Following up the successful 2016 Summer League Campaign, here is our complete report on the 2016-17 Winter League Campaign, based on 18 different top flight leagues.
2016-17 Winter League Profit Curve
We will try to avoid repeating what was said in the summer league article so that it is all new information for you here.
For the sake of completeness, you should still read and digest both reports for a full idea of our strategies and thought processes.
And you will find the current stock of available HDAFU tables via this link.
2016-17 Campaign Report
Measures of Risk
Before beginning to plan any portfolio or placing bets, you will need to review your analyses and rank the systems you have found according to risk exposure based on the values of the upper inflection point odds.
This will allow you to compile a portfolio with a healthy balance of risk, which is essential to the success of any investment plan.
Here is our rough guide:
- Low Risk (Probability 45.00% or more; upper inflection point maximum odds of 2.22)
- Low-medium Risk (Probability 44.99%-35.00%; upper inflection point maximum odds of 2.85)
- Medium Risk (Probability 34.99%-22.50%; upper inflection point maximum odds of 4.44)
- Medium-high Risk (Probability 22.99%-16.00%; upper inflection point maximum odds of 6.25)
- High Risk (Probability 15.99% or less; upper inflection point odds above 6.25)
Discretion is used if a system does not fit these parameters or crosses two or more classifications – In these cases, the harmonic mean odds of all the games in the set is used as the benchmark to guage risk. The Excel formula for a range of odds in cells A1 to A100 would be: =HARMEAN(A1:A100)
Measures of Success
You will also need a definitive framework to be able to judge the final results.
For us, the final results of any league fall into four distinct categories:
- Systems that achieve a six-season-high (i.e. profits larger than any of the previous five seasons). (Over-Achievers).
- Those that make a profit over and above the size of the initial stake (£100 in our case), but fall short of six-season-high results. (Achievers).
- Strategies that break-even or, record a tiny profit or loss up to the size of the initial stake (£100 in our case). (Zero-Sum).
- Leagues that make a loss over and above the size of the initial stake. (Losers).
You can already see that two of these outcomes are favourable, one is neutral, and only one is detrimental.
2016-17 League-by-League Review
Let’s have a brief look at each league to see how our 22 systems fared. (Alphabetically according to the tab codes in the workbook):
1. AUS1 – Austria Bundesliga – Whole Season System
Only eight of 28 bets won, but this was enough to see a profit of £882.00. Hit rate and yield were both below the calculated averages and the resultant profit figure was lower than any of the five previous seasons’ figures.
Result: Achiever
2. BEL1 – Belgium Jupiler League – Whole Season System
Up to, but not including Europa League and Championship Group splits
Both the estimated hit rate (33 out of 97 bets won) and yield figures were surpassed by wide margins, leading to a six-season-high profit figure of £3,108.00.
Result: Over-Achiever
3. CZE1A – Czech Republic 1. Liga – First Half Season System
Up to the 04/12/2016 winter break
Nine out of 22 bets won, and whilst the hit rate and yield both outstripped expectations, the profit figure settled at £1,247.00, the fourth largest in the last six seasons.
Result: Achiever
4. CZE1B – Czech Republic 1. Liga – Second Half Season System
From 18/02/2017 start of the second half of the season
This one suffered its worst result in the last six seasons, but the resultant loss was minimal at -£310.00.
Result: Loser
5. DEN1A – Denmark Superligaen – First Half Season System
This was the only system employed in this league purely because the format of the second half of the 2016-17 season was to change from previous seasons.
Although hit rate and yield were both below estimates, the system still recorded its third highest total for six seasons at £1,238.00.
There were twice as many bets than expected (largely due to the fact that around 40% more games than usual were played in the first half of the season to accommodate the new second half season format).
Result: Achiever
6. ENG1 – England Premier League – Whole Season System
This system suffered the longest losing streak we have ever encountered, almost 14% worse than expected, for a very painful 36 straight losses. (A six-season-low).
However, the situation was mostly recovered by three big winners all carrying odds of over 12.00, for a final loss of just -£32.00.
The last six bets of the season lost, but had only one of these been a winner, this system would have returned a profit. Small margins.
Result: Zero-Sum
7. FRA1 – France Ligue 1 – Whole Season System
Hit rate and yield were both below par, but the league turned in a steady performance for a profit of £1,634.00, ranked fifth largest in the last six seasons.
Result: Achiever
8. FRA2 – France Ligue 2 – Whole Season System
The same story as France Ligue 1, but with a profit of £2,801.00, for its third largest profit figure in six seasons.
This one was unusual for a much higher number of bets than expected. (215 vs. 119 estimate – Profit was at £2,523.00 after 119 bets).
Result: Achiever
9. GER1A – Germany Bundesliga 1 – First Half Season System
Up to 21/12/2016 winter break
Another below par system, but one that still achieved a profit of £1,098.00. (Fourth largest in six seasons).
Result: Achiever
10. GER1B – Germany Bundesliga 1 – Second Half Season System
From 20/01/2017 start of the second half of the season
The very rare inclusion of a system with two non-profitable seasons (the oldest two) in the previous five.
It featured one more bet than expected, which won, to total one more winner than expected. Hit rate and yield both exceeded estimates for a profit of £1,294.00. (Fourth largest in six seasons).
Result: Achiever
11. GRE1 – Greece Super League – Whole Season System
The hit rate here was almost 8% below estimate and resulted in the worst performance for six seasons, and a loss of -£819.00.
Result: Loser
Hi Soccerwidow,
At first glance, the results of your 2016-17 campaign are fantastic. I’m going to go through your campaign in detail and learn what I can about how and why you put it together, and then hopefully go some way towards achieving what you guys did in that season.
One other thing I wanted to say about what you offer at Soccerwidow….the material – free and paid for – is so valuable and really does teach people what they need to know in order to understand the betting market and what is profitable and what is not, the pitfalls and the best way forward.
I’m very much like you in that I don’t see the value in blindly following another person’s picks, but when someone doesn’t know the betting market or how to go about it themselves, blind faith is what a lot of people gravitate towards.
With your products they really do teach people and empower them to be able to make their own decisions and do things the right way. I feel I have learned a tremendous amount since I found your website and am very much looking forward to putting it into practice in this upcoming football season.
Great job on what you guys do at Soccerwidow!
Simon.
Thanks for your very kind words, Simon.
Yes, the results were good, even with a below par set of results in many of the leagues.
We think the achievement is further glossed by the fact that the stakes used were all relatively small. Replicating our success should therefore be within the financial reach of many people.
Keep in touch and let us know how you get on with your own campaigns!
All the best for now.
Hi again,
first of all thx a lot for your valuable insights. Today I have studied the article and the excel sheet a little bit, and two or three questions/remarks are nagging at me:
1) I would have expected more 1A/1B splits in the winter league, especially for leagues with a true winter break like Switzerland and Poland.
2) You also offer Scotland and Croatia in your HDAFU panel, but I’m missing them in the 2011-16 example. On the other side you don’t offer Countries/leagues like Israel, Northern Ireland and Wales or some East European leagues (maybe there is a reason for excluding the latter, but I don’t see any for the first three).
3) Regarding the ratchet/stop-loss scenario: Is there a direct (maybe negative) correlation between the steps (here 1000 units and 5%) and the longest expected win or loss series?
Tanks in advance and keep on your excellent work!
Hello Acepoint,
Thanks for your questions.
1) It was all just a question of time in preparing the analysis – we were so short on time. The Summer League campaign was already in full flow, there was so much work to do on the website, and in the end, we had to go with the whole season analyses in many of the leagues. Indeed, the current stock of Summer and Winter League tables is the first we have offered for sale with the A/B splits.
