One-day International Cricket Research Articles

Forecasting bowler performance in One-Day International cricket using Machine learning

In cricket’s dynamic environment, bowlers’ performances are a key factor in determining success. In this dynamic sport, our study explores the field of bowler performance predictive analytics to enable decision-makers and strategists. Using advanced machine learning techniques, we provide a comprehensive analysis targeted at predicting and analyzing the complex factors impacting bowlers’ performance on the field. This research methodology is based on the sophisticated use of machine learning algorithms to develop a reliable prediction model. Our study reveals an abundance of information about the complex interactions between these diverse factors and how they affect bowler performance. Specifically, we highlight the important impact that opposing dynamics and contextual factors like venue-specific performance trends play, emphasizing the necessity of flexible tactics that depend on contextual circumstances. Prediction methods have significant ramifications not only in cricket but also in other fields. They provide actionable insights for player selection, strategic planning, and ongoing performance evaluation, making them indispensable tools for cricketing companies. Moreover, our study broadens the scope of predictive analytics and holds potential for use in a variety of sports and sectors that depend on complex strategic decision-making. This research demonstrates the critical role that predictive analytics plays in cricket. It offers a rigorous model for predicting and understanding the complex dynamics of bowler performance, greatly enhancing strategic decision-making within the game and expanding its potential into other areas.

Individual and team performance in cricket.

Advancements in technology have recently allowed us to collect and analyse large-scale fine-grained data about human performance, drastically changing the way we approach sports. Here, we provide the first comprehensive analysis of individual and team performance in One-Day International cricket, one of the most popular sports in the world. We investigate temporal patterns of individual success by quantifying the location of the best performance of a player and find that they can happen at any time in their career, surrounded by a burst of comparable top performances. Our analysis shows that long-term performance can be predicted from early observations and that temporary exclusions of players from teams are often due to declining performances but are also associated with strong comebacks. By computing the duration of streaks of winning performances compared to random expectations, we demonstrate that teams win and lose matches consecutively. We define the contributions of specialists such as openers, all-rounders and wicket-keepers and show that a balanced performance from multiple individuals is required to ensure team success. Finally, we measure how transitioning to captaincy in the team improves the performance of batsmen, but not that of bowlers. Our work emphasizes how individual endeavours and team dynamics interconnect and influence collective outcomes in sports.

Cricket performance predictions: a comparative analysis of machine learning models for predicting cricket player’s performance in the One Day International (ODI) world cup 2023

Background and purpose
 Cricket, a globally renowned bat and ball sport, is the second most popular sport worldwide. The objective of the study is to utilize machine learning algorithms to predict the performance probabilities of Indian cricket players participating in the ODI Cricket World Cup 2023. Furthermore, we aim to assess and compare the predictive precision of three machine learning models such as, Random Forest, Support Vector Regression, and XGBoost.
 Materials and Methods
 Data collection centered on Indian One Day International cricket statistics, encompassing matches played, batting and bowling averages, catches taken, and performance predictions. We sourced this data from reputable platforms such as ESPNcricinfo and the International Cricket Council website. Our performance prediction utilized of three machine learning models such as, Random Forest, Support Vector Regression, and XGBoost. Comparative analysis was conducted, evaluating these models through essential metrics including Mean Squared Error, Root Mean Squared Error, Mean absolute Error, and R-squared.
 Results
 The comparative analysis revealed that the XGBoost model consistently outperformed the others. It exhibited lower errors with the lowest Mean Squared Error, Root Mean Squared Error, and Mean Absolute Error, signifying greater predictive accuracy. XGBoost achieved the highest R-squared value, indicating a robust relationship between predictions and actual performance probabilities. Random Forest produced satisfactory results but fell short of XGBoost's accuracy, while Support Vector Regression displayed less accurate predictions across all metrics.
 Conclusions
 This research demonstrates the superior predictive ability of the XGBoost model in the performance probabilities of Indian cricket players in the ODI Cricket World Cup. The practical implications underscore the significance of data-driven insights for team selection and strategy.

