Papers › Collection and Validation of Psychophysiological Data from Professional and Amateur...

Collection and Validation of Psychophysiological Data from Professional and Amateur Players: a Multimodal eSports Dataset

2 Nov 2020arXiv:2011.00958archive 2025-07-28

Anton Smerdov, Bo Zhou, Paul Lukowicz, Andrey Somov

Proper training and analytics in eSports require accurately collected and annotated data. Most eSports research focuses exclusively on in-game data analysis, and there is a lack of prior work involving eSports athletes' psychophysiological data. In this paper, we present a dataset collected from professional and amateur teams in 22 matches in League of Legends video game with more than 40 hours of recordings. Recorded data include the players' physiological activity, e.g. movements, pulse, saccades, obtained from various sensors, self-reported aftermatch survey, and in-game data. An important feature of the dataset is simultaneous data collection from five players, which facilitates the analysis of sensor data on a team level. Upon the collection of dataset we carried out its validation. In particular, we demonstrate that stress and concentration levels for professional players are less correlated, meaning more independent playstyle. Also, we show that the absence of team communication does not affect the professional players as much as amateur ones. To investigate other possible use cases of the dataset, we have trained classical machine learning algorithms for skill prediction and player re-identification using 3-minute sessions of sensor data. Best models achieved 0.856 and 0.521 (0.10 for a chance level) accuracy scores on a validation set for skill prediction and player re-id problems, respectively. The dataset is available at https://github.com/smerdov/eSports Sensors Dataset.

PaperPDFCode

Code

smerdov/eSports_Sensors_Dataset officialmentioned in papermentioned on GitHub report
asmerdov/DataCollectionSystem mentioned on GitHubpytorch report
smerdov/DataCollectionSystem mentioned on GitHubpytorch report

Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.

Code Syntology ran Syntology

Not run by Syntology. Nothing on this page verifies that the listed code works.

Tasks

Feature ImportancePerson Re-IdentificationPhysiological ComputingReal-Time Strategy GamesSensor ModelingSkills AssessmentSkills EvaluationTime Series Analysis

Datasets

Introduced by this paper, per the archive.

eSports Sensors Dataset

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Person Re-Identification eSports Sensors Dataset Random Forest Accuracy 52.1 #1 of 5 Archive leaderboard report
Person Re-Identification eSports Sensors Dataset Random Forest LogLoss 0.01617 #1 of 5 Archive leaderboard report
Person Re-Identification eSports Sensors Dataset Random Forest ROC AUC 0.919 #1 of 5 Archive leaderboard report
Person Re-Identification eSports Sensors Dataset Logistic Regression Accuracy 48.8 #2 of 5 Archive leaderboard report
Person Re-Identification eSports Sensors Dataset Logistic Regression LogLoss 0.01615 #2 of 5 Archive leaderboard report
Person Re-Identification eSports Sensors Dataset Logistic Regression ROC AUC 0.884 #2 of 5 Archive leaderboard report
Person Re-Identification eSports Sensors Dataset SVM Accuracy 45 #3 of 5 Archive leaderboard report
Person Re-Identification eSports Sensors Dataset SVM LogLoss 0.01588 #3 of 5 Archive leaderboard report
Person Re-Identification eSports Sensors Dataset SVM ROC AUC 0.89 #3 of 5 Archive leaderboard report
Person Re-Identification eSports Sensors Dataset KNN Accuracy 41.5 #4 of 5 Archive leaderboard report
Person Re-Identification eSports Sensors Dataset KNN LogLoss 0.05735 #4 of 5 Archive leaderboard report
Person Re-Identification eSports Sensors Dataset KNN ROC AUC 0.84 #4 of 5 Archive leaderboard report
Person Re-Identification eSports Sensors Dataset Random Guess Accuracy 10 #5 of 5 Archive leaderboard report
Person Re-Identification eSports Sensors Dataset Random Guess LogLoss 0.02303 #5 of 5 Archive leaderboard report
Person Re-Identification eSports Sensors Dataset Random Guess ROC AUC 0.5 #5 of 5 Archive leaderboard report
Skills Evaluation eSports Sensors Dataset SVM Accuracy 85.6 #1 of 5 Archive leaderboard report
Skills Evaluation eSports Sensors Dataset SVM LogLoss 0.311 #1 of 5 Archive leaderboard report
Skills Evaluation eSports Sensors Dataset SVM ROC AUC 0.945 #1 of 5 Archive leaderboard report
Skills Evaluation eSports Sensors Dataset Logistic Regression Accuracy 83.8 #2 of 5 Archive leaderboard report
Skills Evaluation eSports Sensors Dataset Logistic Regression LogLoss 0.596 #2 of 5 Archive leaderboard report
Skills Evaluation eSports Sensors Dataset Logistic Regression ROC AUC 0.886 #2 of 5 Archive leaderboard report
Skills Evaluation eSports Sensors Dataset Random Forest Accuracy 80 #3 of 5 Archive leaderboard report
Skills Evaluation eSports Sensors Dataset Random Forest LogLoss 0.456 #3 of 5 Archive leaderboard report
Skills Evaluation eSports Sensors Dataset Random Forest ROC AUC 0.885 #3 of 5 Archive leaderboard report
Skills Evaluation eSports Sensors Dataset KNN Accuracy 74.1 #4 of 5 Archive leaderboard report
Skills Evaluation eSports Sensors Dataset KNN LogLoss 0.442 #4 of 5 Archive leaderboard report
Skills Evaluation eSports Sensors Dataset KNN ROC AUC 0.899 #4 of 5 Archive leaderboard report
Skills Evaluation eSports Sensors Dataset Random Guess Accuracy 50 #5 of 5 Archive leaderboard report
Skills Evaluation eSports Sensors Dataset Random Guess LogLoss 0.693 #5 of 5 Archive leaderboard report
Skills Evaluation eSports Sensors Dataset Random Guess ROC AUC 0.5 #5 of 5 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections