Exploring the influence of judging experience on individual differences in figure skating jump performance evaluations: A case study of novice judges 

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While prior research found that judging accuracy is strongly impacted by previous experience as an athlete, the influence of judging experience on individual differences in evaluation remains unclear. This study investigated how the experience of a novice judge affected their evaluation process. The participants were judges and skaters with similar athletic abilities (n = 3 per group), and individual differences in (1) judging accuracy, (2) gaze coordinates, and (3) intraclass correlations were analyzed. The results revealed that limited judging experience did not significantly affect judging accuracy or individual variability in final GOE scores. However, for five of the six GOE criteria, individual differences were smaller in the judge group than in the skater group. The judge group also showed greater consistency in gaze coordinates during jump evaluation, suggesting that consistency in evaluation for each GOE criterion may be linked to the consistency of gaze coordinates. These findings highlight the potential value of incorporating guidance that promotes consistent visual focus and attentional strategies into judge training programs. sports officials double Axel jump linear mixed model intraclass correlation coefficients Introduction Before 2002, figure skating competitions utilized the “6.0 judging system,” where judges awarded scores between 0.0 and 6.0 in 0.1 increments, to evaluate figure skaters based on technical merit and presentation. The median ordinal rankings of these scores determined their rankings. Although the median-rank-based system provides safeguards against manipulation by a minority of judges (Bassett & Persky, 1994 ), Zitzewitz ( 2006 ) suggested that this may also facilitate vote trading. Indeed, the 1998 and 2002 Olympics saw a significant rise in vote-trading scandals, prompting the International Skating Union (ISU) to introduce changes to the judgment system (Zitzewitz, 2014 ). Under the current scoring system, the successful execution of high-difficulty jumps such as quadruple jumps became crucial, leading to an observed increase in their attempted frequency (Rauer et al., 2022 ). The jump technical score is calculated from the base value, which represents the difficulty, and the grade of execution (GOE) score, which indicates the quality of the technique. The base value is a predetermined score based on the type of jump and the number of rotations (e.g., performing a double Axel jump [2A] results in 3.30 points) (International Skating Union, 2018 ). The GOE score for jump quality is evaluated using an 11-point scale ranging from − 5 to + 5, including 0. One of the features of the new figure skating scoring system is to evaluate jump quality based on positive aspects rather than utilizing point deductions from a perfect score. Table 1 shows the GOE criteria used to assign performance points for jumps. The general guidelines are as follows: +1 = 1 bullet, + 2 = 2 bullets, + 3 = 3 bullets, + 4 = 4 bullets, + 5 = 5 or more bullets. For + 4 and + 5 scores, the first three bullets (shown in bold in Table 1 ) must be present (International Skating Union, 2018 ). In the positive evaluation criteria, the term “very good” is used to describe height and distance rather than simply indicating that these values are “large.” Previous research has shown that jumps with a greater horizontal and landing distance, rather than vertical height, tend to receive higher GOE scores in ladies’ double Axel jumps (Hirosawa, 2025 , November 10; Hirosawa et al., 2022 ). In addition, descriptors such as “unexpected” and “creative” allow for broader interpretive flexibility. Given these criteria, figure skating judges are expected to assess jump quality swiftly and accurately, as their assessments directly affect competition outcomes. Table 1 GOE scoring guidelines for the positive aspects of jumps. GOE criterion positive aspect 1) Very good height and very good length 2) Good take-off and landing 3) Effortless throughout 4) Steps before the jump, unexpected or creative entry 5) Very good body position from take-off to landing 6) Element matches the music NOTE: For + 4 and + 5, the first three bullets highlighted in bold must be present (International Skating Union, 2018 ). GOE, grade of the execution Figure skating, like gymnastics, is a scoring sport dependent on the ability of judges to evaluate athletic skills. Machida ( 2020 ) proposed the distinction between “artistic sports” and “formalistic sports,” where the former involves interpretative acts of artistic expression in addition to technical execution, while the latter focuses solely on the perfection of techniques. In this definition, the technical evaluation criterion of artistic sports should ideally minimize room for interpretation. MacMahon & Mildenhall ( 2012 ) pointed out that human judgment can be vulnerable to errors when the tasks surpass cognitive capacity. Furthermore, biases influence judges in figure skating (Findlay & Ste-Marie, 2004 ; Vincent Dumoulin, 2020 ). As such, this leaves us with a crucial question: how should novice judges gain the experience necessary to make accurate and immediate judgments based on the evaluation criteria? Although sports science research has traditionally focused on athletes and coaches, recent studies show a growing interest in sports officials and their decision-making processes (Hancock et al., 2015 ; Plessner et al., 2023 ). Researchers have primarily investigated how experience affects the accuracy of sports officials’ judgments and visual behaviors. The demand placed on sports officials varies across disciplines. Prior research classified officials as “reactors,” “monitors,” and “interactors” depending on the required level of physical activity, athlete interaction, and the number of cues involved in decision-making (MacMahon et al., 2014 ). Within this framework, monitors are defined as officials with low-to-medium levels of physical demand and interaction with athletes who are required to process a medium-to-large number of visual cues during evaluation, typical of judges in sports such as gymnastics. This classification also closely aligns with figure skating judges, who must assess numerous technical and artistic cues simultaneously while having minimal physical involvement. Hancock et al. ( 2021 ) noted that most research reports on sports officiating have focused on interactors, with few studies on reactors or monitors. Ziv et al. ( 2020 ) suggested that knowledge transfer may be possible among sports officials for cases with similar job descriptions, although Kittel et al. ( 2019 ) cautioned that such generalization may not always hold across different sports contexts. Thus, further research is needed to develop and validate methods for assessing the decision-making performance of officials across a broader range of sports. To date, most studies of monitors have focused on gymnastics, underscoring the need to extend such investigations to other judged sports, including figure skating. Pizzera & Raab ( 2012 ) reported that the effects of officiating, motor, and visual experience on judging accuracy varied across sports. Among monitors, such as trampoline judges, motor experience as an athlete most strongly influenced judging accuracy, whereas officiating or visual experience showed minimal impacts. Similarly, Pizzera ( 2012 ) found that in artistic gymnastics balance beam judging, judges with specific motor experience (SME) in the task more accurately detected joint angles compared with judges without SME. In contrast, for the vault event, no significant differences in judgment accuracy were observed between SME and non-SME judges (Pizzera et al., 2018 ). Other studies examining gymnastics judging accuracy and visual behavior across different participant groups, such as amateurs, athletes, coaches, and judges, have reported mixed findings (Flessas et al., 2015 ; Luis del Campo & Espada Gracia, 2018; Mack, 2020 ; Mack et al., 2021 ; Ste-Marie, 2000 ). Beyond gymnastics, visual-behavior differences linked to experience level have also been reported. For example, in dressage, judges of all experience levels focused longest on the horse’s forehand during Grand Prix performances, but experts devoted more attention to the horse’s legs, whereas novices focused more on the rider (Wolframm et al., 2024 ). In figure skating, Hirosawa et al. ( 2023 ) reported no significant differences in GOE judging accuracy between judges and skaters with comparable athletic experience, suggesting that athletic experience might have a greater influence on the judging accuracy of monitors compared with judging itself. Conversely, Avugos et al. ( 2021 ) highlighted the fact that most officiating studies overlooked variability among individuals, which is important for referee selection, development, and performance evaluations. Given the subjective nature of positive GOE criteria in figure skating, assessing whether judging experience could reduce individual differences is critical. Therefore, the present study examined whether novice-level judging experience decreases individual differences in GOE scoring among judges and skaters with similar athletic abilities, which could otherwise act as a confounding factor. Materials and Methods This study was conducted in accordance with the principles of the Declaration of Helsinki and was approved by the Ethics Committee of Anonymous University (protocol codes anonymous). Participants The participants included three judges (one male and two females) and three skaters (three females), who were categorized into the judge and skater groups, respectively. Both groups had similar athletic backgrounds. According to the Japan Skating Federation (JSF) technical test system, which evaluates skaters across nine grades (8, 7, 1, and beginner grades, ranked from highest to lowest), all participants had passed the grade 6 technical test but not the grade 7 test. Because the successful execution of a double Axel (2A) jump is required for grade 6 certification, all participants possessed equivalent athletic ability and SME relevant to the judging tasks. All three participants in the judge group completed JSF certification for judging qualifications, each obtaining a grade B qualification. Based on the five categorized levels (NR, N, A, B, and T, from high to low), judges with grade N qualification can officiate senior-class All-Japan Figure Skating Championships, those with grade A qualification can officiate senior-class East or West Japan Figure Skating Championships and All-Japan Junior and Novice Championships, and those with grade B qualification can officiate other lower competitions such as regional qualifying competitions across senior, junior, and novice levels as well as All Japan Intercollegiate Figure Skating