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!

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.

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

Image: Marino Bocelli (Shutterstock)

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

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

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

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

1) Before a Ball is Kicked

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

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

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

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

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

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

2) The Weather

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

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

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

3) Domestic and European Competition Formats

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

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

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

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

4) Player Fatigue, Injuries and Suspensions

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

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

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

5) Other Observations

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

6) Example and Summary

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

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

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

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

Japan J-League Home Win Comparison – 1st Half vs. 2nd Half of Five Seasons 2012-16

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

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

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

Japan J-League Home Win – Whole of Five Seasons 2012-16

7) Conclusion

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

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

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

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

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

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

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

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!

In conjunction with the course and based on its teachings, Soccerwidow also publishes a set of dedicated Over/Under Goals Cluster Tables (summer and winter leagues), which are a one-touch solution to identifying value within the over/under ‘X’ goals market for individual matches in a particular league.

What is a Cluster Table?

A Cluster Table is an Excel spreadsheet containing an interactive data set, which displays goal distributions in a particular league during the five complete seasons immediately prior to the season currently in play.

Results are split into four equal-sized groups, or ‘clusters’, according to the historical home and away odds of each match within the five-season-data-set (highest odds at close of ante post market), which act as a gauge of public opinion (perceived strength of the teams), for the match under consideration.

Here are two cluster examples taken from a game in the English Premier League (EPL) during the 2017-18 season. The screenshots come directly from the EPL 2012-17 Cluster Table.

Click on the images below to enlarge them in new tabs:

Man Utd Home – Over ‘X’ Goals Cluster Table 2012-2017 – Over 2.5 Goals Highlighted

Liverpool Away – Over ‘X’ Goals Cluster Table 2012-2017 – Over 2.5 Goals Highlighted

As you can see, the four clusters representing 95 respective home and away games over five seasons are divided into almost equal sets (3x 24 games; 1x 23 games), which determines the division of their HO/AO quotients (Home Odds divided by Away Odds).

Two rows are highlighted: these are the corresponding rows for the match between these two teams on 10th March, 2018.

The home odds of Manchester United were 3.30. Liverpool’s away odds were 2.61. The HO/AO quotient was therefore 1.26 (3.30 divided by 2.61).

How to Interpret the HO/AO Figures

The HO/AO clusters for all teams are different from one another. But why?

Odds are determined according to a team’s historical (statistical) strength (success), or lack of it, and no two teams perform exactly the same, which will of course produce different quotient figures.

How then is it possible to compare two teams in this fashion?

The home odds and the away odds are set by the bookmakers according to historical distributions (statistical results) and therefore provide a constant benchmark to a team’s past performance (looking backwards).

By the time the ante post market closes, they also contain a deal of public perception in terms of demand for the bet in question (looking forwards).

Therefore, the HO/AO clusters take the correlation between the ‘perceived’ strength of the teams involved (based on historical results) AND the market pressures (demand and supply) faced by the bookmakers when setting their odds.

The HO/AO quotient is therefore an ideal method of comparing two teams by selecting from their historical results the nearest batch of equivalent games against teams of a similar perceived strength to the opponent under analysis.

If United are 3.30 to beat Liverpool and Liverpool are 2.61 to beat United, it makes sense to look at comparable results where both teams carried similar prices in their respective home and away games in the past (i.e. the closest United home games to their price of 3.30 in this game, and the closest Liverpool away games to their price of 2.61).

Splitting five seasons’ worth of games into four clusters does not divide exactly. Each team plays 19 games at home and 19 away per season. This makes a total of 95 home and 95 away games for each team, hence why for United’s home games and Liverpool’s away games (and any other team) there are three clusters of 24 games grouped together, and one cluster of 23.

In our example, it is coincidental that the most relevant clusters for both teams to the calculated HO/AO quotient of 1.26 each contain 24 games over the last five seasons.

What the Clusters say about the Comparative Strength of Teams

When looking at the tables in the EPL for any team, the following categories become apparent when dividing games into ‘perceived strength’:

1. HO/AO: up to 0.2248
   The home team is the clear favourite with a very good chance of winning (the weight of money makes the home team the overwhelming favourite)
2. HO/AO: 0.2249 to 0.4902
   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)
3. HO/AO: 0.4903 to 0.7730
   It is not really clear in which direction the game will develop (no overwhelming favourite)
4. HO/AO: 0.7731 to 1.6922
   The chance of a draw is quite high as both teams are perceived to be of equal strength (no overwhelming favourite)
5. HO/AO: over 1.6923
   The home team is weaker than the away team; it could be an away win (the perceived favourite is the away team)

Why are ‘Zero’ odds important?

