When presenting an alternative rankings for the players, the first question that we are asked is: “Why another ranking of players?” For the ODIs, the ICC has its own ranking system, so it is a valid question to ask whether we need another ranking system. Before we go in-depth into our method, let us first state the reason for the new ranking system:

- The ranking we present here is quite simple and easy to understand. It can be computed easily by anyone having the required data in a spreadsheet (or, by executing a single query on a database). On the other hand, ICC only gives general guidelines about how it calculates player rankings.
- The two statistics we present here (Runs Above Average and Wins) actually have a real meaning, and is therefore more intuituve.

First, we present the batsmen rankings. Recall the post on the average ODI batsman. We use that as a baseline to evaluate the performance of the batsmen. We consider only three attributes of a batsman: Total runs scored, total balls faced, and the total outs. The idea is to capture in one single number, the total runs scored by the batsman, his strike rate and the rate at which he gets out.

This single number is called **Runs Above Average (RAA)**. It has two parts, one which captures the strike rate of the batsman and the second which captures the rate at which a batsman gets out. The first part is computed for batsmen as follows. Recall that in 2011, the average batsman scored 0.79 runs per ball. If a batsman scores 900 runs in 1000 balls, he is scoring 900 – 0.79 * 1000 runs above average, which is 110.

The second part is computed by looking at the rate at which the batsman gets out. In 2011, the average batsman made 0.028 outs per ball. If the particular batsman we discussed above, in addition, made 20 outs in 1000 balls, then he mad 0.028 * 1000 – 20 outs less than the average player over 1000 balls, which is 8. The overall average of ODI batsmen in 2011 was 28.31. So overall, he contributed 8 * 28.31 = 226.5 runs more than the average player, in terms of getting out. Taking the sum of both the parts, this particular batsman made 110 + 226.5 = 336.5 runs above average.

Thus, the formula for computing the runs above average is given by

RAA = (Batsman runs – Average batting strike rate * Balls faced by batsman ) + Overall batting average * Balls faced by batsman * (Average out rate – Batsman out rate)

We can go one step further, and convert the RAA into another statistic, called the **Wins**. The idea behind this statistic is that there are 10 wickets that the opposition needs to take to win a match (well, sort of), so the value of 10 “average” batsmen is equal to one win. Thus, wins can be calculated as

Wins = RAA / (10 * Overall batting average)

The RAA and wins of the top batsmen in 2011 are given in the table below. We will very soon post a complete year-by-year list of all batsmen on the website.

Batsman | Runs | Balls | Innings | Outs | RAA | Wins |
---|---|---|---|---|---|---|

IJL Trott | 1318 | 1644 | 28 | 26 | 582 | 2.056 |

SR Watson | 1140 | 1046 | 22 | 20 | 574 | 2.027 |

V Kohli | 1382 | 1615 | 34 | 29 | 561 | 1.982 |

KC Sangakkara | 1132 | 1537 | 25 | 22 | 509 | 1.799 |

Misbah-ul-Haq | 965 | 1417 | 26 | 17 | 484 | 1.709 |

MJ Clarke | 901 | 1130 | 22 | 17 | 420 | 1.483 |

DPMD Jayawardene | 1033 | 1249 | 24 | 23 | 382 | 1.349 |

MS Dhoni | 764 | 851 | 22 | 14 | 368 | 1.299 |

WU Tharanga | 827 | 1092 | 21 | 17 | 346 | 1.221 |

BRM Taylor | 743 | 868 | 17 | 15 | 318 | 1.125 |

Similarly, the RAA and Wins for bowlers can be computed using the following formula:

RAA = (Average runs per ball * Total balls by bowler – Total runs given by bowler ) + Overall bowling average * Total balls by bowler * (Bowler wickets per ball – Overall average wickets per ball)

Wins = RAA / (10 * Overall bowling average)

And here is the list of top ODI bowlers in 2011, together with their RAA and Wins:

Bowlers | Runs | Balls | Innings | Wickets | RAA | Wins |
---|---|---|---|---|---|---|

SL Malinga | 932 | 1154 | 23 | 48 | 603 | 1.885 |

Shahid Afridi | 938 | 1345 | 26 | 46 | 533 | 1.666 |

Saeed Ajmal | 580 | 1000 | 20 | 33 | 475 | 1.486 |

MG Johnson | 820 | 1105 | 22 | 39 | 427 | 1.335 |

M Morkel | 461 | 619 | 14 | 26 | 370 | 1.158 |

Z Khan | 620 | 768 | 14 | 30 | 339 | 1.061 |

B Lee | 720 | 938 | 19 | 33 | 335 | 1.048 |

DW Steyn | 484 | 663 | 14 | 25 | 315 | 0.986 |

Imran Tahir | 150 | 237 | 5 | 14 | 298 | 0.931 |

R Rampaul | 603 | 723 | 14 | 28 | 292 | 0.914 |

Hi Shashi,

There seems to be a glitch in your RAA calculation. For example, say both A and B have batting avg 50, and accumulated 1000 runs over the season. Let’s say A had a SR of 200, and B had SR 100. RAA for both A and B will be same. Although, A intuitively had a bigger impact than B.

Basically, RAA is not at all capturing impact of SR. It can be simply written as

RAA = (no. of outs)*(difference in batting avg. wrt overall batting avg in the season)

Check this out

https://docs.google.com/spreadsheet/ccc?key=0AvwWgn7MZoPQdFh2ZExWNm5kcHR3ZENPdFRGM19xU3c

Read “Extra” as your RAA/(innings batted)

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