Loading…

VisuaLeague: Visual Analytics of Multiple Games

One of the most popular eSports (electronic sports) game types practiced is the Multiplayer Online Battle Arena (MOBA) genre, represented by one of the most popular competitive games, League of Legends (LoL). As in many traditional sports, to improve player and team performance, players and coaches...

Full description

Saved in:

Bibliographic Details
Main Authors:	Afonso, Ana Paula, Carmo, Maria Beatriz, Afonso, Rafael
Format:	Conference Proceeding
Language:	English
Subjects:	aggregated data visualization Data visualization Filtering Game data visualization Games MOBA games Spatial databases spatio-temporal data Training Visual analytics
Online Access:	Request full text
Tags:	Add Tag No Tags, Be the first to tag this record!

cited_by
cites
container_end_page	62
container_issue
container_start_page	54
container_title
container_volume
creator	Afonso, Ana Paula Carmo, Maria Beatriz Afonso, Rafael
description	One of the most popular eSports (electronic sports) game types practiced is the Multiplayer Online Battle Arena (MOBA) genre, represented by one of the most popular competitive games, League of Legends (LoL). As in many traditional sports, to improve player and team performance, players and coaches analyze all the game events, such as, the positions and trajectories of the players, representing their movements, events and actions they performed during the game (spatial and temporal data). This paper presents VisuaLeague, a visualization tool for analysis of LoL matches for single player, teams, professional matches, and multiple games. The tool offers interaction with the visualizations, filtering and aggregation of data, and clustering to solve the common problems presented in analysis with voluminous amount of data, like cluttering and overlapping. We evaluated VisuaLeague through a user study covering the various types of analysis with two professional coaches. Results indicate that the tool was overall intuitive, useful, efficient and innovative and coaches show a particular interest in the analysis of professional training matches and multiple games as those provide visualizations that often lack in common tools, specially, regarding spatio-temporal data.
doi_str_mv	10.1109/IV53921.2021.00019
format	conference_proceeding
fullrecord	<record><control><sourceid>ieee_CHZPO</sourceid><recordid>TN_cdi_ieee_primary_9582703</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>9582703</ieee_id><sourcerecordid>9582703</sourcerecordid><originalsourceid>FETCH-LOGICAL-i203t-b0d75ded7ac5d52c208afa656f9a24fbea9ae16cb6847fca8570a2b077fe27e63</originalsourceid><addsrcrecordid>eNotj0FOwzAQRQ0SEqX0ArDJBdKO7YzHZldVUCoFsYFuq0kyRkYpVHWy6O2pgM1_epsnfaXuNMy1hrDYbNEGo-cGzgMAOlyoWSCvncPKekP-Uk2MJSxBW3-tbnL-BKgQCSdqsU155Fr4Y5SH4lf6YvnF_WlIbS6-Y_Ey9kM69FKseS_5Vl1F7rPM_jlV70-Pb6vnsn5db1bLukwG7FA20BF20hG32KFpDXiO7NDFwKaKjXBg0a5tnK8otuyRgE0DRFEMibNTdf_XTSKyOxzTno-nXcDzHbD2B3GEQ_A</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>VisuaLeague: Visual Analytics of Multiple Games</title><source>IEEE Xplore All Conference Series</source><creator>Afonso, Ana Paula ; Carmo, Maria Beatriz ; Afonso, Rafael</creator><creatorcontrib>Afonso, Ana Paula ; Carmo, Maria Beatriz ; Afonso, Rafael</creatorcontrib><description>One of the most popular eSports (electronic sports) game types practiced is the Multiplayer Online Battle Arena (MOBA) genre, represented by one of the most popular competitive games, League of Legends (LoL). As in many traditional sports, to improve player and team performance, players and coaches analyze all the game events, such as, the positions and trajectories of the players, representing their movements, events and actions they performed during the game (spatial and temporal data). This paper presents VisuaLeague, a visualization tool for analysis of LoL matches for single player, teams, professional matches, and multiple games. The tool offers interaction with the visualizations, filtering and aggregation of data, and clustering to solve the common problems presented in analysis with voluminous amount of data, like cluttering and overlapping. We evaluated VisuaLeague through a user study covering the various types of analysis with two professional coaches. Results indicate that the tool was overall intuitive, useful, efficient and innovative and coaches show a particular interest in the analysis of professional training matches and multiple games as those provide visualizations that often lack in common tools, specially, regarding spatio-temporal data.