After the setting and publishing of opening odds for sale, the price of a bet is then influenced by:

• The popularity for that bet amongst punters (demand)
• A balancing act of monies received between the outcomes carried out by the bookmaker via price fluctuations to create its margin/profit (supply)

The price fluctuations (changes in the odds) from the opening of the market right up until the end of the event are therefore driven by both demand (punters) and supply (bookmakers), and contrary to popular belief, not dictated solely by the bookmaker.

If the zero odds of an event are known it is possible to identify temporary or lasting pricing ‘errors’, large and small, caused by these fluctuations in demand and supply. These errors can then be used to ensure that every bet placed contains ‘value’, the essential element in making long-term profits from gambling.

As a reminder:

• Prices offered above zero odds represent value back bet opportunities
• Prices offered below zero odds represent value lay bet opportunities

Zero odds are those at which, if every bet were placed at this price, the overall outcome of any number of bets would be a ‘zero’ sum game.

Finding ‘value’ is therefore about determining the implied (actual estimated) probability of an event (based on historical results), and obtaining odds representing a lower probability (i.e. higher odds) if backing, or a higher probability (lower odds) if laying.

Of course, the higher the odds obtained above zero odds are, the more profitable your long-term back bet portfolio should be and the lower the odds obtained below zero odds are, the more profitable your long-term lay bet portfolio should be.

Manchester United vs. Liverpool

The HO/AO quotient was 1.26, suggesting that public perception of the event was that the draw was probably the most likely outcome.

In the images above, the Over 2.5 Goals bet type is highlighted.

HO/AO 1.26 sits in the fourth cluster of United’s cluster table, and the percentage chance of Over 2.5 Goals for their home games within this cluster was 37.60%

HO/AO 1.26 sits in the third cluster of Liverpool’s cluster table, and the percentage chance of Over 2.5 Goals for their away games within this cluster was 62.40%

Calculate the average of these percentages: 37.60% + 62.40% = 100.00% / 2 = 50.00%

Calculate the zero odds: 1 / 50.00% = 2.00

It just so happens that the highest Over 2.5 Goals odds on offer for this event were also 2.00, providing no value in backing or laying.

The result was 2-1 to United, meaning that public perception of the event most likely being a draw was proved to be wrong. Public perception of likely outcomes and the eventual reality are very difficult to reconcile, which is why odds movements should never be relied upon as a guide to potential outcomes.

You should also note that the most popular games to bet on are usually those most intensely analysed (United vs. Liverpool is just about the most high-profile club game in the world). Because of this, the highest pre-match odds available for many of the different bet types are usually very accurate compared to the statistical likelihoods. In this case, we calculated 2.00 as the zero odds and indeed, 2.00 was the highest pre-match price available.

Once again, we reiterate just how accurate the Cluster Tables are in calculating probabilities.

Try the Power of the Cluster Tables for Only £2

The Cluster Tables are an extremely powerful tool for checking market odds against ‘true’ odds in order to select bets containing ‘value’ for long-term profit.

The tables can also be utilised for predicting odds movements before kick-off and much, much more, but we will write about these benefits in other articles.

A German reader once commented that he couldn’t believe we were selling the Cluster Tables because to him, “these five season tables are something like a ‘money printing machine’!“.

If you wish to play around with the table used in our example above, you can purchase the EPL Cluster Table for the 2017-18 season here for just £2:

This table comes with the added bonus of a £5 discount voucher applicable to the Fundamentals of Sports Betting Course – Over/Under Goals.

You can use this table for backtesting the 2017-18 EPL season. Randomly select any weekend and carry out the calculations as demonstrated in this article. Try experimenting a little and perhaps compile different portfolios such as:

• Choosing only Under 3.5 Goal bets
• Choosing bets which have at least a 60% probability to win
• Choosing bets with a strong home favourite only
• … and so on! Use your wits and imagination to find a system that actually works for you!

Once you understand how the Cluster Tables work and have found a system to focus on, picking bets for a weekend becomes truly very easy!

Please note that after the 2017-18 EPL season finishes this sample table will expire and should not be relied upon for betting purposes after that. Sorry, you will have to buy the 2018-19 replacement table. However, the 2017-18 version will certainly give you a good idea of the table’s full functionality.

If you have any further questions on how to use the cluster tables, please use the comments section below.

Thanks for reading and good luck with your value betting!