Top 50 Batmen with Highest Batting Average in ODI Cricket

virat kohli

We as a cricket fan always curious to see players total runs and batting average when he takes guard in middle of the pitch. Cricket fans always impresses with Batmen’s batting average. We loved to see batting average of Sachin Tendulkar, Virender Sehwag, AB de Villiers, David Warner and many other players when they came out to bat in any One Day International match.

In any form of cricket batting average measures as total number of scores divided by number of times batsmen out. These are the primary measures to rate a batsmen. Batting average is considered as a good performance metric for a player which determines his skill as a batsman and how consistent he is. Batting average defines the average score a batsman is scoring per ODI match.

If you notice it closely than you will find that batting average is always lower than the test average because of limited over’s cricket and batsmen has to play fast to maintain strike rate as well.

Here I compiled the top 20 batsmen with highest batting average in ODI cricket. If you analyze below list than you will find that some batsmen even has only 1000 runs and their batting average is much higher than other batsmen. A true cricket fan can understands the number because a consistent player is only that who frequently scores huge runs in maximum matches like AB de Villiers, Virat Kohli and David Warner doing from past couple of years.

In this list the highest batting average in ODI belongs to Netherland’s batsmen Ryan Ten Doeschate. He retired from international cricket before few years because of his age and he only played 33 ODI matches. In top 10 players 3 players are from India and Sachin Tendulkar is on 20th position.

Highest Batting Average in ODI Cricket

1RN ten Doeschate (NL)333215411196787.759
2AB de Villiers (Afr/SA)2142059080162*54.041002451
3MG Bevan (AUS)2321966912108*53.5874.16646
4V Kohli (INDIA)179171775518353.1190.762739
5Babar Azam (PAK)2323116812353.0990.5446
6IJL Trott (ENG)6865281913751.2577.06422
7MS Dhoni (Asia/INDIA)2862499275183*50.9688.981061
8HM Amla (SA)148145683215950.689.242431
9AT Rayudu (INDIA)34301055124*50.2376.2826
10TLW Cooper (NL)232297610148.870.1618
11MEK Hussey (AUS)1851575442109*48.1587.16339
12Zaheer Abbas (PAK)6260257212347.6284.8713
13IVA Richards (WI)1871676721189*4790.21145
14GM Turner (NZ)41401598171*4768.0539
15KS Williamson (NZ)1091034332145*46.5883.87829
16JE Root (ENG)8075314912546.385.61819
17AC Voges (AUS)3128870112*45.7887.1714
18CG Greenidge (WI)1281275134133*45.0364.921131
19DA Warner (AUS)9391394617944.8496.851316
20SR Tendulkar (INDIA)46345218426200*44.8386.234996
21Q de Kock (SA)7777326717844.7595.551212
22DM Jones (AUS)164161606814544.6172.56746
23MJ Clarke (AUS)245223798113044.5878.98858
24JH Kallis (Afr/ICC/SA)3283141157913944.3672.891786
25ML Hayden (AUS/ICC)1611556133181*43.878.961036
26SPD Smith (AUS)9581310116443.6787.72816
27LRPL Taylor (NZ)1811676070131*43.6682.271732
28Misbah-ul-Haq (PAK)162149512296*43.473.75042
29Haris Sohail (PAK)222177489*4382.8607
30F du Plessis (SA)105101382518542.9788.17824
31S Dhawan (INDIA)7675309013742.9189.95917
32RR Sarwan (WI)1811695804120*42.6775.74538
33MJ Guptill (NZ)1411385230237*42.5286.81132
34JWA Taylor (ENG)272688710142.2380.1217
35HH Dippenaar (Afr/SA)107953421125*42.2367.78426
36RT Ponting (AUS/ICC)3753651370416442.0380.393082
37KC Sangakkara (Asia/ICC/SL)4043801423416941.9878.862593
38Mohammad Yousuf (Asia/PAK)2882739720141*41.7175.11564
39Javed Miandad (PAK)2332187381119*41.767.01850
40S Chanderpaul (WI)268251877815041.670.741159
41JJ Roy (ENG)3534132916241.53105.5538
42CJ Ferguson (AUS)302566371*41.4385.3205
43RG Sharma (INDIA)153147513126441.3784.431029
44DL Haynes (WI)2382378648152*41.3763.091757
45L Klusener (SA)1711373576103*41.189.91219
46SC Ganguly (Asia/INDIA)3113001136318341.0273.72272
47G Kirsten (SA)1851856798188*40.9572.041345
48DR Martyn (AUS)2081825346144*40.877.73537
49KP Pietersen (ENG/ICC)136125444013040.7386.58925
50GJ Bailey (AUS)9085304415640.5883.51322

Note – We update above data at the end of each month so data can be delayed.

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