Bowling Averages for Tests+ODIs+T20Is in the 2016 IN Zimbabwe

Bowling averages
Player
Span
Mat
Inns
Balls
Mdns
Runs
Wkts
BBI
Ave
Econ
SR
5
10
Ct
St
SJ Benn2016-2016331501138---5.52---1-
D Bishoo2016-2016116013033/3010.003.0020.0----
TA Boult2016-2016244022514664/5224.332.1767.0----
CR Brathwaite2016-201644204-19864/4833.005.8234.0----
KC Brathwaite2016-20164160-5611/5656.005.6060.0--2-
JJ Bumrah2016-2016662274135144/229.643.5616.2--3-
JL Carter2016-20164324-28---7.00-----
YS Chahal2016-201666216416693/2518.444.6124.0--3-
RW Chakabva2016-20161-0-----------
BB Chari2016-2016716-3---3.00---4-
J Charles2016-20164-0-----------
TL Chatara2016-20163311439411/2094.004.94114.0----
CJ Chibhabha2016-20161210353524452/1348.804.1470.6--1-
E Chigumbura2016-20169236-54---9.00---2-
MT Chinouya2016-2016233421418831/4562.663.29114.0----
TS Chisoro2016-20164314429622/2348.004.0072.0--1-
AG Cremer2016-201615171651171139164/9171.184.13103.1--3-
ML Cummins2016-20161160140---4.00-----
DM de Silva2016-20167442-2811/1028.004.0042.0--8-
R Dhawan2016-20161124-4211/4242.0010.5024.0--2-
MS Dhoni2016-20166-0---------5-
N Dickwella2016-20165-0---------51
CR Ervine2016-201610-0---------4-
FY Fazal2016-20161-0-----------
N Pradeep2016-201633162214152/2128.205.2232.4----
ST Gabriel2016-201633139-10563/3117.504.5323.1--1-
DAS Gunaratne2016-201676210214683/1018.254.1726.2--2-
MJ Guptill2016-2016224851133/113.661.3716.0--3-
HMRKB Herath2016-20162469628287198/6315.102.4736.6211-
JO Holder2016-201644228-17893/5719.774.6825.3--1-
SD Hope2016-20164-0---------5-
KM Jadhav2016-20166-0---------2-
GSNFG Jayasuriya2016-20163366-63---5.72---1-
FDM Karunaratne2016-20162-0---------3-
KMDN Kulasekara2016-201655240318082/2322.504.5030.0--1-
DS Kulkarni2016-201655200314582/2318.124.3525.0----
CBRLS Kumara2016-201624384623742/4559.253.7096.0----
RAS Lakmal2016-20167971018393143/6928.073.3250.7--1-
TWM Latham2016-20162-0---------3-
E Lewis2016-20164-0---------2-
N Madziva2016-20164491-11021/3255.007.2545.5----
Mandeep Singh2016-20163-0---------1-
T Maruma2016-20162-0---------1-
H Masakadza2016-20161162491013232/3444.003.1883.0--5-
PS Masvaure2016-20162284-61---4.35-----
TMK Mawoyo2016-20163-0---------1-
BKG Mendis2016-2016723611811/1018.003.0036.0--5-
PJ Moor2016-201612-0---------71
CB Mpofu2016-2016566242036962/9661.503.54104.0--2-
CT Mumba2016-2016354911232984/5041.124.0261.3--2-
T Mupariwa2016-20161136-43---7.16-----
TK Musakanda2016-20161-0-----------
CT Mutombodzi2016-20162-0---------1-
R Mutumbami2016-20164-0-----------
T Muzarabani2016-2016449017922/3139.505.2645.0----
KK Nair2016-20162-0-----------
HM Nicholls2016-20162-0---------1-
AR Nurse2016-201644228118683/2723.254.8928.5--3-
J Nyumbu2016-2016122463129---3.14---1-
MK Pandey2016-20166-0---------1-
T Panyangara2016-20161145-3711/3737.004.9345.0----
AR Patel2016-201666237312351/1624.603.1147.4--2-
SS Pathirana2016-201655189-15642/2639.004.9547.2--3-
MDK Perera2016-201624357819383/3424.123.2444.6--1-
MDKJ Perera2016-20167-0---------21
R Powell2016-20164-0---------4-
KL Rahul2016-20166-0---------4-
AT Rayudu2016-20166-0---------3-
MJ Santner2016-2016244682316862/1628.002.1578.0--2-
MD Shanaka2016-20162224-23---5.75-----
V Sibanda2016-20164-0-----------
Sikandar Raza2016-20161110337526621/7133.004.73168.5--2-
JK Silva2016-20162-0---------1-
IS Sodhi2016-2016243621819984/6024.873.2945.2--1-
TG Southee2016-2016244443220462/2834.002.7574.0----
BB Sran2016-2016551922140104/1014.004.3719.2----
LRPL Taylor2016-20162-0---------3-
WU Tharanga2016-20167-0---------1-
DT Tiripano2016-2016131497825545133/2041.923.3475.2--2-
JD Unadkat2016-20161124-43---10.75-----
JDF Vandersay2016-20162160-5033/5016.665.0020.0--1-
BV Vitori2016-20161154-5233/5217.335.7718.0----
N Wagner2016-20162448522187116/4117.002.3144.01-1-
MN Waller2016-20167396-6521/1732.504.0648.0--5-
BJ Watling2016-20162-0---------41
SC Williams2016-2016108412526572/1837.853.8558.8--7-
KS Williamson2016-20162161--------5-
Adjust:Most recentPast weekPast MonthPast year4 years10 years25 years
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