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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 & […]

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January 11, 2025

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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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The upper-body recordings in an MP4 format, full HD resolution (1920 × 1080) constitute 820 GB of space³¹. The files are grouped into 19 components 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 ‘Video_S3_255_280’ indicates that the component included a video record from Stage 3 for participants from 255 to 280. To ease the video data analysis, we included the time intervals for each experimental epoch in the “CEPAV_data/video_timing” sheet²⁴ (e.g., for Stage 1 for Participant 8, the baseline starts at 1:17 minutes of video).Article
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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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Esports refers to competitive video gaming where individuals compete against each other in organized tournaments for prize money. Here, we present the Competitive Esports Physiological, Affective, and Video (CEPAV) dataset, in which 300 male Counter Strike: Global Offensive gamers participated in a study aimed at optimizing affect during esports tournament¹. The CEPAV dataset includes (1) physiological data, capturing the player’s cardiovascular responses from before, during, and after over 3000 CS: GO matches; (2) self-reported affective data, detailing the affective states experienced before gameplay; and (3) video data, providing a visual record of 552 in-laboratory gaming sessions. We also collected (affect-related) individual differences measures (e.g., well-being, ill-being) across six weeks in three waves. The self-reported affective data also includes gamers’ natural language descriptions of gaming affective situations. The CEPAV dataset provides a comprehensive resource for researchers and analysts seeking to understand the complex interplay of physiological, affective, and behavioral factors in esports and other performance contexts.
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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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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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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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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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Women’s Track & Field Earns Three All-Region Honors at the New England Division III Championships

Story Links NEW LONDON, Conn.—Junior Brooke Jankowski (Derry, N.H.), sophomore Colleen Corman (Quaker Hill, Conn.) and first-year Amrit Rehal (Alpharetta, Ga.) all earned all-region honors as the Babson College men’s and women’s track & field teams wrapped up competition at the New England Division III Championships on Saturday afternoon. The Babson women totaled 12 points over the two-day […]

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May 4, 2025

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NEW LONDON, Conn.—Junior Brooke Jankowski (Derry, N.H.), sophomore Colleen Corman (Quaker Hill, Conn.) and first-year Amrit Rehal (Alpharetta, Ga.) all earned all-region honors as the Babson College men’s and women’s track & field teams wrapped up competition at the New England Division III Championships on Saturday afternoon.

The Babson women totaled 12 points over the two-day meet to finish 22nd among scoring teams, while the Babson men tied for 34th with two points.

The Beavers claimed a pair of top-six finishes in the women’s high jump as Rehal placed fourth by clearing 5-feet, 3.25-inches, while Jankowski took sixth with a mark of 5-feet, 1.25-inches. Their other all-region honor came in the 400-meter hurdles as Corman posted a time of 1:07.21, which was good for fifth place and just 0.11 seconds off her best result of the year.

Junior Robyn Wilkes (Atlanta, Ga.) came in 16th in the 200 meters with a time of 25.88, while classmate Trista Sicard (Mansfield, Mass.) finished 22nd in the triple jump with a leap of 35-feet, 3.75-inches. Wilkes time in the 200 meters was just 0.01 seconds off her season best set back on April 19.

First-year Noa Wong (Kaneohe, Hawaii) registered the top result of the day for the Babson men by coming in 17th with a season-best time of 49.96 in the 400 meters. Classmate Jackson Adams (Norwell, Mass.) finished 20th in the 400-meter hurdles with a time of 57.19.

The Beavers are back in action next Friday and Saturday with both the NEICAAA Championships at UMass Amherst and the Farley Inter Regional at Williams.

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Miners boys volleyball falls to West Field in playoff opener

It wasn’t to be Saturday afternoon for the Miners boys volleyball squad, falling in their playoff opener to West Field three sets to two. Park City left everything on their home floor however, sending a moderate-but-exuberant crowd into a frenzy with each twist-and-turn of the match. The Miners took the first set with relative ease, […]

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May 4, 2025

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It wasn’t to be Saturday afternoon for the Miners boys volleyball squad, falling in their playoff opener to West Field three sets to two.

Park City left everything on their home floor however, sending a moderate-but-exuberant crowd into a frenzy with each twist-and-turn of the match. The Miners took the first set with relative ease, falling in the second in a similar manner. The third set was contentious, with the Miners falling on a set point marred by a bad call, and in the fourth set, Park City rallied well to force the deciding fifth.

In the fifth set, it appeared the Miners simply ran out of juice. West Field had a bit more size up front, and they used that to dominate. West Field also played a bit cleaner throughout the match, avoiding penalties and other mistakes.

