Predicting the Outcome of a Tennis Tournament: Based on Both Data and Judgments View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2019-06-06

AUTHORS

Wei Gu, Thomas L. Saaty

ABSTRACT

This paper is about predicting the outcome of tennis matches of the Association of Tennis Professionals (ATP) and the Women’s Tennis Association (WTA) using both data and judgments. There are many factors that influence that outcome. An important question is which factors have significant influence on the outcome. We have identified numerous factors and systematically prioritized them subjectively and objectively, so as to improve the accuracy of the prediction. We then used them to predict the win-lose outcome of the 2015 US OPEN tennis matches (63 men and 31 women’s games) before they took place. The tennis match prediction in sports literature thus far reported an accuracy rate of 70%.The accuracy of our proposed model which combines data and judgment reaches 85.1% More... »

PAGES

317-343

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11518-018-5395-3

DOI

http://dx.doi.org/10.1007/s11518-018-5395-3

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1111935007


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/01", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Mathematical Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/08", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Information and Computing Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/09", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Engineering", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Donlinks School of Economics and Management, University of Science and Technology Beijing, 100083, Beijing, China", 
          "id": "http://www.grid.ac/institutes/grid.69775.3a", 
          "name": [
            "Donlinks School of Economics and Management, University of Science and Technology Beijing, 100083, Beijing, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Gu", 
        "givenName": "Wei", 
        "id": "sg:person.014614575371.42", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014614575371.42"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Distinguished University Professor, University of Pittsburgh, 15260, Pittsburgh, PA, USA", 
          "id": "http://www.grid.ac/institutes/grid.21925.3d", 
          "name": [
            "Distinguished University Professor, University of Pittsburgh, 15260, Pittsburgh, PA, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Saaty", 
        "givenName": "Thomas L.", 
        "id": "sg:person.010070352303.49", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010070352303.49"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1007/0-387-23081-5_9", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1085141247", 
          "https://doi.org/10.1007/0-387-23081-5_9"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2019-06-06", 
    "datePublishedReg": "2019-06-06", 
    "description": "This paper is about predicting the outcome of tennis matches of the Association of Tennis Professionals (ATP) and the Women\u2019s Tennis Association (WTA) using both data and judgments. There are many factors that influence that outcome. An important question is which factors have significant influence on the outcome. We have identified numerous factors and systematically prioritized them subjectively and objectively, so as to improve the accuracy of the prediction. We then used them to predict the win-lose outcome of the 2015 US OPEN tennis matches (63 men and 31 women\u2019s games) before they took place. The tennis match prediction in sports literature thus far reported an accuracy rate of 70%.The accuracy of our proposed model which combines data and judgment reaches 85.1%", 
    "genre": "article", 
    "id": "sg:pub.10.1007/s11518-018-5395-3", 
    "isAccessibleForFree": false, 
    "isFundedItemOf": [
      {
        "id": "sg:grant.8326202", 
        "type": "MonetaryGrant"
      }, 
      {
        "id": "sg:grant.8325567", 
        "type": "MonetaryGrant"
      }, 
      {
        "id": "sg:grant.8322572", 
        "type": "MonetaryGrant"
      }
    ], 
    "isPartOf": [
      {
        "id": "sg:journal.1135899", 
        "issn": [
          "1004-3756", 
          "1861-9576"
        ], 
        "name": "Journal of Systems Science and Systems Engineering", 
        "publisher": "Springer Nature", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "3", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "28"
      }
    ], 
    "keywords": [
      "outcomes", 
      "association", 
      "Women\u2019s Tennis Association", 
      "numerous factors", 
      "factors", 
      "tennis matches", 
      "professionals", 
      "data", 
      "Tennis Professionals", 
      "tennis", 
      "important questions", 
      "rate", 
      "accuracy rate", 
      "tennis tournaments", 
      "literature", 
      "judgments", 
      "significant influence", 
      "Predicting", 
      "questions", 
      "influence", 
      "accuracy", 
      "model", 
      "match", 
      "sport literature", 
      "place", 
      "prediction", 
      "tournament", 
      "win\u2013lose outcomes", 
      "paper"
    ], 
    "name": "Predicting the Outcome of a Tennis Tournament: Based on Both Data and Judgments", 
    "pagination": "317-343", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1111935007"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s11518-018-5395-3"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s11518-018-5395-3", 
      "https://app.dimensions.ai/details/publication/pub.1111935007"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2022-11-24T21:05", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20221124/entities/gbq_results/article/article_822.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://doi.org/10.1007/s11518-018-5395-3"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s11518-018-5395-3'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s11518-018-5395-3'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s11518-018-5395-3'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s11518-018-5395-3'


 

This table displays all metadata directly associated to this object as RDF triples.

