Case Studies – Soccerwidow

Over Under Betting Experiment July 2020 ~ Final Report & Further Findings

Soccerwidow — Fri, 13 Nov 2020 13:03:43 +0000

From 1st July until 2nd August 2020, we carried out a public experiment to showcase Over/Under ‘X’ goals picks based on the teachings of our Over/Under Odds Calculation coursebook.

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:

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

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

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

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

79.2 % plus 82.6% = 161.8%

161.8% divided by 2 = 80.9%

This percentage was then converted into the expected Zero odds:

1 divided by 80.9% = 1.24

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:

Expected Profitability between -40% and 95%

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:

Expected Yield between -15% and 30%

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!

Results of our 2020 Summer League HDAFU Tables’ Real Time Test

Right Winger — Thu, 10 Sep 2020 09:41:22 +0000

From July 1st to August 26th we ran a Summer League HDAFU Tables Live Picks Trial to see if and how coronavirus lockdowns have affected match outcomes and betting results.

Towards the end of this summary article, we also discuss the statistical evidence of the lockdown effects as matches continue to be played without fans.

The Systems Employed

For the purposes of this test nine systems were selected across four leagues in close geographical proximity to one another.

Please note that it is normally our mandate when selecting systems for real-play portfolios only to pick the best single system in a league (e.g. whole-of-season or first half-season or second half-season), or best two systems (first half-season and second half-season).

However, for the purposes of this live simulation, we trialled a number of different first half-season systems from the four leagues. This was done because the choice of leagues in which to generate bets for this experiment was extremely limited at the time the simulation began.

Immediately below you will find screenshots taken from the actual 2020 Summer League HDAFU Tables showing each system in detail. The systems were determined after scrutinising the profit/loss curves shown in the dedicated ‘Inflection Points by HO/AO quotient’ tab in these tables. (Home Odds divided by Away Odds).

(Clicking on all of the images below enlarges them in a new tab):

FINLAND Veikkausliiga

HDAFU Table Finland Veikkausliiga: 2x Draw Systems

2x Draw Systems: (a) at and between HO/AO quotients of 0.405 & 0.543 (corresponding draw odds 3.39 to 3.60); (b) at and above the HO/AO quotient of 1.372 (corresponding draw odds 3.37 to 6.81).

Note that although the draw odds of these two systems overlap, it is important to understand that they apply to two separate tranches of games (i.e. the HO/AO quotients do not overlap). System (a) represents smaller quotients indicative of games involving short-priced home favourites. System (b) applies to quotients above 1 indicating games where the away team is the shorter-priced favourite. All nine of the systems in this simulation are non-overlapping to provide stand-alone comparisons.

ICELAND Úrvalsdeild

HDAFU Table Screenshots – Iceland Úrvalsdeild: 1x Draw & 1x Favourite System

1x Draw System; 1x Favourite System: Draw at and between HO/AO quotients of 0.152 & 0.33 (corresponding draw odds 4.11 to 5.26); Favourite at and between HO/AO quotients of 0.418 & 1.63 (corresponding favourite odds 1.85 to 2.15).

NORWAY Eliteserien

HDAFU Table Screenshots – Norway Eliteserien: 1x Draw, 1x Favourite System & 1x Home Win System

1x Draw System; 1x Favourite System; 1x Home Win System: Draw at and between HO/AO quotients of 0.238 & 0.374 (corresponding draw odds 3.94 to 4.40); Favourite at and between HO/AO quotients of 0.445 & 0.67 (corresponding favourite odds 1.90 to 2.22) ; Home Win at and between HO/AO quotients of 1.117 & 2.042 (corresponding home odds 2.87 to 3.96).

SWEDEN Allsvenskan

HDAFU Table Screenshots – Sweden Allsvenskan: 1x Draw & 1x Favourite System

1x Draw System; 1x Favourite System: Draw at and between HO/AO quotients of 0.383 & 0.904 (corresponding draw odds 3.45 to 3.76); Favourite at and between HO/AO quotients of 0.0 & 0.343 (corresponding favourite odds up to and including 1.72).

Results of the 2020 ‘Live’ HDAFU Tables Trial

Profit Loss Curve

The graph below shows the final profit/loss curve after 44 rounds of betting (i.e. separate days) comprising a total of 129 bets:

HDAFU Tables – Summer Leagues 2020 Live Simulation: Bank Development

If you were following our live picks on a daily basis you will remember that on August the 3rd, after 26 betting rounds (91 bets), we decided to cut two systems from the draft and monitor instead the results from the favourite win systems in Iceland and Sweden – both were underperforming badly and stood little or no chance of recovering into profit taking into consideration the likely number of remaining expected bets.

The 129 picks therefore included bets in both of these systems up to and including the 2nd of August but no further.

System-by-System Analysis

HDAFU Tables – Live Simulation: 1X2 Portfolio Analysis

What do the results of this live picks simulation tell us?

In truth, not much, but it does prove that a profit can be made in a live environment from selecting systems based upon past-performance using the HDAFU Tables. As stated above we would never have chosen to play for real multiple first half-season systems in the same league – Always stick to the single best historical performer and construct portfolios based around these top performers in each of the leagues of your choice.

Although hindsight is a flexible tool, choosing just one system from each of the four leagues would have been relatively easy as the best four stood out markedly from the rest:

FINLAND: Draw system at and between HO/AO quotients of 0.405 & 0.543. The corresponding draw odds between 3.39 and 3.60 were a less risky bet than the wider odds range of the other draw system. In other words, the system we would have played contained games that were more homogeneous (consistent) with each other.
ICELAND: Draw system at and between HO/AO quotients of 0.152 & 0.33. The corresponding draw odds here were between 4.11 and 5.26. Again, it was considered to be a more consistent set of bets than the other system, which relied on choosing home or away favourites – literally two systems in one.
NORWAY: Favourite system at and between HO/AO quotients of 0.445 & 0.67, corresponding to odds between 1.90 and 2.22. We would have picked this over the home win system option purely because the favourites were all going to be the home team anyway (HO/AO quotient below 1): the home win system option was more risky and wholly represented by underdogs at home. The historical profits shown in the analysis for the favourite were also more recent than those posted by the third option, the draw system.
SWEDEN: Draw system at and between HO/AO quotients of 0.383 & 0.904 (corresponding draw odds 3.45 to 3.76). This was a far better set of historical data than betting on the favourite showed. Profits for the draw system were present in each of the previous five seasons and three times larger than those for the favourite system.

These four systems on their own (three winners and one loser) would have netted an overall profit of 556.66 units.

Coronavirus Effects on Home Performance

The four leagues chosen were all Nordic countries to provide some form of meaningful comparison.

(A) Two Leagues Resumed “as Normal”

Finland and Norway produced 1X2 results much in line with previous seasons but, in both leagues, the draw percentage was a little down and the away win was slightly up. But nothing statistically significant enough to say that playing with no crowds has affected the trends:

Finland & Norway: First Half-Season 1X2 Results Comparison

(B) Two Leagues with Notable Discrepancies

However, in Iceland and Sweden, the picture was different:

Iceland & Sweden: First Half-Season 1X2 Results Comparison

In both leagues, it is quite clear to see that home wins have dropped significantly, with draws and away wins increasing in frequency as a result.