2) Scotland and Croatia are newcomers to the fold, so the tables for these weren’t in existence at the start of the 2016-17 seasons. A few seasons ago, we offered Wales and Northern Ireland, but not a single person bought one. What we offer now is simply based on the question of demand and supply.
We try and use the larger, more popular leagues, which tend to be less under-priced than a lot of the fringe leagues.
Remember, the less popular a league is in terms of money wagered by punters, the harder it is for the bookmakers to balance their books, hence their need to under-price far more in, say Wales, than they would in the EPL.
Also, the eastern-European leagues are dogged by match-fixing problems, either criminally, or by mutual consent of the teams involved. You can see this to less of an extent in leagues like Greece or Italy Serie B, where the last few rounds of a season always create strange prices. For example, when a draw is good for both teams, it suddenly becomes the shortest of the 1×2 prices because the bookies know that both teams are likely to play for the draw. Turkey’s last scandal was in 2011.
Hungary, Bulgaria and Serbia may make future appearances in the complement of tables, but Ukraine is in a mess, and personally I wouldn’t wager a penny on the Romanian Liga 1, as sources there tell me it is corrupt to the core, but then again, that’s pretty much public knowledge…
3) No. There didn’t need to be. We knew the portfolio was going to make money, so we have simulated a sensible approach to the staking plan purely as an example to expand readers’ thinking in this direction.
I hope this helps!
Best regards.
Hi! I am just writing to congratulate you on the excellent analysis on both summer and winter leagues. These material in soccerwidow is mandatory for any serious ante-post bettor. Being a statistician, I value these posts in a way you cannot imagine. Thanks again!
Best regards and profits to us all!
Gabriel
Blush!
Thanks for your kind words, Gabriel!
We shall continue to remain humble servants to our valued readers! 🙂
hi Right Winger,
1. how did you calculate starting bank? I used your free excel sheet “winning losing streaks” to calculate starting bank based on your forecast number of 1838 bets and hit rate of 38.87%. With hit rate rounded to 40%, risk coefficient of 3 and 100 units stakes starting bank should be 4500 units. Yours was only 2000 units. Of course, it depends on chosen risk coefficient, but if to take each strategy separately to calculate its starting bank just 3 strategies could easily require larger starting bank than 4500 units. And starting bank for those 22 systems would require quite an amount of money. I’m confused?
2. Why Stake Ratchet & Stop-loss Mechanism was used for the whole portfolio instead of using it for each league separately? This type of approach mean stakes are also increased or reduced for systems that are performing well or failing.
Hello again Jo,
I did mention at the end of the ratchet and stop-loss section on page 4 of this article that the staking plan example was a simulation only.
The vast majority of punters don’t even know what a staking plan is or how to calculate a starting bank, let alone deal with a portfolio of multiple systems all running parallel, or calculate the systems in the first place.
I have therefore kept this section of the article as simple as possible just to switch the majority of people on to thinking about proper money management.
Yes, I remember saying in the Summer League Campaign report that we would apply the ratchet and stop-loss simulation separately to each league and show the results, but I just did not have the time to do this – I had not allowed for the early start to some of the 2017-18 leagues this year (due to the World Cup next year), which only became apparent when putting together the Winter League Tables for sale. It was therefore more important to give people as much time to analyse the tables by getting them published as early as possible.
I’m sorry if you’re disappointed, but my advice is to concentrate on the big picture. Sometimes the small details are just not consequential enough to worry about.
If you want to simulate the ratchet and stop-loss applied to each league individually, then I would be happy to publish your findings on the blog. I am sure this approach would have produced even higher profits, but in practise, it is very complicated to manage and more time-consuming to perform. Probably better to stick to the overall approach mentioned, especially as each system is part of a team anyway.
And you must remember that a portfolio approach is all about synergy. The longest losing streak of any one system is always going to be absorbed by the other systems around it. In the case of our portfolio, the EPL suggested the longest individual streak at 31 losses. As it panned out, the portfolio produced a longest run of just 12 consecutive losses.
