A State-Space Perspective on Modelling and Inference for Online Skill Rating
We summarise popular methods used for skill rating in competitive sports, along with their inferential paradigms and introduce new approaches based on sequential Monte Carlo and discrete hidden Markov models. We advocate for a state-space model perspective, wherein players' skills are represent...
Gespeichert in:
| Veröffentlicht in: | arXiv.org 2024-04 |
|---|---|
| Hauptverfasser: | , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | |
|---|---|
| container_issue | |
| container_start_page | |
| container_title | arXiv.org |
| container_volume | |
| creator | Duffield, Samuel Power, Samuel Rimella, Lorenzo |
| description | We summarise popular methods used for skill rating in competitive sports, along with their inferential paradigms and introduce new approaches based on sequential Monte Carlo and discrete hidden Markov models. We advocate for a state-space model perspective, wherein players' skills are represented as time-varying, and match results serve as observed quantities. We explore the steps to construct the model and the three stages of inference: filtering, smoothing and parameter estimation. We examine the challenges of scaling up to numerous players and matches, highlighting the main approximations and reductions which facilitate statistical and computational efficiency. We additionally compare approaches in a realistic experimental pipeline that can be easily reproduced and extended with our open-source Python package, https://github.com/SamDuffield/abile. |
| doi_str_mv | 10.48550/arxiv.2308.02414 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_arxiv</sourceid><recordid>TN_cdi_arxiv_primary_2308_02414</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2847144939</sourcerecordid><originalsourceid>FETCH-LOGICAL-a954-4c155455a920eced275d2216aa8663f42283fa6fd8b4ebf260239ce0bdc797113</originalsourceid><addsrcrecordid>eNotj11LwzAYhYMgOOZ-gFcGvO5M3iRtejmGH4PKxO6-pMkb6axpTbuh_966eXUuzsPhPITccLaUWil2b-J3c1yCYHrJQHJ5QWYgBE-0BLgii2HYM8YgzUApMSPFipajGTEpe2ORvmIcerRjc0TaBfrSOWzbJrxTExzdBI8Rw4T5LtJtmAqk5UfTtvTNjBN1TS69aQdc_Oec7B4fduvnpNg-bdarIjG5kom0XCmplMmBoUUHmXIAPDVGp6nw000tvEm907XE2kPKQOQWWe1slmecizm5Pc-eVKs-Np8m_lR_ytVJeSLuzkQfu68DDmO17w4xTJ8q0DLjUuYiF7_BQlft</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2847144939</pqid></control><display><type>article</type><title>A State-Space Perspective on Modelling and Inference for Online Skill Rating</title><source>arXiv.org</source><source>Free E- Journals</source><creator>Duffield, Samuel ; Power, Samuel ; Rimella, Lorenzo</creator><creatorcontrib>Duffield, Samuel ; Power, Samuel ; Rimella, Lorenzo</creatorcontrib><description>We summarise popular methods used for skill rating in competitive sports, along with their inferential paradigms and introduce new approaches based on sequential Monte Carlo and discrete hidden Markov models. We advocate for a state-space model perspective, wherein players' skills are represented as time-varying, and match results serve as observed quantities. We explore the steps to construct the model and the three stages of inference: filtering, smoothing and parameter estimation. We examine the challenges of scaling up to numerous players and matches, highlighting the main approximations and reductions which facilitate statistical and computational efficiency. We additionally compare approaches in a realistic experimental pipeline that can be easily reproduced and extended with our open-source Python package, https://github.com/SamDuffield/abile.</description><identifier>EISSN: 2331-8422</identifier><identifier>DOI: 10.48550/arxiv.2308.02414</identifier><language>eng</language><publisher>Ithaca: Cornell University Library, arXiv.org</publisher><subject>Decoupling ; Inference ; Parameter estimation ; Players ; Sports ; State space models ; Statistics - Applications ; Statistics - Machine Learning ; Workflow</subject><ispartof>arXiv.org, 2024-04</ispartof><rights>2024. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>http://creativecommons.org/licenses/by/4.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,776,780,881,27904</link.rule.ids><backlink>$$Uhttps://doi.org/10.48550/arXiv.2308.02414$$DView paper in arXiv$$Hfree_for_read</backlink><backlink>$$Uhttps://doi.org/10.1093/jrsssc/qlae035$$DView published paper (Access to full text may be restricted)$$Hfree_for_read</backlink></links><search><creatorcontrib>Duffield, Samuel</creatorcontrib><creatorcontrib>Power, Samuel</creatorcontrib><creatorcontrib>Rimella, Lorenzo</creatorcontrib><title>A State-Space Perspective on Modelling and Inference for Online Skill Rating</title><title>arXiv.org</title><description>We summarise popular methods used for skill rating in competitive sports, along with their inferential paradigms and introduce new approaches based on sequential Monte Carlo and discrete hidden Markov models. We advocate for a state-space model perspective, wherein players' skills are represented as time-varying, and match results serve as observed quantities. We explore the steps to construct the model and the three stages of inference: filtering, smoothing and parameter estimation. We examine the challenges of scaling up to numerous players and matches, highlighting the main approximations and reductions which facilitate statistical and computational efficiency. We additionally compare approaches in a realistic experimental pipeline that can be easily reproduced and extended with our open-source Python package, https://github.com/SamDuffield/abile.