Highest match aggregates in 2016 in Tests+ODIs+T20Is - India
Highest match aggregates
Team 1 | Team 2 | Team runs | Team wickets | Overs | RR | Ground | Match Date | Scorecard |
India | England | 1457 | 29 | 449.3 | 3.24 | Rajkot | 9 Nov 2016 | Test # 2232 |
India | England | 1443 | 27 | 436.0 | 3.3 | Chennai | 16 Dec 2016 | Test # 2241 |
India | England | 1226 | 30 | 368.1 | 3.33 | Wankhede | 8 Dec 2016 | Test # 2239 |
India | New Zealand | 1225 | 28 | 353.1 | 3.46 | Indore | 8 Oct 2016 | Test # 2223 |
India | New Zealand | 1193 | 35 | 387.4 | 3.07 | Kanpur | 22 Sep 2016 | Test # 2221 |
West Indies | India | 1084 | 25 | 327.4 | 3.3 | Kingston | 30 Jul 2016 | Test # 2211 |
India | England | 1072 | 40 | 393.1 | 2.72 | Visakhapatnam | 17 Nov 2016 | Test # 2235 |
West Indies | India | 1040 | 28 | 330.1 | 3.14 | North Sound | 21 Jul 2016 | Test # 2207 |
India | England | 1040 | 32 | 342.5 | 3.03 | Mohali | 26 Nov 2016 | Test # 2238 |
India | New Zealand | 980 | 40 | 315.5 | 3.1 | Eden Gardens | 30 Sep 2016 | Test # 2222 |
West Indies | India | 903 | 37 | 328.5 | 2.74 | Gros Islet | 9 Aug 2016 | Test # 2215 |
Australia | India | 671 | 18 | 99.2 | 6.75 | Canberra | 20 Jan 2016 | ODI # 3726 |
Australia | India | 661 | 11 | 99.4 | 6.63 | Sydney | 23 Jan 2016 | ODI # 3727 |
Australia | India | 619 | 8 | 99.2 | 6.23 | W.A.C.A | 12 Jan 2016 | ODI # 3723 |
Australia | India | 617 | 11 | 99.0 | 6.23 | Brisbane | 15 Jan 2016 | ODI # 3724 |
Australia | India | 591 | 13 | 98.5 | 5.97 | Melbourne | 17 Jan 2016 | ODI # 3725 |
India | New Zealand | 574 | 13 | 98.0 | 5.85 | Mohali | 23 Oct 2016 | ODI # 3798 |
India | New Zealand | 501 | 17 | 98.4 | 5.07 | Ranchi | 26 Oct 2016 | ODI # 3799 |
India | West Indies | 489 | 10 | 40.0 | 12.22 | Lauderhill | 27 Aug 2016 | T20I # 562 |
India | New Zealand | 478 | 19 | 99.3 | 4.8 | Delhi | 20 Oct 2016 | ODI # 3797 |
Australia | India | 397 | 8 | 40.0 | 9.92 | Sydney | 31 Jan 2016 | T20I # 489 |
India | West Indies | 388 | 5 | 39.4 | 9.78 | Wankhede | 31 Mar 2016 | T20I # 556 |
India | New Zealand | 384 | 14 | 77.0 | 4.98 | Dharamsala | 16 Oct 2016 | ODI # 3796 |
India | New Zealand | 348 | 16 | 73.1 | 4.75 | Visakhapatnam | 29 Oct 2016 | ODI # 3800 |
Zimbabwe | India | 341 | 11 | 92.2 | 3.69 | Harare | 11 Jun 2016 | ODI # 3742 |
Australia | India | 341 | 11 | 40.0 | 8.52 | Melbourne | 29 Jan 2016 | T20I # 486 |
Australia | India | 339 | 13 | 39.3 | 8.58 | Adelaide | 26 Jan 2016 | T20I # 485 |
Zimbabwe | India | 338 | 12 | 40.0 | 8.45 | Harare | 18 Jun 2016 | T20I # 558 |
India | Sri Lanka | 323 | 15 | 40.0 | 8.07 | Ranchi | 12 Feb 2016 | T20I # 497 |
India | Australia | 321 | 10 | 39.1 | 8.19 | Mohali | 27 Mar 2016 | T20I # 553 |
India | Bangladesh | 291 | 16 | 40.0 | 7.27 | Bengaluru | 23 Mar 2016 | T20I # 547 |
Bangladesh | India | 287 | 13 | 40.0 | 7.17 | Mirpur | 24 Feb 2016 | T20I # 509 |
India | Sri Lanka | 280 | 14 | 39.2 | 7.11 | Mirpur | 1 Mar 2016 | T20I # 515 |
Zimbabwe | India | 273 | 12 | 40.0 | 6.82 | Harare | 22 Jun 2016 | T20I # 560 |
Zimbabwe | India | 255 | 11 | 61.2 | 4.15 | Harare | 13 Jun 2016 | ODI # 3744 |
Zimbabwe | India | 249 | 10 | 64.1 | 3.88 | Harare | 15 Jun 2016 | ODI # 3746 |
Bangladesh | India | 242 | 7 | 28.5 | 8.39 | Mirpur | 6 Mar 2016 | T20I # 521 |
India | Pakistan | 237 | 9 | 33.5 | 7 | Eden Gardens | 19 Mar 2016 | T20I # 541 |
India | Sri Lanka | 206 | 15 | 36.5 | 5.59 | Pune | 9 Feb 2016 | T20I # 496 |
India | New Zealand | 205 | 17 | 38.1 | 5.37 | Nagpur | 15 Mar 2016 | T20I # 535 |
Zimbabwe | India | 202 | 9 | 33.1 | 6.09 | Harare | 20 Jun 2016 | T20I # 559 |
India | Pakistan | 168 | 15 | 33.0 | 5.09 | Mirpur | 27 Feb 2016 | T20I # 512 |
India | Sri Lanka | 166 | 11 | 31.5 | 5.21 | Visakhapatnam | 14 Feb 2016 | T20I # 499 |
India | U.A.E. | 163 | 10 | 30.1 | 5.4 | Mirpur | 3 Mar 2016 | T20I # 517 |
India | West Indies | 158 | 10 | 21.4 | 7.29 | Lauderhill | 28 Aug 2016 | T20I # 563 |
West Indies | India | 62 | 2 | 22.0 | 2.81 | Port of Spain | 18 Aug 2016 | Test # 2218 |
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