Don’t worry, it is a good thing to be curious and to want to form your own opinions of things. Nothing is gospel and there is certainly no bible about betting in existence. We write about our own experiences based on our own level of statistical knowledge and maths. Perhaps there are others out there with even higher standards than us, but this is the only website I am aware of prepared to help people increase their levels of knowledge and understanding.
And at the end of the day, we can teach, but we can’t make decisions or place bets on your behalf.
For the sake of good order, I will add a link to the winning and losing streaks article on page 4, and thank you once again for your valuable contribution.
Hi again, RW!
Are HDAFU tables and methods to build a system also appliable to group stage competitions, such as Libertadores, Champions League and Europa League? Or even to Europe or South América World Cup Qualifiers?
I understand that playoffs are off the table, but I see these competitions mentioned above as a middle ground between the regular legues and the playoffs.
What do you think about this?
Hello again Gabriel,
The competitions you mention do have group stages (i.e. a mini league format), but there are simply not enough of those games to form any level of statistical significance over a relevant period of time (say five seasons).
All qualifying-round and knock-out-stage games in those competitions would effectively be cup games, and therefore off the agenda as being too volatile to predict.
In the Champions League, for example, I think there are only 96 group-stage games per season. The smallest league we analyse for the HDAFU tables is Iceland with 132 games per season.
Also, league-games-only is a far more predictable science as each season contains a similar number of the same teams. In other words, over five seasons, the turnover of different teams is likely to be a lot lower in a league than it is in the Champions League.
A lower turnover of teams means a lot more games between the same teams in five seasons and therefore a more standardised data set. You might have some of the same teams represented in the Champions League, such as Real Madrid, Man Utd, Juventus, etc., but they might not ever play each other in five seasons of group games (more statistical noise created).
Keep it simple – stick to top-flight leagues – there are plenty of matches to bet on there!
I hope this helps your thinking and thanks again for writing!
Hi Right Winger,
As always, excellent job, and congratulations on the results achieved!
I noticed two systems in the portfolio with high expected losing streak, England Premier League (31), and Netherlands Eredivisie (29). Both surpasses your previous recommendations of trying to stay below 20, by far. I know in the end it’s all a matter of personal preference and attitude toward risk, i’m just curious to know your thought process that led to the selection of this two particular systems. What made you close an eye on the losing streak figure?
Thanks!
Hello again Daniel,
I’m afraid to say that the decisions made on those two leagues were not scientific.
You will see in a couple of my replies above (see 25/06/17 to Acepoint), that we were simply rushed for time and I was faced with a “take it or leave it” decision.
But with the EPL and Eredivisie being two of the top six most popular leagues, I couldn’t really leave them out of the campaign, even with their 31 and 29 estimated streaks respectively.
This time last year was such a hectic period that I couldn’t finish the analyses in time – some of the leagues did receive the benefit of all three analyses (whole season basis, 1st half season, 2nd half season), whilst with others, I was only able to carry out one analysis (the whole season).
Ordinarily I would say to everyone to stick with the maximum of 20 losing streak as a rule of thumb, with perhaps a little leeway.
At the time, and with my experience of running these campaigns, I didn’t think it would make a huge difference to the results. Perhaps things could have been improved in the EPL, but I was more than happy with the Eredivisie outcome.
Again, I think it shows the power of a portfolio approach based on inflection points that a healthy profit can be made, even if the most optimal systems are not chosen, although I would always recommend taking the time to perform the job properly.
If I had had time to do a perfect job on the analysis, results may have been even better, but ultimately, there was no spilt milk to cry over.
Thanks for your question – it was a good point to bring up.
Hi, about splitting system into 1st and 2nd half, what would you do if a smooth rising line would show up in a whole season analysis but by splitting them it would show more profit?
For example: analysing a whole season, the inflection points I decided to use were from 3.1 to 3.9. But, if I split it in 2 half seasons, I would have better results by using 2.9 to 3.4 in 1st half and 3.2 to 3.9 in 2nd half.
