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The competitive esports physiological, affective, and video dataset

Published

11 months ago

January 11, 2025

The competitive esports physiological, affective, and video dataset

Abstract

National Science Centre in Poland supported preparing this article with a research grant (UMO-2020/39/B/HS6/00685) and a scholarship awarded to M.B. by the Foundation for Polish Science (FNP). The funder had no role in study design, data collection, analysis, publishing decisions, or manuscript preparation. The project was conducted in AMU Psychophysiology Lab: Positive Gaming & Streaming (head: Lukasz Kaczmarek). The authors would like to thank Lukasz Kaczmarek for his help in funding acquisition and consultations on the early versions of the project.

Background & Summary

Participants detailed their highest level of competitive play (ranging from recreational to international), their professional engagement with esports (as a full-time or part-time job or a non-income generating activity), and their weekly gaming duration in hours. Additionally, they reported their in-game ranking (specifically, the top rank achieved in the past year), the total time spent playing the CS: as recorded by the CS: GO game system within the Steam Library (Valve Corp., USA), average weekly hours spent playing against computer-controlled opponents (bots). We also calculated their total gaming hours over the tournament preceding two weeks from their daily logs. Participants also shared their age and Body Mass Index (BMI).Article
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MAPI Research Institute. Generalized Anxiety Disorder – 7 (GAD-7). Available at: https://eprovide.mapi-trust.org/instruments/generalized-anxiety-disorder-7 (accessed July 2023).Dweck, C. S. Mindset: The New Psychology of Success. Random House (2006).

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Crum, A. J., Salovey, P. & Achor, S. Rethinking stress: the role of mindsets in determining the stress response. J. Pers. Soc. Psychol. 104, 716 (2013).
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Bailey, H. Open Broadcasting Software. Retrieved from https://obsproject.com/ (2018).

Received: 01 August 2024

Table 1 Existing Affective Psychophysiological Datasets (Part 1).

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Table 2 Existing Affective Psychophysiological Datasets (Part 2).

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Table 3 Existing Affective Psychophysiological Datasets (Part 3).

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Methods

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Participants

De France, K. & Hollenstein, T. Assessing emotion regulation repertoires: The regulation of emotion systems survey. Pers. Individ. Differ. 119, 204–215 (2017).

Ethics Information

The physiological and behavioral data, in a CSV format, constitute 366 GB of space³². The files are grouped into eight subcomponents with a maximum size of 50 GB due to OSF storage restrictions. The component’s name indicates for which study stage (S1 vs S3) and participants and which person they refer to. For instance, the name ‘Physio_S3_225_300’ indicates that the component included psychophysiological and behavioral data from Stage 3 for participants from 225 to 300. The component contains a set of CSV files for particular subjects. All psychophysiological and behavioral signals recorded during the experiment for each individual are available in a single CSV datafile named “S < stage_id > _P < participant_id >,” where “S” stands for study stage, “P” for participants, e.g., S1_P10.csv, or S3_P224.csv. The “< particpant_id >” is a natural number identifying a participant.

Procedure

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Stage 1

Internal/external validity: our study offered a unique blend of internal and external validity through the use of controlled experiments paired with real-world outcomes. It included a thorough evaluation of affective and physiological dynamics and implemented a robust, theory-driven intervention.

Table 4 Description of Questionnaires Used in the Project (Part 1).

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Table 5 Description of Questionnaires Used in the Project (Part 2).

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Table 6 Description of Questionnaires Used in the Project (Part 3).

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Zhang, Z. et al. Multimodal spontaneous emotion corpus for human behavior analysis. In IEEE Conf. Comput. Vision Pattern Recognit. 3438–3446 (2016).Article
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Stage 2

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Stage 3

Epel, E. S. et al. More than a feeling: A unified view of stress measurement for population science. Front. Neuroendocrinol. 49, 146–169 (2018).Kroenke, K., Spitzer, R. L. & Williams, J. B. The Patient Health Questionnaire-2: validity of a two-item depression screener. Med. Care 41, 1284–1292 (2003).Some information about our study is detailed in the published registered report⁶ – which presents hypothesis testing related to the effects of the Synergistic Mindsets Intervention – including a comprehensive description of the sampling procedures, study procedure, questionnaires, and physiological data. In this paper, we provide additional details on open-text responses, video recordings, and behavioral data. Furthermore, we include new information regarding data quality and present how the measures changed over the course of laboratory visits. Finally, to enhance the usability of the CEPAV dataset, we standardized and merged the physiological and behavioral data collected from three different devices (each with distinct data formats and sampling rates) and uploaded the resulting user-friendly files instead of the raw data.

Measures

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Questionnaires

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Open-Text answers

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Table 7 Description of the Instructions for Open-text Questions (Part 1).

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Table 8 Description of the Instructions for Open-text Questions (Part 2).

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Table 9 Description of the Instructions for Open-text Questions (Part 3).

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Other self-reports

Demographics

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Performance

Brytek-Matera, A. & Kozieł, A. The body self-awareness among women practicing fitness: a preliminary study. Pol. Psychol. Bull. 46, 104–111 (2015).

Video data

Video recordings

Crum, A. J., Akinola, M., Martin, A. & Fath, S. The role of stress mindset in shaping cognitive, emotional, and physiological responses to challenging and threatening stress. Anxiety Stress Coping 30, 379–395 (2017).

Gameplay recordings

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Physiological measures

Impedance cardiography and electrocardiography

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Blood pressure

Shui, X. et al. A dataset of daily ambulatory psychological and physiological recording for emotion research. Sci. Data 8, 161 (2021).

Behavioral measures

Kroenke, K., Spitzer, R. L., Williams, J. B., Monahan, P. O. & Löwe, B. Anxiety disorders in primary care: prevalence, impairment, comorbidity, and detection. Ann. Intern. Med. 146, 317–325 (2007).

Data preprocessing

Koelstra, S. et al. Deap: A database for emotion analysis; using physiological signals. IEEE Trans. Affective Comput. 3, 18–31 (2011).Histograms Presenting Ranges of Means of Collected Signals. Panel A presents data from Stage 1; Panel presents data from Stage 3. HR – Heart rate, bpm, SBP – Systolic Pressure, mmHg; DBP – Diastolic Pressure mmHg; SV – Stroke Volume, ml; LVET – Left Ventricular Ejection Time, ms; PI- Pulse Interval, ms; MS – Maximum Slope; mmHg/s; CO – Cardiac Output; l/min; TPR – Total Peripheral Resistance Medical Unit, mmHg.min/l; TPRCGS – Total Peripheral Resistance CGS; dyn.s/cm5; wr – right wrist movement, custom units; tl – left thigh movement, custom units; tr – right thigh movement, custom units.Article
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Maciej Behnke, Wadim Krzyżaniak, Patrycja Chwiłkowska, Szymon Jęśko Białek, Maciej Kłoskowski, Patryk Maciejewski & Kacper Szymański

Data Records

Rodgers, R. F. et al. A biopsychosocial model of social media use and body image concerns, disordered eating, and muscle-building behaviors among adolescent girls and boys. J. Youth Adolesc. 49, 399–409 (2020).

Dataset Structure

Self-reports and metadata

Ab. Aziz, N. A. K. T. et al. Asian affective and emotional state (A2ES) dataset of ECG and PPG for affective computing research. Algorithms 16, 130 (2023).

Video data

Melhart, D., Liapis, A. & Yannakakis, G. N. The arousal video game annotation (AGAIN) dataset. IEEE Trans. Affective Comput. 13, 2171–2184 (2022).

Physiological and behavioral data

Jankowski, K. S. Is the shift in chronotype associated with an alteration in well-being? Biol. Rhythm Res. 46, 237–248 (2015).

Single physiological file structure

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Technical Validation

Missing data

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Questionnaires reliability

Koldijk, S., Sappelli, M., Verberne, S., Neerincx, M. A. & Kraaij, W. The swell knowledge work dataset for stress and user modeling research. In Multimodal Interaction (2014).

Physiological data – qualitative validation

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Physiological data – quantitative validation

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Yeager, D. S. et al. A synergistic mindsets intervention protects adolescents from stress. Nature 607, 512–520 (2022).Li, J. Psychometric properties of Ten-Item Personality Inventory in China. Chin. J. Health Psychol. 21, 1688–1692 (2013).

Summary of previously completed analyses

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Summary of the physiological, affective, and behavioral activity during the competitive esports

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Usage Notes

Becerra, R., Gainey, K., Murray, K. & Preece, D. A. Intolerance of uncertainty and anxiety: The role of beliefs about emotions. J. Affect. Disord. 324, 349–353 (2023).Gao, Z., Cui, X., Wan, W., Zheng, W. & Gu, Z. ECSMP: A dataset on emotion cognition, sleep, and multi-model physiological signals. Data Brief 39, 107660 (2021).Article
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Declaration of Generative AI and AI-assisted technologies in the writing process

Christy, A. G., Schlegel, R. J. & Cimpian, A. Why do people believe in a “true self”? The role of essentialist reasoning about personal identity and the self. J. Pers. Soc. Psychol. 117, 386–416 (2019).

Code availability

Gameplay during the CS: GO matches was captured using the Open Broadcaster Software²⁵ at 30 FPS to document in-game actions and strategies (Fig. 2).

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Maciej Behnke.
Behnke, M. et al. CEPAV Dataset, Raw_Physio Component. Open Science Framework https://doi.org/10.17605/OSF.IO/HKDUY (2024).Physiological and behavioral data were exported from the acquisition formats by the first author (MB). We used different acquisition software; therefore, the exported data had to be integrated into a common format. The exported TXT and CSV files were preprocessed using Python^28,29 scientific libraries (e.g., pandas 2.2.2, numpy 1.26.4; see Code Availability, for detailed information). In addition to scripts for processing the typical data, we also added scripts for handling problematic cases and exceptions.
In our initial publication, we tested the effects of the Synergistic Mindsets Intervention (SMI) compared to a control intervention⁶. The SMI was positively received, leading participants to adopt more advantageous stress mindsets, more favourable appraisals of the esports tournament, and an increased application of reappraisal strategies for emotion regulation. Despite these positive outcomes, the high-stakes nature of the esports competition was perceived as an enjoyable challenge rather than a negative stressor, reducing the potential for the SMI to significantly influence affective and physiological reactions. The absence of a negative physiological stress response meant there was very little for the intervention to modulate. Consequently, no significant changes were noted in affective responses or gaming performance due to the intervention. Access to the research code, dataset, and findings can be found elsewhere⁶.
We converted the raw acquired data (obtained with proprietary acquisition software) into a consistent format and saved it in CSV files. All signals were resampled to 1 kHz, using the previous neighbor interpolation method. Signals from different devices were time-synchronized using synchronization markers generated by VU-AMS and Finometer devices during experiments. We marked the baselines, matches, and recoveries within the files. Finally, data across studies were exported to normalized form, consisting of a header, predefined file structure, and standardized subject naming convention. The description of labels used for tagging specific epochs is available in the “CEPAV_data” file²⁴, the “epoch_name” sheet.
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Visualization Single Physiological File Structure for Stage 1 (left panel) and 3 (right panel). ECG- electrocardiogram, mV; Z0 – Average thorax impedance, ohm; dZ – Change in impedance due to respiration and heartbeat, ohm; dZ/dt – Impedance CardioGram, ohm/s; SBP – Systolic Pressure, mmHg; DBP – Diastolic Pressure, mmHg; MAP- Mean Pressure, mmHg; HR – Heart rate, bpm; SV – Stroke Volume, ml; LVET – Left Ventricular Ejection Time, ms; PI- Pulse Interval, ms; MS – Maximum Slope; mmHg/s; CO – Cardiac Output; l/min; TPR – Total Peripheral Resistance Medical Unit, mmHg.min/l; TPRCGS – Total Peripheral Resistance CGS; dyn.s/cm5; wr – right wrist movement, custom units; tl – left thigh movement, custom units; tr – right thigh movement, custom units.
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Shifts in Mean Levels of Affective Measures. Red line separates Stage 1 and Stage 3 laboratory visits. HR – Heart rate, bpm, SBP – Systolic Pressure, mmHg; DBP – Diastolic Pressure mmHg; CO – Cardiac Output; l/min; TPR – Total Peripheral Resistance Medical Unit, mmHg.min/l.
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DOI: https://doi.org/10.1038/s41597-024-04364-z
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We collected the number of kills, kills’ assists, and deaths and the match scores as determined by the Counter-Strike: Global Offensive scoring system, which factors in the difficulty of the weapon used and the points earned for each enemy bot eliminated. A higher score reflects superior performance. Gamers’ tournament performance was primarily evaluated based on their total score, making it the main performance index. However, other metrics—kills, assists, and deaths—can provide insight into the participant’s strategy. For instance, a high total score paired with a high number of deaths may indicate a risk-taking approach. Analyzing which strategies proved optimal or suited individual gamers could be valuable for esports coaches, helping them tailor training and game plans effectively. In Stage 2, participants were asked to log their daily match scores, simulating the conditions of the upcoming tournament.Download references
However, the dataset comes with certain limitations. First, this dataset cannot be employed to investigate differences between sexes, ethnicities, or between the group ages, as all participants were male Caucasian young adults. Second, our investigation was confined to affective reactions within a single esports (Counter Strike: Global Offensive) context. Third, as noted in the missing data section, the dataset lacks some data due to technical constraints (e.g., ICG missing due to electrode detachment), lack of consent to share data, and human errors (e.g., not starting data collection for accelerometers). Lastly, the dataset represents a secondary use of data initially collected for a previously published independent study.Accepted: 18 December 2024
We present questionnaires, open-text, other self-reported data, and auxiliary information about the participants in the “CEPAV_data” spreadsheet²⁴. The file includes participants’ ID, sex, age, height, weight, experimental conditions, and questionnaire responses (the “self_reports” sheet). To make it easier to use the database, we also included averages for physiological and behavioral data from selected moments of the study in the file, which were used for the Summary of the Physiological, Affective, and Behavioral Activity During the Competitive Esports (Technical Validation section) and presented in Fig. 5 (the “physio_behav” sheet)²⁴.Uusberg, A. et al. Appraisal shifts during reappraisal. Emotion 23, 1985–2001 (2023).
Participant movement was non-invasively tracked using three tri-axial accelerometers (model wGT3X-BT, Actigraph, USA), placed on the thighs (thigh left and thigh right; TL & TR) next to the knee and the right wrist (WR), allowing for the continuous observation of physical activity and gestures during gameplay. All accelerometers were initialized before the participant’s arrival to collect raw acceleration data at 30 Hz with the same start time using ActiLife software (version 6.13.4). We used the measure of the vector magnitude for the given accelerometer for each 1-second interval extracted with the ActiLife software.Behnke, M., Krzyżaniak, W., Nowak, J. et al. The competitive esports physiological, affective, and video dataset.
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Participants’ upper bodies were continuously captured on video using an HD camera positioned in between the monitors, utilizing the Open Broadcaster Software²⁵. The camera was set approximately 65 cm away from the participants’ heads, with a recording at 30 FPS. We also captured the back view of the experimental view with the camera near the ceiling (Fig. 2). These recordings were primarily used to monitor the study’s progress and ensure participant safety.Medland, H., De France, K., Hollenstein, T., Mussoff, D. & Koval, P. Regulating emotion systems in everyday life: Reliability and validity of the RESS-EMA scale. Eur. J. Psychol. Assess. 36, 437 (2020).
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Affective science is an interdisciplinary field that draws upon methods and findings from psychology, cognitive science, neuroscience, computer science, biology, and other related fields to understand the complexities of affective phenomena and to determine how they influence human behavior. Multimodal data are needed to thoroughly investigate affective phenomena. Different scientific backgrounds equip researchers with unique skills for data collection. For example, psychologists excel in experimental designs, computer scientists excel in data mining from digital platforms, and biomedical researchers excel in collecting biological samples. Interdisciplinary teams leverage these diverse methodologies to approach research questions by collecting comprehensive multi-modal datasets.
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The physiological data quality was assured by following recommendations in affective science³³. First, the data were collected by experimenters who completed at least 30 hours of training in psychophysiological research provided by MB. Second, prior to performing preprocessing, the first author (MB) visually inspected all physiological signals. Before inclusion in the database, MB manually double-checked all datasets for missing or corrupted data.

