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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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Jan Nowak

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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).
Kacper Szymański

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Daniël Lakens

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Network Services Division, Poznan Supercomputing and Networking Center, Poznan, Poland
Kate Petrova

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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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Jeremy P. Jamieson

Contributions

Sample size: 300 participants.

Corresponding author

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).
Benet-Martínez, V. & John, O. P. Los Cinco Grandes across cultures and ethnic groups: Multitrait-multimethod analyses of the Big Five in Spanish and English. J. Pers. Soc. Psychol. 75, 729–750 (1998).
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Volleyball Closes Season Against No. 1 Kentucky in NCAA Tournament

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2 hours ago

December 5, 2025

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LEXINGTON, KY. – The Wofford volleyball team was defeated by the No. 1 seeded Kentucky Wildcats 3-0 on Thursday night inside of Historic Memorial Coliseum in the first round of the NCAA Tournament. The Terriers close the season 17-14 with a 10-6 record in conference action. Kentucky will take on UCLA tomorrow night.

“Our mindset was to come out and leave it out there,” said head coach Lynze Roos. “I feel like we competed in some really good points. They got some separation and that was tough, but I’m really proud of the way that these women competed in the season that we had.”

Wofford totaled 27 kills on 26 assists throughout the match. On defense, the team posted 54 digs and four blocks. The Wildcats finished the match with 45 kills on 44 assists. Kentucky also posted 56 digs and four blocks.

Leading Wofford was Bradley Brown who had 10 kills on a .259 hitting percentage. Brown also led the team with three blocks. Following behind was Chloe Smith with six kills. Maddy Frazier dished out a team-high 13 assists, while Taylor Pecht had 10. Laney Klika recorded a team-high 13 digs, along with 10 from Caroline Przystup. Annemarie Rakoski and Natalie Arnold tallied one block apiece.

“We talk a lot about playing relentless defense and going for every single ball. We knew that tonight was going to be a tough task, but you never really know unless you go for it,” said Laney Klika.

“We talked a lot before the game about playing how we play and not letting their offense or defense change the way that we like to play,” added Annemarie Rakoski.

“It was amazing just to be able to have some family and friends that I don’t get to see very much anymore come watch me play. It was super cool to just have that support,” said Chloe Smith.

Kentucky grabbed the first two points of set one, but Wofford responded with a solo block by Annemarie Rakoski. Another solo block from Bradley Brown kept the Kentucky lead within one point. With the Wildcats leading 13-8, Kentucky would add four unanswered points to bring the Wofford deficit to nine points. The Terriers could not overcome the Kentucky lead, losing set one 25-11.

The teams were back-and-forth to start set two, as the Terriers would take an early 5-4 lead. Wofford took its biggest advantage – a 15-13 lead – after a pair of Kentucky attacking errors. The Wildcats fought back to take a 20-19 lead, and the team scored the final five points of the stanza to take set two 25-19. Bradley Brown totaled eight kills and one block in the second set alone.

Wofford jumped out to a 2-0 lead to start the third set of the match by way of a Bradley Brown kill. Kentucky responded with a 7-1 run, however, to regain the lead. The Wildcats would eventually take a 13-4 advantage. Wofford cut the deficit to six points a few rallies later, but the team would lose set three and ultimately the match.

Wofford concludes the season 17-14 with a 10-6 mark in Southern Conference play. The team entered the conference tournament as the No. 3 seed and defeated both No. 6 Samford and No. 2 Furman to reach the championship match. The Terriers took down No. 1 ETSU to win their third-straight conference championship and earn another bid to the NCAA Tournament.

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Demon Deacons Open Season at Liberty Kickoff

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3 hours ago

December 5, 2025

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LYNCHBURG, Va. – The Wake Forest track and field team opened their 2024-25 indoor track schedule on Thursday afternoon as a pair of Demon Deacons competed in the Liberty Kickoff at the Liberty Indoor Track Complex.

