Horizontal Aiming-Point Variability Differs Between Reduced-Distance and Original-Distance Shooting Conditions in Olympic Rifle Shooters

Horizontal Aiming-Point Variability Differs Between Reduced-Distance and Original-Distance Shooting Conditions in Olympic Rifle Shooters | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Horizontal Aiming-Point Variability Differs Between Reduced-Distance and Original-Distance Shooting Conditions in Olympic Rifle Shooters Tartaruga, Dino, Kredel, Ralf This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9243717/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Reduced-distance dry-firing is widely used in rifle shooting when access to full-distance ranges is limited. However, even when target size and height are adjusted, reduced-distance practice alters the geometric relation between translational and rotational control demands and may therefore affect the comparability of aiming-related performance indicators. The present study examined whether horizontal aiming-point variability differs between reduced-distance and original-distance shooting conditions in Olympic rifle shooters. Routine training-monitoring data from 13 national -squad athletes were retrospectively analysed. For the first research question (RQ1), 5 athletes provided both reduced-distance dry-fire observations during the COVID-19 lockdown and original-distance live-fire observations before or after lockdown. For the second, exploratory research question (RQ2), 8 athletes provided original-distance live-fire observations both before and after lockdown; 2 of these athletes had performed reduced-distance dry-firing during lockdown (exposure group), whereas 6 had not (control group). Aiming-point kinematics were recorded with the SCATT MX-02 system. Horizontal aiming-point variability was quantified within an individualized hold phase identified by an adaptive phase-segmentation algorithm. For RQ1, the final analytic sample comprised 2484 shots. Horizontal aiming-point variability was lower under original-distance conditions (M = 6.91 mm, SD = 2.40) than under reduced-distance conditions (M = 10.00 mm, SD = 4.10). In the final linear mixed effects model, reduced-distance conditions were associated with higher horizontal aiming-point variability than original-distance conditions, Estimate = 1.60 mm, 95% CI [1.02, 2.18], t(2482) = 5.38, p < .001. For RQ2, the final analytic sample comprised 1889 original-distance live-fire shots. The group × phase interaction was significant, Estimate = -1.07 mm, 95% CI [-1.90, -0.24], t(1885) = -2.54, p = .011, indicating that the pre- to post-lockdown change differed between groups. However, this exploratory finding should be interpreted cautiously because the exposure group comprised only two athletes. Reduced-distance dry-firing was associated with acutely higher horizontal aiming-point variability than original-distance live-firing. The present findings may be interpreted in terms of at least three non-exclusive explanations: geometrical scaling effects, altered motor-control compensation under reduced-distance conditions, and distance-dependent projection issues in the measurement system. In addition, the exploratory pre-post pattern suggests that longer-term adaptation cannot be excluded. Controlled research is needed to determine the relative contribution of these explanations to reduced-distance effects on stability of hold. Geometry Sports Medicine and Kinesiology Psychology Aiming Small-Bore Dry-Firing Shooting Motor Control Linear mixed-effects model Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Rifle shooting is a sport discipline in which fine-motor control constitutes a central performance-limiting factor. In the Olympic 50 m small-bore three-position event, the athletes fire 20 shots in each of the three (kneeling, prone and standing) positions. To score a 10.0, the deviation of the projectile’s center from the target center may not exceed 8 mm, which results from the projectile radius (2.8 mm) and the radius of the 10-ring (5.2 mm) (ISSF, 2023 ). Various factors can affect such deviations of the shot locations from the target center. External conditions, such as weather (Mishchenko, 2013 ; O’Brien, 1985 ) and mechanical tolerances of ammunition (Ladommatos, 2021 ) and firearm (Gladyszewska, 2013 ; Sequard-Base et al., 2018 ) contribute to the observed variability, but are largely unrelated to the athlete. By contrast, athlete-related factors, particularly those linked to motor control, have been reported to be associated with shooting performance (Spancken et al., 2021 ; Sundaram et al., 2024 ), especially in the standing position of small-bore shooting, where the largest loss of points is typically observed (Henry et al., 2018 ; Lang et al., 2024 ). Since research on 50 m small-bore standing shooting is scarce, relevant performance characteristics must largely be inferred from the closely related discipline of 10 m air-rifle standing shooting, as summarized in the recent systematic review by Sundaram et al. ( 2024 ). Within this body of literature, horizontal aiming-point variability has been identified as one of the motor-control-related parameters most strongly associated with shooting performance. Typically defined as the horizontal stability of the aiming-point during the final second before shot release, it showed a correlation of -0.63 with shooting performance across three studies, indicating that smaller horizontal fluctuations were associated with higher shooting scores (Ball, 2003 ; Ihalainen, Linnamo, et al., 2016 ; Lang & Zhou, 2022 ). Consistent with this association, Ihalainen, Kuitunen, et al. ( 2016 ) reported that horizontal holding ability accounted for 54% of the variance in shooting performance, and that this relationship was evident in both training and competition. Horizontal stability of hold can therefore be regarded as a key technical parameter in rifle shooting performance. To train this key technical component, several methods are available, of which dry-fire training is a commonly used option. Dry-firing refers to shooting without ammunition and is regularly used both on and off the shooting range (Anderson, 2020 ; Olympic Channel Writer, 2020 ). Many shooters dry-fire at home when team or club ranges are unavailable (Anderson, 2020 , p. 12). For most shooters, dry-firing before qualification and final rounds is standard practice at international competitions. It serves to warm up, attune movement sequences, and prepare mentally. Athletes also use dry-firing during travel or on non-shooting days to maintain kinesthetic feel (Bühlmann & Reinkemeier, 2022 , p. 226). In an intervention study, albeit without a proper control group, training effects were even attributed to dry-firing (Saini et al., 2023 ). Dry-firing is also often used when only restricted space is available, for example in the dry-firing and warm-up areas provided by competition organizers (ISSF, 2023 , p. 199) or in scientific laboratories (Liao et al., 2025 , p. 2010). To preserve shooting stance and natural point of aim (Hariri et al., 2012 ) during reduced-distance dry-firing, target size and target height should be adapted so that relative target size and visual aiming angle correspond to the original competition setup (ISSF, 2023 ; Kucharczyk, 2005 ; Reinkemeier & Bühlmann, 2025 ; Rosner, 2020 ). However, even with such adjustments, reduced-distance practice cannot fully reproduce original-distance conditions because the underlying motor-control demands are not identical. From a kinematic perspective, deviations of the aiming-point can arise from both rotational and translational movements of the rifle as a rigid body (Winter, 2009 ). While the angular precision required to maintain alignment with the target remains essentially unchanged when the setup is geometrically scaled, the tolerance for translational displacement decreases in proportion to target size. Accordingly, a deviation of 8 mm, which is still sufficient to score a 10.0 at 50 m, corresponds to only 0.16 mm in a 1 m setup. Reduced-distance dry-firing therefore alters the relative contribution of rotational and translational control demands, which may affect aiming behavior and limits the direct comparability of technical parameters with original-distance shooting. Several studies over the past two decades have examined shooting under non-standard distances, whether in live- or dry-fire contexts, often because of facility constraints or other practical reasons (Deeny et al., 2009 ; Goodman et al., 2009 ; Konttinen, 2000 ; Liao et al., 2025 ). These studies relied on virtual shot-placement estimates from opto-electronic training systems, although the validity of such estimates is not beyond doubt (Bale & Wilkinson, 2023 ; Zanevskyy et al., 2009 , 2014 ). The standard device used in Olympic shooting practice is the opto-electronic SCATT system (SCATT, 1991 ), which records a two-dimensional aiming-point trace and a virtual shot location on a target plane. However, virtually estimated shot placements are not sufficiently comparable to real shot placements (Zanevskyy et al., 2009 , p. 68), because ballistic factors affect real but not virtual shot location. Therefore, direct effects of reduced-distance dry-fire training on actual shot placement cannot be examined in this context (ISSF, 2013; SIUS, 2021 ). By contrast, horizontal aiming-point variability remains accessible and has repeatedly been identified as one of the stability-of-hold indicators most strongly associated with shooting performance (Ball, 2003 ; Ihalainen, Linnamo, et al., 2016 ; Lang & Zhou, 2022 ). In summary, reduced-distance dry-fire training is widely practiced and offers clear practical advantages, but it also changes the relation of translational and rotational precision demands compared with original-distance shooting. This limits the direct transferability of technical parameters from reduced-distance to full-distance contexts. Nevertheless, horizontal aiming-point variability has consistently emerged as the stability-of-hold parameter with the strongest explanatory value for shooting performance (Ball, 2003 ; Ihalainen, Linnamo, et al., 2016 ; Lang & Zhou, 2022 ). Building on this evidence, the present study focused on the effects of reduced-distance dry-fire training on this key technical component. Specifically, the first research question (RQ1) examined whether horizontal aiming-point variability differs acutely between reduced-distance dry-firing and live-firing. The second research question (RQ2) examined whether the pre- to post-lockdown change in horizontal aiming-point variability differed between athletes who performed reduced-distance dry-firing during lockdown and a control group without such exposure. Methods Design and Participants We used a retrospective longitudinal observational repeated-measures design, capitalizing on the natural experiment created by the COVID-19 lockdown restrictions. This design was considered most appropriate because an experimental allocation of elite and near-elite athletes to reduced-distance dry-fire training would not have been ethically justifiable, given the possibility that such a training intervention might impair performance-relevant motor control through the incongruent relationship between rotational and translational demands. Instead, we analyzed training data that arose under real-world constraints imposed by the lockdown. A total of 13 athletes from the national shooting federation contributed data to the overall dataset. All athletes were members of the national junior or senior squad, representing the elite and near-elite level of competitive shooting in Switzerland. Participants were recruited through the national and regional training centers, where they train regularly under standardized elite sport structures. Eligibility criteria required athletes to be actively competing at the national or international level, free of injuries that would impair shooting performance, and engaged in regular training at one of the national shooting training centers. All procedures performed in the current study were in accordance with the ethical standards of the national shooting federation’s ethics committee and with the Declaration of Helsinki and its later amendments (World Medical Association, 2013 ). All participants gave written informed consent. Apparatus Aiming-point kinematics were recorded with the opto-electronic shooting analysis system (SCATT MX-02) at 100 Hz, which maps the rifle’s aiming-point onto a virtual target plane (SCATT, 1991 ). According to the user manual of the SCATT MX-02 sensor, the system allows users to employ the system also at reduced distances by providing aiming-point trajectories back-scaled on a virtual target at the original shooting distance (Medvedev, 2018 , pp. 8–9). Data Collection Data was derived from routine training monitoring in elite sport practice. Athletes regularly provided SCATT recordings for performance analysis as part of their usual training process, with the timing and frequency of data submission determined by their individual training plans, coaching arrangements, and practical circumstances rather than by a standardized study protocol (Tartaruga & Kredel, 2025 ). Thus, the dataset reflects applied training practice rather than experimentally scheduled measurement