Picking the length: investigating how bowling length influences batter decision-making in international men’s 50-over cricket

ABSTRACT In this study, 19,587 balls bowled by fast bowlers across two One-Day International (ODI) cricket tournaments were analysed, with the aim of exploring the relationship between bowling lengths and foot-based batter decision-making. Initially, a Chi-Square test of independence was used to determine if bowling lengths were associated with the foot-based decision-making of batters. Subsequently, the relationship of specific foot-based strokes with different bowling lengths were further examined through a Logistic Regression. Post-Hoc analysis of standardised residuals from Chi-Square tests suggest an association between foot-based decision-making and dot balls (χ2(1) = 99.798, p < .001). Furthermore, Logistic Regression results reveal that all length zones except the half-tracker length show statistically significant association with front-foot and back-foot strokes. Respective coefficients of these length zones also show an organisation of specific strokes with front-foot stroke dominating lengths close to the batter (yorker, full-toss, half-volley and good length) and back-foot strokes dominating zones further away from the batter (back of a length and short length). The results confirm interdependence between batter-bowler performances and suggest that batters in ODI cricket prefer meeting the ball on the front foot in their search of scoring runs.

Modelling player ratings in One Day International cricket using the Modified Duckworth-Lewis method

Abstract Accepted by: Phil Scarf We present a player rating system for One-Day International (ODI) cricket using the Modified Duckworth-Lewis model. We used ball-by-ball data of 1764 uninterrupted One-Day International (ODI) matches played from January 2004 to June 2021 to estimate model parameters. The proposed method is then used to rate players who appeared in the ICC Men’s World Cup 2019. The method is novel because the rating depends on the state of the match, so that pressure and non-pressure situations are accounted for. The method also considers a pitch effect (high or low run scoring). Moreover, the batsman performance can be compared to all-rounder and bowling performances. The results rate Shakib Al Hassan of Bangladesh as the tournament’s best player, followed by R.G. Sharma (India) and J.J. Roy (England).

Modeling joint survival probabilities of runs scored and balls faced in limited overs cricket using copulas

In limited overs cricket, the goal of a batsman is to score a maximum number of runs within a limited number of balls. Therefore, the number of runs scored and the number of balls faced are the two key statistics used to evaluate the performance of a batsman. In cricket, as the batsmen play as pairs, having longer partnerships is also key to building strong innings. Moreover, having a steady opening partnership is extremely important as a team aims to build such a stronger innings. In this study, we have shown a way to evaluate the performance of opening partnerships in Twenty20 (T20) cricket and the performance of individual batsmen in One Day International Cricket (ODI) by modeling the joint distribution of runs scored and balls faced using copula functions. The joint survival probabilities derived from this approach are then used to evaluate the batting performance of opening partnerships and individual batsmen for different stages of the innings. Results of the study have shown that cricket managers and team officials can use the proposed method in selecting appropriate partnership pairs and individual batsmen in an efficient manner for specific situations in the match.

A Study of Batting out-of-form in One-Day International Cricket

When a batter scores low in a series of innings, lack of self-confidence generated as a result coupled with poor batting strategies may lead to scoring even lower in the immediately preceding innings. In a team game like Cricket, the out-of-form status of a batter may affect the outcome of the game. This motivated us to explore a statistical method to detect batting inconsistency of a batter in Cricket. One-sided Exponentially weighted moving average (EWMA) control chart was employed due to its evident capability in rapid detection of upward or downward changes in the process mean. The analysis of this study considered ODI batters, who were engaged in the sport during the past decade belonging to seven different cricket nations. An observation of out-of-control state in a control chart was recognized as a batter experiencing an out-of-form performance phase and basic sensitizing rules were utilized to detect non-random and unnatural variation which were explained as warning signals of batting inconsistency. As a demonstration of the proposed method, EWMA control charts of a few popular batters were presented and analyzed which revealed some interesting findings related to their ODI career performance. When an occurrence of a clear downward sequence was detected in the control chart, it was decided that the batter has been out-of-form during that phase. The proposed method can be utilized to dynamically evaluate career batting performance through detection of out-of-form status of batsmen using their recent past scores and this would facilitate effective player selection for the next game.

