The level of detail contained in the tables even stretches to distinguishing between the **first and second halves of a league season**, where markedly different patterns of results are usually found.

To help you achieve the same results, here is our calendar of leagues showing you exactly where the mid-season breaks are for the purposes of analysing each HDAFU Table.

Some leagues have still not confirmed their schedules for 2018-19, but updates will be performed when information becomes available.

We begin with the more popular Winter Leagues; the Summer Leagues are detailed on Page 2.

As the A-League has no recognisable mid-season break, and the programme is so short (currently 135 matches per season), just the Whole Season Analysis is offered (single HDAFU table only).

**Start Date:** 19th October, 2018

**Finish Date:** 28th April, 2019

The Austrian Bundesliga has an official winter break every season (approximately six weeks) beginning in mid-December.

**Start Date:** 27th July, 2018

**1st Half Ends:** 15th December, 2018

**2nd Half Starts:** 23rd February, 2019

**Finish Date:** T.B.A. May, 2019*

**The last round of the all-against-all home/away format is 17th March 2019. After this, the league splits into two groups of six where each team plays another 10 games, home and away, against the other five teams in its group. These games are to be included in any HDAFU portfolio strategy. Official finish date of the league will be updated after 17th March.*

The Jupiler League has a three/four-week break beginning after the last round of matches in December each year.

The second half of the season is shorter than the first and for the sake of the HDAFU tables, finishes with the last round of matches before the league splits into Championship* and Europa League Play-off* groups.

(*These latter matches are not included in our analyses and should not form part of any HDAFU portfolio strategy).

**Start Date:** 27th July, 2017

**1st Half Ends:** 26th December, 2018

**2nd Half Starts:** 18th January, 2019

**Finish Date:** 15th March, 2019

The Superligaen has a seven/eight-week break beginning after the last round of matches in December each year.

The second half of the season splits into a Championship Group, a Relegation Group* and includes Europa League playoff games*.

(*The Relegation Group and Europa League playoff games are not included in our analyses and should not form part of any HDAFU portfolio strategy).

**Start Date:** 13th July, 2018

**1st Half Ends:** 16th December, 2018

**2nd Half Starts:** 10th February, 2019

**Finish Date:** T.B.A. May, 2019

The Premier League has no mid-season or winter break, but the season breaks naturally at the end of the calendar year.

**Start Date:** 11th August, 2018

**1st Half Ends:** 26th December, 2018 (Round 19)

**2nd Half Starts:** 29th December, 2018 (Round 20)

**Finish Date:** 12th May, 2019

The Championship has no mid-season or winter break, and the season is broken after Round 23, the halfway point of the season.

**Start Date:** 3rd August, 2018

**1st Half Ends:** 22nd December, 2018 (Round 23)

**2nd Half Starts:** 26th December, 2018 (Round 24)

**Finish Date:** 5th May, 2019

Ligue 1 breaks for around three weeks just before Christmas each year.

**Start Date:** 11th August, 2018

**1st Half Ends:** 22nd December, 2018

**2nd Half Starts:** 12th January, 2019

**Finish Date:** 25th May, 2019

Ligue 2 breaks for around four weeks just before Christmas each year.

**Start Date:** 27th July, 2018

**1st Half Ends:** 21st December, 2018

**2nd Half Starts:** 11th January, 2019

**Finish Date:** 17th May, 2019

The Bundesliga 1 has a break of four/five weeks each year beginning in mid-December.

**Start Date:** 24th August, 2018

**1st Half Ends:** 22nd December, 2018

**2nd Half Starts:** 19th January, 2019

**Finish Date:** 18th May, 2019

The Bundesliga 2 has a break of four/five weeks each year beginning in mid-December.

**Start Date:** 3rd August, 2018

**1st Half Ends:** 22nd December, 2018

**2nd Half Starts:** 29th January, 2019

**Finish Date:** 19th May, 2019

The Super League has a break of around three weeks usually beginning during the third week of December.

**Start Date:** 25th August, 2018

**1st Half Ends:** 21st December, 2018

**2nd Half Starts:** 11th January, 2019

**Finish Date:** 12th May, 2019

Serie A usually has a break of around two weeks beginning after the last round of games immediately before/after Christmas, with the re-start during the first or second week of January each year.

**Start Date:** 19th August, 2018

**1st Half Ends:** 29th December, 2018

**2nd Half Starts:** 20th January, 2019

**Finish Date:** 26th May, 2019

Serie B usually has a break of around two weeks beginning after the last round of games immediately before/after Christmas, with the re-start during the first or second week of January each year.

**Start Date:** 24th August, 2018

**1st Half Ends:** 30th December, 2018

**2nd Half Starts:** 19th January, 2019

**Finish Date:** 11th May, 2019

The Eredivisie has a four-week break beginning after the last round of games immediately before Christmas.

**Start Date:** 10th August, 2018

**1st Half Ends:** 23rd December, 2018

**2nd Half Starts:** 18th January, 2019

**Finish Date:** 12th May, 2019

The Ekstraklasa has a seven/eight-week break beginning after the last round of matches in mid-December each year.

The second half of the season splits into a Championship and Relegation Group, and all matches are included in our analyses for inclusion in any strategy.

**Start Date:** 20th July, 2018

**1st Half Ends:** 22nd December, 2018

**2nd Half Starts:** 9th February, 2019

**Finish Date:** T.B.A. May, 2019*

**The league splits after the final matches of the regular season on 13th April 2019. The Finish Date for the league will be updated thereafter.*

The Primeira Liga has a two-week break beginning after the last round of matches immediately before Christmas each year.

**Start Date:** 12th. August, 2018

**1st Half Ends:** 23rd December, 2018

**2nd Half Starts:** 2nd January, 2019

**Finish Date:** 19th May, 2019

Liga I has up to six weeks’ break beginning immediately before Christmas each year.

**Start Date:** 20th July, 2018

**1st Half Ends:** 20th December, 2018

**2nd Half Starts:** 2nd February, 2019

**Finish Date:** 1st June, 2019

The Russian Premier League needs an extended break to cope with the winter weather. The break usually begins in mid-December, with the re-start not until early March each year.

**Start Date:** 29th July, 2018

**1st Half Ends:** 9th December, 2018

**2nd Half Starts:** 3rd March, 2019

**Finish Date:** 26th May, 2019

The Premiership breaks for around three weeks each season after the last round of matches in the calendar year.

**Start Date:** 4th August, 2018

**1st Half Ends:** 29th December, 2018

**2nd Half Starts:** 23rd January, 2019

**Finish Date:** 6th April, 2019.

La Liga Primera has a break of up to two weeks after the games immediately before Christmas.

**Start Date:** 17th August, 2018

**1st Half Ends:** 22nd December, 2018

**2nd Half Starts:** 6th January, 2019

**Finish Date:** 19th May, 2019

La Liga Segunda has a break of up to two weeks after the games immediately before Christmas.

**Start Date:** 17th August, 2018

**1st Half Ends:** 22nd December, 2018

**2nd Half Starts:** 6th January, 2019

**Finish Date:** 9th June, 2019

The Super League breaks for six/seven weeks in mid-December each year.

**Start Date:** 21st July, 2018

**1st Half Ends:** 15th December, 2018

**2nd Half Starts:** 2nd February, 2019

**Finish Date:** 25th May, 2019

The Süper Lig breaks for around two weeks at the end of each calendar year.

**Start Date:** 10th August, 2018

**1st Half Ends:** 24th December, 2018

**2nd Half Starts:** 27th January, 2019

**Finish Date:** 26th May, 2019

**How high should be a starting bank?
Is 5,000 units enough?**

Well, there is no standard answer to this question. It all depends on the individual strategy.

Image: Sergey Novikov (Shutterstock)

However, what is possible, is to calculate bank fluctuations *(i.e. winning and losing sequences)*.

With the help of knowing the best and worst case scenarios you can determine the ideal starting bank for any betting system of your choice.

At the end of the article you will find a few useful exercises to practise, with the solutions available as a free download to all of you who would like them.

It stands to reason that the smaller the probability of an event occurring *(i.e. higher odds)*, the longer the likely losing streak will be *(in between winning bets)*.

However, the big question is how often and for how long will the losing (and winning) streaks transpire?

It is possible to mathematically calculate many things with statistics, including streaks of luck and bad luck. However, it is important to note that no matter how accurate the results may appear, they are ‘models’ *(a formal representation of a theory)*.

In this article, we are talking about probabilities; what can we ‘predict’ about how things may develop in the future. Please bear in mind that any such hypothesis is always a “could happen” not a “will happen”.

Of course, the larger the sample size *(i.e. number of bets)*, the more likely the prediction is to be correct. But apart from the bookmakers themselves, who else has a betting portfolio comprising thousands of bets every weekend?

The longest expected losing streak *(or winning streak)* can be calculated using the following formula:

**n** = number of trials *(i.e. total number of bets)*

**ln** = natural logarithm^{1}

**P** = probability^{2}

**| .. |** = absolute value or ‘modulus’

^{1}*Suffice to say, explaining what natural logarithm is would be worthy of a series of articles. For the time being, use Excel to calculate this for you.*

^{2}*For winning streak calculations use the positive value (i.e. the probability of winning). For losing streak calculations use the negative probability value. For example, if the probability to win the bet is 33% then the probability that the bet loses (negative probability) is 67%.*

In practice, the formula is best applied to situations where you constantly bet repeatedly on the same probability, for example, on ‘red’ at the roulette wheel: its probability remains exactly the same with every new spin of the wheel.

For football betting the concept is much more difficult to apply as each bet is likely to have a different probability (e.g. one Over 2.5 Goals bet with a 55.3% chance, and the next with a 62.1% chance, etc.).

However, you can group bets in probability clusters – for example, bets with a 55%-60% expected hit rate, bets with a 60%-65% expected hit rate, and so on.

Longest Winning and Losing Streaks, depending on the number of bets (Examples for 50, 500 and 1,000 bets shown)

The tables above show the calculations of the expected maximum number of winning and losing streaks, depending on the expected hit rate *(probability of the bet to win)*.

To read the tables, let’s explain the 70% line *(odds in the region 1.4 and 1.45)*; in other words, bets with a 7 in 10 chance of winning.

The table on the left calculates the expectations of 50 tries *(50 bets in a row, one after the next)*. You can see that the player will experience at least one streak of three lost bets in a row somewhere in the sequence.

On the other hand, he can expect at least one series of 11 winning bets in a row during the same sequence of 50 bets.

