In July 2020, after the first wave of the coronavirus, when most of the leagues resumed their games Soccerwidow performed a **public experiment** to see whether old statistics could be still used and if the Over/Under Betting coursebook remained potent.

If you followed our live experiment last year with real money then you would have increased your starting bank by over 50% in just 25 betting days.

Now, many months have passed and the leagues have just about returned to their regular schedules, albeit without fans in most stadiums. What is quite obvious to all observers, as well as punters, is that there are now more away wins than previously: ‘home advantage’ seems to have shrunk.

Above are the statistics for the four leagues we tested in our portfolio last summer: Italy, Spain, Poland and the EPL. These were four randomly chosen leagues and our campaign covered the last six weeks of the respective seasons.

This time we are adding the German Bundesliga 1. Firstly, because the BILD *(the German broadsheet newspaper)* is going to publish our picks on its website and secondly, to allow our course buyers *(who are in the possession of the German Bundesliga Cluster Table courtesy of their purchase)* to follow the calculations and reasoning.

This season’s campaign will again follow the last six weeks of each featured league and we will once again concentrate on Over/Under selections using our Cluster Tables.

The rules of engagement are the same as last time *(for comparison purposes)* and are explained a little further down in this article.

What’s pretty obvious this season is that in many leagues the ‘home advantage’ seems to have suffered due to the empty stadiums. Apart from the Bundesliga, the other four leagues, Poland, Spain, Italy and the EPL, have seen considerably fewer home wins than in the previous season – for example, a drop of 17.8% in the EPL thus far.

However, despite the shift in the home and away wins the total goals per match have hardly changed. Italy so far this season is down just -0.7% of goals, Spain -0.4%, Poland -2.7% and the EPL -2.2%. The highest change in the observed goals per match is in the German Bundesliga: -6.9%, although they have exactly the same number of home wins as the previous season.

What is interesting here is that the Bundesliga home goals per match do not show a high deviation – only -3.6% – but the away goals scored per match have dropped by -11.0%. In the other leagues, except in Italy, the away teams are currently scoring more goals on average.

Whatever the reason is for these changes it cannot be solely down to the missing crowds at games. It’s truly fascinating – just have a look at the numbers in the above graphs and make your own conclusions.

The picks for the respective day will appear here around 1 p.m. (sometimes earlier) as well as the results of the previous matchday. For the German audience, the picks are also published by the BILD, so no one will be able to tell us that we don’t publish our picks in advance! 🙂

You may have to press the **F5** button to refresh this page if you don’t see the picks for the day. However, please note that there won’t be picks on every day as not every day of the week has qualifying matches.

*Sadly, we have had to suspend our live experiment in association with Bild.de on 5th May 2021 after just 18 rounds of games. We were spending an awful amount of time compiling the data and making the picks entirely for free. Bild.de was using the novelty of a female pundit (yours truly) to attract readership and to entice them into buying subscriptions for the full version of its website. Indeed, every Soccerwidow featured article on Bild was attracting between 20-50,000 views each. Yet, an organisation as large and as powerful as Bild was arrogant enough to take our work for free with no guarantee of payment at the end of it. Apparently, we were supposed to be grateful for the exposure we received as a result of having our hard work taken advantage of. Sorry Bild, but that’s not the way to build lasting associations or bonds with your business partners… We are off to spend our time on more fruitful labours!*

**Best (Odds): The odds at the time the picks were made/published*

The expected probability and zero odds are calculated exactly as described in the coursebook using the Cluster Tables.

**The original selection criteria was:**

- the chance to win the bet has a
**Probability**between**60%**and**80%**, and - the
**expected Yield**is between**-15%**and**30%** - the
**Profitability**of the bet is between**-50%**and**95%** - the
**Disparity of goals**between the home and away team is between**-25%**and**30%**

**According to this season’s statistics so far, the following additional rules were to be applied:**

**ITALY**>> Avoiding ‘Over 2.5’ bets**SPAIN**>> ‘Over 1.5 goal’ will be preferred even if they have a negative value**POLAND**>> Under Bets will be preferred**EPL**>> Under Bets will be preferred**BL1**>> Being careful to place Over 1.5 and Over 2.5 bets

**If all the above criteria were applied and there were 2 bets to choose from, then the last knock-out criteria were:**

- bet has a positive value, and if not,
- the bet with the lowest negative value in the 60% – 80% cluster is selected
- only 1 bet per match is selected

**HOWEVER…**

After the first four betting days, our bank reduced by almost 25%.

Rather than waiting for the stop loss margin *(60% of the bank)* to check the stake amounts and prevent total loss of the bank, we reappraised the portfolio and changed the selection rules with effect from 16th April 2021 *(betting day 5)*.

We are no longer concentrating exclusively on the 60-80% probability range.

