TY - GEN
T1 - Valuing Player Actions in Counter-Strike
T2 - 8th IEEE International Conference on Big Data, Big Data 2020
AU - Xenopoulos, Peter
AU - Doraiswamy, Harish
AU - Silva, Claudio
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/12/10
Y1 - 2020/12/10
N2 - Esports, despite its expanding interest, lacks fundamental sports analytics resources such as accessible data or proven and reproducible analytical frameworks. Even Counter-Strike: Global Offensive (CSGO), the second most popular esport, suffers from these problems. Thus, quantitative evaluation of CSGO players, a task important to teams, media, bettors and fans, is difficult. To address this, we introduce (1) a data model for CSGO with an open-source implementation; (2) a graph distance measure for defining distances in CSGO; and (3) a context-aware framework to value players' actions based on changes in their team's chances of winning. Using over 70 million in-game CSGO events, we demonstrate our framework's consistency and independence compared to existing valuation frameworks. We also provide use cases demonstrating high-impact play identification and uncertainty estimation.
AB - Esports, despite its expanding interest, lacks fundamental sports analytics resources such as accessible data or proven and reproducible analytical frameworks. Even Counter-Strike: Global Offensive (CSGO), the second most popular esport, suffers from these problems. Thus, quantitative evaluation of CSGO players, a task important to teams, media, bettors and fans, is difficult. To address this, we introduce (1) a data model for CSGO with an open-source implementation; (2) a graph distance measure for defining distances in CSGO; and (3) a context-aware framework to value players' actions based on changes in their team's chances of winning. Using over 70 million in-game CSGO events, we demonstrate our framework's consistency and independence compared to existing valuation frameworks. We also provide use cases demonstrating high-impact play identification and uncertainty estimation.
KW - esports data
KW - event stream data
KW - sports analytics
UR - http://www.scopus.com/inward/record.url?scp=85103843367&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85103843367&partnerID=8YFLogxK
U2 - 10.1109/BigData50022.2020.9378154
DO - 10.1109/BigData50022.2020.9378154
M3 - Conference contribution
AN - SCOPUS:85103843367
T3 - Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020
SP - 1283
EP - 1292
BT - Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020
A2 - Wu, Xintao
A2 - Jermaine, Chris
A2 - Xiong, Li
A2 - Hu, Xiaohua Tony
A2 - Kotevska, Olivera
A2 - Lu, Siyuan
A2 - Xu, Weijia
A2 - Aluru, Srinivas
A2 - Zhai, Chengxiang
A2 - Al-Masri, Eyhab
A2 - Chen, Zhiyuan
A2 - Saltz, Jeff
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 10 December 2020 through 13 December 2020
ER -