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

  • S. Hendricks*
  • , K. Till
  • , S. Scantlebury
  • , N. Dalton-Barron
  • , S. den Hollander
  • , N. Gill
  • , S. Kemp
  • , A. Kilding
  • , M. Lambert
  • , P. Mackreth
  • , J. O’Reilly
  • , Cameron Owen
  • , K. Spencer
  • , K.A. Stokes
  • , J.C. Tee
  • , R. Tucker
  • , L. Vaz
  • , D. Weaving
  • , S. W. West
  • , K. Dane
  • F. McKnight, Ben Jones
*Corresponding author for this work
  • University of Cape Town
  • Leeds Beckett University
  • Rugby Football League Limited
  • University of Waikato
  • Rugby Football Union
  • London School of Hygiene and Tropical Medicine
  • Auckland University of Technology
  • Chinese University of Hong Kong
  • University of Bath, Department for Health
  • University of Bath
  • World Rugby
  • Universidade de Trás-os-Montes e Alto Douro
  • University of Calgary
  • Premiership Rugby
  • Australian Catholic University

Research output: Contribution to journalArticle (journal)peer-review

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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.

Original languageEnglish
Article numbere70003
Pages (from-to)1-11
JournalEuropean Journal of Sport Science
Volume25
Issue number8
Early online date12 Jul 2025
DOIs
Publication statusPublished - 30 Aug 2025

Keywords

  • game analysis
  • injury and prevention
  • performance
  • skill
  • team sport
  • Humans
  • Male
  • Logistic Models
  • Competitive Behavior
  • Football/physiology
  • Shoulder
  • Athletic Performance/physiology
  • Video Recording

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