In contrast look at the 30% line *(odds in the region of 3.2 to 3.4)*. In a series of 50 bets the bettor must expect at least one sequence of 11 consecutive losing bets, but will probably see only one set of three consecutive winning bets.

To develop a sense of probabilities and sequences, you can experiment with a dice. It has six faces; in other words, a probability of 16.67% (1 in 6 chance) of successfully landing on a chosen number.

Choose a number and count the number of throws until you succeed to roll it. Count also the number of consecutive successful rolls.

Choose two numbers that you do not want to roll (e.g. 5 and 6).

This means you have a 66.67% chance that one of the remaining four numbers is rolled.

In football betting terms, this equates to wagering on something like the full-time ‘Under 3.5 Goals’ market at odds of 1.50. (This experiment is just a little faster than waiting for 50 games to finish!)

Take a pen and paper and record 100 throws of the dice. If one of your four chosen numbers arrives mark a 1 on your paper; if the 5 or 6 are thrown, mark a 0. Count the number of winning and losing streaks you experience.

What is the maximum number of winning and losing streaks you experience in a sample size of 100 throws (bets)?

*Having learned how to calculate the expected length of winning and losing streaks, the next question to ask is: *

**How many bets is it likely to take before I encounter ‘X’ losses in a row?**

This formula is actually very simple:

= **1** divided by **P**, **to the power of** **a**

**P** = probability (expected hit rate or loss rate)

**a** = number of won or lost bets in a row

In the tables below you can see how many attempts (bets) it needs to experience a specific, expected length of luck or bad luck. Again, the assumption is that the bettor bets all the time on the same probability:

Expected time of occurrence of winning and losing streaks, depending on the hit rate

Looking firstly in the right-hand column at the Losing Sequences, if the expected hit rate is 45% *(what you should ‘expect’ at odds of around 2.2)*, then it is likely that you will experience a sequence of three losing bets in a row by the time your sixth bet is settled.

After 20 such bets it is likely that you will have seen a losing streak as long as five bets in a row.

Looking at the Winning Sequences column: you will win three times in a row at some stage during a series of 11 bets.

However, winning five in a row may only be seen once in every 54 bets.

As we mentioned before, in football betting it is extremely difficult, if not impossible, to find bets, all with the same probability of success.

However, you should at least try to understand the theory behind winning and losing streaks, as it will be **easier on your nerves** when you do encounter the inevitable run of bad fortune.

In particular, a thorough understanding of losing streaks is of enormous importance when setting both the size of your starting bank and stakes per bet.

**Example:**

A bettor prefers bets within the odds range of 2.0 to 2.5 with a hit rate between 40% and 50%. He plans to place 50 bets *(e.g. two bets per round on 25 rounds of matches)*.

After looking at the tables, he knows that the maximum losing sequence expected is likely to be as long as six to eight lost bets in a row. Therefore, he knows that there may be at least one sequence of three or four consecutive rounds *(weekends)* when all bets lose.

After every 5^{th} to 8^{th} bet, he is also aware that he is likely to experience a loss of three consecutive lost bets *(e.g. one weekend loses both bets, the following weekend only one loses)*.

He also knows that every 13 to 32 bets there will even be a streak of five losing bets in a row.

The bettor is fully aware that he has to take this into consideration and plan the starting bank accordingly to be able to ‘sit through’ these losing streaks.

Of course, he also knows that winning sequences will arrive too. In his case, with some ‘luck’, he may experience a winning sequence of five bets in a row after 32 bets. Every eight to 16 bets he will have a ‘lucky’ streak of three wins in a row.

This is certainly quite a fluctuation. When these ‘bad luck’ and ‘good luck’ streaks actually happen, nobody knows. However, what we do know is: They will happen!

A starting bank should be approximately five times the maximum expected losing streak. The reason for this is that a losing streak can happen right at the beginning, immediately followed by another bad run of luck. We are talking statistics here!

So if a bettor wants to stake 10 units per bet, the starting bank must be nine times (expected losing streak) the stake of 10 units multiplied by five = 450 units. Then he can risk 2.2% of his bank each time he bets (10 divided by 450). If losing, the stakes will remain constant at 2.2% and, if winning, raised gradually.

**Questions to ask before setting the starting bank:**

- What hit rate is expected
*(probability to win the bets)*? - How many bets are planned for the season?
- How long will the longest losing streak be?
- What is the desired stake per bet?

**Calculation of the starting bank:**

- A bettor pursues a strategy with a win probability of 60% per bet
*(e.g. Under 3.5 Goals)*. He places one bet after the other; in other words, he waits for the outcome of each bet before placing the next. In total he places 50 bets.What is the longest ‘losing streak’ (bad luck) that he can expect? How long is the longest ‘winning streak’ (luck) that can be expected?

- Same example as in (1): A strategy with a probability of 60% per bet; placing one bet after the other.
This time our punter is hoping for a ‘winning streak’ (luck) of 5 consecutive wins. How often does that happen?

- A gambler pursues a strategy with a probability of 20% per bet
*(e.g. ‘betting on the underdog’)*. Again, he places one bet after the other.With a total of 500 bets, how long is the longest ‘losing streak’ that he must expect? After how many bets can he expect the longest ‘winning streak’?

- Same example as in (3): Strategy with a probability of 20% per bet; placing one bet after another
The bettor was hoping for a ‘winning streak’ (good luck) of five consecutive wins. How often does that happen? After which bet number should he expect ‘bad luck’ of five consecutive losses?

- Following the above two strategies
*(one with a 60% chance to win, the other with 20%)*our bettor stakes 10 units per bet.How high should the starting bank be for the 60% strategy, and how much for the 20% strategy?

*Note: The initial bank should be approximately five times the maximum losing streak based on a total of 500 bets placed.*

*Just click on the button above and click on “Proceed to checkout” button in the new tab, then enter your name and e-mail address. Our automatic service will then deliver the file to you via e-mail, free of charge. The size of the PDF file is 320KB.*

The factor **5** used in this article to determine the betting bank is a risk variable for risk-averse bettors. It is also the factor advisable for strategies with a 45% to 55% win probability *(odds between 1.8 and 2.2)*.

Here is another article: **How to Calculate Losing Streaks & Optimal Bankroll** in which we provide a more detailed account of setting the ideal starting bank.

Risk management in sports betting is the foundation stone upon which all of your betting transactions should be built.

Risk management encompasses risk assessment, risk control and capital requirements, all of which cannot be addressed until you understand how winning and losing streaks are likely to impact upon your starting bank.

]]>*In the articles Judging the Risk of a Football Betting Portfolio and Finding a System Using the HO/AO Quotient (and many other articles), I have always referred to portfolios that span a whole season.*

*To stop betting when the bank has reached a comfortable level of profit isn’t a problem but your main question probably is…*

Thankfully, it doesn’t!

As long as you play with a mathematical advantage on your side, you can start your betting venture at any point throughout the year/season. You can take one or more breaks at anytime and you can re-start again at anytime.

The only requirement is that your portfolio must be inherently **mathematically sound**.

One problem you may think of when starting a betting campaign in the middle of a season is that there will already have been a good number of matches played. You may believe that the number of potential winning candidates has been already reduced dramatically. But bear in mind that so has the number of losers.

I’m going to show you how the portfolio of bets showcased in the article **2017-18 Winter League Report – 35k in 138 Days** would have performed if betting would have commenced and ceased at random times during the season.

**Random Scenario 1** Start Date: 5th October 2017, incorporating Christmas holiday break from say 15th December 2017 to 10th January 2018.

**Random Scenario 2** Start Date : 1st January 2018 (second half of season start date for many leagues), incorporating Easter holiday break from say 26th March 2018 to 13th April 2018.

**Random Scenario 3**: Start Date: 1st December 2017, until 1st May 2018 without any breaks.

**Random Scenario 4**: Start Date: 15th August 2017; breaking on 1st October 2017; re-starting on 10th November 2017; breaking 15th January 2018; re-starting 1st March 2018.

Curious about the results? Then let’s have a look…

Please remember that all scenarios relate to the SAME portfolio; our 2017-18 winter league season portfolio example (using the first 10 European League HDAFU Tables in alphabetical order).

The chosen scenarios are all random and the point that I will illustrate is that so long as there is a sound portfolio in place, it doesn’t matter when you start betting or if you decide to pause, whether for a few weeks or even months.

In order to make direct comparisons, the following analyses are all based on 100 unit flat stakes across the board without **ratcheting**.

Scenario 1: Portfolio of bets for the 2017-18 winter league season

**Scenario 1** a late start; an example perhaps where the bettor decided to paper-test his/her system for two months before committing money to it. And then, like most people, took time off over Christmas before recommencing.

With this campaign, there would have been **95 days of betting** with 364 bets placed; the average winnings would have been 74.76 units per betting day, averaging a respectable win of **19.51 units per bet** (100 unit flat stakes).

Total Profit: **7,102 units**

Scenario 2: Portfolio of bets for the 2017-18 winter league season

**Scenario 2** another late start. Perhaps someone who felt more comfortable with a longer period of paper testing? And this time, a holiday break for Easter.

Here, there would have been **55 days of betting** with 195 bets placed; the average winnings would have been 61.60 units per betting day, averaging a decent win of **17.37 units per bet** (100 unit flat stakes).

Total Profit: **3,388 units**

Scenario 3: Portfolio of bets for the 2017-18 winter league season

**Scenario 3** a five month continuous run without any breaks.

During this time scale there would have been **74 days of betting** with 265 bets placed; the average winnings would have been 51.05 units per betting day, averaging a very acceptable **14.26 units per bet** (100 unit flat stakes).

Total Profit: **3,778 units**

Scenario 4: Portfolio of bets for the 2017-18 winter league season

**Scenario 4** is a much more random pattern of betting – someone who dips in and out whenever they have time or money, or whenever they feel like it. Sometimes, there are just better things to do in life!

During the scattered times there would have been **87 days of betting** with 349 bets placed; the average winnings would have been 98.75 units per betting day, averaging **20.79 units per bet** (100 unit flat stakes).

Total Profit: **7,257 units**

So, the main lesson you should have learned from this summary is that IF you have compiled a sound portfolio of bets then it really doesn’t matter at what time you start during the season, or when you take a break.

Compile your portfolio in the same way you would when looking to begin betting at the start of the season/year.

For example, if you buy HDAFU Tables in October, you will still need to compile your portfolio as if you would have started at the beginning of the season *(July/August)*.

The beauty of starting late is that you can even paper-test your portfolio and check if it is working. If you find that it is then you can safely assume that it will work for the remainder of the season too. If not, then adjust until you get it right.

