(P100108)| Volume 25, SUPPLEMENT 2, S18, November 2022

Determining stroke and movement profiles in competitive tennis match-play from wearable sensor accelerometry

      Introduction: The external load profile of tennis consists of repeated hitting and running actions, though appropriate technology to capture these concurrent demands are limited. Recent innovations in commercial wearable technology have revealed tennis-specific algorithms are able to detect forehand, backhand and serve stroke events alongside traditional movement metrics. Consequently, this study determined stroke and movement accelerometry metrics from a wearable sensor and compared between court surface (grass vs. hard) and match outcome (win vs. loss) during competitive tennis match-play.
      Methods: Eight junior high-performance tennis players wore a trunk-mounted GPS, with in-built accelerometer, magnetometer and gyroscope during singles matches on hard and grass courts. Manufacturer software calculated accelerometer-derived total Player Load (tPL). A prototype algorithm classified forehands, backhands, serves and “other” strokes, thereby calculating stroke player load (sPL) from individual strokes. Movement player load (mPL) was calculated as the difference between tPL and sPL, with all metrics reported as absolute and relative (.min-1, %, .stroke). Analysis of accelerometer load and stroke count metrics were performed via a two-way (surface [grass vs. hard] x match outcome [win vs. loss]) ANOVA (p < 0.05) and effect sizes (Cohen’s d).
      Results: Respective mPL and sPL were reported at 431 ±185 and 116 ±55 arbitrary units (AU) during typical hard court match-play. No interaction effects for surface and match outcome existed for absolute tPL, mPL and sPL (p>0.05). Increased mPL% featured on grass courts compared to hard courts (83 ±2) vs. 79 ±5), while sPL% was increased on hard courts (p=0.04, d=1.18[0.31-2.02]). Elevated sPL.min-1 existed on hard courts (p=0.04, d=1.19[0.32-2.04]), but no differences in tPL.min-1 and mPL.min-1 were evident for surface or outcome (p>0.05). Relative forehand sPL (FH-sPL.min-1) was higher on hard courts (p=0.03, d=1.18[0.31-2.02]) alongside higher forehand counts (p=0.01, d=1.29[0.40-2.14).
      Discussion: Hitting demands are heightened on hard courts from increased sPL and stroke counts. Conversely, increased mPL% on grass courts likely reflect the specific movement demands from point-play. In combination, these findings suggest that grouping the physical demands of hard and grass courts are likely inappropriate. Physical preparation strategies during training blocks can be tailored towards movement or hitting loads to suit competitive surfaces. Within grass court tournament blocks, detraining effects due to match-play exposures may be heightened due to lower time spent in point-play (i.e., reduced sPL.min-1) and could require supplementary drills from conditioning staff to mitigate this occurrence. Lastly, technical coaches can utilise stroke count measures to improve understandings of hitting load exposures across stroke type during competitive periods.
      Impact and Application to the Field
      • For sport science practitioners, load monitoring surveillance via accelerometry measures can be confidently implemented during training blocks given the sensitivity of sPL to court surface changes, which is reflective of different stroke types used and overall hitting volumes.
      • Strength and conditioning staff working in tennis can maximise available training block time in targeting movement- or stroke-specific physical adaptations dependant on the competitive surface.
      Conflict of Interest Statement: Three of the five authors are currently employed by Tennis Australia and one author is employed by Catapult Sports.