Continuous Improvement and Showjumping: Getting 1% Better at Jumping Clear Rounds

 

JumpClear analysis enables approaching showjumping training through the lens of continuous improvement, aka, the 1% Improvement Rule. Under this approach, we would use JumpClear Fault Source Analysis to set measurable, achievable goals for progress based on incrementally reducing our most common faults.

I first read about 1% Growth in a biography by Valorie Kondos Field, the former coach of the amazing UCLA gymnastics team who compared the philosophy of getting 1% better day after day favorably to one of striving for - and falling short - of a standard of 100% perfection. She writes:

"The philosophy I do embrace is getting 1 percent better...Imagine an athlete one day getting 1 percent better in technique, form and mental discipline. Then getting 1 percent better in technique, form and mental discipline the next day. And the next day. Wow! The compounding effect of such incremental improvement would reach near super-hero status, but it is also absolutely achievable."  

Takeaway 1:  set measurable, achievable goals

Takeaway 2: such goals add up to big results 

James Clear explains this theory further (and adds some math) in his book, Atomic Habits, Tiny Changes, Remarkable Results. Clear cites the effects of simply improving 1 percent every day and calculates, if you were to improve at an activity 1%, you would improve results by 37 times in one year! 

How does this relate to showjumping?  

It's actually kind of hard to align showjumping with a 1% growth approach.  

While the outcome of a round itself is clear - we have a score and placing - what we often understand as the drivers of success and failure - the things that we chip away at daily - aren't measurable.  (Am I 1% better at shortening my reins?).  As a consequence, regardless of level - and especially if we've been in the sport for a long time - progress towards goals can feel really amorphous.  

JumpClear was designed to add a layer of quantifiability to some of the things which go into jumping a clear round - and while we can't measure improvement in rein length, Fault Source Analysis delivers a data-driven understanding of a horse & rider's most common faults.  In doing so, we aim to give riders a tool to see the goal of jumping more clear rounds through the lens of measurable, achievable components, i.e, focusing on and reducing specific fault areas.    

Performance Goals Part 2: Using Data to Make a Plan

JumpClear is a great tool for identifying your best opportunities to improve your results and setting measurable goals.

Yesterday we gave an example of using JumpClear data to identify an opportunity for improvement and set a goal.  But now what?  What do you do with all these numbers?  

(To be clear, JumpClear data isn't going to be a silver bullet.  The goal is to give a rider & trainer information to identify a challenge and be part of their overall tool kit for horse management and training). 

Fault Source Analysis encompasses 6 metrics.  We only used Jump Detail to set our goal but the data around the other metrics tells us a lot more about this particular challenge.

Using the same example - which full disclosure, is one of my horses - this is what happens when you zoom in on just Skinny jumps:

100% of faults are off the left lead

100% of faults are with a front leg

60% are at a single technicality

Hmmm...We now have a pretty consistent defined situation to problem solve and maybe come up with some mental cues. 

We also have a really concrete and bite-sized goal.  Recalling the "Model" course jump distribution, Skinny Jumps were calculated to be 4.7% of total jumps over 12 months.  If we use some other data around the average number of classes JumpClear members contest, that comes out to 24.8 Skinny Jumps.  If we assume they are evenly spread across leads, we're down to 12.4 Skinny Jumps to make a particular focus.  

Over a year of jumps, that feels like we've narrowed it down to a pretty achievable goal!

Using JumpClear to Set Performance Goals

JumpClear uses Fault Source Analysis to identify opportunities for horses & riders to improve their performance by showing the areas where they have a higher percentage of faults.  

Recently, we've been expanding this analysis through comparing an individual member's performance to average or baseline fault distribution.

We use this approach as the basis for setting unique performance goals for horses: the logic is essentially finding fault metrics where the horse significantly differs from average performance and calculating how it could be performing if its faults were more like the average.

Here's how it works (this is a real example):

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We start with a horses faults for the past 12 months.

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We compare that to the Average Fault Detail distribution.  This is where we can identify Target Areas where a horse's faults differ significantly from the average.  Here, we highlight Skinny jumps which are more than 5x average!

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Then we multiple the Average by the horse's Actual Total Faults for the past 12 months to get the expected number of faults for the jump detail in our Target Areas.

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Finally we calculate the difference between the Expected and Actual Faults.  This tells us the change in faults possible if the horse improved its Target Areas to the Average.

Here, this horse could decrease its Total Faults by 15% if she improved this one fault area!