Championships. All participants involved in this study provided informed consent after being fully briefed on the study’s aims, procedures, and potential impacts. Before their participation, they were informed of their rights, including the freedom to withdraw from the study at any point without consequence. Each participant signed a consent form acknowledging their understanding and agreement to participate under these conditions. Video The study utilized video recordings featuring 30 2A jumps from the ladies’ short program at the 2019 World Championship. Each selected jump had received a GOE score of 0 or higher from all nine official judges in the competition. Although the jump lasts only a few seconds from take-off to landing, judges must evaluate the overall performance, including the approach, take-off, air phase, and landing movements. Accordingly, each video clip was approximately 30 second to 1 minute in length, and was compiled from official footage provided on the International Skating Union’s (ISU) YouTube channel (International Skating Union, 2019 ). Because the eye tracker used in the present study operated at 60 Hz, all video stimuli were converted to 30 frames per second (FPS) to synchronize temporal resolution between the gaze data and video playback. The original program music and audience sounds were retained; however, accompanying commentary and on-screen scores were removed to avoid potential bias in evaluation. Two videos (Videos 1 and 2) were created for this study, each containing the first 15 skaters and the last 15 skaters of the event, respectively, presented in the same order as the actual competition. Tasks The participants completed two tasks in a fixed order: Task 1: Consistent with official competition procedures, the participants assigned GOE scores for each jump on an 11-point scale from − 5 to + 5, including 0. Task 2: After completing Task 1, the participants evaluated whether each jump met each of the six positive GOE criteria in separate judgments. This process was repeated for all six criteria, resulting in six evaluations per video clip for each participant. As described by Kittel et al. ( 2019 ), video-based testing was employed because it provides a reliable and standardized means of assessing individual decision-making processes among sports officials. Moreover, off-ice video evaluation is a common practice in figure skating judge training. Eye Tracker This study employed the Tobii Pro Spectrum (Tobii AB, Stockholm, Sweden) to track participants’ gaze coordinates for both eyes. According to the official specifications, the display size was 23.8 inches (527.0 × 296.5 mm), with a resolution of 1920 × 1080 pixels and a pixel pitch of 0.275 mm (Tobii, 2025 ). The data acquisition frequency was 60 Hz. Procedure Before the experiment, the participants received the following information and instructions: GOE criteria (Table 1 ) Stimuli overview: The participants judged 30 senior-category ladies’ 2A jumps, all without under-rotation or other deductions affecting the base value. Evaluation protocol: The participants were instructed to wait until the response time appeared on the screen before beginning their evaluation, to observe each video attentively, and to move their eyes naturally without turning their heads. Practice session: Before the main experiment, the participants completed a practice session involving three jumps with GOE scores near zero. These clips served as the basis for judging this experiment and were excluded from the experiment video because some judges gave minus scores, even though the final GOE scores were close to zero. The actual GOE scores were 0.05, 0.05, and − 0.05. During the experiment, the participants first evaluated Video 1, which contained 15 jumps. Each jump was presented with a 15-second evaluation period followed by a 5-second interval. After a 10-minute break, the participants evaluated Video 2, which contained the same parameters as Video 1. Eye tracker calibration was performed before the participants viewed both videos. Parameters and Statistical Analysis Based on the hypotheses discussed below, this study examined and analyzed the following four parameters: Judging performance (GOE) : Judging accuracy was assessed using the mean absolute error (MAE) for each group, calculated relative to the official GOE scores from the 2019 World Championships. Official GOE scores represent the trimmed mean of the nine judges’ scores after excluding the highest and lowest values (International Skating Union, 2019 ). Previous studies have shown that SME strongly influences judging accuracy (Pizzera et al., 2018 ). Because both the judge and skater groups in this study had similar athletic experience, minimal differences in judging performance were expected if SME were the dominant factor. A linear mixed model was used to examine group and individual differences. The fixed effects included participant attributes (judge or skater), while the random effects included participant ID (1–6) and jump video number (1–30). Analyses were performed using R (v4.3.3) with maximum-likelihood estimation. Statistical significance was set at p < 0.05. Gaze coordinates : Gaze coordinates reflect cognitive processes beyond attention, encompassing aspects such as perception, memory, and decision-making (Carter & Luke, 2020 ). Previous frame-by-frame analysis study of gaze distribution across body parts (Hirosawa et al., 2023 ) risked misclassification because visually close gaze positions may be assigned to different categories due to predefined segmentation. To achieve higher spatial precision, the present study quantified gaze consistency by calculating Euclidean distances between all possible participant pairs within each group (judges and skaters) for every video clip. Because judges share professional training and were hypothesized to attend more consistently to visual cues relevant to GOE evaluation, the judge group was expected to exhibit smaller gaze dispersion (i.e., shorter Euclidean distances) than the skater group. The eye tracker recorded gaze data at 60 Hz, and the videos were presented at 30 FPS, resulting in two gaze samples corresponding to each video frame. Although the duration of each jump clip ranged from approximately 30 seconds to 1 minute, the analysis frames were standardized. The frame on which the blade completely left the ice was defined as the take-off frame, and 150 frames before take-off and 89 frames after landing were extracted, yielding a total of 240 frames for analysis. Accordingly, each jump video clip contained 480 gaze samples. Using these 480 gaze samples per video clip, the two-dimensional gaze coordinates \(\:\left(x,y\right)\) of the three members in each group were used to compute the Euclidean distances for three participant pairs (A–B, A–C, and B–C). In the equation below, i and j denote two different participants within the same group: $$\:{d}_{i,j,t}=\sqrt{{\left({x}_{i,t}-{x}_{j,t}\right)}^{2}+{\left({y}_{i,t}-{y}_{j,t}\right)}^{2}\:}$$ 1 The overall spatial consistency of each group was then obtained by averaging these three pairwise distances across all video clips. The computational processing was performed using Python 3(3.10.12). GOE scoring : Following the scoring method performed in the actual competition, the participants assigned GOE scores from − 5 to + 5 for each jump. The judge group was hypothesized to exhibit lower individual variations in GOE scores due to their experience. The intraclass correlation coefficient (ICC(2,1)) (Shrout & Fleiss, 1979 ) of each group was calculated, and group differences were assessed using Python (3.10.12). Statistical significance was set at 5%. The ICC(2,1) of the actual GOE scores assigned by the nine international-level judges was also calculated as a reference. Evaluation by GOE criterion : The participants provided “yes” or “no” responses for each jump based on the specific GOE criterion being evaluated. The judge group was hypothesized to exhibit smaller individual variations due to their experience. As was done for GOE scoring, ICC(2,1) was calculated for each group, and group differences were assessed using Python (3.10.12). Statistical significance was set at 5%. Results Judging Performance Table 2 presents the MAE of the GOE scores given by each participant compared with the GOE scores given by the judges during the competition, as reported previously (Hirosawa et al., 2023 ). Examination of the random-intercept model (Model 1) and random-intercept-slope model (Model 2) for random effects showed that Model 1 provided a better fit than Model 2. Overall, the skaters demonstrated greater judging accuracy than the judges, although fixed effects of the attributes were not significant. Meanwhile, random effects analysis indicated smaller individual differences in the intercepts of the participants than those of the jump videos, with almost no individual differences. Table 2 Summary of judging performance results Participants MAE ± SD 95% CI Judge A 1.181 ± 0.662 0.934–1.428 Judge B 0.986 ± 0.786 0.692–1.279 Judge C 1.010 ± 0.679 0.756–1.263 Skater A 0.871 ± 0.595 0.649–1.094 Skater B 0.900 ± 0.668 0.650–1.150 Skater C 0.971 ± 0.627 0.737–1.206 Judges 1.059 ± 0.107 0.794–1.323 Skaters 0.914 ± 0.052 0.786–1.042 MAE, mean absolute error; SD, standard deviation; CI, confidence interval Table 3 Comparison of linear mixed model accuracy, and fixed and random effects on judgmental performance Model AIC BIC p-value Model 1 377.550 393.515 Model 2 383.261 411.998 0.683 Fixed-effects estimate standard error t-value p-value Attribute (skaters) -0.145 0.097 -1.487 0.139 Random-effects variance standard deviation Participants 0.000 0.000 Jump video 0.037 0.191 AIC, Akaike information criterion; BIC, Bayesian information criterion Gaze coordinates Euclidean distances were calculated for each group using 2D gaze coordinates obtained from the eye tracker. The mean values of the two groups were compared. On average, the judge group exhibited shorter Euclidean distances, suggesting lesser individual variability in gazing coordinates compared with the skater group. Final GOE scoring The ICC for the final GOE scores was 0.336 (95% confidence interval [CI]: 0.10–0.57) for the judges and 0.409 (95% CI: 0.18–0.63) for the skaters. Although the point estimate was higher for the skaters, the wide and overlapping confidence intervals indicated that no clear group differences could be concluded. Evaluation according to each GOE criterion Table 4 presents the ICC(2,1) values for each GOE criterion. The judge group demonstrated higher values for five of the six criteria, except for Criterion 4 (steps before the jump, unexpected or creative entry), which showed a minimal group difference. Additionally, statistically significant ICC(2,1) values were observed in the judge group for Criteria 1–5. Both groups’ ICC(2,1) displayed statistically insignificant and small values for Criterion 6 (element matches the music), indicating large