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.

But what about the goals?

Spain soccer stats 2019-20 to 2020-21

Poland soccer stats 2019-20 to 2020-21

Italy soccer stats 2019-20 to 2020-21

EPL soccer stats 2019-20 to 2020-21

BL1 soccer stats 2019-20 to 2020-21

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.

Slideshow Picks

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!

Day 18 Picks & Results: 2021.05.03

Odds from (and published on SW): 23:22 GMT+1, 2nd May

Day 17 Picks & Results: 2021.05.02

Odds from (and published on SW): 23:08 GMT+1, 1st May

Day 16 Picks & Results: 2021.05.01

Odds from (and published on SW): 22:02 GMT+1, 30th April

Day 15 Picks & Results: 2021.04.30

Odds from (and published on SW): 10:06 GMT+1, 30th April

Day 14 Picks & Results: 2021.04.26

Odds from (and published on SW): 10:47 GMT+1, 26th April

Day 13 Picks & Results: 2021.04.25

Odds from (and published on SW): 21:54 GMT+1, 24th April

Day 12 Picks & Results: 2021.04.24

Odds from (and published on SW): 22:24 GMT+1, 23rd April

Day 11 Picks & Results: 2021.04.23

Odds from (and published on SW): 09:47 GMT+1, 23rd April

Day 10 Picks & Results: 2021.04.22

Odds from (and published on SW): 09:41 GMT+1, 22nd April

Day 09 Picks & Results: 2021.04.21

Odds from (and published on SW): 14:11 GMT+1, 21st April

Day 08 Picks & Results: 2021.04.20

Odds from (and published on SW): 17:58 GMT+1, 19th April

Day 07 Picks & Results: 2021.04.18

Odds from (and published on SW): 21:49 GMT+1, 17th April

Day 06 Picks & Results: 2021.04.17

Odds from (and published on SW): 12:25 GMT+1, 17th April

Day 05 Picks & Results: 2021.04.16

Odds from (and published on SW): 18:25 GMT+1, 15th April

Day 04 Picks & Results: 2021.04.12

Odds from (and published on SW): 12:58 GMT+1, 12th April
Next Picks due 16/04/2021

Day 03 Picks & Results: 2021.04.11

Odds from (and published on SW): 22:09 GMT+1, 10th April

Day 02 Picks & Results: 2021.04.10

Odds from (and published on SW): 10:37 GMT+1, 10th April

Day 01 Picks & Results: 2021.04.09

Odds from (and published on SW): 09:12 GMT+1, 9th April

*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:

1. the chance to win the bet has a Probability between 60% and 80%, and
2. the expected Yield is between -15% and 30%
3. the Profitability of the bet is between -50% and 95%
4. 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).

Duration of the Experiment

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.

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.

Important information about the risk!

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 General Outcome of 25 Betting Rounds and 77 Bets

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

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.

How the Bets were Chosen

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 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:

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):

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 (Value I)

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 (money spent) of running the venture.

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 and use the expected Profitability as 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 (Value II)

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 use our 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.

Slideshow Picks

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.

Day 25 - Picks & results 2020.08.02

Odds from the early morning; published on SW at 11:22 am

This last pick of the experiment was somewhat dodgy. There was actually far too much value in the bet but there wasn't any other bet in the 60% to 80% probability cluster for the day. The bet won! It proved one more time that probabilities don't lie.

Day 24 - Picks & results 2020.08.01

Odds from the early morning; published on SW at 11:33 am

Day 23 - Picks & results 2020.07.29

Odds from the early morning; published on SW at 10:51 am

Day 22 - Picks & results 2020.07.28

Odds from the early morning; published on SW at 10:23 am

Day 21 - Picks & results 2020.07.26

Odds from the early morning; published on SW at 09:26 am

I cautioned you as I published the picks... We were experiencing a pretty nice long winning streak with 8 winning bet in a row and from the 16 EPL picks in total there wasn't a single bet lost.

However, please remember that we are playing probabilities 60% to 80%. There must be the occasional bet that loses!

Do you remember? I was advising you that we were expecting a few losing bets.

