So last week after I shared the numbers on Pakistan’s percentage of dot balls, a number of people asked to know how it compared with some of the other teams. I looked at the top 3 teams in terms of win/loss percentage over that period - Australia, South Africa, and India - and pulled some more data.
Here’s what it looks like (Pakistan's numbers having been updated to include the last 2 one-dayers):
June 2001 to present:
2011 to present:
Difference between 2011 - and 2001 -
South Africa are still top and they've gotten a lot better. They now score on more than 50% of balls faced. India have improved a lot as well, and are almost at the 50% mark. The largest difference for both teams comes in the 1s column, with a very noticeable increase of around 4% in the number of singles both teams take. This offsets the small drop from scoring fewer boundaries, and helps increase the overall strike rate.
Here’s what it looks like (Pakistan's numbers having been updated to include the last 2 one-dayers):
June 2001 to present:
| Team | Balls | 0s | 1s | 2s | 3s | 4s | 5s | 6s | Runs | SR% | Dot% | 1.23 | 4,6% | SS% |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| RSA | 66549 | 35071 | 21515 | 3650 | 491 | 5088 | 8 | 726 | 55036 | 82.7 | 52.7 | 38.6 | 8.7 | 47.3 |
| AUS | 88237 | 46940 | 27568 | 5235 | 847 | 6617 | 12 | 1018 | 73215 | 83.0 | 53.2 | 38.1 | 8.7 | 46.8 |
| TOT | 326809 | 176012 | 101677 | 18094 | 2470 | 24874 | 53 | 3629 | 266810 | 81.6 | 53.9 | 37.4 | 8.7 | 46.1 |
| IND | 93224 | 50434 | 28742 | 4897 | 534 | 7568 | 24 | 1025 | 76680 | 82.3 | 54.1 | 36.7 | 9.2 | 45.9 |
| PAK | 78799 | 43567 | 23852 | 4312 | 598 | 5601 | 9 | 860 | 61879 | 78.5 | 55.3 | 36.5 | 8.2 | 44.7 |
So the average for these four teams is around 54% dots, with South Africa and Australia above, and India and Pakistan below this number. India is able to beat the average strike rate because of its high boundary rate, though more interestingly South Africa and Australia are able to do the same with just an average boundary rate, because they have a greater proportion of 1s, 2s, and 3s.
In the last post in an effort to save space I had combined the 1s, 2s and 3s, and the 4s and 6s together. I think it's more helpful to split these out because they affect the strike rate differently, and doing so can help answer questions such as why Australia has a slightly higher strike rate than South Africa even though their scoring shot percentage is lower.
June 2001 to present:
Australia score fewer singles, and are also a touch lower on 4s, but more than make up for it because of the higher percentage of 2s and 3s, and a hair's width increase in the number of 6s. So, score more of shots that produce more runs and you can afford to pick up fewer singles without hurting your strike rate. Hopefully that's easy enough to understand.
June 2001 to present:
| Team | SR% | Dot% | 1s% | 2s% | 3s% | 4s% | 6s% | SS% |
|---|---|---|---|---|---|---|---|---|
| RSA | 82.7 | 52.7 | 32.3 | 5.5 | 0.7 | 7.7 | 1.1 | 47.3 |
| AUS | 83.0 | 53.2 | 31.2 | 5.9 | 1.0 | 7.5 | 1.2 | 46.8 |
| TOT | 81.6 | 53.9 | 31.1 | 5.5 | 0.8 | 7.6 | 1.1 | 46.1 |
| IND | 82.3 | 54.1 | 30.8 | 5.3 | 0.6 | 8.1 | 1.1 | 45.9 |
| PAK | 78.5 | 55.3 | 30.3 | 5.5 | 0.8 | 7.1 | 1.1 | 44.7 |
Australia score fewer singles, and are also a touch lower on 4s, but more than make up for it because of the higher percentage of 2s and 3s, and a hair's width increase in the number of 6s. So, score more of shots that produce more runs and you can afford to pick up fewer singles without hurting your strike rate. Hopefully that's easy enough to understand.
Also, if you want to take one of these differences and translate it into a strike rate differential, the math is very simple. For example, a 1% increase in the number of 4s hit means a 1% x 4 = 4% increase in strike rate. Looking at India and Pakistan's numbers, that pretty much explains the difference in strike rates.
Now, given that the above is over the last 12 years, how about more recent numbers? What do things look like this decade i.e. 2011 onwards.
