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!

The opening month of the 2020/21 Premier League season was one of the most entertaining in living memory.

Round two, spanning the 19th-21st of September, was particularly outstanding with 44 goals scored across ten fixtures – for the fans it could only be described as pure entertainment.

This tally broke the existing record from February 2011 for the most goals scored in a single Premier League weekend under the 20-team format (number of goals that weekend: 43).

With such a high quantity of matches making an impact on the ‘Over/Under’ sportsbook, there is inevitably a ripple-effect on other staples of Premier League wagering, such as HT/FT, handicap markets and BTTS (Both Teams to Score).

Feet Up, For the Big Match! (photo courtesy of www.pxfuel.com)

It almost seems that the absence of fans from Premier League games may lead to a shift in several key markets… Really?

Here are a few thoughts. Feel free to share yours in the comment section.

Will Over/Under 2.5 Goals ‘Benchmark’ Become Less Focal?

As can be seen from the wide variety of live sportsbook betting markets out there, there is now ample opportunity to explore a number of niche markets related purely to goal scoring.

Given the normal average of goals per week across previous seasons, it is widely accepted that using 2.5 goals as a division between ‘high’ and ‘low’ scoring encounters provides an optimal, and easy-to-negotiate meridian.

But perhaps further weekends of high scoring games with questionable defending from once-reliable teams may lead to Over/Under 3.5 goals becoming the new baseline in goal betting?

Naturally, the coming months will see player stamina impacted by European involvement for last season’s high-flyers and, for the newcomers, the continuing culture shock and adjustment needed to survive the rarified atmosphere of the Premier League.

With the glut of games ahead the use of the ‘2.5’ figure to make vital decisions in the total goals market may return to a balance.

What does seem certain is another boom in people backing both teams to score within Premier League multiples, accumulators and proposition bets. So too will there be a greater scrutiny upon teams that are often involved in such matches, such as Leeds United, who found themselves at both ends of two 4-3 scorelines, in consecutive games at the start of this campaign:

Can a Change in Underdog Results lead to HT/FT Impact?

Again, this depends on continued shock results, such as Crystal Palace and Leicester winning by multiple-goal margins at Manchester clubs United and City respectively.

The absence of home-biased crowds, whether complete or partial shutouts, has undeniably played its part. When using last season as a source of information for future betting decisions, it has become common practice for many punters to split leagues into before and after the lockdown began.

Last season, there was little fluctuation in the Premier League, except for away underdogs drawing less often and winning or losing more without a hostile home crowd to face. The hosts’ lack of a ‘twelfth man’ (the crowd) seems to be a leveller, helping unfancied away teams achieve unlikely results at normally difficult venues.

A more attacking-style of play is now evident and it is becoming rarer to see away underdogs defending deep and attempting to play on the counter-attack. This sea change will undoubtedly be significant for the HT/FT and Goal Time markets, though public opinion will continue to play its part.

Backing goals earlier in live play can only become more of a phenomenon if underdogs continue to be adventurous from the start. And so too will backing late goals, as the effects of an energetic start are felt more amongst squads less accustomed to the rigors of Premier League action.

Is this the same Across Europe?

On early evidence, the unprecedented inflation of importance on the ‘Over 3.5’ threshold will certainly transfer to other major European leagues. For example, Bayern Munich’s opening two Bundesliga games illustrated this newfound sense of unpredictability in the Over/Under market. The two games produced a total of 13 goals – an 8-0 win and a shock 4-1 defeat.

Bayern Munich’s Allianz Arena (photo courtesy of www.pxfuel.com)

Both games paid out for anyone backing Over 5.5 goals, which represents the point at which the goal odds begin to surge upwards, regardless of how good the favourite is compared to the underdog.

The opening Saturday of Serie A also produced a number of high scores, with three of the four matches producing over 4.5 goals, and threatening the long-held stereotype that Italian football is focused more on defence.

Last season, the Bundesliga was also notable for seeing a decline in favourites losing away from home, with only 12.2% of teams losing to home underdogs between May and August.

Other leagues have seen a similar trend, albeit less drastically, and this certainly provides an opportunity for bettors. With or without fans, home advantage is usually observed as a factor for travelling favourites in many odds starting off longer than they otherwise would be. In turn, away favourites will perhaps become more of a staple than ever when it comes to placing the bets.

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

What we know at the moment of writing is that the starts of the new league seasons in China, South Korea and Japan have been postponed. Many of Italy’s Serie A matches are currently being played in empty stadiums. Which leagues will follow suit?

The problem is that no-one truly knows in which direction things will develop. There is a great amount of uncertainty everywhere and the press is filled with reports about new outbreaks and rising numbers of infected people. It is no wonder that many of us feel a slight sense of panic creeping up.

But please remember, the Soccerwidow website is purely about numbers and we will, therefore, look at the statistics pragmatically (although always with a sympathetic nod to the growing situation).

Current Trends of the Coronavirus

As per 26th February 2020, some countries have started to mass test for the Covid-19 virus. At the time of writing, the UK had concluded 7,132 tests, 13 of which, were positive (0.2% positivity rate). Italy had concluded 9,462 tests, 470 of which, were positive (5.0% positivity rate). France has also been carrying out mass tests as well as Austria and the United States. No doubt more countries will follow.

The virus has the potential to reach pandemic levels and, therefore, every single country in this world is taking this threat very seriously and working very hard to reduce the risk faced by their populations in order to halt the spread of the virus.

Despite the apparent hysteria, as per the 26th of February…

[Source]

The Facts We Know About the Coronavirus

You can see from the numbers above that the risk of catching this virus is as low as it is to win the lottery and become a millionaire. It is a cold fact that there is a statistically lower chance of dying from Coronavirus than winning at least a million on the UK National Lottery.

Then Why Is There So Much Hype?

The really serious problem with this highly infectious virus is the very high amount of people (82%) that are carriers of this potentially deadly infection but don’t notice it because they don’t have any symptoms. That’s a real big problem because if not controlled it will lead to a massive spread of the virus and collapse the medical systems in the countries affected.

Hence, the very strong control measures that are currently being observed all over the world. And strong control measures include high public awareness and, therefore, mass-media press coverage. That’s simple cause and effect, a phrase you may be familiar with.

However, please remember that high-level press coverage doesn’t mean that the real risk is higher than the actual statistical numbers show.

Therefore, in my opinion, as a scholar of numbers, there is absolutely no need for panic (on a personal scale).

With all of the precautionary measures currently being put in place (closing schools, closing towns and even regions, limiting travel, self-isolation, putting places into quarantine, etc.), it is very unlikely that the virus will spread in an uncontrolled manner.

No Need for Any Panic. Life Will Go On as Usual!

I have been criticised for the title of this chapter but it is a cold fact that life will go on as usual, just with a few more precautions in place.

Look to The Facts We Know About the Coronavirus and, as per its date, just 0.0057% of China’s population is affected by Coronavirus, with the trend in decline. There is a sharp increase in cases outside of China and the two trends need to be analysed separately. For example, 0.0031% of South Korea’s population is affected by Coronavirus and, as harsh it may sound, these numbers will rise but are very unlikely to topple China’s figures.

Looking at all of this statistically, what can be probably said is that the maximum expectation is an infection of 0.01% of the population of any country and, the good news is that from these infected people, 82% will only suffer from very mild symptoms.

A maximum of 0.0018 % of a country’s population may come down with severe symptoms from this virus outbreak but probably far less.

0.0018% means that of 100,000 people there may be up to 2 cases. As stated previously, it is much more likely that you (or your favourite football player) will win a substantial amount on the UK National Lottery than suffer severe symptoms from Coronavirus.

There Shouldn’t Be Any Notable Effects on Match Results

Of course, all these quarantines and lock-downs do affect the economy and businesses but the psychological effects of the situation are probably worse.

However, please always keep in mind that professional football clubs are businesses and, like every other sound business, they will do everything possible to continue performing at the same high level as usual and not be affected by any virus outbreaks and panic.

In Italy, for example, many Serie A games have recently been played behind closed doors. However, there shouldn’t be any noticeable adverse effect on match results.

Do you remember the Japanese Tsunami in 2011 that caused a mighty number of 15,899 deaths? Although the league was halted after one round for seven weeks this pause had no effects on the statistical patterns of the J1 League during that season. And neither will Coronavirus; not in Japan or anywhere else.

Please be careful about making hasty judgements! At this stage, with comparatively low numbers of virus-related severe illnesses in each country, it is very unlikely that the virus will have any effect on the long-term outcome of a group of matches.

Currently, the newspapers are full every day with this topic (public awareness has to be raised! Newspapers have to be sold!) but please force yourself to think statistically and put everything into perspective.

Precaution and Risk Management

Please bear in mind that seasons always have the habit of starting somewhat unpredictably, with or without Coronavirus. It always takes six to eight rounds to start rolling ‘statistically correctly’. Just have a look at our League reports each season.

People who calculate matches individually, using the Value Calculator or the Coursebook and its Cluster Tables, should find that any effects of Coronavirus (if there are any) will be taken into account when following the calculations as usual. The odds offered will always be a measure of the possible outcomes whatever the extraneous circumstances may be.

System betters, using the HDAFU Tables, also don’t need to worry. There shouldn’t be any impact on the distribution of the results, neither for the 1st or 2nd half of season systems.

As a suggestion, perhaps pick your Summer League systems this year in a normal way but only monitor them for a while without committing big money. It doesn’t do any harm to start betting with real money a little bit later.

My general advice is: The first 6-8 weeks of every season always tends to be a bumpy ride, with or without something like Coronavirus in the background. There is no shame in abstaining from betting during this period and using the time for paper testing.

“I have a question for you since your strategy in the German Bundesliga was Underdog Whole Season, why do you show only Home Underdog bets in the system’s performance?”

So, why?

The answer is very simple: Because the HDAFU Tables identify Value Bet clusters. Although an HDAFU Table may be perceived as a ‘System Betting’ tool the most profitable historical betting clusters are the ones packed with Value Bets. And in the German Bundesliga, in the last five seasons (and beyond) these have mostly been home underdog bets.

It doesn’t matter which angle you approach betting from, to make a stable profit you must always ensure ‘value’ is on your side.

You may now ask Why do certain clusters of the HDAFU Tables contain Value Bets?

To shed light on this you really need to understand…

How do Bookmakers Set their Odds?

The bookmaker trade is a business aimed at making profits like any other business. Although they claim that their odds are ‘fair’ this doesn’t implicitly mean that their odds represent the ‘true’ probabilities of each event occurring.

Something classed as being ‘fair’ means only that it is carried out without wishing to cheat or to achieve an unjust advantage. However, it is neither ‘cheating’ nor ‘unjust advantage’ to optimise profits, is it? Otherwise, you could blame every profit-making company for setting ‘unfair’ prices only because they calculate with high-profit margins.

Take the example of a popular team like Bayern Munich. Playing at home, they win approximately 85% of their matches give or take every season. The ‘true’ average odds should, therefore, be in the region of 1.18 (1/85%). However, with the weight of money (the majority of bets being placed on the most probable winner), the bookmakers can reduce the odds on Bayern to win, say, to 1.15, increasing their profits in the long run, but still offering ‘fair’ odds on a match to match basis.

Of course, this also applies to Bayern’s away games. When playing away, Bayern wins approximately 65% of their matches, which in odds is 1.53 (1/65%). However, the average odds offered by the bookmakers for Bayern to win away are 1.44. That’s a clear and significant reduction. Are you with me?

Now, let’s dive a little bit deeper into odds calculation to help you understand what makes the HDAFU Tables so very special…

Changing One Side Affects the Other Side

To show you the above illustration in numbers we will look in more detail at one of the Bundesliga matches in our 2017-18 Winter League Report.

