ANALYSIS OF FAULTS IN
INTERNATIONAL
SHOWJUMPING COMPETITION
D. Marlin, G. Tabor and J. Williams
Performance analysis (PA)
Understanding the complexity which underpins
performance can inform interventions designed to support the individual to enhance their success /
extend their career (Hughes et al., 2001)
Intention
Perception
Actions
Behavioural
change
& altered
decision-
making
SUCCESS!
Performance analysis in equestrian sport
• Reliability of historic & anecdotal training practices used for the horse are increasingly being questioned (Williams, 2013; Ely et al., 2010; McGreevy & McLean, 2010)
• Need for objective, evidence-based practice in horse (and rider) training (Williams, 2013; Randle & Waran, 2017)
Benefits = enhanced health, welfare & success
To measure performance need to define what ‘IT’ is
IT is what YOU want to know or change
Goals for training & competition
1. prepare horse & rider for demands of ‘X’
2. Identify patterns, trends to explain / change ‘Y’
SJ riders and trainers believe faults
don’t occur by random
AIMS: 1. to characterise faults in International showjumping competition 2. to establish if relationships existed between fault accumulation & fence related factors
Getting a competitive analysis: strategic PA
What causes faults?
Notational analysis
Study of movement patterns,
strategies and tactics in sport
Can be individual or team
approach, singular event or across
time in training or competition
Reliability in execution is key to
validity
Hughes and Franks (2004), Duthie et al. (2003)
Barris and Button, 2008, Di Salvo et al, 2006
Horse
Rider
Course
Faults
Tactics
Warm up
Training
Management
Past record
Environment
Comparison
to successful
combinations
SJ: variables?
TEAM approach: Coach [±rider & wider team] & PA:
Identify questions or goals
DEFINE CONTEXT
Training or competition What level? team, rider, horse, H&R, course, jump etc
Collect data
Use frequency & notational analysis (video), & MV modelling
ID associations behaviours (ACTIONS) & outcomes (GOALS)
McGarry, 2009
What did we do?Reviewed video footage: 170 combinations, 8 events and 2550 jumping efforts for 2nd round Nations Cup, European league, 2017
>45°
Fence & fault type
Fence number: sequential and per ¼ course
Approach: straightness
<45°
>45°
Analysis:Correlations & MV logistic regression ID relationships between fault accumulation and fence related variables
What did we find?
Relationship between sequential jumping efforts and fault accumulation
Distribution of faults
• Most faults = poles
• More faults occur as course progresses (P<0.05; r=0.7)
• More faults in 3rd
and 4th 1/4’s
What did we find: MV modelling?
• Faults 9 times more likely in the 2nd half of the course (P < 0.006 )
• Straight approach reduced chance of scoring faults by 48% compared to non-straight approach (P < 0.0001)
• For every 0.1s over optimum time, combinations were 1.1 times more likely to gain faults (P<0.05)
• Fence type NOT significant, but influential improved model fit (predictability: ROC 68%)also hint for future study: verticals in combinations
How could you use this information?
• Similar findings to Harris et al. (2018)
• Why does slower = increased faults?
• In these Nations Cup competitions, faults were not randomly distributed
• Feedback and application!
• Riders and coaches can use this information to inform training regimens and design competition strategies win!
KEY step: PA Feedback to performance team
1. PA feedback in PLAIN ENGLISH via report and face to face meetings to
performance team / coaches discuss and interpret results
2. Team (including PA) feedback to riders discuss and apply to training and
competition strategies
3. Further analysis team / H&R level / other?
4. Analysis implement further changes / keep doing what works review
impact
Going forwards?
Continue to establish the evidence base
More detail: retrospective and prospective PA of rider, horse, course and
fence related factors
Practical context: performance goals and questions to be set in
partnership with National team riders & across teams
Prospective testing: frequency and notational analysis + MV modelling
ID if patterns in competition strategies on performance
Thank you for listening
Any questions