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