Under Form Portfolio System Performance Review
June 6, 2011 Leave a comment
Overview
The Under Form Portfolio System uses form-based trends to identify matches that offer value in a bet on there being under 2.5 goals by full-time. At least that’s the theory. An average season will see approximately 480 bets placed with a return of around 70pts at a ROI close to 15%.
Performance Summary
Table 1 (below) provides a summary of the Under Form Portfolio System’s performance this season along with figures from backtesting over the previous five seasons. Starting with the number of bets, it seems that 2010/11 was very much in line with previous seasons. There were 462 bets this season compared to an average of 478 in each of the previous five years. Even allowing for the period during which I was unavailable the number of qualifiers is perfectly normal with previous seasons seeing between 452 and 529 bets. However, the number of winners recorded this season is way down on previous years, something that seems to have become somewhat of a recurring theme throughout these performance reviews. The strike rate of 43.51% is over 20% down on the five-year average. The previous lowest SR was 62.63% from the 2007/08 season so this year was a long way off that even. To put this year’s SR into context it is equivalent to 95 fewer winners than in an average season. Worse still the strike rate is nearly 10% below the level that would be achieved by backing all games to end with under 2.5 goals or to put it another way the system did far worse than a monkey with a pin would.
| 2010/11 | 2005/06 – 2009/10 | ||||||
| Bets | Winners | SR | Bets | Winners | SR | ||
| 462 | 201 | 43.51% | 2393 | 1533 | 64.06% | ||
| Ave Odds | Max Odds | Ave Odds | Max Odds | ||||
| Profit | ROI | Profit | ROI | Profit | ROI | Profit | ROI |
| -84.12 | -18.21% | -69.48 | -15.03% | 361.42 | 15.10% | – | – |
An average season nets around 72pts profit, although this figure has varied from 58.58pts to 86.57pts in recent years. Either way it is a long way ahead of this season’s actual returns: a loss of 84.12pts to average odds. Settling all bets to the maximum available odds improves the returns by 15pts and adds 3% to the ROI but we are still talking about a loss of close to 70pts at a rate of over 15%. The system was designed to return a profit of around 15% of stakes, not a loss of around the same amount. Something has gone badly wrong here!
In the past five seasons this system has been able to rack up a string of 20 consecutive winning bets earning 16.42pts profit in the process. The worst run saw a loss of 9pts across seven bets. This season there was a streak of 11 consecutive losers taking 16pts out of the bank while the longest winning run was only six bets long and made only 7.59pts profit. When the longest losing run is more than 50% longer than the previous longest you know you’re in trouble. Combine that with a winning run that couldn’t exceed a third of the length of the previous best and you’re in deep.
Where did all those winners go to? In an attempt to answer that I have put together the data in Table 2 below. The first thing to notice is the year-on-year increase in the average number of goals per game, a trend that saw the average increase by more than a quarter of a goal in the past six seasons. It may not sound like a great deal but it equates to an increase of 10% over that period. You can see what effect this increase has had on the chances of a match going under.over 2.5 goals in the table below. As well as the average number of goals per game increasing every season, the number of games that had ended with over 2.5 goals has increased each year, obviously meaning the number of unders has decreased annually. This has not been a slow transition either. Back in 2005/06 nearly 55% of games finished with fewer than 2.5 goals, there was close to parity in 2009/10 and the trend reversed last season with more games going over the line than under it. That’s a worrying statistic for a system that focuses on under 2.5 goals bets.
| Ave Goals | Under 2.5 | Over 2.5 | |||
| 05/06 | 2.48 | 1116 | 54.81% | 920 | 45.19% |
| 06/07 | 2.51 | 1099 | 53.98% | 937 | 46.02% |
| 07/08 | 2.53 | 1089 | 53.49% | 947 | 46.51% |
| 08/09 | 2.55 | 1069 | 52.50% | 967 | 47.50% |
| 09/10 | 2.64 | 1034 | 50.79% | 1002 | 49.21% |
| 10/11 | 2.74 | 984 | 48.33% | 1052 | 51.67% |
It’s not even as though the average odds have really changed in line with the above trends either. Back in 2005/06 the average odds for a bet on under 2.5 goals was 1.78 compared to 1.94 on over 2.5 goals. OK, last season the averages were 1.86 for the unders and 1.91 on the overs. There have been a few slight variations in the intervening years but nothing to really write home about. So the odds on an unders bet is, on average, shorter than that for an overs bet despite the fact that the average number of goals is on the rise and for the first time a match is more likely to end with over 2.5 goals than go under that mark. Hmmm, something’s not right there.