We will now focus on two specific ranges of over/under options: from OVER 1.5 goals to OVER 5.5 *and* UNDER 3.5 goals to UNDER 0.5 (0:0).

If there are two bets with a very similar probability in a single game, such as O 1.5 and U 3.5, both will be played with the stake evenly distributed between them. (For example, if the higher odds option represents 2.5% of the bank, then this amount is split 1.25% on one result and 1.25% on the other).

If there are several qualifying bets in a single game, for example, O 1.5 – O 2.5 – O 3.5 – O 4.5 – O 5.5, all bets that contain value are played. In this case, we will stick to the maximum stake of the bet with the *highest probability* and split this equally between all of the bets.

With this approach, more bets with higher odds will enter into the scope of the portfolio – for example, Under 0.5 and Over 5.5 goals.

Here is an example calculation of a bet that would have gone really well:

**The basis for calculating the stakes has changed from this:**

- Odds up to 1.1:
**5%**from the bank - Odds between 1.1 – 1.16:
**4%**from the bank - Odds between 1.16 – 1.39:
**3.8%**from the bank - Odds between 1.4 – 2.25:
**2.5%**from the bank - Odds between 2.25 – 7.50:
**1.5%**from the bank - Odds over 7.50:
**0.5%**from the bank

**…To this with effect from 16th April 2021:**

- Over 1.5 Goals = 3.5% of bank
- Over 2.5 Goals = 2.5% of bank
- Over 3.5 Goals = 1.5% of bank
- Over 4.5 Goals = 1.0% of bank
- Over 5.5 Goals = 0.5% of bank
- Under 0.5 Goals = 0.5% of bank
- Under 1.5 Goals = 1.0% of bank
- Under 2.5 Goals = 1.5% of bank
- Under 3.5 Goals = 2.5% of bank

Stakes are always rounded up to the nearest whole number.

However, not only are the stakes calculated according to the risk but a ratchet system will also be applied. This means that the stakes increase with each round in accordance with the highest bank total achieved and remain at that level even if the bank then decreases again. The stakes are only reduced if the bank reduces to 60% of the starting bank *(i.e. starting bank loses 40%)*.

**Starting Bank** *(at the start of the experiment on April 9, 2021)*: **3,000.00**

**Highest Bank** *(9th April 2021)*: **3,000.00**

*Bank will increase each day if there are winnings; bank for calculating stakes will only reduce when it drops below 1.600,00 (60% of starting bank).*

The first pick is due on Friday 9th April 2021 and we will continue until the end of the seasons in our five selected leagues.

The **EPL** concludes on **May 23rd 2021**.

**Germany**’s last match is on **May 22nd 2021**.

**Italy**’s last match is on **May 23rd 2021**.

**Poland** finishes on **May 16th 2021**.

**Spain**’s last match will be on **May 23rd 2021**

So, we are looking to cover seven full weekends and the midweek games in-between them. Whether we continue publishing picks using Summer Leagues thereafter will be decided at a later date.

Even if we trust our **own coursebook** and **cluster tables** and are pretty sure that the published picks will lead to a profit, we urge you to play it safe by not risking more money than you can afford to lose.

Please stick to the above staking plan and do not carry out any experiments with the staking. Don’t let your emotions get the better of you and increase stakes if there is a good spell going on. And please don’t chase any losses if there are a few bad days in a row. Please always remember that we are playing statistics and that they never line up in a regular manner.

It is interesting to see that the total goals in the games haven’t really changed despite the fluctuations of home and away wins but what we do not know for sure is whether the Cluster Tables, which are based on the statistics of the last five full seasons of the teams involved, are robust enough to cope with this change.

Therefore, we urge you once more, be careful! Should you follow our picks with real money, then please stake only what you are prepared to lose and stick strictly to the staking plan!!!

Fingers crossed that things go our way again!

Enjoy & share, Your Soccerwidow

The experiment was prompted by the outbreak of the coronavirus and the fact that many leagues suspended their games for a period of a few months and afterwards resumed in empty stadiums. We wanted to see whether historical statistics could still be used and what could be observed after this unexpectedly long break.

The bank grew from an initial figure of **3,000.00** units to an impressive total of **4,617.56** using ratcheted stakes during the course of just one month.

It was very pleasing to see that the Cluster Tables performed so reliably well despite the coronavirus outbreak and the consequent very long pauses in our featured leagues:

Table 1: Corona experiment July-August 2020

Profit/Loss graph after 25 rounds

Profit/Loss graph after 25 rounds

During this 33-day period a total of **77** bets were placed within the **60%** to **80%** probability range.

Here’s the distribution of those bets and the Profit/Loss achieved split into clusters of **2%** probability increments:

Table 2: July-August 2020 – Over/Under experiment P/L results graph by Probability

From the above chart, you can see that all but one of these clusters produced a profit. However, the number of bets varied in each cluster. For example, there were four bets with a probability between 60% and 62%, and nine bets in the 62% to 64% cluster, and so on.