On the 9th September 2017 Bayern played away against Hoffenheim. Bayern’s ‘true’ chances to win that game were 47.78% (see Value Calculator results below):

The ‘true’ odds corresponding to a 47.78% probability are 2.09 (1/47.78%).

The problem bookmakers probably had with this particular game, especially if they would have offered odds in the region of 2.09, 2.0 or even 1.9 for Bayern to win, is that there wouldn’t have been enough bets on either of the two other possible results, the home win and draw: The book would be unbalanced with the bookmaker facing a huge potential liability if Bayern were to win.

Football followers with a low understanding of probabilities know that Bayern, even playing away, will probably win the match. Regular punters would be expecting odds in the region of 1.5 or 1.6.

Odds around 2.09 would have encouraged far more money on Bayern as punters would have perceived the odds as an opportunity to cash-in on ‘higher than normal’ odds for a Bayern away win.

Therefore, to avoid too many bets on this outcome the bookmakers were literally forced to reduce Bayern’s odds to match public expectations.

So, instead of pricing the odds close to their ‘true’ probability of 47.78% (in odds: 2.09), the bookmakers had to offer the away win close to the ‘expected’ probability (65%). Hence, they offered odds for Bayern to win of 1.46 – an implied probability of 68.5% (1/1.46).

Of course, Bayern’s statistical chances didn’t suddenly increase by 20% to win that match, although the odds offered may have swayed people into believing this.

Probabilities: Home Win + Draw + Away Win = 100%

Statistically speaking, the sum of the probabilities for any match outcome is always 100%; it is either a home win, a draw or an away win.

Therefore, if the odds (applied probabilities) for an away win are changed due to market pressure, it naturally affects the draw and home odds (implied probabilities).

In this example:

• The ‘true’ probability for Hoffenheim to win of 24.5% (in odds: 4.07) was reduced to 14.9% (odds increased to 6.72)
• The ‘true’ probability for the draw of 27.7% (in odds: 3.61) was reduced to 20.3% (odds increased to 4.92)
• The ‘true’ probability for Bayern to win was increased from 47.8% (odds of 2.09) to 68.5% (odds reduced to 1.46)

In this example, the ‘underdog’ at home (Hoffenheim) became even more of an ‘outsider’ and hence a Value Bet (the price offered was much higher than that of its statistical probability).

Just as a side note, Hoffenheim won the game 2-0

The HDAFU Tables Help You to Discover Value Bet Clusters

As shown in the example above there was a clear gap between public expectations and the ‘true’ probabilities, which literally forced the bookmakers to adjust their odds for Bayern, who were shown as a much stronger away favourite than they actually were.

In the EPL the same can be said of the Draw expectation; in Italy, it’s the Away Win and so on. For specifics, you will have to dive deeper into the analysis of the 2017-18 Winter League Report, where the patterns in the leagues chosen for that season are revealed.

Each league has its own betting patterns and punter preferences and the bookmakers react accordingly.

What makes the HDAFU Tables so special is that they highlight where the odds or HO/AO (home odds divided by away odds) clusters are profitable for the bettor if bets are placed constantly and consistently within the parameters of these clusters. The majority of bets made within these clusters are Value Bets.

So, just remember: There is a public expectation of match outcomes and the bookmakers react by reducing or increasing odds and balancing these changes by changing the odds for the other two outcomes. It’s as simple as that.

Time Saving ~ Risk Diversification ~ Value Betting

Value Bettors who calculate each game individually will find it very challenging to identify enough bets for each weekend to diversify risk sufficiently enough. Every match requires time to be analysed.

The calculations for just one match and checking its bets for viability could take as long as two to three hours. If you’re adept at using our Value Calculator, one match might take you 15-20 minutes to analyse. Even then if it takes only 25 minutes per match in total to identify, choose and place a single bet, if you want a Saturday portfolio of at least 15 matches for diversification, it’s going to take you more than six hours to achieve – every Saturday.