Reprints and permissionsPanzeri, A. et al. Factors impacting resilience as a result of exposure to COVID-19: The ecological resilience model. PLoS One 16, e0256041 (2021).

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We collected data on individual differences at the beginning of Stage 1 and Stage 3 and during the 1-month follow-up. Details related to each questionnaire are presented in Tables 4–6. Additional details on the included questionnaires (e.g., which items measure specific subscales) are included in the “CEPAV_data” spreadsheet²⁴ (the “self_reports” sheet).

Published: 11 January 2025

We collected six types of measures in this project, namely questionnaires, open-text, other self-reports, video, physiology, and behavior.

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Multistage: participants provided data on multiple occasions, namely during two laboratory sessions, daily gaming, daily descriptions of the day, and one-month follow-up after the esports tournaments.

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Behnke, M. et al. Supplementary materials for applying a synergistic mindsets intervention to an esports context. Open Sci. Framework https://osf.io/62yge (2024).Article
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To describe competitive gaming from the affective, physiological, and behavioral perspectives, we calculated the averages for the one-minute intervals for given situations within the study: five minutes of resting baseline, pre-match baselines, during the matches, and recoveries after the matches. Figure 5 visualizes the levels of the given parameters: HR, HRV, SBP, DBP, CO, TPR, WR, TL, and TR. As shown, there are clearly visible differences between gaming and resting periods for HR, HRV, and accelerometry. Furthermore, we identified the time-related drift in HR, which may indicate the healthy habituation process^39,40. Our observations may serve as an inspiration for the quantitative analysis of the observed phenomena.

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Kate Petrova & James J. Gross

Virtanen, P. et al. SciPy 1.0: Fundamental algorithms for scientific computing in Python. Nat. Methods 17, 261–272 (2020).Upon their first visit to the lab, participants were briefed on the study’s procedures and gave informed consent, specifying which data they were willing to share publicly (for details on how many participants were unwilling for their data to be shared publicly, see Technical Validation/Missing Data section). Next, psychophysiological sensors were attached, and participants completed baseline questionnaires (Tables 4–6). Instructions and questionnaire responses were managed through two 23-inch PC monitors (one dedicated to gaming and the other to experimental tasks). Following the setup and questionnaire completion, the researcher checked the signals quality and moved from the experimental room to the control room, signaling the start of the experiment. The researcher monitored the safety and participant behavior using the camera installed behind the participant.

We also prepared examples in Python Jupyter Notebooks presenting how to load and visualize psychophysiological data from sample files (Fig. 3). Both the conversion scripts and the Notebooks can be obtained from our source code repository available at CEPAV Code Component³⁰ and at GitHub https://github.com/psychosensing/CEPAV.

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During the period between laboratory visits, participants were instructed to play CS: GO as per their usual gaming routine. On gaming days, they were asked to designate one match to be played in “performance mode”, mirroring tournament conditions. This involved playing a match that included a 2-minute preparation phase and providing affective self-reports prior to the game (see Fig. 1). Post-match, they were asked to log their scores. Additionally, at the day’s end, participants were asked to report their affect and the total time spent gaming that day.We organized a large-scale study around esports competition⁶. We conducted a three-phase experiment involving a novel Synergistic Mindsets Intervention, compared to a control group focused on brain education⁷, to explore its impact on training and competition performance. This included monitoring participants’ progress over two weeks and their performance in a tournament. A one-month follow-up assessed the intervention’s long-term effects. We recorded gamers’ functioning in the laboratory sessions with continuous, non-invasive measurements of physiological, behavioral, and video data (Stages 1 & 3). Strengths of the CEPAV dataset include:

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Download citationEach of the CSV files in the dataset has a 1-line header. The header includes channel/sensor name e.g., “timestamp”, “ECG”, “SBP”, or “marker”. Following the header, each CSV file contains 20 columns. The first column of the data table (except for the header) contains timestamps, as provided by a clock on the main data acquisition (logging) computer – the timestamp shows the time of data collection. In the last column, there is a marker that identifies the specific phase of the experiment. The “epoch_name” sheet provides a full explanation of the epoch IDs used to mark the specific phase of the experiment, e.g., “1” indicates the experimental baseline, while “13” indicates the first minute of the baseline match during Stage 1. The columns in between timestamp and marker contain the physiological data and behavioral data, namely ECG (column 2), DZ (3), DZDT (4), Z0 (5), SBP (6), DBP (7), MAP (8), HR (9), SV (10), LVET (11), PI (12), MS (13), CO (14), TPR (15), TPRCGS (16), WR (17), TL (18), TR (19). For the visualization of a single physiological file structure for Stages 1 and 3, see Fig. 3.

