Comparative Analysis of Pitch Ratings in All Formats of Cricket

Abstract

The article identifies measurable attributes to find the consistency in pitch ratings which are otherwise subjective decisions made by referees for international test match cricket. To do so, the article uses statistics related to test matches, one-days and T20s played among all test playing nations between March 2017 and March 2019 (53 tests, 142 T20s and 172 one-day matches: the next two seasons ending May 2020/2021 were hit by COVID-19 and hence excluded). To measure the consistency of pitch ratings (very good, good, above average, average, below average and poor), measurable attributes like runs/day, wickets/day, runs/over, runs/wicket and overs/wicket were identified. To rank pitch ratings using these attributes, the multi-criteria decision-making technique—PROMETHEE II was used. We found that the referee pitch ratings are largely consistent and the attributes developed can be utilized to further analyse future judgements regarding pitch ratings. Further, six-pitch ratings can be clustered into two distinct groups that are significantly different from each other. The article is among the first to analyse sports pitch ratings by using team performance-based statistics. This study paves the way for similar studies and development of newer statistical flow-based attributes.

RunsGuard Framework: Context Aware Cricket Game Strategy for Field Placement and Score Containment

“…It surpasses the current methodology and ICC rankings in the Test, One Day International (ODI), and Twenty20 (T20) formats and generates the Commitment Index Rank (CIR) for teams. Kumar et al [22] proposed a special technique for evaluating the pitch consistency in cricket matches played in international tests. It includes games played between March 2017 and March 2019, omitting the seasons impacted by COVID-19.…”

“…In addition, previous studies [5,9,[17][18][19][20][21][22] collectively addressed various aspects of cricket analytics, encompassing the learning strength and weakness rules of players [17], extracting regions of batsmen through text-commentary analysis [18], machine learningbased team selection [19], and understanding the impact of contextual factors on team performance in T20 cricket through an interpretable machine learning approach [20]. Furthermore, studies have explored the utilization of big data for cricket match outcome prediction [9], sports analytics for cricket games [21], offering a unique approach to ranking cricket teams [23], and conducting a comparative analysis of pitch ratings for all formats of cricket [22].…”

“…In addition, previous studies [5,9,[17][18][19][20][21][22] collectively addressed various aspects of cricket analytics, encompassing the learning strength and weakness rules of players [17], extracting regions of batsmen through text-commentary analysis [18], machine learningbased team selection [19], and understanding the impact of contextual factors on team performance in T20 cricket through an interpretable machine learning approach [20]. Furthermore, studies have explored the utilization of big data for cricket match outcome prediction [9], sports analytics for cricket games [21], offering a unique approach to ranking cricket teams [23], and conducting a comparative analysis of pitch ratings for all formats of cricket [22]. Finally, studies [24][25][26][27], collectively explored strategic decision-making and performance evaluation in cricket, covering optimal playing strategies against specific bowling types using game theory [24], player-aware resource compensation in interrupted matches [25], investigating the influence of the T20 cricket on Test cricket performance and team quality [26], and estimating a batsman's shot selection in T20 cricket to guide strategic decisions related to fielder placement [27].…”