Although it is too early to draw conclusions after just half a season, this is a situation that suggests monitoring and will perhaps be the subject of a follow-up article if trends continue in this manner.

Get the Analysis Spreadsheet!

If you would like the accompanying Excel spreadsheet (541Kb) for this article please click on the button below. It is priced at £9.99 and includes a host of useful formulas including the Excel logarithm formula to calculate the expected Longest Losing Streak once the expected hit-rate is known. Other analyses included:

Full, country-by-country breakdown of each system and its performance
First half-season Home, Draw and Away result comparison with previous five seasons
Charts, graphs and tables representing the final results
Template for identifying the bets, which can be tailored to your own requirements
The usual array of Excel formulas for your own system selection purposes including monitoring
…and much, much more!

HDAFU Table Discount Offer

In addition, you will receive a lifetime discount code with your product delivery note providing £35.00 off of any purchase of three or four HDAFU Tables (i.e. bridging the gap to the previous minimum five-table discount code). If you want a gentle introduction to the world of HDAFU Tables, or wish to target your leagues in smaller bundles, then this offer is for you!

Click here to get your Analysis Spreadsheet:

>>> 2020 HDAFU coronavirus test excel spreadsheet <<<

(Then click on the green Checkout button, fill in your name/email address details, submit your order and pay securely with PayPal or a credit card. A download link will then be sent to your email address. Check your junk mail if it doesn’t arrive in your inbox immediately).

Important Guidance on Picking Systems from a League

Despite choosing multiple systems from the four leagues for the purposes of the live test, our guidance for compiling a 1X2 portfolio from the HDAFU Tables remains unchanged: when selecting (i) a first half-season system and/or a second half-season system or (ii) a whole-of-season system, choose only one option from each league.

Choosing both a first half and second half-season system in the same league is completely viable (i.e. two systems in a single league), but don’t mix and match by implementing a whole-of-season system plus a first and/or second half system or multiples of any of these types in the same league:

HDAFU Tables: Viable Combinations of Chosen Systems in Any One League – One Tick = One System

2020 Summer League HDAFU Tables – Coronavirus Trial – ‘Live’ Picks

Right Winger — Wed, 05 Aug 2020 04:02:31 +0000

This article will be updated at least once a day until the first halves of the league seasons highlighted finish. Clear your browser cache and then press the F5 key to refresh.

Live Simulation: Nine HDAFU Systems in Four Leagues

Many of you are asking what effects coronavirus is having on football betting. Whilst we envisage that the disruptions caused will not have any noticeable change on long-term statistical trends, it is impossible to say with any certainty what will happen in these unprecedented times.

Therefore, we have decided to carry out a very public experiment using 1X2 systems taken from our most recent set of HDAFU Tables – those for the 2020 Summer Leagues, limited to four leagues that will hopefully play out the entirety of their seasons, albeit a little late in starting (or restarting).

We will publish the picks a day or so in advance and keep you abreast of progress. We have decided to suspend our own betting for the time being, so this exercise will be purely a simulation with a ratcheted staking plan in operation and is for first-half-of-season systems only.

The four leagues are:

Finland Veikkausliiga (2x draw systems)

Iceland Úrvalsdeild (AKA Pepsideild) (1x draw system; 1x favourite win system)

Norway Eliteserien (1x draw system; 1x home win system; 1x favourite win system)

Sweden Allsvenskan (1x draw system; 1x favourite system)

Portfolio Expectations

Portfolio Probability = Hit Rate 47.07%

Mathematical Advantage (Expected Yield) = 27.61%

This collection of nine systems, therefore, represents a MEDIUM-RISK portfolio

Two Different Approaches:

1X2 HDAFU Profit/Loss Curve Betting & Over/Under ‘X’ Goals Cluster Table Betting

In tandem with this HDAFU test, we are doing the same with our Cluster Tables in four Winter Leagues that are due to finish at the beginning of August. Picks will be published daily and will appear on our German-language sister site, Fussballwitwe.de. Don’t understand German? then try Google auto-translation into English here.

In short, both are value betting systems, but with different methods.

1X2 bets are system bets: you choose a system and place the bet whenever the selected criterion fits – in this case, the quotient arrived at by dividing the current home odds by the corresponding away odds.

Over/Under bets are based on calculating the betting odds for each game (according to the historical statistics over the five preceding seasons) and placing the bet where there is value. Here, we will consciously choose the bets according to probabilities as well as their profitability/yield potential. (Over/Under course readers will know what I am talking about here!)

The 1X2 bets are much easier to play; you make the selection criteria once (i.e. formulate the system at the beginning), using the historical information and profit curves contained in our HDAFU Tables. These also provide a risk assessment and simulation of how future betting expectations might go. Once the parameters of each system are known, it only takes a short time to find the appropriate bets for each game round. However, good value usually lies only in low probability bets (longer odds such as the draw, or underdog win), and, therefore, it is a better idea to mix these selections with higher probability bets (favourite wins), to avoid long losing streaks.

With Over/Under betting you can decide which probabilities you want to play depending on your personal risk attitude. Higher probabilities = lower risk of losing the bet = easier for the nerves. The challenge here, however, is that in order to gain this extra level of precision you have to calculate each individual game individually.

Slideshow of Value Betting Picks

The picks for the respective day will appear here around noon/13.00 GMT +1, including the results from the previous day.

Please click on the arrows at either end to scroll through and view the entire history of the picks.

Hover over the table with your cursor for pop-up day-to-day notes.

Note: Following the interim report detailed in our report after 91 picks, two systems have been dropped (Sweden favourite win; Iceland favourite win) and are shown from Round 27 (August 3rd) for monitoring purposes only.

Want this slideshow larger? Either hold down the ctrl key on your keyboard and press the + (plus) key until you reach a comfortable size. (Afterwards, hold ctrl and press the − (minus) key to reduce your view). Alternatively, with a scroll wheel mouse, hold down the ctrl key on your keyboard and push the scroll wheel forwards. Hold ctrl and reverse the direction of the scroll wheel to reduce the view. The URL bar at the top of your screen will guide you in returning to 100% normal size.

Staking Plan

The starting bank is 3,000 units and the basis for calculating the stakes is as follows:

Odds below 1.10: 5% of highest bank achieved*
Odds between 1.10 – 1.16: 4% of highest bank achieved*

Odds between 1.16 – 1.39: 3.8% of highest bank achieved*

Odds between 1.40 – 2.25: 2.5% of highest bank achieved*

Odds between 2.26 – 7.50: 1.5% of highest bank achieved*

Odds over 7.50: 0.5% of highest bank achieved*

This is the same staking plan used by the counterpart Cluster Tables experiment, but you should note that the chosen HDAFU systems are likely to utilise only the three lines highlighted as all odds satisfying our criteria will be 1.30 or over and will never exceed 7.50.

Not only will the stakes be scaled according to risk, but a ratchet system* will also be employed. Stakes will begin based on the starting bank of 3,000 units and will continue to be so until there is an increase in the bank after a day’s games. Thereafter, it will be calculated based on the highest end-of-day bank total achieved (even if the actual bank drops below 3,000 units in future). Only if the starting bank reduces by 40% will the stakes be scaled back. (i.e. A stop-loss mechanism is activated if the bank falls below 1,800 units).

N.B. All stake calculations will be rounded-up to the nearest whole unit (e.g. 77.19 becomes 78).