Junior Seren Rodgers secured a third place finish in the pentathlon, totaling 3,771 points. With the result, the Taunton, England, native now sits sixth all-time in program history in the women’s indoor pentathlon.

Overall, Rodgers recorded three podium finishes during the competition, including a pair of runner up results in the long jump, where she recorded a jump of 10.32 meters, and the 800m, crossing the line in 2:19.62. Rodgers also claimed third place in the 60m hurdles after clocking a time of 8.86 seconds.

Meanwhile, freshman Julia Aere also competed in the pentathlon, securing eighth place with 3,462 points in her collegiate debut. The Delray Beach, Fla., native placed inside the top-10 in all five events, highlighted by a third place finish in the shot put after recording a distance of 11.13 meters, as well as a fifth place result in the 800m after recording a time of 2:27.34.

Notable Finishes

Pentathlon

2025-26 Indoor Track and Field Top-10 Marks in School History

From the Staff
“I’m really pleased with how the competition progressed today. Julia and Seren competed well and this meet was a great measure of how hard we have worked throughout the fall semester. It gave us a chance to get out, perform at a high level and still recognize that we left some points on the table, which is exciting. We are in a great place heading into the holiday break and this will keep us motivated and hungry. These two ladies set the tone early for the team and we are eager for the rest of the team to compete this weekend.” – Assistant Coach Ryan Grinnell

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The Demon Deacons return to action on Saturday with a pair of meets. One group of Wake Forest athletes will travel to Boston to compete in the 5K race at the Sharon Colyear-Danville Season Opener. Meanwhile, several Deacs will compete at the Visit Winston-Salem College Kick-off at the JDL Fast Track.

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Lopes unveil 2025-26 indoor slate

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3 hours ago

December 5, 2025

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Grand Canyon unveiled its 2025-26 indoor track and field schedule, which starts this week with the first of eight regular-season meets and concludes with the Mountain West Indoor Championships in late February.

After the Lopes dominant WAC indoor run of 14 conference titles between the men’s and women’s teams, GCU will compete in its first season as a Mountain West member.

Coach Flood celebrates with sprinter after final

Coach Flood celebrates with sprinter after final

“This schedule and competition should prepare both our men’s and women’s teams to make a lot of noise at our inaugural Mountain West Conference Indoor Championships, as well as send a lot of our athletes to the NCAA Indoor Championships,” GCU head coach Tom Flood said.

Grand Canyon’s indoor schedule will feature meets in trips to Flagstaff, Arizona; Albuquerque, New Mexico, and Reno, Nevada.

“Again, we are really excited about our indoor schedule,” Flood said. “We will again be competing at some of the finest indoor facilities in the country and against some of the best track and field programs in the country.”

The 2025-26 season begins Thursday as the Lopes travel to Reno, Nevada, before taking a break until the new year. From there, they will travel to Flagstaff and Albuquerque before heading back to Reno for the Mountain West Indoor Championships.

GCU aims to represent at the NCAA Indoor Championships, which will be held March 13-14 in Fayetteville, Arkansas.

For the full indoor schedule, click here.

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Men’s and Women’s Track and Field 2026 Season Preview

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4 hours ago

December 5, 2025

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A new era in Hope College track and field begins today with the first indoor meet of the 2026 season.

Beginning at noon, the Flying Dutch and Flying Dutchmen are competing at the Grand Valley State University Holiday Open under the leadership of first-year head coach Jordan Bartolazzi, the 11th women’s head coach in program history and the 13th men’s coach.

Bartolazzi, who built his alma mater, Elmhurst University (Illinois), into a College Conference of Illinois and Wisconsin power before coming to Hope, is thrilled with the progress both teams made during preseason workouts.

“There was a lot of good stuff going on here to build on,” Bartolazzi said. “Our focus, with every practice, has been preparing to compete, whether we’re an All-American or trying to crack the conference lineup. We have a lot of student-athletes working really hard. We’ve been having a ball. There has been really good energy. I think it’s been a good start for us.”