2011 to present:
| Team | SR% | Dot% | 1s% | 2s% | 3s% | 4s% | 6s% | SS% |
|---|---|---|---|---|---|---|---|---|
| RSA | 85.0 | 48.7 | 36.8 | 5.4 | 0.8 | 7.3 | 1.0 | 51.3 |
| IND | 85.7 | 50.2 | 34.6 | 5.7 | 0.5 | 7.9 | 1.1 | 49.8 |
| TOT | 82.0 | 52.1 | 33.4 | 5.5 | 0.7 | 7.3 | 1.1 | 47.9 |
| AUS | 81.9 | 53.1 | 31.7 | 5.9 | 0.9 | 7.0 | 1.2 | 46.9 |
| PAK | 75.9 | 55.3 | 31.4 | 4.8 | 0.7 | 6.8 | 0.9 | 44.7 |
Difference between 2011 - and 2001 -
| Team | SR% | Dot% | 1s% | 2s% | 3s% | 4s% | 6s% | SS% |
|---|---|---|---|---|---|---|---|---|
| RSA | 2.3 | (4.0) | 4.5 | (0.1) | 0.1 | (0.4) | (0.1) | 4.0 |
| IND | 3.5 | (3.9) | 3.8 | 0.4 | (0.0) | (0.3) | 0.0 | 3.9 |
| TOT | 0.4 | (1.7) | 2.2 | (0.1) | (0.0) | (0.4) | (0.0) | 1.7 |
| AUS | (1.0) | (0.1) | 0.5 | (0.0) | (0.1) | (0.5) | 0.1 | 0.1 |
| PAK | (2.6) | 0.1 | 1.2 | (0.7) | (0.1) | (0.3) | (0.2) | (0.1) |
South Africa are still top and they've gotten a lot better. They now score on more than 50% of balls faced. India have improved a lot as well, and are almost at the 50% mark. The largest difference for both teams comes in the 1s column, with a very noticeable increase of around 4% in the number of singles both teams take. This offsets the small drop from scoring fewer boundaries, and helps increase the overall strike rate.
Australia see their strike rate decrease slightly mostly because of fewer 4s hit.
Pakistan as I had mentioned the last time see a drop in their strike rate even though the overall SS% hasn't really moved. But what has changed is the actual composition of their scoring shots. Previously I had said it's the fewer boundaries. Which is true when looking at 4s and 6s combined. But the single biggest driver is actually the lower proportion of 2s, something I didn't pick up on the last time because I was too focused on dot balls. In this time period (mostly under Misbah if you ignore the first half of 2011) this 4.8% of 2s is the lowest out of all the other Pakistani teams I discussed. Is it because of the older legs in the middle order of the current lot? Grounds aren't that big where they play? Or is it related to fewer boundaries i.e. they're just not hitting the ball far enough? I don't know the reason but my guess would be it's more to do with lazy running.
Anyway the main point here I guess is that it's not good enough to maintain status quo if you're Pakistan. It's great that the number of dot balls hasn't increased but they need to go further down. Other teams are moving in the opposite direction, and in South Africa and India's case it's simply by scoring more singles. Which I would imagine involves taking fewer risks than trying to hit more boundaries. This last point largely addressed to those who were questioning the case for Fawad Alam by pointing out his low percentage of boundaries. He more than compensates for it with his high ratio of 1s and 2s.
Now, on to the individual players themselves. I had to put a qualifier of 'balls faced 3000 or greater' otherwise it would be too many. But you can see the complete list in the tables I post at the end.
June 2001 to present (qualification: balls faced > 3000):
June 2001 to present (qualification: balls faced > 3000):
| No. | Name | Ave | SR% | Dot% | 1s% | 2s% | 3s% | 4s% | 6s% | 123% | 4,6% | SS% |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | Duminy | 40.0 | 83.6 | 44.8 | 42.0 | 6.1 | 0.8 | 5.3 | 1.0 | 48.9 | 6.3 | 55.2 |
| 2 | Afridi | 23.2 | 126.8 | 45.1 | 29.5 | 7.8 | 0.8 | 10.7 | 6.1 | 38.1 | 16.8 | 54.9 |
| 3 | Amla | 58.8 | 92.2 | 45.6 | 37.9 | 5.9 | 0.9 | 9.2 | 0.5 | 44.7 | 9.7 | 54.4 |
| 4 | Hussey | 48.2 | 87.2 | 45.7 | 39.7 | 6.4 | 0.7 | 6.2 | 1.3 | 46.8 | 7.5 | 54.3 |
| 5 | ABdeV | 50.2 | 93.8 | 46.9 | 36.7 | 5.4 | 0.8 | 8.6 | 1.6 | 42.9 | 10.2 | 53.1 |
| 6 | Boucher | 31.7 | 89.4 | 47.2 | 37.3 | 6.6 | 0.4 | 6.7 | 1.8 | 44.2 | 8.5 | 52.8 |
| 7 | Raina | 32.1 | 91.6 | 47.9 | 36.4 | 5.6 | 0.5 | 7.7 | 1.9 | 42.4 | 9.7 | 52.1 |
| 8 | Kohli | 42.1 | 86.0 | 48.5 | 36.0 | 6.3 | 0.6 | 8.1 | 0.5 | 42.9 | 8.7 | 51.5 |
| 9 | Dhoni | 48.2 | 87.6 | 48.6 | 36.3 | 6.2 | 0.5 | 6.7 | 1.8 | 43.0 | 8.5 | 51.4 |
| 10 | Symonds | 42.1 | 91.0 | 50.6 | 31.8 | 7.3 | 0.7 | 7.8 | 1.9 | 39.7 | 9.7 | 49.4 |
| 11 | Gambhir | 41.4 | 85.2 | 51.0 | 33.2 | 5.3 | 0.9 | 9.2 | 0.3 | 39.5 | 9.5 | 49.0 |
| 12 | Razzaq | 32.0 | 90.2 | 51.1 | 32.2 | 6.7 | 0.6 | 6.8 | 2.6 | 39.5 | 9.4 | 48.9 |
| 13 | Younis | 33.2 | 77.4 | 51.5 | 35.2 | 5.6 | 0.8 | 6.3 | 0.6 | 41.6 | 6.9 | 48.5 |
| 14 | Clarke | 44.7 | 78.2 | 51.6 | 34.5 | 6.0 | 1.0 | 6.4 | 0.5 | 41.5 | 6.9 | 48.4 |
| 15 | MoYo | 43.5 | 77.5 | 52.2 | 34.4 | 5.6 | 0.6 | 6.4 | 0.7 | 40.7 | 7.1 | 47.8 |
| 16 | Inzi | 37.8 | 79.1 | 52.5 | 34.2 | 4.8 | 0.6 | 6.8 | 1.0 | 39.6 | 7.9 | 47.5 |
| 17 | Kallis | 47.6 | 76.6 | 52.6 | 35.0 | 4.7 | 0.5 | 6.4 | 0.9 | 40.1 | 7.3 | 47.4 |
| 18 | Malik | 33.7 | 78.9 | 53.1 | 32.6 | 5.8 | 0.9 | 6.7 | 0.9 | 39.3 | 7.6 | 46.9 |
| 19 | Misbah | 41.2 | 73.9 | 53.2 | 34.7 | 5.4 | 0.6 | 5.2 | 1.0 | 40.6 | 6.2 | 46.8 |
| 20 | Martyn | 41.4 | 75.5 | 53.3 | 33.1 | 5.9 | 0.9 | 6.5 | 0.3 | 39.9 | 6.8 | 46.7 |
| 21 | Yuvraj | 44.7 | 87.3 | 53.6 | 30.2 | 5.2 | 0.4 | 9.0 | 1.6 | 35.9 | 10.6 | 46.4 |
| 22 | Sehwag | 37.0 | 104.5 | 53.6 | 24.7 | 5.1 | 0.5 | 14.4 | 1.7 | 30.3 | 16.1 | 46.4 |
| TOT | 34.0 | 81.6 | 53.9 | 31.1 | 5.5 | 0.8 | 7.6 | 1.1 | 37.4 | 8.7 | 46.1 | |
| 23 | Dravid | 45.0 | 73.8 | 54.0 | 33.3 | 5.2 | 0.6 | 6.6 | 0.3 | 39.1 | 6.9 | 46.0 |
| 24 | Watson | 42.1 | 88.8 | 54.1 | 28.7 | 5.9 | 0.6 | 8.8 | 1.9 | 35.2 | 10.7 | 45.9 |
| 25 | Kaif | 35.1 | 72.0 | 54.1 | 33.3 | 5.7 | 0.6 | 6.0 | 0.2 | 39.6 | 6.2 | 45.9 |
| 26 | Ponting | 42.5 | 83.3 | 54.1 | 30.3 | 5.3 | 1.1 | 8.0 | 1.2 | 36.7 | 9.2 | 45.9 |
| 27 | Gilchrist | 37.0 | 102.1 | 55.2 | 22.3 | 6.1 | 1.5 | 13.3 | 1.7 | 29.9 | 15.0 | 44.8 |
| 28 | Sachin | 42.5 | 85.6 | 55.5 | 27.5 | 5.5 | 0.7 | 10.2 | 0.7 | 33.6 | 10.9 | 44.5 |
| 29 | Haddin | 32.1 | 81.7 | 56.2 | 29.0 | 4.7 | 0.7 | 7.6 | 1.8 | 34.4 | 9.4 | 43.8 |
| 30 | Smith | 38.9 | 81.2 | 56.4 | 27.2 | 5.7 | 0.9 | 9.2 | 0.5 | 33.9 | 9.7 | 43.6 |
| 31 | Gibbs | 38.2 | 86.8 | 57.7 | 24.8 | 5.1 | 0.7 | 10.3 | 1.4 | 30.6 | 11.7 | 42.3 |
| 32 | Kakmal | 26.4 | 83.8 | 57.8 | 24.9 | 5.3 | 1.3 | 9.8 | 0.9 | 31.6 | 10.6 | 42.2 |
| 33 | Hayden | 45.0 | 80.2 | 58.1 | 26.2 | 5.1 | 1.1 | 8.3 | 1.2 | 32.4 | 9.5 | 41.9 |
| 34 | Dippenaar | 44.6 | 68.7 | 58.2 | 29.7 | 4.6 | 0.9 | 6.3 | 0.3 | 35.2 | 6.6 | 41.8 |
| 35 | Butt | 36.8 | 76.3 | 59.9 | 24.9 | 4.5 | 0.9 | 9.6 | 0.2 | 30.3 | 9.8 | 40.1 |
| 36 | Ganguly | 42.1 | 73.9 | 60.5 | 26.3 | 4.0 | 0.5 | 7.2 | 1.5 | 30.8 | 8.7 | 39.5 |
| 37 | Hameed | 36.9 | 67.0 | 63.4 | 22.5 | 5.6 | 1.1 | 7.2 | 0.2 | 29.2 | 7.4 | 36.6 |
| 38 | Hafeez | 27.3 | 69.1 | 63.9 | 22.1 | 4.8 | 0.9 | 7.7 | 0.7 | 27.8 | 8.4 | 36.1 |
The top 5 has three South Africans, plus Afridi and Hussey. I would've expected Hussey to be #1, JP Duminy came as a complete surprise. Since he's been out of action of late due to injury, I haven't really seen him play.
Rounding out the top 10 are Raina, Kohli, and Dhoni, who form the backbone of the Indian middle order, with Gambhir not far behind at 11. Except for Gambhir and Hashim Amla, the top half is almost all middle order batsmen until you get to Sehwag at 22.