The loss was emotional for the Miners, whose second-ever boys volleyball season came to a close. For many of the team’s 11 seniors, that was their last competitive sporting action. Supportive parents and friends helped ease the blow as West Field made the Park City High gym their own, celebrating on their opponent’s home floor.

“I told them how proud of them I am,” said Miners head coach Eric Damon on how he consoled the team in the locker room after the loss. “It’s not the result we wanted, but from where we started this season to where we ended, the growth as volleyball players and young men is unreal.”

Miles Bronstein competes for a ball at the net. Credit: David Jackson/Park Record

This year’s group was Damon’s first as varsity head coach. The group means a lot to him, and he knows they’ll go on to do special things in life. Many of the players and parents went up to their head coach, thanking him for his efforts this season.

“I’m feeling really optimistic,” added Damon on the future of Park City boys volleyball. “There’s a lot of young boys that are coming up, and volleyball is growing.”

Damon liked what he saw this year from the program’s underclassmen. He also hopes some of the seniors’ younger siblings felt inspired by what they saw, and decide to come out for future teams. This year’s team certainly electrified their home crowd.

Next year, Damon will look for a little more consistency from his team. He said one of his favorite memories this season was watching his boys dominate Stansbury at home March 28, and he wished they’d have been able to piece together that level a bit more.

“Volleyball is very much a game of ebbs and flows, and you’ve got to ride the waves,” said Damon. “When the waves are too low, sometimes it’s too hard to come back from. … That Stansbury match really showed what kind of team we are.”

West Field advances to face number one 4A seed Orem Wednesday morning at Utah Valley University. To follow the remainder of the boys 4A tournament, see their bracket on MaxPreps. The state title game will be held Thursday, also at UVU.

Credit: David Jackson/Park Record
Credit: David Jackson/Park Record
Credit: David Jackson/Park Record

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Redondo Union claims Southern Section girls’ beach volleyball title

The moment of truth arrived … and Bella Jones rose to the occasion. Leaping as high as she could from her right-side position, the Redondo Union senior angled a crosscourt kill that clipped the top of the net and dropped into the open court to complete a 12-21, 21-14, 15-13 comeback for Jones and partner […]

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May 4, 2025

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The moment of truth arrived … and Bella Jones rose to the occasion.

Leaping as high as she could from her right-side position, the Redondo Union senior angled a crosscourt kill that clipped the top of the net and dropped into the open court to complete a 12-21, 21-14, 15-13 comeback for Jones and partner Kara Namimatsu to lift the Sea Hawks to a thrilling 3-2 dual victory over defending champion Mira Costa in the CIF Southern Section Division 1 beach volleyball final Saturday afternoon at Long Beach City College.

The Bay League rivals had split the first four matches, with Redondo Union winning on the first two courts and Mira Costa prevailing on the last two, so the championship came down to Court 3, where Jones and Namimatsu were down 11-8 to the Mustangs tandem of Lily Sprague and Allyn Hilt before rallying back to take a 14-12 lead on an ace by Jones. Sprague’s spike landed on the baseline to stave off the first championship point, setting the stage for Jones’ heroics.

“I told Kara let’s go out swinging … win or lose I’m going to be aggressive,” said the teary-eyed Jones, who is headed to San Diego State to play indoor volleyball. “I was really nervous and it wasn’t the best swing, it hit the tape, but all these girls mean so much to me and I wanted to win it for them.”

It was only the second time Jones and Namimatsu had played together — the first being two days before in the semifinals.

“We had an injury so we switched our lineup on the bus for JSerra and we just went with it,” Jones confessed. “Yesterday we served and passed to each other for about an hour and that helped.”

Redondo Union’s Abby Zimmerman lofts the ball over Mira Costa’s Simone Roslon.

“Our chemistry works well together and in the timeouts our coach told us to trust in each other, trust in your training,” added Namimatsu, a junior transfer from Bishop Montgomery and early USC beach volleyball commit.

The result was practically a carbon copy of the Sea Hawks’ semifinal triumph, only that time it was Abby Zimmerman and Avery Junk winning 15-13 in the third set of the deciding match on Court 1. On Saturday, the pair swept Mira Costa’s top duo of Olga Nikolaeva and Simone Roslon, 21-17, 21-14.

“This is a really big win for us and it’s really exciting to have all five matches going on at the same time,” said Zimmerman, an All-CIF junior outside hitter who is going to California for indoor. “Our coaches trusted me and Avery to go up against their best. It was just our day.”

