Records
2017/18 Records
FC+LA+T20 Matches Records
Bowling Best Career Economy Rate - vs South Africa

Players - vs South Africa

Best economy rates
Player
Span
Mat
Overs
Mdns
Balls
Runs
Wkts
BBI
Ave
Econ
SR
4
5
10
SPD Smith (AUS)
2018-201837.04429---1.28----
I Sharma (IND)
2018-2018269.01241415083/4618.752.1751.7---
R Ashwin (IND)
2018-2018276.31745921574/11330.712.8165.51--
JR Hazlewood (AUS)
2018-20184160.030960471123/6139.252.9480.0---
CJ Sayers (AUS)
2018-2018149.01129414622/7873.002.97147.0---
PJ Cummins (AUS)
2018-20184155.432934472225/8321.453.0342.421-
Mohammed Shami (IND)
2018-2018383.316501256155/2817.063.0633.411-
KM Jarvis (ZIM)
2017-2017118.021085733/5719.003.1636.0---
NM Lyon (AUS)
2018-20184210.5391265681163/5042.563.2379.0---
MT Renshaw (AUS)
2018-201814.0-2413---3.25----
JJ Bumrah (IND)
2018-201811162.118973591235/5425.693.6442.3-1-
AG Cremer (ZIM)
2017-2017118.0-1086622/6633.003.6654.0---
B Muzarabani (ZIM)
2017-2017113.027848---3.69----
MA Starc (AUS)
2018-20183109.218656413125/3434.413.7754.611-
Shafiul Islam (BAN)
2017-2017240.0624015321/4676.503.82120.0---
Mustafizur Rahman (BAN)
2017-2017263.0737824132/3080.333.82126.0---
MR Marsh (AUS)
2018-2018442.0625216942/2642.254.0263.0---
Subashis Roy (BAN)
2017-2017129.0317411833/11839.334.0658.0---
Soumya Sarkar (BAN)
2017-201745.0-3021---4.20----
B Kumar (IND)
2018-201810111.024666471195/2424.784.2435.011-
CB Mpofu (ZIM)
2017-2017113.31815833/5819.334.2927.0---
HH Pandya (IND)
2018-201812107.07642476102/2747.604.4464.2---
Mehidy Hasan Miraz (BAN)
2017-2017485.0551038342/3195.754.50127.5---
Kuldeep Yadav (IND)
2018-2018651.01306236174/2313.884.6218.02--
CJ Chibhabha (ZIM)
2017-2017111.016651---4.63----
KM Jadhav (IND)
2018-201836.0-3630---5.00----
Sikandar Raza (ZIM)
2017-201715.0-3025---5.00----
Mominul Haque (BAN)
2017-201729.0-544833/2716.005.3318.0---
Sabbir Rahman (BAN)
2017-2017714.018475---5.35----
Taijul Islam (BAN)
2017-2017127.0-162145---5.37----
Mahmudullah (BAN)
2017-2017729.02174173---5.96----
Nasir Hossain (BAN)
2017-2017213.0-7878---6.00----
YS Chahal (IND)
2018-2018860.11361365175/2221.476.0621.211-
Shakib Al Hasan (BAN)
2017-2017535.0-21021452/2242.806.1142.0---
Taskin Ahmed (BAN)
2017-2017661.0536637722/66188.506.18183.0---
Rubel Hossain (BAN)
2017-2017656.0133637274/6253.146.6448.01--
SN Thakur (IND)
2018-2018316.5-10111864/5219.667.0016.81--
Mashrafe Mortaza (BAN)
2017-2017327.5-167201---7.22----
SK Raina (IND)
2018-201833.0-182711/2727.009.0018.0---
JD Unadkat (IND)
2018-201827.4-467532/4225.009.7815.3---
Mohammad Saifuddin (BAN)
2017-2017311.0-6611922/5359.5010.8133.0---
AR Patel (IND)
2018-201811.0-616---16.00----
CT Bancroft (AUS)
2018-201830.0-0---------
RP Burl (ZIM)
2017-201710.0-0---------
JA Burns (AUS)
2018-201810.0-0---------
S Dhawan (IND)
2018-2018100.0-0---------
MS Dhoni (IND)
2018-201890.0-0---------
CR Ervine (ZIM)
2017-201710.0-0---------
PSP Handscomb (AUS)
2018-201810.0-0---------
Imrul Kayes (BAN)
2017-201770.0-0---------
SS Iyer (IND)
2018-201830.0-0---------
KD Karthik (IND)
2018-201810.0-0---------
UT Khawaja (AUS)
2018-201840.0-0---------
V Kohli (IND)
2018-2018110.0-0---------
Litton Das (BAN)
2017-201760.0-0---------
SE Marsh (AUS)
2018-201840.0-0---------
H Masakadza (ZIM)
2017-201710.0-0---------
PJ Moor (ZIM)
2017-201710.0-0---------
Mushfiqur Rahim (BAN)
2017-201770.0-0---------
TD Paine (AUS)
2018-201840.0-0---------
MK Pandey (IND)
2018-201830.0-0---------
PA Patel (IND)
2018-201820.0-0---------
CA Pujara (IND)
2018-201830.0-0---------
AM Rahane (IND)
2018-201870.0-0---------
KL Rahul (IND)
2018-201820.0-0---------
WP Saha (IND)
2018-201810.0-0---------
RG Sharma (IND)
2018-2018110.0-0---------
Tamim Iqbal (BAN)
2017-201720.0-0---------
BRM Taylor (ZIM)
2017-201710.0-0---------
M Vijay (IND)
2018-201830.0-0---------
DA Warner (AUS)
2018-201830.0-0---------
Adjust:Most recentPast weekPast MonthPast year4 years10 years25 years
Performances in matches that overlap decades are credited to the year in which they occurred - this results in some unknown data, especially in regard to bowling figures
Performances in matches that overlap years are credited to the year in which they occurred - this results in some unknown data, especially in regard to bowling figures