Strike rate

Abstract: A single measure that can be used to assess the performance of bowlers in cricket is defined. This study shows how it can be used to rank bowlers. The performance of bowlers is generally measured by using three different criteria, i.e. the average number of runs conceded per wicket taken (A), the economy rate (E), which is the average number of runs conceded per over bowled, and the strike rate (S), which is the average number of balls bowled per wicket taken. Each of these is important in its own right. The average (A) is normally used to rate bowlers. This classification is however not very accurate as it does for example not take into account how many overs have been bowled. Two bowlers might have the same average but one may be more economical than the other. The purpose of this paper is to introduce a single measure that takes the full performance of a bowler into account. The combined bowling rate CBR = 3/[1/A + 1/E + 1/S] is defined for this purpose and is used to rate bowlers. A classification scheme . with ten classes according to which bowlers can be classified is given. The best bowlers are those who fall in class one. (S. African J. for Research in Sport, Physical Ed. and Recreation: 2002 24 (2): 37-44)

Abstract: Applications of machine learning supplemented with data mining techniques has become a hot topic for research worldwide, sports analytics is no exception though. Cricket is one of the most popular sports in Australia, Caribbean, UK and South Asian nations with a net fan base of around 2.5 billion. The game has tremendous spectator support in more than 100 nations and the masses show great interest in predicting the game outcomes. There are lots of pre-game and in-game attributes which decides the outcome of a cricket match. Pre-game attributes like the venue, past track-records, innings(first/second), team strength etc. and the various in-game attributes like toss, run rate, wickets remaining, strike rate etc. influence the result of a match in a predominant manner. In this study, 2 different ML approaches namely Decision Trees and Multilayer Perceptron Network have been used to analyse the effect produced on the outcome of a cricket match due to these varied factors. Based on these results CricAI: Cricket Match Outcome Prediction System has been developed. The designed tool takes into consideration the pregame attributes like the ground, venue (home, away, neutral) and innings (first/second) for predicting the final result of given match.

Abstract: Batting performance measures containing strike rate adjustments take into account the important fact that if two batsmen had scored the same number of runs in a match, the one with the better strike rate had performed best. But match conditions can influence the batting and bowling performances of cricket players. On a good pitch a batsman can get a good score at a high strike rate, but if the pitch was bad, a similar good score is normally accompanied by a much lower strike rate. The main objective of this study is to propose a method that can be used to make batsmen's scores comparable despite the fact that playing conditions might have been very different. The number of runs scored by a batsman is adjusted by comparing his strike rate with the overall strike rate of all the players in the specific match. These adjusted runs are then used in the most appropriate formula to calculate the average of the batsman. The method is illustrated by using the results of the Indian Premier League 2009 Twenty20 Series played during May and June 2009. The main conclusion is that the traditional average is not the most appropriate measure to compare batsmen's performances after conclusion of a short series. Key pointsIt is unfair to compare the score of a batsman obtained on a good pitch under ideal batting conditions with that of a batsman who had to battle under severe conditions.By comparing a batsman's strike rate with the overall strike rate of the players in the specific match, his score can be adjusted to get a better figure for his true performance.The results demonstrate clearly that the use of adjusted scores lead to rankings that differ from those based on the traditional measures.

Abstract: Cricket is one of the most popular sports among every class of people. The contribution of individual team members to the overall team performance is more easily quantifiable in cricket and the performance evaluation of a player is a very critical issue. Indian Premier League T20 cricket tournament dataset has been considered to measure the performance evaluation of bowlers (Fast Bowler and Spinner). The study measures the performance of Fast-bowlers and Spinners of IPL (I, II and III) based on their economy rate, bowling average, bowling strike rate and other different criterion and evaluate their rankings according to their performances with the help of AHP and TOPSIS. Finally, evaluate performance of all players who played in all three IPL (I, II and III) by using AHP-TOPSIS and AHP-COPRAS and provide their rankings.