77 bets is a very small sample size and this becomes even smaller when trying to form conclusions about each of the 2% clusters. However, this is a practical way of maintaining control if you are using the Cluster Tables for your own betting.

Indeed, for monitoring purposes, we recommend that you do sort your bets in small probability clusters and judge the synergy of your portfolio on the basis of its entire performance. You will find it easier to make decisions if there are obvious areas that are letting down the results.

The bets were chosen using our Cluster Table tools that are the product of our coursebook teachings. With these tables, you can very quickly determine the expected probabilities of Over/Under bets for any forthcoming match involving the featured teams *(i.e. only those playing in at least their sixth consecutive season in that league – identified in the tables)*.

To help explain how the bets were chosen, here’s an example using the very last pick of our experiment:

**Sassuolo vs. Udinese on 02/08/2020**

Below is an extract from the Cluster Table used to make this pick:

Table 3: Calculating the Over/Under bets

Sassuolo vs. Udinese 02/08/2020

Sassuolo vs. Udinese 02/08/2020

Sassuolo was the favourite to win the game at odds of 1.95; Udinese was the underdog at odds of 3.84 *(odds taken at 06:57 GMT+1 on the day of the match)*.

With these odds, **the HO/AO quotient** was calculated:

Home Odds (HO) 1.95 divided by Away Odds (AO) 3.84 = 0.5078

Using the 2019/20 **Cluster Table** for Italy, the over and under probabilities for Sassuolo home matches and for Udinese away matches were found using the appropriate HO/AO cluster containing the value of 0.5078.

These percentages were then copied into an extra *‘helper’* spreadsheet *(i.e. the two top lines of the tables on the left)*.

Using the two probability percentages collected from both teams, the average was calculated *(Over 0.5 bets example)*:

**161.8% divided by 2 = 80.9%**

This percentage was then converted into the expected Zero odds:

This process was then repeated for all Over/Under bets.

The third line of our helper spreadsheet is for manual entering of the market odds being offered for these bets.

As we were limiting ourselves during the experiment to bets within the 60% to 80% cluster, there was no difficulty choosing the bets for this particular match as there was only one visible within this probability cluster. The bet ‘Under 2.5 goals’ with a probability to win of 68.2% *(corresponding Zero odds: 1.47)* was being offered at outstandingly good odds of 3.10.

By the way, this bet won as the match ended in a 0:1 result. Of course, there was an element of ‘luck’ involved as on paper it also had a 31.8% probability of losing. Also, the expected ‘Profitability’ as well as the expected ‘Yield’ were artificially high, which would normally have led us to dismiss this bet as viable.

I will summarise these two very important considerations next in the article but if you wish to understand the concepts of profitability and yield in more detail, buying and working through the coursebook is your only option. It simply is too vast a subject to summarise in an article and is not the sort of information I wish to give away for free

Further Reading:

How to Use Soccerwidow’s Over/Under Betting Cluster Tables

5 Simple Steps to Win Over and Under Betting

Profitability is the relation of profit/loss to the money spent. In other words, profitability is an index for measuring financial success *( operational profit)* in relation to the costs

When applied to gambling, profitability measures betting proficiency in relation to its expenses.

Profitability Formula:

If you wish to learn a little more about what profitability in betting means, here’s an article with the definitions and some example calculations:Stake, Yield, Return on Investment (ROI), Profitability – Definitions and Formulas

The nice thing is that it is actually possible to predict the expected profitability if you have calculated the Zero odds and know the market odds of the bet you are thinking of placing.

You can see the results of these calculations in Table 3 *(Sassuolo vs. Udinese calculations)* in the row below the market odds. Try to come up with these numbers yourself!

Of course, all these calculations are about probabilities and a future outcome; they aren’t set in stone and results always come with a deviation. I cannot dive deeper into the matter of deviation at this stage but once again recommend the coursebook, where you will find almost a third devoted to explaining this quite difficult topic in step-by-step detail.

However, what we will look at here is the graph of the distribution of Profit and Losses from our Over/Under experiment by expected Profitability.

For those of you who didn’t follow the experiment as it progressed… During July 2020 we published almost daily Over/Under picks with probabilities between 60% and 80%.Often, there would be only one bet apparent in this cluster

(like in the example Sassuolo vs. Udinese)and we would choose this bet without taking any ‘value’ into consideration or worrying about the expected ‘Profitability’ or expected ‘Yield’.Indeed, the profitability and yield might have carried negative values, but the picks would still be included in our portfolio and published.

The reason for this is that when you calculate Zero odds and consider the deviation, the market odds may be higher or lower but still be ‘fair’.

