouring zis”.

Never-ze-less, ze predictions from ze Frankfurt and Cologne think-tanks produced handsome profits.

As mentioned in our original article * not all predictions will win*. Indeed, it would have been miraculous if they had all won as the mathematical likelihood was just 0.00000031% (326 million to 1 – that’s definitely the way to bankrupt the bookies!).

**Soccerwidow’s pre-tournament profit/loss estimation of the scientists’ predictions using a fixed risk/fixed win staking plan:**

*Total risk of all bets (stakes): 106.55 Units
Maximum potential profit: 140.52 Units
Realistically expected profit (10%-15% yield): 10.66 to 15.98 Units*

However, things turned out better than expected with profits of 27.22 units (25.5% yield) based on our favoured fixed risk/fixed win staking plan:

The second table represents the same bets using a fixed stake staking plan.

We feel that a **Fixed win/Fixed Risk staking plan** is the most solid and reliable form of staking for maximised profits and minimised losses, and we have just found this article **Raceadvisor.co.uk: Fixed Profits or Fixed Stakes?** which includes a mathematical experiment comparing a fixed stake with a fixed risk/win staking plan. It’s an interesting read and a similar outcome to our own findings.

However, the **Fixed Stake Staking Plan** tends to be a more popular method and although this would have also brought a nice profit of 22.65 units (19.7% yield), the **Fixed win/Fixed Risk staking plan** remains ‘smarter’ in our humble opinion.

*If you would like to analyse these calculations in a little more detail you can download our Excel spreadsheet which complements the above tables, free of charge. However, please kindly return the favour and either Twitter this article, like it on Facebook, or Google+ it.*

Excelspreadsheet Euro 2012 Simulation – Staking Plan Comparison

In our original ‘mad scientists’ article we recommended a fixed win/fixed risk staking plan and as we have seen, this method performed better than a fixed stake staking plan.

However, both staking plans produced fairly similar results due mainly to the fact that betting odds between 1.5 and 3.5 were in play. Especially at the lower odds the differences between the stake amounts in both plans were not huge, which ultimately led to similar results.

Whichever staking plan you choose is down to you and it then remains to follow it religiously without emotion and to never chase losses…

*If you now have a great void in your life following the completion of Euro 2012, instead of developing withdrawal symptoms or falling into post-Euro 2012 depression, remember that you always have therapy available by reading this blog from cover to cover. Then, if you are very brave you may wish to dive into Soccerwidow’s Fundamentals of Sports Betting course in order to learn the skills of professional odds calculation and prepare yourself for next season!*

If the prognosis of scientists from the International School of Management (ISM) in Frankfurt and the German Sport University Cologne is to be believed, then Euro 2012 will be nothing more than a re-run of the 2008 finals, with few surprises along the way.

According to the results of their statistical analyses, co-hosts Poland will make it to the semi-finals, but Netherlands will fail in the group phase. England’s chances rely on victory over Sweden to reach the quarter-finals on goal difference.

Of course, without any bias whatsoever the German scientists have predicted their countrymen to reach the final and beat Spain to claim the title.

Of the 24 group games, there are only 7 matches with big favorites, and 17 matches between more well-matched teams. Of these 17 games, the underdogs will prevail in 7.

*Among the 17 more balanced encounters there are three key matches:*

Poland against Russia (victory Poland)

Portugal against Netherlands (victory Portugal)

France against England (victory France)

There will be a surprise in the match between England and Ukraine (only a draw), but England will qualify for the quarter-finals due to a better goal difference.

The top two in each group from the simulation are Poland & Russia, Germany & Portugal, Spain & Italy, and France & England.

*The quarter-finals are less spectacular:*

Germany knock-out Russia and Spain beat England. France get lucky and narrowly defeat Italy.

*Semi-final predictions:*

Brave Poland finally meet their match against Spain. Germany beat France, but this match will probably be the tightest game of the tournament.

*Final heartbreak for holders:*

Germany get lucky and beat Spain.

This is a statistical simulation, and as with all such predictions, pretty clear statements of the likely outcome are often produced, but particular events (i.e. individual matches) do not necessarily comply with the bigger picture.

Unfortunately the report does not specify the expected probabilities (expectations) for each individual event and only presents the final findings. Of course, there is the chance that the scientists will get lucky and every match will enter the history books exactly as predicted in the simulation, but this is probably just wishful thinking!

How do the betting odds compare? It can certainly be expected that some of the predictions from Cologne and Frankfurt will happen exactly as predicted, but not all. Also, do the market odds contain enough value to make it worthwhile following the forecast? Again, this is left up to the reader.

It is however interesting to follow such predictions, which must have been time-consuming to compile. We have therefore added a little to the analysis by checking the odds and bets on the forecasts. The 1st July will be the day of reckoning!

You’ll need a good staking plan if you wish to follow the German scientists’ predictions and it is also important to follow through with all their forecasts rather than cherry-picking (this is always important when betting on statistical predictions).

Good luck with your Euro 2012 betting, and don’t forget that your family may not care so much for football as you do. Please find some time for your loved ones in between the excitement of what is considered to be the best quality international football tournament in the world…

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Because bookmakers are profit-making enterprises who offer bets on the market, it is understandable that they build-in a winning margin to their odds computations. This margin is called **overround**.

The overround differs from bookie to bookie and can be at its lowest around 2.5% *(e.g. Pinnaclesports pride themselves on being the bookmaker with the highest odds and the lowest margins)* but can reach 12% or even higher

The overround is the bookmaker’s mathematical advantage and ensures in the long run that they will collect more money (stakes) than they will have to pay out to bettors.

It should be obvious that a bookmaker needs this margin in order to pay staff and running costs, and of course to make a profit.

In an **accumulator bet** (also called ‘multiple bet’ or ‘parlay’), the overround is **exponentiated**, which increases the profits of the bookmaker by multiples.

Here’s an example: If the overround is, for example, 6%, then in a five-fold accumulator bet the **compound interest** is 33.8%.

The above calculation means that a five-fold bet containing individual bets each with an overround of 6% generates a profit margin of 33.8% for the bookmaker. **But what does this mean in reality?**

Here is a table for five cup finals in mid-May 2012 (Swiss Cup, Scottish Cup, UEFA Champions League, Portuguese Cup, Coppa Italia), and the odds offered by the bookmaker, **Tipico**:

You will find the mathematically correct probabilities calculated for these five cup finals in our article **The Permutations of 5 Cup Finals in 5 Days**, and the following table shows the corresponding * true* odds (or ‘zero’ odds):

Comparing the above tables, you can see straight away that the true odds differ dramatically from the bookmaker’s odds. For example, Basel’s true odds of winning their match were 1.89, but the bookmaker offered this bet at 1.67. The draw in the Swiss Cup final had true odds of 4.05, whereas the bookmaker offered only 3.80, et cetera.

Sometimes bookmakers odds are higher than the true odds. Using our example again, betting on Bayern to win was available at 1.8, although their true odds were nearer 1.54. However, of the 15 home, draw and away bets only five were offered at higher prices than the true odds, and 10 were lower.

Tipico’s average overround was between 4.13% and 6.22% per match:

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