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Following these matches, they were asked to evaluate the negative appraisals and undertake an emotion recall task. In an emotion recall task, participants were instructed to reflect on, detail, and assess two distinct moments from the tournament: one that elicited positive emotions and another that elicited negative emotions. Participants evaluated these situations on the dimensions of positive and negative affective experience, appraisals, and affect regulation strategies.Kang, S. et al. K-EmoPhone: A mobile and wearable dataset with in-situ emotion, stress, and attention labels. Sci. Data 10, 351 (2023).

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As shown in Tables 4–6, the questionnaires used present acceptable reliability.

M.B., D.L., K.P., P.C, J.P.J & J.J.G designed research. M.B., & J.J.G secured funding. M.B., D.L., K.P., J.P.J & J.J.G wrote the initial manuscript. All authors reviewed and edited the final version of the manuscript. M.B. and P.C. coordinated the experimental research and managed the data processing, analysis, and OSF repository of the project. M.B. & D.L. contributed to the analysis plan. M.B., P.C., S.J.M., M.K., W.K., P.M., & K.S. performed the experiments, collected data, and pre-processed the data. M.B., run the analysis. W.K. coded the software for the data preprocessing. M.B., J.N., S.K., W. K., developed the dataset, M.B., W. K., and J.N. contributed to the technical validation. S.K. supervised the data processing and technical validation.Article
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Cognitive Neuroscience Center, Adam Mickiewicz University, Poznan, PolandDaniël Lakens

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Cardiovascular biosignals were captured using the Finometer MIDI device (Finapres Medical Systems, Netherlands). This device employs the volume-clamp technique with finger cuffs placed on the third digit of the left hand to measure arterial pressure waveforms in the finger, enabling the estimation of Systolic Pressure (mmHg; SBP); Diastolic Pressure (mmHg; DBP); Mean Pressure (mmHg; MAP); Heart rate (bpm; HR); Stroke Volume (ml; SV); Left Ventricular Ejection Time (ms; LVET); Pulse Interval (ms; PI); Maximum Slope (mmHg/s; MS); Cardiac Output (l/min; CO); Total Peripheral Resistance Medical Unit (mmHg.min/l; TPR); Total Peripheral Resistance CGS (dyn.s/cm5; TPRCGS);Two weeks following the initial laboratory session, participants returned to compete in the esports tournament. This session mirrored the setup of Stage 1, starting with physiological sensor attachment, completing questionnaires, and undergoing a 5-minute physiological baseline. Subsequently, participants played eight tournament matches.

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Levenson, R. W. The autonomic nervous system and emotion. Emotion Rev. 6, 100–112 (2014).Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

In the first week of this phase, the SMI participants were prompted to use reappraisal before their match to regulate their affective responses. The following week, this prompt was not given to assess if participants would autonomously utilize reappraisal techniques in their daily gaming. The SMI participants also documented their adherence and progress within the affect regulation training by reporting gaming scenarios where they employed reappraisal (also serving as an intervention booster). Control participants noted their gaming situations without any specific affective characteristics. We used the Smartphone Ecological Momentary Assessment 3 (SEMA 3) application for Stage 2 data collection²³.Behnke, M. et al. CEPAV Dataset, Code Component. Open Science Framework https://doi.org/10.17605/OSF.IO/GFZ3M (2024).

Jan Nowak & Szymon KupińskiSample of the Video Data Collected During Stage 1 and Stage 3. The depicted participant provided explicit consent for the open publication of his image.

Siqueira, E. S. et al. An automated approach to estimate player experience in game events from psychophysiological data. Multimedia Tools Appl. 82, 19189–19220 (2023).Maciej Behnke

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We present data collected in the Psychophysiological Laboratory at the Faculty of Psychology and Cognitive Science, Adam Mickiewicz University, by the International team of researchers for the study aimed at optimizing affective responses during esports tournament⁶ from April 2023 to September 2024 in Poznan, Poland. Additional information on research questions, methods, and analysis are available elsewhere⁶.Matar Boumosleh, J. & Jaalouk, D. Depression, anxiety, and smartphone addiction in university students—A cross-sectional study. PLoS One 12, e0182239 (2017).

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The sample consisted of 300 male players of Counter-Strike: Global Offensive (CS: GO), aged between 18 and 32 years, with a mean age of 21.95 years (SD = 2.29). Competitive experience varied within the group: 200 players (67%) had no experience, 76 (25%) had competed in local tournaments, 17 (6%) had participated nationally, and six (2%) had taken part in international competitions. One participant did not disclose his competitive background. Esports provided an additional income source for 17 participants, while the rest did not earn money through gaming. On average, participants had been playing CS: GO for 9.13 years (SD = 5.22), with a mean total gameplay time of 2225.69 hours (SD = 1980.55) as recorded in their Steam Library (Valve Corp., SA). The number of participants varied across CS: GO ranks, with 9 participants ranked as Silver I, 2 as Silver III, 7 as Silver IV, 4 as Silver Elite, 4 as Silver Elite Master, 10 as Gold Nova I, 14 as Gold Nova II, 12 as Gold Nova III, 11 as Gold Nova Master, 28 as Master Guardian I, 21 as Master Guardian II, 26 as Master Guardian Elite, 27 as Distinguished Master Guardian, 29 as Legendary Eagle, 32 as Legendary Eagle Master, 19 as Supreme Master First Class, 44 as Global Elite. Details related to inclusion and exclusion criteria and the process of sample size determination are described elsewhere⁶.

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Project and Match Procedures. The red frames represent a procedure for all performances (to simplify the figure, we depicted it in detail only for baseline performance), namely prematch physiology, affective experience, Counter-Strike: Global Offensive match, and recovery. Baseline and post-intervention questionnaires include negative prior mindsets, positive and negative affective experiences, affect regulation strategies, well-being, ill-being, alexithymia, and emotion belief measures. Affective self-report includes affective experience and demands and resources evaluation. Emotion recall tasks include recalling and describing situations from the tournament that elicited positive and negative affective experiences and evaluating them using affective experience, situational appraisals and affect regulation strategies measures. One month after Stage 3, participants were asked to fill in follow-up questionnaires, the same set as at baseline and post-intervention. Figure reproduced from our previous article⁶, used under a CC BY license.

Participants were asked to provide open-text answers while learning about the SMI and control intervention, in daily reports, and during the Emotion Recall Task. The full script of Polish and English materials is available elsewhere⁶. The exact instructions for open-text questions are presented in Tables 7–9.Article

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The session concluded with a study evaluation, after which participants were debriefed, screened for any suspicions regarding the study’s aims, and compensated. Finally, we identified esports tournament winners, namley players that collected the most points in eight matches (separately, in the SMI and control group). The top three players in each group were awarded prizes of 2500, 1500, and 1000 PLN for first, second, and third places, respectively, equivalent to approximately 600, 360, and 240 USD. We also awarded a single player in each group that scored the most points in a single match, with the award of 500 PLN.Behnke, M., Buchwald, M., Bykowski, A., Kupiński, S. & Kaczmarek, L. D. Psychophysiology of positive and negative emotions dataset of 1157 cases and 8 biosignals. Sci. Data 9, 10 (2022).