110 TRIPLES      21 PREDICATES      55 URIs      45 LITERALS      6 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s11518-018-5395-3 schema:about anzsrc-for:01
2 anzsrc-for:08
3 anzsrc-for:09
4 schema:author Na99318e0444046fa8e7e2077a670e869
5 schema:citation sg:pub.10.1007/0-387-23081-5_9
6 schema:datePublished 2019-06-06
7 schema:datePublishedReg 2019-06-06
8 schema:description This paper is about predicting the outcome of tennis matches of the Association of Tennis Professionals (ATP) and the Women’s Tennis Association (WTA) using both data and judgments. There are many factors that influence that outcome. An important question is which factors have significant influence on the outcome. We have identified numerous factors and systematically prioritized them subjectively and objectively, so as to improve the accuracy of the prediction. We then used them to predict the win-lose outcome of the 2015 US OPEN tennis matches (63 men and 31 women’s games) before they took place. The tennis match prediction in sports literature thus far reported an accuracy rate of 70%.The accuracy of our proposed model which combines data and judgment reaches 85.1%
9 schema:genre article
10 schema:isAccessibleForFree false
11 schema:isPartOf N02837cf2f9654408ad9277cab4ce35f8
12 N30bad62353124bb1b4ca92b12ea9da88
13 sg:journal.1135899
14 schema:keywords Predicting
15 Tennis Professionals
16 Women’s Tennis Association
17 accuracy
18 accuracy rate
19 association
20 data
21 factors
22 important questions
23 influence
24 judgments
25 literature
26 match
27 model
28 numerous factors
29 outcomes
30 paper
31 place
32 prediction
33 professionals
34 questions
35 rate
36 significant influence
37 sport literature
38 tennis
39 tennis matches
40 tennis tournaments
41 tournament
42 win–lose outcomes
43 schema:name Predicting the Outcome of a Tennis Tournament: Based on Both Data and Judgments
44 schema:pagination 317-343
45 schema:productId N3de097eff5b24283b4ed94cfa9240c99
46 Ne4236393e0ac4f318a353002df8f4ebe
47 schema:sameAs https://app.dimensions.ai/details/publication/pub.1111935007
48 https://doi.org/10.1007/s11518-018-5395-3
49 schema:sdDatePublished 2022-11-24T21:05
50 schema:sdLicense https://scigraph.springernature.com/explorer/license/
51 schema:sdPublisher N39d29e0f6deb499498a9afe11ae29a0f
52 schema:url https://doi.org/10.1007/s11518-018-5395-3
53 sgo:license sg:explorer/license/
54 sgo:sdDataset articles
55 rdf:type schema:ScholarlyArticle
56 N02837cf2f9654408ad9277cab4ce35f8 schema:issueNumber 3
57 rdf:type schema:PublicationIssue
58 N30bad62353124bb1b4ca92b12ea9da88 schema:volumeNumber 28
59 rdf:type schema:PublicationVolume
60 N39d29e0f6deb499498a9afe11ae29a0f schema:name Springer Nature - SN SciGraph project
61 rdf:type schema:Organization
62 N3de097eff5b24283b4ed94cfa9240c99 schema:name doi
63 schema:value 10.1007/s11518-018-5395-3
64 rdf:type schema:PropertyValue
65 Na99318e0444046fa8e7e2077a670e869 rdf:first sg:person.014614575371.42
66 rdf:rest Naed227a0a500424c9d1c6cd9f5a0d2ba
67 Naed227a0a500424c9d1c6cd9f5a0d2ba rdf:first sg:person.010070352303.49
68 rdf:rest rdf:nil
69 Ne4236393e0ac4f318a353002df8f4ebe schema:name dimensions_id
70 schema:value pub.1111935007
71 rdf:type schema:PropertyValue
72 anzsrc-for:01 schema:inDefinedTermSet anzsrc-for:
73 schema:name Mathematical Sciences
74 rdf:type schema:DefinedTerm
75 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
76 schema:name Information and Computing Sciences
77 rdf:type schema:DefinedTerm
78 anzsrc-for:09 schema:inDefinedTermSet anzsrc-for:
79 schema:name Engineering
80 rdf:type schema:DefinedTerm
81 sg:grant.8322572 http://pending.schema.org/fundedItem sg:pub.10.1007/s11518-018-5395-3
82 rdf:type schema:MonetaryGrant
83 sg:grant.8325567 http://pending.schema.org/fundedItem sg:pub.10.1007/s11518-018-5395-3
84 rdf:type schema:MonetaryGrant
85 sg:grant.8326202 http://pending.schema.org/fundedItem sg:pub.10.1007/s11518-018-5395-3
86 rdf:type schema:MonetaryGrant
87 sg:journal.1135899 schema:issn 1004-3756
88 1861-9576
89 schema:name Journal of Systems Science and Systems Engineering
90 schema:publisher Springer Nature
91 rdf:type schema:Periodical
92 sg:person.010070352303.49 schema:affiliation grid-institutes:grid.21925.3d
93 schema:familyName Saaty
94 schema:givenName Thomas L.
95 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010070352303.49
96 rdf:type schema:Person
97 sg:person.014614575371.42 schema:affiliation grid-institutes:grid.69775.3a
98 schema:familyName Gu
99 schema:givenName Wei
100 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014614575371.42
101 rdf:type schema:Person
102 sg:pub.10.1007/0-387-23081-5_9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1085141247
103 https://doi.org/10.1007/0-387-23081-5_9
104 rdf:type schema:CreativeWork
105 grid-institutes:grid.21925.3d schema:alternateName Distinguished University Professor, University of Pittsburgh, 15260, Pittsburgh, PA, USA
106 schema:name Distinguished University Professor, University of Pittsburgh, 15260, Pittsburgh, PA, USA
107 rdf:type schema:Organization
108 grid-institutes:grid.69775.3a schema:alternateName Donlinks School of Economics and Management, University of Science and Technology Beijing, 100083, Beijing, China
109 schema:name Donlinks School of Economics and Management, University of Science and Technology Beijing, 100083, Beijing, China
110 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...