Manipulated Betting Fixed Matches
Manipulated Betting Fixed Matches
Soccer predictions winning tips 1×2
Day: Wednesday Date: 07.09.2022
League: ICELAND Besta-deild karla
Match: Vikingur Reykjavik – Leiknir
Tip: Over 2.5 Goals
Odds: 1.50 Result: 9:0 Won
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The way bettors process information is important to their success. What is binary bias? What can YouTube and the Baltimore Ravens tell us about betting psychology? Read on to find Manipulated Betting Fixed Matches out.
What is binary thinking?
Binary thinking involves sorting information into mutually exclusive options, not unlike the way a computer thinks in binary code. Something is either a 1 or a 0 and those are the only two options. There is no grey area.
Many argue that humans instinctively sort information in this kind of way, naturally jumping into this kind of binary method of thinking.
For primitive humans this made sense. The kind of judgements that needed to be made to survive lent themselves well to such a way of thinking, especially when it came to quick decision-making. Decisions such as whether a rustle heard in the bush is a predator or non-predator for guaranteed profits, Manipulated Betting Fixed Matches.
The reward offered by spending valuable time weighing up the information available about the sound (whilst a predator could be preparing to strike) is not worth the risk of being eaten. Simply categorising the rustle in the bush as a predator and fleeing makes much more sense from a risk vs reward perspective.
Richard Dawkins claims such a desire for straight yes-or-no solutions to neatly categorise information is “The tyranny of the discontinuous mind”. He suggests that people seek the reassurance of an either-or classification because it’s much easier for the brain to think in binary, as our distant ancestors did, rather than consider the shades of grey between two conclusions.
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This kind of binary decision making is perfectly fine for basic snap decision making, but we now live in a world of nuance. Nowhere is this reflected more acutely than in the world of betting fixed matches.
Binary bias: caffeine and YouTube ratings
How does binary decision making affect the way we process information?
Fisher and Keil set out to ascertain this in a series of studies on what they called “Manipulated Betting Fixed Matches”. For these studies, participants were given evidence about a topic, before being asked to summarise the evidence and give a rating that best captured their overall impression of the strength of the argument.
If people are evaluating data from different studies investigating the relationship between caffeine and health, for example, they would quickly categorize data as either showing an effect or not, regardless of the relative strength of the evidence.
Overall they found that: “Across a wide variety of contexts, we show that when summarizing evidence, people exhibit a binary bias: a tendency to impose categorical distinctions on continuous data. Evidence is compressed into discrete bins, and the difference between categories forms the summary judgment.”
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In other words the participants tended to ignore the relative strength of the evidence presented to them, instead favouring categorising them into discrete categories and looking at the sum total of evidence within those categories.
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This stripped out all the continuous data. As a result, a conclusion with a 25% likelihood in one direction was simply bucket with all the conclusions that lean in that direction regardless of their strength. This made the data easier to process for the test subjects but meant that the value of the information diminished.
YouTube discovered this whilst trying to refine their rating system for videos. Their star ratings proved to be ineffective since the vast majority of votes were either for one star or five star.
This was a consequence of binary decision making. If the user likes the video they categorise it as a five, whilst if they didn’t like the video then they categorised it as a one. All of the information in the middle of these two discrete categories was lost. This resulted in YouTube switching to a simpler thumbs up/down system.
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As shown above, humans prefer to sort information into two distinct categories where possible. This is also the case within betting.
To an inexperienced bettor, a good bet is simply one that wins. A bad bet is one that loses. Those two buckets are easy to grasp and make intuitive sense to somebody without a good grasp of the nuances behind Manipulated Betting Fixed Matches.
This however, is completely false. A winning bet fixed matches can be a terrible bet whilst the best bet ever place may have turned out to be a loser. By categorising bets in such a simple way, all of the useful information gets stripe away.
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This desire to attribute a data point into a “good” or “bad” category due to the outcome of an event was shown during the debate around the Baltimore Ravens’ failed two point conversion attempt from the 2019 NFL season.
Mathematically, the decision to go for the two point conversion was the correct one by the Ravens. However, because the attempt failed, some pundits categorised the call into the “bad decision” bucket.