Duration of Experiment

As mentioned, we will be running the HDAFU 1X2 systems calibrated for the first half of the season only in each of the selected four leagues, starting on the 1st of July, 2020.

Picks will appear in our slideshow until the midway point of each league is reached.

At this stage, with the coronavirus interruptions, no certain dates have yet been confirmed as to when each league will reach its natural break (if indeed there is one), but we will advise in advance of each system drawing to its conclusion.

Final Whistle

We have no idea how things like the lack of a crowd, the apparent nullification of home advantage, players being able to hear instructions shouted between themselves and from the sidelines, referees no longer being mobbed over their decisions by over-enthusiastic and supporter-conscious players, lack of fitness, or psychological factors, will affect the game we all know and love.

It is safe to say that we are as much in the dark as you are concerning the future of football and sports betting.

The unknowns are too many to take great risks and, as stated previously, we are not chancing our money on these picks – the systems we are using together form a ‘live’ experiment based on our previously successful HDAFU Tables and Cluster Tables.

If you wish to play these picks yourself, then please do so with money you can afford to lose and try to stick to our stake recommendations.

Let’s hope for something to cheer us all up during these very strange times!

Interim Report after 91 Picks & 26 Betting Rounds ~ HDAFU Tables

Soccerwidow — Tue, 04 Aug 2020 12:11:56 +0000

2020 Summer League HDAFU Tables: Coronavirus Trial – ‘Live’ Picks

Since the 1st July, we have been running a live picks experiment using systems selected from our HDAFU Tables to see if Covid-19 is affecting results one way or another.

As of Sunday, 2nd August 2020, a total of 91 picks had been published in advance of kick-off times in four different ‘summer’ leagues.

The HDAFU systems chosen are all 1st half-season systems only, with the experiment due to finish on the 26th of August when the latest scheduled of these, the Finnish Veikkausliiga, reaches its midway point.

The four leagues are:

Finland Veikkausliiga (2x draw systems)
Iceland Úrvalsdeild ~ AKA Pepsideild (1x draw system; 1x favourite win system)
Norway Eliteserien (1x draw system; 1x home win system; 1x favourite win system)
Sweden Allsvenskan (1x draw system; 1x favourite system)

Expectations for the portfolio as a whole were as follows:-

Portfolio Probability = Hit Rate 47.07%
Mathematical Advantage (Expected Yield) = 27.61%
Total number of bets expected until the end of the 1st halves in each season = 196

So far, from the expected number of 196 bets, 91 have been played (46.4%), with 36 winning (hit rate: 39.6%). The hit rate is minus 6.9% of expectations. Later in this article, we will see if we can identify a culprit (or culprits) in our chosen systems.

Betting Bank Development

The Portfolio Probability or average expected hit rate was 47.07%. This probability is very close to throwing a dice (i.e. 50/50) and, as with a dice roll, an element of luck is required to see your choice of heads or tails coming up more often than not.

Unfortunately, luck was not on our side as the experiment developed…

It did have a promising start and grew from 3,000.00 units (starting bank) to 3,213.54 units in the first four days before diving into a long run of unprofitable rounds. This illustrated in the following graph showing the running total:

After day four, the portfolio saw a losing streak of 11 betting rounds (rounds 5 to 15: July 5th to July 18th, 2020) taking the bank down to its lowest point of 2,177.79 units (67.8% of the highest bank), almost activating our Stop-Loss mechanism (set at 60% of starting bank), where stakes would have been reduced on future bets.

The ratchet system we used kept the stakes constant using the high-water mark of 3,213.65 units (i.e. highest point the bank reached) as the gauge against which to calculate our exposure on each bet. Read here for more explanations of what a ratcheted staking plan actually is: Sound Staking: Flat Stakes & Ratcheting

The ratcheting method of staking helped the bank to recover faster as soon as results once again began to go our way – it is now, after a truly bumpy ride, at a new high of 3,282.73.

Lesson #1: For those who use the HDAFU Tables for betting, sit tight and give yourself a chance! Do not give up too quickly! Continue religiously choosing the picks according to the criteria you set when choosing your systems. More often than not, your bank will eventually recover.

Always remember: You are playing statistics that behave randomly; your luck may come in big chunks of good or bad, or may be more regularly distributed.

System Performance Review

Of course, with the deep trough at the start of our portfolio, we went digging to see what may have been the root cause.

You can see in the graph below the nine different systems and the number of bets played as at August 2nd, 2020: e.g. Finland Draw (II) had 11 bets played at this date. The graph also compares the expected hit rates and the observed hit rates of each system.

Most of the systems have, after 26 betting rounds, achieved their expected hit rates.

The systems that have developed hit rates far below expectations are:-

Sweden Allsvenskan: Favourite system
Iceland Úrvalsdeild: Draw system & Favourite system

Below is a graph showing the Profit/Loss made by all nine systems after 26 match days:

As you can see from looking just at the observed hit rates, the ‘Favourite Win’ systems of Sweden and Iceland are producing deficits.

The Draw system in Iceland isn’t doing as bad and may even recover. But, we must be careful as the originally expected hit rate was low at 37.7% and from 11 expected bets five of these have already been played.

The main culprits that seem to be affecting the portfolio as a whole are obviously the ‘Favourite Win’ systems in Sweden and Iceland.

Nevertheless, it is too early to conclude that Covid-19 is affecting these systems, although it would be easy to associate the lack of crowds and thus, reduced home advantage, with the failure of these home win dominated systems. After all, the Norway ‘Favourite Win’ system is performing quite well.

To shuffle the pack, we will remove the two losing systems from our running total calculations and separate them from the other picks so we can monitor their progress in isolation. If you are following our Sweden and Iceland favourites with real money then perhaps hold fire for the time being and stick to the other seven systems.

Lesson #2: For those who use the HDAFU Tables for betting, review your systems once per month (or at least after every 100 bets)!

If you have obvious duds bringing the house down, don’t be afraid to remove them from your betting portfolio.

Of course, you can, if you wish, try to replace the losing systems with other systems. It doesn’t matter if you start and stop a system during the middle of any season: Starting or Pausing Your 1X2 Portfolio in the Middle of a Season

2019 Summer League HDAFU Tables – Campaign Report: £8k+ in 288 Days!

Right Winger — Sat, 13 Jun 2020 13:09:00 +0000

The best test of any theory is always how it works in practice:

Below is the full account of our FT 1X2 results campaign for the 2019 ‘Summer Leagues’ based around Soccerwidow’s HDAFU ~ Home – Draw – Away – Favourite – Underdog Tables.

419 bets in 288 days = min. £8,433 profit from £100 unit flat stakes
The final profit curve for the 2019 Summer League Campaign (comprising all 419 bets) is shown below:

2019 Summer League Campaign Profit Curve

The systems employed in each league were all picked based on observations of the profit curves produced from the accumulated data (match odds and results) of the previous five complete seasons.

Read on to find out exactly how and why we picked the systems we did…

Campaign Report 2019 Summer Leagues

If you would like the accompaniment for this article please click on the button below to get your FREE 2019 Summer League Campaign spreadsheet, which pares down each system to individual match detail.

>>> 2019 summer league campaign <<<

How the Systems Were Chosen

Our long-held tried and tested method is to use the HO/AO quotient (i.e. the figure derived from dividing the home odds by the away odds) as the most reliable benchmark for categorising an individual match and allow comparisons or groupings with others.