variability in the ratings. Interestingly, the order of ICC(2,1) values across criteria remained consistent for both groups (4 > 2 > 1 > 3 > 5 > 6). Table 4 Comparison of Euclidean distances for two-dimensional (2D) gaze coordinates Euclidean distances of 2D gaze coordinates Judges Skaters Participant A - Participant B 116.48 ± 101.86 pixel (3.20cm ± 2.80 cm) 157.35 ± 146.82pixel (4.33 ± 4.04cm) Participant A - Participant C 139.11 ± 121.28 pixel (3.83 ± 3.34 cm) 134.28 ± 130.41 pixel (3.69 ± 3.59cm) Participant B - Participant C 122.84 ± 103.58 pixel (3.38 ± 2.85 cm) 153.79 ± 156.90pixel (4.23 ± 4.31cm) Average 126.15 ± 109.67 pixel (3.47 ± 3.16 cm) 148.47 ± 145.47pixel (4.08 ± 4.00cm) NOTE: Values for the groups with larger values are in bold. Table 5 Comparison of inter-rater reliability of evaluation by final GOE and each GOE criterion Judges Skaters GOE guideline ICC(2,1) p-value 95%CI ICC(2,1) p-value 95%CI Final GOE 0.336 0.000*** 0.10–0.57 0.409 0.000*** 0.18–0.63 1 Very good height and very good length 0.334 0.001** 0.11–0.56 0.301 0.003** 0.08–0.54 2 Good take-off and landing 0.436 0.000*** 0.22–0.65 0.373 0.000*** 0.14–0.60 3 Effortless throughout 0.284 0.002** 0.08–0.52 0.073 0.192 -0.08-0.29 4 Steps before the jump, unexpected or creative entry 0.438 0.000*** 0.21–0.65 0.446 0.000*** 0.21–0.66 5 Very good body position from take-off to landing 0.239 0.009** 0.04–0.47 0.047 0.217 -0.06-0.22 6 Element matches the music 0.045 0.274 -0.09-0.24 -0.012 0.563 -0.11-0.14 *p < 0.05, **p < 0.01, ***p < 0.001 NOTE: Values for the groups with larger values are in bold. GOE, grade of execution Discussion This study investigated how judging experience affects individual differences in GOE evaluation of figure skating jumps, with all participants having similar athletic experience. Our findings revealed the following: Both groups exhibited comparable accuracy in judging GOE scores, with minimal individual variations. The judge group displayed shorter Euclidean distances in their gaze coordinates compared with the skater group. The skater group demonstrated less interrater variability in final GOE scores. Conversely, the judge group showed less variation in five of the six criteria based on individual GOE criterion evaluations. Previous studies have shown that the judging accuracy of monitors is more strongly influenced by athletic experience than by judging experience (Pizzera, 2012 ; Pizzera & Raab, 2012 ). Accordingly, we expected that figure skating judges and skaters in our study with comparable athletic experience would demonstrate similar levels of judging accuracy. Indeed, our findings confirmed that the fixed effect of participant attributes (judge vs. skater) was not significant, consistent with previous studies. This suggests that monitors can leverage their athletic experience to evaluate both the negative and positive aspects of performance emphasized in the updated GOE criteria. Furthermore, the greater influence of the random effect of the jump video compared with the participant effect indicated that the specific jump being evaluated exerted more influence on GOE judgment than individual variability, supporting the robustness of the controlled experimental setting. The analysis of gaze behavior revealed smaller Euclidean distances among gaze coordinates in the judge group compared with the skater group. A prior study reported that judges and skaters differ in the body areas they focus on during evaluation: while both groups primarily fixated on the upper body, skaters tended to focus more on the face, whereas judges concentrated on the lower body and skating boots(Hirosawa et al., 2023 ). However, the previous study did not address the spatial similarity of gaze locations among participants. In the present study, the smaller Euclidean distances observed within the judge group suggest that even novice-level judging experience may reduce individual spatial differences in where participants look during jump evaluation. Regarding individual differences in jump GOE evaluation, the skater group exhibited smaller ICC(2,1) values for the final GOE score, whereas the judge group demonstrated significantly smaller individual variations for five of the six GOE criteria. These results indicate that novice judging experience did not contribute to reducing individual differences in evaluating the final GOE score; however, it can reduce individual differences regarding each evaluation criterion of GOE. Notably, Criterion 6 (element matches the music) showed the highest individual variability among all criteria, and novice judging experience reduced individual differences for Criteria 1, 2, 3, and 5. As the GOE guidelines (Table 1 ) state that Criteria 1–3 must be applied to achieve high GOE scores (+ 4 or + 5), these three criteria are critical for evaluating jump performance. Consequently, this suggests that novice judges should prioritize acquiring a consistent understanding of these criteria as they gain experience. Regarding Criteria 3 (effortless throughout) and 5 (very good body position from takeoff to landing), the skater group showed low ICC(2,1) values, as opposed to the improved values and reduced individual differences in the judge group. The differences in judging experience were most apparent in the large individual differences between the two criteria. However, Criterion 6 exhibited low reliability and was not statistically significant in either group, even if the judge group's ICC(2,1) value (0.045) indicated larger individual differences compared with the skater group. Determining musical congruence remains a highly interpretive process for novice judges, underscoring the need for targeted training programs that promote a standardized understanding of how jump execution aligns with music. For reference, the ICC(2,1) value calculated for the World Championship judges (n = 9) in final GOE scoring was 0.635 (p = 0.000, 95% CI [0.50–0.77]), which was higher than the values observed in both groups (judges and skaters) in the present study. However, direct comparison with actual competition judges is difficult due to differences in evaluation environments. Nevertheless, this supplementary evidence suggests that accumulated judging experience may contribute to improved consistency in performance evaluation. Despite its insights, this study has some limitations. First, as a case study with a small sample size, future studies with larger sample sizes are needed to generalize the results. Specifically, this study considered only novice-level judging experience. Investigating how top-level international judges perceive and evaluate jump performance and assessing the extent of individual variations within this group are crucial for a more comprehensive understanding. The relatively small number of figure skating competitions and judges, as compared to other sports such as soccer, presents a challenge in addressing these limitations. As such, securing the cooperation of international and national federations is essential to facilitate larger-scale studies with higher-level judges. Additionally, in this study, the judge and skater groups were intentionally matched in terms of athletic ability. Thus, whether similar patterns of individual differences would emerge in groups with lower or higher levels of athletic ability remains unclear. As a methodological limitation, this study prioritized providing participants with a natural viewing environment to reduce their burden and, therefore, did not strictly control the height of the chair or the viewing distance from the monitor. Although previous studies have reported acceptable accuracy without the use of a chin rest, they also recommend this tool to stabilize head position and maximize measurement precision (Carter & Luke, 2020 ). Future studies examining gaze-position distributions in greater detail should use a chin rest. Despite these limitations, to the best of our knowledge, our study is the first to clarify the relationship between novice judging experiences and individual differences in figure skating jump performance, providing a new perspective on the decision-making processes of sports officials. Conclusion This study examined the effect of novice judging experience on reducing individual differences in GOE evaluation of figure skating jumps, with a particular focus on positive evaluation criteria. The participants consisted of judges and skaters with comparable athletic experience. The results showed that novice judging experience did not reduce individual differences in final GOE scores. However, for five of the six GOE criteria, individual differences were smaller in the judge group than in the skater group, particularly for Criteria 3 (effortless throughout) and 5 (very good body position from takeoff to landing), indicating the impact of judging experience. Furthermore, the judge group showed greater consistency in gaze coordinates during jump evaluation, suggesting that consistency in evaluation for each GOE criterion may be linked to the consistency of gaze coordinates. These findings highlight the potential value of incorporating guidance that promotes consistent visual focus and attentional strategies into judge training programs. Declarations Funding details No funding was received Disclosure statement The authors report no potential competing interests. Author Contribution SH made substantial contributions to the conception, design, acquisition, analysis, and interpretation of data for this research and drafted the manuscript. TK made significant contributions to the conception and design of the work and critically revised it for important intellectual content. YA critically revised the manuscript for important intellectual content and approved the final version for publication. 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Current Psychology , 37 (4), 934–941. https://doi.org/10.1007/s12144-017-9572-1 Machida, T. (2020). Artistic Sport Management: Interdisciplinary Studies Based on Figure Skating . Hakusuisha. Mack, M. (2020). Exploring Cognitive and Perceptual Judgment Processes in Gymnastics Using Essential Kinematics Information. Advances in Cognitive Psychology / University of Finance and Management in Warsaw , 16 (1), 34–44. https://doi.org/10.5709/acp-0282-7 Mack, M., Schmidt, M., & Heinen, T. (2021). The Relationship Between the Perceived Movement Quality and the Kinematic Pattern of Complex Skills in Gymnastics. Journal of Human Kinetics , 77 , 5–13. https://doi.org/10.2478/hukin-2021-0007 MacMahon, C., Mascarenhas, D., Plessner, H., Pizzera, A., Oudejans, R., & Raab, M. (2014). Sports Officials and Officiating: Science and Practice . Routledge. https://play.google.com/store/books/details?id=GyWcBQAAQBAJ MacMahon, C., & Mildenhall, B. (2012). A Practical Perspective on Decision Making Influences in Sports Officiating. International Journal of Sports Science & Coaching , 7 (1), 153–165. https://doi.org/10.1260/1747-9541.7.1.153 Pizzera, A. (2012). Gymnastic judges benefit from their own motor experience as gymnasts. Research Quarterly for Exercise and Sport , 83 (4), 603–607. https://doi.org/10.1080/02701367.2012.10599887 Pizzera, A., Möller, C., & Plessner, H. (2018). Gaze behavior of gymnastics judges: Where do experienced judges and gymnasts look while judging? Research Quarterly for Exercise and Sport , 89 (1), 112–119. https://doi.org/10.1080/02701367.2017.1412392 Pizzera, A., & Raab, M. (2012). Perceptual Judgments of Sports Officials are Influenced by their Motor and Visual Experience. Journal of Applied Sport Psychology , 24 (1), 59–72. https://doi.org/10.1080/10413200.2011.608412 Plessner, H., Ermark, F., Schütz, L.