occasions. Data were collected under two shooting conditions: original-distance live-firing, defined as shooting with ammunition at 50 m, and reduced-distance dry-firing, defined here as ammunition-free training performed indoors in home-training environments at distances between 3 m and 7 m, depending on the space available to the respective athlete (Table 1 ). The sequence of conditions was not experimentally manipulated but followed the governmental COVID-19 restrictions. Accordingly, the first phase comprised original-distance live-firing before lockdown (17.01.2020–25.03.2020), the second phase consisted predominantly of reduced-distance dry-firing during lockdown (26.03.2020–28.04.2020), and the third phase comprised original-distance live-firing after lockdown (29.04.2020–09.08.2020), when access to shooting ranges was restored (Table 2 ). Athletes were instructed to calibrate the SCATT system according to the user manual specifically for their shooting distance (Medvedev, 2018 ). Because the two research questions required different data structures, analytic samples differed between analyses. For RQ1, athletes were included if they had provided both reduced-distance dry-fire observations during lockdown and original-distance live-fire observations before or after lockdown, enabling a comparison between shooting conditions. For RQ2, athletes were included if they had provided at least one original-distance live-fire session before and at least one original-distance live-fire session after lockdown. Within this subgroup, athletes who had additionally completed at least one reduced-distance dry-fire session during lockdown formed the exposure group, whereas athletes without such lockdown exposure formed the control group. The distribution of live- and dry-fire observations across phases is shown in Table 1 . Data Processing All processing steps were performed in MATLAB 2024b (Mathworks, 2024 ). SCATT MX-02 aiming-point recordings sampled at 100 Hz were imported from pre-exported spreadsheet files (Žliobaitė, 2014 ) and regularized to an evenly sampled time base using linear interpolation. Stability of hold and related aiming-point variables have traditionally been quantified over the final second prior to shot release (Ball, 2003 ; Ihalainen, Kuitunen, et al., 2016 ; Ihalainen, Linnamo, et al., 2016 ; Lang & Zhou, 2022 ). However, aiming duration may vary both between and within shooters, making a fixed pre-release window less suitable from a motor-control and coaching perspective. We therefore applied a previously developed adaptive phase-segmentation algorithm (Tartaruga & Kredel, 2024 ), which separates each aiming trajectory into an approach phase and a hold phase. Conceptually, the approach phase reflects the directed movement of the aiming-point toward the shooter’s individual holding area. By contrast, the hold phase represents the subsequent period in which the rifle is relatively stabilized, and the aiming-point fluctuates around this individual holding area in a more stationary manner. Thus, the approach phase captures the active approach toward the individual holding area, whereas the hold phase reflects the period during which the athlete attempts to maintain aim within that area prior to shot release. For each shot, the algorithm estimated the onset of the individualized hold phase, and the hold window was defined from that shot specific time frame to 0.2 s before shot release. Horizontal aiming-point variability (StdX) was then calculated as the standard deviation of the horizontal aiming-point coordinates within this individualized hold phase (Fig. 1 ). To distinguish live-fire from dry-fire shots, a recoil-based heuristic was applied. Specifically, the gradient-based radial movement magnitude of the aiming trace was computed for each shot. Shots were classified as live-fired when radial movement exceeded a threshold of 1500 mm/s within a window from − 0.05 to + 0.05 s around shot release; otherwise, they were classified as dry-fired. The threshold was determined across sessions from visual inspection of radial-movement time courses, which typically showed a pronounced post-release increase for live-fired shots but not for dry-fired shots. To reduce undue influence of atypical observations, outlier screening was performed prior to statistical modelling using a Mahalanobis-distance approach (Li et al., 2019 ). Outlier detection was based on horizontal aiming-point variability together with the detected onset of the hold phase. Including this onset allowed us to identify not only unusually large variability values but also observations with atypical phase timing, which could reflect rare edge cases in the automated phase segmentation. Observations exceeding the 95th percentile of the corresponding chi-square distribution were excluded prior to modelling. Table 1 Descriptive characteristics of the 13 athletes who contributed data during the study period. The table reports age, sex, season mean performance, and inclusion in the analytic samples for RQ1 and RQ2. ID Age Sex Season Mean Performance Reduced Distance RQ1 RQ2 1 23 m 580.5 5m 1 1 2 23 m 584.1 3m 1 1 6 24 m 586.6 1 8 27 m 579.5 1 9 19 f 578.9 4m 1 11 24 f 577.8 1 12 21 f 580.3 1 13 28 m 586.5 1 15 15 f 565.4 7m 1 16 27 f 576.5 1 22 22 f 580.1 3m 1 M 21.6 577.9 4.4m SD 3.8 6.3 1.7m Sum 5 8 Table 2 Training exposure of athletes included in the RQ1 analysis across pre-lockdown, lockdown, and post-lockdown phases. The table reports the number of sessions as well as the number of shots performed under original-distance live-fire and reduced-distance dry-fire conditions in each phase. ID Pre-lockdown Lockdown Post-lockdown Sessions Shots Sessions Shots Sessions Shots N N live N dry N N live N dry N N live N dry 1 6 125 85 6 38 182 10 378 63 2 3 196 2 3 119 5 214 8 9 2 59 3 120 15 4 120 5 80 127 22 1 31 6 226 8 230 81 Sum 10 321 118 21 38 706 31 1022 279 Table 3 Training exposure of athletes included in the RQ2 analysis before and after lockdown. Athletes in the exposure group (EG) had performed reduced-distance dry-firing during lockdown, whereas athletes in the control group (CG) had not. The table reports the number of original-distance live-fire sessions and shots available for the pre- and post-lockdown phases. ID Pre-lockdown Post-lockdown Group N sessions N shots live N sessions N shots live 1 EG 4 125 10 375 2 EG 3 189 5 206 6 CG 1 40 1 38 8 CG 2 76 2 51 11 CG 1 26 2 71 12 CG 2 78 7 278 13 CG 5 198 1 40 16 CG 1 32 2 66 Sum 19 764 30 1125 Statistical Analysis All statistical analyses were conducted in MATLAB 2024b (Mathworks, 2024 ) using linear mixed-effects models fitted with fitlme (Pinheiro & Bates, 1996 ). Mixed-effects modelling was used because the data were hierarchical and unbalanced, with shots nested within sessions and sessions nested within athletes. Random intercepts for athletes and for sessions nested within athletes accounted for between-athlete differences in baseline horizontal aiming-point variability and session-specific fluctuations. Because the data were derived from routine practice, shot counts varied within and between athletes. All available shots were therefore retained, and the resulting imbalance was addressed within the mixed-effects framework (Cnaan et al., 1997 ; Schielzeth et al., 2020 ; Wiley & Rapp, 2019 ). Given the limited number of higher-level units, random-effects structures were kept parsimonious, and more complex random-slope specifications were not pursued because they were not sufficiently supported by the data and would have reduced model stability and interpretability (Bates et al., 2018 ; Matuschek et al., 2017 ; Meteyard & Davies, 2020 ; Wiley & Rapp, 2019 ). Prior to modelling, observations with zero values or missing values in the dependent variable were removed. Outlier screening was then performed using a Mahalanobis-distance approach (Li et al., 2019 ; Mahalanobis, 1936 ). For the primary outcome horizontal aiming-point variability during the individualized hold phase (StdX), outlier detection was based on StdX together with the estimated onset of the hold phase, allowing the identification of observations with atypical variability or atypical phase timing. Observations exceeding the 95th percentile of the corresponding chi-square distribution were excluded from the analyses. Model assumptions were evaluated for the final mixed-effects models using residual diagnostics, including inspection of residual-versus-fitted plots and normal probability plots of residuals. Where these diagnostics indicated potential deviations from normality, supplementary robustness checks were performed using transformed versions of the dependent variable, specifically log-transformed outcomes. Robustness with respect to outlier handling was additionally examined in supplementary analyses. Model building followed a stepwise and predefined increase in complexity. Specifically, progressively more complex fixed-effects structures were fitted and compared along a clear decision path, such that each increase in model complexity was evaluated against the preceding model. These comparisons were based on maximum likelihood estimation and assessed using log-likelihood, Akaike’s information criterion (AIC), and Bayesian information criterion (BIC), together with likelihood-ratio tests for nested models. This stepwise approach was used to determine whether the added model complexity was supported by the data before proceeding to the next level of specification. Final parameter estimates were then obtained by refitting the selected models with restricted maximum likelihood estimation. Fixed effects in the final models were evaluated using coefficient t-statistics and term-wise F-tests. Effect sizes are reported as unstandardized fixed-effect estimates on the original millimeter scale together with 95% confidence intervals. For RQ1, the fixed-effects structure was specified to test whether horizontal aiming-point variability differed between live-fire and reduced-distance dry-fire observations. Model comparisons therefore examined whether inclusion of condition improved fit over simpler specifications, with modality retained as the principal fixed effect of interest in the final model. For RQ2, analyses were restricted to live-fire observations collected at the original distance during the pre- and post-lockdown phases. The fixed-effects structure was specified to test whether longitudinal change differed between the exposure and control groups, with the group-by-phase interaction as the central term of interest. Model comparisons therefore examined whether inclusion of this interaction improved fit over simpler specifications, and it was retained in the final model. Results The overall dataset comprised training-monitoring data from 13 athletes (Table 1 ). Because the two research questions required different observation structures, separate analytic samples were defined retrospectively (Tables 1 – 3 ). After removal of 3 zero values, 6 missing values, and 139 multivariate outliers, the final RQ1 dataset comprised 2484 shots from 5 athletes. Of these, 1381 shots were recorded under original-distance conditions and 1103 under reduced-distance conditions. For RQ2, observations outside the defined phase and group criteria were first excluded, after which 10 zero values, 20 missing values, and 72 multivariate outliers were removed, resulting in a final dataset of 1889 shots from 8 athletes. Of these, 2 athletes had performed reduced-distance dry-firing during lockdown and were assigned to the exposure group (EG), whereas 6 athletes without such reduced distance dry-firing exposure formed the control group (CG). RQ1: Comparison between original-distance and reduced-distance shooting conditions Horizontal aiming-point variability was descriptively lower under original-distance conditions than under reduced-distance conditions. Mean horizontal aiming-point variability was 6.91 mm (SD = 2.40) at original distance and 10.00 mm (SD = 4.10) at reduced distance. In the final REML-fitted model, shooting condition significantly predicted horizontal aiming-point variability, F(1, 2482) = 28.96, p < .001. Reduced-distance conditions were associated with higher horizontal aiming-point variability than original-distance conditions, with an estimated difference of 1.60 mm (SE = 0.30), t(2482) = 5.38, p < .001, 95% CI [1.02, 2.18]. The pattern of results remained unchanged in supplementary analyses based on log-transformed outcomes and analysis with and without outlier exclusion. RQ2: Exploratory pre-post comparison Horizontal aiming-point variability in the CG descriptively increased slightly from 5.21 mm (SD = 1.88) before lockdown to 5.40 mm (SD = 2.21) after lockdown. In the EG, horizontal aiming-point variability decreased from 7.69 mm (SD = 2.21) before lockdown to 6.67 mm (SD = 1.97) after lockdown. To examine whether the pre- to post-lockdown change differed between groups, model complexity was increased stepwise as specified in the Methods. The final model retained the group × phase interaction and session-level random intercepts nested within athletes. In the final REML-fitted model, the group × phase interaction was statistically significant, F(1, 1885) = 6.44, p = .011. The estimated interaction effect was − 1.07 mm (SE = 0.42), indicating that the pre- to post-lockdown