Employing the quotients of all matches in a league over the previous five complete seasons provides a significant data set with which to carry out in-depth analyses.

Then, by producing graphs for each of the Home-Draw-Away-Favourite-Underdog results, it is easy to visualise betting results and trends.

Constructing a balanced portfolio of ‘systems’ in our chosen leagues is then just a matter of identifying and choosing historically lucrative segments of the profit curves upon which to target our bets in the forthcoming season.

For the purposes of diversification, we chose two systems for each league: one representing the 1st half of the season and the other for the 2nd half of the season.

In all of the leagues summarised below, you will see images taken directly from the 2019 HDAFU tables. (Containing the data from the five seasons prior: from 2014 to 2018)

When deciding upon which segment of the profit/loss curves to choose as our systems in each league, we compared the relevant 1st and 2nd half-season graphs (i.e. representing half of the data set in each case) with that representing the Whole Season (the whole of the data set).

If the segment of interest in, say, the 1st half-season graph was mirrored to some degree in the Whole Season analysis, then it was considered more likely to be influential on the whole-of-season picture. Ditto with the 2nd half-season.

In almost every case, you will see visual similarities between the chosen half-season system and the whole-of-season graph, whereas the graph belonging to the other half of the season is usually very different.

However, in a few examples, the chosen segment of the graph is mirrored in all three graphs (1st, 2nd and Whole-of-season), making it even more likely that the chosen system for the future had a significant influence on the historical picture across the previous five seasons.

Judgment of Risk

You will find a Risk Table (Image 8) and explanatory commentary in our dedicated risk judgment article.

League-by-League Breakdown

In alphabetical order…

League 01) Brazil: Série A

System 1 – 1st Half: Away Win
High-risk

Here, the visually similar peaks in the profit curves between the 1st half and whole-of-season graphs were indicative of a potentially lucrative system:

(Clicking on all of the images below enlarges them in a new tab):

Brazil Série A: 1st Half & Whole-Season Away Win Combined Graphs

The beginning and end of the peak in the 1st half-season graph corresponded with HO/AO quotients between 0.104 and 0.167. These points have also been marked in the whole-of-season graph alongside to show how similar these two graphs look. This is a high-risk system with odds between around 9.00 and 16.50 (top row label).

By comparison, the 2nd half-season graph looks very different:

Brazil Série A: 2nd Half-Season Away Win Graph

As you can see, there is a similar peak towards the left-hand side of the 2nd half graph, but it corresponds to odds of around 6.00 to 8.00.

Between odds of 6.00 and 8.00 in the whole-of-season graph above is a less definite picture containing peaks and troughs of ‘statistical noise’.

To conclude, the 1st half-season system of choosing away wins between HO/AO quotients of 0.104 and 0.167 seems to be more influential on the overall picture in this league.

RESULT

In our campaign from 28/04 to 15/09/2019, there was a total of 17 bets satisfying the HO/AO criteria. 15 of these bets lost and only two won. The odds of the bets placed ranged from 8.45 to 12.39.

However, the two bets that won were sufficiently high priced to earn this system a profit.

Profit: £366.00

Running Total: £366.00

System 2 – 2nd Half: Home Win
High-risk

Brazil Série A: 2nd Half & Whole-Season Home Win Combined Graphs

Hopefully, these graphs are now self-explanatory. Once again, the section of the profit curve peaking in the 2nd half-season graph in Brazil is mirrored in the whole-of-season graph alongside it.

And again, the 1st half-season graph looks very different:

Brazil Série A: 1st Half-Season Home Win Graph

Our chosen system incorporates HO/AO quotients between 1.157 and 1,607, which correspond to odds of around 2.95 to 3.50.

In the 1st half graph, this area does show an incline but it represents a gain of fewer than 1,000 units.

By comparison, the 2nd half-season system employed spanned a gain of 2,500 units in the previous five seasons. (Between 2,930 and 5,412 units)

The equivalent whole-of-season graph shows a gain of 3,400 units. (Between 3,979 and 7,394 units)

RESULT

In our campaign from 22/09 to 01/12/2019, there was a total of 13 bets satisfying the HO/AO criteria. Seven of these bets lost and six won. The odds of the bets placed ranged from 3.01 to 3.45.

Profit: £588.00

Running Total: £954.00

League 02) China: Super League

System 3 – 1st Half: Away Win
Low-risk

China Super League: 1st Half & Whole-Season Away Win Combined Graphs

There weren’t many similarities between the 1st half and whole-of-season graphs in China and only the very tail of the profit curve seemed to be an acceptable match.

But, the away win selections highlighted in the chosen quotient of 3.332 to 15.175 were all odds-on favourites between around 1.20 and 1.80.

The 2nd half-season graph did not show anything as pronounced in this area.

China Super League: 2nd Half-Season Away Win Graph

As you can see from the labels shown on the combined graphs above the five-season profit figure in this HO/AO zone totalled only 500-600 units.

We knew from the outset that we wouldn’t be playing for massive gains with this 1st half system.

But, when there is little else to find in the analysis, choosing a low-risk system or a home-win or a favourite-based system are the prudent choices.

RESULT

In our campaign from 30/03 to 30/06/2019, there was a total of six bets satisfying the HO/AO criteria. Two of these bets lost and four won. The odds of the bets placed ranged from 1.37 to 1.60.

Profit: £21.00

Running Total: £975.00

System 4 – 2nd Half: Draw
High-risk

China Super League: 2nd Half & Whole-Season Draw Combined Graphs

Here, the whole-of-season graph showed a massive historical profit exceeding 9,000 units over five seasons between the chosen quotients of 0.455 and 0.988. (the closest mark of 0.451 is shown on this graph owing to the shortcomings of Excel graphs)

Looking at the 1st half-season graph it was evident that the draw in the second half of the Chinese season showed the stronger trendline.

China Super League: 1st Half-Season Draw Graph

Odds for the draw between the chosen quotients were roughly 3.30 to 4.30.

The upper quotient wasn’t extended further: it could have been taken to the next and final peak of around 1.07.

However, it was decided that the final peak represented diminishing returns for a higher risk.

On the 2nd half-season graph you can see before the last peak after 0.988 was a trough of roughly equal size. Therefore, the chances of increasing the profit beyond the 0.988 parameters were roughly 50/50 (i.e. ‘heads’ and the bottom line increases; ‘tails’ and it reduces – this snakes-and-ladders game wasn’t worth playing).

RESULT

In our campaign from 06/07 to 27/11/2019, there was a total of 26 bets satisfying the HO/AO criteria. 18 of these bets lost and eight won. The odds of the bets placed ranged from 3.32 to 4.29.

Profit: £355.00

Running Total: £1,330.00

League 03) Finland: Veikkausliiga

System 5 – 1st Half: Draw
High-risk

Finland Veikkausliiga: 1st Half & Whole-Season Draw Combined Graphs

The nice peak showing on the 1st half-season graph between quotients of 0.443 and 0.522 was mirrored well in the whole-of-season graph.

It may seem paradoxical that this part of the curve appears below the profit line in the graph (i.e. in the ‘red’) but as you can see the curve begins around -5,000 units and climbs to around -2,000 units, meaning the results in this sector are combining to create a profit of around +3,000 units (i.e. an improvement from -5,000 to -2,000).