-M., & Geoffrey Schweizer. (2023). Sports performance judgments - an update from a social cognitive perspective. Asian Journal of Sport and Exercise Psychology , 3 (1), 13–23. https://doi.org/10.1016/j.ajsep.2023.01.002 Rauer, T., Pape, H.-C., Knobe, M., Pohlemann, T., & Ganse, B. (2022). Figure skating: Increasing numbers of revolutions in jumps at the European and World Championships. PloS One , 17 (11), e0265343. https://doi.org/10.1371/journal.pone.0265343 Shrout, P. E., & Fleiss, J. L. (1979). Intraclass correlations: uses in assessing rater reliability. Psychological Bulletin , 86 (2), 420–428. https://doi.org/10.1037//0033-2909.86.2.420 Ste-Marie, D. M. (2000). Expertise in women’s gymnastic judging: an observational approach. Perceptual and Motor Skills , 90 (2), 543–546. https://doi.org/10.2466/pms.2000.90.2.543 Tobii, A. B. (2025, March). Tobii Pro Spectrum user manual (Version 3.2) . https://go.tobii.com/tobii-pro-spectrum-user-manual Vincent Dumoulin, H. M. (2020). Accuracy and national bias of figure skating judges: The good, the bad and the ugly. 14th MIT Sloan Sports Analytics Conference , PaperTrack:Business of sports ID1548706. Wolframm, I., Reuter, P., Zaharia, I., & Vernooij, J. (2024). In the eye of the beholder-visual search behavior in equestrian dressage judges. Animals: An Open Access Journal from MDPI , 14 (14), 2025. https://doi.org/10.3390/ani14142025 Zitzewitz, E. (2006). Nationalism in winter sports judging and its lessons for organizational decision making. Journal of Economics & Management Strategy , 15 (1), 67–99. https://doi.org/10.1111/j.1530-9134.2006.00092.x Zitzewitz, E. (2014). Does Transparency Reduce Favoritism and Corruption? Evidence From the Reform of Figure Skating Judging. Journal of Sports Economics , 15 (1), 3–30. https://doi.org/10.1177/1527002512441479 Ziv, G., Lidor, R., Zach, S., Brams, S., & Helsen, W. F. (2020). Gaze behavior of referees in sport-A review. Frontiers in Sports and Active Living , 2 , 572891. https://doi.org/10.3389/fspor.2020.572891 Additional Declarations No competing interests reported. Supplementary Files highlited.docx Cite Share Download PDF Status: Published Journal Publication published 28 Jan, 2026 Read the published version in Sport Sciences for Health → Version 1 posted Editorial decision: Accepted 31 Dec, 2025 Reviews received at journal 22 Dec, 2025 Reviewers agreed at journal 02 Dec, 2025 Reviewers invited by journal 01 Dec, 2025 Submission checks completed at journal 26 Nov, 2025 First submitted to journal 25 Nov, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Hirosawa","email":"data:image/png;base64,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","orcid":"","institution":"Toin University of Yokohama","correspondingAuthor":true,"prefix":"","firstName":"Seiji","middleName":"","lastName":"Hirosawa","suffix":""},{"id":551054998,"identity":"f179e4e2-d41a-44d4-aaca-4555b8492a04","order_by":1,"name":"Takaaki Kato","email":"","orcid":"","institution":"Keio 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16:12:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":786842,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4661368/v1/0a1b83cd-de08-4208-b921-7cd5bff65cc2.pdf"},{"id":96981366,"identity":"208c4031-6306-4d21-b829-01906d2da483","added_by":"auto","created_at":"2025-11-28 09:21:08","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":138631,"visible":true,"origin":"","legend":"","description":"","filename":"highlited.docx","url":"https://assets-eu.researchsquare.com/files/rs-4661368/v1/bdb7c60859360afb02361fb3.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Exploring the influence of judging experience on individual differences in figure skating jump performance evaluations: A case study of novice judges ","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBefore 2002, figure skating competitions utilized the \u0026ldquo;6.0 judging system,\u0026rdquo; where judges awarded scores between 0.0 and 6.0 in 0.1 increments, to evaluate figure skaters based on technical merit and presentation. The median ordinal rankings of these scores determined their rankings. Although the median-rank-based system provides safeguards against manipulation by a minority of judges (Bassett \u0026amp; Persky, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1994\u003c/span\u003e), Zitzewitz (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) suggested that this may also facilitate vote trading. Indeed, the 1998 and 2002 Olympics saw a significant rise in vote-trading scandals, prompting the International Skating Union (ISU) to introduce changes to the judgment system (Zitzewitz, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eUnder the current scoring system, the successful execution of high-difficulty jumps such as quadruple jumps became crucial, leading to an observed increase in their attempted frequency (Rauer et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The jump technical score is calculated from the base value, which represents the difficulty, and the grade of execution (GOE) score, which indicates the quality of the technique. The base value is a predetermined score based on the type of jump and the number of rotations (e.g., performing a double Axel jump [2A] results in 3.30 points) (International Skating Union, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The GOE score for jump quality is evaluated using an 11-point scale ranging from \u0026minus;\u0026thinsp;5 to +\u0026thinsp;5, including 0. One of the features of the new figure skating scoring system is to evaluate jump quality based on positive aspects rather than utilizing point deductions from a perfect score. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the GOE criteria used to assign performance points for jumps. The general guidelines are as follows: +1\u0026thinsp;=\u0026thinsp;1 bullet, +\u0026thinsp;2\u0026thinsp;=\u0026thinsp;2 bullets, +\u0026thinsp;3\u0026thinsp;=\u0026thinsp;3 bullets, +\u0026thinsp;4\u0026thinsp;=\u0026thinsp;4 bullets, + 5\u0026thinsp;=\u0026thinsp;5 or more bullets. For +\u0026thinsp;4 and +\u0026thinsp;5 scores, the first three bullets (shown in bold in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) must be present (International Skating Union, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In the positive evaluation criteria, the term \u0026ldquo;very good\u0026rdquo; is used to describe height and distance rather than simply indicating that these values are \u0026ldquo;large.\u0026rdquo; Previous research has shown that jumps with a greater horizontal and landing distance, rather than vertical height, tend to receive higher GOE scores in ladies\u0026rsquo; double Axel jumps (Hirosawa, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2025\u003c/span\u003e, November 10; Hirosawa et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In addition, descriptors such as \u0026ldquo;unexpected\u0026rdquo; and \u0026ldquo;creative\u0026rdquo; allow for broader interpretive flexibility. Given these criteria, figure skating judges are expected to assess jump quality swiftly and accurately, as their assessments directly affect competition outcomes.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eGOE scoring guidelines for the positive aspects of jumps.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"1\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGOE criterion positive aspect\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e1) Very good height and very good length\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e2) Good take-off and landing\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e3) Effortless throughout\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4) Steps before the jump, unexpected or creative entry\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5) Very good body position from take-off to landing\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6) Element matches the music\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"1\"\u003eNOTE: For +\u0026thinsp;4 and +\u0026thinsp;5, the first three bullets highlighted in bold must be present (International Skating Union, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). GOE, grade of the execution\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eFigure skating, like gymnastics, is a scoring sport dependent on the ability of judges to evaluate athletic skills. Machida (\u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e) proposed the distinction between \u0026ldquo;artistic sports\u0026rdquo; and \u0026ldquo;formalistic sports,\u0026rdquo; where the former involves interpretative acts of artistic expression in addition to technical execution, while the latter focuses solely on the perfection of techniques. In this definition, the technical evaluation criterion of artistic sports should ideally minimize room for interpretation. MacMahon \u0026amp; Mildenhall (\u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e) pointed out that human judgment can be vulnerable to errors when the tasks surpass cognitive capacity. Furthermore, biases influence judges in figure skating (Findlay \u0026amp; Ste-Marie, \u003cspan class=\"CitationRef\"\u003e2004\u003c/span\u003e; Vincent Dumoulin, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). As such, this leaves us with a crucial question: how should novice judges gain the experience necessary to make accurate and immediate judgments based on the evaluation criteria?\u003c/p\u003e\u003cp\u003eAlthough sports science research has traditionally focused on athletes and coaches, recent studies show a growing interest in sports officials and their decision-making processes (Hancock et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Plessner et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Researchers have primarily investigated how experience affects the accuracy of sports officials\u0026rsquo; judgments and visual behaviors.