Multimodality: participants provided 750 hours of cardiovascular, behavioral, and video data during the laboratory sessions. Furthermore, participants answered multiple questionnaires related to gaming-related affect, well-being and ill-being, and answered multiple open-text questions related to affective gaming situations.Monsoon, A. D., Preece, D. A. & Becerra, R. Control and acceptance beliefs about emotions: Associations with psychological distress and the mediating role of emotion regulation flexibility. Aust. Psychol. 57, 236–248 (2022).

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The calculated SNR indicated the high quality of all collected signals³⁵, with mean SNR ranging from 21.90 dB to 68.05 dB depending on the physiological signal and study. We identified outliers with the median absolute deviation, with a cutoff of 3, as recommended by Leys et al.^36,37, resulting in 416 signals (3.94% of all calculated SNR values) identified as SNR outliers. Next, the first author (MB) visually inspected all flagged data to determine whether the signal should be deleted and classified as an artifact, resulting in 17 signals being identified as artifacts. Outlying signals are marked with yellow color in the “SNR” sheet with additional comments from the first author on reasons that potentially influenced the outlying values (e.g., disconnecting ECG and ICG electrodes after the study while the signals were still being recorded).

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Miranda-Correa, J. A., Abadi, M. K., Sebe, N. & Patras, I. Amigos: A dataset for affect personality and mood research on individuals and groups. IEEE Trans. Affective Comput. 12, 479–493 (2018).The study received approval from the Bioethical Committee at Poznan University of Medical Sciences in Poland (802/22). Written informed consent was obtained from all participants, specifying which types of data—self-reports, physiological, behavioral, and video—they agreed to share publicly. This process ensured participants explicitly consented to data publication and the sharing of their images while also allowing them the option to opt out of sharing any specific type of data. For their participation, individuals were compensated with a shopping voucher worth 400 PLN (approximately 0). In addition, esports tournament winners were awarded prizes of 2500, 1500, and 1000 PLN for first, second, and third places, respectively, equivalent to approximately 600, 360, and 240 USD.

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The initial phase of the experiment involved a 5-minute period for establishing physiological baselines, requiring participants to sit quietly. This was followed by the first CS: GO match to assess baseline performance in a format that would mirror the subsequent tournament without any experimental manipulations (baseline match). Post-match, participants were randomly divided into either the SMI or a control group, where they engaged in a 30–40-minute self-guided intervention. Participants were randomly assigned using a random number generator from RandomLists. The assigned condition was linked to the participant number and hardcoded into the intervention presentation software. Upon reviewing the intervention materials, participants were prompted to integrate the newly acquired strategies into their esports training, which involved playing two additional matches (training matches 1 & 2). To conclude Stage 1, instructions were provided on how to log daily measures. Synergistic mindsets participants were advised to consistently apply affect regulation techniques during their gaming sessions over the next two weeks. Control participants were encouraged to incorporate the learned brain facts into their daily gaming practices.Article
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Participants played Counter-Strike: Global Offensive (CS: GO) in a deathmatch mode on the Dust II map, competing against the highest difficulty level of computer-generated opponents (bots) without random weapons. All matches were structured into prematch baseline measurements (2 minutes), gaming (2 minutes), and recovery (2 minutes) (Fig. 1). During all match phases, we collected cardiovascular, behavioral, and video data. Before each match, participants also shared their affective experience and provided demands and resources evaluations.

William, W. K. Zung self-rating depression scale. Encycl. Qual. Life Well Being Res 7317 (2014).The “CEPAV_data” spreadsheet details missing data for various types, organized under the columns: Missing_ICG_ECG, Missing_Blood_Pressure, Missing_Accelerometry, and Missing_Video (the “missing” sheet)²⁴. Specifically, we were unable to collect some ICG/ECG data from 10 participants, blood pressure data from 10 participants, accelerometry data from 9 participants, and video data from 5 participants. Furthermore, for 7 participants, we deleted some signals that were identified as artifacts during SNR analysis. The spreadsheet also specifies the study stage at which the data is missing; for example, S3_m15 indicates missing data during Stage 3, encompassing tournament performance 5 recovery and tournament performance 6 baseline. Additionally, the absence of ICG and ECG data impacted our ability to compile complete data files for 11 sessions for 10 participants. Furthermore, 22 participants withheld consent to share their video data. None of the participants withheld consent to share their self-reports and behavioral and physiological data.

Huang, W., Liu, G. & Wen, W. MAPD: A Multi-subject Affective Physiological Database. In Computational Intelligence and Design (2014).The code can be accessed via the Code component³⁰ on OSF and via the public GitHub repository: https://github.com/psychosensing/CEPAV. It is licensed under MIT OpenSource license, i.e., the permission is granted, free of charge, to obtain a copy of this software and associated files (e.g., the Jupyter IPython Notebooks), subject to the following conditions: the copyright notice and the MIT license permission notice shall be included in all copies or substantial portions of the software based on the scripts we published. Scripts for converting data from proprietary acquisition software formats into consistent CSV files, as well as IPython Jupyter Notebooks presenting how to load the data from CEPAV CSV files into Python Pandas DataFrame structure, are available in the Code component³⁰ on OSF and at the following GitHub repository: https://github.com/psychosensing/CEPAV. Scripts that we used to transform the data from proprietary acquisition formats into coherent CSV files utilized Python 3.10.12⁴¹. The list of the specific modules and their versions is available in the “requirements.txt” file in the GitHub repository. Jupyter Notebooks use Python version: 3.10.12, as well as the following Python modules: packages related to Jupyter Notebook: jupyterlab v. 4.0.8; jupyter-core module v. 5.5.0, jupyter-client v. 8.6.0; jupyter_server: 2.10.0, Nbclient v. 0.9.0, nbconvert v. 7.11.0, nbformat v. 5.9.2, traitlets v. 5.13.0ipython v. 8.17.2; ipykernel v. 6.26.0⁴²; and a data organization and manipulation module – pandas v. 2.2.2²⁸.

