Although I have posted some of my thoughts on this issue in the 420 thread, the comments by Dave got me thinking. (CatManDo - by the way, Dave, I love the gif, but I don’t believe you really feel that way.). Of course, one of the big problems with understanding the effects of “weight” on “performance” is the nebulousness of the term “performance”, in that it typically means different things to different people, depending on their expectations for the boat
. However, most of us generally include within those expectations the time it takes to get from point A to point B. So, let’s take that as at least one common denominator. Unfortunately, even defining “weight” is complicated by the differing measurement methods used by the manufacturers and how different people outfit their boat
. “Light” displacement
, “weight when loaded to the waterline”, “maximum” displacement
, “full” displacement (and there are probably a few others) are cited by someone or another.
From my perspective, I really don’t care how much a boat weighs, as a unitary variable. I do care, very much, how it performs in getting me from point A to point B. Many choose to examine these using a variety of theoretically related variables and, from them, speculate about the ultimate performance. That is probably a worthwhile exercise, especially for designers and builders. But, for a consumer like me, I am most interested in how a boat performs in the “real world”.
Fortunately, there are some data available. Every year at about the same time, the Atlantic Rally for Cruisers (ARC) leaves from the same place and arrives at the same place. The elapsed time from start to finish is logged. They have a separate multihull
class and the data are posted to their website. It is a pretty popular rally and, given the quasi-competitive nature, we can expect that most of the boat crews are doing their best to log fast times. Also, all of the boats go through an inspection
they must pass before departure and carry a minimum set of equipment
I took the data from the ARC
for the last three years (’04 - ’06). These are particularly “good” years in that they represent a variety of conditions. 2004 was a particularly slow passage
year (mean crossing time = 23379 minutes) with all of the boats encountering light winds. 2005 was a fast year (mean crossing time = 14342 minutes) and 2006 was close to average (mean crossing time = 18315 minutes). So, using these three years together should give us some decent overall estimates of performance in a variety of conditions. (Note: all data are multihulls only and DNF boats are not included.)
A total of 57 multihulls competed and finished during the three years (20 in 2004, 17 in 2005, and 20 in 2006). 20 boats competed during two years, representing a variety of brands and models, ranging from an Island Spirit 400 to a Privilege
585 and including a Dragonfly 1200, several Lagoons and Catanas of different models, an Outremer
, a Kelsall
Space 52, and a St Francis 50. (Notably absent are Australian cats, probably due to the area. That’s too bad, since if they are superior to the predominantly Euro cats, then we can’t determine that from these data.)
For brands and models that had more than one boat represented in a given year, I analyzed the data in two ways, both separately (in other words, keeping each boat listed by itself) and aggregately (averaging the crossing times for all of the same boats and using only the average crossing time). So, if there were three Lagoon
410’s in a given year, with the aggregate method I averaged the crossing times for the three boats and used that result to represent the Lagoon
410 for that year. As it turned out, the method used did not matter in the results, so the results presented below will use the aggregate method since that compensates for one boat having a particularly skilled captain
or favorable winds, or vice-versa.)
Data regarding the elapsed time (uncorrected - remember this is “real world” and I don’t care if they had to motor
some to get through a lull, plus, this keeps the correction formulas out of the mix) and boat brand/model were obtained from the official ARC
websites for the respective years. Other data about the boats (length at the waterline and displacement) were obtained from the manufacturer’s official data.
A note about the displacement figures quickly became apparent: there is a wide variety of number cited by the manufacturers and finding a single
figure that can be compared from one to the other is difficult. The most common figure cited is “light” displacement. However, what each means by this number is not necessarily specified and may vary. So, although this was the only common figure and thus was the one used in the analyses, it is still prudent to treat it with caution.
One additional data point was also collected: current
for the boat/model. This was obtained by searching the Yachtworld listings and taking the average of the asking prices boats of the same model that were no more than four years old. This was rounded off to tenths of a million in US dollars.
Some may already be asking, why no data for sail area? Two reasons: (1) Working sail area (believe it or not) is not always listed by the manufacturers; and (2) Given the wide variety and sizes of downwind sails
, as well as the amount of time in which they have may have been used, this was viewed as being too unreliable a variable for inclusion. Again, since I am assuming that each crew was approaching the rally with at least a bit of competitive attitude, tempered by the fact that they were doing a transatlantic run, I decided to assume that each crew used their best judgment as to the sails
they flew and when they flew them.
Data were analyzed using SSPS (a statistical analysis program with which I have over 25 years of experience). On to the results.
Starting with most obvious hypothesis that lighter displacement boats will have shorter crossing times, I ran a Pearson
correlation coefficient. It was: -.08, which is not significant. As a matter of fact, this is a very small number and basically means that there was no relationship between crossing time and displacement. (For the more statistically focused among us, the significance test, 2-tailed, was .59. Correlation coefficients run from -1 to +1, with zero meaning “no relationship”.)
Next, perhaps the crossing time is more related to waterline length. This correlation was also not significant at -.24 (sig, 2-tailed, .12). However, it is at least getting closer to it and in the expected direction (longer boats having slightly, but insignificantly
shorter crossing times, thus the minus sign).