It seems like a paradox but having negative ‘value’ attached to a bet calculation doesn’t mean that it is a bet without ‘value’.

Table 4: July 2020 – Over Under experiment P/L results graph by expected Profitability

You can see from the graph above that at its beginning the P/L curve wanders around the -200 mark and then starts rising. The starting point for the rise is around 95% and it stops at -40%. This can be used as a knock-out criteria when selecting bets to place:

Advice for those of you who are actively using the Cluster Tables for investment purposes…If you wish to play a similar system to the picks showcased in our experiment, then please choose your bets by sticking religiously to the 60% to 80% probability cluster

anduse theexpected Profitabilityas a knock-out criterion.

If you have only one bet in this probability cluster, and it carries an artificially high profitability value like the one shown in this article (Sassuolo vs. Udinese U2.5 goals), then you need to make the tough decision whether or not to play the bet or leave it alone.

Yield is the Profit/Loss ratio applied to the total capital employed *(total staked)*. When applied to gambling, Yield measures betting effectiveness compared to total turnover. *(The interest received from securities, i.e. stakes)*

Yield Formula:In football betting, any yield over 7% is considered to be a very good result. Be careful when you hear people talk about their betting strategies or offering betting systems for sale with a high yield. This is intended to impress the reader, but a high yield is always an indication of high-risk strategies employed!

Like with the expected profitability in the section above, it is also possible to calculate the expected yield simply by having calculated the Zero odds and knowing the market odds.

Please have another look at Table 3 *(Sassuolo vs. Udinese calculations)* in the row below the Profitability. Again, see if you can match these figures with your own formulas or calculations.

Once again, high yield systems mean high risk. Usually, you will need to play many bets to move forwards with systems of this nature. The reason is simple: High risk means low probability and that means a very irregular distribution of winning bets – and lots of losers along the way!

You can see this for yourself in the graph below, which represents the experiment’s distribution of Profit and Losses by expected Yield:

Table 5: July 2020 – Over Under experiment P/L results graph by expected Yield

You’ll see from the curve that expected Yield over 30% didn’t produce any profits and neither did an expected Yield below -15%. That there even was a negative expected Yield is because of deviation.

This factor can be used as a second knock-out criteria when choosing bets:

Advice for those who actively useour Cluster Tables…Don’t take our guidance here as gospel. Of course, you can choose whichever probability clusters suit your personal acceptance of risk. You don’t need to stick religiously to the 60% to 80% range that we used in this public experiment.

But, ideally, what you then need to do is to select only matches in your chosen clusters(you can do this retrospectively)and analyse their performance by expected Profitability as well as expected Yield. In doing this, you should then be able to build your own knock-out criteria and adjust accordingly.

I really hope you enjoyed this article and learnt something along the way. Please don’t hesitate to ask any questions in the comment section below.

Lastly, keep faith in statistics! Despite the pandemic, every league will continue playing on a professional level and hence, past statistics can be applied to predict future performance. How else do you think bookmakers set their odds?

Note:

And if you need further incentive to investigate our Cluster Tables further, don’t forget that the **169-page Odds Calculation coursebook** comes with a free German Bundesliga Cluster Table. Buy the coursebook, snap up a bargain in the process, and begin betting on the over/under markets straightaway!

Since the 1st of July, we have been running **an HDAFU Tables experiment** on Soccerwidow, and a parallel **Over/Under Goal betting experiment** on our German-language sister site **Fussballwitwe.de**.

Whilst it is too early to say whether the HDAFU Tables will perform to expectations, the Over/Under picks are doing outstandingly well. The original starting bank of 3,000 increased by over 50% in 25 betting days.

The picks for the respective day appeared here **around 1 p.m.** *(sometimes earlier)* as well as the results of the previous day.

Please click on the arrows to scroll through the entire history of the picks.

Below are all the picks that were published during the July 2020 Corona experiment *(the 2019-20 Winter League seasons finished now)*. The bank grew from a starting point of 3,000.00 to impressive **4,617.56** during just one month. It was very pleasing to see that the statistics taught in the coursebook in combination with the Cluster Tables did so reliably well despite this Corona outbreak and very long breaks of the leagues.

The expected probability and zero odds are calculated exactly as described in the coursebook using the Cluster Tables. The selection criteria is:

- if it has a minimum probability of 60%, and
- if it has a positive value, and if not,
- the bet with the lowest negative value in the 60% – 80% cluster is selected
- only 1 bet per match is selected

The basis for calculating the stakes is the following risk adjustment

- Odds up to 1.1:
**5%**from the bank - Odds between 1.1 – 1.16:
**4%**from the bank - Odds between 1.16 – 1.39:
**3.8%**from the bank - Odds between 1.4 – 2.25:
**2.5%**from the bank - Odds between 2.25 – 7.50:
**1.5%**from the bank - Odds over 7.50:
**0.5%**from the bank

Stakes are always rounded to the nearest whole number.