Day 20 - Picks & results 2020.07.25

Odds from the early morning; published on SW at 11:29 am

Day 19 - Picks & results 2020.07.24

Odds from the early morning; published on SW at 10:19 am

Day 18 - Picks & results 2020.07.23

Odds from the early morning; published on SW at 09:06 am

Day 17 - Picks & results 2020.07.22

Odds from the early morning; published on SW at 14:07 pm

Day 16 - Picks & resuslts 2020.07.21

Odds from the early morning; published on SW at 12:05 pm

Day 15 - Picks & results 2020.07.20

Odds from around 12:30 pm; published on SW at 13:27 pm

Day 14 - Picks & results 2020.07.19

Odds from the morning, around 10:45 am; picks published on SW at 13:04 pm

Day 13 - Picks & results 2020.07.16

Odds from the early morning, around 09:30 am; picks published on SW at 13:01 pm

Day 12 - Picks & results 2020.07.15

Day 11 - Picks & results 2020.07.13

Day 10 - Picks & results 2020.07.12

Day 9 - Picks & results 2020.07.11

Day 8 - Picks & results 2020.07.09

Day 7 - Picks & results 2020.07.08

Day 6 - Picks & results 2020.07.07

Day 5 - Picks & results 2020.07.06

Day 4 - Picks & results 2020.07.05

Day 3 - Picks & results 2020.07.04

Day 2 - Picks & results 2020.07.02

Day 1 - Picks & results 2020.07.01

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

1. if it has a minimum probability of 60%, and
2. if it has a positive value, and if not,
3. the bet with the lowest negative value in the 60% – 80% cluster is selected
4. 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).

Duration of the experiment

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.

Important information about the current risk!

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

Image: ross-edward cairney (Shutterstock)

The outcome of the research shows that people who describe themselves as lucky share one common characteristic: they are all exceptionally good at looking for and recognising opportunities. Wiseman’s luck strategies are actually banal: maximise random chances, listen to intuition, have confidence in your future, recognise luck in misfortune.

Opportunity Knocks

One explanation as to why some people are less lucky (less successful) than others can be found in an experiment carried out by Wiseman in 2003. His experiment follows that of Stanley Milgram Small World Experiment which suggests that each human (social member of society) is connected to every other human in the world by a surprisingly short chain of acquaintances.

Wiseman’s experiment began with Internet posts inviting the general public to participate and some 500 people applied. 100 were then randomly selected and handed a package with the task of forwarding it to ‘Katie’, a 27-year-old student of art and history at Manchester University, who once worked in public relations in London, and enjoys cycling.

All the initial volunteers and subsequent recipients were asked to send the parcel only to someone they knew on first-name terms. Ten per cent of the parcels eventually reached Katie and amazingly, there were just four people in total linking the initial volunteers with Katie.

The tiny number of people linking the volunteers to Katie supports the idea that humans may indeed all be linked to one another via a remarkably short human chain. ‘The Luck Factor’, published in 2004 by Richard Wiseman, reveals that lucky people frequently experience the ‘small-world’ phenomenon, and that such lucky meetings have a dramatic and positive effect on their lives. In contrast, unlucky people rarely report small-world experiences.

However, in his experiment approximately 20% of the volunteers did not forward their parcels at all, guaranteeing that their packages would never reach Katie. These volunteers had gone to considerable lengths to ensure that they participated in the study, but had then dropped out at the very first stage. Interestingly, the vast majority of these people had previously rated themselves as unlucky. These people hadn’t even tried to achieve success and had given up at the first opportunity (here is the complete description of the experiment).

The conclusion of this experiment is that luck and success depend on taking opportunities and that people who are not shy in creating and seizing opportunities seem to be more successful.

Belief in Luck

Have you ever heard the term “self-fulfilling prophecy”? Proof of this concept was recently attempted by Derren Brown in a UK Channel 4 TV show. He tried to show that if people begin to believe in luck, their attitude changes positively towards opportunities and as a result they become better off or more successful.

To conduct this experiment, a small northern English town was selected and a rumour begun about a statue in a local park which was bringing luck. The Channel 4 team helped a little by engineering a few lucky coincidences and within only three months the experiment took on a life of its own. The rumour spread, attracted the interest of the regional press and many people started to report lucky experiences and coincidences (without any further effort from Channel 4!).

The Channel 4 team also carried out a few experiments in the background and observed that the people who believed in the lucky statue became surprisingly more successful than others. These people were more open to new opportunities, concentrated on reaching success and thereby catapulted themselves into the newly formed group of lucky people.

