TrueSkill

TrueSkill™: a Bayesian skill rating system. We present a new Bayesian skill rating system which can be viewed as a generalisation of the Elo system used in Chess. The new system tracks the uncertainty about player skills, explicitly models draws, can deal with any number of competing entities and can infer individual skills from team results. Inference is performed by approximate message passing on a factor graph representation of the model. We present experimental evidence on the increased accuracy and convergence speed of the system compared to Elo and report on our experience with the new rating system running in a large-scale commercial online gaming service under the name of TrueSkill.


References in zbMATH (referenced in 22 articles )

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  1. Šarčević, Ana; Vranić, Mihaela; Pintar, Damir: A combinatorial approach in predicting the outcome of tennis matches (2021)
  2. Chen, Ye; Ryzhov, Ilya O.: Technical note: Consistency analysis of sequential learning under approximate Bayesian inference (2020)
  3. Kharrat, Tarak; McHale, Ian G.; Peña, Javier López: Plus-minus player ratings for soccer (2020)
  4. Pananjady, Ashwin; Mao, Cheng; Muthukumar, Vidya; Wainwright, Martin J.; Courtade, Thomas A.: Worst-case versus average-case design for estimation from partial pairwise comparisons (2020)
  5. Ricatte, Thomas; Gilleron, Rémi; Tommasi, Marc: Skill rating for multiplayer games. Introducing hypernode graphs and their spectral theory (2020)
  6. Viappiani, Paolo; Boutilier, Craig: On the equivalence of optimal recommendation sets and myopically optimal query sets (2020)
  7. Latif, Naveed; Pečarić, Đilda; Pečarić, Josip: Majorizatiuon and Zipf-Mandelbrot law (2018)
  8. Oliveira, I. F. D.; Zehavi, S.; Davidov, O.: Stochastic transitivity: axioms and models (2018)
  9. Oliveira, Ivo F. D.; Ailon, Nir; Davidov, Ori: A new and flexible approach to the analysis of paired comparison data (2018)
  10. Pan, Yuangang; Han, Bo; Tsang, Ivor W.: Stagewise learning for noisy (k)-ary preferences (2018)
  11. Ryzhov, Ilya O.: The local time method for targeting and selection (2018)
  12. Weng, Ruby Chiu-Hsing; Coad, D. Stephen: Real-time Bayesian parameter estimation for item response models (2018)
  13. Lampropoulos, Leonidas; Gallois-Wong, Diane; Hriţcu, Cătălin; Hughes, John; Pierce, Benjamin C.; Xia, Li-yao: Beginner’s Luck: a language for property-based generators (2017)
  14. Latif, Naveed; Pečarić, Đilda; Pečarić, Josip: Majorization, Csiszár divergence and Zipf-Mandelbrot law (2017)
  15. Chen, Xi; Jiao, Kevin; Lin, Qihang: Bayesian decision process for cost-efficient dynamic ranking via crowdsourcing (2016)
  16. Pigozzi, Gabriella; Tsoukiàs, Alexis; Viappiani, Paolo: Preferences in artificial intelligence (2016)
  17. Shah, Nihar B.; Balakrishnan, Sivaraman; Bradley, Joseph; Parekh, Abhay; Ramchandran, Kannan; Wainwright, Martin J.: Estimation from pairwise comparisons: sharp minimax bounds with topology dependence (2016)
  18. Borgström, Johannes; Gordon, Andrew D.; Greenberg, Michael; Margetson, James; Van Gael, Jurgen: Measure transformer semantics for Bayesian machine learning (2011)
  19. Pahikkala, Tapio; Waegeman, Willem; Tsivtsivadze, Evgeni; Salakoski, Tapio; de Baets, Bernard: Learning intransitive reciprocal relations with kernel methods (2010)
  20. Beygelzimer, Alina; Langford, John; Ravikumar, Pradeep: Error-correcting tournaments (2009)

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