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.    

Old Salem Farm Spring Grand Prix Week 2

The Grand Prix during the second week of the Old Salem Farm Spring series, like Week 1, was designed by Alan Wade and held during the day on the new sand international arena at the OSF Venue. It was a CSI4* versus the CSI3* held Week 1. We'll provide some analysis of this week's class and some overall thoughts comparing the two weeks.  

We saw a total of 45 faults (meaning a rail or refusal) in Round 1.  There were 31 competitors so each entry averaged 1.42 faults.  13% of the class jumped Clear in Round 1.   

Interestingly, while the average faults per entry is similar to last week where the 33 competitors averaged 1.42 faults, the percent clear last week was twice as high (27%).  (Simply, many more horses had just a single rail this week.)  A likely factor: the time allowed played a significantly greater role this week; more than half the class incurred at least one time fault (just 3 entries did last week).      

The most faulted obstacle Week 2 was a Combination-Out Oxer, (Vertical-Oxer combination).  This jump was responsible for 18% of faults in Round 1.  

The next most faulted jumps were the A element of the same combination (16%) and the related liverpool vertical immediately following this combination (13%).  

In total, the Double Combination at 10AB was responsible for a third of Round 1 Faults.  In comparison, the Triple Combination caused relatively little trouble - accounting for just 11% of faults.

If we compare both Grand Prixs, both classes utilized a Triple Combination and one Double and Combinations were clearly a key driver of faults!  A combination element was the most faulted jump both weeks.  All combination elements combined accounted for 57% of Faults Week 1 and 44% Week 2.   (Both are significantly higher than the average for Combination Faults in a High Performance Class which we are seeing around 37%.)

The class was won by Mclain Ward on HH Azur.  

Old Salem Farm Grand Prix Analysis (Spring 1. May 15, 2022)

 

The Grand Prix during the first week of the Old Salem Farm Spring series was a CSI3*, designed by Alan Wade.  It was held during the day on the new sand international arena at the OSF Venue. 

We saw a total of 47 faults in Round 1.  The most faulted obstacle was a Combination-Mid Oxer, (Vertical-Oxer-Vertical combination).  This jump was responsible for 17% of faults in Round 1.  

The next most faulted jumps were the A element of the same combination and a single other oxer (the jungle themed oxer with green rails immediately preceding this combination).  Each respectively caused 13% of faults.

In total, the Triple Combination was responsible for 38% of Round 1 Faults; moreover, if we also include faults from the Double Combination at 10AB, Total Combination Faults = 57% of Round 1 faults!  (This is significantly higher than the average for Combination Faults in a High Performance Class which we are seeing around 37%.)

All jumps except 2, an Other Related Oxer, were faulted at least once. 7 other jumps were each faulted only once.  All in, of the 16 course elements, half were responsible for 85% of the faults in Round 1!

The class was won by Jordan Coyle on Centriko Volo.  

JumpClear Member Statistics April

 

Overall, we saw significantly less activity from JumpClear members during April, as might be expected during what many use as a transition month between the winter and summer season.  

Only 1 in 4 members were active.  For those who did compete, Average Rounds per Month were lower than usual - 3.5 versus an average just under 5.0.  75% of horses competed in just one competition.  

Members who showed averaged a 38% Clear Round Average.  This is significantly below the baseline.  

Looking at Fault Source metrics, the most faulted course elements continue to come from the "other" Jump Detail with Other Vertical driving 28% of Faults and Other Oxer 17%.  The next most faulted were Combination-In Vertical (14%) and Liverpool Vertical (14%).  

Keeping in mind we are considering a fairly small number of faults, a few more detailed points of note:

• 70% faults were at Related jumps (versus 53% in the baseline)
• 72% of faults were at Vertical jumps (versus 49% in the baseline)
• for classes 1.40 and above, 47% of faults were at Other; 41% were at a combination element. For classes below 1.40, 42% of faults were at Other.  Just 8% were at a combination element.  (Note: this is an exaggeration of a trend previously discussed that Combinations comprise a higher percentage of faults as jump height increases).  

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!

Comparing Faults Types Between Different Horses

In the last post, we analyzed the baseline distribution of the Jump Detail fault metric and compared it to a hypothetical modeled course.  Our conclusion was on average, faults aligned well with the frequency different types of jumps occurred.

However, there are significant differences between individual horses.  These different fault distributions reflect the behavioral asymmetries of horses & riders and are the basis of JumpClear's Fault Source Analysis methodology.  

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Standard Deviation measures the variance within a range of numbers. Here, it is used to capture the difference between the fault patterns of the individual JumpClear member horses.  We see the SDs are all large compared to the absolute values for the Baseline values.

Likewise, when we calculate a Fault Range of + or - one Standard Deviation, there is huge variance in possible outcomes.  There are 7 metrics alone where horses could vary from no faults (a negative value is calculated) to up to 10%! 

Going forward, JumpClear will use this approach of comparing a horse's fault distribution to the Baseline and Standard Deviation Range as part of identifying each horse's unique opportunities for improvement.