Amla's position at 3 is an anomaly almost of Bradmanesque proportions. The fact that he's so far ahead of other openers is pretty astounding. I think it points to his ability to convert his starts into high scores with great consistency. His last innings being a good example. After the first 10 overs he was 11 off 19 with 2 fours and 5 scoring shots in total i.e. 14 of 19 were dots. Of his next 94 balls only 23 were dots and he completed a half century just in singles. (Of course being dropped before 50 also helped.) Him and AB de Villiers are the best partnership in ODI cricket - possibly ever - and it's easy to see why. There's just simply no way to keep them quiet. They can reach the boundary regularly and also pick up singles with the utmost of ease.
Don't laugh when you look at Hafeez at the bottom. Seriously, his problem is the exact opposite of Hashim Amla, which is that he wastes too many good starts. What ends up happening is he plays a greater proportion of deliveries in the period where the ball's newer and there are more fielders in the ring, and just never gets to the point where run-scoring gets easier.
In general for Pakistan, what they have currently out there - Younis Khan the highest ranked frontline batsman at 13 - just isn't good enough. In order to keep up with other countries they need more busy bodies in there, especially in the middle order.
Addendum: One thing I'd like to add is: the South Africa effect. Their batsmen are consistently above the 50% scoring shots level, especially the middle/lower middle order. Some of the players I wasn't able to show here because of the 3000 ball qualifier are guys like Jonty Rhodes, Lance Klusener, Shaun Pollock, Albie Morkel, Nicky Boje, Johan Botha, and from among the newer guys Faf du Plessis. They're all above 50%.
South Africa have always had a very clinical, professional approach, especially to ODI cricket. Just looking at the numbers and seeing how many of them score so efficiently, I think this reflects on how those players are coached.
India's ODI surge one can argue began under the MS Dhoni captaincy, which started right after they won the T20 World Cup in 2007. Who did they hire as coach soon afterwards? Gary Kirsten from South Africa.
Is it a coincidence that India's scoring shot percentage has now started to trend upwards? I would put my money on no. I think this is the result of Kirsten instilling the same professional approach in India's ODI batting, especially among the newer guys like Raina and Kohli, that has been part and parcel of South African cricket throughout this time. And now even with Kirsten gone this approach is flourishing.
People had also mentioned trying to do this analysis based on over splits (e.g. what is the dot ball percentage between overs 15-35) but I'm afraid I don't have the data for that at the moment. I imagine for something like that I would need to parse Cricinfo's ball-by-ball commentary, but I haven't quite had the time to figure that out. The problem is deciding how nimble I want such a system to be. For if I'm collecting data on how batsmen score their runs, might as well analyze it from the perspective of bowlers as well. And extras, which are completely ignored here. Etc, etc. The more I want it to do, the more complex it'll be. Anyway, probably something to look for in the future.
Here's a zip file containing in CSV format the full list of 120 players (30 per team) as well as all innings data that I collected.
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