The Flying Dutch, who finished third in the MIAA Women’s Indoor Track and Field Preseason Coaches Poll, feature a roster of 73, with 17 seniors, 17 juniors, 14 sophomores and 25 freshmen.

The Flying Dutchmen, who also tookl thjird in the MIAA Men’s Indoor Track and Field Preseason Coaches Poll, have a roster of 64, with 18 seniors, nine juniors, 17 sophomores and 20 freshmen.

Coaching Staff

Head coach: Jordan Bartolazzi, first season

Distance coach: Mark Northuis

Distance assistant coaches: Dan Campbell, Mike Northuis

Throws coach: Paul Markel

Jumps coach: Addy Gerig

Pole vault coaches: Jon Lunderberg, Ben Turner

Women’s Roster

Senior Sara Schermerhorn (Traverse City, Michigan / Traverse City West) is Hope’s top returner after claiming All-America honors in four events for the second consecutive season in 2025: indoor and outdoor 200 meters and indoor and outdoor 400 meters.

The exercise science major swept MIAA Most Valuable Indoor and Outdoor Track Athlete honors as a junior and earned MIAA Most Valuable Indoor Track Athlete accolades in back-to-back seasons. She became the first sprinter in league history to win titles in the 60, 200 and 400 at the same meet.

Schermerhorn set MIAA records in the indoor 200 and 400 meters last season. She also ran on the MIAA champion 4×400 relay, which returns two other sprinters: senior Frances Cozzens (Lyman, New Hampshire / Saint Johnsbury Academy) and sophomore Sofia Fisher (Lombard, Illinois / Montini Catholic).

Hope returns three runners from the MIAA champion distance medley relay: senior Molly Durow (Glenview, Illinois / Glenbrook South), junior Amanda Markham (Hoffman Estates, Illinois / William Fremd) and sophomore Lily Sackrider (St. Johns, Michigan / St. Johns).

Durow is coming off an All-America cross country campaign this fall. The special education major finished 32nd at the Division III national championships and was runner-up at the Great Lakes Regional and MIAA Championships.

“We have great leadership,” Bartolazzi said. “Not only do we have some great upperclassmen, but we have some really wonderful seniors who have made an effort to welcome our freshmen and newcomers to the program. It’s a gift to have great senior leadership in year one.”

Men’s Roster

Hope returns senior sprinter Liam Danitz, the 2025 First Team All-MIAA honoree and MIAA Most Valuable Men’s Indoor Track Athlete.

Danitz (West Branch, Michigan / Ogemaw Heights) set an MIAA record in the 200-meter dash (21.59), earned First Team All-America honors with a fifth-place national finish in the 200 (21.93), and took second in the 60-meter dash (6.83) for All-MIAA Second Team honors.

The exercise science major also contributed to an All-MIAA Second Team 4×400 relay alongside returning junior Dylan Terpstra (Hudsonville, Michigan / Hudsonville).

Senior Erickson Kunzler (Marne, Michigan / Grand Rapids Catholic Central) returns as the MIAA 800-meter champion after posting a winning time of 1:56.09.

Senior Carston Cole (Holland, Michigan / West Ottawa) and junior Carter Dean (Traverse City, Michigan / Traverse City West) also return from last year’s All-MIAA First Team distance medley relay.

Cole recorded Hope’s top cross country finish at nationals this fall. The Flying Dutchmen placed 23rd in the nation as a team and made history with their first Great Lakes Regional title since 1980 and first MIAA crown since 1986.

Sophomore Logan Begeman (Portage, Michigan / Portage Central) ran away with the Great Lakes Regional and MIAA individual championships.

“They’re hungry,” Bartolazzi said of the Flying Dutchmen. “Having that breakthrough season in cross country is such a gift. They felt like they were close and believed they could compete at the conference and national levels. They showed they could. That carries over to the track season.”

