A recent study using reinforcement learning with a Q-functions to quantify the impact of individual player actions in ice hockey has shown promising results. The model takes into account the context of the actions and captures internal dynamic features of the play which simple common metrics e.g., counting goals or assists, do not. It also performs look ahead which is important in a low scoring game like ice hockey. However, it does not capture the chemistry between the players i.e., how well the players play together which is important in a team sport like ice hockey. In this paper, we, therefore, extend this earlier work on individual player performance with new metrics on player pairs impact when on ice together. Our resulting top pairings are compared to NHL’s official statistics and extended analysis is performed that investigate the relationship with time on ice which provides insights that could be of relevance to coaches.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:liu-175928 |
| Date | January 2021 |
| Creators | Ljung, Dennis |
| Publisher | Linköpings universitet, Institutionen för datavetenskap |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
Page generated in 0.0024 seconds