</description><identifier>EISSN: 2375-0138</identifier><identifier>EISBN: 9781665438278</identifier><identifier>EISBN: 1665438274</identifier><identifier>DOI: 10.1109/IV53921.2021.00019</identifier><identifier>CODEN: IEEPAD</identifier><language>eng</language><publisher>IEEE</publisher><subject>aggregated data visualization ; Data visualization ; Filtering ; Game data visualization ; Games ; MOBA games ; Spatial databases ; spatio-temporal data ; Training ; Visual analytics</subject><ispartof>2021 25th International Conference Information Visualisation (IV), 2021, p.54-62</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9582703$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,27923,54553,54930</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/9582703$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Afonso, Ana Paula</creatorcontrib><creatorcontrib>Carmo, Maria Beatriz</creatorcontrib><creatorcontrib>Afonso, Rafael</creatorcontrib><title>VisuaLeague: Visual Analytics of Multiple Games</title><title>2021 25th International Conference Information Visualisation (IV)</title><addtitle>IV</addtitle><description>One of the most popular eSports (electronic sports) game types practiced is the Multiplayer Online Battle Arena (MOBA) genre, represented by one of the most popular competitive games, League of Legends (LoL). As in many traditional sports, to improve player and team performance, players and coaches analyze all the game events, such as, the positions and trajectories of the players, representing their movements, events and actions they performed during the game (spatial and temporal data). This paper presents VisuaLeague, a visualization tool for analysis of LoL matches for single player, teams, professional matches, and multiple games. The tool offers interaction with the visualizations, filtering and aggregation of data, and clustering to solve the common problems presented in analysis with voluminous amount of data, like cluttering and overlapping. We evaluated VisuaLeague through a user study covering the various types of analysis with two professional coaches. Results indicate that the tool was overall intuitive, useful, efficient and innovative and coaches show a particular interest in the analysis of professional training matches and multiple games as those provide visualizations that often lack in common tools, specially, regarding spatio-temporal data.</description><subject>aggregated data visualization</subject><subject>Data visualization</subject><subject>Filtering</subject><subject>Game data visualization</subject><subject>Games</subject><subject>MOBA games</subject><subject>Spatial databases</subject><subject>spatio-temporal data</subject><subject>Training</subject><subject>Visual analytics</subject><issn>2375-0138</issn><isbn>9781665438278</isbn><isbn>1665438274</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2021</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNotj0FOwzAQRQ0SEqX0ArDJBdKO7YzHZldVUCoFsYFuq0kyRkYpVHWy6O2pgM1_epsnfaXuNMy1hrDYbNEGo-cGzgMAOlyoWSCvncPKekP-Uk2MJSxBW3-tbnL-BKgQCSdqsU155Fr4Y5SH4lf6YvnF_WlIbS6-Y_Ey9kM69FKseS_5Vl1F7rPM_jlV70-Pb6vnsn5db1bLukwG7FA20BF20hG32KFpDXiO7NDFwKaKjXBg0a5tnK8otuyRgE0DRFEMibNTdf_XTSKyOxzTno-nXcDzHbD2B3GEQ_A</recordid><startdate>202107</startdate><enddate>202107</enddate><creator>Afonso, Ana Paula</creator><creator>Carmo, Maria Beatriz</creator><creator>Afonso, Rafael</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>202107</creationdate><title>VisuaLeague: Visual Analytics of Multiple Games</title><author>Afonso, Ana Paula ; Carmo, Maria Beatriz ; Afonso, Rafael</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i203t-b0d75ded7ac5d52c208afa656f9a24fbea9ae16cb6847fca8570a2b077fe27e63</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2021</creationdate><topic>aggregated data visualization</topic><topic>Data visualization</topic><topic>Filtering</topic><topic>Game data visualization</topic><topic>Games</topic><topic>MOBA games</topic><topic>Spatial databases</topic><topic>spatio-temporal data</topic><topic>Training</topic><topic>Visual analytics</topic><toplevel>online_resources</toplevel><creatorcontrib>Afonso, Ana Paula</creatorcontrib><creatorcontrib>Carmo, Maria Beatriz</creatorcontrib><creatorcontrib>Afonso, Rafael</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE/IET Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Afonso, Ana Paula</au><au>Carmo, Maria Beatriz</au><au>Afonso, Rafael</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>VisuaLeague: Visual Analytics of Multiple Games</atitle><btitle>2021 25th International Conference Information Visualisation (IV)</btitle><stitle>IV</stitle><date>2021-07</date><risdate>2021</risdate><spage>54</spage><epage>62</epage><pages>54-62</pages><eissn>2375-0138</eissn><eisbn>9781665438278</eisbn><eisbn>1665438274</eisbn><coden>IEEPAD</coden><abstract>One of the most popular eSports (electronic sports) game types practiced is the Multiplayer Online Battle Arena (MOBA) genre, represented by one of the most popular competitive games, League of Legends (LoL). As in many traditional sports, to improve player and team performance, players and coaches analyze all the game events, such as, the positions and trajectories of the players, representing their movements, events and actions they performed during the game (spatial and temporal data). This paper presents VisuaLeague, a visualization tool for analysis of LoL matches for single player, teams, professional matches, and multiple games. The tool offers interaction with the visualizations, filtering and aggregation of data, and clustering to solve the common problems presented in analysis with voluminous amount of data, like cluttering and overlapping. We evaluated VisuaLeague through a user study covering the various types of analysis with two professional coaches. Results indicate that the tool was overall intuitive, useful, efficient and innovative and coaches show a particular interest in the analysis of professional training matches and multiple games as those provide visualizations that often lack in common tools, specially, regarding spatio-temporal data.</abstract><pub>IEEE</pub><doi>10.1109/IV53921.2021.00019</doi><tpages>9</tpages></addata></record>
fulltext	fulltext_linktorsrc
identifier	EISSN: 2375-0138
ispartof	2021 25th International Conference Information Visualisation (IV), 2021, p.54-62
issn	2375-0138
language	eng
recordid	cdi_ieee_primary_9582703
source	IEEE Xplore All Conference Series
subjects	aggregated data visualization Data visualization Filtering Game data visualization Games MOBA games Spatial databases spatio-temporal data Training Visual analytics
title	VisuaLeague: Visual Analytics of Multiple Games
url	http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-14T12%3A38%3A55IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-ieee_CHZPO&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=VisuaLeague:%20Visual%20Analytics%20of%20Multiple%20Games&rft.btitle=2021%2025th%20International%20Conference%20Information%20Visualisation%20(IV)&rft.au=Afonso,%20Ana%20Paula&rft.date=2021-07&rft.spage=54&rft.epage=62&rft.pages=54-62&rft.eissn=2375-0138&rft.coden=IEEPAD&rft_id=info:doi/10.1109/IV53921.2021.00019&rft.eisbn=9781665438278&rft.eisbn_list=1665438274&rft_dat=%3Cieee_CHZPO%3E9582703%3C/ieee_CHZPO%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-i203t-b0d75ded7ac5d52c208afa656f9a24fbea9ae16cb6847fca8570a2b077fe27e63%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=9582703&rfr_iscdi=true