Pakistan’s T20 Cricket in the last decade had one problem most of the time, and that was batting. To find role-based advanced solutions for that, I devised a batting impact that not just shows their overall impact, but also allows you to deep dive into the players’ specific phases and their matchups against pacers and spinners.
Findings from the PSL 2026
For PSL 2026, looking at the model, it shows the most impactful batters: Kusal Mendis, Reeza Hendricks, Mark Chapman, Fakhar Zaman, and Sahibzada Farhan.
However, these results combine both aspects of T20 batting, which are run accumulation and a high rate of scoring. If you want to look at these factors separately for individual players, you can see which player is a good player of pace but not a good hitter, or similar against spin as well.
For example, Reeza Hendricks was great this year against spinners in terms of run accumulation and similarly Saud Shakeel as well, but they were both similarly below par in terms of spin hitting.
Similarly, since the model goes into every phase as well, it also factors in which players did well in powerplay, middle overs, and death overs.
Why The Need for This Model
So the nature of T20 cricket is such that you cannot just blindly see a tournament’s most runs and most wickets leaderboard to judge a player’s tournament-wise performance.
Also, even if you divide it into a phase-wise leaderboard, someone like Maaz Sadaqat and someone like Kusal Mendis are different because of their styles of play.
Even if someone like Mark Chapman scores 60 (30) and Babar Azam scores 60 (30), both of them should be judged differently and are.
Another aspect is that you cannot just assign a value that a strike rate of 180 is great in death overs or powerplay; it needs to be assessed in terms of match situation, pitch conditions, etc. So this model takes all this into account by using the tournament average in each phase, against each bowling type.
For example, Usman Khan is one of the top 10 most impactful batters overall in this tournament. However, if we look at his impact in the powerplay, his average impact is 1.0, and his strike rate impact is below that, meaning relatively, he accumulated or anchored the innings in an average manner in this phase, and his strike rate was poor.
However, his middle overs impact is off the charts, especially in terms of the strike rate he is basically an outlier.
He thus covers the gap against spinners mostly in the middle overs after starting slow in the powerplay to reconstruct the innings.
Another interesting thing you can see in comparison in both powerplay and middle over charts is Maaz Sadaqat, who goes berserk in the powerplay and most of the time won’t survive the 6-over phase. When he does, he slows down a bit relatively, but improves on the reliability and the accumulator factor.
The other need for this is to show innings-wise progression of players like Maaz Sadaqat, for example, in some games, there might not have been a huge target, and he blasted off in the powerplay, while also coming off successful and surviving the powerplay, to eventually slow down when there’s not a big chase left.
Similarly, it also shows the value of not being out in a phase, for example, Maaz’s 40 off 20, and he survives the powerplay, that’s way better than his 40 off 20 and getting out in the powerplay.
This is in no way to say that you need to save wickets and be conservative in the powerplay; however, if you go on to attack and still play long, that is just the next level of insanity and that deserves to be rewarded more than just a cameo.
Thus, the averages were adjusted to incorporate not outs in this model.
Similarly, if I give the example of an overseas player, Dian Forrestor played some cameos for the Rawalpindiz, you might remember him as a Death Overs hitter, and he was spectacular in terms of that, with a 1.33 SR IMPACT against pacers at the death.
However, another great return from him might be forgotten by most; he had an SR IMPACT of 1.25 against spinners in Middle Overs as well, but his SR IMPACT against pacers in the middle overs was very poor, 0.49. Even in terms of raw numbers, he scored 11 off 16 balls in this scenario.
Brief Working of this Impact Model
It takes the relativity into account to judge a player’s performance not blindly, but after considering the conditions as well. However, it later combines the impact of average and strike-rate through geometric mean instead of just adding, as in statistics like BASRA, which are effectively inaccurate, as you cannot just add average and strike rate, two variables that have the same numerator.
Similarly, it has to take into account one or two cameos, especially in the middle overs or in the powerplay, and for that, the impacts when combined across bowler types or phases also take into account the number of balls faced.
About the Author: Zaid Babar Khan
Data geek, Cricket fanatic, Haris Rauf and Lahore Qalandars fan and an article writer