Derren Brown succeeded to show that believing in the existence of a lucky charm has a positive effect on attitude and leads to more frequent opportunities. It was rather fascinating to watch the broadcast “Derren Brown: The Experiments, Series 1 Episode 4” and observe the development of the Todmorden experiment: Channel4

However, please be cautious! Believing only in luck and grabbing opportunities is not a warranty for success (although Derren Brown’s show seems to suggest this at the end, be reminded that it was only a show and he is a master of illusion!). On the other hand, surely a strong belief in hard luck and permanently omitting opportunities does not lead to constant failure?

Seneca, a Roman philosopher and politician (5 BC-65 AD) is known for his saying: Luck is what happens when preparation meets opportunity.

Therefore, in our opinion the interpretation of Richard Wiseman’s and Derren Brown’s experiments should be as follows:

Sending a package to a stranger via acquaintances – a large circle of friends and an excellent social network requires a lot of effort to build and maintain and therefore it surely belongs to the set of “preparation”. If then an opportunity comes along, success (luck) strikes faster since there is simply a larger intersection between the two sets: preparation and opportunity [expressed in mathematical terminology].

Believing in a lucky charm – preparation (a lot of work) is not really much use if opportunities are hardly ever taken [the intersection of the two sets, luck, then remains empty or is much smaller than compared to other people].

The Gambler’s Fallacy

Despite belief in luck and many opportunities to gamble the gambler must never forget that luck (success) is a subset of preparation and opportunity. Gamblers, however, seem to have the tendency to focus on the many opportunities and in a firm belief in luck; somehow the ‘preparation’ falls behind. Unfortunately this is not a formula for success: If luck is defined as a subset of preparation and opportunity then one can omit neither of these elements.

When gambling it seems that there is exactly the opposite psychological rule compared to social behavior: If people in social environments are inclined to work hard (preparation) and are quite careful and sometimes even reluctant in seizing new opportunities, it seems to be the opposite when gambling. The majority of gamblers seem to be people who are rather inclined to seize opportunities but keep the preparation (work) at a minimum.

Image: archerix (Shutterstock)

Be reminded of the fact that casinos and bookmakers earn their profits via statistics and probabilities (percentages) and there is a memorable saying by the statistician Chip Denman (Statistics Laboratory at the University of Maryland): Luck is only probability which one takes personally.

If a coin is thrown ten times and lands eight times as a ‘head’, it is tempting to believe that the next throw must be a tail. Unfortunately this is wrong thinking. The coin does not have a memory and the next throw has exactly the same chance as all the preceding ten throws: a 50% probability for ‘heads’ and 50% for ‘tails’.

Margaret Thatcher once commented upon praise she was given when receiving a school prize at the age of nine, “I wasn’t lucky, I deserved it!”.

Nevertheless gamblers often believe in ‘lucky streaks’ or ‘hot tables’ or even attach superstition to a particular number. There is simply no need to be fooled as the majority of gamblers tend to lose more than they will ever win.

Soccerwidow’s blog is about statistical analyses, probabilities, evaluation of historical data, strategies of sporting bets, gambling explanations, bookmaker mathematics, odds, et cetera. Perhaps you will take this opportunity and become a Guest Author for Soccerwidow. Who knows where this opportunity will lead you in life?


Further literature on this topic:

• Quirkology: The Curious Science Of Everyday Lives (Richard Wiseman)
• Supersense: Why We Believe in the Unbelievable (Bruce M. Hood)
• What’s Luck Got to Do with It?: The History, Mathematics, and Psychology of the Gambler’s Illusion (Joseph Mazur)

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.

Length of Winning and Losing Streaks

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?

Winning and Losing Streaks Formula

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 logarithm¹
P = probability²
| .. | = absolute value or ‘modulus’

¹Suffice 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.

²For 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.

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?

Timing of Winning and Losing Streaks

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!

Starting Bank – Rule of Thumb

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:

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

Calculation of the starting bank:

Exercises: Losing & Winning Streaks

Answers to the Exercises

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.

Optimising Your Bankroll

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.

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

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

Image: Cartoonresource (Shutterstock)

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

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

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

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

Why Is This So?