“We beat them earlier this year which gave us confidence that we could do it but we lost to them in the finals the last two years so we knew we had to play our best,” added Junk, a senior head to Florida State to play beach with twin sister Addy, who won 22-20, 20-17 on Court 2 with partner Leah Blair.

Mira Costa juniors Audrey Flanagan and Anabelle Redaelli prevailed easily 21-11, 21-18 on Court 4 while sophomores Lerin Rosenthal and Sammy Nammack won 21-17, 25-23 on Court 5.

On March 4, Redondo Union snapped Mira Costa’s 170-match winning streak spanning over a decade (the majority of them coming in Interscholastic Beach Volleyball League play before the CIF sanctioned beach volleyball). Mira Costa won the rematch 5-0 on March 26 and was poised for a three-peat.

Saturday, however, belonged to the Sea Hawks.

A similar scenario unfolded in the first dual Saturday morning and the ending was equally dramatic as Long Beach Poly’s Simone Millsap and Alyssa Luna overcame a match point on Court 4 to win the deciding set 17-15, with Millsap serving an ace to clinch the Jackrabbits’ 3-2 triumph over Anaheim Canyon in the inaugural Division 3 final.

Long each Poly celebrates its 3-2 triumph over Anaheim Canyon.

“My thought was ‘I need to get this in,’” Millsap said of her last serve ag ainst Canyon’s Erin Ly and Hannah Huang. “I just closed my eyes and envisioned it going to the left corner— and luckily it did.”

Poly’s Aleeya Salima and Lindyn Foster pulled out a 13-21, 21-17, 15-11 win on Court 1 to level the score.

“Going into the last set we knew it come down to us and I think it was about 8-8 when we heard cheering and saw everyone running over to our court that we knew for sure,” Millsap said.

“When a game’s going point by point, your goal is to get the last two,” said Luna, who was called up from JV to play with Millsap in the third round of the playoffs. “Our indoor team won CIF in November so this is extra special.”

The second match of Saturday’s championship tripleheader pitted two Long Beach schools against each other and again it came down to Court 4, where Wilson sophomore Iyla Alvarado and junior Jane Morrison prevailed 21-19, 18-21, 15-11 over Millikan’s Mikayla Brumbelow and Johanna Swerdloff to secure the title for the Bruins.

Moments later, Rams juniors Sophia Orbiso and Aubrey Greene pulled out a 21-17, 17-21, 21-19 win on Court 2 but by then Wilson had already clinched. Millikan was trying to avenge 3-2 and 4-1 losses in Moore League play.

Long Beach Wilson’s Sadie Calderone is defended at the net by Millikan’s Bethany Arnold.

“It was 2-1 at that point, but I was like ‘we need to do this!,” Morrison said. “It helps having people there supporting me. Even though we’d beaten them twice this is the finals and we knew it could go either way.”

“No matter what’s going on with the other matches, we want to win ours,” Alvarado added. “This is our second year playing together, we have good chemistry, we talk strategy and pride ourselves on being mentally stronger than the other team.”

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Wabash Captures 11th NCAC Outdoor Track and Field Title

Story Links GREENCASTLE, Indiana — Wabash captured its 11th North Coast Athletic Conference outdoor track and field championship and 20th track and field title overall by winning the 2025 NCAC Men’s Outdoor Track and Field Championship on Saturday afternoon at DePauw University. Wabash scored 172.5 points to outdistance Denison […]

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May 4, 2025

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GREENCASTLE, Indiana — Wabash captured its 11th North Coast Athletic Conference outdoor track and field championship and 20th track and field title overall by winning the 2025 NCAC Men’s Outdoor Track and Field Championship on Saturday afternoon at DePauw University.

Wabash scored 172.5 points to outdistance Denison University in second place with 137.5 points. Wittenberg, the pre-championship favorite to win the men’s title, finished in third place with 130.5 points. DePauw finished in fourth place with 110 points, followed by Ohio Wesleyan (83.5 points), Oberlin (69 points), Wooster (52 points), Kenyon (40 points), and Hiram (24 points).

The 11 outdoor titles extend the Wabash record for NCAC titles. The Little Giants have won five championships in the past seven years.

Head coach Clyde Morgan, associate head coach Tyler McCreary, and assistant coaches Emile Conde, Mitchell Kessler, Dillon Frederickson, and emeritus track and field coach Robert Johnson were named the NCAC Men’s Outdoor Track and Field Staff of the Year.

“We dedicated this meet to former Wabash Dean of Students Tom Bambrey,” Morgan said after the meet. “Tom (who passed away earlier this year) was dedicated to the sport from his time as an athlete, as a dean at his alma mater, and after he retired as director of athletics of the College. We definitely felt his presence throughout the weekend.”