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Faculty of Psychology and Cognitive Science, Adam Mickiewicz University, Poznan, Poland

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Participants attended two individual laboratory sessions (Fig. 1). During the first session, baseline data and training-related measures were collected (Stage 1). Participants were then randomly assigned to one of two groups: a control group, which learned general facts about the brain, or the Synergistic Mindsets Intervention (SMI) group, which focused on using reappraisal techniques to approach both the performance situation and their responses to it more productively. Further information on the SMI, control interventions, and cover story and transcripts of all instructions given to participants (in Polish and their English translations) were published as the supplementary materials of the hypothesis testing article and can be found elsewhere²². Those in the SMI group practiced reappraisal strategies over a two-week period, documenting their adherence and improvements (Stage 2). The follow-up laboratory visit saw participants competing in an esports tournament (Stage 3). We then explored the SMI’s enduring effects with a 1-month follow-up.John, O. P., Donahue, E. M. & Kentle, R. L. The Big Five Inventory – Versions 4a and 54. (University of California, Berkeley, Institute of Personality and Social Research, 1991).
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The Vrije Universiteit Ambulatory Monitoring System (VU-AMS, Netherlands) was employed to collect cardiac biosignals, incorporating both impedance cardiography (ICG) and electrocardiography (ECG) for continuous and noninvasive monitoring of heart activity. In accordance with psychophysiological standards^26,27, we utilized pre-gelled AgCl electrodes (Kendall Abro, H98SG) positioned according to the standard Lead II setup for ECG measurements and arranged in a four-spot pattern for ICG assessments. This device enables the continuous recording of ECG, Average thorax impedance (ohm; Z0); change in impedance due to respiration and heartbeat (ohm; dZ), and Impedance CardioGram (ohm/s; dZ/dt) to check for proper quality of the recorded signals. Raw data exports were carried out using the VU-AMS Data, Analysis & Management Software (VU-DAMS 5.4.20).Article
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Maciej Behnke

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Kanafa-Chmielewska, D. & Bartosz, B. Poczucie kontroli w sferze osobistej, interpersonalnej i socjopolitycznej, nastawienie na stałość lub zmienność a oceny na studiach. Pol. J. Appl. Psychol. 16, (2018).
Wadim Krzyżaniak

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Szymon Kupiński

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Patrycja Chwiłkowska

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Preece, D. A. et al. Emotion generation and emotion regulation: The role of emotion beliefs. J. Affect. Disord. Rep. 9, 100351 (2022).
Szymon Jęśko Białek

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Daly, M., Sutin, A. R. & Robinson, E. Depression reported by US adults in 2017–2018 and March and April 2020. J. Affect. Disord. 278, 131–135 (2021).
Maciej Kłoskowski

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Patryk Maciejewski

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We also calculated the mean values of each channel for Stage 1 and Stage 3. Histograms (Fig. 4) present that collected signals had standard ranges. For instance, most participants presented a healthy SBP and DBP range during the experiments³⁸. Figure 4 presents data for easily interpretable measures (e.g., HR, SBP, DBP collected with Finometer) and does not present raw recordings (e.g., ECG in mV) that require further processing (e.g., heart rate based on peak analysis).
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Network Services Division, Poznan Supercomputing and Networking Center, Poznan, Poland
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Jeremy P. Jamieson

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Sharma, K. et al. A dataset of continuous affect annotations and physiological signals for emotion analysis. Sci. Data 6, 196 (2019).
James J. Gross

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Sample size: 300 participants.

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Klussman, K., Lindeman, M. I. H., Nichols, A. L. & Langer, J. Fostering stress resilience among business students: The role of stress mindset and self-connection. Psychol. Rep. 124, 1462–1480 (2021).

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Eysenck, H. J. & Eysenck, S. B. G. Manual of the Eysenck Personality Questionnaire (Junior & Adult). Hodder and Stoughton Educational (1975).

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In conclusion, the CEPAV database offers a rich and detailed source of data shared with the scientific community to support research and development in the field of affective science. One of the principles of open science is that researchers are free to use the dataset without the need to contact the authors as long as their usage complies with the applicable licenses. However, we encourage researchers to reach out to us at macbehnke@gmail.com if they have any questions or require assistance. We are happy to provide guidance and support to help maximize the utility of the dataset.
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Here, we present the Competitive Esports Physiological, Affective, and Video (CEPAV) dataset¹. Esports represents a rapidly growing field in which well-trained individuals – gamers – compete using video games. In esports, gamers compete while seated in front of a screen, creating an ideal environment to study affective responses, including emotional experiences and real-time cardiovascular reactions to performance^2,3,4,5. This setting allows for the examination of high-stakes performance with continuous real-time monitoring of affective responses at multiple levels. Using esports as a model allowed us to gain insights into the interplay between emotional states and physiological responses during intense gameplay sessions.
Behnke, M., Gross, J. J. & Kaczmarek, L. D. The role of emotions in esports performance. Emotion 22, 1059–1070 (2022).
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Deniz Dakak Named an AVCA All-American

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16 seconds ago

December 18, 2025

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WASHINGTON – Deniz Dakak adds an AVCA All-American honorable mention selection to her outstanding sophomore season, announced on Wednesday morning by the American Volleyball Coaches Association. She is the ninth player in program history to receive the honor in addition to her All-Region selection on Dec. 9.

Dakak’s young career was put into the spotlight this season after she led the Patriot League in assists each week of the 2025 campaign. She was the quarterback of AU’s offense, which put up staggering numbers. The Eagles hit .294 at the end of the regular season, ranking 12th in the country before they hit a Patriot League Tournament record .500 in the conference championship match.

The Istanbul, Turkey native averaged 10.53 assists per set this year, and finished the season with over 1,028 after the NCAA Tournament. Dakak was named the Patriot League Player and Setter of the Year, just the third player to ever earn both awards. With two seasons left, Dakak is on pace to finish in the top 10 of AU’s all-time assists list.

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For the latest on American University Volleyball, stay tuned to AUEagles.com and follow the team on Twitter (@AU_Volleyball), Facebook (/AU.Volleyball) and Instagram (@au_volleyball).

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Skinner, Sheffield Set to Meet in NCAA Volleyball Semifinals – UK Athletics

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16 minutes ago

December 18, 2025

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In Thursday night’s NCAA Volleyball national semifinals, two coaches who are very familiar with one another will square off in the night’s second match. Kentucky head coach Craig Skinner and Wisconsin skipper Kelly Sheffield have a relationship that goes back decades.

Skinner and Sheffield are both from the Muncie, Indiana, area. In 1990, they led a Muncie Burris High School junior varsity team to an undefeated record.

Skinner and Sheffield would go their separate ways before both eventually became Division I head coaches. Skinner was named the head coach at Kentucky in 2004, while Sheffield was the head coach at Albany and Dayton before landing the Wisconsin job in 2012.

The UK head coach had nothing but praise for his counterpart in Thursday’s match.

“Kelly has earned everything he’s gotten,” Skinner said. “He’s come from humble beginnings, both in school and in coaching. He’s been on — coached and packed his car in an evening, had to be in Houston 20 hours later to start his first coaching job probably making about $10,000 a year. I have a lot of respect for someone that earned their way to this point in time.”

Skinner is also appreciative of the Muncie roots that both he and Sheffield have.

“You have to give a lot of credit to the Shondell family and Don Shondell for starting the Ball State program,” Skinner said. “Steve Shondell, the oldest Shondell son, played in and started the Muncie Burris program and Munciana Volleyball Club. When I started coming through Ball State, yeah, I’ll try this coaching thing. I just fell in love with what they were about.

