This may come from a number of areas: One design sails, old hull design, basic deck gear, too light, too narrow etc.
"Old sails" should never become part or a rating system. A crew keeps their craft in racing order or not. Factoring in "old sails" is a nightmere. It leads to many new questions (what is old for dacron what is old for pentex ?) that are very hard to answer and requires detailed compliance checks at events that nobody really wants.
Other factors like width can very easily be factored in into a measurement based system and these element has been factored in recently in Texel.
Again, making a measured based rating system reflect such differences is not a difficult task. Getting the race committees to actually accept and use the new (multiple) rating numbers is however a very difficult task.
Ergo, the problem is not technical in nature but human (as most problems in the world are)
How do you address this problem in a formula rule?
Ohh that is very simple, there are standard numerical tools for such a thing.
Basically you load a large dataset of race results into your computer memory with data about crew skill level, wind conditions and boat specs and do a regression.
Regression means that you first select a limited number of influence factors, say length, width, height, weight, area, boarded (yes/no) and crew makeup. Then you choose a model structure, most often a linear relationship in such cases. And then you have the numerical algorithms find those weighting factors that make the inherently smooth "graph" approximate the race results the best as is possible given this limited number of influence factors.
Why is this advantagious ?
Well, say you have regressed the system on a dataset that is composed only of actively raced fleets that are spread evenly over the range of beach cats; from 14 foot to 23 foot. The produced results is pretty accurate in rating these fleets. Now when a new class is formed that shares some aspects with a 23 footer and other aspects with say a 16 and 14 footer then its rating will be darn accurate the first time around. Because the regression will tune the describing formula to each individual influence factor on a well conditions and dependable dataset. It will therefor give a pretty accurate weight to each, making any new combination of specs a sum of well known factors.
This means that accuracy achieved by regression on other fleets is passed over onto fleets and designs that are not actively raced. This is an advantage that can NEVER be had with methods based solely on pure statistics. Here only actively raced fleets will get accurate numbers while all others will just swing about some incorrect rating that is more reflective of the skill level inside a class then of the design itself.
Again similar aspects are used to accuraty descibe systems in engineering despite strong disturbances.
Don't get me wrong I believe in the formula route but obliviously it needs constant adjustment to even the playing field and still holding on to the single number rating. This is a tough nut to crack.
All rating systems suffer to the same exend when forced to predict performance over a wide range of conditions and course by a single numbers. Being "Yardstick" or "Measurement" has nothing to do with it.
Also it is actually the Yardstick systems that need to continiously adjust themselves when gaining more data and NOT the measurement based systems. Measurement based systems, as described above, are very succesful in getting a rather accurate rating the first time around by using "knowlegde" gained when rating older more active classes. When the initial rating is already rather accurate then future adjustments are negligiable or very small indeed.
Maybe the best solution is to have both systems in one, so you have the information from Colin sailwave program and it pumps out an PY adjustment (fudge value) number that's added to the SCHRS or Texel rules and you should have a more precise rating system, the best of both wolds.
Not a bad idea, but the very best solution is to have Sailwave create a very large pool of DEPENDABLE race data that is then used for a new regression of Texel/ISAF. Of course a re-evaluation of the influence factors is then also in other. One of the more interesting ones being the factor where a spinnaker is added to a sloop rig making the jib much less effective on downwind legs.
I'm against Fudge factors as they are tuned by human beings with convictions and believes that are often counter scientific. I would just have a human being or a committee decide on the influence factors and model structure and then have a numerical algorithm produce the actuall regression.
Okay Wouter and Simon I'm ready to get flamed.
]
A heated discussion does not equal personal disrespect !
So don't worry here.
On a different tack which is giving better results on the water between Texel and SCHRS, with both being formula rules they should be coming out close, from Claus attachment you get big difference the further you go away from the F18 base line ratios, the Hobie 16 is old and a OD and the A-Class too light. What you are seeing is type forming, it happens in every formula rule, it's the nature of the beast.
There are some differences between both systems at the fundament. Texel is a pure regression executed on a very simple modelstructure where human inspired modifications were later added to tweak the number. SCHRS uses a far more elaborate modelstructure (or used to, prior to changes made in 2007) with a less pure regression, it also has less afterthough modifiers.
In my opinion both systems should just merge into one system and do a new PURE regression on a larger set of influence factors. The new resulting system will then be more accurate, more transparant and simplier then both.
Wouter