The 2nd half-season graph was again very different.

Finland Veikkausliiga: 2nd Half-Season Draw Graph

A second possibility was the peak on the right-hand side of both curves in the double-image above between the quotients of 2.802 and 8.911.

But, as you can see from the odds label at the top of these graphs, a system in this zone would involve backing draws at considerably longer odds (i.e. a far higher risk system), and with only 2,000 units profit made in the previous five seasons.

RESULT

In our campaign from 03/04 to 19/06/2019, there was a total of five bets satisfying the HO/AO criteria. Four of these bets lost and only one won. The odds of the bets placed ranged from 3.33 to 3.63.

Loss: -£137.00

Running Total: £1,193.00

System 6 – 2nd Half: Favourite
High-risk

Finland Veikkausliiga: 2nd Half & Whole-Season Favourite Win Combined Graphs

There was a lot of statistical noise in the 2nd half-season graph and only one peak of any interest.

The 1st half-season graph showed once again what huge differences exist between the 1st and 2nd halves of league seasons as far as match results and odds are concerned.

Finland Veikkausliiga: 1st Half-Season Favourite Win Graph

The five-season profit was around 1,700 units between the chosen quotients of 0.694 and 0.790.

Dividing this by five to give a seasonal expectation meant that nothing spectacular could be expected from this system.

However, the favourites alluded to would all be home favourites between odds of 2.30 and 2.45 meaning that a profit would be secured even if there were an equal number of winning and losing bets.

RESULT

In our campaign from 27/07 to 19/10/2019, there was a total of four bets satisfying the HO/AO criteria. Two of these bets lost and two won. The odds of the bets placed ranged from 2.33 to 2.43.

Profit: £68.00

Running Total: £1,261.00

League 04) Iceland: Úrvalsdeild

System 7 – 1st Half: Home Win
Medium-risk

Iceland Úrvalsdeild: 1st Half & Whole-Season Home Win Combined Graphs

Another couple of similar-looking graphs, both strikingly different from the 2nd half-season results.

Iceland Úrvalsdeild: 2nd Half-Season Home Win Graph

The chosen quotients of 0.527 and 0.929 (0.515 being the nearest point available on the whole-of-season graph) targeted a tranche of home favourites.

The home odds for these bets were likely to be between around 2.00 and 2.60.

Dividing the five-season profit figure of the proposed system produced an expectation of just 400 units profit per season.

RESULT

In our campaign from 27/04 to 06/07/2019, there was a total of 18 bets satisfying the HO/AO criteria. Seven of these bets lost and 11 won. The odds of the bets placed ranged from 2.09 to 2.56.

Profit: £668.00

Running Total: £1,929.00

System 8 – 2nd Half: Draw
High-risk

Iceland Úrvalsdeild: 2nd Half & Whole-Season Draw Combined Graphs

The selected portion of the graph is around 40% the length of the whole, so we can expect to bet on around 40% of matches (25-26 games) during the second half of the season.

Once again, the 1st half-season graph bears little resemblance.

Iceland Úrvalsdeild: 1st Half-Season Draw Graph

The quotients between 1.108 and 4.304 inclusive are aimed at draws carrying odds between 3.35 and 4.75.

In all cases, the away team will be the favourite to win the match.

Expectations are higher than the 1st half-season home win system (here, around 700 units per season on average) but, with higher rewards on offer, the risks are also greater.

RESULT

In our campaign from 13/07 to 22/09/2019, there was a total of 20 bets satisfying the HO/AO criteria. 14 of these bets lost and six won. The odds of the bets placed ranged from 3.44 to 4.21.

Profit: £188.00

Running Total: £2,117.00

League 05) Ireland: Premier League

System 9 – 1st Half: Home Win
High-risk

Ireland Premier League: 1st Half & Whole-Season Home Win Combined Graphs

Another two similar-looking graphs – note a similar rising curve at the same points in the 2nd half-season picture, which adds promise to this system.

Ireland Premier League: 2nd Half-Season Home Win Graph

Here, we are looking at quotients spanning 1.448 and 2.584.

This places the home win bets in a zone of odds between around 3.40 and 5.00: In other words, every bet we make will be on a home underdog to win.

The rewards are not great (around 500 units per season on average), but there is little else to aim in the 1st half of the season in this league.

It may be high-risk, but the consolation is that it is also a home-win-based system.

RESULT

In our campaign from 22/02 to 18/05/2019, there was a total of 15 bets satisfying the HO/AO criteria. 11 of these bets lost and four won. The odds of the bets placed ranged from 3.42 to 4.81.

Profit: £158.00

Running Total: £2,275.00

System 10 – 2nd Half: Away Win
High-risk

Ireland Premier League: 2nd Half & Whole-Season Away Win Combined Graphs

The curve beginning near to 0.204 and peaking around 0.233 looks more appealing than the one we chose, but remember that the more vertical the peak, the fewer bets will be involved. It can also be the sign of an anomaly created by one or two out-of-kilter results. Hence why gentler upward peaks from left to right are usually a far better bet.

The 1st half-season picture contains a similar shallow rise towards the right-hand side of the profit curve but, like the other two graphs, this is in a zone where odds-on favourites are winning very small increments to the bottom line (amounts which are barely worth the work involved).

Ireland Premier League: 1st Half-Season Away Win Graph

The chosen quotients of 0.398 and 0.738 contain a zone of away odds between around 3.15 and 4.75.

All of our bets will, therefore, be backing away underdogs to win.

Remembering the more vertical spike from earlier, you will see the odds there are far higher (between around 6.80 and 7.50). At these odds, it would only take one or two anomalous results to produce a misleading peak of this nature.

RESULT

In our campaign from 31/05 to 22/10/2019, there was a total of 12 bets satisfying the HO/AO criteria. Eight of these bets lost and four won. The odds of the bets placed ranged from 3.25 to 4.54.

Profit: £434.00

Running Total: £2,709.00

League 06) Japan: J-League 1

System 11 – 1st Half: Away Win
Medium-risk

Japan J-League 1: 1st Half & Whole-Season Away Win Combined Graphs

The five-season 1st half picture suggests an average of 1,000 units on offer per season for the chosen system of quotients between 0.695 and 1.015.

The 2nd half-season image suggests a reversal of fortunes between these marks.

Japan J-League 1: 2nd Half-Season Away Win Graph

In terms of odds, most of the bets we are expecting to place will be away underdogs.

However, those over 1.000 (before 1.015) will be slight favourites to win.

Looking at the 1st half-season image, the odds range of our bet placements is likely to be between around 2.60 and 3.40.

It is worth noting that Japan always seems to contain an away win system in one half of the season or the other.

RESULT

In our campaign from 23/02 to 30/06/2019, there was a total of 42 bets satisfying the HO/AO criteria. 25 of these bets lost and 17 won. The odds of the bets placed ranged from 2.60 to 3.41.

Profit: £705.00

Running Total: £3,414.00

System 12 – 2nd Half: Draw
High-risk

Japan J-League 1: 2nd Half & Whole-Season Draw Combined Graphs

The peak that was chosen as the 2nd half-season system also appears in the 1st half-season image.

Japan J-League 1: 1st Half-Season Draw Graph

Here, the quotients of 0.189 and 0.328 (0.331 the closest available point in the whole-of-season graph) indicate odds in the region of 3.75 to 5.50.