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

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

If a bookmaker’s betting odds are not aligned to public opinion then a disproportionately large amount of money will be placed on only one side of a bet. This would be a gamble for the bookmaker. However, bookmakers are not in the business of speculating on an outcome.

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

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

The bookmakers’ priority is balancing their books

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

Always remember

1. Bookmakers set odds based on a mixture of statistical probabilities and public opinion.
2. Bookmakers do not speculate (gamble). Their priority is balancing the books.

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

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

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

How do Bookies Manage their Risk?

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

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

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

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

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

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

The bookmakers’ mantra is very simple:

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

Learn from the Bookmakers!

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

The goal of bookmakers is not to predict the outcome of a game correctly. This means that their odds often do not reflect the expected probability distribution.

Bookmakers’ odds usually reflect public opinion about a match and their primary objective is to ensure a well balanced book.

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

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

They rely on dividing historical data (previous five complete seasons) into clusters according to the “HO/AO quotient” to provide a reliable comparison with future matches under analysis.

Betting odds are a mixture of statistical fact and public opinion (people voting with their money) as to what the likely outcome of an event will be.

Introducing the HO/AO quotient allows us to to ‘cluster’ groups of past matches and with that, to quantify the mutual relationship between the number of goals scored in matches and the strength of the teams involved. (The HO/AO quotients are a practical application of corellation).

This allows us to put an upcoming game into perspective.

In other words, we use the group of past matches bearing HO/AO quotients most similar to the match under analysis in order to make more accurate assessments about its likely number of goals.

The number of goals scored↔team strength relationship is a hugely strong correlation known to the bookmakers and used to a greater or lesser degree when setting their opening odds.

However, as public opinion (market pressure) leads to ‘errors’ in market pricing (odds), using the knowledge of the correlation allows us to spot ‘value’.

Following on from our Betting with Cluster Tables introductory article, here are the four simple steps needed to calculate pinpoint zero odds for intelligent value betting decisions in the Over/Under ‘X’ Goals market:

We shall look at each of these steps using the English Premier League as example.

Let’s look in fine detail at the EPL match: West Ham vs. Southampton from 31st March, 2018.

Step 1 – Find the Home and Away Odds

One of the most important components of the Cluster Tables is the HO/AO quotient (home odds divided by away odds), hence the need for both odds before referring to the tables.

To find the latest, up-to-date odds for any fixture you can employ bookmakers or betting exchanges of your choice, or make use of an odds comparison site. For the sake of our example, we are using OddsPortal.com as they are the only site showing time-stamped odds to support our illustrations.

Oddsportal Ante Post Odds Composite Screenshot – West Ham vs. Southampton 31/03/2018

The screenshot on the left is a composite image showing both the home and away odds just before this game started. Click on the image to enlarge it in a new tab.

Betsafe offered a price of 2.90 on West Ham six minutes before kick-off, whilst 5Dimes gave best price of 2.73 on Southampton seconds before the start.

Despite the multitude of odds movements throughout the entire ante post market, you will find in the vast majority of cases that the relationship between the home and away odds will stay pretty much the same throughout the ante post market.

Usually, the HO/AO quotient locates the match firmly between the two ends of a cluster, and the quotient tends to remain in that same cluster group no matter how the odds move during the lead up to kick-off.

This means that the timing of the analysis is not critical; you can perform it at any period during the ante post market before the match kicks-off. And of course, bet placement timing then also becomes just a matter of finding market odds containing value.

Timing only becomes an issue in the very rare event that the HO/AO quotient places the match very close to one of the ends of the cluster range for either team. It is then always wise to check odds close to kick-off to ensure that you have the match in the right HO/AO clusters for both teams.

Most of our tables are based on Pinnacle bookmaker odds, and for these leagues, you need only find which odds Pinnacle is offering at that time.

A small number of our tables use the highest audited bookmaker odds from a select panel included at Oddsportal. For these leagues, you will need to find the highest home and away odds being offered from a small range of bookmakers at the time.

Okay, we have our home and away odds – onto the next step…

Step 2 – Calculate the HO/AO Quotient

Easy! Take a calculator or enter the figures into a spreadsheet and just divide the home odds by the away odds to provide a quotient.

