Methodology

 

JumpClear offers a unique approach to equestrian competition statistics and sports data based on Fault Source Analysis.  

Fault Source Analysis analyzes jumping faults based on 6 metrics:

• Jump Type (vertical or oxer)
• Jump Detail (for example, combination in, or liverpool)
• Lead 
• Technicality (single or related)
• Leg (front or hind, the leg which strikes the faulted rail)
• Rail (front or back, for oxers only)

This Fault Source methodology is a core part of the analytics offered to JumpClear members and used to identify the most common faults at a horse & rider level.  

We also apply it for general competition analysis, including high performance or Grand Prixs.

Data for Fault Source Analysis is primarily sourced through review competition video.  

JumpClear does all the work for members of collecting and analyzing video.

Clear Round Average:

JumpClear also conducts analysis of competition results using Clear Round Average as the primary metric.

We believe Clear Round Average is a useful metric - as opposed to simply looking at rank - because it can be applied consistently across all competition levels and regardless of the number of competitors in a class.  Our ambition is to use Clear Round Average as a meaningful tool for comparison, whether showing the performance of an individual rider across time or analyzing Grand Prix outcomes between different course designers.

Some Other Lingo:

TTM and Trend:

Most JumpClear analysis is based on the prior 12 months of historical data - the "TTM" or trailing twelve months.  We adopted this method to ensure a sufficient amount of rounds and faults are captured in the calculations, given the average frequency a horse shows.  (Otherwise said, we don't want to draw conclusions based on just a couple of rounds).  

Often, the TTM will be compared to Trend, which is the most recent 3 months.  

Baseline:

As the JumpClear database grows, we will increasingly offer insights based on Baseline or Average behavior.  We also use the Baseline for Individual members to compare their statistics and identify particular opportunities where their data differs from the average.  

Most Common Fault:

We use a combination of the 6 Fault Source metrics to identify the most common fault, defined as the course element responsible for the highest percentage of total faults.  This is a metric used for both Member Statistics and High Performance Analysis.

In general, we strive to use as many metrics as significant, for example, JUMP TYPE + JUMP DETAIL (Combination-In Oxer) or JUMP TYPE + JUMP DETAIL + RAIL (Combination-In Oxer Front Rail).