Lateral preference in complex combat situations: Prevalence and relationship with general measures of hand and foot preference

ABSTRACT

Laterality is considered relevant to performance in combat sports with particular emphasis being placed on fighters’ handedness and combat stance. Such approach, however, may fall too short to understand the role of laterality in sports where fighters are allowed to use their hands and feet standing and on the ground. Here, we referred to grappling sports (i) to estimate lateral preferences in selected combat situations and (ii) to test for an association between those preferences and common measures of hand and foot preference. Based on the responses of 135 experienced grapplers who participated in an online questionnaire lateral preference, at the group-level, was revealed in 12 out of 18 combat situations. At an item-level, common measures of lateral preference and grappling-specific lateral preference were related in three out of 36 conditions (footedness only, not handedness). Across items, scores in a grappling-specific laterality index were positively related with foot but not with hand preference scores. Implications for the assessment of lateral preference in combat sports and the use of item-specific terminology in this context are discussed. On a broader scale, we also elaborate on potential consequences of our findings with regard to evolutionary explanations of the maintenance of left-handedness in humans.

KEYWORDS:

• Brazilian Jiu-Jitsu
• wrestling
• Luta Livre
• judo
• self-report

Acknowledgements

We thank all grapplers who supported the research voluntarily. Also, we thank our colleagues in the Department of Psychology at the Institute of Psychology, German Sport University, for commenting on a previous version of the manuscript. Florian Loffing: Conceptualization, Data curation, Formal analysis, Methodology, Supervision, Writing—original draft, review & editing. Ole Deeken: Conceptualization, Data curation, Formal analysis, Methodology, Writing—original draft, review & editing. Jörg Schorer: Supervision, Writing—original draft, review & editing.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Supplementary material

Supplementary Tables on the classification of situation-specific images depicting either left or right preference (Table S1) and exploratory results from chi²-tests of independence and on absolute frequencies for handedness/footedness by grappling-specific lateral preference combinations (Tables S2–S4).

Data availability statement

The datasets generated and/or analysed during the current study are available in the OSF repository, https://osf.io/w6u2z/.

Notes

1 Luta Livre (“freestyle fighting” in Portuguese) originates from Brazil and is similar to Brazilian Jiu-Jitsu. Key differences are, among others, that Luta Livre is performed without a gi and that in one of its two variants (Luta Livre Vale Tudo) strokes and kicks are allowed (not allowed in the other variant Luta Livre Esportiva) (“Luta Livre,” 2023).

2 Cohen (1988) proposed g = .05, g = .15, and g = .25 as tentative thresholds for small, medium, and large effects, respectively, while at the same time reiterating (as for any other standardized effect size he proposed) “that a basis for positioning g which comes from theory or experience should automatically take precedence over these conventions” (p. 147). Here, we refrain from classifying effects as falling in one of these categories; instead, our interpretation mainly relates to the frequencies observed along with the precision in the respective parameters’ estimation (i.e., 95% confidence intervals).

3 The SPSS-script provided online by Michael Smithson calculates the non-centrality parameter for the non-central distributions under which a particular chi²-value cuts off an area of α% (here: 5%) to the right (λ_upp) and left (λ_low). Based on these calculations, the lower (w_low) and upper (w_upp) bound of the confidence interval on Cohen’s w are obtained via the formula w_low = sqrt[λ_low/N] and w_upp = sqrt[λ_upp/N], respectively.

Additional information

Funding

The author(s) reported there is no funding associated with the work featured in this article.