And, at this end of the scale, the matches included were likely to be between odds-on home favourites and longer-priced away underdogs.

But the fact the peak in the profit curve showed in both the 1st and 2nd half-season graphs was indicative of a good chance of repeated profits from this zone.

RESULT

In our campaign from 07/07 to 07/12/2019, there was a total of 12 bets satisfying the HO/AO criteria. Eight of these bets lost and four won. The odds of the bets placed ranged from 3.72 to 5.36.

Profit: £445.00

Running Total: £3,859.00

League 07) Norway: Eliteserien

System 13 – 1st Half: Home Win
Medium-risk

Norway Eliteserien: 1st Half & Whole-Season Home Win Combined Graphs

Despite a lot of statistical noise in the two graphs above, the zone chosen suggested a similar return from both: a five-season haul of around 2,000 units, or 400 on average per season.

The 2nd half-season graph suggested half the size of reward between the same quotient points.

Norway Eliteserien: 2nd Half-Season Home Win Graph

The quotients of 1.019 and 2.133 suggested home wins in the region of odds between 2.70 and just above 4.00.

In reality, every bet would be backing a home underdog, although not a great distance apart from the away favourite odds.

The profit curve for the 1st half-season contains many jagged peaks and troughs and, at these odds, it was expected that more bets would lose than would win.

RESULT

In our campaign from 31/03 to 15/07/2019, there was a total of 29 bets satisfying the HO/AO criteria. 18 of these bets lost and 11 won. The odds of the bets placed ranged from 2.74 to 4.11.

Profit: £778.00

Running Total: £4,637.00

System 14 – 2nd Half: Away Win
High-risk

Norway Eliteserien: 2nd Half & Whole-Season Away Win Combined Graphs

For the 2nd half-season system, a five-season historical profit of over 3,000 units attracted us to the first peak in the away win graph.

In the 1st half-season graph, there was a similar rise in the profit curve but the five-season haul was around 2,000 units.

Norway Eliteserien: 1st Half-Season Away Win Graph

As you can see from the 2nd half graph above, our away back bets were all likely to be underdogs – not rank outsiders, but the odds were in the range from around 4.60 to 5.80.

The part of the curve containing our system existed within a fairly narrow margin, so not many bets were expected.

But, at the odds highlighted in the graph, not many bets would need to win to turn a profit here.

RESULT

In our campaign from 04/08 to 01/12/2019, there was a total of 11 bets satisfying the HO/AO criteria. Eight of these bets lost and three won. The odds of the bets placed ranged from 4.55 to 5.56.

Profit: £348.00

Running Total: £4,985.00

League 08) South Korea: K League 1

System 15 – 1st Half: Draw
Medium-risk

South Korea K League 1: 1st Half & Whole-Season Draw Combined Graphs

Two very similar-looking graphs including a long and steadily increasing profit line.

The 2nd half-season graph is once again very different in appearance.

South Korea K League 1: 2nd Half-Season Draw Graph

Our chosen system spanned quotient points from 0.639 to 3.993 (4.435 the closest point of reference in the whole-of-season graph) and represents around two-thirds of all matches during the 1st half of the season.

The potential rewards are large – around 6,000 units profit from the five-season historical trend or 1,200 per season on average.

The season is 228 matches in length. Our expectations were around 75 bets (66% of 114).

RESULT

In our campaign from 01/03 to 07/07/2019, there was a total of 79 bets satisfying the HO/AO criteria. 54 of these bets lost and 25 won. The odds of the bets placed ranged from 2.87 to 4.50.

Profit: £684.00

Running Total: £5,669.00

System 16 – 2nd Half: Draw
Medium-risk

South Korea K League 1: 2nd Half & Whole-Season Draw Combined Graphs

An interesting league from the perspective that the draw looked profitable in both halves of the season but at different sets of quotients.

A reminder of the 1st half graph in isolation is shown below.

South Korea K League 1: 1st Half-Season Draw Graph

In the 2nd half-season, the quotients that were chosen, 0.701 to 0.887, were part of the 1st half-season system but the rest of that system was not historically profitable in the 2nd half.

Here, we are looking at draws between well-matched teams, with the home team being the slight favourite in each case.

The quotients represent a small margin of the entire graph, so we are not expecting anywhere near as many bets as in the 1st half-season.

RESULT

In our campaign from 20/07 to 30/11/2019, there was a total of 14 bets satisfying the HO/AO criteria. Nine of these bets lost and five won. The odds of the bets placed ranged from 3.03 to 3.52.

Profit: £265.00

Running Total: £5,934.00

League 09) Sweden: Allsvenskan

System 17 – 1st Half: Draw
Medium-risk

Sweden Allsvenskan: 1st Half & Whole-Season Draw Combined Graphs

The chosen section in the above graphs is, to some degree, mirrored at 0.405 to 0.631 in the 2nd half-season graph below.

Sweden Allsvenskan: 2nd Half-Season Draw Graph

Our chosen parameters of 0.380 to 0.895 encompass games involving closely-matched teams but where the home side is the favourite in each case.

The feel from the odds range is that every match selected ‘could go either way’, which is always prime territory for backing the draw.

The five-season historical profit is over 3,750 units or around 750 on average per season.

RESULT

In our campaign from 01/04 to 15/07/2019, there was a total of 36 bets satisfying the HO/AO criteria. 19 of these bets lost and 17 won. The odds of the bets placed ranged from 2.99 to 3.80.

Profit: £2,100.00

Running Total: £8,034.00

System 18 – 2nd Half: Underdog
High-risk

Sweden Allsvenskan: 2nd Half & Whole-Season Underdog Combined Graphs

There wasn’t much to get excited about in this analysis, especially so when looking at the fruitless descent of the 1st half-season underdog graph below.

Sweden Allsvenskan: 1st Half-Season Underdog Graph

In the chosen portion of our graphs, we are looking at games between fairly evenly-matched teams where the away side is the slight underdog in each case.

The ‘could go either way’ factor might be beneficial if enough underdogs prevail at the higher odds they ultimately must carry (i.e. more ‘value’) to offset the weight of money placed on the favourites.

The margins are once again narrow between 0.666 and 0.943, so not a huge number of games are expected to qualify for betting upon.

And underdog betting usually suits the second half of a season:

Teams towards the lower echelons of a league tend to fight harder for survival
Perhaps a team near the bottom has already faced most of its toughest matches in the first half of the programme and goes into the second half at misleadingly longer odds to win its games

RESULT

In our campaign from 20/07 to 28/10/2019, there was a total of 15 bets satisfying the HO/AO criteria. 10 of these bets lost and five won. The odds of the bets placed ranged from 2.74 to 3.45.

Profit: £68.00

Running Total: £8,102.00

League 10) U.S.A.: Major League Soccer

System 19 – 1st Half: Favourite
Medium-risk

U.S.A. Major League Soccer: 1st Half & Whole-Season Favourite Combined Graphs

A small window of opportunity, which was mirrored in the 2nd half-season graph below – the 1st half quotients are also marked on the 2nd half-season image to highlight the similarity.

U.S.A. Major League Soccer: 2nd Half Favourite Graph

The narrow margin between 0.875 and 0.960 indicates few bets will qualify to be placed.

But from the historical perspective, this small zone has accrued over 2,000 units of profit in the previous five seasons.