Senior Brayden Curnutt earned NCAC Men’s Distance / Mid-Distance Runner of the Year honors. He broke the NCAC championship meet record by winning the 5,000-meter run in 14:40.54, breaking the old mark of 14:45.50 set by former Wabash runner Paul Christian in 2019.

Will Neubauer won the 800-meter run by finishing in 1:52.84. Teammates Haiden Diemer-McKinney and David Adesanya added second and fourth-place finishes. Diemer-McKinney earned all-conference honors with his runner-up performance, crossing the finish line in 1:54.82. Adesanya placed fourth overall with a time of 1:56.70.

Wabash earned six of the top-eight places in the discus throw, led by Quinn Sholar’s second-place finish with an all-conference mark of 45.83 meters (150 feet, 4 inches). Evan Furuness was named the NCAC Men’s Field Athlete of the Year, adding a fourth-place finish in the discus with his effort of 42.06 meters (138 feet) to his other meet accomplishments. He won the shot put on Friday and earned all-conference honors with a third-place finish in the hammer throw.

Jordan Tate finished fifth in the discus with a top effort of 41.59 meters (136 feet, 5 inches). Jakob Van Pelt added a sixth-place finish with a season-best throw of 41.26 meters (135 feet, 4 inches). Oostman scored a seventh-place finish with his toss of 41.25 meters (`35 feet, 4 inches).
Owen Smith took second place in the 400-meter hurdles, earning all-conference honors with his personal-best time of 54.83.

Carter Leininger scored a fourth-place finish in the 100-meter dash, posting a time of 10.77. Tanner Brooks placed eighth overall in the finals with a time of 11.00. Leininger also earned a fifth-place finish in the 200-meter dash by crossing the line in 21.76.

Ryan Papandria placed fourth overall in the javelin throw with a toss of 51.61 meters (169 feet, 4 inches). Jake Oostman scored a sixth-place finish with his throw of 49.92 meters (163 feet, 9 inches).

Kannon Chase earned a fourth-place finish in the high jump by clearing a season-best height of 1.90 meters (6 feet, 2.75 inches). Oostman placed sixth in the high jump with his season-best height of 1.85 meters (6 feet, 0.75 inches).

Mike Holsclaw produced a leap of 12.65 meters (41 feet, 6 inches) to finish in seventh place in the triple jump.

Smith, Leininger, Nate Joven, and Brooks combined to finish fourth in the 4×100-meter relay with a combined season-best time of 42.15. Leininger, Smith, Diemer-McKinney, and Adesanya combined to finish the 4×400-meter relay in 3:24.08 to place seventh overall.

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TCU Headed to the NCAA Finals

Story Links GULF SHORES, Ala. – For the first time in school history, TCU Beach Volleyball (31-5) is headed to the NCAA Finals to compete for the National Championship. After clocking both No. 7-seeded Texas and No. 6-seeded Cal Poly on Saturday, the Horned Frogs will face the No. 4-seeded LMU Lions […]

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May 4, 2025

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GULF SHORES, Ala. – For the first time in school history, TCU Beach Volleyball (31-5) is headed to the NCAA Finals to compete for the National Championship. After clocking both No. 7-seeded Texas and No. 6-seeded Cal Poly on Saturday, the Horned Frogs will face the No. 4-seeded LMU Lions in the finals on Sunday at 9:30 a.m. CT on ESPN.

TCU remains the best in Texas after sweeping the Longhorns 3-0 in the quarterfinals. In the all-time series, the Horned Frogs improved to 42-0 against teams from the Lone Star State.

In the semifinals against the Mustangs, TCU’s AVCA Top Flight honorees propelled the Frogs to the title dual: Daniela Alvarez/Tania Moreno, Anhelina Khmil/Ana Vergara and Allanis Navas/Sofia Izuzquiza.

It was the first point for the No. 1 duo of Alvarez/Moreno this weekend. The two have yet to drop a dual in the four years they’ve been to Gulf Shores. Khmil and Vergara remain undefeated, notching their 20th win together this season.

TCU improved to 6-6 all-time in the NCAA Beach Volleyball Championship.

Quick Hits against No. 7-seeded Texas

• TCU took all the first sets against the Longhorns to take an early lead.

• Anhelina Khmil and Ana Vergara notched point one at the No. 4 spot: 21-13, 21-12.

• Sofia Izuzquiza and Allanis Navas notched point two at the No. 3 spot: 21-15, 21-15.

• To clinch a spot in the semifinals, Hailey Hamlett and Maria Gonzalez notched point three at the No. 2 spot: 21-19, 21-19.