\u003c/p\u003e\u003cp\u003eThe demand placed on sports officials varies across disciplines. Prior research classified officials as \u0026ldquo;reactors,\u0026rdquo; \u0026ldquo;monitors,\u0026rdquo; and \u0026ldquo;interactors\u0026rdquo; depending on the required level of physical activity, athlete interaction, and the number of cues involved in decision-making (MacMahon et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Within this framework, monitors are defined as officials with low-to-medium levels of physical demand and interaction with athletes who are required to process a medium-to-large number of visual cues during evaluation, typical of judges in sports such as gymnastics. This classification also closely aligns with figure skating judges, who must assess numerous technical and artistic cues simultaneously while having minimal physical involvement.\u003c/p\u003e\u003cp\u003eHancock et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) noted that most research reports on sports officiating have focused on interactors, with few studies on reactors or monitors. Ziv et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) suggested that knowledge transfer may be possible among sports officials for cases with similar job descriptions, although Kittel et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) cautioned that such generalization may not always hold across different sports contexts. Thus, further research is needed to develop and validate methods for assessing the decision-making performance of officials across a broader range of sports. To date, most studies of monitors have focused on gymnastics, underscoring the need to extend such investigations to other judged sports, including figure skating.\u003c/p\u003e\u003cp\u003ePizzera \u0026amp; Raab (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) reported that the effects of officiating, motor, and visual experience on judging accuracy varied across sports. Among monitors, such as trampoline judges, motor experience as an athlete most strongly influenced judging accuracy, whereas officiating or visual experience showed minimal impacts. Similarly, Pizzera (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) found that in artistic gymnastics balance beam judging, judges with specific motor experience (SME) in the task more accurately detected joint angles compared with judges without SME. In contrast, for the vault event, no significant differences in judgment accuracy were observed between SME and non-SME judges (Pizzera et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOther studies examining gymnastics judging accuracy and visual behavior across different participant groups, such as amateurs, athletes, coaches, and judges, have reported mixed findings (Flessas et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Luis del Campo \u0026amp; Espada Gracia, 2018; Mack, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Mack et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Ste-Marie, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Beyond gymnastics, visual-behavior differences linked to experience level have also been reported. For example, in dressage, judges of all experience levels focused longest on the horse\u0026rsquo;s forehand during Grand Prix performances, but experts devoted more attention to the horse\u0026rsquo;s legs, whereas novices focused more on the rider (Wolframm et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn figure skating, Hirosawa et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) reported no significant differences in GOE judging accuracy between judges and skaters with comparable athletic experience, suggesting that athletic experience might have a greater influence on the judging accuracy of monitors compared with judging itself. Conversely, Avugos et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) highlighted the fact that most officiating studies overlooked variability among individuals, which is important for referee selection, development, and performance evaluations.\u003c/p\u003e\u003cp\u003eGiven the subjective nature of positive GOE criteria in figure skating, assessing whether judging experience could reduce individual differences is critical. Therefore, the present study examined whether novice-level judging experience decreases individual differences in GOE scoring among judges and skaters with similar athletic abilities, which could otherwise act as a confounding factor.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003eThis study was conducted in accordance with the principles of the Declaration of Helsinki and was approved by the Ethics Committee of Anonymous University (protocol codes anonymous).\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eParticipants\u003c/h2\u003e\u003cp\u003eThe participants included three judges (one male and two females) and three skaters (three females), who were categorized into the judge and skater groups, respectively. Both groups had similar athletic backgrounds. According to the Japan Skating Federation (JSF) technical test system, which evaluates skaters across nine grades (8, 7, 1, and beginner grades, ranked from highest to lowest), all participants had passed the grade 6 technical test but not the grade 7 test. Because the successful execution of a double Axel (2A) jump is required for grade 6 certification, all participants possessed equivalent athletic ability and SME relevant to the judging tasks. All three participants in the judge group completed JSF certification for judging qualifications, each obtaining a grade B qualification. Based on the five categorized levels (NR, N, A, B, and T, from high to low), judges with grade N qualification can officiate senior-class All-Japan Figure Skating Championships, those with grade A qualification can officiate senior-class East or West Japan Figure Skating Championships and All-Japan Junior and Novice Championships, and those with grade B qualification can officiate other lower competitions such as regional qualifying competitions across senior, junior, and novice levels as well as All Japan Intercollegiate Figure Skating Championships.\u003c/p\u003e\u003cp\u003eAll participants involved in this study provided informed consent after being fully briefed on the study\u0026rsquo;s aims, procedures, and potential impacts. Before their participation, they were informed of their rights, including the freedom to withdraw from the study at any point without consequence. Each participant signed a consent form acknowledging their understanding and agreement to participate under these conditions.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eVideo\u003c/h3\u003e\n\u003cp\u003eThe study utilized video recordings featuring 30 2A jumps from the ladies\u0026rsquo; short program at the 2019 World Championship. Each selected jump had received a GOE score of 0 or higher from all nine official judges in the competition. Although the jump lasts only a few seconds from take-off to landing, judges must evaluate the overall performance, including the approach, take-off, air phase, and landing movements. Accordingly, each video clip was approximately 30 second to 1 minute in length, and was compiled from official footage provided on the International Skating Union\u0026rsquo;s (ISU) YouTube channel (International Skating Union, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eBecause the eye tracker used in the present study operated at 60 Hz, all video stimuli were converted to 30 frames per second (FPS) to synchronize temporal resolution between the gaze data and video playback. The original program music and audience sounds were retained; however, accompanying commentary and on-screen scores were removed to avoid potential bias in evaluation. Two videos (Videos 1 and 2) were created for this study, each containing the first 15 skaters and the last 15 skaters of the event, respectively, presented in the same order as the actual competition.\u003c/p\u003e\n\u003ch3\u003eTasks\u003c/h3\u003e\n\u003cp\u003eThe participants completed two tasks in a fixed order:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eTask 1: Consistent with official competition procedures, the participants assigned GOE scores for each jump on an 11-point scale from \u0026minus;\u0026thinsp;5 to +\u0026thinsp;5, including 0.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eTask 2: After completing Task 1, the participants evaluated whether each jump met each of the six positive GOE criteria in separate judgments. This process was repeated for all six criteria, resulting in six evaluations per video clip for each participant.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003eAs described by Kittel et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), video-based testing was employed because it provides a reliable and standardized means of assessing individual decision-making processes among sports officials. Moreover, off-ice video evaluation is a common practice in figure skating judge training.\u003c/p\u003e\n\u003ch3\u003eEye Tracker\u003c/h3\u003e\n\u003cp\u003eThis study employed the Tobii Pro Spectrum (Tobii AB, Stockholm, Sweden) to track participants\u0026rsquo; gaze coordinates for both eyes. According to the official specifications, the display size was 23.8 inches (527.0 \u0026times; 296.5 mm), with a resolution of 1920 \u0026times; 1080 pixels and a pixel pitch of 0.275 mm (Tobii, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The data acquisition frequency was 60 Hz.\u003c/p\u003e\n\u003ch3\u003eProcedure\u003c/h3\u003e\n\u003cp\u003eBefore the experiment, the participants received the following information and instructions:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eGOE criteria (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eStimuli overview: The participants judged 30 senior-category ladies\u0026rsquo; 2A jumps, all without under-rotation or other deductions affecting the base value.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eEvaluation protocol: The participants were instructed to wait until the response time appeared on the screen before beginning their evaluation, to observe each video attentively, and to move their eyes naturally without turning their heads.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003ePractice session: Before the main experiment, the participants completed a practice session involving three jumps with GOE scores near zero. These clips served as the basis for judging this experiment and were excluded from the experiment video because some judges gave minus scores, even though the final GOE scores were close to zero. The actual GOE scores were 0.05, 0.05, and \u0026minus;\u0026thinsp;0.05.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eDuring the experiment, the participants first evaluated Video 1, which contained 15 jumps. Each jump was presented with a 15-second evaluation period followed by a 5-second interval. After a 10-minute break, the participants evaluated Video 2, which contained the same parameters as Video 1. Eye tracker calibration was performed before the participants viewed both videos.