There's a very interesting statistical analysis of bowlers on Cricinfo.
It rationalises bowling analyses in a manner that reflects the quality of the batsmen who were dismissed. The best bowlers by this criteria are those who dismiss batsmen for the greatest margins under their career average. The method's is explained as this:
In the wickets column of scorecards there is the bland pronouncement that a bowler has captured x number of wickets. There is no information on whose wickets he captured. This analysis seeks to secure such information.
The computation is simple. Every wicket captured by a bowler in the 1865 Test matches played so far is analysed, and the sum of career batting averages of the batsmen dismissed is calculated. It is then divided by the number of wickets captured by each bowler and a Batting Quality Index (BQI) arrived at. It's a simple but exhaustive calculation, which is impossible manually.
However let us seek to address this situation by looking at two other measures. The first is the difference between BQI and the career bowling average for the bowler. While it is true that having a high BQI means that the bowler has picked up better quality wickets, it might be more than offset by a high bowling average, which means the bowler has conceded a lot of runs for each wicket captured. The difference between these two figures will give a clear indication of the bowler's quality. The higher the difference, the better the bowler.
And two of the top three - Marshall and Ambrose - made my Greatest Test XI, which is nice. What is very intriguing is how spinners fare. Murali and especially Warne are lower down the list than you would expect, reflecting the fact many of their wickets have been against low-quality players (Bangladesh and Zimbabwe in Murali's case, tailenders in Warne's). But while there are a plethora of spinners in the lower echelons of the list (most not too surprisingly), there are two spinners in the top seven: O'Reilly and Laker. As ever, the capacity of cricket to find fascinating statistics is astounding.
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