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.   

Are Some Jumps Faulted More Frequently? Analyzing Fault Distribution for Showjumping

 Are some jumps faulted more than others?  Is it because they're "harder" or just used more frequently?  We looked at the data to find out.

The Approach:

We designed a model course to arrive at an approximate distribution of jump types.  The Course was based on an average of 10 different jump configurations to capture different Combination Types, and use of elements like Liverpools, Skinny Jumps, Planks, Walls and Water.  

We calculated the percent each element comprised within the model course and compared that to Baseline Data for JumpClear members for their Fault Distribution for Average Round 1 Faults over a 12 month period.  

Findings

Overall, the Model Course aligns very well with Average Faults.  This data suggests that faults - viewed as an average across multiple horses and rounds - occur roughly in proportion to the frequency of the type of course element.  Otherwise said, no particular jump is a bogey causing a hugely disproportionate amount of faults.

We know, however, that faults vary significantly between individual horses.  We'll look more closely at this in the next post.

Showjumping Course Analysis Combination Elements

As the JumpClear database grows, it's exciting to deliver more insights based on total member data.  Here we take a deeper look at Combination Faults...

Starting with the basics, Combinations - across all elements - made up 34% of Round 1 Faults for JumpClear members over the past 12 months.  They were a much smaller percent of faults in the jumpoff - just 15%.  

There numbers are interesting because they suggest that combination faults come down to simple math.  Combinations are faulted roughly in proportion to the percent they make up of total jumps on the course*; they are not - in fact - horrible bogey obstacles that stand in the way of every horse & rider's clear round.

(If you want to check that math: the average first round has between 12 - 14 numbered jumps with somewhere around two doubles and a triple or two doubles, so 19 to 21 obstacles of which combinations comprise 6 or 7.  The average jumpoff has about 7 - 9 numbered jumps with one double, so about 9 to 11 obstacles of which combination elements make up two).  

However, we see that combination faults differ quite significantly by division.

As a general rule, the lower heights have fewer combination faults.  This is largely - but not entirely - driven by the impact of the "mid" element and likely reflective of the limited use of triple combination in the 1.30 and 1.20 national classes.

By a slight margin, the most rails come from the Combination In Element.  Across all elements, faults are split nearly evenly between oxers and verticals.

WEF 2022 Grand Prix Analysis

 

Each week of WEF, JumpClear analyzed the Grand Prix to provide stats on the most faulted course element.  We also tracked overall faults across the 13 weeks.  Here's what we learned!

There were 551 First Rounds throughout the Season (data includes the highest level grand prix each week).  119 (22%) were clear.  The highest clear round average came in Week 6, a 3* where Catalina Cruz was the course designer.

There were 772 unique Round 1 Faults.  

• The most frequently faulted course elements were an Other Oxer (13%) and Other Vertical (10%)
• The least faulted were a wall, a triple bar, the first jump (all jump types) and a Last Jump Vertical.  All were 1% or less of total faults.  (Interestingly, last jump oxer was much more heavily faulted (7%)
• Combinations made up 37% of faults in total: the most faulted combination element was a Combination-in Vertical
• The most faulted Combination Type by a significant margin was an Oxer-Vertical (27%).  Vertical-Oxer (18%) and Vertical-Oxer-Vertical (15%) followed.  

Industry Insights Based on JumpClear Member Statistics (March 2022)

 

JumpClear members averaged a 53% clear round average in March.  This is in line with the 12 month average.  

This is the first month where no horse achieved a perfect 100% clear round average.  The highest average was 80%

Average Rounds/Month was 6.9 which is the highest we've seen.  

Looking at Fault Source metrics, the most faulted course elements continue to come from the "other" Jump Detail with Other Oxer driving 28% of Faults and Other Vertical 22%.  The next most faulted were three Combination elements: Combination-In Oxer, Combination Out-Vertical (both 11%) and Combination-In Vertical (10%).