Compile it the same way as advised in various articles and comply with the ‘magic number’.

- At least 50% ‘medium risk’ strategies.
- At least 50% of the systems within the portfolio with at least 50 bets
*(for the whole season/year)*. - An overall expected hit rate of the portfolio of around 50%.
- A minimum of 500 bets in the portfolio = an average of 50 bets in 10 different systems

If you can achieve more than 50 – the magic number – in any of the above equations, then your portfolio should have an even stronger chance of succeeding.

Once you have compiled your full year/season portfolio and you are happy with it then you can start straight away, at anytime of the year. Period.

As always I wish you good luck with your betting adventures!

*I hope this article has cleared up any confusion about starting in the middle of a season, breaking for holidays, and so on… However, if you are still not sure, then please feel free to ask any questions via the comment section below.*

*Of course, bookmakers are in the business of setting odds and determining prices which are offered for certain betting events.*

If I had to use just one word to describe how bookmakers think…

Image: Cartoonresource (Shutterstock)

Image: Cartoonresource (Shutterstock)

When viewing odds in betting exchanges such as **Betfair**, **Betdaq**, **Smarkets**, or **WBX**, you should understand that it is neither the exchange platform or the traders using them who set the odds.

The fact is that the bookmakers are used as the market guide for traders on the betting exchanges, and it is the bookies who compile and publish their odds weeks in advance of the events in question *(sometimes even months)*, and certainly well before the exchanges even open their markets for trading.

If you have ever calculated odds you will have noticed that the bookmakers’ offers often do not represent the ‘true’ picture, in other words, the **‘true’ mathematically calculated values** *(the statistically expected values)*.

Only occasionally (probably in less than half of all cases) are odds close to the statistical expectations of the betting event. However, in the vast majority of games, odds are either considerably higher than mathematically expected or far lower…

You have to appreciate that bookmakers do not really intend to predict an outcome (correctly). If you enjoy statistical analysis, then take a little time to do a simple calculation for any league of your choice. Simply **convert bookmaker odds into probabilities** and compare them to the actual distribution of the results.

Bookmakers have been around for thousands of years in one form or another. Their main goal is of course to make a profit. They price their odds to ensure that sufficient ** action** is taking place on both sides of a bet.

If a bookmaker’s betting odds are * not* aligned to

The role of bookmakers is, strictly speaking, rather the function of an intermediary, similar to a stockbroker. They take money from various people on various outcomes and after the game is finished they pay out the winners.

In return for this service, the bookies take a “fee” known as the **overround**.

The closer to the kick-off of a game, the more ‘fluid’ the odds become, as salient information such as team news becomes public knowledge, and this then has a knock-on effect with bettors’ opinions being confirmed or changed on the outcome of the match in question. Thus, the odds tend to change more as the start of the match gets nearer and nearer and more money changes hands.

**Always remember**

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

In an ideal world, bookmakers would like to see the same amount of money (risk) on both sides of a bet outcome. However, utopia is virtually unknown in the world of bookmaking and firms are rarely able to equalise their level of risk on both sides.

Therefore, you will often see a bookmaker adjusting his odds for an event over time. This fluidity aims to achieve an * acceptable* money line on both sides of the bet outcome.

**Please note!** Because it is rarely possible to “**equalise**” the risk on both sides, bookmakers instead look for an “**acceptable**” level of risk. This is the only ‘gamble’ bookmakers take.

You will have certainly noticed the plethora of various betting offers used by the bookmakers to woo their customers. Unsurprisingly, these are the bets where they expect to make the highest profits *(for example, pushing accumulator bets with offers such as, “If team A (usually a short priced favourite) is the one which lets down your five fold, we will return your stake!”)* (how generous of them!!).

Bookmakers apply all kinds of marketing tricks to divert the sports bettor into a direction which is most profitable; **for them** but * not* for the bettors!

I risk repeating myself but the truth is that bookies’ odds never aim to predict an outcome of a match with utmost accuracy *(therefore the calculated probabilities of ‘true’ odds often do not match the betting odds offered in the market)*. A bookmaker’s main goal is to __balance__ the books and to do this, public opinion is taken into account.

This is the key to bookmaking success. **This is the key to sports betting success**.

Of course, each sport is different, but in the end bookmaking methods are always the same. Bookmakers make money with these same methods, regardless of the sport or other type of betting event.

- Their books are not perfect.
- They do not have a crystal ball.
- Bookmakers have a
**business plan**!

**The bookmakers’ mantra is very simple:**

Calculate the statistical chances of the matches for a weekend and set the odds by taking into account the probabilities and public opinion. Collect enough money to pay off losing bets. Keep the profit.

Bookmakers are not able to balance their books for each single game. To them, it is always about “acceptable” amounts of money (profits or losses) and spreading risk.

The goal of bookmakers is __ not__ to predict the outcome of a game

Bookmakers’ odds usually **reflect public opinion** about a match and their __ primary objective__ is to ensure a

If you wish to become successful with any form of betting you must understand the way of thinking (the business plan) of the bookmakers.

Why? Because these firms survive and thrive from the money they encourage you to lose through nothing more than your own ignorance of how their ‘system’ works.

]]>

Please note that this article addresses.system betting(= a predetermined selection of bets to bet on the same criteria over a longer period of time)System betting simply means that you are not judging the risk of any individual bet prior to placing it

(this would be ‘value betting’). System bettors identify an overall ‘edge’ or ‘advantage’ that is likely to persist over a longer period of time and then follow-through from the start to the finish of a campaign. Of course, there is ‘value’ involved (a ‘mathematical advantage’), otherwise it wouldn’t work.

The examples in this article are our actual picks for the 2017-18 season that were identified using the **HDAFU Tables**.

A portfolio is a package of bets where extensive analysis has determined the choices (picks). Diversifying the portfolio is an essential part of betting strategy with the aim of reducing the risks of losing.

Please do not confuse the term ‘portfolio’ with ‘best timing of placing bets’. A portfolio is planned well in advance of a weekend (or round, or even season) and determines the assortment of bets that are to be placed later.

A portfolio of bets is therefore a varied group of individual bets, not just one particular match on a particular day.

To judge the success of any particular betting portfolio it is necessary to evaluate the performance of all of its systems together as one, not just the individual group members (bets in just one league or system).

Diversification is a technique that reduces risk by allocating bets among various leagues, bet types, and other categories such as times or seasons. The rationale behind this technique contends that a portfolio constructed of different kinds of bets will, on average, pose a lower risk than any individual bet (system) found within the portfolio.

Diversification strives to smooth out unsystematic or anomalous risk events (outcome of matches/leagues) in a portfolio so that the positive performance of some bets (winnings) neutralizes the negative performance of others (losers).

*The questions that probably arise right now are:* How big should my portfolio be? How much should I diversify?

I will answer these two questions at the end of the article but for the time being let us first look at our portfolio for the 2017-18 Winter League season. I will explain step-by-step how I came up with the choices and what my thinking was behind them. This will probably already answer many of your questions.

For the sake of brevity, I am showing only the first 10 European leagues we used in alphabetical order: Austria, Belgium, Denmark, England, France, Germany, Greece, Italy, Netherlands and Poland.

The overall results you will see would have been similar whichever 10 leagues we chose to illustrate with.

For betting, it doesn’t matter which leagues you choose so long as you have easy access to all of them with the bookmakers available to you. A well-balanced portfolio normally delivers the results as expected.

Here is our portfolio for 2017-18:

Image 1: Portfolio of bets for the 2017-18 winter league season

As you can see it is a conservative portfolio with a good number of low risk and medium risk systems.

As a side note, this article isn’t about how the individual systems (within a portfolio) are picked. However, for the 53 bets in the EPL, you will find a detailed explanation in this article:Finding a System Using the HO/AO Quotient

For the other nine leagues you will just have to trust that the systems were picked in a similar manner to the EPL example.

The terms probability, expectation and hit rate are all closely related, and express more or less the same thing. The main differences are that before a game starts *(or a whole system of bets/portfolio is played)* the terms ‘probability’, ‘expectation’ and ‘prediction’ are used but, once results are known, these terms are supplanted by actual ‘hit rate’.

The observed distribution of the past *(as displayed in the HDAFU Tables)* becomes the probability for the future *(= expected hit rate)*.

Referring to image 1 above, there was an expected hit rate of 80% in the Greek Super League. This meant that from every 10 bets placed, on average, 8 were likely to win. Therefore, the longest losing streak when placing 50 bets in a row was 2 *(see article: The Science of Calculating Winning and Losing Streaks)*. This individual system within the portfolio was evaluated as being ‘low risk’.

On the other hand, the expected hit rate of 31.58% in Germany meant that from every 10 bets placed, on average, 7 were likely to lose. The longest losing streak expected when placing all 57 bets in a row was 11. This is an example of low probabilities, or ‘high risk’ classification.

The ‘longest losing streak expected’ is not necessarily the ‘longest losing streak observed’.

For example, in the EPL 2012-17 the observed longest losing streak was 6 in a row and this happened 3 times during the previous 5 seasons.

Image 2: EPL HO/AO group 0.603 to 1.396 – Backing the Draw 1st Half

Nevertheless, to judge the risk for the future you have to allow for the worst-case scenario even if it hasn’t happened for a very long time – one day it will happen, believe me!

It is always safer to prepare for the worst rather than relying on luck.

At the end of this article

(image 6)you will see that 2 of the 10 systems(Austria, EPL)reached their expected longest losing streaks, and one system even outstripped its calculated(predicted)expectations(Denmark).

Here’s a screenshot from the EPL 2012-17 HDAFU Table representing the HO/AO quotient cluster group (0.603 to 1.396) for backing the draw. The expected total number of bets and expected hit rate for the forthcoming season are circled in red.

Image 3: EPL 2012-17 HO/AO group 0.603 to 1.396 – Backing the Draw 1st Half

To calculate the longest expected losing streak use the following formula:

**n** = number of trials *(i.e. total number of bets expected)*

**ln** = natural logarithm

**P** = probability / expected hit rate *(for losing streaks: expected losing rate)*

**| .. |** = absolute value or ‘modulus’

Here’s the formula in action for the EPL example:

The winning probability of 39.62% means of course that the losing probability is its inverse:

100% minus 39.62% = 60.38%To calculate the longest losing streak use in Excel the following formula:

=ABS(LN(53)/LN(60.38%))

Read more about the above formula, its use and interpretation in our article:The Science of Calculating Winning and Losing Streaks

The result of our calculations meant that for the 2017-18 EPL season, when backing ‘Draw in the first half’ within an HO/AO Quotient between 0.603 and 1.396, the expected maximum losing streak was 8 losses in a row.