change in horizontal aiming-point variability differed between groups. Specifically, the EG showed a larger decrease from pre- to post-lockdown than the CG, 95% CI [− 1.90, − 0.24], t(1885) = − 2.54, p = .011. The main effect of phase was not statistically significant, Estimate = 0.39 mm, SE = 0.29, 95% CI [− 0.18, 0.95], t(1885) = 1.33, p = .182. By contrast, the main effect of group indicated higher pre-lockdown variability in the EG than in the CG, Estimate = 2.32 mm, SE = 1.06, 95% CI [0.24, 4.39], t(1885) = 2.19, p = .028. Supplementary robustness checks based on log-transformed outcomes and analyses with and without outlier exclusion showed the same overall pattern for the group × phase interaction, whereas the main effect of group was no longer statistically significant. Discussion The present study examined horizontal aiming-point variability in elite and near-elite rifle shooters under original-distance and reduced-distance shooting conditions, using training data collected before, during, and after the COVID-19 lockdown in Switzerland. The main finding was that reduced-distance dry-firing was associated with higher horizontal aiming-point variability than live-firing. In addition, the exploratory pre-post analysis suggested that the change in live-fire variability from before to after lockdown differed between athletes with and without reduced-distance dry-fire exposure during lockdown. Because this second finding was based on a very small exposure group, it should be interpreted cautiously. Difference Between Reduced-Distance and Original-Distance Shooting The higher horizontal aiming-point variability observed under reduced-distance conditions is plausibly related to altered task demands. Even when target size and target height are adapted to preserve the general visual geometry of the task, reduced-distance setups do not fully reproduce original-distance conditions. In particular, the relative weighting of translational and rotational control demands changes. Whereas rotational deviations scale proportionally with target distance, translational deviations become relatively more consequential when athletes aim at smaller scaled targets over shorter distances. Under such conditions, even very small translational displacements represent a larger fraction of the target area, which may make horizontal hold control effectively more demanding. This has important implications for the interpretation of previously reported technical parameters such as horizontal aiming-point variability. Studies that do not specify shooting distance may report values that are not directly comparable, because shorter distances systematically alter the biomechanical demands of stability of hold. A correction factor accounting for different distances could therefore be considered in future meta-analyses to help distinguish genuine technical differences from distance-dependent scaling effects. Importantly, in the most recent systematic review (Sundaram et al., 2024 ), all included studies reporting horizontal aiming-point variability were conducted at the standardized distance of 10 m. However, these studies varied in firearm type, including air rifle (Ihalainen et al., 2018 ; Ihalainen, Kuitunen, et al., 2016 ; Ihalainen, Linnamo, et al., 2016 ; Lang, 2022 ), air pistol (Hawkins, 2011 ; Olsson & Laaksonen, 2021 ), and assault rifle (Mononen et al., 2007 ), all of which entail different target sizes. Thus, although the nominal shooting distance was identical, the underlying motor demands and precision requirements were not. Direct comparison of horizontal aiming-point variability across shooting disciplines should therefore be made with caution. The present findings may be interpreted in terms of at least two non-exclusive explanations. The first explanation is a geometrical scaling explanation . According to this account, the underlying translational movements of the rifle may be similar under reduced- and original-distance conditions, but these movements are projected onto a geometrically scaled target. Because the SCATT system automatically back-scales the recorded aiming-point trace to the target plane corresponding to the original shooting distance, the same absolute translational displacement may occupy a larger proportion of the reduced target area and may therefore appear as inflated horizontal aiming-point variability when represented on the target plane. A second explanation is a distance-specific compensation explanation . In this view, skilled shooters may have learned over many years to stabilize the aiming point at original distance not only by minimizing translational disturbances, but also by compensating for them through fine rotational adjustments of the rifle (Arutyunyan et al., 1968 , 1969 ; Zatsiorski & Aktov, 1990 ). Under reduced-distance conditions, however, the coordination patter may no longer be equally effective, because keeping the aiming point within the smaller scaled target area may require a different balance between translational control and rotational compensation. The resulting increase in horizontal aiming-point variability would therefore reflect not merely geometrical inflation, but also a reduced fit between a well-practised compensation strategy and the altered task geometry. Possible Adaptation Following Reduced-Distance Practice The exploratory pre-post analysis suggested a difference in original-distance live-fire horizontal aiming-point variability between athletes with and without reduced-distance dry-fire exposure during lockdown. Descriptively, the exposure group showed lower live-fire variability after lockdown than before lockdown, whereas the non-exposure group showed little overall change. However, this finding must be interpreted with caution. Most importantly, the exposure group comprised only two athletes, and exposure was not experimentally assigned. In addition, supplementary robustness checks preserved the overall interaction pattern but not the baseline group difference, which is reassuring insofar as it reduces concern that the observed pattern was driven primarily by a generally worse exposure group. Overall, the analysis does not support causal inference and should be regarded as hypothesis-generating rather than confirmatory. Within these constraints, the observed pattern does not exclude the possibility that reduced-distance practice may, under some conditions, support later adaptation. One possibility is that repeated exposure to reduced-distance conditions encouraged shooters to reduce the magnitude of translational disturbances themselves, for example through improved postural control. Another possibility is that practice under altered geometry challenged the established coupling between translational disturbances and compensatory rotational adjustments, thereby inducing adaptation in the underlying control strategy. If so, reduced-distance practice may have acted as a perturbation or a form of “desirable difficulty” (Guadagnoli & Lee, 2004 ; Thomas et al., 2025 ) that required athletes to recalibrate a previously learned motor-control solution. This could help explain why reduced-distance conditions were associated with acutely higher horizontal aiming-point variability, while the exploratory longitudinal pattern did not rule out the possibility of later adaptation under original-distance live-fire conditions. At present, however, these mechanisms remain speculative, and the present design does not allow conclusions as to whether the observed pattern was attributable to reduced-distance dry-fire exposure itself. Comparison with related training contexts The challenges of reduced-distance aiming are not unique to dry-firing. In competitions such as the National Collegiate Athletic Association (NCAA) in the United States athletes frequently compete indoors at reduced distances (15.24m) with live ammunition with smallbore rifles (Goldschmied & Kowalczyk, 2014 ). Although ballistic feedback is preserved in such contexts, the altered scaling of targets still changes the motor requirements of hold control. How elite Olympic shooters perceive the transition from NCAA-style shooting (15.24m) to Olympic standard (50m) ranges remains an open question. More generally, elite rifle shooters are repeatedly confronted with changes in task geometry across disciplines, for example when switching from 10 m air rifle to 50 m smallbore shooting. From a translational perspective, the tolerance for scoring a 10 differs substantially between these disciplines, being 2.5 mm in air rifle and 8.0 mm in 50 m smallbore. At the same time, the corresponding rotational demands also differ, amounting to approximately 0.014° in air rifle and 0.0092° in smallbore shooting. Comparative investigations could therefore clarify whether transitions between such shooting contexts produce similar destabilization effects. Practical implications for training The acute difference between shooting conditions is likely the most robust practical message of the present study. Reduced-distance dry-firing should not be assumed to be technically equivalent to live-firing at original distance when horizontal aiming-point variability is used as the technical outcome measure. This does not mean that reduced-distance dry-firing lacks value. It may still help maintain technical routines, trigger control, attentional structure, and aspects of mental preparation when access to shooting ranges is limited. However, coaches and athletes should be aware that it appears to impose different immediate demands on stability of hold than original-distance live-firing. From a practical perspective, it is also important to distinguish between dry-firing and holding exercises. Dry-firing refers to aiming at a target and executing a shot without ammunition (Saini et al., 2023 ), whereas holding exercises typically involve stabilizing the rifle for a defined period without a specific visual aiming task (Laaksonen et al., 2011 ). Reduced-distance dry-firing and holding exercises specifically without aiming at a visual reference point should therefore not be treated as equivalent training forms, as they likely impose different demands on sensorimotor control and motor learning. Future studies should distinguish clearly between these forms of practice when examining training at reduced distances. Accordingly, reduced-distance dry-firing may be less suitable as a direct substitute for original-distance live-firing immediately before competition, when maximal task specificity is required. In preparatory phases, it may still be useful, but its application should ideally be documented more systematically, including actual training distance, target scaling, session frequency, and exposure volume, to enable inference of specific effects on successive performance. Limitations Several limitations should be considered. First, the exact dry-fire distances differed between athletes and were therefore not uniform over dry-fire exposure. Second, the study was based on an observational natural experiment rather than controlled assignment, which limits causal interpretation. Third, the exploratory group comparison in RQ2 was based on only two athletes in the lockdown dry-fire exposure group, making this part of the study especially vulnerable to instability and overinterpretation. Fourth, the data structure was substantially unbalanced across athletes, phases, and sessions. Mixed-effects models are well suited to such hierarchical and unbalanced data (Meteyard & Davies, 2020 ; Wiley & Rapp, 2019 ), but they do not compensate for the limited number of higher-level units (i.e., athletes), which constrains the generalizability of the findings. Nevertheless, this limitation should be considered in context: the study did not rely on a selective subsample, but included all available athletes, sessions and shots meeting the predefined inclusion criteria, thereby the athletes represented a near-elite to elite performance level, that is, the population for whom questions about reduced-distance dry-fire training are especially consequential. In this performance range, technical monitoring is commonly interpreted at the level of the individual athlete, because even small changes may be meaningful in practice. Thus, while the limited number of athletes restricts statistical generalizability, the findings may still provide practically useful guidance for individualized training decisions in high-performance settings. A further limitation concerns the use of the SCATT system itself. Previous work has questioned the one-to-one accuracy of SCATT-derived aiming-point coordinates, suggesting that the displayed trajectory may not perfectly reflect actual rifle movement (Zanevskyy et al., 2014 ). Although the present study relied primarily on within-dataset comparisons and on SCATT’s back-scaled target representation, some part of the observed reduced-distance effect may still reflect characteristics of the measurement system rather than motor control alone. Directions for future research Future research should address the present limitations by systematically manipulating dry-fire distance, clearly differentiating between dry-firing and holding exercises, and examining both acute and longer-term effects of reduced-distance practice. More importantly, future work should follow three main directions: testing the