Quantifying the pressure on the teams batting or bowling in the second innings of limited overs cricket matches

Bhattacharjee, Dibyojyoti; Lemmer, Hermanus H.

doi:10.1177/1747954116667106

Cited by 9 publications

(9 citation statements)

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“…Pressure Index Bhattacharjee and Lemmer (2016) have forwarded the concept and formula of pressure index as a tool to analyse cricket data. According to them, the formula for pressure index is given by 2where T is the target runs to be scored by the team, and B is the total number of balls available to the team in any innings.…”

Section: Methodsmentioning

confidence: 99%

“…Bhattacharjee and Lemmer (2016) have forwarded the concept and formula of pressure index as a tool to analyse cricket data. According to them, the formula for pressure index is given by …”

Section: Methodsmentioning

confidence: 99%

“…In this article, an attempt is made to study the role of opening partners during a run chase in deciding the outcome of a Twenty20 match using Pressure Index (introduced by Bhattacharjee & Lemmer, 2016). It is a ball-by-ball measure, which quantifies the pressure experienced by Team B (team batting second).…”

Section: Introductionmentioning

confidence: 99%

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Investigating the Role of Opening Partners While Chasing on the Outcome of Twenty20 Cricket Matches

Talukdar

2020

Management and Labour Studies

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In cricket, irrespective of the format of the game, batting always happens in pairs. The two batsmen who bat together are called as batting partners. The pair of batsmen who come to bat at the beginning of any innings are called opening batsmen or opening partners. In Twenty20 cricket, the opening partners must start their innings with a definite strategy. In one hand, they have the advantage of only two fielders outside the 30-yard circle for the first six overs (technically called as the powerplay overs), and so both openers are expected to play high scoring shots and attempt to score runs quickly. On the other hand, the odds against them are the ball is new, so is the pitch and the bowlers are fresh and energetic. When any one of the opening batsmen loses his wicket, the partnership comes to an end. This study tries to figure out the influence of the opening partnership of the second innings on the outcome of Twenty20 matches. Pressure Index (developed by earlier researchers), effects of venue or ground and target score are used as explanatory variables in the logistic regression model to check if the performance of opening partnership influences the outcome of Twenty20 matches along with other variables. The data used for the exercise is from Twenty20 international cricket matches played within the period January 2012 to June 2018. The study finds that opening partnership while chasing is a significant factor in deciding the match outcome during the run chase for the said dataset. Also, the best opening batting partners have been identified.

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Section: Methodsmentioning

confidence: 99%

“…Bhattacharjee and Lemmer (2016) have forwarded the concept and formula of pressure index as a tool to analyse cricket data. According to them, the formula for pressure index is given by …”

Section: Methodsmentioning

confidence: 99%

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Investigating the Role of Opening Partners While Chasing on the Outcome of Twenty20 Cricket Matches

Talukdar

2020

Management and Labour Studies

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“…The pressure of large target tempts the bat-second team to take additional risks that may negatively affect their natural batting performance. Even highly competitive teams have been found vulnerable to such high target pressure as their batting performance often collapses after scoring unusually low total while chasing a high target, 17 which is often termed as 'choking' or 'strangling' and has been studied by Lemmer. 18 All these phenomena do not guarantee a level playing field for both teams since the playing order may ultimately influence the outcome of the game.…”

Section: Proposed Researchmentioning

confidence: 99%

A probabilistic approach to identifying run scoring advantage in the order of playing cricket

Samad

Sen

2021

International Journal of Sports Science & Coaching

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In the game of cricket, the decision to bat first after winning the toss is often taken to make the best use of superior pitch conditions and set a big target for the opponent. However, the opponent may fail to show their natural batting performance in the second innings due to several factors, including deteriorated pitch conditions and excessive pressure of chasing a high target score. The advantage of batting first has been highlighted in the literature and expert opinions. However, the effect of batting and bowling order on match outcome has not been investigated well enough to recommend an adjustment of potential bias. This study proposes a probability-based model to study venue-specific scoring and chasing characteristics of teams with different match outcomes. A total of 1117 one-day international cricket matches held in ten popular venues are analyzed to show substantially high scoring likelihood when the winning team bat in the first innings. In a high scoring match, results suggest that the same bat-first winning team is very unlikely to score or chase the same high score if they bat in the second innings. We use the Bayesian rule to identify the bias in the scoring likelihood due to the playing order (bat-first versus bat-second). The bias is adjusted by revising the second innings target in a way that equalizes winning and run scoring likelihoods of both teams. The data and source code have been shared publicly for future research in creating competitive match outcomes by eliminating the advantage of batting order in run scoring.