Furthermore, sorting the 2012-17 data falling within this HO/AO Quotient group into rounds revealed that there weren’t more than 4 available bets in any round of matches; sometimes there was only one, and occasionally there were none.

With a losing streak of 8 bets possibly spanning a period of 5 to 6 weeks *(= 1.5 months)*, this system was not likely to be easy on the nerves.

To guard against nerves becoming mass panic, you will need to ensure that your portfolio does not contain too many systems with lower probabilities. Although not very likely, it is quite possible that they may all experience their longest losing streaks at the same time *(2017-18 being a case in point, where the season began badly across the board)*.

A high yield ALWAYS indicates high risk!

…Until it doesn’t!

I have included in the example Netherlands Eredivisie *(Backing the Favourite 2nd Half)* because the analysis came out with a 75% probability of winning. However, the expected yield of 32.23% was exceptionally high for this probability group and there were only 15 bets expected.

Normally, high probability of winning and high yield do not go together. Therefore, it may have been just an observed anomaly because of the small number of matches on average each year in this group.

Nevertheless, because only 15 bets were expected *(of over 500 in the whole portfolio)* and with an expected high hit rate, the overall judgment for this particular pick was ‘medium’ although the high yield would have normally marked this system as ‘high risk’.

Moral 1: There are no rules without exceptions.

Next rule:

In Germany, the expected hit rate was 31.58% (high risk), whilst expected yield was 29.38% (medium risk)

The combined expectations of hit rate and yield here would perhaps have labelled this system as ‘high risk’ but downgraded to ‘medium risk’ as expected yield was under 30%.

Portfolio compilation and decision-making involved the balancing act of achieving as high an expected hit rate as possible, together with an acceptable yield.

- The overall expected hit rate of the portfolio should be in the region of ± 50%
*(of course, if you can get it higher, the easier it will be on your nerves)* - An acceptable yield is anything in the region of ± 20%

To calculate the overall expected hit rate of the whole portfolio, list your systems like I have done in image 1 (Portfolio of bets for the 2017-18 winter league season)

- First you multiply the number of expected bets with the expected hit rate for each system, e.g.
**Austria:**72 times 31.94% = 22.9968

**Belgium:**30 times 86.67% = 26.001

**Denmark:**36 times 41.67% = 15.0012

…and so on - Then add them all up:
22.9968 plus 26.001 plus 15.0012 plus etc., etc. .. = 243.2526

- Now divide the result by the total number of bets expected:
243.2526 divided by 505 = 0.4816883 ≫≫ 48.17%

To calculate the overall expected Yield the same calculation applies.

The portfolio example in this article had 505 expected bets over the period of a whole season, with an expected Hit Rate of 48.17%, and an expected Yield of 22.22%.

It was therefore a ‘medium risk’ portfolio.

‘Medium risk’ means that you must be prepared for bumpy rides – substantial ups and downs in your betting bank. But with a sensible staking plan, this shouldn’t mean bankruptcy before the situation picks up again.

Despite a ‘high’ hit rate of 48.17% *(which isn’t as ‘high’ as you may think)* you may experience a betting campaign like this one if you don’t get off to a good start:

Image 4: Profit/ Loss **Scenario I** for an expected hit rate of 48.17% and 22.22% yield

However, you could get a good start and a very smooth curve over the whole season.

Please note, these screen captures relate to the same portfolio of bets. Nothing has changed: 48.17% probability of winning; 22.22 % expected yield.

Image 5: Profit/ Loss **Scenario II** for an expected hit rate of 48.17% and 22.22% yield

Scenario 3 has an extremely good start (up to bet number 80) and then gives way to a bumpy ride to the end of the season:

Image 6: Profit/ Loss **Scenario III** for an expected hit rate of 48.17% and 22.22% yield

Have a think about these profit/loss curve simulations.

The first (unlucky) scenario even has an average hit rate of 52%, much higher than the second and third. However, the second and third are much smoother on the nerves and both have a better financial result at the end.

What I’m trying to say is that you must always remember that you are gambling. Although you are using sound statistics for compiling your portfolio, never lose sight of the fact you are still gambling!

The two very simple rules to negotiate a season with a profit at the end are:

- If you have a sound portfolio do not give up too quickly.
- If you have at the beginning a very good start don’t become over-excited and start increasing your stakes more than originally planned.

Always remember, gambling has a lot similarities with rolling a dice; you may have to wait for ages until the sixes start popping up or you have them all at the beginning and then for a long time none.

At the end of the season, the portfolio produced the expected result *(the overall hit rate was more or less as predicted)* although the leagues themselves within the portfolio performed erratically with peaks and troughs during their seasons.

Image 7: Final performance of the 2017-18 winter league portfolio

All three ‘high risk’ systems produced, as expected, very long losing streaks: Austria, EPL, Poland.

Denmark produced a longer losing streak than expected. It started on the 26/8/2017 and continued until 29/10/17 – more than 9 rounds of games! Nevertheless, they recovered to finish with a positive return, although far lower than expected.

I will write more about the final performance of that portfolio in another article where you will also be able to download the whole monitoring spreadsheet including staking and ante post odds movements.

Betting, in general, is a very unreliable venture and to be able to ‘control’ risk *(reduce on losing streaks)* a good number of high probability bets is required.

However, the big challenge with football betting is that especially the lower odds *(up to 2.5)* are often reduced because market demand forces bookmakers to reduce prices for favourites and increase the prices for the opposite bets (underdogs and occasionally draws).

You will therefore find plenty of draw and underdog strategies in the HDAFU tables but unfortunately, these two bet types come normally with very low probabilities of winning *(= low hit rate expectation/high losing rate expectation)*.

And, if you have too many low probability bets in your portfolio and experience several weeks in a row where the favourites are mostly winning, your betting bank may become distressed beyond your comfort levels.

As I have shown you in the example, to get a balanced portfolio of 500 bets requires approximately 10 leagues. If one league doesn’t play according to statistics *(e.g. Denmark)* another league will hopefully make up for it *(e.g. Germany)*.

Of course, the larger your portfolio, the better.

Reduce on ‘high risk’ as much as you can.

Image 8: Relationship between a systems expected hit rate and its yield

Pay attention that you don’t have too many systems with just 10 to 15 bets in your portfolio as they are much more volatile than systems with 50 bets or more.

However, in the example portfolio, there were only 5 systems with 50 or more bets. It’s not always easy to stick to every rule.

- At least 50% ‘medium risk’ strategies.
- At least 50% of the systems within the portfolio with at least 50 bets
*(for the whole season/year)*. - An overall expected hit rate of the portfolio of around 50%.
- A minimum of 500 bets in the portfolio = an average of 50 bets in 10 different systems

If you can achieve more than 50 – the magic number – in any of the above equations, then your portfolio should have an even stronger chance of succeeding.

Good luck with your betting!

*I hope this article has answered the questions we receive about judging the risk of a system identified with the HDAFU Tables, and will help you to construct a well-balanced portfolio. However, if you are still not clear, then please feel free to ask any questions via the comment section below.*

But when it comes down to the essence of football betting systems, the keen observers amongst you will appreciate that every season is a season of two halves also.

Image: Marino Bocelli (Shutterstock)

Many of the continental European leagues operate with a winter break: the German Bundesliga (18 teams – 306 matches) pauses for a month in late December of every year (the average German winter break was 31 days in the five seasons 2011-16); the Russian Premier League breaks for three months in early December of each year, and so on.

Some leagues without a recognised mid-season break contain a natural break. The English Premier League (20 teams – 380 matches) is a good example. The league schedule here is for all Round 19 matches (halfway stage of the campaign) to be completed in the last few days of the calendar year. Round 20 always begins early in the new calendar year.

But why is the impact of these breaks such an important consideration for the discerning bettor?

Let’s first examine some of the more relevant differences between the two halves of a season.

Before the start of any new season, the destinies of every team are completely unknown.

Public opinion (punters, press, TV, betting companies, etc.) dictates that some teams are earmarked as potential title winners or challenging for Europe; others as relegation candidates; the rest a mixture of unknown quantities, or teams set for a season of struggles.

In this way, matches are initially priced by the bookmakers based purely on the past performance of the teams involved. (There are no current statistics as the league hasn’t yet kicked-off). These prices are then adjusted based on the strength of public opinion. (In other words, according to the weight of money staked by punters).

The initial odds-setting exercises are often wide of the mark. They are guesses based on what has happened in the past. It takes several rounds of competition before the real mix of potential title challengers and relegation candidates begins to take shape and odds settle accordingly.

A classic example is the 5000/1 arbitrary price offered for Leicester City to win the EPL before the start of both the 2014-15 and 2015-16 seasons. 2014-15 saw them back in the EPL for the first time in 10 years with no relevant statistical form whatsoever. Surviving by the skin of their teeth was only good enough for bookmakers and punters alike to give them no chance again the following season. Having miraculously won the EPL title in 2015-16, they were lower than 60/1 with several bookies prior to the start of 2016-17, with only two seasons of relevant statistics behind them.

Therefore, ante-post odds setting becomes a more reliable exercise as the season progresses, when more results are recorded by each team, and league position and form become more apparent.

Many European leagues have a formal Winter Break out of necessity to avoid the worst of the winter weather. When you have lived in Berlin during a December day that plummets to -25°C, and experienced petrol and diesel freezing at -40°C and below during a Russian winter, it is easy to understand why!

Most European leagues start in late summer. The first half of a season sees games played in gradually deteriorating weather conditions as summer enters autumn, and autumn enters winter.

The second half of a season usually begins during or at the end of winter, continues through the following spring, and into the beginning of the summer. It’s a complete reversal of conditions, and each team will have their own regional variations to contend with as well.

At the start of every season, for most teams in a league, there are fewer competitions to contend with. Most teams begin a season with a fully fit squad of players but a manager might not know his strongest team at the start.

Squad rotation only becomes an issue for teams with large enough squads to rotate and is observed when teams wish to rest key players in less important games. Most of the EPL teams enter the League Cup in the last week of August, whilst those with European commitments have almost an extra month before League Cup duties commence.

Invariably, one or two teams begin their seasons before the league campaign kicks-off. Pre-qualification games for the Europa and Champions Leagues begin as early as June. As a result of playing up to three two-legged competitive ties, these teams may already be more ‘match fit’ before commencing their league campaigns.