geometrical scaling explanation, testing the distance-specific compensation explanation, and examining whether reduced-distance dry-firing can, under certain conditions, serve as a beneficial longer-term training intervention. To examine the geometrical scaling explanation, future studies could combine aiming-point analysis with biomechanical measures of body sway and rifle movement. For example, force-plate recordings of mediolateral center-of-pressure motion could be used to determine whether reduced-distance and original-distance shooting are accompanied by similar postural sway despite different values of horizontal aiming-point variability. Although center-of-pressure measures are not typically subsumed under the definition of stability of hold, previous studies have reported associations between postural balance, aiming-point stability, and shooting performance (Ball, 2003 ; Ihalainen, Linnamo, et al., 2016 ). In parallel, three-dimensional kinematic assessment of rifle motion could help quantify the relative contribution of translational and rotational components of rigid-body movement under both conditions. However, three-dimensional motion capture of rifle movement would not, by itself, be sufficient to determine aiming-point behaviour as projected onto the target plane, because small measurement errors may be amplified during geometric projection (Yılmaz & Amca, 2025 ). If postural sway and rifle movement were largely comparable across distances, whereas projected horizontal aiming-point variability still differed, this would support the interpretation that part of the effect reflects geometrical scaling rather than a genuine change in movement execution. To examine the distance-specific compensation explanation, future studies should test whether shooters adapt to reduced-distance conditions over repeated exposure. If the higher horizontal aiming-point variability reflects a mismatch between altered task geometry and a compensation strategy learned under original-distance conditions, the effect should not necessarily remain constant. Instead, repeated practice at reduced distance may lead to recalibration of the relation between translational disturbances and compensatory rotational adjustments. This prediction could be tested in longitudinal designs with repeated reduced-distance sessions and re-tests at original distance. A particularly informative extension would be to manipulate reduced shooting distance across several graded levels in order to determine whether compensation demands change gradually or whether specific distance thresholds disproportionately disrupt established control strategies. A third direction for future research concerns the possibility that reduced-distance dry-firing may, under certain conditions, support longer-term adaptation rather than merely inducing acute changes. Although the present evidence is limited and based on a very small number of athletes, the exploratory pre–post pattern observed in this study does not exclude the possibility that reduced-distance practice may contribute to improvements in original-distance stability of hold over time. This also mitigates the previously raised ethical concerns that reduced-distance dry-firing interventions might have detrimental effects. Conclusion Reduced-distance dry-firing was associated with acutely higher horizontal aiming-point variability than original-distance live-firing. At the same time, the exploratory pre-post pattern suggests that later adaptation cannot be excluded. Reduced-distance dry-firing should therefore not be considered a technically equivalent substitute for original-distance live-firing immediately before competition. The observed effects may reflect at least three non-exclusive mechanisms: geometrical scaling effects, altered motor-control compensation under reduced-distance conditions, and distance-dependent projection issues in the measurement system. Because key training parameters were not standardized, systematic controlled research is needed to determine the relative contribution of these mechanisms and to clarify whether, and under which conditions, reduced-distance dry-firing may also support longer-term adaptation in stability of hold. Declarations All procedures performed in the current study were approved by the Swiss National Shooting Federation's ethics committee and are in accordance with the Declaration of Helsinki and its later amendments (World Medical Association, 2013). All participants gave written informed consent. Acknowledgements We used generative artificial intelligence for translation, and support in drafting analysis scripts in MATLAB. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9243717","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":613239026,"identity":"818e0962-0f46-44b1-8049-0d830bd28ccb","order_by":0,"name":"Tartaruga, Dino","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABLElEQVRIie2PwUrDQBBAZ1lILpN4TYj0G1YCQTE0v7IhsB79AAP2lFOxv+JJvBlZSC7RXpV6CAjBYwoi8SKmScE2be1VMO8yM7vzmBmAnp6/iFwvXQBaBzKCNgGItyscANtS/Ci4S4nXleXQRllpWEVPtfucVMNzT30ozPdw6rFUfZ1/3roDT9XkG4QvXcWUesCABydjPHOsw2TmX0t0LC0TNlJdHENSdBVWNxjAKUMQimUoM+5IVCiJpD+m6DAykjuUS4YHRa18PXqOXCy2X5EMDaGY8ygmNxKYobWKnW9RTIk24yJl+FTQU3IV+HfNLVF9S50ATzYUfZod5aV7wdSJIM/Vx9AzJ+liMXegTjK7LMMNpYEvI8XOh2Jw+B1SdR5oucfo6enp+Rd8AyHCaNw2qsXQAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0001-8307-9890","institution":"University of Bern","correspondingAuthor":true,"prefix":"","firstName":"Dino","middleName":"","lastName":"Tartaruga","suffix":""},{"id":613239027,"identity":"5a2d9436-9568-4d5e-b962-b6fde46f43c8","order_by":1,"name":"Kredel, Ralf","email":"","orcid":"https://orcid.org/0000-0001-6279-8132","institution":"University of Bern","correspondingAuthor":false,"prefix":"","firstName":"Ralf","middleName":"","lastName":"Kredel","suffix":""}],"badges":[],"createdAt":"2026-03-27 10:43:32","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-9243717/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9243717/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106090422,"identity":"1edb6682-331c-4591-811f-0a01d2f98f70","added_by":"auto","created_at":"2026-04-03 10:48:21","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":173434,"visible":true,"origin":"","legend":"\u003cp\u003eExample of aiming-point trajectories illustrating the separation of approach and hold phases (left) and comparison with a fixed 1-s pre-release window (right). The approach phase (green) reflects the directed movement of the aiming-point toward the individual holding area (yellow), whereas the hold phase represents relatively stationary fluctuations around this area prior to shot release. Horizontal aiming-point variability (StdX) is calculated as the standard deviation of horizontal aiming-point coordinates within the hold phase.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9243717/v1/7950c2054e990f158357071c.png"},{"id":106090425,"identity":"252352f5-13cf-4dca-8fca-822fcf5c5d70","added_by":"auto","created_at":"2026-04-03 10:48:23","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":157477,"visible":true,"origin":"","legend":"\u003cp\u003eClassification of live-fired and dry-fired shots based on radial movement magnitude. Top panels show radial movement (red) around shot release (black) for representative live-fire (left) and dry-fire (right) shots, with the classification threshold (cyan). Bottom panels illustrate corresponding aiming-point trajectories, highlighting the pronounced post-release movement (red) in live-fire (left) compared with dry-fire shots (right).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9243717/v1/0d934c6705eb2d36aeed08b0.png"},{"id":106090423,"identity":"ec6b7557-fee8-4537-9620-d69cab55a7b7","added_by":"auto","created_at":"2026-04-03 10:48:22","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":241612,"visible":true,"origin":"","legend":"\u003cp\u003eHorizontal aiming-point variability (StdX) across shooting conditions (original-distance live-firing vs. reduced-distance dry-firing). Individual shots are shown as semi-transparent points. Colored circles and connecting lines represent athlete-specific means, and black markers indicate overall means across all shots. The significance annotation refers to the fixed effect of shooting condition in the linear mixed-effects model.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9243717/v1/4500f3733cdd107113bce31b.png"},{"id":106094807,"identity":"2dd10ef3-5ef7-470f-8032-dad6ad98a78b","added_by":"auto","created_at":"2026-04-03 11:43:18","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":177783,"visible":true,"origin":"","legend":"\u003cp\u003eHorizontal aiming-point variability (StdX) before and after lockdown for the exposure group (EG) and control group (CG). The exposure group performed reduced-distance dry-firing during lockdown, whereas the control group did not. Individual shots are shown as semi-transparent points. Colored circles and connecting lines represent athlete-specific means within each phase, and black markers indicate overall means. The significance annotation refers to the group × phase interaction in the linear mixed-effects model.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9243717/v1/0080c99d4a3f2ad209b005fa.png"},{"id":106095888,"identity":"d7637dc9-f57e-4de2-82f3-5d9fd6485e98","added_by":"auto","created_at":"2026-04-03 11:51:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1342508,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9243717/v1/04c4912a-c0ce-4477-be3c-6dbfbf8fc010.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eHorizontal Aiming-Point Variability Differs Between Reduced-Distance and Original-Distance Shooting Conditions in Olympic Rifle Shooters\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eRifle shooting is a sport discipline in which fine-motor control constitutes a central performance-limiting factor. In the Olympic 50 m small-bore three-position event, the athletes fire 20 shots in each of the three (kneeling, prone and standing) positions. To score a 10.0, the deviation of the projectile\u0026rsquo;s center from the target center may not exceed 8 mm, which results from the projectile radius (2.8 mm) and the radius of the 10-ring (5.2 mm) (ISSF, \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eVarious factors can affect such deviations of the shot locations from the target center. External conditions, such as weather (Mishchenko, \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e; O\u0026rsquo;Brien, \u003cspan class=\"CitationRef\"\u003e1985\u003c/span\u003e) and mechanical tolerances of ammunition (Ladommatos, \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e) and firearm (Gladyszewska, \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e; Sequard-Base et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e) contribute to the observed variability, but are largely unrelated to the athlete. By contrast, athlete-related factors, particularly those linked to motor control, have been reported to be associated with shooting performance (Spancken et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Sundaram et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), especially in the standing position of small-bore shooting, where the largest loss of points is typically observed (Henry et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Lang et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eSince research on 50 m small-bore standing shooting is scarce, relevant performance characteristics must largely be inferred from the closely related discipline of 10 m air-rifle standing shooting, as summarized in the recent systematic review by Sundaram et al. (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). Within this body of literature, horizontal aiming-point variability has been identified as one of the motor-control-related parameters most strongly associated with shooting performance. Typically defined as the horizontal stability of the aiming-point during the final second before shot release, it showed a correlation of -0.63 with shooting performance across three studies, indicating that smaller horizontal fluctuations were associated with higher shooting scores (Ball, \u003cspan class=\"CitationRef\"\u003e2003\u003c/span\u003e; Ihalainen, Linnamo, et al., \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e; Lang \u0026amp; Zhou, \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). Consistent with this association, Ihalainen, Kuitunen, et al. (\u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e) reported that horizontal holding ability accounted for 54% of the variance in shooting performance, and that this relationship was evident in both training and competition. Horizontal stability of hold can therefore be regarded as a key technical parameter in rifle shooting performance.