With the HDAFU Tables life is much easier. You don’t need to carry out any individual calculations once you have identified the profitable clusters and checked them carefully before you start placing real bets – and you only need to decide upon your systems once (whole season systems) or twice (half-season systems) per season. Once you have prepared a large enough and diversified portfolio of systems from different leagues, you can let the statistics do the work for you.

Of course, an additional finishing touch for those of us with time when compiling the weekly portfolio of bets is to cross-check those highlighted by the HDAFU Tables (portfolio builder) against the Value Calculator (individual match investigator) and ensure that the majority are actual Value Bets on the day.

One thing you can take for granted is if a cluster has been packed with Value Bets during the previous five seasons it’s likely that the same cluster will continue to churn out Value Bets in the following season.

Many thanks, João, for your question and I hope this article helps clarify things for a wider audience!

The following series of key articles is aimed at helping you fully understand the fundamentals of profitable 1X2 portfolio betting using our revolutionary HDAFU Tables.

Jump Link Menu:

(1) Introductory Articles
(2) Building Profitable Portfolios of Bets
(3) The Scale of Rewards on Offer
(4) The Mechanics of the HDAFU Tables
(5) All Other Important Matters

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(1) Introductory Articles

Football Betting System Concepts

Explore the realities of constructing promising betting strategies together with the mathematical expectations involved.

The absolute basics of system betting.

Learn the principles of acceptable accuracy, and the advantages of filtering a data set for a targeted, more successful approach to football betting. << read more >>

Why Inflection Points are Vital

An inflection point is a point in a profit/loss line graph where the curve changes direction, either from rising to falling or vice versa.

What does this have to do with betting?

Inflection point graphs are the heart of our HDAFU Tables and highlight the various profit and loss areas visually. It is then easy to identify where significant changes from either profits to losses, or from losses to profits occur. The turning points in each curve indicate the natural cut-off points in the odds or odds quotients showing us where to bet (in profitable areas only) and where to stop (before entering unprofitable zones). << read more >>

Why Mid-season Breaks Matter

The HDAFU Tables contain automated analyses from three different perspectives: the whole of season analysis; the first half of season analysis; the second half of season analysis.

Why is it important to split the analyses in this fashion?

Especially in leagues with distinct mid-season or winter breaks, statistics are sometimes vastly different in each half of a season. Explore why this should be and why it is good sense to analyse historical statistics in this much detail. << read more >>

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(2) Building Profitable Portfolios of Bets

Finding Systems using the HO/AO Quotient

HO/AO means home odds divided by the away odds, and the resultant figure is known as a quotient. In order to identify the likely outcome of a future game, an historical benchmark of some description has to be used.

But why use the HO/AO Quotient?

The HO/AO quotient is by far the most powerful method of clustering groups of matches together for comparison purposes based on the perceived strength of the teams involved in a match. Because the ratios between the home and away odds don’t alter that dramatically during ante post it is a more reliable benchmark than using a comparison of each team’s odds alone. << read more >>

Judging the Risk of a Football Betting Portfolio

Risk and reward are inherently linked and their relationship should be thoroughly understood before committing money to any betting adventure.

How risky are my individual systems or my portfolio in general?

Gain control over your portfolio with a grounding in risk diversification and learn the relationships between its likely hit rate, winning and losing streaks and potential yield in order to gauge the overall risk involved. << read more >>

Sound Staking Strategies

Learn about the different results of flat staking, and ratcheting with a stop-loss mechanism.

What are the differences between flat and progressive staking?

When things are going well, ramp up the stakes! And choose a stop-loss percentage of bank to haul the stake size back in when your campaign hits a sticky patch. << read more >>

Bank Management and Stake Size

No one bookmaker or betting exchange will give you best price (or close to it) every time. For best results, a portfolio of accounts is needed.

But how large should the initial starting bank and stakes be?

