Here, the red curve is shifted stepwise from left to right along the black curve. For each step we try to quantify how well the red and black curves fit together at that point.
Let the curves lay over each other on a table with the red curve plotted on transparent paper. After looking at the mismatch you can set up a score telling how bad it all looks. Then shift the upper curve one step (one year) to the right and set up a second score for this mismatch. While stepping and scoring you suddenly see - if you are lucky - a really good match which will give a high score.
Plot your scores, one for each step, and it may look as the picture above. As you can see there is only one high peak. This occurs when the two curves lay over each other with their very first years overlapping. In real world this means that the two logs were cut the same year.
We may also sort out he highest piles above and present their values in a table like this:
--Rel Over *P2Yrs (year) -year lap CorrC 0 84 0.68 -54 30 0.30 72 30 0.29 35 67 0.28
When the felling year is known for the black curve sample, a column with corresponding year numbers could also be printed.
When you start sliding the red curve from left to right over the black curve, there are at first only a short part of the curves that "overlap". To look at how one or two or even five rings match is not meaningfull. A reasonable value for a minimum overlap is 30. So probably you should start with the two curves overlapping 30 years and then calculate your score values from that point and on while you successively slide the red curve over the black one.
In CDendro the minimum overlap can be set through the command "Settings/Options for matching and normalization/Least overlap in years between samples when correlating"
The curves compared during the Curve sliding process could be pure ring width curves or normalized ring width curves.
There are many alternative algorithms that can be used while scoring the curve matching quality at each point of the curve sliding process described above. The most common method is Correlation analysis. In this case the curves have to be normalized to make the selection process work.