\u003c/p\u003e\n\u003cp\u003eTo train this key technical component, several methods are available, of which dry-fire training is a commonly used option. Dry-firing refers to shooting without ammunition and is regularly used both on and off the shooting range (Anderson, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Olympic Channel Writer, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). Many shooters dry-fire at home when team or club ranges are unavailable (Anderson, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e, p. 12). For most shooters, dry-firing before qualification and final rounds is standard practice at international competitions. It serves to warm up, attune movement sequences, and prepare mentally. Athletes also use dry-firing during travel or on non-shooting days to maintain kinesthetic feel (B\u0026uuml;hlmann \u0026amp; Reinkemeier, \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e, p. 226). In an intervention study, albeit without a proper control group, training effects were even attributed to dry-firing (Saini et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). Dry-firing is also often used when only restricted space is available, for example in the dry-firing and warm-up areas provided by competition organizers (ISSF, \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e, p. 199) or in scientific laboratories (Liao et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e, p. 2010).\u003c/p\u003e\n\u003cp\u003eTo preserve shooting stance and natural point of aim (Hariri et al., \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e) during reduced-distance dry-firing, target size and target height should be adapted so that relative target size and visual aiming angle correspond to the original competition setup (ISSF, \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e; Kucharczyk, \u003cspan class=\"CitationRef\"\u003e2005\u003c/span\u003e; Reinkemeier \u0026amp; B\u0026uuml;hlmann, \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e; Rosner, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, even with such adjustments, reduced-distance practice cannot fully reproduce original-distance conditions because the underlying motor-control demands are not identical. From a kinematic perspective, deviations of the aiming-point can arise from both rotational and translational movements of the rifle as a rigid body (Winter, \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e). While the angular precision required to maintain alignment with the target remains essentially unchanged when the setup is geometrically scaled, the tolerance for translational displacement decreases in proportion to target size. Accordingly, a deviation of 8 mm, which is still sufficient to score a 10.0 at 50 m, corresponds to only 0.16 mm in a 1 m setup. Reduced-distance dry-firing therefore alters the relative contribution of rotational and translational control demands, which may affect aiming behavior and limits the direct comparability of technical parameters with original-distance shooting.\u003c/p\u003e\n\u003cp\u003eSeveral studies over the past two decades have examined shooting under non-standard distances, whether in live- or dry-fire contexts, often because of facility constraints or other practical reasons (Deeny et al., \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e; Goodman et al., \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e; Konttinen, \u003cspan class=\"CitationRef\"\u003e2000\u003c/span\u003e; Liao et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e). These studies relied on virtual shot-placement estimates from opto-electronic training systems, although the validity of such estimates is not beyond doubt (Bale \u0026amp; Wilkinson, \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e; Zanevskyy et al., \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e). The standard device used in Olympic shooting practice is the opto-electronic SCATT system (SCATT, \u003cspan class=\"CitationRef\"\u003e1991\u003c/span\u003e), which records a two-dimensional aiming-point trace and a virtual shot location on a target plane. However, virtually estimated shot placements are not sufficiently comparable to real shot placements (Zanevskyy et al., \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e, p. 68), because ballistic factors affect real but not virtual shot location. Therefore, direct effects of reduced-distance dry-fire training on actual shot placement cannot be examined in this context (ISSF, 2013; SIUS, \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). By contrast, horizontal aiming-point variability remains accessible and has repeatedly been identified as one of the stability-of-hold indicators most strongly associated with shooting performance (Ball, \u003cspan class=\"CitationRef\"\u003e2003\u003c/span\u003e; Ihalainen, Linnamo, et al., \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e; Lang \u0026amp; Zhou, \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eIn summary, reduced-distance dry-fire training is widely practiced and offers clear practical advantages, but it also changes the relation of translational and rotational precision demands compared with original-distance shooting. This limits the direct transferability of technical parameters from reduced-distance to full-distance contexts. Nevertheless, horizontal aiming-point variability has consistently emerged as the stability-of-hold parameter with the strongest explanatory value for shooting performance (Ball, \u003cspan class=\"CitationRef\"\u003e2003\u003c/span\u003e; Ihalainen, Linnamo, et al., \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e; Lang \u0026amp; Zhou, \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). Building on this evidence, the present study focused on the effects of reduced-distance dry-fire training on this key technical component. Specifically, the first research question (RQ1) examined whether horizontal aiming-point variability differs acutely between reduced-distance dry-firing and live-firing. The second research question (RQ2) examined whether the pre- to post-lockdown change in horizontal aiming-point variability differed between athletes who performed reduced-distance dry-firing during lockdown and a control group without such exposure.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eDesign and Participants\u003c/p\u003e\n\u003cp\u003eWe used a retrospective longitudinal observational repeated-measures design, capitalizing on the natural experiment created by the COVID-19 lockdown restrictions. This design was considered most appropriate because an experimental allocation of elite and near-elite athletes to reduced-distance dry-fire training would not have been ethically justifiable, given the possibility that such a training intervention might impair performance-relevant motor control through the incongruent relationship between rotational and translational demands. Instead, we analyzed training data that arose under real-world constraints imposed by the lockdown.\u003c/p\u003e\n\u003cp\u003eA total of 13 athletes from the national shooting federation contributed data to the overall dataset. All athletes were members of the national junior or senior squad, representing the elite and near-elite level of competitive shooting in Switzerland. Participants were recruited through the national and regional training centers, where they train regularly under standardized elite sport structures. Eligibility criteria required athletes to be actively competing at the national or international level, free of injuries that would impair shooting performance, and engaged in regular training at one of the national shooting training centers.\u003c/p\u003e\n\u003cp\u003eAll procedures performed in the current study were in accordance with the ethical standards of the national shooting federation\u0026rsquo;s ethics committee and with the Declaration of Helsinki and its later amendments (World Medical Association, \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e). All participants gave written informed consent.\u003c/p\u003e\n\u003cp\u003eApparatus\u003c/p\u003e\n\u003cp\u003eAiming-point kinematics were recorded with the opto-electronic shooting analysis system (SCATT MX-02) at 100 Hz, which maps the rifle\u0026rsquo;s aiming-point onto a virtual target plane (SCATT, \u003cspan class=\"CitationRef\"\u003e1991\u003c/span\u003e). According to the user manual of the SCATT MX-02 sensor, the system allows users to employ the system also at reduced distances by providing aiming-point trajectories back-scaled on a virtual target at the original shooting distance (Medvedev, \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e, pp. 8\u0026ndash;9).\u003c/p\u003e\n\u003cp\u003eData Collection\u003c/p\u003e\n\u003cp\u003eData was derived from routine training monitoring in elite sport practice. Athletes regularly provided SCATT recordings for performance analysis as part of their usual training process, with the timing and frequency of data submission determined by their individual training plans, coaching arrangements, and practical circumstances rather than by a standardized study protocol (Tartaruga \u0026amp; Kredel, \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e). Thus, the dataset reflects applied training practice rather than experimentally scheduled measurement occasions.\u003c/p\u003e\n\u003cp\u003eData were collected under two shooting conditions: original-distance live-firing, defined as shooting with ammunition at 50 m, and reduced-distance dry-firing, defined here as ammunition-free training performed indoors in home-training environments at distances between 3 m and 7 m, depending on the space available to the respective athlete (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eThe sequence of conditions was not experimentally manipulated but followed the governmental COVID-19 restrictions. Accordingly, the first phase comprised original-distance live-firing before lockdown (17.01.2020\u0026ndash;25.03.2020), the second phase consisted predominantly of reduced-distance dry-firing during lockdown (26.03.2020\u0026ndash;28.04.2020), and the third phase comprised original-distance live-firing after lockdown (29.04.2020\u0026ndash;09.08.2020), when access to shooting ranges was restored (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Athletes were instructed to calibrate the SCATT system according to the user manual specifically for their shooting distance (Medvedev, \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eBecause the two research questions required different data structures, analytic samples differed between analyses. For RQ1, athletes were included if they had provided both reduced-distance dry-fire observations during lockdown and original-distance live-fire observations before or after lockdown, enabling a comparison between shooting conditions. For RQ2, athletes were included if they had provided at least one original-distance live-fire session before and at least one original-distance live-fire session after lockdown. Within this subgroup, athletes who had additionally completed at least one reduced-distance dry-fire session during lockdown formed the exposure group, whereas athletes without such lockdown exposure formed the control group. The distribution of live- and dry-fire observations across phases is shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003eData Processing\u003c/p\u003e\n\u003cp\u003eAll processing steps were performed in MATLAB 2024b (Mathworks, \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). SCATT MX-02 aiming-point recordings sampled at 100 Hz were imported from pre-exported spreadsheet files (Žliobaitė, \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e) and regularized to an evenly sampled time base using linear interpolation.\u003c/p\u003e\n\u003cp\u003eStability of hold and related aiming-point variables have traditionally been quantified over the final second prior to shot release (Ball, \u003cspan class=\"CitationRef\"\u003e2003\u003c/span\u003e; Ihalainen, Kuitunen, et al., \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e; Ihalainen, Linnamo, et al., \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e; Lang \u0026amp; Zhou, \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, aiming duration may vary both between and within shooters, making a fixed pre-release window less suitable from a motor-control and coaching perspective. We therefore applied a previously developed adaptive phase-segmentation algorithm (Tartaruga \u0026amp; Kredel, \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), which separates each aiming trajectory into an approach phase and a hold phase.\u003c/p\u003e\n\u003cp\u003eConceptually, the approach phase reflects the directed movement of the aiming-point toward the shooter\u0026rsquo;s individual holding area. By contrast, the hold phase represents the subsequent period in which the rifle is relatively stabilized, and the aiming-point fluctuates around this individual holding area in a more stationary manner. Thus, the approach phase captures the active approach toward the individual holding area, whereas the hold phase reflects the period during which the athlete attempts to maintain aim within that area prior to shot release.