In this case, the quotient is: 2.90 divided by 2.73 = 1.0623 (rounded-up)

Step 3 – Identify the Relevant Cluster and Percentage Result

Cross-checking any team’s HO/AO quotient against their statistical percentages for any of the over/under 0.5 to 6.5 options in any match under analysis is extremely easy.

Within the Cluster Table for the appropriate league, click on the Betting Tables tab. This reveals a one-touch spreadsheet for obtaining both team’s results.

Here is the table of figures for West Ham (click on the image below to enlarge it in a new tab – and then use the magnifier to enlarge again if necessary):

Over/Under Cluster Table – Betting Table Screenshot

To change the team, simply click on the orange team name in the top left-hand corner to access the drop-down menu of all teams with five-season data sets.

By clicking on the team you are looking for, the figures in the table will automatically revert to those of that team.

The first half of the sheet contains the home figures: Summary at the top, Over ‘X’ Goals, and then Under ‘X’ Goals. The bottom three panels are the away results.

For this example, let’s decide to go for the most popular ‘Over 2.5 Goals’ bet.

For West Ham’s home figures, using the second panel from the top, you can see on the left-hand side in dark blue, their dedicated HO/AO clusters.

The HO/AO quotient we have calculated is 1.0623 and this fits neatly into the third cluster down. Looking under ‘Running Total Probability’, we simply record the percentage figure, in this case, 73.9%.

Here are West Ham’s top two panels with the relevant cluster row and percentage result for Over 2.5 Goals highlighted:

Over/Under Cluster Table – Betting Tables Tab – West Ham’s Cluster Row Highlighted

And after changing the team name, here are Southampton’s away figures in their fourth and fifth panels:

Over/Under Cluster Table – Betting Tables Tab – Southampton’s Cluster Row Highlighted

You will also note that the HO/AO quotient fitted very firmly inside the relevant cluster group of both teams, and not too close to its edges (West Ham’s cluster group was 0.7301-1.9345, whilst Southampton’s was 0.8211-1.3880).

Again, you will rarely encounter situations that will need monitoring – most games will see the same cluster groups used despite the odds movements throughout the ante post market. This means that neither analysing nor placing the bets is time-sensitive, and both exercises need not be performed at the same time either.


Next Page: Step 4 – Do the Maths!

Furthermore, the HO/AO Quotient removes the need to stay in front of the computer in the last hour before kick-off and allows you to compile your portfolio of bets well in advance, hence reducing the stress factor and possible human error.

Image: StockLite (Shutterstock)

What is a HO/AO Quotient?

The division of HO (Home Odds) by AO (AO Odds) reflects the “strength” of the teams. This makes upcoming games comparable with past matches, of which the results are known.

When building the quotient, we’re not asking ourselves questions like “Are these teams really equal?” or, “Is the favourite priced correctly?” We do not recalculate 1X2 odds; we simply use market prices and assume that the bookmakers have taken statistics into account and reflected public opinion (market pressures) as well as they possibly could do when setting their odds.

The HO/AO quotient is therefore an ideal device for comparing an upcoming match with the nearest batch of equivalent games against teams of a similar perceived strength to the opponent under analysis.

If for example, Liverpool are 1.34 to beat West Ham and West Ham are 11.0 to beat Liverpool, it makes sense to look at comparable matches where other teams carried similar prices in their respective home and away games in the past.

How to Calculate the HO/AO Quotient

As an example we will use the match Arsenal against Liverpool on 22/12/2017.

The evening before the match, the home win (Arsenal) was priced at 2.60, and the away win (Liverpool) at 2.95.

For the HO/AO quotient you simply divide the home odds (HO) by the away odds (AO).

2.60 divided by 2.95 = 0.881

That’s it!

Of course, if you would use the odds at kick-off then your quotient will be different, the home win (Arsenal) was then priced at 2.68, and the away win (Chelsea) at 2.95.

2.68 divided by 2.95 = 0.908

We will have closer look at these difference in the course of this article and explore if they actually matter and how much they matter.

What does the HO/AO Quotient Tell Us About the Comparative Strength of Teams?