However, not only are the stakes calculated according to the risk but a ratchet system is also applied. This means that the stakes increase with each round in accordance with the highest bank total achieved and remain at the same level even if the bank then decreases again. The stakes are only reduced if the bank erodes to 60% of the starting bank *(i.e. starting bank loses 40%)*.

**Starting Bank** *(at the start of the experiment on July 1, 2020)*: **3,000**

**Highest Bank** *(25th July 2020)*: **4,729.44**

*Bank will increase each day if there are winnings; bank for calculating stakes will only reduce when it drops below 2837.66 (60% of starting bank).*

We all know that the coronavirus interrupted/paused the leagues for different lengths of time.

The **EPL** broke on March 9th and, after a 100-day break, started playing again on June 17th.

**Italy** also stopped on March 9th and started playing after a 103-day break on June 20th.

**Poland** suspended on March 13th; their break was only 81-days and they started playing again on May 29th. The league concluded on 19 July 2020 and all matches of 31–37 round have been played with “no more than 25 percent of the number of seats allocated to the public”.

**Spain** suspended on March 10th and started playing again since June 11th after a 93-day break. There were matches played nearly every day for 39 days – concluding on Sunday 19 July.

Each league will make up the lost time differently, however, the last game of this winter season is scheduled to be played on August 2nd. This will end our experiment. In summary, we are expecting from the 1st July until the close a total of 85 matches for cluster table betting.

Even if we trust our own coursebook and statistics and are actually pretty sure that the published picks will lead to a profit, we are currently playing safe by not risking real money on this experiment.

Just like everyone else at the moment, we can only guess what effect playing in empty stadiums will have on match results. Will home advantage be affected?

How do psychological factors affect results? Like all of us, the players were locked up in their houses for months and subjected to strict curfews.

Did everyone continue to train equally? What effect has this break on the fitness of the players?

There are currently so many questions and unknown factors that could potentially affect game results. Therefore, be careful! Should you follow our picks with real money, then please stake only as much as you can afford to lose and please adhere strictly to the staking plan!!!

Fingers crossed that things go our way!

Enjoy & share, Your Soccerwidow

Since the 1st of July, 42 matches have been evaluated and ‘live’ betting recommendations ahead of the games were published.

After 14-days into this trial what can be said is that, at the moment, it is debatable whether one can take past statistics and select bets based purely on mathematical formulas and calculations.

Here are our observations so far:-

People who bought the

coursebookknow about the recommended use of theProfitability/Yield quotient. Unfortunately, the quotient currently proves to be very volatile and using it for choosing bets may lead to losses.Selecting by ‘value’ only is also backfiring at the moment. There is a clear trend of more goals than usual in the matches and bookmakers are adjusting their odds to reduce their payout risks. Hence, bets that look on paper like they contain ‘value’ are probably ‘valueless’.

Nevertheless, every cloud has a silver lining and, although the probabilities seem to have shifted a little, it seems that the 60% to 80% probability cluster has an especially higher hit rate than actually expected

(i.e. mathematically speaking, using past statistics). If the expected Zero-odds are calculated using theCluster Tables, it can be clearly observed that bookmakers are reacting to this current change by lowering their prices(betting odds).

Therefore, the current course of action suggested is to consciously search in this probability cluster(60% to 80%)and to include bets in the portfolio within this range that have a low or even negative ‘value’.

As you’ve seen in the above graph, with these conditions in place, the bank grew from 3,000 units to a respectable amount of 4,308 units in just 14-days…

Fingers crossed that these observations and conclusions are correct. We are only halfway through this experiment so time will tell.

The Coronavirus experiment is coming to its end and it can definitely be said that it is going very well indeed… So far, in just 19 days of betting, the bank has increased from a starting point of **3,000.00** to **4,642.44** units (**54.7%**).

I have been asked by some in the comments section below why I have been including not only positive values but also negative ones in the published picks.

The reason was that I wanted to give everyone the opportunity to see how and if statistics (and my coursebook) are still applicable both during the pandemic itself and when taking into consideration some pretty long lockdown suspensions/breaks of various leagues.

Below is a graph showing the profit curve applied to the Profitability/Yield quotients:

As you can see on the red curve the point 2.0 is the transition point *(Profitability/Yield Quotient: 2.0)*. The profit up to this point is

The lesson therefore is…Past statistics are certainly still applicable and so are the teachings in mycoursebook. Should you be using theCluster Tablesthen it is prudent to choose the bet selections by applying the Profitability/Yield quotient.(do not choose any bets below a P/Y quotient of 2.0!)Nevertheless, for the public, I will continue to publish the picks until the end of this experiment using the same criteria

(positive as well as a negative value), but from now on I will also publish the P/Y quotient with the picks.