Resuming: what is the main variable to be analysed in cases (there are many) like this? The stability? The bigger profit? The clear half cut in the season during winter, for example?
Thanks!
Hello again Gabriel,
The main decision-making factor has to be a combination of personal choice and risk acceptance.
Although I was pressed for time in making the analyses for the article above, I made sure, from the results I had at my disposal, to compile a portfolio containing an even balance of low, medium and high risk.
So, yes, find the systems with the highest profit and incorporate them into a team of systems.
Once you have your best team of systems planned out on paper, you may have to do some rearranging (i.e. substituting a split analysis in one league, for the whole season analysis instead, or vice versa), to ensure the portfolio is balanced from a risk perspective.
It’s stability in the whole portfolio you are looking for, not necessarily within individual leagues, but if your inflection points show odds clusters above the zero odds in each system (remember page 3 of the User Guide), you will certainly make money in the long run.
Thanks again for your question!
hi Right Winger,
my current portfolio with 30 strategies has uneven balance of risk. 40% low risk, 16% low-medium risk, 33% medium risk and 10% medium-hight risk strategies. Majority, 67% of them, have no losing season out of 5 and substituting them to balance the risk more evenly would mean to incorporate strategies that have 1 losing season out of 5 instead of none and (or) less balanced hit rate and (or) less balanced number of matches and (or) lower profits over 5 seasons. Of course, you won’t see exact numbers to judge, but in order to balance the risk more evenly, would it be a better trade in your eyes?
And what is better to you: a strategy that has zero odds below first infection point, but 1 losing season over 5 years, or zero odds above first inflection point, but profit in all 5 previous seasons?
Hi Jo,
The portfolio you have suggested is a little bottom heavy. In other words, you are going to see a larger percentage of games at lower odds, which will tend to produce a more jagged profit curve (you may not see your portfolio constantly in profit from the start).
The main purpose of the lower odds games is to fill-in the gaps between the losing bets at higher odds. If you remember the inflection point graph in the 2016 Summer League Campaign spreadsheet, it was a zero-sum game up to odds of around 2.28. Because you have a higher proportion of lower risk strategies, you may see this figure pushed upwards (e.g. a zero sum game up to odds of 2.50?).
Your profit curve will look more like the Summer League Campaign than the more efficient Winter League strategy. Your profits will be lower because you are playing a lower risk strategy. It will also be harder to recover losses if your wins are relatively small at that end of the spectrum. You might find it frustrating as the going will be slow – three steps forwards and two back. If it were me, I would find a better balance.
It is always better to have the zero odds below the lower inflection point as every bet you make will be a value bet, which is a strategy that will guarantee long-term profits. If you start surrendering value, you will hamstring yourself. I would rather have value on my side than an analysis with all five seasons in profit.
I hope this assists!
An additional question: Isn’t the number of (possible) games in each league also important for the risk strategy? I would assume that for judging the portfolio’s risk you have to weight the risk in each league.
A simple example:
Two leagues, one with a low risk strategy (e.g. home wins between 1.8 – 2.2 and 200 games), combined with a medium-high to high risk strategy in another league (e.g. underdog wins between 4.5 and 7.5 and 30 games).
Hello again Acepoint,
Your example is a good one, dealing with two opposite strategies in two different leagues.
With a lower risk strategy, you will always be faced with a higher number of bets than with a higher risk strategy, and vice versa.
In other words, with lower odds, you need a higher number of winning bets to create the profit, and vice versa with higher odds.
The risk of each strategy never changes – every bet within the inflection points chosen carries the implied probabilities of those odds. It is not affected by what happens in a different strategy bearing different implied probabilities.
Combining the strategies or weighting them in accordance with each other to obtain some sort of overall measure of risk is therefore going to distort the picture.
Therefore, what we don’t want to do is dilute the extremes of our risk spectrum (i.e. the lowest and highest ends). We therefore take each system in each league on its own merits and line them up alongside each other.
I hope I have understood your question correctly.