The third round of the FA Cup is usually the first set of fixtures for EPL teams to face in the new calendar year. The 1st of January is, therefore, a natural split and heralds the start of the second half of the season in England.

As players accumulate more game-time during a season, their chances of missing matches through injury or suspension naturally increase.

It takes time for a totting-up suspension to attach to any player. In the EPL, the yellow card suspension system recognises the midway point of the season. Five yellows in the first half of a season lead to a ban. With up to four yellows to a player’s name, an armistice applies to allow him to continue playing in the second half of the season with the threat of a totting-up ban reset at the ten yellow cards mark.

Therefore, with the rules of the game and the limits of the human body, it is therefore only natural that more suspensions and fatigue-related-injuries will occur later rather than earlier in a season.

- The more successful a team becomes the more games in a season that team will play and vice versa. Successful teams will subsequently tend to play more matches in the second half of a season.
- Games become ‘six-pointers’ towards the end of a season when there is something more definite to play for.
- Some squads become thinner as the season progresses, more so during the second half because of injuries, suspensions, African Cup of Nations call-ups, etc.
- Attitude towards cup competitions may change depending upon the league standing at the time of the club involved.
- Targets become more visible and tangible as competitions draw to a close. The attitude of ‘taking each game as it comes’ is replaced by a more focused approach as the prize money and the glory gets closer.
- Players with personal targets or seasonal records to achieve or maintain will, of course, be more incentivised the closer it gets towards the end of the season. E.g. Golden Boot and Golden Glove candidates.
- Teams experiencing managerial changes during the season will be affected in different ways. A relegation-haunted team may suddenly perform like champions-elect under their new manager. A different team may be doomed already and no amount of managerial changes can help.
- League position tends to be a psychological factor for everyone concerned. A cursory glance at the league table will lead punters to view teams at the bottom as generally weaker than those at the top.
- The pre-season transfer window is far longer than the mid-season window.
- If you know your football and have many seasons of observation under your belt, you will surely know in your heart that both halves of a season are entirely different from each other.

Taking all of these factors into consideration it stands to reason that what happens in the first half of a season is likely to be totally different to how the second half pans out. The variables are different. The mentality of teams is different. Everything is different.

With betting systems, what works well in the first half of a season may be totally inappropriate once the second half commences.

The following graphic shows a great example from the Japanese J-League, and is based on flat stakes of 100 units per match.

*Click on the image to enlarge it – opens in a new tab:*

The left-hand graph shows the results of backing the home win in all 765 games during the first half of the five seasons 2012-16. (The first 17 rounds of matches in each season).

The right-hand graph shows the same bet type for the 765 matches occurring in the second half of the same seasons. (The second 17 rounds of matches in each season).

You can see quite clearly that backing the home win during the first half of each of the five seasons is unviable and leads to heavy losses – you are better off laying the home win. However, in the second half of the season, there are healthy profits to be made by backing it.

However, these opportunities would be hard to spot with the analysis of all 1,530 matches together:

Looking at the whole J-League picture for five full seasons reveals a more chaotic picture and one that tempts neither a backing nor laying strategy.

As an aside, whilst Japan shows patterns which are typical of many leagues, it also has its peculiarities, such as being the home of the **world’s oldest footballer**, still playing professionally well into his fifties!

More often than not, there will be different bet types applying to the first and second halves of the season. For example, it might be the underdog or away win during the first half of the season, and home wins and favourites in the second half.

Sometimes, the same bet type applies to both halves of the season, just with slightly different parameters. You might be chasing favourites priced between 2.01 and 2.76 in the first half, and favourites priced between 1.89 and 2.56 in the second half. Every league is different.

So, we have explained why and shown how each half of a season has its own patterns. Analysing both halves separately is usually a far more revealing method than analysing what happens in whole seasons.

**Whole season analyses tend to represent a blend of what has happened across both halves**, rather than pinpointing what is likely to happen in each half. (Just ask the Russians – their winter break is so long that both halves might just as well be separate league seasons).

However, some leagues just don’t have any recognisable break at all. In Europe, for example, the Finnish Veikkausliiga. M.L.S. in the United States is another example.

**For leagues such as this, it is sometimes better to analyse the season as a whole and forget about breaking it down into halves.**

These two leagues are examples of what we call ‘**Summer Leagues**’ – ones where the entire season is fitted into a single calendar year rather than bridging two as is the norm in the top-flight European leagues.

But which staking plan is the best?

The answer is: A simple and straightforward staking plan. Nothing complicated; just plump for **flat staking** with or without a **ratcheting** mechanism.

All other staking plans contain one or another problem and I can guarantee that there is definitely not a single staking method in existence, which makes a failing betting system work.

Therefore, firstly work out a sound betting system and then secondly, adhere to a modest and plain staking plan. Keep the money management as simple as possible because it is already difficult enough to keep up with everything that goes into **monitoring a betting system**. You will perhaps also have to think about juggling your bank between various bookmakers and exchanges if needs be.

In today’s article I’m going to show you a fuss-free staking plan using the example of our **2017-18 Winter League portfolio** .

As mentioned, it’s a combination of a **flat staking plan** and a **ratcheting mechanism**.

With the help of our HDAFU Tables and, in particular, their **Inflection Point** graphs, it is now easy to develop a portfolio of bets with a definite mathematical edge.

And in conjunction with our chosen staking plan, the portfolio of 2017-18 Winter Leagues performed as follows:

Image 1: 2017-18 Winter League Campaign – Profit Curve with Ratcheting

We started with a betting bank of 4,000 units and finished after 47 weeks and 518 bets with a total 38,925 units.

Of course, it wasn’t a smooth ride the whole time. Especially at the start of the season, the first 15 weeks (up to 27/10/2017) were very tough. 153 bets were placed but the result was pretty much a zero sum game. It did eventually rise to 5,562, but for all the time invested and work performed it was quite a frustrating experience.

From this point until the end of December, results were better and the ratcheting system helped the bank up to 13,792 units. But then another rough period started.

Nevertheless, it was worth it! A very long slog (47 weeks!) for a profit of 34,925 units. A great result vindicating the soundness of both the **portfolio of bets** and its staking plan.

Just as a side note, if you want to learn more about how the portfolio was originally compiled and how it performed in detail then you will find our report here:System Football Betting: 2017-18 Winter League Report – 35k in 138 Days

But let’s get back to the topic of the article:proper staking…

Image 1 showed you the performance of the portfolio using ratcheting, but if we would have applied a flat staking plan only *(without ratcheting)*, then the Profit/Loss curve would have looked like this:

Image 2: 2017-18 Winter League Campaign – Profit Curve Flat Stakes

You can see straight away that the simple flat stakes (without ratcheting) also produces profits, but the curve is much flatter – here, the betting bank *only* increases from 4,000 units to 13,909 units. Flat staking lacks the exponential element of a ratcheting system to grow a bank, but on the other hand, it is much easier on the nerves as I will show you later in this article.

But first here are a few definitions…

Flat staking simply means that you wager on every bet exactly the same amount of money, without any deviations. But this may include some consideration towards the risk of each bet. You may therefore wish to stagger your stakes according to the implied probability (odds) of winning each bet:

**VERY SIMPLE:**decide to stake a flat 100 units on every bet in the portfolio.**SIMPLE:**decide to stake a flat 100 units on bets with odds below 1.50, 50 units on bets with odds between 1.50 and 2.50, and so on.

But whichever of the two options you choose, you are in effect still ‘flat staking’.

Ratcheting is a progressive money management approach where the size of the stakes move by degrees, upward or downward, depending upon results.

With ratcheting the stakes are variable and depend on the size of the bank. However, the percentage of the ratchet (in our case 2.5%) always remains constant.

If at the end of a round of matches (or week) your bank has grown, all bets placed the following week should be adjusted to the higher bank.

For example, if the bank increases from 4,000 to 4,500, the stake increases from a flat 100 units to 112.50 per bet in the following round (i.e. stake remains at the base level set of 2.5% of bank).

**The percentage of the bank used per bet stays ‘flat’.**

Nevertheless, you also need to guard against bankruptcy. If your portfolio experiences a losing round, reduce the stakes for each bet but not before the bank drops to **75%** (or below) of its highest point.

You may think that this method is simply a stop-loss strategy, but it isn’t quite the same. I will explain further down in the article why we used 75% as the margin for our downwards ratcheting and not any other number.

Should you lose at the end of a round (week), continue to play each bet with the same, unchanged stake until the bank’s previous high has shrunk by 25%.

This means that in the event of a short-term loss, the stake continues to refer to the bank at the highest level it has reached so far and does not adjust to the lower bank until the bank has dropped to 75% of its peak size.

Only then is the stake recalculated (reduced) and the ratchet process begins again.

To be clear on this point, in the event of a run of losses, the stake size per bet always remains in relation to the highest bank to date and should not decrease until the threshold of 75% of highest bank ever is reached. *(If you have for example, a very bad start to a campaign, the 75% trigger point may well apply to your starting bank)*.

Only then will the stakes be adjusted (reduced) to this lower bank size. This will then be your new starting bank. All further bets from then on refer to this bank and the ratcheting process begins again.

**Example 1:**

The bank drops from 4,000 to 3,800: The stakes remain unchanged, flat 100.00 (= 2.5% of the starting bank of 4,000) for the next period (round/ week).

After the next round the bank closes with 3,520. Still, the stakes remain unchanged, flat 100.00, using the previous bank of 4,000 for its calculations.

Only if the bank closes with under 3,000 (75% of 4,000) will the stake sizes be recalculated.

**Example 2:**

Using the starting bank from our previous example, the bank has dropped to 2,800. This has now become the new starting bank and the stake is recalculated:

**2,800 x 2.5% = 70.00**

With the reduction of over 75% of the bank from its former highest level of 4,000 (100 unit stakes) to 2,800, the stake size is recalibrated and remains flat at 70 units.

Afterwards, if the bank starts to rise, you will need to begin increasing the stakes again.

Say, after the next round you bank has gone up to 3,150.

3,150 x 2.5% = 78.75

The adjusted ‘new’ stake is now 78.75 and remains in place until either the bank drops to 75% of 3,150 (2,362) or the bank grows above 3,150, when stake amounts will be 2.5% of the new, larger bank size.

We have seen that ratcheting is purely a method of ‘flexible flat staking’ to encourage exponential bank growth.

The idea is to start off with stakes of 100 units and, if everything goes according to plan, by the end of the season the stake sizes should hopefully be in the multiples of 100 units.

Looking at the other side of the coin, the losses during this time will be in the same proportions, and not everyone is comfortable when losing a few thousand units in an afternoon, even if its ‘just winnings’ from previous rounds.