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eParameters and Statistical Analysis\u003c/h2\u003e\u003cp\u003eBased on the hypotheses discussed below, this study examined and analyzed the following four parameters:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cem\u003eJudging performance (GOE)\u003c/em\u003e: Judging accuracy was assessed using the mean absolute error (MAE) for each group, calculated relative to the official GOE scores from the 2019 World Championships. Official GOE scores represent the trimmed mean of the nine judges\u0026rsquo; scores after excluding the highest and lowest values (International Skating Union, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003ePrevious studies have shown that SME strongly influences judging accuracy (Pizzera et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Because both the judge and skater groups in this study had similar athletic experience, minimal differences in judging performance were expected if SME were the dominant factor.\u003c/p\u003e\u003cp\u003eA linear mixed model was used to examine group and individual differences. The fixed effects included participant attributes (judge or skater), while the random effects included participant ID (1\u0026ndash;6) and jump video number (1\u0026ndash;30). Analyses were performed using \u003cb\u003eR (v4.3.3)\u003c/b\u003e with maximum-likelihood estimation. Statistical significance was set at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cem\u003eGaze coordinates\u003c/em\u003e: Gaze coordinates reflect cognitive processes beyond attention, encompassing aspects such as perception, memory, and decision-making (Carter \u0026amp; Luke, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Previous frame-by-frame analysis study of gaze distribution across body parts (Hirosawa et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) risked misclassification because visually close gaze positions may be assigned to different categories due to predefined segmentation. To achieve higher spatial precision, the present study quantified gaze consistency by calculating Euclidean distances between all possible participant pairs within each group (judges and skaters) for every video clip.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eBecause judges share professional training and were hypothesized to attend more consistently to visual cues relevant to GOE evaluation, the judge group was expected to exhibit smaller gaze dispersion (i.e., shorter Euclidean distances) than the skater group. The eye tracker recorded gaze data at 60 Hz, and the videos were presented at 30 FPS, resulting in two gaze samples corresponding to each video frame. Although the duration of each jump clip ranged from approximately 30 seconds to 1 minute, the analysis frames were standardized. The frame on which the blade completely left the ice was defined as the take-off frame, and 150 frames before take-off and 89 frames after landing were extracted, yielding a total of 240 frames for analysis. Accordingly, each jump video clip contained 480 gaze samples. Using these 480 gaze samples per video clip, the two-dimensional gaze coordinates \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\left(x,y\\right)\\)\u003c/span\u003e\u003c/span\u003eof the three members in each group were used to compute the Euclidean distances for three participant pairs (A\u0026ndash;B, A\u0026ndash;C, and B\u0026ndash;C). In the equation below, \u003cem\u003ei\u003c/em\u003e and \u003cem\u003ej\u003c/em\u003e denote two different participants within the same group:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:{d}_{i,j,t}=\\sqrt{{\\left({x}_{i,t}-{x}_{j,t}\\right)}^{2}+{\\left({y}_{i,t}-{y}_{j,t}\\right)}^{2}\\:}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe overall spatial consistency of each group was then obtained by averaging these three pairwise distances across all video clips. The computational processing was performed using Python 3(3.10.12).\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cem\u003eGOE scoring\u003c/em\u003e: Following the scoring method performed in the actual competition, the participants assigned GOE scores from \u0026minus;\u0026thinsp;5 to +\u0026thinsp;5 for each jump. The judge group was hypothesized to exhibit lower individual variations in GOE scores due to their experience.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eThe intraclass correlation coefficient (ICC(2,1)) (Shrout \u0026amp; Fleiss, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1979\u003c/span\u003e) of each group was calculated, and group differences were assessed using Python (3.10.12). Statistical significance was set at 5%. The ICC(2,1) of the actual GOE scores assigned by the nine international-level judges was also calculated as a reference.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cem\u003eEvaluation by GOE criterion\u003c/em\u003e: The participants provided \u0026ldquo;yes\u0026rdquo; or \u0026ldquo;no\u0026rdquo; responses for each jump based on the specific GOE criterion being evaluated. The judge group was hypothesized to exhibit smaller individual variations due to their experience.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eAs was done for GOE scoring, ICC(2,1) was calculated for each group, and group differences were assessed using Python (3.10.12). Statistical significance was set at 5%.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003eJudging Performance\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the MAE of the GOE scores given by each participant compared with the GOE scores given by the judges during the competition, as reported previously (Hirosawa et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Examination of the random-intercept model (Model 1) and random-intercept-slope model (Model 2) for random effects showed that Model 1 provided a better fit than Model 2. Overall, the skaters demonstrated greater judging accuracy than the judges, although fixed effects of the attributes were not significant. Meanwhile, random effects analysis indicated smaller individual differences in the intercepts of the participants than those of the jump videos, with almost no individual differences.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSummary of judging performance results\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParticipants\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMAE\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJudge A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e1.181\u0026thinsp;\u0026plusmn;\u0026thinsp;0.662\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.934\u0026ndash;1.428\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJudge B\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e0.986\u0026thinsp;\u0026plusmn;\u0026thinsp;0.786\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.692\u0026ndash;1.279\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJudge C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e1.010\u0026thinsp;\u0026plusmn;\u0026thinsp;0.679\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.756\u0026ndash;1.263\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSkater A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e0.871\u0026thinsp;\u0026plusmn;\u0026thinsp;0.595\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.649\u0026ndash;1.094\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSkater B\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e0.900\u0026thinsp;\u0026plusmn;\u0026thinsp;0.668\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.650\u0026ndash;1.150\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSkater C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e0.971\u0026thinsp;\u0026plusmn;\u0026thinsp;0.627\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.737\u0026ndash;1.206\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJudges\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e1.059\u0026thinsp;\u0026plusmn;\u0026thinsp;0.107\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.794\u0026ndash;1.323\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSkaters\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e0.914\u0026thinsp;\u0026plusmn;\u0026thinsp;0.052\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.786\u0026ndash;1.042\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eMAE, mean absolute error; SD, standard deviation; CI, confidence interval\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparison of linear mixed model accuracy, and fixed and random effects on judgmental performance\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAIC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBIC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e377.550\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e393.515\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e383.261\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e411.998\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.683\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFixed-effects\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eestimate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003estandard error\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003et-value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAttribute (skaters)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.145\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.097\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-1.487\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.139\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRandom-effects\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003evariance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003estandard deviation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParticipants\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJump video\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.037\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.191\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAIC, Akaike information criterion; BIC, Bayesian information criterion\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eGaze coordinates\u003c/h2\u003e\u003cp\u003eEuclidean distances were calculated for each group using 2D gaze coordinates obtained from the eye tracker. The mean values of the two groups were compared. On average, the judge group exhibited shorter Euclidean distances, suggesting lesser individual variability in gazing coordinates compared with the skater group.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eFinal GOE scoring\u003c/h2\u003e\u003cp\u003eThe ICC for the final GOE scores was 0.336 (95% confidence interval [CI]: 0.10\u0026ndash;0.57) for the judges and 0.409 (95% CI: 0.18\u0026ndash;0.63) for the skaters. Although the point estimate was higher for the skaters, the wide and overlapping confidence intervals indicated that no clear group differences could be concluded.