So the fact my SR is so low compared to previous seasons is perhaps due in part to the fact that fewer than half of all games ended under the goal line. If the average goals per game count is going up then obviously winners on the unders market are going to be harder to come by. That said I still finished over 5% lower than the SR I would have attained by backing unders in all matches so there are still major flaws in the system. A strike rate as low as the one I recorded this season requires average odds of 2.30 in order to breakeven. And as we saw earlier the average under 2.5 odds this season was only 1.86. There was no way this system could possibly return a profit on that basis.
A look at the staking of this system now. The previous average stake was 1.17pts per bet with a maximum stake of 3pts. A stake larger than a single point was employed in 16.14% of matches. This season a total of 462pts were staked on 377 games for an average stake of 1.23pts while multi-point stakes were required for 20.16% of matches with one game calling for a stake of 4pts to be put down (the bet lost). A greater average stake, a higher frequency of larger bets and a new record maximum stake – the staking certainly seems slightly out of kilter this year, and when combined with the lack of winners we have already observed we have a surefire recipe for disaster.
Finally it is time to review the advised betting bank figure. Based on the previous largest drawdown of 9pts a small bank of 30pts was advised despite the fact that there had previously been a point whereby an outlay of 33pts had been called for. Given the figures we have already seen regarding the strike rate and average odds it is clear that in the long run that no bank would have been big enough as the system was destined for heavy losses. As it happens the system losses exceeded 20pts for the first time on New Year’s Day and the system never recovered. This season saw a maximum exposure of 34pts due to concurrent bets so at least something remained in line with previous seasons.
It may be pushing it slightly to call that a summary but never mind.
Detailed Analysis
Table 3 below shows a monthly comparison of this season’s performance compared to the total performance over the previous five seasons. This season’s data on the left-hand side has a lot of red profit figures in there, as one might reasonably expect knowing that the season as a whole resulted in heavy losses. The losses built right from the start of the season although January’s losses really do take the biscuit when you consider I was out of action and not posting bets for part of the month.
The number of bets placed each month seems to have followed the pattern of previous seasons, starting with very few bets before building to a crescendo after christmas. This season August, September and October saw an average number of bets but November and December were quieter than expected. Based on previous seasons one would have expected around 10 bets in November and around 37 in December. The rest of the season was very much in line with expected bet numbers though.
However, the number of winners is way down on the anticipated numbers in all month with the possible exception of February, although even then we are probably a few short of where we should have been. The quiet months at the start of the season can be excused on the basis of small samples that could easily be distorted by single games and even odd goals here and there but from new year onwards serious questions must be asked. January’s strike rate was less than half the expected figure and we certainly felt the effects with huge losses. Things improved slightly in February, as I have already touched on, but it was a poor run in to the end of the season still.
| 2010/11 | 2005/06 – 2009/10 | ||||||||||
| Bets | Winners | SR | Profit | ROI | Bets | Winners | SR | Profit | ROI | ||
| August | 4 | 2 | 50.00% | -0.69 | -17.15% | August | 36 | 24 | 66.67% | 5.52 | 15.33% |
| September | 9 | 0 | 0.00% | -9.00 | -100.00% | September | 38 | 25 | 65.79% | 6.02 | 15.84% |
| October | 9 | 4 | 44.44% | -1.79 | -19.85% | October | 41 | 30 | 73.17% | 13.16 | 32.10% |
| November | 2 | 1 | 50.00% | -0.12 | -5.86% | November | 50 | 31 | 62.00% | 6.09 | 12.18% |
| December | 19 | 6 | 31.58% | -7.46 | -39.26% | December | 184 | 113 | 61.41% | 20.81 | 11.31% |
| January | 110 | 34 | 30.91% | -46.52 | -42.29% | January | 529 | 334 | 63.14% | 82.56 | 15.61% |
| February | 128 | 71 | 55.47% | 7.67 | 5.99% | February | 647 | 407 | 62.91% | 82.76 | 12.79% |
| March | 74 | 37 | 50.00% | -5.85 | -7.91% | March | 300 | 205 | 68.33% | 62.21 | 20.74% |
| April | 82 | 39 | 47.56% | -8.41 | -10.26% | April | 398 | 255 | 64.07% | 47.98 | 12.06% |
| May | 25 | 7 | 28.00% | -11.96 | -47.85% | May | 170 | 109 | 64.12% | 34.31 | 20.18% |
It’s not entirely clear why the results fell as they did this season. It’s easy to blame the weather but there is no hard and fast evidence that it was really a factor. December’s big freeze meant some games that were scheduled for then didn’t happen till later in the season but that shouldn’t really have mattered. We have already seen that there is an underlying trend for more goals these days, even compared to just a few years back, and that is likely to impact systems selecting under 2.5 goals bets but again I can’t lay the blame firmly at that door either, especially when you look at how rapidly the wheels fell off in January.