I then ran a new variable in which I took the length at the waterline and divided it by the displacement to result in a variable called “Pounds of displacement per foot at the waterline”, since this would better represent not just displacement or length, but how much “boat per foot” they were trying to push through the water
. The correlation of this variable with the crossing time was also not significant (Pearson r
= -.004, sig., 2-tailed, .98)
Now, does this mean that weight doesn’t matter? No, at least not to me. But, what it does mean to me is that after the designers and builders finish doing their jobs, they design and build a “total” boat. In that context, weight is compensated in such a way that it no longer, by itself, can be used to predict the overall performance of the boat -- even when adjusted for the length at the waterline, at least in “real world” conditions.
However, I still wasn’t satisfied with just correlations. So, I divided the boats up by categories so I could directly compare Light, Average, and Heavy boats, using both the raw displacement numbers and the “pounds per foot” number. I considered “light” boats to be those weighing less than 9 tons, “heavy” boats to be those weighing 14 or more tons, and “average” boats being those that weighed in between. (There were 17 light boats, 17 average boats, and 10 heavy boats.) I then ran an Analysis of Variance (ANOVA) between the three groups, which again was not significant (F = .23, sig. = .79)
However, just using “light”, “heavy” and “average” can be misleading, since smaller boats (like a Lagoon 380
and a Fountaine Pajot
38) that have a low gross weight could also be included with a boat like an Outremer
45. So, the boats were again re-categorized using the “pounds per foot at the waterline” variable. This resulted in 8 boats being categorized as “light”, 10 being categorized as “heavy” and 26 being considered as “average”. Example boats from each category include:
St. Francis 50
When we look at the mean crossing times for each category of the boats (based on “pounds per foot of waterline length”, here are the actual average crossing times (in minutes) for each category:
18868 (the average crossing time for all of the boats)
Although it probably doesn’t take an ANOVA to tell you this, here it is, anyway: There were no significant differences in crossing times.
(F = .54, Sig. = .59)
Since I also had the pricing data, I was curious about another matter: what is the relationship between performance, comfort, and price
? Is there a way to quantify this? After all, there are a number of these boats that are very nice, and very expensive (prices ranged from $250K to $1.6M, with an average price of $760K). Granted, most of the less expensive boats were shorter and/or older models no longer produced (including a Prout 39). So, I computed a Performance/Price Index ( Crossing Time divided by Price, which was further divided by 1000 in order to get the result into smaller numbers). This was further refined by multiplying this by Length at the Waterline as a measure of “comfort” and divided by 100, again to obtain results with smaller numbers. Having done so, here are some of the results. In this type of index, the numbers themselves do not have an absolute meaning, they are only relative and useful for comparison between the boats. Smaller numbers indicate a “higher price” for the performance and comfort, whereas larger numbers could be interpreted as indicating greater “value” for the performance and comfort:
Note: The average Performance/Price/Comfort Index for all boats is 13.8 (SD = 6.6)
Catana 43 13.2
Catana 471 11.8
Catana 472 7.5
Catana 521 5.2
Catana 582 8.1
Dragonfly 1200 16.4
F - P 38 26.0
Island Spirit 400 10.4
Lagoon 410 24.9
Lagoon 500 14.3
Lagoon 55 17.8
Lagoon 570 4.8
Nautitech 40 15.9
Outremer 45 16.4
Privilege 435 17.5
Privilege 495 16.9
Privilege 585 7.5
Prout 39 20.2
Space 52 31.7
St Francis 50 10.4
Boat that rate as better than average in the Performance/Price/Comfort Index include some that may be surprises: The Fountaine-Pajot 38, the Lagoon 380 and 410, the Prout 39 and the Kelsall Space 52. Boats that fall lower than average include some pretty luxurious and expensive boats: the larger Catanas, the Lagoon 570 and the Privilege 585.
Perhaps what these data are saying is that if you equate performance as meaning, primarily, the ability to get from point A to point B and comfort as equating to waterline length, leaving luxury accommodations out of the equation, there are some clearly better values in meeting that expectation. However, if you place a higher value on the luxuries, you will pay much more for it, but don’t expect that to also pay an additional dividend in performance. From the ARC data, it won’t.
You can certainly criticize this analysis and the basis for the criticisms has been made plain, above. It represents a largely European group of boats. It is a transatlantic crossing that is usually (though not always) a downwind run. You can argue that the lack of sa/d numbers is a problem, and, if we could somehow keep that constant across a one to two week period of sailing, I would agree with you. You can argue that there are other, better variables to use. Please do, and while you’re at it, give us the data
As I’ve said, before, I am not a boat designer
. Neither have I ever built a boat. But, I’m not stupid, either. I know there are sales pitches-a-plenty out there and more than enough “opinions”. This is an attempt on my part to bring some sense of logic to the discussion that is based on actual data from real world experience and not from a salesman’s promises or some preconceived notions acquired somewhere in the distant past. If you’ve got other data, then please, share it. Not opinions or conclusions, please, but real, able to be reproduced, checked, and analyzed data.
I hope some of you find this interesting and helpful.
“I love the smell of sacred cow in the morning -- it smells like, Bar-B-Q!”