Bear this in mind before you decide to try ratcheting. Are you a disciplined person? Are you able to function when you have a few thousand riding on a few matches?

If your answer to these questions is ‘no’, then please do yourself a favour and stick to flat staking only! Do not try ratcheting, at least not to the end, and stop increasing your stakes when you reach the limits of your comfort zone (or have achieved target).

–

You may find it helpful to follow the explanations in this article with the help of our dedicated Excel workbook detailing our 2017-18 Winter League portfolio. Not only does it contain the match data and calculates the flat and ratchet staking results, but it also shows how the portfolio was composed and provides many other useful snippets of information.

We are sure that you will feel the nominal **£5.00 GBP** charge is a real bargain.

*The size of this .XLSX Excel file is 568KB:*

**>>> 2017-18 winter league campaign <<< **

–

Even the soundest portfolio of bets will experience bad periods where one bet after another (or even one round after another) is losing. It happened to us from the 09/12/2017 (2017 week 50) – 11/03/2018 (2018 week 11). Three months of more losing rounds than winning ones! Tough indeed!

Here are two images that show the profits/losses together with the bank development during this rough period using flat stakes versus ratcheting:

Image 3: 2017-18 Winter League Campaign – Using Flat Stakes only

Image 4: 2017-18 Winter League Campaign – Ratcheting the Stakes

Both staking plans produced profits but, to put the choppy ride into even better perspective, you will need to note that the results summarised in Image 4 were by this time already based on a ratcheted bet size of 344.80 units *(as at 09/12/2017)*, and this has grown to 707.82 units by 11/03/2018.

The period spanned more than 15 weeks with nine losing rounds (60%). From the 23/12/2017 – 12/01/2018 there were many losses, not huge, but enough to be nerve-racking!

The biggest losing round of bets with flat staking was: – 578

The biggest losing round of bets with ratcheting was: – 4,091

**Tough!** This again highlights the difference in volatility between flat staking and ratcheting. Steel hearts only required here!

However:

The biggest profit round with flat staking was: 1,785

The biggest profit round with ratcheting was: 6,155

**Great!** But please don’t get carried away too much!

*Moral #1:* **If you are a person that finds it challenging to keep emotions under control, stick to flat stakes! The best laid plans fall to pieces if you can’t cope during the really rough times.**

**It is always better to be a modest winner than a brave loser.**

In the previous section you saw the monetary effects of winning and losing when using flat stakes only or when ratcheting. However, the differences become even more obvious if you look at the profits/losses in relation to the bank:

Image 5: 2017-18 Winter League Campaign – Profit/Losses in relation to the bank: Flat Stakes

When staking flat our example portfolio only produced a maximum loss of – 8.1 % of the bank. The winnings too were pretty ‘modest’: a maximum of 20.7%.

Ratcheting involves a far greater rollercoaster. The maximum loss was as high as – 15.2 % of the bank. The maximum winnings were: 52.6%.

Image 6: 2017-18 Winter League Campaign – Profit/Losses in relation to the bank: Ratcheting

*Moral #2:* **As I have already said, simple flat stakes (without ratcheting) are much easier on the nerves than ratcheting. The exponential growth a ratcheting system produces goes hand-in hand with exponential losses.**

In the article **Bank Management & Stake Size** I explained the ‘scientific’ calculation of the percentage of starting bank that should be used for betting.

It was based on the average of the three largest losing rounds (weeks):

12%, 15.2% and 14.6% >>> average: 13.9% (rounded: 14%).

We can use this figure of 14% to calculate the stop-loss margin. You see, everything is somehow connected. The stake size, the stop-loss margin, and much more.

To be able to sit through a run of at least two losing rounds in a row where the bank is depleted by 14% each time you need to calculate as follows:

86% x 86% = 73.96%

Let’s round this up to 75% to be more risk averse (safety conscious).

Hence, if your portfolio loses, reduce the stakes * but not before* the

Please bear in mind that all the calculations and explanations are based on a portfolio of just over 500 bets with an expected hit rate of around 50%. Should your portfolio be different (no two are alike), then you will need to carry out all the calculations using your own figures.

If you cannot calculate this for yourself in such great detail then either stick to the 75% threshold, or perhaps lower it to 65% *(if you have a lower risk aversion)* as advised in previous articles.

*Moral #3:* **Better to be safe than sorry. If you are new to ratcheting it’s probably better if you play with smaller stakes than the calculations actually permit (e.g. 1.5% of your betting bank instead of 2.5%) then you won’t reach the stop-loss margin too quickly. **

*I hope you’ve enjoyed this article and learned something about sound staking and ratcheting. However, if you are still unsure on any point, please feel free to ask any questions via the comment section below.*

*We cannot say in advance with accuracy how many fish there are to catch or, after the event, how many fish were not caught…*

Image: tobkatrina (Shutterstock)

System betting is similar to fishing with a net.

Of course, it is easy to say how many bets were won over the course of a full season (i.e. how many fish were caught).

But it is also easy to evaluate how many opportunities to win were missed (i.e. bets not placed; fish not caught).

And calculating how many winning opportunities there were in total (i.e. how many fish there were to catch) is also straightforward.

With this much control over the available information we assure you that it is possible to identify, formulate, refine, test, and finally deploy a successful betting system.

The ultimate goal of system betting is to create a strategy that becomes just a numbers exercise, and where obtaining the highest price for the desired outcome is the primary objective for achieving ‘value’ in order to gain maximum reward.

The equatorial circumference of the Earth is widely accepted as 40,075 km. However, the circumference from pole to pole is slightly less as the Earth is not exactly spherical.

Furthermore, if you were to bring into the equation all the geographical features in the path of the measuring tape and calculate the circumference including every peak and trough of every mountain and valley, then this would provide yet another different value.

But which is the correct measurement?

Let’s look at another example: Motor car producers test their vehicles and provide guarantees but occasionally they get it wrong and mass returns are ordered to rectify a common fault.

Of course, a vehicle’s longevity also depends where in the world and on which terrain it is used, how it is driven and maintained, plus a host of other immeasurable factors.

If you were to ask a vehicle manufacturer how long a particular model will last it will be impossible for him to say with any degree of accuracy, although he can predict that a rough percentage of vehicles will still be on the road in one year from now, less in two-years’ time, fewer in three-years’ time, and so on.

What we are trying to say is that too many extraneous factors prevent total accuracy with any mathematical calculation and scholars of math will readily agree that theirs is not an exact science; some allowance for error has to be accepted in any equation or calculation. (For example, the formula for Pi (Π) produces an infinitely long number, not an exact one).

Likewise, all betting strategies based on historical figures **can never be 100% accurate**.

It is impossible to predict for sure the outcome of any one single event but it is much easier to say, for example, that between 40 and 50% of the 380 matches in league ‘X’ will be home wins next season based on the historical results in that league.

This is where system betting or ‘blanket betting’ becomes interesting.

But how do we create a strategy to catch as many of the 40-50% home wins as possible using the fewest number of wagers?

The statistics we keep reveal that just three scorelines account for around 70% of all half-time scores. It does not matter which top-flight league you look at, the same three scores repeat themselves in more or less the same quantities per league, every season.

Can you guess what these three scorelines are before checking out **this article**?

**Patterns such as this** are ideal for planning a betting system; in fact anything you can find which happens often enough and regularly enough to warrant continual betting on the same desired outcome for consistent profits.

It goes without saying that bookmakers are not stupid and it is rare to find a result over the course of time uniformly priced in your favour.

In other words, it is hard to find an event which will be profitable when staking or risking the same amount on the same desired outcome in *every* match in a league during the course of an entire season. But whilst it is difficult, it is not impossible.

When looking at system betting, we always prefer to look at **the last five seasons’ results** as this usually provides a sufficiently large pool of statistics to be significant. Working with too few data is more of an accuracy issue than working with too many, but five seasons’ worth is plenty/ideal.

The **key to unlocking the potential of any system** is your ability to filter-out matches which are statistically less likely to bring the desired outcome and, when such games are analysed collectively as a cluster, do not bring enough winning bets to make a profit.

It is clear that excluding unprofitable betting clusters such as this will increase the efficiency/profitability of any strategy.

In other words, narrow down the field to find where the majority of winning bets occur and then concentrate your strategy within this area, dispensing with the other groups of matches that are historically unprofitable.

When sorting data and filtering it, cut-off points often appear at both ends or either end of the odds spectrum. You will find that bets may become unprofitable at a certain price and lower or, at a certain price and higher, or both (and vice versa).

A good initial filter is to sort your collection of results into ascending or descending order according to the size of the home team’s odds to win.

Ultimately, you will sort the entire list of matches by as many criteria as you can think of, such as: home, away, or draw odds (or these odds converted into probability percentages – remember, this is 1 divided by the decimal odds, expressed as a percentage); desired event odds; home odds multiplied by the away odds; in fact any numerical factor you can think of to find one or more filters to discard the unprofitable matches.

In this way, filtering out undesirable matches turns system betting into a form of **value betting**.

Once you have filtered the entire list, it is worth looking at each individual season to see if the same elimination process works in a micro format. Paper test everything until you are happy that the system can be profitable.

The next step is to test with live events without committing money to the outcomes. Does the elimination filter or set of filters still work?

Keep observing and recording, refining and correcting, until you are happy that your system really does have a future.

Never be delusional about system or blanket betting. What works for one league several seasons in a row may fail due to an abnormal season ahead.

It is therefore important never to count on one league or one system at any one time. It is far better to spread your risk and have several systems in play at the same time. Successful system betting relies on the law of large numbers to spread risk.

The final fundamental ingredient of any betting strategy is a **flexible and effective staking plan** to act as the glue which holds everything together. A stop-loss (to prevent total loss of betting bank) and ratchet provision (increasing stakes incrementally) is the gloss on any staking plan.

Thus, building a solid betting system is just a series of ingredients and interconnected steps. Rather like baking a cake without a recipe; you may have to keep experimenting with the ingredients of your betting system until the end result becomes palatable.

To conclude this article, we return to our earlier metaphor. (Oh my cod!).

Dip your net (i.e. your betting system) into the pool of chosen (filtered) matches.

How closely knitted the mesh of your net is will determine how many big fish (winning bets) your filter(s) will allow you to catch (win), and how many pass through the mesh of the net (the minnows, which are unprofitable to catch).

Through trial and error, adjust the size of the mesh to catch only what you want in order to achieve as many winning bets with as few wagers as possible.

Thinking along these lines will increase your chances of developing a successful strategy.

