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Analysis of performance indicators that most significantly affect International Twenty20 cricket

Irvine, Scott, Kennedy, Rodney·2017·International Journal of Performance Analysis in Sport
DOI 10.1080/24748668.2017.1343989
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Abstract

Twenty20 (T20) cricket has rapidly become the most lucrative and desirable format of the game, with performance analysis pivotal in shaping successful tactics and strategies. The purpose of this study was to determine the performance indicators that most significantly affect the outcome of a game of international T20 cricket, in different parts of the world. Cricket-specific analysis software was used to analyse 40 international matches from 2012 to 2016, across 7 different countries. Magnitude-based inferences determined that the total number of dot balls bowled (ES = 1.49), the total number of wickets taken (ES = 1.46) and the innings run rate (ES = 1.21) were the most significant indicators of success across the four environments. This indicates the need to select wicket-taking bowlers, for captains to gamble with attacking fields throughout the innings and for aggressive batters with a high strike rate and boundary % to be selected where possible. Furthermore, the main indicators of success remained constant across environments, with run-scoring and batting determinants greatest when in sub-continent conditions.

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Crossref · 10.1080/24748668.2017.1343989

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6 of 6 references verified0 citation errors · 0 not checkable

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  • Winning teams have been reported to score a higher number of total runs from boundaries.

    “As winning teams have been reported to score a higher number of total runs from boundaries (Bhardwaj & Dwyer, 2022;Douglas & Tam, 2010;Irvine & Kennedy, 2017;Moore et al., 2012;C. Petersen et al., 2008)”

    From Position-specific fielding instances and utilisation in professional men’s T20 cricket (2026) · cites this paper as Irvine & Kennedy, 2017

  • Previous performance analysis research in T20 cricket has examined performance indicators related to batting and bowling or applied machine learning techniques to predict match outcomes.

    “While previous performance analysis research in T20 cricket has examined performance indicators related to batting and bowling (Bhardwaj & Dwyer, 2022;Douglas & Tam, 2010;Irvine & Kennedy, 2017;Moore et al., 2012;Najdan et al., 2014;Petersen et al., 2008) or applied machine learning techniques to predict match outcomes (Basit et al., 2020;Dubey et al., 2020;Ishi & Patil, 2021;Kapadia et al., 2020;”

    From Position-specific fielding instances and utilisation in professional men’s T20 cricket (2026) · cites this paper as Irvine & Kennedy, 2017

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Related

Authors
Irvine, S., & Kennedy, R.
Affiliations
  • Cricket Ireland, Belfast, UK
  • University of Ulster (GB)
Corresponding author
Kennedy, Rodney
ORCID
Kennedy, Rodney
Venue
International Journal of Performance Analysis in Sport
Publisher
Informa UK Limited
Published
2017-05-04
Volume
17
Issue
3
Pages
350-359
ISSN
  • 1474-8185 (ISSN-L)
  • 2474-8668
DOI
10.1080/24748668.2017.1343989
OpenAlex
W2736291280
Cited by
32
References
6
Citations by year
2026: 4 · 2025: 9 · 2024: 4 · 2023: 4 · 2022: 3 · 2021: 5 · 2020: 2 · 2018: 1
FWCI
1.48
Type
journal article
Language
en
Open access
closed
Locations
  • International Journal of Performance Analysis in Sport (publishedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
Topics
  • Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Sports Analytics and Performance
  • Health Sciences › Medicine › Orthopedics and Sports Medicine › Sports Performance and Training
  • Social Sciences › Psychology › Developmental and Educational Psychology › Sport Psychology and Performance
Keywords
T20 cricket, performance indicators, match outcome, wickets, batting strategy, international cricket

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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

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