The bank grew from an initial figure of **3,000.00** units to an impressive total of **4,617.56** using ratcheted stakes *(from a starting point of 100 units per bet)* during the course of just one month.

It was very pleasing to see that the Cluster Tables performed so reliably well despite the coronavirus outbreak and the consequent very long pauses in our featured leagues.

Read the full reports and its findings here: **Over Under Betting Experiment July 2020 ~ Final Report & Further Findings**

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.*

**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.

]]>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.*

Have you ever wondered how bookmakers set their odds? They must be pretty good at it to remain trading in a high risk industry built on small margins!

Remember folks it’s all about finding the “edge”.

Image: Cartoonresource (Shutterstock)

Image: Cartoonresource (Shutterstock)

It is common knowledge that the gambling industry as a whole relies to a large extent on the ignorance of its customers and, only by understanding how bookmakers think and act, will you ever be able to compete with them on a level playing field.

This course is designed to give you the essential, fundamental knowledge necessary to understand odds calculation and the bookmaker market.

It deals with relatively simple descriptive statistics and teaches you how to look at data sets, calculate your own probabilities and odds, analyse the market odds on offer, and make informed decisions when predicting football results.

Amongst the topics you will work through are distributions, deviations, graphs and charts, odds calculation, financial terminologies, risk management, and of course, how to identify ‘value’ in the betting market.

You will gain a deep understanding of the many different elements required to understand the bookmaker market and odds calculation. Many false beliefs that the majority of gamblers and fans of football have about betting will be exposed and stripped away.

Readers will also be enlightened to learn about how odds are set in the market, where to find ‘errors’ in market prices, how to evaluate data and graphs, and much, much more.

The course comes in electronic format and the bundle includes the **course book (PDF*)** and a **Bundesliga cluster table (Excel)** for studying. This course employs a didactic method of teaching, which is an educational technique. It is a very structured style of learning and as such, each section of the course should be mastered before moving on to the next.

To further encourage you to learn, there is a plethora of exercises to practice what you have learned, and the solutions to the exercises are found at the end of the book, sometimes embellished with further explanations.

All of the course lessons are presented in a pragmatic, easy-to-follow, step-by-step fashion, with no more than passing respect towards the sport of football and the passion of its fans. After all, this book has been written by a lady who loves maths but dislikes football…!

In addition, you will receive **the very latest Bundesliga table for the current season**, so that you can put into practice what you have learned and within the course, you will find **a discount code** enabling you to download **for free the German Bundesliga sheet in future seasons**, meaning that you will always have access to the most current Bundesliga cluster table, **forever**: it doesn’t matter when you bought the course.

**By the way, the PDF is a professionally formatted document and if you have a double-sided printer you can print your own book for studying.*

Course with Cluster Tables for the German Bundesliga

Over/Under ‘X’ Goals

PRODUCT SUMMARY

- Format:
**PDF & XLS** - Download Size:
**Course (PDF): 5.2 MB & Excel file: 782 KB** - Publisher:
**Soccerwidow Ltd; 2nd Edition***(July 2016)* - Pages:
**169***(almost 38,000 words)* - Tables and graphs:
**70** - Example tasks:
**more than 80***(with solutions)* - Language:
**ENGLISH**

* 0% VAT to UK customers, 0% VAT to Non-EU customers **Read more:** EU VAT Legislation

For traditionalists, the PDF has been professionally formatted for double-sided printing. If you like, you can print your book in full-colour with a cover and add slick spiral binding.

Those who prefer to read the document on their computers will find the chosen font *(Myriad Pro)* easy to read on the screen. Myriad Pro has a clean sans-serif aesthetic which makes it highly accessible.

The electronic version also contains a plethora of easy-to-navigate links to help the learner find his way around the document.

**Probability, Betting Odds, Value, Yield, Profitability**

**Basic Statistical Terminology**

- Goal Distribution and Percentage Calculation
- Deviation from the Mean
- Standard Deviation – The Main Measure of Variability
- Precision, Trueness and Accuracy

**Betting Odds Calculation**

- Probability and Betting Odds
- Opening Betting Odds Range
- Calculation of ‘Zero’ (Fair) Odds

**Risk Management Control**

- Financial Terms: Yield, ROI and Profitability
- Risk Forecasting and Evaluation: Value of a Trade (Bet)
- Preventative Measures: Setting the Starting Bank

**Market Dynamics, Cluster Groups and Betting Tables**

**Market Dynamics**

- Betting Odds are Prices of Bets
- Falling Odds represent Price Increase
- Bookmakers adjust Betting Odds to Public Opinions

**Building of Cluster Groups**

- What are Cluster Groups?
- Clustering Depending on ‘Strength’ of the Team

**Betting Tables: Over/Under ‘X’ Goals**

- Goal Distribution by Team
- Team Calculation Tables
- Standard Deviation Tables
- Using Calculation Tables to Determine Betting Odds

**Finding Value Bets**

- Everything that Glitters is not Gold
- Method I: Value Betting using Cluster Group Tables
- Method II: Value Betting using the Value Calculator
- Cluster Groups, Value Calculator and Bets Identification

…or ask any questions in the comment section below. ]]>

**Do you know how bookmakers set their odds? We do! And we will happily teach you too!**

Image: Cartoonresource (Shutterstock)

The 2nd edition is not just a ‘revised’ version, it is a **total overhaul** answering many questions raised by our original audience such as deviations and correction factors, which are explained in great detail.