\u003c/p\u003e\n\u003cp\u003eFor each shot, the algorithm estimated the onset of the individualized hold phase, and the hold window was defined from that shot specific time frame to 0.2 s before shot release. Horizontal aiming-point variability (StdX) was then calculated as the standard deviation of the horizontal aiming-point coordinates within this individualized hold phase (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eTo distinguish live-fire from dry-fire shots, a recoil-based heuristic was applied. Specifically, the gradient-based radial movement magnitude of the aiming trace was computed for each shot. Shots were classified as live-fired when radial movement exceeded a threshold of 1500 mm/s within a window from \u0026minus;\u0026thinsp;0.05 to +\u0026thinsp;0.05 s around shot release; otherwise, they were classified as dry-fired. The threshold was determined across sessions from visual inspection of radial-movement time courses, which typically showed a pronounced post-release increase for live-fired shots but not for dry-fired shots.\u003c/p\u003e\n\u003cp\u003eTo reduce undue influence of atypical observations, outlier screening was performed prior to statistical modelling using a Mahalanobis-distance approach (Li et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). Outlier detection was based on horizontal aiming-point variability together with the detected onset of the hold phase. Including this onset allowed us to identify not only unusually large variability values but also observations with atypical phase timing, which could reflect rare edge cases in the automated phase segmentation. Observations exceeding the 95th percentile of the corresponding chi-square distribution were excluded prior to modelling.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDescriptive characteristics of the 13 athletes who contributed data during the study period. The table reports age, sex, season mean performance, and inclusion in the analytic samples for RQ1 and RQ2.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eID\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSeason Mean Performance\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eReduced Distance\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRQ1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRQ2\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003em\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e580.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003em\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e584.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003em\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e586.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003em\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e579.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e578.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e577.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e580.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003em\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e586.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e565.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e576.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e580.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e577.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.4m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.7m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003cdiv align=\"char\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eTraining exposure of athletes included in the RQ1 analysis across pre-lockdown, lockdown, and post-lockdown phases. The table reports the number of sessions as well as the number of shots performed under original-distance live-fire and reduced-distance dry-fire conditions in each phase.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eID\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003ePre-lockdown\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eLockdown\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003ePost-lockdown\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSessions\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eShots\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSessions\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eShots\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSessions\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eShots\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eN live\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eN dry\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eN live\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eN dry\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eN live\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eN dry\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e378\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e127\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e226\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e321\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e706\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e279\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\" class=\"fr-table-selection-hover\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eTraining exposure of athletes included in the RQ2 analysis before and after lockdown. Athletes in the exposure group (EG) had performed reduced-distance dry-firing during lockdown, whereas athletes in the control group (CG) had not. The table reports the number of original-distance live-fire sessions and shots available for the pre- and post-lockdown phases.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eID\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ePre-lockdown\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ePost-lockdown\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGroup\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eN sessions\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eN shots live\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eN sessions\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eN shots live\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e375\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e189\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e206\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e278\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e764\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1125\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\n\u003cp\u003eAll statistical analyses were conducted in MATLAB 2024b (Mathworks, \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e) using linear mixed-effects models fitted with \u003cem\u003efitlme\u003c/em\u003e (Pinheiro \u0026amp; Bates, \u003cspan class=\"CitationRef\"\u003e1996\u003c/span\u003e). Mixed-effects modelling was used because the data were hierarchical and unbalanced, with shots nested within sessions and sessions nested within athletes. Random intercepts for athletes and for sessions nested within athletes accounted for between-athlete differences in baseline horizontal aiming-point variability and session-specific fluctuations.\u003c/p\u003e\n\u003cp\u003eBecause the data were derived from routine practice, shot counts varied within and between athletes. All available shots were therefore retained, and the resulting imbalance was addressed within the mixed-effects framework (Cnaan et al., \u003cspan class=\"CitationRef\"\u003e1997\u003c/span\u003e; Schielzeth et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Wiley \u0026amp; Rapp, \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). Given the limited number of higher-level units, random-effects structures were kept parsimonious, and more complex random-slope specifications were not pursued because they were not sufficiently supported by the data and would have reduced model stability and interpretability (Bates et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Matuschek et al., \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e; Meteyard \u0026amp; Davies, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Wiley \u0026amp; Rapp, \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003ePrior to modelling, observations with zero values or missing values in the dependent variable were removed. Outlier screening was then performed using a Mahalanobis-distance approach (Li et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Mahalanobis, \u003cspan class=\"CitationRef\"\u003e1936\u003c/span\u003e). For the primary outcome horizontal aiming-point variability during the individualized hold phase (StdX), outlier detection was based on StdX together with the estimated onset of the hold phase, allowing the identification of observations with atypical variability or atypical phase timing. Observations exceeding the 95th percentile of the corresponding chi-square distribution were excluded from the analyses.\u003c/p\u003e\n\u003cp\u003eModel assumptions were evaluated for the final mixed-effects models using residual diagnostics, including inspection of residual-versus-fitted plots and normal probability plots of residuals. Where these diagnostics indicated potential deviations from normality, supplementary robustness checks were performed using transformed versions of the dependent variable, specifically log-transformed outcomes. Robustness with respect to outlier handling was additionally examined in supplementary analyses.\u003c/p\u003e\n\u003cp\u003eModel building followed a stepwise and predefined increase in complexity. Specifically, progressively more complex fixed-effects structures were fitted and compared along a clear decision path, such that each increase in model complexity was evaluated against the preceding model. These comparisons were based on maximum likelihood estimation and assessed using log-likelihood, Akaike\u0026rsquo;s information criterion (AIC), and Bayesian information criterion (BIC), together with likelihood-ratio tests for nested models. This stepwise approach was used to determine whether the added model complexity was supported by the data before proceeding to the next level of specification. Final parameter estimates were then obtained by refitting the selected models with restricted maximum likelihood estimation. Fixed effects in the final models were evaluated using coefficient t-statistics and term-wise F-tests. Effect sizes are reported as unstandardized fixed-effect estimates on the original millimeter scale together with 95% confidence intervals.\u003c/p\u003e\n\u003cp\u003eFor RQ1, the fixed-effects structure was specified to test whether horizontal aiming-point variability differed between live-fire and reduced-distance dry-fire observations. Model comparisons therefore examined whether inclusion of condition improved fit over simpler specifications, with modality retained as the principal fixed effect of interest in the final model.\u003c/p\u003e\n\u003cp\u003eFor RQ2, analyses were restricted to live-fire observations collected at the original distance during the pre- and post-lockdown phases. The fixed-effects structure was specified to test whether longitudinal change differed between the exposure and control groups, with the group-by-phase interaction as the central term of interest. Model comparisons therefore examined whether inclusion of this interaction improved fit over simpler specifications, and it was retained in the final model.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe overall dataset comprised training-monitoring data from 13 athletes (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Because the two research questions required different observation structures, separate analytic samples were defined retrospectively (Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). After removal of 3 zero values, 6 missing values, and 139 multivariate outliers, the final RQ1 dataset comprised 2484 shots from 5 athletes. Of these, 1381 shots were recorded under original-distance conditions and 1103 under reduced-distance conditions.\u003c/p\u003e \u003cp\u003eFor RQ2, observations outside the defined phase and group criteria were first excluded, after which 10 zero values, 20 missing values, and 72 multivariate outliers were removed, resulting in a final dataset of 1889 shots from 8 athletes. Of these, 2 athletes had performed reduced-distance dry-firing during lockdown and were assigned to the exposure group (EG), whereas 6 athletes without such reduced distance dry-firing exposure formed the control group (CG).\u003c/p\u003e \u003cp\u003eRQ1: Comparison between original-distance and reduced-distance shooting conditions\u003c/p\u003e \u003cp\u003eHorizontal aiming-point variability was descriptively lower under original-distance conditions than under reduced-distance conditions. Mean horizontal aiming-point variability was 6.91 mm (SD\u0026thinsp;=\u0026thinsp;2.40) at original distance and 10.00 mm (SD\u0026thinsp;=\u0026thinsp;4.10) at reduced distance.\u003c/p\u003e \u003cp\u003eIn the final REML-fitted model, shooting condition significantly predicted horizontal aiming-point variability, F(1, 2482)\u0026thinsp;=\u0026thinsp;28.96, p \u0026lt; .001. Reduced-distance conditions were associated with higher horizontal aiming-point variability than original-distance conditions, with an estimated difference of 1.60 mm (SE\u0026thinsp;=\u0026thinsp;0.30), t(2482)\u0026thinsp;=\u0026thinsp;5.38, p \u0026lt; .001, 95% CI [1.02, 2.18]. The pattern of results remained unchanged in supplementary analyses based on log-transformed outcomes and analysis with and without outlier exclusion.