Let’s have a quick look at how the returns each month compared to previous seasons. It is easy to excuse the first four months of the season as I said earlier because the odd goal here and there can distort such small samples although September delivering a blank certainly wasn’t part of the plan. December also offers up a small sample size but rather than losing close to 7.5pts we should have picked up a few points profit based on the five-year averages. The things really explode. Rather than netting around 16pts as one would expect from previous years January saw a loss of over 46pts. That’s a swing of more than 60pts against us and there is just no recovering from incidents like that. February showed a profit this year but was still around 9pts shy of the average figure. The season ended with three months each of which delivered returns that were approximately 18pts below the expected figures. The system underachieved right from the start really but as soon as the bets increased in number the difference between actual and expected profits widened.
A breakdown of this season’s performance by division is shown on the left of Table 4 with the right hand side providing aggregate data from the previous five seasons for comparison. The striking thing about this table is the fact that the system shows heavy losses in all four divisions. There are double-digit losses at all levels and double figure negative ROIs to go with them. This is obviously in complete contrast to previous years where each division has recorded positive double-digit ROIs. If we look at the strike rates recorded in each division we see they are all under 50% this season whereas the five-year averages are all in excess of 60%. Obviously that means we have a serious lack of winners as we have already seen several times in this review. To put that into context somewhat though the season ended with around half as many winners in the Premiership as one would expect on average. The Championship was ‘missing’ over 17 winners, we were owed around 25 more League One winners and in League Two we fell short by nearly 30. In terms of bet numbers though things weren’t too far off the mark this season. An average season would have seen a few more bets in the Premiership and Championship than we actually saw but the lower divisions were certainly in the right ballpark.
| 2010/11 | 2005/06 – 2009/10 | ||||||||||
| Bets | Winners | SR | Profit | ROI | Bets | Winners | SR | Profit | ROI | ||
| Premiership | 50 | 16 | 32.00% | -19.86 | -39.71% | Premiership | 313 | 201 | 64.22% | 51.64 | 16.50% |
| Championship | 85 | 37 | 43.53% | -17.52 | -20.61% | Championship | 514 | 329 | 64.01% | 60.21 | 11.71% |
| League One | 168 | 75 | 44.64% | -24.67 | -14.69% | League One | 824 | 531 | 64.44% | 138.23 | 16.78% |
| League Two | 159 | 73 | 45.91% | -22.08 | -13.89% | League Two | 742 | 472 | 63.61% | 111.34 | 15.01% |
I’ve already briefly touched on the double-figure losses and ROIs but I thought it might be useful to make it clear how poorly this system performed compared to the five-year averages. The returns from bets on Premiership matches were around 30pts lower than expected. That’s a 30pt difference from just 50 bets which I find quite staggering. The same difference can be seen between actual and expected returns for the Championship but from more bets while League Two came out 45pts under the expected returns and it was a massive 50pt difference for League One. The top two divisions show a smaller points difference but as there were fewer bets the ROI differences are actually much worse than for League One and League Two.
Conclusions
I don’t think it’s too strong to call this an unmitigated disaster! The average odds were always going to come out a good shade below evens due to the markets we’re betting in which means a strike rate up around 60% is a must. For this system to end with an SR of 43.51% is nothing short of embarrassing really. That kind of deficit is not down to an unlucky season and were I to run the system again next season I don’t see any reason why it wouldn’t also end in disaster. There is something fundamentally wrong here. I have to look back at those five-year averages to see how the data was compiled and how the system was constructed to identify where it went wrong but I suspect it won’t take too much effort to identify flaws with the development process given the disparity in actual and expected performances.
I must admit too that I wasn’t aware of the goals trends I highlighted earlier in this review until it came to compiling the data for Table 2. I certainly didn’t spot the year-on-year increase in the average number of goals per game or the trend towards over 2.5 goals while I was developing the system. That’s probably a mistake but almost certainly not the biggest mistake I made during that process. It just shows that you need to be fully aware of your data and check it from numerous angles in case you miss anything during previous analysis.
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