All teams backed will be slight home favourites in very evenly matched games.

Neither many bets nor huge profits were expected.

With several hundred games in this league during a season, and with the format regularly changing (i.e. increasing numbers of teams in recent seasons) we felt that choosing systems in the M.L.S. with fewer potential selections would be better for the overall health of our portfolio.

RESULT

In our campaign from 31/03 to 02/06/2019, there was a total of five bets satisfying the HO/AO criteria. Three of these bets lost and two won. The odds of the bets placed ranged from 2.55 to 2.66.

Profit: £14.00

Running Total: £8,116.00

System 20 – 2nd Half: Home Win
Medium-risk

U.S.A. Major League Soccer: 2nd Half & Whole-Season Home Win Combined Graphs

Here is another example of a system’s quotients also appearing to be profitable in the counterpart half of the league.

U.S.A. Major League Soccer: 1st Half Home Win Graph

The quotients that were chosen between 0.353 to 0.518 also form part of a rising part of the profit curve in the 1st half-season graph.

All of the bets expected will be home favourites at odds of 2.00 or below and above 1.60.

At these odds, the hit rate must remain high to make a profit knowing that approximately one-quarter of all games in the 2nd half-season will qualify for betting.

This is a medium-risk system with the potential for low gains. But, in keeping with our philosophy about concentrating upon more stable leagues, it also had the security of potentially low losses.

RESULT

In our campaign from 04/07 to 06/10/2019, there was a total of 40 bets satisfying the HO/AO criteria. 16 of these bets lost and 24 won. The odds of the bets placed ranged from 1.69 to 2.00.

Profit: £327.00

Grand Total: £8,443.00

Conclusion

The overall profit total was pleasing and, to reiterate, the profit curve graph at the start of this article shows what would have been achieved with flat 100 unit stakes.

However, by using a ratcheting system to incrementally increase stakes in tandem with stop-loss stake reduction at 85% of the bank each round, the profit figure would have been almost 10k higher. Ratcheting without stop-loss in place, in this case, would have netted a figure almost 14k higher.

We will shortly be publishing an article showing an articulate staking plan in action on this portfolio of bets.

The hit rate achieved was just over 38% using predominantly medium and high-risk systems, with just one low-risk system in use.

But, it should be noted that 64% of the bets placed were low-medium risk (269 bets), whilst 36% were in high-risk systems (150 bets).

The longest losing streak was only nine bets (once). The longest winning streak was eight bets (twice).

Of course, the stark reality of the campaign sticks out like a sore thumb in the profit curve graph. Using flat stakes, betting could have been stopped in mid-September having achieved the level of profit registered by mid-December. In effect, the last three months of the portfolio were a zero-sum game. Hindsight always offers up a wonderful reality check.

Emphasis on Strategy

In a word, the strategy is diversification.

Using all 10 of the Summer League HDAFU tables available we decided this term to maximise the number of systems gleaned from them by choosing a 1st half and a 2nd half-season system from each league. In other words, using the 10 tables to come up with a total of 20 different systems.

(See 6) Example and Summary in this link to illustrate why we split the profit and loss curves into the halves of each season.

The other important reason for splitting each Summer League into two systems (and ignoring whole season systems in all Summer Leagues), is the fact that the leagues themselves are not as popular with punters as the main European leagues.

Because of the lack of demand in some areas of the Summer Leagues (i.e. less money wagered makes it harder for bookmakers to balance their book), odds are sometimes adversely affected with higher over-rounds: In other words, there are less ‘value’ opportunities available for punters.

In addition, a tendency exists for bookmakers to react to historical results trends at the beginning of a new season by lowering odds across the board to counter the efficacy of punters trying to take advantage of them.

Therefore, without the ability to second-guess what is likely to happen with the odds trends in games not yet played, by concentrating on one bet type in any league for no longer than half a season, the chances of picking a system discriminated against by the bookmakers are minimised as much as is possible.

Also, if a system is going to go wrong, then better to limit the damage to half a season, rather then endure the misery of continuing losses (and hoping in vain that they improve) throughout the full term.

Final Words

Soccerwidow uses the term ‘Summer Leagues’ to describe those where the season begins and ends during the same calendar year.

In other words, leagues outside the usual format of those beginning in one calendar year and completing in the next (what we call ‘Winter Leagues’).

Although our own HDAFU campaigns are traditionally a mixture of ‘Summer’ and ‘Winter’ leagues it is difficult to provide a summary of the whole due to its overlapping and ever-running nature. In effect, the Soccerwidow campaign is one long, never-ending portfolio: The betting equivalent of perpetual motion.

In this way, it is as close to the bookmakers’ own business model as possible but on a micro-level by comparison.

Bear in mind that with the Winter League portfolio running concurrently, this article represents an isolated snapshot of what happened to our Summer Leagues during the time frame represented by 419 bets placed in 10 leagues (20 systems) over a period of 288 days from the 22nd of February to the 7th of December 2019, both dates inclusive.

Here again, is that free download link – Click on the button below to get your 2019 Summer League Campaign spreadsheet.

>>> 2019 summer league campaign <<<

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Coronavirus Thoughts

The effects of coronavirus on the various leagues around the world are still unknown.

After the March 2011 Tsunami in Japan, the J1-League was halted for seven weeks. When the league restarted it assumed exactly the same statistical pattern as expected from previous seasons: the elongated break between rounds had no effects on the trends of the observed results (although only one round of matches had been played in the 2011 season up to that moment).

Coronavirus has suspended leagues for far longer and at a different point in each, and the possibility of many leagues resuming against a backdrop of empty stadia is another factor to consider. Will home advantage be to some extent neutralized by the lack of fans?

All in all, differences like this may affect individual results but, from a statistical perspective, previous trends will in all likelihood remain evident. But don’t hold us to that.

Whilst things stabilize around the world it is probably better to pause betting portfolios for a while and monitor your systems on paper. However, Value Calculation and Over/Under goal betting are probably still valid as the concepts are applied to and rely on calculating each game individually.

Stay safe.

Value Betting in Operation: Why the HDAFU Tables Work

Soccerwidow — Fri, 11 Oct 2019 18:36:52 +0000

We recently received a very valid question from a reader who went through our 2017-18 Winter League Report with a fine-tooth comb:

“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)

The ‘true’ probabilities add up to 100%: 24.5% plus 27.7% plus 47.8%
The ‘fair’ probabilities add up to 103.7%: 14.9% plus 20.3% plus 68.5%

The 3.7% difference is called the bookmakers’ overround, but that’s another topic. However, what you should have learned by now is that if the probability (odds) of one side is massively changed the probabilities (odds) of the other two outcomes must consequently be affected.

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.

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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!

Judging the Risk of a Football Betting Portfolio

Soccerwidow — Thu, 08 Nov 2018 18:30:20 +0000

Understanding how risk can be controlled when betting is sometimes quite a challenge. Learn today how risk can be reduced & controlled when betting on football. Risk management of a portfolio should be an essential part of your betting strategy.

Please note that this article addresses system betting (= a predetermined selection of bets to bet on the same criteria over a longer period of time).

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

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

What Exactly is a Portfolio?

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

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

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

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

What is Diversification?