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eRQ2: Exploratory pre-post comparison\u003c/p\u003e \u003cp\u003eHorizontal aiming-point variability in the CG descriptively increased slightly from 5.21 mm (SD\u0026thinsp;=\u0026thinsp;1.88) before lockdown to 5.40 mm (SD\u0026thinsp;=\u0026thinsp;2.21) after lockdown. In the EG, horizontal aiming-point variability decreased from 7.69 mm (SD\u0026thinsp;=\u0026thinsp;2.21) before lockdown to 6.67 mm (SD\u0026thinsp;=\u0026thinsp;1.97) after lockdown.\u003c/p\u003e \u003cp\u003eTo examine whether the pre- to post-lockdown change differed between groups, model complexity was increased stepwise as specified in the Methods. The final model retained the group \u0026times; phase interaction and session-level random intercepts nested within athletes.\u003c/p\u003e \u003cp\u003eIn the final REML-fitted model, the group \u0026times; phase interaction was statistically significant, F(1, 1885)\u0026thinsp;=\u0026thinsp;6.44, p = .011. The estimated interaction effect was \u0026minus;\u0026thinsp;1.07 mm (SE\u0026thinsp;=\u0026thinsp;0.42), indicating that the pre- to post-lockdown change in horizontal aiming-point variability differed between groups. Specifically, the EG showed a larger decrease from pre- to post-lockdown than the CG, 95% CI [\u0026minus;\u0026thinsp;1.90, \u0026minus;\u0026thinsp;0.24], t(1885)\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;2.54, p = .011.\u003c/p\u003e \u003cp\u003eThe main effect of phase was not statistically significant, Estimate\u0026thinsp;=\u0026thinsp;0.39 mm, SE\u0026thinsp;=\u0026thinsp;0.29, 95% CI [\u0026minus;\u0026thinsp;0.18, 0.95], t(1885)\u0026thinsp;=\u0026thinsp;1.33, p = .182. By contrast, the main effect of group indicated higher pre-lockdown variability in the EG than in the CG, Estimate\u0026thinsp;=\u0026thinsp;2.32 mm, SE\u0026thinsp;=\u0026thinsp;1.06, 95% CI [0.24, 4.39], t(1885)\u0026thinsp;=\u0026thinsp;2.19, p = .028. Supplementary robustness checks based on log-transformed outcomes and analyses with and without outlier exclusion showed the same overall pattern for the group \u0026times; phase interaction, whereas the main effect of group was no longer statistically significant.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present study examined horizontal aiming-point variability in elite and near-elite rifle shooters under original-distance and reduced-distance shooting conditions, using training data collected before, during, and after the COVID-19 lockdown in Switzerland. The main finding was that reduced-distance dry-firing was associated with higher horizontal aiming-point variability than live-firing. In addition, the exploratory pre-post analysis suggested that the change in live-fire variability from before to after lockdown differed between athletes with and without reduced-distance dry-fire exposure during lockdown. Because this second finding was based on a very small exposure group, it should be interpreted cautiously.\u003c/p\u003e \u003cp\u003eDifference Between Reduced-Distance and Original-Distance Shooting\u003c/p\u003e \u003cp\u003eThe higher horizontal aiming-point variability observed under reduced-distance conditions is plausibly related to altered task demands. Even when target size and target height are adapted to preserve the general visual geometry of the task, reduced-distance setups do not fully reproduce original-distance conditions. In particular, the relative weighting of translational and rotational control demands changes. Whereas rotational deviations scale proportionally with target distance, translational deviations become relatively more consequential when athletes aim at smaller scaled targets over shorter distances. Under such conditions, even very small translational displacements represent a larger fraction of the target area, which may make horizontal hold control effectively more demanding.\u003c/p\u003e \u003cp\u003eThis has important implications for the interpretation of previously reported technical parameters such as horizontal aiming-point variability. Studies that do not specify shooting distance may report values that are not directly comparable, because shorter distances systematically alter the biomechanical demands of stability of hold. A correction factor accounting for different distances could therefore be considered in future meta-analyses to help distinguish genuine technical differences from distance-dependent scaling effects. Importantly, in the most recent systematic review (Sundaram et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), all included studies reporting horizontal aiming-point variability were conducted at the standardized distance of 10 m. However, these studies varied in firearm type, including air rifle (Ihalainen et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Ihalainen, Kuitunen, et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Ihalainen, Linnamo, et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Lang, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), air pistol (Hawkins, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Olsson \u0026amp; Laaksonen, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and assault rifle (Mononen et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), all of which entail different target sizes. Thus, although the nominal shooting distance was identical, the underlying motor demands and precision requirements were not. Direct comparison of horizontal aiming-point variability across shooting disciplines should therefore be made with caution.\u003c/p\u003e \u003cp\u003eThe present findings may be interpreted in terms of at least two non-exclusive explanations. The first explanation is a \u003cem\u003egeometrical scaling explanation\u003c/em\u003e. According to this account, the underlying translational movements of the rifle may be similar under reduced- and original-distance conditions, but these movements are projected onto a geometrically scaled target. Because the SCATT system automatically back-scales the recorded aiming-point trace to the target plane corresponding to the original shooting distance, the same absolute translational displacement may occupy a larger proportion of the reduced target area and may therefore appear as inflated horizontal aiming-point variability when represented on the target plane.\u003c/p\u003e \u003cp\u003eA second explanation is a \u003cem\u003edistance-specific compensation explanation\u003c/em\u003e. In this view, skilled shooters may have learned over many years to stabilize the aiming point at original distance not only by minimizing translational disturbances, but also by compensating for them through fine rotational adjustments of the rifle (Arutyunyan et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1968\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1969\u003c/span\u003e; Zatsiorski \u0026amp; Aktov, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e1990\u003c/span\u003e). Under reduced-distance conditions, however, the coordination patter may no longer be equally effective, because keeping the aiming point within the smaller scaled target area may require a different balance between translational control and rotational compensation. The resulting increase in horizontal aiming-point variability would therefore reflect not merely geometrical inflation, but also a reduced fit between a well-practised compensation strategy and the altered task geometry.\u003c/p\u003e \u003cp\u003ePossible Adaptation Following Reduced-Distance Practice\u003c/p\u003e \u003cp\u003eThe exploratory pre-post analysis suggested a difference in original-distance live-fire horizontal aiming-point variability between athletes with and without reduced-distance dry-fire exposure during lockdown. Descriptively, the exposure group showed lower live-fire variability after lockdown than before lockdown, whereas the non-exposure group showed little overall change. However, this finding must be interpreted with caution. Most importantly, the exposure group comprised only two athletes, and exposure was not experimentally assigned. In addition, supplementary robustness checks preserved the overall interaction pattern but not the baseline group difference, which is reassuring insofar as it reduces concern that the observed pattern was driven primarily by a generally worse exposure group. Overall, the analysis does not support causal inference and should be regarded as hypothesis-generating rather than confirmatory.\u003c/p\u003e \u003cp\u003eWithin these constraints, the observed pattern does not exclude the possibility that reduced-distance practice may, under some conditions, support later adaptation. One possibility is that repeated exposure to reduced-distance conditions encouraged shooters to reduce the magnitude of translational disturbances themselves, for example through improved postural control. Another possibility is that practice under altered geometry challenged the established coupling between translational disturbances and compensatory rotational adjustments, thereby inducing adaptation in the underlying control strategy. If so, reduced-distance practice may have acted as a perturbation or a form of \u0026ldquo;desirable difficulty\u0026rdquo; (Guadagnoli \u0026amp; Lee, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Thomas et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) that required athletes to recalibrate a previously learned motor-control solution. This could help explain why reduced-distance conditions were associated with acutely higher horizontal aiming-point variability, while the exploratory longitudinal pattern did not rule out the possibility of later adaptation under original-distance live-fire conditions. At present, however, these mechanisms remain speculative, and the present design does not allow conclusions as to whether the observed pattern was attributable to reduced-distance dry-fire exposure itself.\u003c/p\u003e \u003cp\u003eComparison with related training contexts\u003c/p\u003e \u003cp\u003eThe challenges of reduced-distance aiming are not unique to dry-firing. In competitions such as the National Collegiate Athletic Association (NCAA) in the United States athletes frequently compete indoors at reduced distances (15.24m) with live ammunition with smallbore rifles (Goldschmied \u0026amp; Kowalczyk, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Although ballistic feedback is preserved in such contexts, the altered scaling of targets still changes the motor requirements of hold control. How elite Olympic shooters perceive the transition from NCAA-style shooting (15.24m) to Olympic standard (50m) ranges remains an open question.\u003c/p\u003e \u003cp\u003eMore generally, elite rifle shooters are repeatedly confronted with changes in task geometry across disciplines, for example when switching from 10 m air rifle to 50 m smallbore shooting. From a translational perspective, the tolerance for scoring a 10 differs substantially between these disciplines, being 2.5 mm in air rifle and 8.0 mm in 50 m smallbore. At the same time, the corresponding rotational demands also differ, amounting to approximately 0.014\u0026deg; in air rifle and 0.0092\u0026deg; in smallbore shooting. Comparative investigations could therefore clarify whether transitions between such shooting contexts produce similar destabilization effects.\u003c/p\u003e \u003cp\u003ePractical implications for training\u003c/p\u003e \u003cp\u003eThe acute difference between shooting conditions is likely the most robust practical message of the present study. Reduced-distance dry-firing should not be assumed to be technically equivalent to live-firing at original distance when horizontal aiming-point variability is used as the technical outcome measure. This does not mean that reduced-distance dry-firing lacks value. It may still help maintain technical routines, trigger control, attentional structure, and aspects of mental preparation when access to shooting ranges is limited. However, coaches and athletes should be aware that it appears to impose different immediate demands on stability of hold than original-distance live-firing.\u003c/p\u003e \u003cp\u003eFrom a practical perspective, it is also important to distinguish between dry-firing and holding exercises. Dry-firing refers to aiming at a target and executing a shot without ammunition (Saini et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), whereas holding exercises typically involve stabilizing the rifle for a defined period without a specific visual aiming task (Laaksonen et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Reduced-distance dry-firing and holding exercises specifically without aiming at a visual reference point should therefore not be treated as equivalent training forms, as they likely impose different demands on sensorimotor control and motor learning. Future studies should distinguish clearly between these forms of practice when examining training at reduced distances.\u003c/p\u003e \u003cp\u003eAccordingly, reduced-distance dry-firing may be less suitable as a direct substitute for original-distance live-firing immediately before competition, when maximal task specificity is required. In preparatory phases, it may still be useful, but its application should ideally be documented more systematically, including actual training distance, target scaling, session frequency, and exposure volume, to enable inference of specific effects on successive performance.