When looking at the 2013-18 EPL HDAFU table, the following classifications become apparent when dividing games into ‘perceived strength’:

1. HO/AO: up to 0.225
   The home team is the clear favourite with a very good chance of winning (the home team is the overwhelming favourite)
   For example, Liverpool vs. Brighton on the 13/5/2018
2. HO/AO: 0.226 to 0.499
   The home team is definitely stronger than the away team, but there is also a good chance of a draw in the game (fluctuating opinion between home or draw)
   For example, Swansea vs. Stoke on the 13/5/2018
3. HO/AO: 0.500 to 0.788
   It is not really clear in which direction the game will develop (no overwhelming favourite)
   For example, West Ham vs. Everton on the 13/5/2018
4. HO/AO: 0.789 to 1.751
   The chance of a draw is quite high as both teams are perceived to be of equal strength (no overwhelming favourite)
   For example, Stoke vs. Crystal Palace on the 05/05/2018
5. HO/AO: over 1.752
   The home team is perceived as being much weaker than the away team; the public feels that it could be an away win (the perceived favourite is the away team)
   For example, Southampton vs. Man City on the 13/5/2018

If you are in the possession of the 2013-18 EPL HDAFU table then go to the data tab and check a few matches and their HO/AO quotients and see if you agree with the games being in the above classifications.

Alternatively, calculate the HO/AO quotients using before kick-off odds that you can retrieve from any odds comparison site.

Now, continue with the exercise and check against the actual distribution of results. You will quickly see that they hardly distribute according to the expectations (HO/AO clusters). That’s what makes 1X2 betting so very tricky and the HO/AO quotient so handy.

Finding a System Using the HO/AO Quotient

We now come to the practical part of this article. If you have not yet downloaded our HDAFU Show Table, then you can do it now here; all the following examples are being carried out using this show table.

It is a fully functional table and its only limitation is that it is an expired table and cannot be used for the forthcoming season. Of course, it can be used for backtesting its functionality against the previous season (2017-18).

The size of this Excel .XLSX workbook is 4.2 MB

(1) Find an HO/AO Cluster in the ‘IP’s by HOAO’ tab in which the Curve Goes Steadily Upwards

Image 1: P/L Simulation of ‘Backing the Draw’ in the first half

Above screenshot shows you ‘Backing the Draw’ in the first half.

It is a nice, steady upwards curve and stretched over almost a third of the whole graph. This is why we are going to look at this HO/AO cluster for this article.

(2) Check in the ‘System Picker’ tab what to Expect

Image 2: System Picker example

(3) Decide if to Include the System in your Portfolio

Our example above was definitely a system worthy of inclusion in the 2017-18 portfolio. It had everything you would be looking for:

• 50 bets minimum per annum
• an acceptable hit rate and
• delightfully low ‘Zero’ odds for the draw

Feel free to check yourself how this system would have performed for you during the 2017-18 season.

Here’s the answer: In the end 55 bets were played, of which 21 won (38.18% hit rate) with a profit of over 1,500 units using 100 unit flat stakes.

Why does the HO/AO Quotient Make an Analysis Less Volatile?

Here’s a close up screenshot from the Inflection Point graph above (image 1) and the odds seem to be disorganised and unordered:

Image 3: Inflection Point graph – corresponding draw odds

The HO/AO Quotient is a device for grouping matches into ‘comparable strengths’ of the teams involved (home odds divided by away odds), with the draw variable removed.

If you would be picking your system by odds then draw odds up to 3.54 could be draws odds for all kind of matches; matches between a strong favourite at home, equally matched teams, or the favourite being the away team, and so on.

With the HO/AO Quotient in our example: 0.603 to 1.396 we are only targeting teams in group 3 ≫ It is not really clear in which direction the game will develop (no overwhelming favourite) and group 4 ≫ The chance of a draw is quite high as both teams are perceived to be of equal strength (no overwhelming favourite)

For example, if there is a strong favourite involved in the game then the HO/AO Quotient approach filters these games out, even if the draw odds may be lower than 3.54.

Using the HO/AO Quotient slims down the number of available bets to an average of 53 bets with an expected hit rate of 39.62%. The expected profit is: 2,046.60 units per annum when staking 100 units flat.

Would you have used the ‘IP’s by Odds’ instead you would have likely chosen the odds cluster from 3.30 to 3.65. This would have provided you with a higher number bets per annum (87) but with only a 33.33% expected hit rate and a lower profit expectation of 1,322.40 units. Furthermore, the system would have been more volatile: an expected deviation of 2.78% (as compared with 2.55%) with a maximum deviation of 6.66% (as compared with 5.48%).