Or, why not look into our **HDAFU Tables** where the whole job is done and presented on a plate for you to pick systems from the leagues of your choice.

**Happy fishing!**

Managing and following through with the plan is illustrated in the **2017-18 Winter League Report – 35k in 138 Days**, where the full performance of our selections is analysed and dissected.

But remember, the best compiled portfolio won’t work without proper bank management and staking.

Please have a look at the distribution of bets during the 2017-18 Winter League Campaign:

Image 1: 2017-18 Winter League Campaign – Bets per Week of the Year (per round)

Week 38 was the busiest with 24 bets in total, whilst week 50 saw 23 bets. Within a relatively small portfolio of just over 500 bets, the average number of bets per round was 11, but the observed spread was anything between 0 and 24 *(2 rounds without bets)*.

Even with a small portfolio, bank management poses challenges, especially when juggling numerous bookmaker/exchange accounts. Monitoring the balance of each account can become difficult, which may lead to liquidity issues (perhaps not enough in the account) and the constant hassle of moving money between your accounts (or playing with smaller stakes than intended).

Trying to get the highest odds possible in the market all the time becomes unrealistic as the total betting bank would need to be spread over many, many bookmakers.

As harsh as it may sound, you will have to accept that aiming to achieve the highest odds available in the entire market at any moment in time is virtually impossible, and the choice of bookmakers/exchanges has to be limited from the start. I recommend limiting your bookmaker/exchange accounts to a maximum of five or six, and simply choosing best price between them at the time you wish to place a bet.

Here is the list of bookmakers/exchanges we recommend to use in addition to Betfair: –

**BetDaq
Matchbook
Smarkets
Pinnacle
William Hill
888Sport**

It makes little difference to the results when limiting your accounts to just a handful and you will see evidence of this in our **2017-18 Winter League Report – 35k in 138 Days**.

The screenshot below shows the bet placement distribution by bookmaker/exchange.

Image 2: 2017-18 Winter League Campaign – Distribution by Bookmakers/ Exchanges

*Lesson 1:* **Limit your choice of bookmakers/exchanges!**

To determine the stake size there are a few things to consider. Firstly of course, the size of your starting bank. This is an easy one to call – You know what funds you have.

But once you’ve settled on the starting bank, things quickly become a little more complicated…

You can go to great length and once you have compiled your portfolio carry out following exercise:

- Go to the ‘data’ tab in the HDAFU Tables, copy and paste all applicable matches and amalgamate them in one spreadsheet.
- Go to any odds comparison site of your choice and check the bookmakers that would have been available for you in those matches; log them into your spreadsheet together with the name of the bookmaker/exchange
*(a hugely time-intensive exercise!)* - Sort the data into chronological order and group it by weeks/rounds.
- Count the number of bets with each bookmaker/exchange in each round.

Once complete, you will be able to use the data to come up with a similar graph to this one:

Image 3: 2017-18 Winter League Campaign – Distribution by Bookmaker/Exchange in more detail

In our example, you can clearly see that Pinnacle had the majority of bets (up to 15 in one round), Betfair and Matchbook were the next most used (up to seven bets per round), followed by 888sport (up to five bets per round), and lastly, William Hill (up to four bets).

Going forwards, the scientific split of our bank for the forthcoming new season based on experience from last season is therefore:

40% Pinnacle: 1,600 units

20% Betfair: 800 units

20% Matchbook: 800 units

10% 888sport: 400 units

10% William Hill: 400 units

But, if you have no past experience it may be harder to estimate which of your chosen bookmakers/exchanges are most likely to have the highest odds when you are ready to place the bets, and how future bets may be distributed between your accounts.

If you have no past figures to guide you, then it is probably best to distribute your betting bank equally between your chosen bookmakers/exchanges. In this case (five accounts), an even split of the starting bank of 4,000 units means 800 units in each account.

*Lesson 2:* **If you cannot calculate the most probable distribution of your bets between the bookmakers of your choice, then simply distribute funds in equal amounts between your accounts!**

The major question is how each betting round is going to perform. Is the portfolio going to make a profit or a loss? Which account will accrue the biggest proportion of profits? Which will be the most high-maintenance, requiring the most re-deposits?

Have a look at our profit/loss distribution over time:

Image 4: 2017-18 Winter League Campaign – Profit/Loss Distribution per Week (per Round)

In total, there were 47 rounds (weeks) of betting. 17 of these (36.2%) finished with losses. (Effectively one losing round in every three).

There were four months (15 rounds) between week 50 (9/12/2017) and week 11 (11/3/2018) where the portfolio experienced a seemingly never-ending rollercoaster ride.

During this time, eight rounds (over 50%!) produced losses as seen in the image above. Those three months were especially difficult on the nerves.

However, this still doesn’t tell us the best stake size. There is also no sense looking at which bookmakers incurred losses, as the past distribution will very likely not repeat itself during the forthcoming new season.

The best guide is to have a look at the profits/losses in relation to the bank size:

Image 5: 2017-18 Winter League Campaign – Profit/Loss in relation to the bank

As you can see, there were three occasions where the bank dropped by more than 12% of its total. In the worst single round (week) the size of the bank contracted by over 15%. Furthermore, we know from Image 1 *(distribution of bets per week)* that if we are going to employ a similar portfolio of bets in future, that there will be up to 25 bets in a week (round).

We have no idea which of the 25 bets will win and which won’t, and the intention of the staking plan not to be bankrupt after any one week of ‘bad luck’. Also, we don’t want to be in a position after experiencing a ‘bad week’ where we are forced to reduce the size of our stakes. Better to arrange the plan so that we can ride through any longer periods of ‘bad luck’.

We have distributed our funds between accounts but we don’t know which of the chosen bookies/exchanges are going to receive the bets.

**The ‘scientific’ calculation is as follow:**

The average of 12%, 15.2% and 14.6% = 13.9% (rounded: 14%).

Although very unlikely, there may be a run of losses (say three rounds in a row) where the bank is depleted by 14% each time, and should this happen, we still need to continue placing bets without having to reduce the stakes.

As mentioned, we are expecting a maximum of 25 bets in a single round.

25 divided by 86% divided by 86% divided by 86% = 39.3 (risk averse rounded: 40)

1 divided by 40 = 0.025 (= 2.5%)

So, if we risk 2.5% of our betting bank per bet, we will still be able to survive three hefty losing runs without having to reduce the stakes.

**The ‘short cut’ calculation is as follow:**

If you have no past figures or estimates, take a short-cut and use the following calculation:

- Estimate as best you possibly can the expected maximum number of bets in one betting round
- Double this number
- Divide 1 by this number

*Example:*

- Expected number of bets: 25
- 25 x 2 = 50
- 1 divided by 50 = 2%

*Lesson 3:* **Limit your stake to 2.5% if you have a portfolio of around 500 bets coming up for one season. If you have a larger portfolio, use your past experience or figures to calculate your maximum stake size as shown above!**

Yes, it can! Our portfolio produced a profit during and despite three very tough months:

Image 6: 2017-18 Winter League Campaign – Analysis of Most Challenging Period

Prior to week 50 the bank size stood at 13,792 units, but after enduring a really volatile period (where more than one round lost in every two) the bank had risen to 23,960 units.

This was only possible by using a strict staking plan in combination with stake ratcheting.

Please remember:

- Limit yourself to just a handful of bookmakers/exchanges: five or six is plenty
- Split your bank (more or less) equally between the chosen accounts
- Limit your individual bet stakes to 2.5% of your entire betting bank (or less if you are expecting more than 25 bets at any betting round)
- Apply and stick strictly to the set rules, and NEVER experiment and/or change your staking midway through a campaign! Once decided. Stick to it!

–

You may find it helpful to follow the explanations in this article with the help of our Excel monitoring workbook. Not only does it contain the match data and calculates the bet distribution, but includes many more Excel formulas and ideas to help you grasp how to record and control your own betting ventures.

We are sure that you will feel the nominal **£5.00 GBP** charge is a real bargain.

*The size of this .XLSX Excel file is 568KB:*

**>>> 2017-18 winter league campaign <<< **

–

*I hope you enjoyed this article and learned something about the unavoidable randomness of the distribution of bets within a portfolio and how to keep your bank under control despite the inevitable peaks and troughs. However, if you are still unsure on any point, please feel free to ask any questions via the comment section below.*

Despite a rocky start and other challenging periods throughout 2017-18, a starting bank of 4,000 units was eventually turned into 38,925.

Image 1: Winter League Campaign Development Graph

(Bank Development ↔ green profit curve; Profit/Loss ↔ weekly columns)

(Bank Development ↔ green profit curve; Profit/Loss ↔ weekly columns)

Regarding the selection of systems we will expand a little on our decision-making process in the league-by-league review below. For compiling this portfolio we used our **HDAFU Tables**.

How the systems were originally chosen, you’ll find a detailed explanation using the EPL pick as example here: **Finding a System Using the HO/AO Quotient**

To read about the compilation of the portfolio used for this article here’s a link to our analytical article: **Judging the Risk of a Football Betting Portfolio**.

There you will find explanations on how to calculate the overall probability/expected hit rate of a portfolio and how to judge the risk. You will also learn how to predict winning and losing streaks, and what kind of profit/loss curve to expect when compiling your own portfolio of bets.

Let’s now dive in to the performance report without further ado.

We start with a league-by-league review and then see how they all put together reduced the risk and finally produced a result as expected.

In Image 2 below you can see that hardly any individual system (league) within the portfolio met its expectations (predictions), yet the overall performance of the portfolio fully met the expectations, profit-wise, yield-wise and hit rate-wise.

Image 2: Final performance of the 2017-18 winter league portfolio

Risk Judgement: **HIGH RISK**

Hit rate expected: **31.94%** *(associated risk: high)*

Yield expected: **30.36%** *(associated risk: high)*

Despite its low hit rate this system was included because a healthy yield was expected. When judging a system fit for inclusion, healthy yield indicates that a profit can still be made even if something goes wrong, e.g. the hit rate is a little lower than expected.

Hit rate achieved: **30.67%** *(just below expectation)*

Yield achieved: **9.75%** *(far below expectation)*

The hit rate was more or less achieved, but the profit was 65% below budget. Perhaps the odds that were obtained were too low or there were just not enough larger priced winners in this cluster.

Risk Judgement: **LOW RISK**

Hit rate expected: **86.67%** *(associated risk: low)*

Yield expected: **13.85%** *(associated risk: low)*

Included because of the high hit rate indication to add reliability and stability to the portfolio, despite a low yield expectation.

