T20 Stats Explained

A team has 120 balls and ten wickets. Wickets are relatively plentiful and balls are scarce, which inverts the Test-match logic most fans grew up with. That is why a batter who scores quickly and gets out is often more valuable than one who survives while consuming deliveries someone else could have used.

The limitation is sample size and situation. A strike rate built over ten balls at the death of an innings is not comparable to one built across a full powerplay, and neither is comparable across formats. Always ask how many balls the number came from before you use it in an argument.

Because not-outs do not count as dismissals, a middle-order batter who is regularly stranded at the end of an innings can carry a high average without having changed many results. Meanwhile an opener asked to attack the powerplay will show a lower average for doing exactly the job the team wants. Neither number is wrong; both need the role attached to be meaningful.

That gap is what 'impact' metrics try to close, by weighting runs against the match situation, the phase of the innings and the difficulty of scoring at the time. They are useful as a supplement and terrible as a single verdict - different models disagree with each other, and none of them replaces watching the game.

In T20, economy usually matters most, because containing an over is a direct contribution to the balls-versus-runs equation. But a bowler who takes early wickets can be worth more than a cheaper one who takes none, since a new batter costs the batting side tempo. Phase matters too - death overs will always look expensive next to a powerplay spell.

The honest reading is always contextual: which overs did the bowler bowl, against which batters, on what surface. A season economy figure with none of that attached is a starting point for a question, not an answer to one.

Start with the total and the overs, then look at the fall-of-wickets column to see when the innings changed shape. Only then read individual scores, with balls faced beside them. A 35 from 18 balls in a collapse is a different innings from a 35 from 34 balls in a stable chase, and the runs column alone cannot tell them apart.

Finish with the bowling figures and check which overs each bowler sent down. Once you have done this half a dozen times it becomes automatic, and viral graphics stop being persuasive because you can see what they left out.

The tells are consistent: an unusually specific filter ('most fours in the second powerplay of away T20Is since 2023'), no sample size shown, formats quietly mixed together, or a comparison between players at completely different career stages. If the graphic does not name its source, assume it is decoration.

The fix is one step: trace the number to a scorecard on ESPNcricinfo or an official board release. And whatever the number says, it does not forecast the next match. We publish no odds, no betting content and no predictions - statistics describe what happened, they do not tell you what will.