Find out what the optimum stake size should be for any size of starting bank and how to divide and manage the money in your various bookmaker and betting exchange accounts. << read more >>

Calculating Winning and Losing Streaks

A specialist article detailing the calculations to establish longest expected winning and losing streaks.

Why is this important?

In addition to explicit formulas to help you calculate the maximum expected winning and losing streaks, the relationship between these and the size of the initial starting bank is explored. The article comes with a free ‘examination paper’ download so you can test yourself until it becomes second nature. << read more >>

Starting or Pausing in the Middle of a Season

Can I afford a break and then restart the campaign again after a holiday? And what happens if I miss the start of the season or the start of the first or second half?

Does a campaign require ‘whole Season’ betting?

So long as you play with mathematical advantage on your side, a betting campaign can be started at anytime during the year/season. You can also take one or more breaks and resume again whenever you wish. Or just quit when you have reached a predetermined target. << read more >>

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(3) The Scale of Rewards on Offer

2017-18 Winter League Report – 35k in 138 Days

Our own campaign report on the 2017-18 Winter League season encompassing 138 match days.

Comes with an Excel workbook download (paid) detailing all systems in use and each individual bet.

A full league-by-league report detailing how the profits were achieved and showing the ratcheting system in action to improve results exponentially beyond a flat staking approach. << read more >>

2016-17 Winter League Report – 20k in 214 days

Our own campaign report on the 2016-17 Winter League season encompassing 214 match days.

Comes with an Excel workbook download (paid) detailing all systems in use and each individual bet.

A full league-by-league report detailing how the profits were achieved using flat stakes only. The Excel workbook also simulates what would have been achieved with basic ratcheting and stop-loss mechanisms in place. << read more >>

2016 Summer League Report – 10k in 178 days

Our own campaign report on the 2016 Summer League season encompassing 178 match days.

Comes with an Excel workbook download (free) detailing all systems in use and each individual bet.

A full league-by-league report detailing how the profits were achieved using flat stakes only. << read more >>

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(4) The Mechanics of the HDAFU Tables

Summer and Winter League Calendar

Date information for every league brought together in one place so that you don’t miss a match.

Are the mid-season breaks mentioned?

Yes, here you will find the start and end dates of each half of each league season together with notes on excluded matches, if applicable. (Usually, most end-of-season play-off games are excluded unless they include every team in that league). << read more >>

Understanding the Settings in Oddsportal

To identify which games to bet on in the leagues of your choice, you will need access to a live odds comparison site.

Oddsportal.com is by far the most comprehensive odds comparison site on the Net at present.

A guided tour around the Oddsportal website including detailed summaries of how to handle each of its settings. << read more >>

Understanding the Odds Toggle Function

The Odds Toggle is an important feature of the HDAFU Tables and allows you to adjust the results of each simulation.

Why would I wish to adjust the figures?

As the HDAFU Tables are powered by bookmaker odds, you may wish to simulate betting exchange results by inputting an adjustment figure in the System Picker tab. You will also find there a commission rate input feature. Adding figures to these fields will automatically change the simulations across the entire workbook. << read more >>

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(5) All Other Important Matters

FAQ’s – Before Purchasing HDAFU Tables

A selection of reader’s questions answered in full.

Product-specific, general, and online store checkout questions answered.

Everything you need to know before purchasing your first HDAFU Table. << read more >>

FAQ’s – HDAFU Table Owners

A selection of user’s questions answered in full.

Bet timing, troublshooting, and miscellaneous questions answered.

A selection of user questions to further help you understand the nature of the product you have purchased. << read more >>

The Gambler’s Worst Enemy: Emotions!

It takes a particular mindset to be a successful gambler.

But isn’t compiling a very promising system enough?

Unemotional follow-through is vital to the success of any betting strategy. Explore the worst of what can happen if your nerves of steel turn to rust and you allow emotions to get the better of you! << read more >>

SUMMARY

Hopefully this article provides a logical walk-through the mechanics and the power of Soccerwidow’s HDAFU Tables.

