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Record W4367669112 · doi:10.1519/jsc.0000000000004403

The Relationship of Anthropometric and Physical Performance Characteristics on Competitive Success in Amateur, Elite, and Professional Rodeo Athletes

2023· article· en· W4367669112 on OpenAlexaff
Dustin J. Oranchuk, LeeAnne K. Gullett, Michael Kicia, Brandon Thome, Alex Game

Bibliographic record

VenueThe Journal of Strength and Conditioning Research · 2023
Typearticle
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsUniversity of AlbertaAlberta Health
Fundersnot available
KeywordsAnthropometryIsometric exerciseAthletesPhysical therapySprintVertical jumpAmateurCompetitive athletesJumpingPhysical medicine and rehabilitationPsychologyJumpMedicineGeography

Abstract

fetched live from OpenAlex

ABSTRACT: Oranchuk, DJ, Gullett, LK, Kicia, M, Thome, B, and Game, A. The relationship of anthropometric and physical performance characteristics on competitive success in amateur, elite, and professional rodeo athletes. J Strength Cond Res 37(7): 1495-1506, 2023-Reference anthropometric and physical performance qualities can improve understanding of sporting needs and streamline preparation and rehabilitation programs. However, these data and their relationships with competitive success are absent in rodeo athletes. We hypothesized that riding performance would be most correlated with hip adductor, neck, and grip strength, whereas jump, reactive strength index (RSI), and change of direction abilities would best predict bull-fighting performance. Forty-three amateur ( n = 9), professional ( n = 23), or internationally ranked ( n = 11) male rodeo athletes (bareback = 9, bull riders = 16, saddle bronc = 7, bullfighters = 11) (26.8 ± 5.6 years) volunteered for this study. Anthropometrics included body mass, height, and body fat percentage. Performance measures included isometric hip adduction and abduction, neck flexion and extension, handgrip strength, squat and countermovement jump heights, eccentric utilization ratio, reactive strength index, change of direction, bike sprints, and several pneumatic power measures. Bullfighters were taller and heavier than bull riders (effect size [ES] = 0.84-0.87, p = 0.008-0.017). Bull riders were leaner than bullfighters (ES = 0.74, p = 0.012). Fighters had greater RSI than riders (ES = 0.73-1.47, p < 0.001-0.030). Competitive level of rodeo riders ( n = 32) correlated with age, rodeo experience (ρ = 0.37-0.43, p = 0.013-0.049), bent-leg abduction (ρ = 0.43, p = 0.014), straight-leg hip adduction and abduction (ρ = 0.49-0.56, p < 0.001-0.005), neck flexion force (ρ = 0.43, p = 0.016), and rotational power (ρ = 0.50, p = 0.004). The competitive level of the fighters correlated with age (ρ = 0.64, p = 0.036) and time trial performance (ρ = -0.76, p = 0.006). This is the first study providing normative and correlational strength and power performance data in a rodeo population. These data highlight the need for more event-specific physical preparation. Riders should focus their physical preparation on hip and neck strength and rotational power. Bullfighters should prioritize stiffness and anaerobic power.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.055
GPT teacher head0.384
Teacher spread0.330 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2023
Admission routes1
Has abstractyes

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