The updated version is now a hefty document at **169 pages** and **almost 40,000 words**, three times the length of the first course.

One enormous enhancement is that in this new version, we actually give away **two proven methods of bet selection**, making it as easy as possible to spot candidates for successful value betting portfolios.

Another substantial upgrade is that buyers of the 2nd edition will now automatically get the current, up-to-date German Bundesliga cluster table supplied with the course. In addition, you will find a discount code within the course enabling you to download **for free** the German Bundesliga sheet in future seasons, meaning that you will always have access to the most current Bundesliga cluster table, **forever**: it doesn’t matter when you bought the course.

**Attila**

The course is really very well done! Concentrated expertise that cannot be found anywhere else.

**Socrates**

Finally an unbiased contribution to football betting, which goes beyond a simple description of betting options and also explains bookmaker calculations as well as betting odds formation in detail.

What really surprised me is how easy it actually is to calculate odds and even predict Betfair market prices to find value bets. However, I will still need a long time to work through the entire course, but now I have some guidance about how to change my betting behaviour.

**Onheimlech**

Hello Soccerwidow,

Your course is written very well. My bet portfolio now considers mathematical formulas and somehow I like this.

It is amazing to see how close my own calculated odds come to the actual odds for different bookmakers. Without your course I wouldn’t be able to find value.

**André**

I just working through the Over/Under X Goals course.

The examples described are simply fabulous. When I look back and see what I have previously evaluated and what is recommended by you – they are worlds if not galaxies apart.

**Marcel Brand**

Dear Soccerwidow,

Firstly many thanks for the great Over/Under Goals course. I would already personally call myself an advanced bettor, but your course has brought me further forwards, especially in the topic of cluster grouping.

**Pete**

Just bought your guide, and learned a lot. Many, many thanks! I can recommend it.

Worth every penny!

**Ian**

I’ve just purchased the Over/Under course and Value Bet detector. They are both excellent products and bring together all the knowledge that I have accumulated over a few decades.

Unfortunately, it has taken me this long to figure out the simple fact that value bets + staking plan = steady profits

**Janice**

When I bought your course I was completely overawed by it and didn’t read it properly for a couple of months – in fact I stopped betting altogether…

But when I had nothing to do one day I re-opened it, gradually overcame my fear, and began making some satisfying progress.

The exercises and questions really are a great idea and help to commit the concepts to memory, and having the answers provided at the back of the book is hugely reassuring, especially when you get them right.

The course **Fundamentals of Sports Betting – Over/Under ‘X’ Goals** explains statistical applications required to calculate odds and find value in the market.

All the concepts explained apply basic maths, financial management, quantitative analysis, and statistics to football betting.

The course reinterprets maths covered in A-levels at school *(such as analysing graphs, calculating the mean, etc.)*, and also introduces some statistics from the first year of university studies *(e.g. standard deviation)*.

Bankroll management is one of the most important pillars for success in sports betting.

Image: Alex Roz

A portfolio of sports bets placed over time can be compared to investing in the money markets on a portfolio of stocks and shares.

Indeed, the term ‘bankroll management’ comes from the financial sector and describes the use of the *seed capital* (i.e. in betting terminology, the *initial stake*).

Bankroll is the ‘starting bank’, and the intention is to manage it and increase it at the same time.

Bankroll management therefore deals with how to properly manage your starting bank.

**The good news**: It is actually possible to calculate the required starting bank mathematically.

**The bad news**: The calculations are naturally dependent upon statistics, and the ‘significance’ of the results relies on the amount of data used.

For example, any strategy based on one German Bundesliga team’s home games during a season produces a sample of precisely 17 sets of data, which is a very small number, statistically speaking.

The **Law of Large Numbers** is omnipresent so far as statistical accuracy is concerned: The larger the data sample, the more accurate the final results are likely to be, although a line has to be drawn between sample size and an acceptable level of error.

One way of coping with small data sets is to incorporate a **risk discount** into the equation. More about this later…

On face value, you might assume that calculating the necessary starting bank for a betting strategy can be derived solely from the stake multiplied by the number of bets (n).

