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Competing with humans at fantasy football: team formation in large partially-observable domains

Record type: Conference or Workshop Item (Paper)

We present the first real-world benchmark for sequentially optimal team formation, working within the framework of a class of online football prediction games known as Fantasy Football. We model the problem as a Bayesian reinforcement learning one, where the action space is exponential in the number of players and where the decision maker’s beliefs are over multiple characteristics of each footballer. We then exploit domain knowledge to construct computationally tractable solution techniques in order to build a competitive automated Fantasy Football manager. Thus, we are able to establish the baseline performance in this domain, even without complete information on footballers’ performances (accessible to human managers), showing that our agent is able to rank at around the top percentile when pitched against 2.5M human players

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Citation

Matthews, Tim, Ramchurn, Sarvapali and Chalkiadakis, Georgios (2012) Competing with humans at fantasy football: team formation in large partially-observable domains At Twenty-Sixth Conference of the Association for the Advancement for Artificial Intelligence, Canada. 22 - 26 Jul 2012. 8 pp, pp. 1394-1400.

More information

Published date: 22 July 2012
Venue - Dates: Twenty-Sixth Conference of the Association for the Advancement for Artificial Intelligence, Canada, 2012-07-22 - 2012-07-26
Keywords: multi-agent systems, team formation, optimisation, sequential decision making
Organisations: Agents, Interactions & Complexity

Identifiers

Local EPrints ID: 340382
URI: http://eprints.soton.ac.uk/id/eprint/340382
PURE UUID: 9aa61ec1-832c-4305-8c5a-a54640568a28
ORCID for Sarvapali Ramchurn: ORCID iD orcid.org/0000-0001-9686-4302

Catalogue record

Date deposited: 22 Jun 2012 09:10
Last modified: 18 Jul 2017 05:44

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Contributors

Author: Tim Matthews
Author: Sarvapali Ramchurn ORCID iD
Author: Georgios Chalkiadakis

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