\u003c/p\u003e \u003cp\u003eLimitations\u003c/p\u003e \u003cp\u003eSeveral limitations should be considered. First, the exact dry-fire distances differed between athletes and were therefore not uniform over dry-fire exposure. Second, the study was based on an observational natural experiment rather than controlled assignment, which limits causal interpretation. Third, the exploratory group comparison in RQ2 was based on only two athletes in the lockdown dry-fire exposure group, making this part of the study especially vulnerable to instability and overinterpretation. Fourth, the data structure was substantially unbalanced across athletes, phases, and sessions. Mixed-effects models are well suited to such hierarchical and unbalanced data (Meteyard \u0026amp; Davies, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Wiley \u0026amp; Rapp, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), but they do not compensate for the limited number of higher-level units (i.e., athletes), which constrains the generalizability of the findings. Nevertheless, this limitation should be considered in context: the study did not rely on a selective subsample, but included all available athletes, sessions and shots meeting the predefined inclusion criteria, thereby the athletes represented a near-elite to elite performance level, that is, the population for whom questions about reduced-distance dry-fire training are especially consequential. In this performance range, technical monitoring is commonly interpreted at the level of the individual athlete, because even small changes may be meaningful in practice. Thus, while the limited number of athletes restricts statistical generalizability, the findings may still provide practically useful guidance for individualized training decisions in high-performance settings.\u003c/p\u003e \u003cp\u003eA further limitation concerns the use of the SCATT system itself. Previous work has questioned the one-to-one accuracy of SCATT-derived aiming-point coordinates, suggesting that the displayed trajectory may not perfectly reflect actual rifle movement (Zanevskyy et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Although the present study relied primarily on within-dataset comparisons and on SCATT\u0026rsquo;s back-scaled target representation, some part of the observed reduced-distance effect may still reflect characteristics of the measurement system rather than motor control alone.\u003c/p\u003e \u003cp\u003eDirections for future research\u003c/p\u003e \u003cp\u003eFuture research should address the present limitations by systematically manipulating dry-fire distance, clearly differentiating between dry-firing and holding exercises, and examining both acute and longer-term effects of reduced-distance practice. More importantly, future work should follow three main directions: testing the geometrical scaling explanation, testing the distance-specific compensation explanation, and examining whether reduced-distance dry-firing can, under certain conditions, serve as a beneficial longer-term training intervention.\u003c/p\u003e \u003cp\u003eTo examine the geometrical scaling explanation, future studies could combine aiming-point analysis with biomechanical measures of body sway and rifle movement. For example, force-plate recordings of mediolateral center-of-pressure motion could be used to determine whether reduced-distance and original-distance shooting are accompanied by similar postural sway despite different values of horizontal aiming-point variability. Although center-of-pressure measures are not typically subsumed under the definition of stability of hold, previous studies have reported associations between postural balance, aiming-point stability, and shooting performance (Ball, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Ihalainen, Linnamo, et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In parallel, three-dimensional kinematic assessment of rifle motion could help quantify the relative contribution of translational and rotational components of rigid-body movement under both conditions. However, three-dimensional motion capture of rifle movement would not, by itself, be sufficient to determine aiming-point behaviour as projected onto the target plane, because small measurement errors may be amplified during geometric projection (Yılmaz \u0026amp; Amca, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). If postural sway and rifle movement were largely comparable across distances, whereas projected horizontal aiming-point variability still differed, this would support the interpretation that part of the effect reflects geometrical scaling rather than a genuine change in movement execution.\u003c/p\u003e \u003cp\u003eTo examine the distance-specific compensation explanation, future studies should test whether shooters adapt to reduced-distance conditions over repeated exposure. If the higher horizontal aiming-point variability reflects a mismatch between altered task geometry and a compensation strategy learned under original-distance conditions, the effect should not necessarily remain constant. Instead, repeated practice at reduced distance may lead to recalibration of the relation between translational disturbances and compensatory rotational adjustments. This prediction could be tested in longitudinal designs with repeated reduced-distance sessions and re-tests at original distance. A particularly informative extension would be to manipulate reduced shooting distance across several graded levels in order to determine whether compensation demands change gradually or whether specific distance thresholds disproportionately disrupt established control strategies.\u003c/p\u003e \u003cp\u003eA third direction for future research concerns the possibility that reduced-distance dry-firing may, under certain conditions, support longer-term adaptation rather than merely inducing acute changes. Although the present evidence is limited and based on a very small number of athletes, the exploratory pre\u0026ndash;post pattern observed in this study does not exclude the possibility that reduced-distance practice may contribute to improvements in original-distance stability of hold over time. This also mitigates the previously raised ethical concerns that reduced-distance dry-firing interventions might have detrimental effects.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eReduced-distance dry-firing was associated with acutely higher horizontal aiming-point variability than original-distance live-firing. At the same time, the exploratory pre-post pattern suggests that later adaptation cannot be excluded. Reduced-distance dry-firing should therefore not be considered a technically equivalent substitute for original-distance live-firing immediately before competition. The observed effects may reflect at least three non-exclusive mechanisms: geometrical scaling effects, altered motor-control compensation under reduced-distance conditions, and distance-dependent projection issues in the measurement system. Because key training parameters were not standardized, systematic controlled research is needed to determine the relative contribution of these mechanisms and to clarify whether, and under which conditions, reduced-distance dry-firing may also support longer-term adaptation in stability of hold.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cspan\u003eAll procedures performed in the current study were approved by the Swiss National Shooting Federation\u0026apos;s ethics committee and are in accordance with the Declaration of Helsinki and its later amendments (World Medical Association, 2013). All participants gave written informed consent.\u003c/span\u003e\u003c/p\u003e\n\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eWe used generative artificial intelligence for translation, and support in drafting analysis scripts in MATLAB. The AI was not used to generate research ideas, design the study, or interpret the results. We reviewed critically, validated and edited all outputs from AI tools. We retain full responsibility for the scientific content, analyses, and conclusions of this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAnderson, G. (2020). \u003cem\u003eTraining\u0026mdash;Part I The Way to Success in Shooting\u003c/em\u003e. https://thecmp.org/wp-content/uploads/2021/01/TRAINING-%E2%80%93-Part-I-The-Way-to-Success-in-Shooting.pdf\u003c/li\u003e\n \u003cli\u003eArutyunyan, G. A., Gurfinkel, V. S., \u0026amp; Mirskii, M. L. (1968). Investigation of aiming at a target. \u003cem\u003eBiofizika\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e(3), 536\u0026ndash;538.\u003c/li\u003e\n \u003cli\u003eArutyunyan, G. A., Gurfinkel, V. S., \u0026amp; Mirskii, M. L. (1969). 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(1990). Biomechanics of highly precise movements: The aiming process in air rifle shooting. \u003cem\u003eJournal of Biomechanics\u003c/em\u003e, \u003cem\u003e23\u003c/em\u003e(1), 35\u0026ndash;41.\u003c/li\u003e\n \u003cli\u003eŽliobaitė, I. (2014). \u003cem\u003eScatt-analysis\u003c/em\u003e [Python]. https://github.com/zliobaite/Scatt-analysis\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"University of Bern","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Aiming, Small-Bore, Dry-Firing, Shooting, Motor Control, Linear mixed-effects model","lastPublishedDoi":"10.21203/rs.3.rs-9243717/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9243717/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eReduced-distance dry-firing is widely used in rifle shooting when access to full-distance ranges is limited. However, even when target size and height are adjusted, reduced-distance practice alters the geometric relation between translational and rotational control demands and may therefore affect the comparability of aiming-related performance indicators. The present study examined whether horizontal aiming-point variability differs between reduced-distance and original-distance shooting conditions in Olympic rifle shooters.\u003c/p\u003e\n\u003cp\u003eRoutine training-monitoring data from 13 national -squad athletes were retrospectively analysed. For the first research question (RQ1), 5 athletes provided both reduced-distance dry-fire observations during the COVID-19 lockdown and original-distance live-fire observations before or after lockdown. For the second, exploratory research question (RQ2), 8 athletes provided original-distance live-fire observations both before and after lockdown; 2 of these athletes had performed reduced-distance dry-firing during lockdown (exposure group), whereas 6 had not (control group). Aiming-point kinematics were recorded with the SCATT MX-02 system. Horizontal aiming-point variability was quantified within an individualized hold phase identified by an adaptive phase-segmentation algorithm.\u003c/p\u003e\n\u003cp\u003eFor RQ1, the final analytic sample comprised 2484 shots. Horizontal aiming-point variability was lower under original-distance conditions (M = 6.91 mm, SD = 2.40) than under reduced-distance conditions (M = 10.00 mm, SD = 4.10). In the final linear mixed effects model, reduced-distance conditions were associated with higher horizontal aiming-point variability than original-distance conditions, Estimate = 1.60 mm, 95% CI [1.02, 2.18], t(2482) = 5.38, p \u0026lt; .001. For RQ2, the final analytic sample comprised 1889 original-distance live-fire shots. The group × phase interaction was significant, Estimate = -1.07 mm, 95% CI [-1.90, -0.24], t(1885) = -2.54, p = .011, indicating that the pre- to post-lockdown change differed between groups. However, this exploratory finding should be interpreted cautiously because the exposure group comprised only two athletes.\u003c/p\u003e\n\u003cp\u003eReduced-distance dry-firing was associated with acutely higher horizontal aiming-point variability than original-distance live-firing. The present findings may be interpreted in terms of at least three non-exclusive explanations: geometrical scaling effects, altered motor-control compensation under reduced-distance conditions, and distance-dependent projection issues in the measurement system. In addition, the exploratory pre-post pattern suggests that longer-term adaptation cannot be excluded. Controlled research is needed to determine the relative contribution of these explanations to reduced-distance effects on stability of hold.\u003c/p\u003e","manuscriptTitle":"Horizontal Aiming-Point Variability Differs Between Reduced-Distance and Original-Distance Shooting Conditions in Olympic Rifle Shooters","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-03 10:48:17","doi":"10.21203/rs.3.rs-9243717/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bed5f07d-e210-40cd-aa12-90fa3bf84545","owner":[],"postedDate":"April 3rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":65254724,"name":"Geometry"},{"id":65254725,"name":"Sports Medicine and Kinesiology"},{"id":65254726,"name":"Psychology"}],"tags":[],"updatedAt":"2026-04-03T10:48:18+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-03 10:48:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9243717","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9243717","identity":"rs-9243717","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}