An Investigation of Survival Abilities of Opening Batsmen in One-Day International Cricket

This paper quantifies the survival abilities of openers in one-day international cricket matches to fulfill the ultimate goal of analyzing the batting performance of Sri Lankan openers in Cricket World Cup 2019. An inconsistent opening batting performance in one-day cricket could have a negative impact on the match result and it could be one of the reasons for the current downfall of Sri Lankan cricket team. Survival abilities were investigated based on two aspects: number of balls faced till dismissal and strike rate at dismissal. Performances of Sri Lankan openers were evaluated and compared with that of the openers of other teams in Cricket World Cup 2019 through smooth estimated survival functions and hazard functions. Survival abilities of past Sri Lankan openers were also evaluated. Impact of opening batting performance on the match result was investigated through a novel measure: Total Hazard. Results indicate that survival abilities of Sri Lankan openers in Cricket World Cup 2019 are low compared to the openers of other teams and past Sri Lankan openers are more reliable and consistent. It is also found that there is an impact of opening batting performance on the match result. Hence, opening batting performance should be strengthened to rectify the current downfall of Sri Lankan cricket.

Winning one-day international cricket matches: a cross-team perspective

ABSTRACT The study analyses the predictors of a win for four international cricket teams in the one-day international cricket format. A binary logistic regression is used to determine the relationship between the independent variables, i.e., fours and sixes scored, bowling economy, extras conceded, fielding dismissals, the number of debutants from each side, umpire’s nationality, pitch condition, and season of play vis-à-vis odds of a win. The study found that the number of fielding dismissals and bowler economy significantly influence the odds of winning for all four teams. Further, the nationality of the umpire did not affect any team, while other variables influenced the fortunes of different teams differently. Proposed models in the paper can be used by team management and coaches in devising match strategy and player selection for higher win outcomes based on a combination of historical trend data for specific variables and actual data for the others.

Predicting the winning percentage of limited-overs cricket using the Pythagorean formula

The Pythagorean Win-Loss formula can be effectively used to estimate winning percentages for sporting events. This formula was initially developed by baseball statistician Bill James and later was extended by other researchers to sports such as football, basketball, and ice hockey. Although one can calculate actual winning percentages based on the outcomes of played games, that approach does not take into account the margin of victory. The key benefit of the Pythagorean formula is its utilization of actual average runs scored and actual average runs allowed. This article presents the application of the Pythagorean Win-Loss formula to two different types of limited-overs cricket formats, namely One Day International cricket (ODI) and Twenty20 cricket. The data for the application was used from the matches played by the top 10 International Cricket Council (ICC) members who participated in the 2019 ICC Cricket World Cup. For matches for which the second batting team won, runs scored were estimated by considering the remaining amount of resources, based on the Duckworth–Lewis method.

Development of a Comprehensive Multi-Factor Method for Comparing Batting Performances in One-Day International Cricket

In cricket, one-day-international (ODI) batsmen have traditionally been compared on the dimensions of batting average (BA) and strike rate (SR). The conventional method of computing BA assumes that runs scored by a batsman follow an exponential or geometric distribution. This results in unreasonably equating batting inconsistency with batting mean. Our study shows that a Weibull distribution model gives a very sensible assessment of a batsman’s inconsistency, independent of his BA. It also provides a superior fit to batting scores of ODI batsmen. We also introduce a measure for ‘quality-runs’ scored by a batsman which takes into account the difficulty level of opposition. Additionally, longevity index and opposition diversity index are defined to make comparisons more holistic. A substantial amount of data engineering effort is made in segregating available data into home, away and neutral matches. The measures proposed in this paper are more comprehensive and granular than those found in the literature. Various combinations of these six criteria are used to rank a select group of great ODI batsmen by assigning objective weights derived from principal component analysis. Finally, multivariate statistical outlier detection procedure produces different lists of outstanding players corresponding to different combinations of criteria. Our proposed methodology may be gainfully used by a team management to select best batsmen in a given situation.