It’s an opportunity to blow your own trumpet or let off a bit of steam!

What does this have to do with betting?

Image: FXQuadro (Shutterstock)

We speak about ‘Inflection Point graphs’ in our HDAFU Profit/Loss simulations because these graphs have turning points. Turning points are points at which a significant change occurs.

In layman’s language – Turning points are the points in the ‘inflection point graphs’ where profits turn to losses or, where losses turn to profits.

For the mathematicians amongst you, we use the term ‘turning point’ although the academically correct mathematical expression would be ‘maximum’ and ‘minimum’.

However, ‘maximum’ and ‘minimum’ in normal language signifies ‘best’ and ‘worst’ but turning points in a curve have nothing to do with any judgement of being ‘good’ or ‘bad’.

Apologies for any academical incorrectness in the use of the terminology, but somehow we have to make ourselves understood by everybody…

Home ~ Draw ~ Away Profit/ Loss Curves

If you have ever calculated your own odds you will certainly have noticed that bookmaker prices often do not show the ‘true’ picture.

In other words, their odds seldom adhere to mathematically calculated values (the statistically expected values).

In the majority of games, odds are either higher than mathematically expected or lower…

Is That Really True? Can You Prove That?

Here’s an image showing the Profit/Loss Curves for betting on the Home win ~ Draw ~ Away win with equal 100 units stakes over a period of five seasons; the straight red line is the bookmakers’ profit trendline:

English Premier League – Profit/Loss curves by ODDS for 2013-14 to 2017-18 seasons

What you can see in the above graph is that if one curve rises, e.g. betting on the away win if the home odds are between 1.7 and 2.15 (orange curve), then another curve falls.

In this example, betting on the draw (brown curve) produces a loss for the bettor and hence, a profit for the bookmakers.

What is most revealing in the above graph is the red curve. It shows how the bookmakers profits vary depending on the home odds, but the most interesting part is the straight line, the red trendline. It is positive for all odds and perpendicular. The interpretation of this is that for the bookmakers it doesn’t matter in which cluster a particular match is in (in the example, home odds), their odds guarantee them the same constant profit across the whole spread.

Why Is This So?

A bookmaker’s aim is to make a profit and they price their odds to ensure that sufficient action is taking place on both sides of a bet, with enough profit retained whatever the outcome.

In addition, bookmaker betting odds are often adjusted according to public opinion to guard against a disproportionately large amount of money being placed on just one side of a bet.

How can the Bettor Make Use of This Knowledge?

The bettor can take advantage of the knowledge that each betting market contains certain odds clusters that are regularly under-priced, whilst other odds clusters are habitually over-priced. But attention, please! These under- and over-priced clusters are different from league to league.

Bookmakers take into account gamblers’ preferences and these vary depending on the cultural background (e.g. risk-aversion) of the locals. This is perhaps because local bettors are the majority takers for any league bookmakers, so bookmakers take local tastes into account.

It is obvious that, for example, Italians will be betting primarily on Italian matches; Spanish people on Spanish matches, and so on.

However, please be aware that the EPL, the most prominent league in the world, naturally has a huge amount of supporters worldwide. This makes the EPL the most unreliable league (system betting-wise) in terms of its odds. Although odds patterns can be spotted easily, it is always a gamble whether or not they will be returning a profit the forthcoming year.

Nevertheless, for this article, we will use the EPL as an example.

The Significance of the Turning Points on the Inflection Point Graphs

Profit turning points can be easily spotted in the Inflection Point tabs of the HDAFU Simulation Tables using visualisations (see example below) in the form of Profit/Loss curves based on five seasons’ data within these tables.

For example, the EPL: Between 2013-18, if you had gambled unemotionally and systematically on all English Premier League matches to be away wins (at the highest bookmaker odds) and placed a constant stake of 100 units per fixture, then at the end of the fifth season, you would not have seen a large change in your bank: -125 units after 1,900 bets.

