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1×2 HDAFU Tables User Guide: 4 Easy Steps to Find the most Lucrative Betting Systems


Our HDAFU tables have evolved tremendously over the years – the 5th Generation of Summer Leagues is now available for sale.

They are a complete statistical analysis of historical performance over the previous five seasons of the Home win, Draw, Away win, Favourite win and Underdog win (H-D-A-F-U). They serve to identify the most profitable odds ranges in each bet type.

To help you understand why we value this product so highly, here is the Definitive Guide for using the tables to their maximum potential.

If you wish to work along with the example in this article you can download an abridged version of one of those tables here (the one used for this analysis):

>>> Free hdafu table download <<<



Just click on the button above and click on “Continue Checkout” button in the new tab, then enter your name and e-mail address. Our automatic store will then deliver the file to you via e-mail, free of charge. The size of this Excel workbook is 864 KB.

It’s difficult for us to put into words how important the HDAFU tables are to us and our own betting adventures. But what we can say is that we have complete confidence in them to do their job. And from testing them in a live setting, we know that they are an extremely reliable method of building lucrative betting portfolios.

Quite simply, they are the best and most user-friendly tools available for nailing down value betting systems in every league you apply them to.

They reveal the DNA of a league, and provide a hidden level of detail that makes finding and exploiting the sweet spots so easy and so rewarding.

The next four steps will probably change the way you think about betting…

Step 1 – Observation

The first thing you will see when opening any of the HDAFU tables is the Data Tab summary of each bet type.

(Click on the image below to enlarge it in a new tab):

HDAFU Table Data Tab: Whole Season Results Summaries

HDAFU Table Data Tab: Whole Season Results Summaries


The totals along the top row show the effects of betting on every match over five seasons to be (from left to right) either a home win (-7,329), a draw (-835), an away win (+8,236), or wins for the favourites (-2,594) or the underdogs (+3,501).

You can see from this graphic that away wins look the most promising backing system with a profit of 8,236 units from 100 unit stakes.

To customise the stake amount enter what you want in the Fixed Stake box at the top of each bet type in the Data Tab.

The image above shows the full five season cold analysis. If you enter a different stake amount the financial values will change, but the percentages will always remain the same. This being the case, we have fixed these percentages as a benchmark to better gauge the improvements we will make with our filtering exercise later.

The Odds Toggle is for testing the effects of the odds you are getting when playing the systems for real – you can ignore it during your analysis.

You can also leave the betting exchange commission rate at zero. Again, use it for backing system monitoring purposes when you start betting on or paper testing your systems of choice.

Okay, we fancy away wins in this particular league but let’s now have a look at the Inflection Points Tab to see if this backs-up our observation.

(Click on the image below to enlarge it in a new tab):

HDAFU Table Inflection Points Tab: Inflection Points Overview

HDAFU Table Inflection Points Tab: Inflection Points Overview


Away wins are certainly financially the most profitable bet type but the profit curve doesn’t really begin rising until odds of 3.30 are reached. Overall profit at this point is 463 units and this rises to a peak of 13,502 units around odds of 8.60.

These two points on the graph would therefore be our two inflection points: Odds of 3.30 where the curve begins to rise; Odds of 8.60 at the pinnacle, the point at which profits begin to fall again.

However, notice there is a big portion of the away wins curve which is a zero-sum game. This ‘hole’ in our profit curve begins around odds of 3.75 (6,653 units). At this point, the curve falls away again, encounters what we call ‘statistical noise’, and only recovers at odds of around 6.52, when the profit figure surmounts its previous high at 7,184.

HDAFU Table Inflection Points Tab: Inflection Points Odds Intervals 3.75 - 6.52

HDAFU Table Inflection Points Tab: Inflection Points Odds Intervals 3.75 – 6.52

In between these two points is the potential for a lot of wasted effort and not a lot of gain.

We can see the extent of this by scrolling down and looking at the inflection point intervals.

(Click on the right-hand image to enlarge it in a new tab):

This image shows the start of the 3.75 odds sector at the top and the end of the 6.52 odds sector at the bottom.

The yellow column indicates the running total of matches up to each cluster of matches.

We can see that our two odds of 3.75 and 6.52 encompass roughly 330 matches – the difference between 1,115 indicated at the 6.52 break-off point and 785 at the starting point of the 3.75 cluster.

That’s 330 bets over a five season period that are simply not worth making; or 66 bets in a season.

HDAFU Table Inflection Points Tab: Inflection Points Odds Intervals 3.30 - 8.60

HDAFU Table Inflection Points Tab: Inflection Points Odds Intervals 3.30 – 8.60

We can see this clearer by looking at the same snapshot between our original inflection points of 3.30 and 8.60.

:(Click on the left-hand image to enlarge it in a new tab)

In this odds range, we have roughly 587 bets (1,221 minus 634). We now know that more than 56% of these (330 bets) are not worthwhile making.

This leaves only 257 bets but the away win profit sectors between the inflection points seem to be split into two areas of the curve: from odds of 3.30 to 3.75 (medium risk system, accounting for around 160 bets), and then from odds of 6.52 to 8.60 (high risk system; around 100 bets).

If we were to continue our analysis of away wins we would eventually see that the three elements (the medium risk sweet spot, the high risk sweet spot, and the statistical noise in-between) combine to give us a bumpy ride.

Our expected hit-rate will be tempered by that area of noise, and yield will be lower because of the size of the zero-sum area and the number of pointless bets within it.

This means a lot of unpaid work to perform, placing many bets that maintain the status quo and not much else. On top of this, the losing streaks will be greater.

Therefore, why not split into two systems in this league?

The synergy we have mentioned before about many systems supporting each other is what makes the HDAFU betting systems so viable.