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

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

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

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

Our Portfolio for the 2017-18 Season

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

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

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

Here is our portfolio for 2017-18:

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

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

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

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

Probability / Expected Hit Rate / Risk

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

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

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

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

Losing Streak Observed vs. Maximal Losing Streak Expected

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

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

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

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

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

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

Calculating the Expected Maximum Longest Losing Streak

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

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

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

n = number of trials (i.e. total number of bets expected)
ln = natural logarithm
P = probability / expected hit rate (for losing streaks: expected losing rate)
| .. | = absolute value or ‘modulus’

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

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

100% minus 39.62% = 60.38%

To calculate the longest losing streak use in Excel the following formula:

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

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

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

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

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

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

Rules of Thumb to Judge Risk

A high yield ALWAYS indicates high risk!

High Yield (over 30%) = High Risk (normally)

…Until it doesn’t!

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

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

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

Moral 1: There are no rules without exceptions.

Next rule:

Low Probability (expected hit rate under 35%) = High Risk

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

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

Calculating the overall Probability / Expected Hit Rate / Risk of the whole portfolio

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

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

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

First you multiply the number of expected bets with the expected hit rate for each system, e.g.
Austria: 72 times 31.94% = 22.9968
Belgium: 30 times 86.67% = 26.001
Denmark: 36 times 41.67% = 15.0012
…and so on
Then add them all up:
22.9968 plus 26.001 plus 15.0012 plus etc., etc. .. = 243.2526
Now divide the result by the total number of bets expected:
243.2526 divided by 505 = 0.4816883 ≫≫ 48.17%

To calculate the overall expected Yield the same calculation applies.

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

It was therefore a ‘medium risk’ portfolio.

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

Three Scenarios of the same Medium Risk Portfolio with 500 Bets

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

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

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

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

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

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

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

Have a think about these profit/loss curve simulations.

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

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

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

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

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

The Final Performance of the Portfolio

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

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

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

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

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

How big should my portfolio be? How much should I diversify?

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

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

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

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

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

Of course, the larger your portfolio, the better.

The more ‘low risk’ you can identify the better.
Reduce on ‘high risk’ as much as you can.

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

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

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

The Rule of Thumb is: 50 is the magic number!

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

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

Good luck with your betting!

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

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Why Mid-season Breaks Matter in Football Betting

Right Winger — Sun, 04 Nov 2018 19:50:13 +0000

You may have heard the cliché that football is a game of two halves and, indeed, we have written about the statistical differences between the first and second halves of individual matches before.

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

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

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

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

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

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

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.

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

Right Winger — Sat, 15 Sep 2018 07:09:38 +0000

Soccerwidow’s Cluster Tables are an essential tool for identifying value bets and creating a profitable portfolio in the Over/Under ‘X’ Goals market.

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:

Find the Home and Away Odds

Calculate the HO/AO Quotient

Record the Cluster Table Results

Perform the Final Calculations

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

As you can see, Southampton’s Over 2.5 Goals percentage for an HO/AO quotient of 1.0623 in their away games is shown as 33.3% in the second cluster down.

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!

System Football Betting: 2017-18 Winter League Report – 35k in 138 Days

Soccerwidow — Sun, 05 Aug 2018 04:56:57 +0000

The 2017-18 Winter League Campaign consisted of 138 betting days spanning 10 and-a-half months.

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

Image 1: Winter League Campaign Development Graph
(Bank Development ↔ green profit curve; Profit/Loss ↔ weekly columns)

Portfolio Chosen

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

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

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

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

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

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

2017-18 League-by-League Review

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

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

(1) Austria Bundesliga – Underdog – Whole of Season

Risk Judgement: HIGH RISK

Hit rate expected: 31.94% (associated risk: high)
Yield expected: 30.36% (associated risk: high)

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

Hit rate achieved: 30.67% (just below expectation)
Yield achieved: 9.75% (far below expectation)

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

(2) Belgium Jupiler League – Home Win – Whole of Season

Risk Judgement: LOW RISK

Hit rate expected: 86.67% (associated risk: low)
Yield expected: 13.85% (associated risk: low)

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

Hit rate achieved: 80.95% (below expectation)
Yield achieved: 7.90% (below expectation)

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

(3) Denmark Superligaen – Away Win – Whole of Season

Risk Judgement: MEDIUM RISK

Hit rate expected: 41.67% (associated risk: medium)
Yield expected: 21.18% (associated risk: medium)

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

Hit rate achieved: 37.5% (below expectation)
Yield achieved: 8.1% (far below expectation)

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

(4) England Premier League – Draw – 1st Half of Season

Risk Judgement: HIGH RISK

Hit rate expected: 39.62% (associated risk: medium)
Yield expected: 38.62% (associated risk: high)

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

Hit rate achieved: 38.18% (just slightly under expectation)
Yield achieved: 26.73% (good, although far below expectation)

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

(5) France Ligue 1 – Favourite – 2nd Half of Season

Risk Judgement: MEDIUM RISK

Hit rate expected: 50.00% (associated risk: medium)
Yield expected: 24.93% (associated risk: medium)

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

Hit rate achieved: 43.33% (below expectation)
Yield achieved: 10.8% (far below expectation)

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

(6) Germany Bundesliga – Underdog – Whole of Season

Risk Judgement: MEDIUM RISK

Hit rate expected: 31.58% (associated risk: high)
Yield expected: 29.83% (associated risk: medium)

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

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

Hit rate achieved: 37.04% (above expectation)
Yield achieved: 55.43% (far above expectation)

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

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

(7) Greece Super League – Home Win – Whole of Season

Risk Judgement: LOW RISK

Hit rate expected: 80.00% (associated risk: low)
Yield expected: 9.02% (associated risk: low)

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

Hit rate achieved: 82.98% (slightly above expectation)
Yield achieved: 8.06% (below expectation)

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

(8) Italy Serie A – Away Win – Whole of Season

Risk Judgement: MEDIUM RISK

Hit rate expected: 52.59% (associated risk: medium)
Yield expected: 8.49% (associated risk: low)

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

Hit rate achieved: 61.11% (above expectation)
Yield achieved: 17.51% (far above expectation)

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

(9) Netherlands Eredivisie – Favourite – 2nd Half of Season

Risk Judgement: MEDIUM RISK

Hit rate expected: 75.00% (associated risk: low)
Yield expected: 32.23% (associated risk: high)

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

Hit rate achieved: 73.33% (slightly below expectation)
Yield achieved: 25.33% (below expectation)

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

(10) Poland Ekstraklasa – Underdog – 1st Half of Season

Risk Judgement: HIGH RISK

Hit rate expected: 25.00% (associated risk: high)
Yield expected: 33.00% (associated risk: high)

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

Hit rate achieved: 21.62% (slightly below expectation)
Yield achieved: 16.22% (far below expectation)

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

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

Was there a single system within the portfolio that performed as expected?

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

The expected hit rate for Germany was 31.58% and it achieved 37.04%
The expected hit rate for Italy was 52.59% and it achieved 61.11%

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

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

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

Get the Full Report & Check our Statements

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

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

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

Spreadsheet Features:

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

Final Performance 2017-18 Winter League Portfolio

On the whole, the portfolio performed as expected:

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

Image 3: 2018-18 Winter League Campaign Portfolio Results

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

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

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

35k in 138 Days?

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

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

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

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

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

A Portfolio Reduces the Risk

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

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

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

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

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

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

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

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

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

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

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