Huge losses would have been incurred, had you backed all of the away teams to win and limited your betting to the zone of odds between 2.31 and 4.53. Within this zone your losses would have totalled -9,189 units (3,714 plus 5,475).

However, if your strategy had been based on away wins at odds from 1.68 and up to 2.31, after five seasons your profit would have been 3,202 units (7,714 minus 512).

That’s quite a difference, isn’t it?

English Premier League – Profit/Loss curve for 2013-14 to 2017-18 seasons

Just as a side note… Can you see yourself betting on an EPL match targeting odds between 2.3 and 4.5 when backing the away win?

Different Leagues = Different Inflection Points Graphs

The above screenshot shows the Profit/Loss curve for the EPL if back bets on the away win were placed on all 1,900 matches between 17/08/2013 and 13/05/2018 (at 100 unit stakes).

You can see from the graph that the first turning point is located at 2.31 on the ‘away odds’ axis. At this point, the P/L curve reaches a peak of 3,714 units profit before starting to fall again. The decrease in profits continues until away odds of 4,35 are reached (P/L value: – 5,475 units), where the curve turns for a spell but remains negative until away odds of 9.5 are reached. Then it experiences a large jump before falling again.

Now, we compare this to the German Bundesliga Inflection Point graph:

German Bundesliga 1 – Profit/Loss curve for 2013-14 to 2017-18 seasons

You can see at a glance that both graphs are very different.

Whilst the EPL shows a rising curve (profits) until odds of 2.31 the Bundesliga P/L curve starts to drop straight away from odds of 1.4.

This indicates that the bettors on the Bundesliga are probably much more risk-averse than EPL bettors and it seems that they prefer betting on favourites (lower odds). Using this mentality bookmakers can reduce their odds in this group to optimise their profits and hence, the draw odds and/or home odds are likely to be increased.

Once you start working with inflection point graphs you will not only see the various profit /loss curves (whole season, 1st half and 2nd half) and start recognising patterns but you will be also amazed to learn about the different mentalities of bettors in different countries.

Working With the Inflection Points graphs using the HDAFU Tables

Here’s a handy little tutorial:

How to Use the Knowledge of Turning Points

(1) check your betting pattern

Do you recognise your betting patterns within the most common odds clusters; those which show a falling Profit/Loss curve? (For example, betting on odds between 2.3 and 4.5 when backing an away win in the EPL).

If so, perhaps reconsider your strategy and avoid the odds used by the bookmakers to make their profits. Of course, no matter how hard you try, betting in the zones where bookmakers habitually reduce prices (odds) is asking for long-term losses.

It is very rare for people to succeed in any walk of life by swimming against a strong current and you can safely assume that the bookmakers know their job and have for centuries been making a living from manipulating figures.

(2) don’t even try to “beat” the bookies

Swim along with them. ‘Play’ the market the same way as they do. Start looking at strategies which are not in conflict with the market, but in rhythm with it.

(3) be aware that each league has a different market behaviour

Customs and habits of people vary from country to country. Every nation has different culture or cultures. Surely everybody has noticed regional differences expressed in such tangible goods as food or housing. However, these differences extend into how people think and act.

Unfortunately, differences in betting patterns and the subsequent reaction of bookmakers when setting their odds cannot be spotted without taking a mathematical approach. Customer habits, especially in the betting market, remain well hidden from the bettor.

Whichever league you prefer betting on, identify the odds clusters which are utilised by the bookmakers to turn their profits – and then work around those clusters.

(4) use the market turning points for your own benefit

Concentrate on developing your personal betting strategy by taking the market rules into consideration.

(5) find a strategy and identify matches for producing long-term profits

Using the HDAFU simulation tables and finding the various turning points will provide you with the need knowledge of odds clusters you need to produce a long-term profit when backing.

If you already use our Value Bet Detector for calculating odds for individual matches then the knowledge of profitable odds clusters will help you to pick matches which are worthwhile re-calculating.

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

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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.