TY - GEN
T1 - TeamSkill and the NBA
T2 - 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2013
AU - DeLong, Colin
AU - Terveen, Loren
AU - Srivastava, Jaideep
PY - 2013
Y1 - 2013
N2 - In this paper, we build on our previous work by evaluating several approaches for assessing the skill of players and teams on the basis of both individual performance and group cohesion, or "team chemistry", using game data from the National Basketball Association (NBA). Previously developed for skill assessment in team-based multi-player video games (e.g., Halo 3), we find that group cohesion is a predictive feature in virtual and real-world team-based games, and that methods utilizing such features can often outperform the baseline in both contexts. Additionally, we observe a strong positive correlation between the predictive accuracy of our group cohesion-based approaches and the duration of playing time between a particular configuration of players on a team and their opponents, or "match-up" length.
AB - In this paper, we build on our previous work by evaluating several approaches for assessing the skill of players and teams on the basis of both individual performance and group cohesion, or "team chemistry", using game data from the National Basketball Association (NBA). Previously developed for skill assessment in team-based multi-player video games (e.g., Halo 3), we find that group cohesion is a predictive feature in virtual and real-world team-based games, and that methods utilizing such features can often outperform the baseline in both contexts. Additionally, we observe a strong positive correlation between the predictive accuracy of our group cohesion-based approaches and the duration of playing time between a particular configuration of players on a team and their opponents, or "match-up" length.
UR - http://www.scopus.com/inward/record.url?scp=84893289569&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84893289569&partnerID=8YFLogxK
U2 - 10.1145/2492517.2492628
DO - 10.1145/2492517.2492628
M3 - Conference contribution
AN - SCOPUS:84893289569
SN - 9781450322409
T3 - Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2013
SP - 156
EP - 161
BT - Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2013
PB - Association for Computing Machinery
Y2 - 25 August 2013 through 28 August 2013
ER -