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eEvaluation according to each GOE criterion\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents the ICC(2,1) values for each GOE criterion. The judge group demonstrated higher values for five of the six criteria, except for Criterion 4 (steps before the jump, unexpected or creative entry), which showed a minimal group difference. Additionally, statistically significant ICC(2,1) values were observed in the judge group for Criteria 1\u0026ndash;5. Both groups\u0026rsquo; ICC(2,1) displayed statistically insignificant and small values for Criterion 6 (element matches the music), indicating large variability in the ratings. Interestingly, the order of ICC(2,1) values across criteria remained consistent for both groups (4\u0026thinsp;\u0026gt;\u0026thinsp;2\u0026thinsp;\u0026gt;\u0026thinsp;1\u0026thinsp;\u0026gt;\u0026thinsp;3\u0026thinsp;\u0026gt;\u0026thinsp;5\u0026thinsp;\u0026gt;\u0026thinsp;6).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparison of Euclidean distances for two-dimensional (2D) gaze coordinates\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEuclidean distances of\u003c/p\u003e\u003cp\u003e2D gaze coordinates\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eJudges\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSkaters\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParticipant A - Participant B\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e116.48\u0026thinsp;\u0026plusmn;\u0026thinsp;101.86 pixel \u003c/p\u003e\u003cp\u003e(3.20cm\u0026thinsp;\u0026plusmn;\u0026thinsp;2.80 cm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e157.35\u0026thinsp;\u0026plusmn;\u0026thinsp;146.82pixel\u003c/p\u003e\u003cp\u003e(4.33\u0026thinsp;\u0026plusmn;\u0026thinsp;4.04cm)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParticipant A - Participant C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e139.11\u0026thinsp;\u0026plusmn;\u0026thinsp;121.28 pixel\u003c/p\u003e\u003cp\u003e(3.83\u0026thinsp;\u0026plusmn;\u0026thinsp;3.34 cm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e134.28\u0026thinsp;\u0026plusmn;\u0026thinsp;130.41 pixel\u003c/p\u003e\u003cp\u003e(3.69\u0026thinsp;\u0026plusmn;\u0026thinsp;3.59cm)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParticipant B - Participant C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e122.84\u0026thinsp;\u0026plusmn;\u0026thinsp;103.58 pixel\u003c/p\u003e\u003cp\u003e(3.38\u0026thinsp;\u0026plusmn;\u0026thinsp;2.85 cm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e153.79\u0026thinsp;\u0026plusmn;\u0026thinsp;156.90pixel\u003c/p\u003e\u003cp\u003e(4.23\u0026thinsp;\u0026plusmn;\u0026thinsp;4.31cm)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAverage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e126.15\u0026thinsp;\u0026plusmn;\u0026thinsp;109.67 pixel\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e(3.47\u0026thinsp;\u0026plusmn;\u0026thinsp;3.16 cm)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e148.47\u0026thinsp;\u0026plusmn;\u0026thinsp;145.47pixel\u003c/p\u003e\u003cp\u003e(4.08\u0026thinsp;\u0026plusmn;\u0026thinsp;4.00cm)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003eNOTE: Values for the groups with larger values are in bold.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparison of inter-rater reliability of evaluation by final GOE and each GOE criterion\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u003cp\u003eJudges\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u003cp\u003eSkaters\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGOE guideline\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eICC(2,1)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e95%CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eICC(2,1)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e95%CI\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFinal GOE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.336\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.000***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.10\u0026ndash;0.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.409\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.000***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.18\u0026ndash;0.63\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVery good height and very good length\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.334\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.001**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.11\u0026ndash;0.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.301\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.003**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.08\u0026ndash;0.54\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGood take-off and landing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.436\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.000***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.22\u0026ndash;0.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.373\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.000***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.14\u0026ndash;0.60\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEffortless throughout\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.284\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.002**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.08\u0026ndash;0.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.073\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.192\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.08-0.29\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSteps before the jump, unexpected or creative entry\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.438\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.000***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.21\u0026ndash;0.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.446\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.000***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.21\u0026ndash;0.66\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVery good body position from take-off to landing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.239\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.009**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.04\u0026ndash;0.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.047\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.217\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.06-0.22\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eElement matches the music\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.045\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.274\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.09-0.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.563\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.11-0.14\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c8\" namest=\"c3\"\u003e\u003cp\u003e*p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003eNOTE: Values for the groups with larger values are in bold. GOE, grade of execution\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study investigated how judging experience affects individual differences in GOE evaluation of figure skating jumps, with all participants having similar athletic experience. Our findings revealed the following:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eBoth groups exhibited comparable accuracy in judging GOE scores, with minimal individual variations.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eThe judge group displayed shorter Euclidean distances in their gaze coordinates compared with the skater group.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eThe skater group demonstrated less interrater variability in final GOE scores. Conversely, the judge group showed less variation in five of the six criteria based on individual GOE criterion evaluations.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003ePrevious studies have shown that the judging accuracy of monitors is more strongly influenced by athletic experience than by judging experience (Pizzera, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Pizzera \u0026amp; Raab, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Accordingly, we expected that figure skating judges and skaters in our study with comparable athletic experience would demonstrate similar levels of judging accuracy. Indeed, our findings confirmed that the fixed effect of participant attributes (judge vs. skater) was not significant, consistent with previous studies. This suggests that monitors can leverage their athletic experience to evaluate both the negative and positive aspects of performance emphasized in the updated GOE criteria. Furthermore, the greater influence of the random effect of the jump video compared with the participant effect indicated that the specific jump being evaluated exerted more influence on GOE judgment than individual variability, supporting the robustness of the controlled experimental setting.\u003c/p\u003e\u003cp\u003eThe analysis of gaze behavior revealed smaller Euclidean distances among gaze coordinates in the judge group compared with the skater group. A prior study reported that judges and skaters differ in the body areas they focus on during evaluation: while both groups primarily fixated on the upper body, skaters tended to focus more on the face, whereas judges concentrated on the lower body and skating boots(Hirosawa et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, the previous study did not address the spatial similarity of gaze locations among participants. In the present study, the smaller Euclidean distances observed within the judge group suggest that even novice-level judging experience may reduce individual spatial differences in where participants look during jump evaluation.\u003c/p\u003e\u003cp\u003eRegarding individual differences in jump GOE evaluation, the skater group exhibited smaller ICC(2,1) values for the final GOE score, whereas the judge group demonstrated significantly smaller individual variations for five of the six GOE criteria. These results indicate that novice judging experience did not contribute to reducing individual differences in evaluating the final GOE score; however, it can reduce individual differences regarding each evaluation criterion of GOE. Notably, Criterion 6 (element matches the music) showed the highest individual variability among all criteria, and novice judging experience reduced individual differences for Criteria 1, 2, 3, and 5. As the GOE guidelines (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) state that Criteria 1\u0026ndash;3 must be applied to achieve high GOE scores (+\u0026thinsp;4 or +\u0026thinsp;5), these three criteria are critical for evaluating jump performance. Consequently, this suggests that novice judges should prioritize acquiring a consistent understanding of these criteria as they gain experience. Regarding Criteria 3 (effortless throughout) and 5 (very good body position from takeoff to landing), the skater group showed low ICC(2,1) values, as opposed to the improved values and reduced individual differences in the judge group. The differences in judging experience were most apparent in the large individual differences between the two criteria. However, Criterion 6 exhibited low reliability and was not statistically significant in either group, even if the judge group's ICC(2,1) value (0.045) indicated larger individual differences compared with the skater group. Determining musical congruence remains a highly interpretive process for novice judges, underscoring the need for targeted training programs that promote a standardized understanding of how jump execution aligns with music.