By slimming down the number of bet candidates and targeting bet selection via the HO/AO quotient taking the strength of the teams into account, the system becomes less volatile.

Draw systems especially benefit from the HO/AO Quotient, but others as well.

At What Time Should the HO/AO Quotient be Calculated and Which Odds should be Used

All Summer and Winter League HDAFU Tables use 1X2 odds recorded at or close to the end of the ante-post market. In other words, just prior to kick-off in all games. In the majority of cases, these odds are courtesy of Pinnacle, the most popular low-margin bookmaker around. In all other cases, the 1X2 odds used are the highest bookmaker odds at www.Oddsportal.com from a selected panel of their bookmakers according to our own personal settings.

Ideally, the closer to kick-off you calculate the HO/AO quotients for your system(s), the closer you are going to be to the HO/AO quotients calculated and used in the HDAFU simulations. Preferably, you should wait until team news is released before relying on where the odds settle thereafter.

Calculating the HO/AO Quotient at another Time

If you are for some reason unable to calculate the HO/AO quotient close to kick-off on match days then please, don’t worry too much.

Here’s an example weekend from the first half of the 2017-18 EPL season:

Image 4: Calculating HO/AO Quotients at different times

As you can see all four matches that would have fitted the criteria of being in the HO/AO 0.603 to 1.396 quotient cluster would still have been in the group of betting candidates even when using odds at the close of the match.

Only the Swansea vs. Crystal Palace match may have drifted out of the cluster. The other three games remained solidly within their clusters so that it didn’t matter at all how their odds changed during the ante-post market.

I hope this article has cleared up any confusion caused by the introduction of the HO/AO Quotient to our 7th generation HDAFU tables. However, if you are still not sure, then please feel free to ask any questions via the comment section below.

This is a substantial upgrade taking customer comments and queries into account such as the request of reducing human error when identifying systems, providing more assistance when judging the volatility of an identified system, speeding up the process of constructing a portfolio, providing a better feel of the expected losing sequences, and much more.

Of course, we will be populating Soccerwidow.com with updated User Guides and How To articles over the course of the next few weeks, but for those who already familiar with the filtering process, we are sure that they will very quickly pick up on the new method.

The Mightiness of the HDAFU Tables

(1) bookmaker’s business model

The bookmaker’s business model is primarily based on two factors:

Firstly, they keep a small commission on every game via overround. This is well known.

What is less known is that bookmakers when setting their odds adjust them according to the expected weight of money to be staked (= public opinion), meaning that some odds are being lowered and others are increased.

Of course, bookmakers predict (calculate) the outcomes of matches as good as they possibly can on the basis of historical statistics and whatever price adjustments (odds changes) they make are not huge and only in the region of 2 to 3% of the expected true probabilities and therefore not easy to spot.

(2) the power of the hdafu tables revealed

Due to the market expectations that the sum of the three likelihoods of home ~ draw ~ away must add up to 100% the bookmakers’ business model leads to the fact that if one side of the 1X2 bet is reduced (e.g. reducing the home win odds from ‘true’ odds of 1.65 [60.6%] to say, 1.6 [62.5%]) the other two bets in this example, the draw and away win, have to be adjusted as well (e.g. the draw odds being increased from 3.57 [28.0%] to 3.70 [27.1%] and the away odds from 8.78 [11.4%] to 9.57 [10.4%]).

Of course, another market pressure is that bookmakers tout for market share and some bookmakers will therefore offer certain bets to inflated prices (odds). To stick with the example; although the away win should be priced (according to the above ‘adjusted’ calculation) at 9.57 one bookmaker decides to offer this bet for 15.0 [6.7%]. This leads in the market to a correction of the draw odds from 3.7 to 3.24 [30.8%].

This all doesn’t mean that the true probabilities of the outcome of the match have changed. They are still 60.6% [1.65] for the home win, 28.0% [3.57] for the draw and 11.4% [8.78] for the away win but the implied probabilities now show 62.5% [1.6] for the home win, 30.8% [3.24] for the draw and 6.7% [15.0] for the away win.

Here’s where the HDAFU Inflection Point Graphs come in: They display the turning points in the odds (the previous five seasons in each league) where bookmakers habitually increase or decrease their prices.