</description><subject>Decoupling</subject><subject>Inference</subject><subject>Parameter estimation</subject><subject>Players</subject><subject>Sports</subject><subject>State space models</subject><subject>Statistics - Applications</subject><subject>Statistics - Machine Learning</subject><subject>Workflow</subject><issn>2331-8422</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GOX</sourceid><recordid>eNotj11LwzAYhYMgOOZ-gFcGvO5M3iRtejmGH4PKxO6-pMkb6axpTbuh_966eXUuzsPhPITccLaUWil2b-J3c1yCYHrJQHJ5QWYgBE-0BLgii2HYM8YgzUApMSPFipajGTEpe2ORvmIcerRjc0TaBfrSOWzbJrxTExzdBI8Rw4T5LtJtmAqk5UfTtvTNjBN1TS69aQdc_Oec7B4fduvnpNg-bdarIjG5kom0XCmplMmBoUUHmXIAPDVGp6nw000tvEm907XE2kPKQOQWWe1slmecizm5Pc-eVKs-Np8m_lR_ytVJeSLuzkQfu68DDmO17w4xTJ8q0DLjUuYiF7_BQlft</recordid><startdate>20240412</startdate><enddate>20240412</enddate><creator>Duffield, Samuel</creator><creator>Power, Samuel</creator><creator>Rimella, Lorenzo</creator><general>Cornell University Library, arXiv.org</general><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>L6V</scope><scope>M7S</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>EPD</scope><scope>GOX</scope></search><sort><creationdate>20240412</creationdate><title>A State-Space Perspective on Modelling and Inference for Online Skill Rating</title><author>Duffield, Samuel ; Power, Samuel ; Rimella, Lorenzo</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a954-4c155455a920eced275d2216aa8663f42283fa6fd8b4ebf260239ce0bdc797113</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Decoupling</topic><topic>Inference</topic><topic>Parameter estimation</topic><topic>Players</topic><topic>Sports</topic><topic>State space models</topic><topic>Statistics - Applications</topic><topic>Statistics - Machine Learning</topic><topic>Workflow</topic><toplevel>online_resources</toplevel><creatorcontrib>Duffield, Samuel</creatorcontrib><creatorcontrib>Power, Samuel</creatorcontrib><creatorcontrib>Rimella, Lorenzo</creatorcontrib><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><collection>arXiv Statistics</collection><collection>arXiv.org</collection><jtitle>arXiv.org</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Duffield, Samuel</au><au>Power, Samuel</au><au>Rimella, Lorenzo</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A State-Space Perspective on Modelling and Inference for Online Skill Rating</atitle><jtitle>arXiv.org</jtitle><date>2024-04-12</date><risdate>2024</risdate><eissn>2331-8422</eissn><abstract>We summarise popular methods used for skill rating in competitive sports, along with their inferential paradigms and introduce new approaches based on sequential Monte Carlo and discrete hidden Markov models. We advocate for a state-space model perspective, wherein players' skills are represented as time-varying, and match results serve as observed quantities. We explore the steps to construct the model and the three stages of inference: filtering, smoothing and parameter estimation. We examine the challenges of scaling up to numerous players and matches, highlighting the main approximations and reductions which facilitate statistical and computational efficiency. We additionally compare approaches in a realistic experimental pipeline that can be easily reproduced and extended with our open-source Python package, https://github.com/SamDuffield/abile.</abstract><cop>Ithaca</cop><pub>Cornell University Library, arXiv.org</pub><doi>10.48550/arxiv.2308.02414</doi><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | EISSN: 2331-8422 |
| ispartof | arXiv.org, 2024-04 |
| issn | 2331-8422 |
| language | eng |
| recordid | cdi_arxiv_primary_2308_02414 |
| source | arXiv.org; Free E- Journals |
| subjects | Decoupling Inference Parameter estimation Players Sports State space models Statistics - Applications Statistics - Machine Learning Workflow |
| title | A State-Space Perspective on Modelling and Inference for Online Skill Rating |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-23T13%3A26%3A06IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_arxiv&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=A%20State-Space%20Perspective%20on%20Modelling%20and%20Inference%20for%20Online%20Skill%20Rating&rft.jtitle=arXiv.org&rft.au=Duffield,%20Samuel&rft.date=2024-04-12&rft.eissn=2331-8422&rft_id=info:doi/10.48550/arxiv.2308.02414&rft_dat=%3Cproquest_arxiv%3E2847144939%3C/proquest_arxiv%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2847144939&rft_id=info:pmid/&rfr_iscdi=true |