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“…Given expected runs accounts for the batting teams scoring rate and resources remaining, the pressure metric adopts an assumption like that outlined in Bhattacharjee & Lemmer (2016). Bhattacharjee & Lemmer (2016) suggested that second innings pressure is a function of the remaining runs and required run rate to achieve the target total. This has been incorporated in the pressure metric, as runs remaining and required are embedded in the expected runs model.…”

Section: Pressure Modelmentioning

confidence: 99%

A Novel Framework for Constructing Sport-Based Rating Systems

Patel¹

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<p>This doctoral thesis examines the multivariate nature of sporting performances, expressed as performance on context specific tasks, to develop a novel framework for constructing sport-based rating systems, also referred to as scoring models. The intent of this framework is to produce reliable, robust, intuitive, and transparent ratings, regarded as meaningful, for performance prevalent in the sport player and team evaluation environment. In this thesis, Bracewell’s (2003) definition of a rating as an elegant form of dimension reduction is extended. Specifically, ratings are an elegant and excessive form of dimension reduction whereby a single numerical value provides an objective interpretation of performance. The data, provided by numerous vendors, is a summary of actions and performances completed by an individual during the evaluation period. A literature review of rating systems to measure performance, revealed a set of common methodologies, which were applied to produce a set of rating systems that were used as pilot studies to garner a set of learnings and limitations surrounding the current literature. By reviewing rating methodologies and developing rating systems a set of limitations and communalities surrounding the current literature were identified and used to develop a novel framework for constructing sport-based rating systems which output measures of both team and player-level performance. The proposed framework adopts a multi-objective ensembling strategy and implements five key communalities present within many rating methodologies. These communalities are the application of 1) dimension reduction and feature selection techniques, 2) feature engineering tasks, 3) a multi-objective framework, 4) time-based variables and 5) an ensembling procedure to produce an overall rating. An ensemble approach is adopted because it assumed that sporting performances are a function of the significant traits affecting performance. Therefore, performance is defined as performance=f(〖trait〗_1,…,〖trait〗_n). Moreover, the framework is a form of model stacking where information from multiple models is combined to generate a more informative model. Rating systems built using this approach provide a meaningful quantitative interpretation performance during an evaluation period. These ratings measure the quality of performance during a specific time-interval, known as the evaluation period. The framework introduces a methodical approach for constructing rating systems within the sporting domain, which produce meaningful ratings. Meaningful ratings must 1) yield good performance when data is drawn from a wide range of probability distributions that are largely unaffected by outliers, small departures from model assumptions and small sample sizes (robust), 2) be accurate and produce highly informative predictions which are well-calibrated and sharp (reliable), 3) be interpretable and easy to communicate and (transparent), and 4) relate to real-world observable outcomes (intuitive). The framework is developed to construct meaningful rating systems within the sporting industry to evaluate team and player performances. The approach was tested and validated by constructing both team and individual player-based rating systems within the cricketing context. The results of these systems were found to be meaningful, in that, they produced reliable, robust, transparent, and intuitive ratings. This ratings framework is not restricted within the sport of cricket to evaluate players and teams’ performances and is applicable in any sporting code where a summary of multivariate data is necessary to understand performance. Common model evaluation metrics were found to be limited and lacked applicability when evaluating the effectiveness of meaningful ratings, therefore a novel evaluation metric was developed. The constructed metric applies a distance and magnitude-based metrics derived from the spherical scoring rule methodology. The distance and magnitude-based spherical (DMS) metric applies an analytical hierarchy process to assess the effectiveness of meaningful sport-based ratings and accounts for forecasting difficulty on a time basis. The DMS performance metric quantifies elements of the decision-making process by 1) evaluating the distance between ratings reported by the modeller and the actual outcome or the modellers ‘true’ beliefs, 2) providing an indication of “good” ratings, 3) accounting for the context and the forecasting difficulty to which the ratings are being applied, and 4) capturing the introduction of any subjective human bias within sport-based rating systems. The DMS metric is shown to outperform conventional model evaluation metrics such as the log-loss, in specific sporting scenarios of varying difficulty.</p>

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Quantifying the pressure on the teams batting or bowling in the second innings of limited overs cricket matches

Cited by 9 publications

References 10 publications

Investigating the Role of Opening Partners While Chasing on the Outcome of Twenty20 Cricket Matches

Investigating the Role of Opening Partners While Chasing on the Outcome of Twenty20 Cricket Matches

A probabilistic approach to identifying run scoring advantage in the order of playing cricket

A Novel Framework for Constructing Sport-Based Rating Systems

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