With the 17 matches from our example above, and a constant stake of 100 units per bet, the bank would then be: 100 x 17 = 1700 units. But is this maximal amount *really* needed?

Although this may be true where returns from winning bets cannot be immediately re-invested, such a bank can never be optimal because an inordinate amount of capital would be tied-up.

What you should look for is the most cost-effective bankroll where all the money you have at your disposal is working for you as efficiently as possible.

Optimal bankroll is characterized by two things:

- Cash holdings (i.e. money in reserve) is kept as low as possible
**Gambler’s ruin**is avoided

There are five vital criteria you will need to establish:

- What is the size of your stake per bet?
- How many bets does your strategy expect to be placed?
- What is the expected hit rate of your strategy?
- What is its expected longest losing streak?
- Determine the risk variables and incorporate a ‘risk coefficient’.

Okay, we will stick with the German Bundesliga for demonstration purposes and use a system gleaned from its latest full-time 1×2 **HDAFU Simulation Table**.

If you have already bought this table, you can see the full and detailed analysis of **backing the underdog whenever Hamburg plays at home**: This strategy has realised a yield in excess of 58% over the course of five complete seasons from 2010-11 to 2014-15.

In addition, there has been profit produced in every one of those same five seasons.

It’s an ideal candidate for incorporating into a large portfolio of other systems. (When we say ‘large’ we mean a portfolio that will generate at least 500 bets in a season.)

**(1) Size of Stake per Bet:**

This is determined by your own liquidity, and to keep this calculation simple, a **Constant Stake** (CS) of 100 units per bet will be used.

**(2) Number of Bets:**

For this mini portfolio of Hamburg home games, the **Number of Bets** (n) is 17 for the new season.

**(3) Hit Rate:**

The HDAFU Simulation Table reveals that from 85 Hamburg home games over five seasons, 32 underdogs triumphed: a **Hit Rate** of 38%.

The random selection of only 85 matches is a relatively small sample and the possibility of ‘random sample error’ is therefore relatively large.

To compensate, it is worth applying what is known as a ‘risk discount’ to reduce the actual hit rate experienced and to build-in an extra level of security if statistical expectations for the new season are not realised.

Taking a risk discount figure of 5%, the expected hit rate becomes: 38% – 5% = 33%.

[Have a look at **this article** for more information about hit rates].

**(4) Longest Losing Streak Expected (LLSe):**

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*

**P** = (negative) probability^{†}

**| .. |** = absolute value or ‘modulus’ *(see Wikipedia if you would like to know more about these mathematical symbols)*

**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: to make life easy, the formulas to use are included in the free spreadsheet download below*.

^{†}*For this calculation, the negative probability or hit rate is used. In this case, having adjusted our hit rate down to 33% using a risk discount, the probability that the bet loses (negative probability) is 67%*.

rounded down to

From a pool of 17 bets, you can therefore statistically expect that a maximum of seven in a row may be lost without winning one in between.

**(5) Risk Coefficient (RC):**

The determination of risk variables depends primarily on your **risk aversion**. Risk-averse bettors choose a high coefficient figure (e.g. 5), whilst gamblers who are happier taking risks choose lower coefficients (e.g. 2).

But why are we including a risk coefficient at all?

We can assume that the longest expected losing streak *(in our example, seven lost bets in a row)*, may already start with the first bet.

Although one bet may win after that, with the gains reimbursing the loss and allowing for reinvestment, there can still be a second stroke of bad luck directly after the first bet that you have won.

Neither winning bets nor losing bets ever line up in a uniform manner; they will always appear in a random pattern, so always better to be safe than sorry.

The formula is:

Our Bundesliga example is an underdog backing system, which by its very nature, is risky. However, as there are only a maximum of 17 bets in this mini system, we will choose a risk coefficient of 1.5: we are happy to take the risks!

It is not very likely that there will be two losing streaks of seven games in a row when betting 17 consecutive times. However, we are aware that it may be quite challenging for the nerves to sit through losing streaks watching the bank balance reduce before your eyes!

The optimal bankroll required to run this system for a season is as follows:

If you remember the sub-optimal bank strategy at the beginning of the article where we touched on a bankroll of 1,700 units (100 units per bet x 17), you can see we have now released 650 units for investing in another strategy elsewhere.

With this **free Excel table download**, you can easily and quickly discover what the longest losing streaks are for your own strategies. Just enter your stake, number of bets, and risk coefficient figures and let it calculate everything for you!

**>>> Excel Workbook – Losing Streaks <<<**

*Click on the above button – in the new tab click on the ‘Continue Checkout’ button. Enter your name and email address to allow our automatic shopping cart to deliver the file by email to you, free of charge. The .xls file size is 93 KB. When you receive your confirmation email, just click on ‘View Purchase Online’ (in the email text) to download the file.*