The competitive intelligence process in sport: data collection properties of high-level cricket coaches

Berg, Liandi van den; Coetzee, Ben; Blignaut, Seugnet; Mearns, Martie

doi:10.1080/24748668.2018.1443367

Cited by 3 publications

(1 citation statement)

References 37 publications

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“…This paper provides a report of a tutorial at a recent conference with a brief survey of topics. L. Van den Berg, B. Coetzee, S. Blignaut, and M. Mearns et al, 2018 study indicated that easy available sources are not effectively utilized, data collection processes are not performed in a structured manner and coaches need skill development regarding data collection and analysis. Furthermore, the lack of technology as well as the absence of a person who can collect data and a shortage of skills by the person who is responsible for data collection, are the main challenges coaches face.…”

Section: Related Workmentioning

confidence: 99%

Dynamic cricket match outcome prediction

Goel

Davis²,

Bhatia³

et al. 2021

Journal of Sports Analytics

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To propose a model where match outcome is predicted ball by ball at the start of the second inning. Our methodology not only incorporates the dynamically updating game context as the game progresses, but also includes the relative strength between the two teams playing the match. We used 692 matches from all seasons (2008–2018) to train our model, and we used all 59 matches from the current season (2019) to test its performance. Here we have engineered 11 players and 10 bowlers, and all their metrics are tracked as a function of each ball of each over throughout the match during the second inning, also keeping in the consideration of dynamically changing target score as one of the attributes. Initially, we tried Logistic Regression, Naive Bayes, K-Nearest Neighbour (KNN), Support Vector Machine, Decision Tree, Random Forest, Boosting, Bagging, and Gradient Boosting with an accuracy of 76.47%(+/–3.77%). With deep learning, we tried the various flavours of LSTM and GRU like vanilla, Bidirectional and stacked to train our models and the results found are very impressive with an accuracy of 76.13%(+/–2.59%). All of these flavors were tested using various approaches such as one-to-one sequencing, one-to-many sequencing, many-to-one sequencing, and many-to-many sequencing, which are discussed in this paper. An accurate prediction of how many runs a batsman is likely to score and how many wickets a bowler is likely to take in a match will help the team management select the best players for each match.

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Section: Related Workmentioning

confidence: 99%

Dynamic cricket match outcome prediction

Goel

Davis²,

Bhatia³

et al. 2021

Journal of Sports Analytics

View full text Add to dashboard Cite

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Developing a framework for professional practice in applied performance analysis

Martin

Donoghue

Bradley

et al. 2021

International Journal of Performance Analysis in Sport

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Applied performance analysts are increasingly seen as sports science professionals; however, there is no accepted framework for professional practice. The purpose of this study is to develop and validate a framework for professional practice in applied performance analysis (PA) which identifies the components of practice and the expertise underpinning it.A six-step framework analysis was conducted: (1) An initial conceptual framework was devised based on well-accepted components of applied practice; (2) A systematic review identified 90 papers relating to applied PA practice; (3) Papers were coded to the initial framework and additional themes recorded; (4) Themes were analysed and synthesised to construct a draft framework; (5) This draft was validated by surveying 24 experienced applied performance analysts and academic experts; (6) A revised framework is reported based on stakeholder engagement feedback.Nine components of practice were identified; establishing relationships and defining roles, needs analysis and service planning, system design, data collection and reliability checking, data management, analysis, reporting to key stakeholders, facilitation of feedback to athletes and service review and evaluation. Our evidence suggests that applied PA practice is underpinned by five areas of expertise: contextual awareness, building relationships, performance analysis and sporting expertise, technical expertise and professional behaviours.

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