100 WEEKEND PREDICTION TIPS
Date: 30.09.2022 Day: Tuesday
League: PORTUGAL Liga Portugal
Match: Benfica vs Pacos Ferreira
Tip: HOME WIN / HOME WIN
Odds: 1.55 Halftime 2:1 / Fulltime 3:2
Date: 30.09.2022 Day: Tuesday
League: ITALY Serie A
Match: Inter vs Cremonese
Tip: HOME WIN / HOME WIN
Odds: 1.60 Halftime 2:0 / Fulltime 3:1
Date: 30.09.2022 Day: Tuesday
League: ITALY Serie A
Match: AS Roma vs Monza
Tip: HOME WIN
Odds: 1.30 Result: 3:0
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Rigged football matches 1×2
With 100 WEEKEND PREDICTION TIPS forecasting systems mainly based on past performances, understanding regression to the mean is crucial for sports bettors. The 2015-16 EPL season has been no short of surprises so far, raising the question: Are extreme outcomes sustainable? Here’s what statistics have to say about it.
When dealing with random, or mostly random, systems, variables that are more extreme on an initial measurement show a tendency to be less extreme on a second measurement. This phenomenon is called regression toward the 100 WEEKEND PREDICTION TIPS.
Leicester’s performance during the first of the 2015/16 Premier league season, for example, might gain it a higher team rating than that for Chelsea, who have performed far worse during the same period relative to 100 WEEKEND PREDICTION TIPS. But if much of what contributed to their respective team ratings arose as a consequence of chance factors, the phenomenon of regression to the mean would imply that those ratings might not be sustainable going forward.
Measuring 100 WEEKEND PREDICTION TIPS
One way to measure the performance of a team is to see how it has performed relative to market expectation. For example, if the odds of a team winning are 2.00, this implies that the market believes it has a 50% chance of victory (discounting the influence of the bookmaker’s margin). If it wins, it has overperformed relative to market expectation; if it fails to win, it has underperformed.
Such an approach is qualitatively similar to the Brier Score method, which measures the extent to which a team deviates from what the odds imply.
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The main difference is that it allows us to measure the direction, as well as the magnitude, of the deviation from expectancy. Let’s see how Leicester and Chelsea have performed relative to fixedmatches.cc’ expectation over the first 20 games of the 2015/16 Premiership season. For every game a team wins, it receives a risk adjusted score equal to [1 – 1/odds], whilst for every game it fails to win, it receives a score of [-1/odds].
As the season progresses, these scores are summed cumulatively. The tables below reveal that Leicester has performed far better than fixedmatches.cc betting market expected them to achieve, whilst Chelsea has performed far worse.
How much is performance explained by luck?
A question now arises: should we expect Leicester’s overperformance and Chelsea’s underperformance relative to market expectations to continue? If these trends were largely a consequence of causal factors like player ability and managerial style, then we might expect little regression back towards market expectation; at least not until the market had fully re-evaluated the teams’ new skill levels. If, on the other hand, they were largely a consequence of luck, regression towards the mean should be more rapid and complete.
To determine how much influence regression to the mean, and by implication luck, has on the outcome of soccer matches, we break our data into two halves – the first and second halves of a season – and compare the two. If regression to the mean is small, we would expect extreme performance in the first half to more readily correlate with similarly extreme performance in the second half.
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That is to say, performance would show persistence. Alternatively, if regression to the mean is significant, extreme performance in the first half should show little correlation with extreme performance in the second half.
The chart below illustrates this correlation for English football teams from the Premier and Football Leagues over the 2012/13 to 2014/15 seasons. Each of the 276 data points depicts a first half-second half performance pair for each team during a single season. The dark line represents the average trend of the data points.
Correlation of 1st v. 2nd half season performance
As you can see, there is virtually no correlation and an almost perfect regression to the mean. The value of R2 in a correlation plot like this defines how much the variability in one variable accounts for the variability in the second variable.
A figure of 1 implies perfect correlation whilst a figure of 0 implies no correlation at all. Here we can see that the variability in first half season performances explains virtually none of the variability in the second half season performances, implying there is no causal link between the two, and that deviation away from market expectation is essentially a matter of luck.