Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Odds Ratio01:09

Odds Ratio

289
The odds ratio (OR) is a statistical measure used extensively in epidemiology and research to quantify the strength of association between exposure and outcome across different groups. Unlike relative risk, which compares the probabilities of an event occurring, the odds ratio compares the odds of an event occurring in the exposed group to the odds of it occurring in the unexposed group. The odds, in this context, are calculated as the probability of the event happening divided by the...
289
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

185
Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
185
Observational Studies01:11

Observational Studies

9.8K
Observational studies are a type of analytical study where researchers observe events without any interventions. In other words, the researcher does not influence the response variable or the experiment's outcome.
There are three types of observational studies – Prospective, retrospective, and cross-sectional.
Prospective Study
Prospective studies, also known as longitudinal or cohort studies, are carried out by collecting future data from groups sharing similar characteristics. One...
9.8K
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

569
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
569
Bias01:22

Bias

5.6K
Bias refers to any tendency that prevents a question from being considered unprejudiced. In research, bias occurs when one outcome or answer is selected or encouraged over others in sampling or testing. Bias can occur during any research phase, including study design, data collection, analysis, and publication.
In statistics, a sampling bias is created when a sample is collected from a population, and some members of the population are not as likely to be chosen as others (remember, each member...
5.6K
Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

225
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
225

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Nursing strategies for severe poststroke fatigue: a case report of a structured, multidimensional intervention program.

Frontiers in rehabilitation sciences·2026
Same author

[Effect of moxibustion on serum inflammatory response in rat models of rheumatoid arthritis based on GSDMD].

Zhongguo zhen jiu = Chinese acupuncture & moxibustion·2026
Same author

Uncovering hidden cross-regional environmental risks: Network evidence from off-site penalties and implications for pollution transfer in China.

iScience·2026
Same author

Serum Response Factor Regulates CCN1 to Exacerbate Acute Kidney Injury Through Facilitation of Ferroptosis-Related Injury.

Nephrology (Carlton, Vic.)·2026
Same author

Outside the niche: Gut microbiota relay psychological stress to hematopoietic stem cell dysfunction.

Cell stem cell·2026
Same author

Engineering the flexibility of the β-sheet containing Y58 in L-aspartate-α-decarboxylase to relieve mechanism-based inactivation.

3 Biotech·2026

Related Experiment Video

Updated: Sep 23, 2025

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

7.2K

Predicting Risk Propensity Through Player Behavior in DOTA 2: A Cross-Sectional Study.

Sihua Lyu1,2, Nan Zhao1,2, Yichuan Zhang3

  • 1Institute of Psychology, Chinese Academy of Sciences, Beijing, China.

Frontiers in Psychology
|May 16, 2022
PubMed
Summary

This study used machine learning on Defense of the Ancients 2 (DOTA 2) game data to predict risk propensity. Gaussian process regression showed promise in identifying risk-taking behavior nonintrusively.

Keywords:
DOTA 2MOBAmachine learningplayer behaviorrisk propensity

More Related Videos

Transcranial Direct Current Stimulation for Online Gamers
06:01

Transcranial Direct Current Stimulation for Online Gamers

Published on: November 9, 2019

8.1K
Measuring Engagement of Spectators of Social Digital Games
14:02

Measuring Engagement of Spectators of Social Digital Games

Published on: July 3, 2021

3.6K

Related Experiment Videos

Last Updated: Sep 23, 2025

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

7.2K
Transcranial Direct Current Stimulation for Online Gamers
06:01

Transcranial Direct Current Stimulation for Online Gamers

Published on: November 9, 2019

8.1K
Measuring Engagement of Spectators of Social Digital Games
14:02

Measuring Engagement of Spectators of Social Digital Games

Published on: July 3, 2021

3.6K

Area of Science:

  • Behavioral Economics
  • Computational Psychology
  • Game Analytics

Background:

  • Traditional risk propensity assessments (e.g., questionnaires) have limitations in certain contexts.
  • A nonintrusive, automated method for assessing risk propensity is needed.
  • Online gaming behavior may offer insights into individual risk-taking tendencies.

Purpose of the Study:

  • To develop and evaluate machine learning models for predicting risk propensity using data from the video game Defense of the Ancients 2 (DOTA 2).
  • To explore the relationship between in-game behavioral metrics and self-reported risk propensity scores.
  • To assess the feasibility of using gaming data as a proxy for risk propensity measurement.

Main Methods:

  • Collected behavioral metrics and historical statistics from DOTA 2 single matches.
  • Utilized self-reported risk propensity scores from 218 DOTA 2 players.
  • Trained and compared various machine learning models, with Gaussian process regression identified as the best performer.

Main Results:

  • The Gaussian process regression model achieved a root mean square error of 1.10.
  • A correlation of 0.44 was observed between predicted and self-reported risk propensity scores.
  • The model demonstrated a test-retest reliability of 0.67 and an R-squared value of 0.17.

Conclusions:

  • Selected behavioral features in DOTA 2 can contribute to predicting individual risk propensity.
  • This machine learning approach offers a potential nonintrusive method for assessing risk propensity.
  • Further research is recommended to refine the models and explore broader applications.