“Ball State University started as a teacher’s college. Coaching is teaching,” Skinner said. “The joy and passion and interest in helping players do something better than they have ever have before. You really felt the essence of what coaching is.”

Skinner believes that his early beginnings showed him just how much he really wanted to be a coach.

“I tried to get away from coaching for a while,” Skinner said. “I had an accounting degree, got into banking. It sucked me back in because I love the competition and the teaching aspect. That started in Muncie, Indiana, in 1988 or ‘89 for me.”

For those who want to get into to coaching, starting off the way Skinner and Sheffield did is not rare. But Skinner knows that it has to be something you love.

“I always tell people that if you’re going to get into coaching, don’t get in it because you like it and you can make some money,” he said. “Get into it because you have a passion for helping people go above and beyond where they are. Kelly has demonstrated that for a long time.”

Two coaches who once coached a high school junior varsity team together square off in Thursday’s national semifinals when Kentucky plays Wisconsin. It’s a lesson in how much hard work and dedication can pay off.

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Colyer, Booth earn AVCA All-American honors

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32 minutes ago

December 18, 2025

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MADISON, Wis. – After a pair of stellar individual seasons, seniors Mimi Colyer and Carter Booth of the Wisconsin volleyball team each garnered All-America honors from the American Volleyball Coaches Association, as announced on Wednesday morning by the AVCA.

Colyer led the way with a First Team All-American award at outside hitter, followed by Booth with a Third Team All-American nomination at the middle blocker position. In head coach Kelly Sheffield‘s tenure, 21 individual athletes have garnered 47 separate All-American honors in 13 seasons.

Colyer joined that group of athletes, putting together one of the most impressive individual seasons in UW history en route to her fourth All-American award of her career. The Lincoln, California, native recently eclipsed the 2,000-kill mark in her career against No. 2 Stanford in the NCAA Regional Semifinal—combining for 50 kills over the Badgers’ pair of matches in Austin, Texas.

The outside hitter broke the school record for most kills in a season, as she enters this week’s National Semifinals with 566. Colyer has accumulated double-digit kill totals in every match except for one this season, as she currently holds a 19-match streak of 10 or more kills, good for the second-longest streak in program history.

In the national ranks, Colyer places third in kills per set at 5.39. No other Badger in the Rally Scoring Era (since 2008) has finished the season with over five kills per set. She also ranks third in points per set at 5.97.

In leading the Badgers to a second-place finish in the Big Ten Conference, Colyer broke the school record for most kills at 345. Sarah Franklin was the only other athlete at UW to surpass 300 kills in the 20-match span.

Colyer excelled on the defensive end as well, recently surpassing the 1,000-dig mark in her storied career. The senior accumulated nine double-doubles in digs and kills in the 2025 season.

Right by her side, Booth strung together another impressive campaign for the Badgers—notching her second-career All-American award.

Offensively, the 6-foot-7 middle blocker has been efficient in her attack, as she is currently on pace to shatter UW’s record for highest hitting percentage in a single season. Booth sits at .454 entering the National Semifinal match, good for second-highest in the country. She has turned in zero errors in 11 matches this year.

The Denver, Colorado, native recently put together her strongest weekend of the season, highlighted by a .700 (14 – 0 – 20) swinging percentage turned in against No. 2 Stanford. Her 14 kills tied a career-best, as she followed it up with 11 more versus No. 1 Texas to help punch the Badgers’ ticket to Kansas City.

The 6-foot-7 middle blocker currently leads the team in blocks (123) and blocks per set (1.17) as well—showcasing her continued physical presence at the net.

Booth and Colyer are set to be teammates next season for the Dallas Pulse of Major League Volleyball, as they were recently selected in the draft less than a month ago.

Badger fans can catch both All-Americans in action on Thursday, Dec. 18, as UW will look to continue their postseason run. The Badgers are slated to face No. 1 Kentucky at the T-Mobile Center in Kansas City, as first serve will take place 30 minutes after the conclusion of the first National Semifinal between No. 3 Texas A&M and No. 1 Pittsburgh, which is set to start at 5:30 p.m. CT. Both matches will be televised on ESPN.

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Pitt volleyball reaches Final Four again but can it win championship?

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47 minutes ago

December 18, 2025

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Updated Dec. 17, 2025, 11:38 p.m. ET

KANSAS CITY, MO ― Upon arriving at last year’s NCAA volleyball Final Four in Louisville, Kentucky, Pittsburgh Panthers head coach Dan Fisher received several commemorative Louisville Slugger bats.

Throughout the 2025 season, they would sit in his office in special holders, serving as a reminder of what could have been. Last December, Pitt’s title push ended shy of the championship game. After making four consecutive Final Four appearances without winning a trophy, the No. 1 overall seed fell short ― again. The Panthers lost 3-1 to the Louisville Cardinals in the semifinals and went home empty-handed.

On Wednesday, Fisher, well aware that Pitt let a potential chance at a championship slip away, reflected on the disappointment he felt.

“I can simultaneously be proud of making the Final Four and disappointed we didn’t advance,” he said. “I can hold those two thoughts at once.”

The balancing act of holding space for pride and disappointment has likely been something the Panthers coach and his team have been silently juggling all season. At some point, it probably had to become a small part of what fueled them to get back to volleyball’s biggest stage and return for a fifth straight Final Four ― even if they may never admit it out loud. Multiple opportunities to win a championship don’t happen often, and when a team can’t bring home any hardware, the questions about winning inevitably become louder.

Will Pitt ever be the “bride” and not the “bridesmaid”? Is this the year the Panthers finally put it all together?

Six total players at the 2025 Final Four have experience playing at this level. Fisher’s roster has five of the six Final Four veterans. Pitt opposite Olivia Babcock and middle blocker Bre Kelley have been to two prior Final Fours. Several other Panthers players have been to at least one. Wisconsin Badgers middle blocker Carter Booth, with one appearance, is the remaining player. With so many young and new faces on all four tournament rosters, Pitt’s experience could prove valuable if it wants to reach the national championship on Sunday. Babcock addressed the potential advantage during Wednesday’s Panthers press conference.

“Since so many people have been here before, we were able to prepare the newer players coming into this experience what to expect,” Babcock said. “We’re also able to just remind them constantly that even though there will be a lot of media ― things there wouldn’t typically be ― stay locked in at the goal at hand. It is very easy to get distracted. I think those players are going to help our newer players be able to hone in on the task.”

Babcock stressed that it’s an honor to be on the Final Four stage. Still, she says the Panthers are focused. Their preparation has been better, including how they practice and scout opponents and visualize matches. Kelley shed a bit more light on how the Panthers are staying grounded as they approach Thursday’s semifinal match.

“In the past, I feel like we’ve always made it a point that we have to win the Final Four. This year, we have really emphasized, especially with our sports psychologist, to play ball,” Kelley said. “Obviously, we’re trying to embrace this moment and be where our feet are. This game is supposed to be fun. It’s not supposed to be severely taxing on your mind and body.”