Schedule

The Flying Dutch and Flying Dutchmen are scheduled to compete in 10 indoor meets and eight outdoor meets.

The MIAA Indoor Championships are Saturday, Feb. 28, at Trine University.

The NCAA Indoor Championships are Friday-Saturday, March 13-14, in Birmingham, Alabama.

Hope will host the MIAA Outdoor Championships on Friday-Saturday, April 30-May 1, at Brewer Track.

The NCAA Outdoor Championships are Thursday-Saturday, May 21-23, in La Crosse, Wisconsin.

“One thing I wanted to do this year was get our student-athletes on banked tracks a little more,” Bartolazzi said. “Nationals will be on a banked track, so I want them to know what that feels like. It’s a great thing in West Michigan — you don’t have to go far to race against really good people.”

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Mustangs Open Track and Field Season This Weekend

Published

4 hours ago

December 5, 2025

Your Sports Nation

Blue-Gold Invite

Loftus Sports Center – South Bend, Indiana

Friday, December 5, 2025

Live Results

McFerrin-12 Degree Invite

Fasken Indoor Track – College Station, Texas

Saturday, December 6, 2025

Live Results | Watch

Sharon Colyear Danville Season Opener

BU Track & Tennis Center – Boston, Massachusetts

Saturday, December 6, 2025

Live Results | Watch

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DALLAS, Texas (SMU) — The Mustangs will open up their 2025-2026 track and field season across three different meets this weekend. The distance ponies will split between Notre Dame’s Blue-Gold Invite and Boston University’s Sharon Colyear Danville Season Opener. The rest of the team will travel to Texas A&M’s McFerrin-12 Degree Invite.

This meet will serve as a soft opener for the Mustangs with the remainder of the season beginning in mid-January. The distance athletes are coming off a successful cross country season, which concluded with an appearance at the national championship for Rose Mburu, but this will be the first competition for the sprints, jumps, and multis after fall training.

The action will begin on Friday at Notre Dame and continue at the other two meets on Saturday.

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Sooners Set to Host OU Winter Field Fest Season Opener

Published

4 hours ago

December 5, 2025

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NORMAN — The University of Oklahoma track and field team is set to host the OU Winter Field Fest on Friday, Dec. 5 at the Mosier Indoor Facility in Norman, Okla.

The meet is scheduled to begin at 1:30 p.m. and will include a combination of field and sprint events.

Admission is free to fans. Live results from the OU Winter Field Fest can be found via TimingInc.

The Sooners are coming off their first season in the SEC, where they claimed seven individual conference titles.

OU Winter Field Fest Schedule of Events
Triple Jump (M) – 1:30 p.m.
Weight Throw (M) – 1:30 p.m.
High Jump (W) – 2:30 p.m.
Triple Jump (W) – 3 p.m.
Pole Vault (W) – 3 p.m.
Weight Throw (W) – 3 p.m.
60M Hurdles Prelims (W) – 3 p.m.
60M Hurdles Prelims (M) – 3:15 p.m.
60M Dash Prelims (W) – 3:30 p.m.
60M Dash Prelims (M) – 3:45 p.m.
Long Jump (M) – 4:30 p.m.
Shot Put (M) – 4:30 p.m.
60M Hurdles Finals (W) – 4:30 p.m.
60M Hurdles Finals (M) – 4:40 p.m.
60M Dash Finals (W) – 4:50 p.m.
60M Dash Finals (M) – 5 p.m.
Pole Vault (M) – 5:30 p.m.
Long Jump (W) – 6 p.m.
High Jump (M) – 6 p.m.
Shot Put (W) – 6 p.m.

For more information on Oklahoma Track & Field, follow the Sooners on Twitter and Instagram (@OU_Track) and like Oklahoma Sooners Track & Field and Cross Country on Facebook.

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