Tackle Height and Tackle Success—An Analysis of 52,204 Tackle Events

To compare the probability of tackle success (the tackler preventing the ball-carrier and ball from progressing towards the tackler try-line) when contacting the ball-carrier at different heights (shoulder, mid-torso and legs) for different types of tackles (active, passive, smother and arm) while accounting for other tackler situational factors within seven playing levels. Video footage of 271 male rugby union matches were analysed across seven playing groups (Under [U] 12, n = 25 matches; U14, n = 35; U16, n = 39; U18 Amateur n = 39; U18 Elite n = 38; Senior Amateur, n = 40 and Senior Elite, n = 50) across England, New Zealand, South Africa, Portugal and USA (a total of 51,106 tackles). A multi-level logistic regression model with tackle success as the outcome variable and first point of contact and type of tackle as the explanatory variables were computed. Included in the model as cofounders were the situational variables tackle direction, tackle sequence, number of players in the tackle and attacker intention. Post-estimation marginal effects were used to calculate the probabilities (expressed as a percentage %) of tackle success for each interaction between tackle type (active shoulder, smother, passive shoulder and arm) and the first point of contact (shoulder, mid-torso and legs). The probability of tackle success in relation to where the ball-carrier is contacted varied by tackle type and within each age group. The probabilities (Pr) for contacting the shoulder versus mid-torso at the senior levels (elite and amateur) did not differ in relation to tackle success (for instance, for active shoulder tackles within senior elite; shoulder Pr 86% 95% CI 82-89 and mid-torso Pr 82% 95% CI 77-86), whereas at the junior levels, contacting the shoulder had a higher probability than other points of contact. Active shoulder tackles had the highest probability of tackle success across the different playing levels across the different contact heights, whereas arm tackles had the lowest probability (for instance, for mid-torso tackles within senior elite, active Pr 82% 95% CI 77-86 vs. arm Pr 69% 95% CI 64-75). Coaches and practitioners can use this information to improve tackle training design and planning within the different age groups and facilitate player development.

Authors

Institutions

Publication Details

Journal
European Journal of Sport Science
Published
2025-07-12
DOI
https://doi.org/10.1002/ejsc.70003
Primary Topic
Sports injuries and prevention
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Tackle Height and Tackle Success—An Analysis of 52,204 Tackle Events

Ross Tucker, Dan Weaving, Andrew E. Kilding, Nicholas Dalton‐Barron et al.
European Journal of Sport Science
Sports injuries and prevention
article

Tackle Height and Tackle Success—An Analysis of 52,204 Tackle Events

Ross Tucker, Dan Weaving, Andrew E. Kilding, Nicholas Dalton‐Barron, Sean Scantlebury, Kenneth Till, Luís Vaz, Christopher G. Owen, Peter Mackreth, Nicholas Gill, Sharief Hendricks, Steve den Hollander, Stephen West, Keith Stokes, Kathryn Dane, J. O’Reilly, Steve Kemp, Mark Lambert, Ben Jones, Jin W. Tee, K. Spencer, F. McKnight
article en

Abstract

To compare the probability of tackle success (the tackler preventing the ball-carrier and ball from progressing towards the tackler try-line) when contacting the ball-carrier at different heights (shoulder, mid-torso and legs) for different types of tackles (active, passive, smother and arm) while accounting for other tackler situational factors within seven playing levels. Video footage of 271 male rugby union matches were analysed across seven playing groups (Under [U] 12, n = 25 matches; U14, n = 35; U16, n = 39; U18 Amateur n = 39; U18 Elite n = 38; Senior Amateur, n = 40 and Senior Elite, n = 50) across England, New Zealand, South Africa, Portugal and USA (a total of 51,106 tackles). A multi-level logistic regression model with tackle success as the outcome variable and first point of contact and type of tackle as the explanatory variables were computed. Included in the model as cofounders were the situational variables tackle direction, tackle sequence, number of players in the tackle and attacker intention. Post-estimation marginal effects were used to calculate the probabilities (expressed as a percentage %) of tackle success for each interaction between tackle type (active shoulder, smother, passive shoulder and arm) and the first point of contact (shoulder, mid-torso and legs). The probability of tackle success in relation to where the ball-carrier is contacted varied by tackle type and within each age group. The probabilities (Pr) for contacting the shoulder versus mid-torso at the senior levels (elite and amateur) did not differ in relation to tackle success (for instance, for active shoulder tackles within senior elite; shoulder Pr 86% 95% CI 82-89 and mid-torso Pr 82% 95% CI 77-86), whereas at the junior levels, contacting the shoulder had a higher probability than other points of contact. Active shoulder tackles had the highest probability of tackle success across the different playing levels across the different contact heights, whereas arm tackles had the lowest probability (for instance, for mid-torso tackles within senior elite, active Pr 82% 95% CI 77-86 vs. arm Pr 69% 95% CI 64-75). Coaches and practitioners can use this information to improve tackle training design and planning within the different age groups and facilitate player development.

European Journal of Sport ScienceVol. 25(8)
University of Trás-os-Montes and Alto Douro (PT), University of Cape Town (ZA), University of Calgary (CA), Chinese University of Hong Kong (HK), The Football Association (GB), Auckland University of Technology (NZ), London School of Hygiene & Tropical Medicine (GB), World Rugby (IE), Irish Rugby Football Union (IE), University of Bath (GB), University of Waikato (NZ), Leeds Beckett University (GB), Australian Catholic University (AU)
Openalex Percentile: Top 78%
Sports injuries and prevention
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.