The Panthers revealed that the team has broken down every intense matchup, as far back as a September regular-season sweep against the No. 1 Kentucky Wildcats, to just one point at a time. The team said even in a dominant 3-0 win like that one, the focus this year has been on being “good” after getting to 20 points in a set. Once the Panthers get to that point, Babcock says they are “able to flip a switch”. Then, it becomes a matter of which team gets to 25 first. She believes that if Pitt is playing “the best points of (their lives)”, they’ll find a lot of success. Fisher seemed to be in lockstep with that thought process.

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Utah State Volleyball Quartet Named to CSC Academic All-District Team

Published

8 hours ago

December 18, 2025

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LOGAN, Utah – Utah State volleyball had four players named to the College Sports Communicators (CSC) Academic All-District Team, it was announced on Tuesday. Senior middle blocker Tierney Barlow, sophomore opposite side hitter Loryn Helgesen, sophomore setter Kaylie Kofe and sophomore outside hitter Mara Štiglic all received the honor, marking the second-straight season USU has received the maximum allotment of four honorees.

Kofe earned the distinction via a 3.81 GPA while majoring in marketing and leading the Aggie offense to a program-record .274 hitting percentage this season, ranking third in the nation with 11.08 assists per set and also earning Mountain West Player of the Year honors. Kofe is the only player in the nation with three matches of 60 or more assists. Her 1,330 total assists this season ranks fifth all-time at USU while she already sits in eighth for career assists at Utah State with 2,290. Kofe also added 32 kills, 28 aces, 285 digs and 37 total blocks on the year.

Barlow received the honor after posting a 3.97 GPA while majoring in integrated studies. This season, Barlow Utah State’s single-season program record for hitting percentage with a mark of .444, shattering the previous mark of .375 (min. 5 attempts per set) held by Denae Mohlman and set in 1997. Barlow is now the career record holder for hitting percentage, sitting at .418 for her career at Utah State, topping current assistant coach/director of operations Kennedi Hansen’s career mark of .362 (min. 1,000 attacks). Barlow recorded six matches of at least 17 attempts and zero hitting errors this season while no other player in the nation had more than four according to ESPN research. Barlow finished with 321 kills, 18 aces, 51 digs and 93 blocks on the season. She earned All-MW honors for the fourth time in her career.

Helgesen earned the award after recording a 3.57 GPA while majoring in psychology. Helgesen finished the season with 391 kills on a .295 hitting percentage, the 10th-highest hitting percentage in program history with at least five attempts per set. Helgesen also ranks seventh all-time for career hitting percentage at USU (min. 1,000 attempts) with a mark of .275 as an Aggie. Helgesen broke USU’s single-game hitting percentage record with at least 20 attempts, hitting .704 against Grand Canyon. Helgesen also added 26 aces, 96 digs and 68 blocks on the year. She earned All-MW honors for the first time in her career this season.

Štiglic earned the honor after posting a 3.68 GPA and majoring in marketing. Štiglic finished the season with a team-high 3.56 kills per set, totaling 431 kills alongside 29 aces, 146 digs and 63 blocks. Štiglic earned all-MW honors this season after ranking seventh in kills per set (3.63) and fourth in points per set (4.27) during conference play. Štiglic also ranked sixth in the MW with 0.31 aces per set, totaling 21. She hit double-digit kills in 17 of 18 matches during MW action, totaling nine kills in her lone match not reaching the plateau. Štiglic also recorded seven matches with multiple aces.

Fans can follow the Aggie volleyball program on Twitter, @USUVolleyball, on Facebook at /USUVolleyball or on Instagram, @usuvolleyball. Aggie fans can also follow the Utah State athletic program on Twitter, @USUAthletics, Facebook at /USUAthletics and on Instagram, @USUAthletics.

– USU –

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Sports

Ptacek, Zelenovic Named AVCA All-Americans

Published

8 hours ago

December 18, 2025

Your Sports Nation

LAWRENCE, Kan. – Kansas volleyball’s Reese Ptacek and Jovana Zelenovic were named American Volleyball Coaches Association (AVCA) Honorable Mention All-Americans, the organization announced on Wednesday.

The Honorable Mention All-America honors come after both Ptacek and Zelenovic were named First Team All-Big 12, leading one of the league’s most efficient and balanced offenses. Under first year head coach Matt Ulmer, the Jayhawks finished with a 24-11 and the program’s fourth appearance in the NCAA Sweet 16 all-time.

Ptacek, a native of Prescott, Wis., earns her first career All-America honors after hitting .314 with 331 kills, 136 blocks and 27 service aces during the 2025 season. Ptacek was recently named to the AVCA All-Region Team and was named to the AVCA Player of the Year Watch List during the 2025 season.

Zelenovic, a freshman from Novi Sad, Serbia, finished a standout freshman season for the Jayhawks, leading the team with 485.5 total points, 375 kills, 46 service aces and a .276 hitting percentage. Defensively, Zelenovic posted 123 total blocks. Zelenovic was also named to the AVCA All-Region Team and was named as the Central Region’s Freshman of the Year.

Ptacek and Zelenovic are the latest Jayhawks to earn All-America honors, becoming the 14th and 15th Jayhawks to earn All-America honors all-time. Kansas has had multiple All-Americans in just eight seasons all-time, including 2025, 2024, 2023, 2017, 2016, 2015, 2014 and 2013.

See below for a full list of Kansas volleyball All-American honors:

Josi Lima 2003 Honorable Mention Caroline Jarmoc 2013 Third Team 2012 Second Team Chelsea Albers 2014 Honorable Mention 2013 Honorable Mention Sara McClinton 2013 Honorable Mention Erin McNorton 2013 Honorable Mention Cassie Wait 2016 Honorable Mention Ainise Havili 2017 Honorable Mention 2016 Third Team 2015 First Team 2014 Honorable Mention Kelsie Payne 2017 Third Team 2016 First Team 2015 First Team Madison Rigdon 2017 Honorable Mention 2016 Honorable Mention Caroline Bien 2021 Honorable Mention Reagan Cooper 2023 Third Team Camryn Turner 2024 Third Team 2023 Honorable Mention Toyosi Onabanjo 2024 Honorable Mention

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The competitive esports physiological, affective, and video dataset

Abstract

Background & Summary

Methods

Participants

Ethics Information

Procedure

Stage 1

Stage 2

Stage 3

Measures

Questionnaires

Open-Text answers

Other self-reports

Demographics

Performance

Video data

Video recordings

Gameplay recordings

Physiological measures

Impedance cardiography and electrocardiography

Blood pressure

Behavioral measures

Data preprocessing

Data Records

Dataset Structure

Self-reports and metadata

Video data

Physiological and behavioral data

Single physiological file structure

Technical Validation

Missing data

Questionnaires reliability

Physiological data – qualitative validation

Physiological data – quantitative validation

Summary of previously completed analyses

Summary of the physiological, affective, and behavioral activity during the competitive esports

Usage Notes

Declaration of Generative AI and AI-assisted technologies in the writing process

Code availability

References

Acknowledgements

Author information

Authors and Affiliations

Contributions

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