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

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.

Bradley-Terry Model for Assessing the Performance of Ten Odi Cricket Teams Adjusting for Home Ground Effect

The analysis of sports data, especially cricket is an interesting field for the statisticians. Every year, a large number of cricket tournaments take place among the cricket playing nations. It is of interest to study their performance when they play with each other in a one-day international (ODI) match or a test match. In this study, we assess the performance of top ten cricket teams in the ODI cricket match and make a comparison among them. The abilities of teams change over time. As a result, not a single team dominates the game over a long period. Therefore, a paired comparison method is more reliable and appropriate to compare more than two teams at the same time based on the outcomes of the matches they play. Arguably, a team’s performance also depends on whether they play at home or away. In this study, we consider Bradley-Terry model, a widely accepted model for pairwise comparison. In that, we consider home and away effect to demonstrate how the home advantages differ among these teams.

Ranking of Pakistan’s One-Day International Cricket Players Based on Link Fusion

Cricket is one of the most popular game in the world. This game has been played between two teams; every team has 11 players. Every cricket team in the world belongs to a country having a cricket board that selects the best players and manage the team activities. Cricket matches are of three forms One-Day International (ODI) cricket matches, First class or Test Matches and T-20 matches. The main issue in this game is to selects the best suitable players for the team and ranks them according to their performance. We mainly focus on Pakistan's One-day International ODI cricket players ranking. We proposed a method that consist of statistical formulas apply to the significant features of current players in Pakistan's One-Day International ODI cricket and rank them according to the rank score calculated by our method. For the implementation, we make application named TeamMaker that automatically scarp the information, and after applying our algorithm display, the results in the form of ranking tables. Similarly, ranking tables also available on the International Cricket Council ICC website, and too many researchers rank the cricket players of all teams in the world with different algorithms. Still, these methods are universal and not for a specific country. Our methodology based on link fusion that focuses on single country Pakistan with 27 One-Day International (ODI) cricket players that are currently available for the team. This application selects the exclusive features that are affected by their performance. TeamMaker application displays a ranking table based on these features. In the end, we compare and analyze the players ranking results of our methodology with the players ranking of International Cricket Council ICC. Our findings show that previous universal ranking methods neglect many countries and other prominent features of players, that's why many cricket player does not present in ranking tables.

Forecasting the Outcome of the Next ODI Cricket Matches to Be Played

Cricket is one of the most popular sports in the contemporary world. The ebullience of securing victory in a particular match has motivated the rudimentary part of this research aspect. Winning in Cricket depends on various aspects like weather, track records of the performances of players, performances at a specific venue, match experiences, performance against a specific team and the current form of the team and the player. In this paper the main goal is to estimate a win prediction for ODI (One Day International) cricket match. For purposes of model building, various method has been applied retrospectively to the data that are already obtained from previously played matches. From them, Random Forest has given the best outcome of 92.6%.

A Markov model for ranking cricket teams playing one day international matches

Main purpose of ranking teams in One Day International (ODI) Cricket matches is to identify challenging teams of each team. In the sports science literature, various statistical procedures have been proposed by different researchers to rank teams. International Cricket Council (ICC) is using a ranking system as the standard practice. However, it does not work well to realize some head-to head match results. This paper provides a stochastic Markov chain model to rank teams. A comprehensive analysis on head-to-head match prediction for each cricket team is given based on steady-state probabilities. Performance of the proposed model is investigated with ICC ODI rankings. It is shown that the proposed model outperforms to predict results of head-to-head ODI Cricket matches.