However, we also mentioned that you should find the single best system in a league to play alongside the other best systems in the other leagues within your portfolio.

In our away win example, we would need to choose the better of the two systems we have identified. Either backing away wins at odds between 3.30 and 3.75, or between 6.52 and 8.60. Choose one or the other, not both.

We recommend never to play multiple systems in the same bet type. The synergy effect is diminished as ultimately, one of the two systems is not the best we can find.

Ideally, we are looking for synergy between the absolute single best systems in each league within our portfolio, without creating a situation where one system supports another within an individual league.

With different bet types in the same league (e.g. 1×2 market and over/under goals market) this is not an issue, but we would go as far as avoiding the conflict of interest between HT and FT 1×2 systems in the same league, for example.

Away wins initially looked great but is there something better?

Have a look once again at the Inflection Points graphs to try and see what it is.

As is typical of an underdog backing profile, the high risk/high return nature of this bet type produces a noisy curve, one full of jagged peaks and troughs. There are only small rising areas to analyse. Anything you can analyse into promising profits will contain few betting opportunities in a season, with long runs of losing bets to cope with.

Backing the favourite has one area between odds of 1.90 and 2.10 but we can see at these odds not a huge profit is created over five seasons (less than 3,500 units).

The home win is a misery for backing. Again, the sweet spot is between 1.90 and 2.10 but the profit is less than 2,000 units.

That leaves us with backing the draw. There is a large, rising area in the curve beginning at draw odds of 3.32 (-2,008 units), and peaking at 3.65 (7,170 units). It represents a potential profit chunk of 9,178 units over five seasons.

This is better illustrated by superimposing our inflection points onto our graph – We are interested in only the portion of the curve in-between the red arrows:

HDAFU Tables: Inflection Points Graphic

The shape of this curve is what you should be looking for when identifying the first system to analyse in your leagues of choice.

It is the classic gently rising curve from bottom left to top right. It is relatively smooth, with a far smaller amount of statistical noise.

Therefore, this is the bet type we will analyse as our example.

Next Page: Step 2 – Hiding, Sorting & Filtering


learn to think like a bookmaker!
deciphering bookmaker mathematics

Last Update: 23 February 2017

Categories:1x2 Betting Betting Advice Betting Systems Case Studies



28 Responses to “1×2 HDAFU Tables User Guide: 4 Easy Steps to Find the most Lucrative Betting Systems”

  1. jo
    25 March 2017 at 10:59 am #

    Hi Right Winger,

    how do you determine if betting opportunities or hit rate has too big deviations?

    • Right Winger
      3 April 2017 at 4:10 pm #

      Hello again Jo,

      I’ve been trying to backtrack for the answer to your question as, after many years of analysis and betting, identifying deviations almost becomes second nature.

      Firstly, I think that ‘anomalies’ is probably a more useful word to use than deviations.

      The number of potential bets covered by a system and the expected hit rate from it are usually pretty uniform when looking at a filtered set of data from five seasons.

      Anything that stands out from the pack should be looked at more closely. Was it an anomalous season that caused the deviation? Is there a history of anomalous seasons in that particular league? Are there any other reasons for the anomaly?

      Certainly, if the betting opportunities are anomalous in one of the seasons analysed this can point to a change in odds setting for that season, perhaps due to the composition of the league (i.e. an historically strong team is relegated and replaced by a far weaker team, which has a knock-on effect in the odds compilation).

      Filter the results in your chosen system by team, and see if there any anomalies there. You may even be able to filter out certain teams from your analysis – ones that continually let the system down, for example. See if this makes any difference to the deviations you can see.

      I hope this helps!

  2. JVR
    27 April 2017 at 10:25 am #

    I have a few questions regarding the HDAFU tables:

    – Would it help to add in more seasons to the analysis?
    – Would it help to weight the seasons?
    – Do you backtest your picks? For example, omit betting on the first 25% of the season, use these matches to validate, and then start betting on them. And if so, how about splitting the season in half?

    Thanks!

    • Right Winger
      27 April 2017 at 10:29 am #

      Thanks for the good questions, JVR,

      Yes, we performed literally years of analysis before settling on five full seasons of data. It doesn’t seem to make a significant difference adding any more. In fact, adding further seasons tends to dilute the data pool with information that becomes more irrelevant the older it is.

      So, five seasons is enough to provide a data pool which carries statistical significance, and that’s all we need when analysing any snapshot of past performance. You can add more seasons if you want (and create more work for yourself), but the fringe benefits of the extra stats become more and more negligible the further back in time you go.

      And don’t forget, maths is not an exact science. Our aim is to get as close to the answer as we can, knowing that there is never an absolutely correct one.

      My feet are different sizes for example. Most peoples’ are. Almost all people buy their shoes from a shop. We buy shoes that fit as best they can but they are never a ‘perfect’ fit for both feet. But they do the job they are intended to do. This end result metaphor is all we can ever hope to achieve with a statistical analysis – it will never be pinpoint accurate is what I am trying to say.

      Weighting the seasons again doesn’t make much sense as we want to compare ‘apples with apples’ and see how they stack up against each other on their own merits. By doing this, we get a more rounded picture of what is likely to happen in the future, without any bias being placed on past performance.

      For example, there is no point identifying an anomalous season and compensating for it. We want to see the effects of that season in all its ugliness as such a season could quite easily repeat itself in the new season we are analysing for.

      I could write a book longer than the Bible with the amount of back-testing we have performed over the years, and the benefits of this are that we now split the seasons at their natural mid-season interval. The 2017 Summer League HDAFU tables started this trend and in a few weeks, when the Winter Leagues end in Europe, we will again be providing three tables for each league (the whole season analysis, first half split, and second half split).

      Hope this helps and thanks again for contacting us!

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