Hit rate achieved: **80.95%** *(below expectation)*

Yield achieved: **7.90%** *(below expectation)*

The hit rate was below expectation and hence the yield too. However, a profit is a profit albeit small here, but no complaints. In the end, Belgium produced a profit and helped the portfolio to remain balanced over the season.

Risk Judgement: **MEDIUM RISK**

Hit rate expected: **41.67%** *(associated risk: medium)*

Yield expected: **21.18%** *(associated risk: medium)*

Another judgement call asking the anticipated yield of around 20% to buffer the expected hit rate and still turn a profit.

Hit rate achieved: **37.5%** *(below expectation)*

Yield achieved: **8.1%** *(far below expectation)*

The hit rate was below expectation and hence the yield too. But still a profit from a medium risk venture.

Risk Judgement: **HIGH RISK**

Hit rate expected: **39.62%** *(associated risk: medium)*

Yield expected: **38.62%** *(associated risk: high)*

Despite its low hit rate this system was included because a healthy yield was expected. It was felt that the risk was worth the potential reward.

Hit rate achieved: **38.18%** *(just slightly under expectation)*

Yield achieved: **26.73%** *(good, although far below expectation)*

The EPL is probably the most analysed league in the world and it is difficult to spot long-term value for betting systems that are reliable and come out to expectations. The hit rate was in line with expectations but the yield was not. Nevertheless, a healthy profit was achieved.

Risk Judgement: **MEDIUM RISK**

Hit rate expected: **50.00%** *(associated risk: medium)*

Yield expected: **24.93%** *(associated risk: medium)*

The yield looked slightly too high for an expected hit rate of 50%. This may have been an arbitrary positive result observed in the inflection point graphs, especially as the expected number of bets was only 36. Nevertheless, this system was included in the portfolio because of the risk-reward balance.

Hit rate achieved: **43.33%** *(below expectation)*

Yield achieved: **10.8%** *(far below expectation)*

Only 30 bets were played (from expected 36) and both hit rate and yield were much lower than expected.

Risk Judgement: **MEDIUM RISK**

Hit rate expected: **31.58%** *(associated risk: high)*

Yield expected: **29.83%** *(associated risk: medium)*

Normally, systems with an expected hit rate of under 35% (classified as ‘high’ risk) and with an expected yield between 15% and 30% (‘medium’ risk) get an overall risk judgement: HIGH RISK.

Although the combined expectations of hit rate and yield here would have labelled this system as ‘high risk’ we downgraded the overall risk judgement to ‘medium risk’ as it was Germany and the Bundesliga is probably the statistically most reliable league we are aware of.

Hit rate achieved: **37.04%** *(above expectation)*

Yield achieved: **55.43%** *(far above expectation)*

German underdogs (not sausage dogs) were a (woman’s) best friend, and proved to be the most successful of all our systems.

If the EPL is just about the most ‘statistically unreliable’ league, then the Bundesliga is at the other end of the scale. There is something typically German about the Bundesliga’s constant conformity to statistics, season in, season out – very correct and very efficient.

Risk Judgement: **LOW RISK**

Hit rate expected: 80.00% *(associated risk: low)*

Yield expected: 9.02% *(associated risk: low)*

The Greek system was chosen for risk diversification in our portfolio. Despite associated lower yields, bets with a high probability of winning break up losing streaks in the portfolio and add stability.

Hit rate achieved: **82.98%** *(slightly above expectation*)

Yield achieved: **8.06%** *(below expectation)*

Not a huge profit but a profit nonetheless. Low priced home wins are naturally odds-on favourites also. Better prices can be achieved on these by placing bets further back in time before the event.

Risk Judgement: **MEDIUM RISK**

Hit rate expected: **52.59%** *(associated risk: medium)*

Yield expected: **8.49%** *(associated risk: low)*

Like Greece, this one was chosen for risk diversification combining a relatively low yield with a probability over 50%.

Hit rate achieved: **61.11%** *(above expectation)*

Yield achieved: **17.51%** *(far above expectation)*

Italy was our second best performer with its mixture of odds-on and odds-against away teams. However, almost all of these were the favourites and just a 15% swing in hit rate (not unnatural with favourites), was enough to double the yield harvest.

Risk Judgement: **MEDIUM RISK**

Hit rate expected: **75.00%** *(associated risk: low)*

Yield expected: **32.23%** *(associated risk: high)*

The yield here stuck out as being too high for a system with such a high probability. This may have therefore been an arbitrary positive result observed in the inflection point graphs, especially as the expected number of bets was as few as 15. Nevertheless, this system was included in the portfolio and judged as medium risk.

Hit rate achieved: **73.33%** *(slightly below expectation)*

Yield achieved: **25.33%** *(below expectation)*

A very tiny system with only 15 bets expected and indeed, 15 bets played in the end. However, not 12 wins as ‘expected’ but only 11. With margins so tight it was no wonder that the yield figure dropped. Otherwise, despite initial indecision whether to include this system, a profit was made.

Risk Judgement: **HIGH RISK**

Hit rate expected: **25.00%** *(associated risk: high)*

Yield expected: **33.00%** *(associated risk: high)*

With such low probability on the table, unreliability was expected, but the reward (yield) was worth pursuing. Furthermore, only 40 bets were expected. This system therefore had all the hallmarks of ‘high risk’.

Hit rate achieved: **21.62%** *(slightly below expectation)*

Yield achieved: **16.22%** *(far below expectation)*

Betting on underdogs is notoriously volatile and it was lucky that the very last two bets of the season both won to avoid a deficit here. 40 bets were expected, but only 37 played that fitted the selection criteria. 10 wins were expected but only 8 materialised.

This was the only system in the portfolio with such a low hit rate expectation in connection with very few bets expected. It was always going to be a shaky, unreliable ride, and it didin’t disappoint.

There was not a single system that performed *as expected* within the portfolio. Of the 10 chosen systems illustrated here, eight underperformed, and only Italy and Germany overperformed (both ‘medium’ risk systems).

The expected hit rate for Germany was 31.58% and it achieved 37.04%

The expected hit rate for Italy was 52.59% and it achieved 61.11%

Although some of the other eight systems succeeded to surpass their expected hit rates, the profit at the end was less than expected. This indicates that perhaps the odds played across the board were a little below those used in the HDAFU simulation tables.

**Please remember!** Concentrate only on individual leagues whilst compiling your portfolio. Once you start placing bets shift your focus to the overall portfolio performance and bank management. Only after a set period of time, review the individual systems in your portfolio. There will be an article on that topic coming soon…

After you have compiled your portfolio with due care you need to trust your own judgements and follow through fully with your betting endeavour as planned!

The information contained in the spreadsheet is invaluable and we are sure that you will feel the nominal **£5.00 GBP** charge is a real bargain: It is an ideal template for your own portfolio structuring and monitoring processes, and in addition it provides the opening and closing odds for the 518 bets included in the portfolio.

*The size of this .XLSX Excel file is 568KB:*

**>>> 2017-18 winter league campaign <<< **

**Spreadsheet Features:**

- The spreadsheet details every bet in every system used.
- Includes tabs for the overview of the portfolio, match data of the 10 systems used, as well as their opening and closing odds, and finally the odds used and the staking plan.
- The most important feature is the
**Ratchet**staking tab, which will provide you with the ideas and tools to manage your betting bank professionally.

On the whole, the portfolio performed as expected:

- A hit rate of 48.32% was expected; 45.37% was achieved
- A profit (flat stakes of 100 units) of 11,253.60 was expected;
**9,909.00**was achieved (if using 100 unit flat stakes during the whole campaign)

Image 3: 2018-18 Winter League Campaign Portfolio Results

You might say that the portfolio underachieved. But this is betting! The only goal is to finish with a profit! Any achieved profit may be below expectations or above, but as long as there is a profit, the portfolio must be considered as having been successful!

Therefore, remember to look at the performance of the whole portfolio for judging if it was successful. Do not concentrate on the performance of individual systems; not even after the season has finished. Potentially fatal if your mindset is affected by worries about individual systems, especially whilst your campaign is in-play.

However, there are exceptions to this rule and I will write about this in another article and explain when a system can be abandoned in-play, or when a new system can (or should) be added during the season.

You certainly noticed that in the above chapter we wrote that the portfolio achieved a profit of 9,909 but stated in the title of this article “35k in 138 Days”.

To predict the most likely performance of a portfolio you only can simulate a profit/loss using flat stakes. The same applies to judging the performance of a portfolio. You can only use flat stakes to appraise the effectiveness of a portfolio.

It doesn’t matter what staking plan has been used; in order to evaluate *(and compare with predictions)* the performance of a portfolio, you will need to see what would have happened when using flat stakes. Otherwise you will be comparing apples with pears.

The sequence of winning and losing bets is pretty random and any staking plan applied would therefore skew the review process. Therefore remember always to use flat stakes for retrospective analysis.

However, the portfolio used to illustrate this article produced a final profit of 34,925 units using ratcheting *(= gradual increase of stakes when winning)*. More details you’ll find in the article: **Winter Leagues 2017-18: Bank Management, Staking and Timing** *>> This article will be published within the next few days.*

If you have read the article about judging the risk of a portfolio you may remember the following statement about **diversification**:

The rationale behind this technique contends that a portfolio constructed of different kinds of bets will, on average, pose a lower risk than any individual bet (system) found within the portfolio.

Diversification strives to smooth out unsystematic or anomalous risk events (outcome of matches/leagues) in a portfolio so that the positive performance of some bets (winnings) neutralizes the negative performance of others (losers).

Have another look at Image 2 for the losing streak sequences.

The expected individual longest losing streaks within the leagues (systems) were as long as 11 bets in a row.

All three ‘high risk’ systems produced, as expected, very long losing streaks: Austria, EPL, Poland.

Denmark produced an even longer losing streak than expected. It started on the 26/8/2017 and continued until 29/10/17 – more than 9 rounds of games! Nevertheless, they recovered to finish with a positive return, although far lower than expected.

However, the portfolio as a whole only experienced a losing streak of seven bets in a row *(chronologically)*.

But try and look at the portfolio as a whole and consider one round (week) as one ‘single bet’. This perspective reveals a longest streak of four *(out of 47 rounds)* – just about bearable!

*Please remember* that in order to judge the success of any betting portfolio it is necessary to evaluate the performance of all of its systems together as one, not just individual group members (i.e. bets in just one league or system).

*I hope you enjoyed this article and learned how to judge the performance of a portfolio. However, if you are still not clear, then please feel free to ask any questions via the comment section below.*