Thanks for sharing your experience with Novel Pliance and the TeamSattletest systems. What a worthwhile effort!

You probably know about the pressure-testing “research” (and I use the term quite loosely) that has been going on in the UK, and several European countries for a number of years. Some of this work in the UK is under the aegis of the Society of Master Saddlers, and some of it has been done independently in the UK by equine product manufacturers, and even by individuals who are involved in saddle fitting in one way or another.

Periodically the SMS holds open demonstrations in the UK for SMS members and other interested professional parties. About five or six years ago, when some industry people I know in the UK began working with these testing systems, I LONGED to have easy access to both the research and the technology. I still do, but I think I am more circumspect now about the complications and potential pitfalls of interpreting the data generated.

The feedback in the saddlery industry from people who have been working with this type of technology for a number of years has been thought-provoking on many levels. Not surprisingly, the bottom line seems to be that the use of this technology raises far more questions than it answers, but that’s not a bad thing. Questions should rightly come before answers, rather than the other way around.

I think you are dead right when you point out that the technology does not give you answers; it provides data, subject to interpretation. What one aims for is objectivity by means of hardware and software. What one risks getting is the timeless dilemma described in the Sufi/Buddhist/Jain myth of the Blind Men and the Elephant.

This ancient story, which has been told in various ways for thousands of years, is the best warning ever against the fallacy of inferring the whole of the truth from only the limited information that is close at hand.

Essentially the story is this: A king brings an elephant before a group of blind men, and asks each to describe the beast. One feels the tusk and describes the elephant as being curved and rigid like a plow blade; one runs his hands up the leg says no, the elephant is cylindrical like a sturdy pillar; rubbish, says the one holding the elephant’s tail; the one feeling the belly says you’re all wrong; the elephant is like a huge brick wall; the blind man grasping the trunk strongly disagrees, and so forth.

What hasn’t changed in the thousands of years this story has been told is that whatever conclusions are drawn from data are ultimately based on a set of assumptions, and assumptions, by definition, are not objective. If they were, they’d be facts!

I have heard a few anecdotes from the UK pressure-testing trials on saddles that make quite entertaining pub chat, and I have heard enough thought-provoking information arising from this testing that it has had a real impact on my way of thinking about fitting as a dynamic process rather than a static one.

What I am guessing is that, at this primordial stage of research, the greatest utility of this technology is in gathering comparative data on very modest, very circumscribed questions. Yes, I hate that the idiosyncratic, brain-based data I personally rely on to make judgments about saddle fitting is so subjective. I’ve kept multi-year records on thousands of horses and I still find myself having to make guesses every day about what is going to be “good enough” for a particular horse.

I would love to have the use of state-of-the-art technology to settle these matters — or at least to help me make an honest examination of my own working assumptions — but given that we are starting with not much of a baseline of settled, foundational knowledge in this area, the best I am hoping for initially is to be able to test some of my humbler hunches about why one solution might work better for a certain horse than another, based, of course, on what I am assuming “better” means.

Here’s something that I think will be interesting to keep an eye on as events unfold. One of the major frustrations in my personal experience with manufacturers in this industry has been how far removed they often are from the day-to-day reality of how their products function in the field. By the same token, I think that many of us who work exclusively in the field — I’m certainly guilty of this — don’t understand nearly enough about the constraints and the possibilities of the technology we are using (crude as it may be). Our individual spheres don’t have enough overlap for us to be able to work very effectively as a team.

On some level, each one of us who is seeking “The Truth” is a blind man scoping out an elephant, so unless we can come together as a team, each bringing our own experience-based perspective to bear on the problem, I don’t have a lot of faith in the utility of research results. What we learn has to work in real life to be useful and true in any meaningful way.