The extra information given by the analytics behind such a play was removed for these pundits due to a mixture of outcome bias (a failed attempt must have been caused by a poor decision) and binary bias (the need to place the play into a distinct category). Had the play proved succesful their opinions would, in all likelihood, have been different. What is a good bet?
Thinking like a bettor
In order to get into a successful betting fixed matches mind-set the bettor must learn to avoid such biases. The grey area between win and lose is what distinguishes a good bet from a bad one.
Bettors work in percentages. If the bettor’s percentage is more accurate than that of the bookmaker’s he will win in the long run. But is it even possible to ascertain whether the bettor’s percentages are even accurate?
Without a large sample size it is almost impossible to answer that question definitively.
Take one famous percentage figure as an example. Statistics website FiveThirtyEight gave Donald Trump a 30% chance of winning the 2016 REALITY TIPS FIXED MATCHES. Of course, Trump went on to become president.
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The reaction to this prediction from some quarters was to label it as “wrong”. Given the binary approach people take to such things, you can see why it would be tempting to do so. As the work on binary bias done by Fisher and Keil show, people remove the weaken strength of the prediction to place the prediction in the “wrong” category they are comfortable with.
But this is obviously nonsense. According to the prediction, Manipulated Betting Fixed Matches should win three times in ten. The fact the scenario played out to become one in which Trump won shows us nothing new about the accuracy of the prediction.
The sample size would need to be extend to a meaningful level by running the same election repeatedly (which is of course impossible). Only then could we see how close FiveThirtyEight’s prediction of 30% Trump wins was to reality.
Controlling the chaos
This is understandably disconcerting. It goes against our instincts to say that we actually don’t know and may never know whether an individual prediction was a good one.
There have certainly been bets I have placed where I intuitively felt the percentages were in my favour, but outside of a model run across a large sample of similar events, there is no way to definitively say that I was correct.
As bettors we are operating in that grey area between the “good” and “bad” bet buckets. To be successful you have to step away from easy classifications and embrace the percentages on an individual bet for what they are. Simply attempts to create a “good” bet with the knowledge that we may never truly know whether we can ever classifiy them as such.
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Manipulated Betting Fixed Matches As mentioned earlier, fractional Kelly is often advocate to moderate risks. The distribution above illustrates why. Whilst the probability of poor performance is reduce significantly (compare the area to the left of bank growth = 1 for the blue and green distributions), the median bankroll is only marginally smaller (2.55 compare to 3.49).
Granted, your expected (mean) bankroll growth is much bigger with full Kelly but most of the time you won’t see that. The median is arguably a better measure of what you should expect to happen in this context. With full Kelly you still have a 21.5% chance of making a loss. For half Kelly, that reduces to 11.8%.
Expected and median bankroll growth
We can use the calculator to see how the expected bankroll growth will vary with the number of bets. We already know from our equations above that this will be logarithmic. The median bankroll growth also varies logarithmically.
Variable odds, variable stakes
The equations and calculator described relying on all bets having the same odds and same stake percentage. How robust will they be under real world scenarios where both may vary? Testing against Monte Carlo simulations reveals that odds can vary considerably without having too much impact on the reliability of the calculator’s outputs, but only provided the stake percentages are all the same. Obviously, that will not be the case with Kelly staking.
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The calculator is also robust for variable stake percentages, for example those advised by the Kelly strategy, provided the odds don’t vary too much. A typical example would be Asian Handicap or point spreads, where most odds are close to 1.95, with minimal deviation.
Reliability is weaker when EV for these bet types also varies, but again provided neither the odds nor the EV for those bets varies too much. The calculator offers a reasonable STRONG FREE FIXED MATCHES of providing quick estimates of performance expectation.
We know that defining expectations from level staking is relatively straight forward. However, this article has shown that we can do the same for percentage staking too. Whilst level staking performances will be normally distribute, those from percentage staking are distribute log-normally. Using this information, I have been able to develop a simple calculator to help bettors define their range of expectations should they choose to follow this money management strategy.