Our HDAFU tables visualise the profit/loss curves that a bettor would have attained would he have been betting on the all the matches during a period of five seasons and with that they reveal the bookmakers best kept secret – where they ‘adjust’ their odds due to market pressures and public opinion.

With the help of the Inflection Point graphs it is very easy to visually identify the turning points and with the interactive ‘system picker’ tab to narrow down the field of view and identify profitable betting systems.

The Main Upgrades from the Previous Version

All three scenarios (whole of season, 1st half, 2nd half) are now in one file. Instead of the three HDAFU tables per league like last season you now get only one. But this doesn’t mean that you get less for your money. On the contrary, you’re getting much more!

The HDAFU Tables are in a new style Excel workbook, with everything fully automated. No more time wasted filtering and amending formulas manually, one-by-one – it’s all touch of a button stuff now.

The fully automated ‘System Picker’ tab removes the need of sorting through the data and carrying out any manual calculations. All the necessary calculations are done automatically for you and you will see the results in an instant.

In addition to inflection point graphs by odds the new tables now also provide 15 additional analyses by ‘home odds divided by away odds quotient’ (HO/AO Quotient). The relationship between the home and away odds remains very constant throughout the ante post market. This allows identification of bets and their placement at any time during the ante post market, removing the need to place bets close to kick-off. In short, there are no more time restrictions and results should be more reliable.

The price per league remains sensible at £35.00 each.

We truly hope that you are going to love the new format of the tables and we would be very interested to hear any feedback on the new style table once you’ve had a chance to look at it.

>>> buy your hdafu tables <<<

Image: Jeff Banke (Shutterstock)

Here are some of the most popular misconceptions…

(1) Betting on low odds is not worth the effort because winnings are very little

This is absolute nonsense. Successful betting is not about the pay-out of a bet, but the main goal is to make a profit. Very often, especially in the small odds markets excellent value can be found.

In addition, low odds have the huge advantage that they stand for high probabilities and therefore losses are considerably fewer in number than with higher priced, lower probability events. Also, small odds experience shorter losing streaks and the patience of the gambler is never stretched too far.

However, what really matters is the price of the bet.

If, for example, the calculations for a match throw up an 85% chance that there will be under 3.5 goals (corresponding betting odds: 1.18), and the market is offering odds of 1.25 then this is a fantastically good value back bet opportunity.

(2) Betting on higher odds is more profitable

Again this is a very common misconception. Higher odds do not automatically hold more value and are therefore more profitable than lower odds.

Many punters believe that better returns can be achieved in the long run if playing at odds between 2.5 to 3.0. Of course, it is understandable that these bets are quite popular as the potential winnings are 1.5 to 2.0 times the stake.

However, what most gamblers do not consider (or perhaps simply do not know!) is that these betting odds represent chances of 33% to 40% of winning, meaning that on average six to seven out of 10 bets in this group will lose.

Since winning and losing bets are never evenly distributed it may well happen that, with a little luck, a few bets in a row may be won but, much more likely is that many, many consecutive bets will lose until the betting bank is depleted beyond recovery or is drained altogether.

(3) If odds are very low it’s a good idea to combine bets

Accumulator bets are the bedfellow of every bookmaker. Full stop! So far as the bookies are concerned the more people combining their betting choices, the better. It is an absolutely nailed on profit for the bookmaker, and only long-term misery for the punters.

We have already mentioned that bets at very low odds have a pretty high chance to win. There is no question about this. Nevertheless, even a bet with a 90% chance of winning still has a 10% probability of losing. Multiples of bets greatly increase the chances of losing. Only one bet needs to go wrong for a busted flush.

In addition, there is of course the overround of the bookmakers. When combining your bets, you usually have to pick one bookmaker limiting you to accepting their fixed prices. You can be sure that somewhere in the accumulator package you have bought will be bets priced well below their true value. No bookmaker offers best price on every event – if they did they would soon be out of business.

With accumulators you are invariably buying negative value and we have seen throughout this blog that the only way of making consistent profits through gambling is by having value on your side, constantly.

This sermon is therefore to encourage you to avoid multiple, accumulator or parlay bets. You can be sure that the bookmaker is god in this particular arena and the gambler always loses in the long run, guaranteed.