Can We Ex Post Facto Justify Duckworth-Lewis Rule?

In a one-day international cricket match, due to disturbances such as rain or storm, at least one of teams cannot bat for stipulated fifty overs. The Duckworth-Lewis rule is then applied so that the match possibly ends up with a decision. We explore whether the rule can be justified based on statistical analysis of outcomes of one-day matches in which all the stipulated overs were bowled as well as matches in which it was needed to apply the rule. Our analysis shows that the rule is quite fair.

An innovative super-efficiency data envelopment analysis, semi-variance, and Shannon-entropy-based methodology for player selection: evidence from cricket

The increasing interest in club cricket and online fantasy cricket league games raises the importance of player selection from the perspective of financial and sports performance. Most previous studies focus only on player efficiency and ignore consistency and the player’s importance in a team strategy. This scenario motivates us to design a holistic player selection method based on a player’s efficiency, consistency, and importance in a team strategy. For efficiency measurement, we apply a modified data envelopment analysis (DEA) method, namely, the non-increasing return-to-scale ‘super-efficiency DEA model,’ that provides improved results compared with the conventional Banker, Charnes, and Cooper DEA model in the presence of a higher number of efficient players. We design a modified consistency index based on the semi-variance approach. Unlike the existing methods that apply a player-specific reference frame to capture variability, we use a common reference frame that calculates the consistency in a more effective manner. We aggregate different consistency indices into a single consistency index using Shannon’s entropy concept and introduce a novel ‘value index’ to determine player importance, which can also be used as an indirect measure of the player’s fitness level. Finally, we design a player performance index by aggregating the efficiency and consistency scores using the Shannon-entropy method and incorporating the value index. We perform a rigorous numerical analysis to determine the all-time best one-day international Cricket XI team for the time span of January 5, 1971 to March 29, 2015. Next, we explain the advantages as well as rationale behind the improvement in the proposed measures compared with the existing methods and highlight the key insights. Finally, we perform a comparative analysis of the proposed team, the team announced by the ICC in 2011, and the team announced by the BBC in 2015.

Key match activities of different fielding positions and categories in one-day international cricket

ABSTRACTFielding has gained considerable importance in the game of cricket; however, there is a paucity of research in this area compared to batting and bowling. The purpose of this study therefore was to quantify the movement demands of one-day international (ODI) fielding. Television footage of eight games (16 innings) from the 2011 ODI World Cup was analysed using the video analysis package SportsCode. The variables of interest included 16 fielding-related activities and two efficiency calculations. These variables were calculated for fielding positions (excluding wicketkeepers) and categories of fielding. There were on average 177 ± 52 fielding contacts and 12 ± 6 fielding mistakes per innings. The activity in the field was split between the fielding categories: 20% close fielding, 51% inner circle fielding and 29% outer circle fielding. The positions with the most fielding activity per innings were bowler (26), cover (24), backward point (14), mid-off (14) and midwicket (14). Understanding the movement demands of cricket fielding provides value to coaches when designing assessments, and individualising training programmes to match positional and categorical (close, inner and outer) fielding demands.

Devising a fairer method for adjusting target scores in interrupted one-day international cricket

One-day international cricket matches face the problem of weather interruption. In such circumstances, a so-called rain rule is used to decide the outcome. A variety of approaches for constructing such rules has been proposed, with the Duckworth-Lewis method being preferred in the sport. There are a number of issues to consider in reasoning about the effectiveness of a rain rule, notably accuracy (does the rule make the right decision?) and fairness (are both teams treated equally?). We develop an approach that is a hybrid of resource-based and so-called probability-preserving approaches and provide empirical evidence that this hybrid method is superior in terms of fairness while competitive in terms of accuracy.