\u003c/p\u003e\u003cp\u003eFor reference, the ICC(2,1) value calculated for the World Championship judges (n\u0026thinsp;=\u0026thinsp;9) in final GOE scoring was 0.635 (p\u0026thinsp;=\u0026thinsp;0.000, 95% CI [0.50\u0026ndash;0.77]), which was higher than the values observed in both groups (judges and skaters) in the present study. However, direct comparison with actual competition judges is difficult due to differences in evaluation environments. Nevertheless, this supplementary evidence suggests that accumulated judging experience may contribute to improved consistency in performance evaluation.\u003c/p\u003e\u003cp\u003eDespite its insights, this study has some limitations. First, as a case study with a small sample size, future studies with larger sample sizes are needed to generalize the results. Specifically, this study considered only novice-level judging experience. Investigating how top-level international judges perceive and evaluate jump performance and assessing the extent of individual variations within this group are crucial for a more comprehensive understanding. The relatively small number of figure skating competitions and judges, as compared to other sports such as soccer, presents a challenge in addressing these limitations. As such, securing the cooperation of international and national federations is essential to facilitate larger-scale studies with higher-level judges. Additionally, in this study, the judge and skater groups were intentionally matched in terms of athletic ability. Thus, whether similar patterns of individual differences would emerge in groups with lower or higher levels of athletic ability remains unclear. As a methodological limitation, this study prioritized providing participants with a natural viewing environment to reduce their burden and, therefore, did not strictly control the height of the chair or the viewing distance from the monitor. Although previous studies have reported acceptable accuracy without the use of a chin rest, they also recommend this tool to stabilize head position and maximize measurement precision (Carter \u0026amp; Luke, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Future studies examining gaze-position distributions in greater detail should use a chin rest.\u003c/p\u003e\u003cp\u003eDespite these limitations, to the best of our knowledge, our study is the first to clarify the relationship between novice judging experiences and individual differences in figure skating jump performance, providing a new perspective on the decision-making processes of sports officials.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study examined the effect of novice judging experience on reducing individual differences in GOE evaluation of figure skating jumps, with a particular focus on positive evaluation criteria. The participants consisted of judges and skaters with comparable athletic experience. The results showed that novice judging experience did not reduce individual differences in final GOE scores. However, for five of the six GOE criteria, individual differences were smaller in the judge group than in the skater group, particularly for Criteria 3 (effortless throughout) and 5 (very good body position from takeoff to landing), indicating the impact of judging experience. Furthermore, the judge group showed greater consistency in gaze coordinates during jump evaluation, suggesting that consistency in evaluation for each GOE criterion may be linked to the consistency of gaze coordinates. These findings highlight the potential value of incorporating guidance that promotes consistent visual focus and attentional strategies into judge training programs.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding was received\u003c/p\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n\u003ch2\u003eDisclosure statement\u003c/h2\u003e\n\u003cp\u003eThe authors report no potential competing interests.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n\u003ch2\u003e\u0026nbsp;\u003c/h2\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eSH made substantial contributions to the conception, design, acquisition, analysis, and interpretation of data for this research and drafted the manuscript. TK made significant contributions to the conception and design of the work and critically revised it for important intellectual content. YA critically revised the manuscript for important intellectual content and approved the final version for publication. All authors agree to be accountable for all aspects of the work, ensuring that questions related to the accuracy or integrity of any part are appropriately investigated and resolved.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eThe study participants did not give written consent for their data to be shared publicly; therefore, due to the sensitive nature of the research, supporting data are not available.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAvugos, S., MacMahon, C., Bar-eli, M., \u0026amp; Raab, M. (2021). 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Citation network analysis of research on sport officials: a lack of interconnectivity. \u003cem\u003eInternational Review of Sport and Exercise Psychology\u003c/em\u003e, \u003cem\u003e8\u003c/em\u003e(1), 95\u0026ndash;105. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/1750984X.2015.1022202\u003c/span\u003e\u003cspan address=\"10.1080/1750984X.2015.1022202\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHirosawa, S. (2025, November 10). Can Machine Learning Robustly Predict Grade Execution Figure Skating Jumps from Kinematic Features Across Competitions-A Case Study Ladies\u0026rsquo; Double Axel World Championships. \u003cem\u003eResearch Square\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.21203/rs.3.rs-6233774/v1\u003c/span\u003e\u003cspan address=\"10.21203/rs.3.rs-6233774/v1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHirosawa, S., Kato, T., Yamashita, T., \u0026amp; Aoki, Y. (2023). 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Gaze behavior of referees in sport-A review. \u003cem\u003eFrontiers in Sports and Active Living\u003c/em\u003e, \u003cem\u003e2\u003c/em\u003e, 572891. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fspor.2020.572891\u003c/span\u003e\u003cspan address=\"10.3389/fspor.2020.572891\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"sport-sciences-for-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ssfh","sideBox":"Learn more about [Sport Sciences for Health](http://link.springer.com/journal/11332)","snPcode":"11332","submissionUrl":"https://submission.nature.com/new-submission/11332/3","title":"Sport Sciences for Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"sports officials, double Axel jump, linear mixed model, intraclass correlation coefficients","lastPublishedDoi":"10.21203/rs.3.rs-4661368/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4661368/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eJudges in figure skating are tasked with the proper evaluation of the skater's grade of execution (GOE) score. While prior research found that judging accuracy is strongly impacted by previous experience as an athlete, the influence of judging experience on individual differences in evaluation remains unclear. This study investigated how the experience of a novice judge affected their evaluation process. The participants were judges and skaters with similar athletic abilities (n = 3 per group), and individual differences in (1) judging accuracy, (2) gaze coordinates, and (3) intraclass correlations were analyzed. The results revealed that limited judging experience did not significantly affect judging accuracy or individual variability in final GOE scores. However, for five of the six GOE criteria, individual differences were smaller in the judge group than in the skater group.\u003cbr\u003e\nThe judge group also showed greater consistency in gaze coordinates during jump evaluation, suggesting that consistency in evaluation for each GOE criterion may be linked to the consistency of gaze coordinates. These findings highlight the potential value of incorporating guidance that promotes consistent visual focus and attentional strategies into judge training programs.\u003c/p\u003e","manuscriptTitle":"Exploring the influence of judging experience on individual differences in figure skating jump performance evaluations: A case study of novice judges ","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-28 09:21:04","doi":"10.21203/rs.3.rs-4661368/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Accepted","date":"2025-12-31T14:15:12+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-22T18:07:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"8449725497380415265317152457968788867","date":"2025-12-02T18:39:15+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-01T08:31:05+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-26T09:13:28+00:00","index":"","fulltext":""},{"type":"submitted","content":"Sport Sciences for Health","date":"2025-11-25T12:24:21+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"sport-sciences-for-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ssfh","sideBox":"Learn more about [Sport Sciences for Health](http://link.springer.com/journal/11332)","snPcode":"11332","submissionUrl":"https://submission.nature.com/new-submission/11332/3","title":"Sport Sciences for Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"e51c5f77-af7d-49d0-a04a-af42c7226f65","owner":[],"postedDate":"November 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-02-02T16:09:06+00:00","versionOfRecord":{"articleIdentity":"rs-4661368","link":"https://doi.org/10.1007/s11332-025-01645-x","journal":{"identity":"sport-sciences-for-health","isVorOnly":false,"title":"Sport Sciences for Health"},"publishedOn":"2026-01-28 15:59:32","publishedOnDateReadable":"January 28th, 2026"},"versionCreatedAt":"2025-11-28 09:21:04","video":"","vorDoi":"10.1007/s11332-025-01645-x","vorDoiUrl":"https://doi.org/10.1007/s11332-025-01645-x","workflowStages":[]},"version":"v1","identity":"rs-4661368","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4661368","identity":"rs-4661368","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}