• With their win over the Longhorns, the Horned Frogs maintained their lead in the all-time series, stretching to 4-0.

• The sweep over Texas is the 15th for TCU this season.

Quick Hits against No. 6-seeded Cal Poly

• The Olympic duo of Daniela Alvarez and Tania Moreno notched the first point for the Frogs at the No. 1 spot: 27-25, 21-12.

• The pairing is now 15-4 on the season together.

• The undefeated No. 4 pairing of Khmil/Vergara notched point two: 25-23, 21-18.

• The two are now 20-0 on the season together.

• Hamlett/Gonzalez dropped in a three-set battle at the No. 2 spot: 15-21, 21-17, 12-15.

• The duo moved to 11-3 on the season together.

• To propell TCU to its first appearance in the final title match, Izuzquiza/Navas notched point three at the No. 3 spot: 21-13, 18-21, 15-13.

• The freshman and senior duo are now 25-2 together on the season.

• The win over the Mustangs stretched TCU’s record in the NCAA Beach Volleyball Championship to 6-6.

• The Horned Frogs maintained their lead in the all-time series against Cal Poly, extending to 6-4.

Up Next

No. 2-seeded TCU will take on No. 4-seeded LMU. The title match for the NCAA Beach Volleyball Championship will be live at 9:30 a.m. CT on ESPN.

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Since moving to Orlando 15 years ago, I’ve watched my fellow residents get the shaft in almost every category: wages, education, infrastructure and housing affordability. It’s the same tired story of local governments serving at the behest of Big Tourism, while their employees barely scrape by. Now, after decades of billionaire Rays baseball owner Stuart […]

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May 4, 2025

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Now, after decades of billionaire Rays baseball owner Stuart Sternberg twisting the arm of Tampa Bay-area taxpayers, another wealthy group wants to bring their version of the grift to Orange County. It appears the investors, who haven’t been named, likely have their sights set on building a stadium on 35.5 acres of valuable county-owned land near SeaWorld. Outside of famed former major-league player Barry Larkin, the Dreamers are quiet on who exactly is behind the push. Armed only with vague verbal commitments and letters of intent, it seems the group is taking the temperature of local lawmakers.

Mafia-style tactics are common in the sports stadium world, as almost every market in the country usually bows at the knee to team owners, giving corporate welfare to the 1%. Make no mistake, these are anonymous rich folks wanting a public resource for free.

Our home in Central Florida is unique, as the Tourism Development Tax (TDT) provides a bounty of extra monies. This makes the area extra enticing for future Major League Baseball owners, more so than even a Nashville or Charlotte market.

The consultant and ruling class make excuses for not spending the money on pressing local needs, saying their hands are tied unless changes are made to state laws in Tallahassee. Good timing. As of this writing, two bills have passed the Florida House of Representatives, (HB7033 and HB1221) in an effort to add flexibility to how municipalities can spend the public money. In theory, this cash could be used for residents, as opposed to Big Tourism expenditures. Per usual good ole boy network cowardice, our local Orange County and City of Orlando politicians sit silent, knowing exactly which donors will line their campaign’s pocket next election.

The usual leeches are bothered by the new legislation: Chambers of Commerce, quasi-corporate tourism boards, lobbyists, connected lawyers, career politicians and corporate overlords. Their argument is simple: why should we advertise and spend on ourselves, when public funds can foot the bill? It’s an absurd notion, as if people will simply stop going to Disney if bed-tax money isn’t used to market the household name.

I believe the International Drive stadium location would be a disservice to locals and tourists alike, with I-4’s pain felt by everyone in the region at any time of day. For anyone who thinks Bay area fans will routinely make the traffic-ridden drive, keep dreaming.

State and local leaders (both Republicans and Democrats) have been replete with opportunities to build rail and improve public transport, which ironically could have been used to bolster the convention center/I-Drive stadium argument. Instead, they blew these community minded improvements off again and again. Instead, opting to dump $400 million or more into the FDR-era Citrus Bowl, gifting Universal new hotel-adjacent infrastructure and advertising (via Visit Orlando) on New York City subways.

As a longtime baseball fan, it’s no secret that I’d love to see professional baseball back in Orlando. But it should likely be minor-league, centrally located and most important — privately funded.

Central Florida isn’t just for tourists. It consists of hardworking hospitality employees changing beds, serving guests and making the theme parks thrive. I’d like to see us do better. That starts with how our “public servants” respond to billionaire asks.

Jonathan Beaton is president of Inside Advantage PR, a media relations agency and crisis PR firm based in Orlando.