Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Player impact measures for scoring in ice hockey
Linköping University, Department of Computer and Information Science, Database and information techniques. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Computer and Information Science, Database and information techniques. Linköping University, Faculty of Science & Engineering. (IDA/ADIT)ORCID iD: 0000-0003-1367-1594
Linköping University, Department of Computer and Information Science, Database and information techniques. Linköping University, Faculty of Science & Engineering. (IDA/ADIT)ORCID iD: 0000-0002-9084-0470
2019 (English)In: Proceedings of MathSport International 2019 Conference / [ed] Dimitris Karlis, Ioannis Ntzoufras, Sotiris Drikos, Athen: Athens University of Economics and Business , 2019, p. 307-317Conference paper, Published paper (Refereed)
Abstract [en]

A commonly used method to evaluate player performance is to attribute values to the different actions that players perform and sum up these values every time a player performs these actions. In ice hockey, such metrics include the number of goals, assists, points, plus-minus statistics and recently Corsi and Fenwick. However, these metrics do not capture the context of player actions and the impact they have on the outcome of later actions. Therefore, recent works have introduced more advanced metrics that take into account the context of the actions and perform look-ahead. The use of look-ahead is particularly valuable in low-scoring sports such as ice hockey. In this paper, we first extend a recent approach based on reinforcement learning for measuring a player's impact on a team's scoring. Second, using NHL play-by-play data for several regular seasons, we analyze and compare these and other traditional measures of player impact. Third, we introduce notions of streaks and show that these may provide information about good players, but do not provide a good predictor for the impact that a player will have the next game. Finally, streaks are compared for different player categories, highlighting differences between player positions and correlations with player salaries.

Place, publisher, year, edition, pages
Athen: Athens University of Economics and Business , 2019. p. 307-317
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:liu:diva-157992OAI: oai:DiVA.org:liu-157992DiVA, id: diva2:1328660
Conference
MathSport International Conference, Athens, 1-3 July 2019
Available from: 2019-06-22 Created: 2019-06-22 Last updated: 2019-06-26Bibliographically approved

Open Access in DiVA

Player impact measures for scoring in ice hockey(530 kB)47 downloads
File information
File name FULLTEXT02.pdfFile size 530 kBChecksum SHA-512
f5187a9d94e1a7f9875291b0b2faa4edbe9c509b324811e68c7626abba0b7ebc5670bd883dedefd7350a628af28475a8893de6e1a33d4b1b6464f9b849e3f821
Type fulltextMimetype application/pdf

Search in DiVA

By author/editor
Sans Fuentes, CarlesCarlsson, NiklasLambrix, Patrick
By organisation
Database and information techniquesFaculty of Science & Engineering
Computer Sciences

Search outside of DiVA

GoogleGoogle Scholar
Total: 47 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 90 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf