By Jaideep Varma and Jatin Thakkar
That the conventional statistical system in cricket of averages and aggregates is skewed is widely agreed. It has been so for years, decades even. However, in the five-day version, because of the nature of the format, the players who normally went up the averages list were usually the best ones, so no one fretted too much. One-Day-Internationals changed that a bit – not outs skewed things considerably but other measures (like tallies of centuries and wickets) were usually summoned up to differentiate between players.
The T20 format has comprehensively queered the pitch. All the skews are wildly magnified. No one knows what the standard measures are anymore. Centuries are a rarity as are four or five wicket hauls. Regular not outs render averages way-off-centre. Strike rates mean nothing by themselves, nor do economy rates. The result is that no one knows who the best T20 players in the world are, in internationals or in domestics, not in a sustained or statistical way.
Ironically, the format of the game most popularly associated with innovation is actually the most backward. When it comes to individual player analysis over multiple matches, anarchy reigns. Man-of-the-match awards are hit-and-miss affairs like never before. Tournament awards go to the wrong candidate…sometimes embarrassingly off-the-mark.
There are a few evolving systems that can actually identify some of these and we use ours to bring some of these anomalies to light. (Impact Index measures the impact a player makes on a match - and the series/tournament - relative to the other 21 players in the same match, therefore it is able to factor in context, hitherto immeasurable.)
In IPL 2010, for example, Pragyan Ojha won the Purple Cap for taking the maximum wickets (21) in 16 matches. His economy rate was 7.29. The award should rightfully have gone to Muralitharan who took 15 wickets in 12 matches at an economy rate of 6.85 (his impact was 14% higher). Similarly, Tendulkar got the Orange Cap that year for scoring 618 runs at an average of 48 and strike rate of 133 in 15 matches. However, Raina, with 520 runs (avg 47; SR 143) in 16 matches, with a tournament-defining performance in the final, should have got it (his impact was 9% higher). The nature of T20 demands scientific ways of combining various parameters – which the conventional systems of evaluation cannot do.