There is always this jostle with stats, science and mathematics to extricate the right numerals to arithmetically manipulate and find who in the team had a major say in the victory.

Cricket is a team game – a game that is supposed to be won collectively as a team, yet it is not a game where the contribution from everyone is equal. Neither is cricket a feudal society where plutocracy has a major say. Cricket vacillates in the space between both extremes, lingering in the cloud of uncertainty. One man cannot be the whole team, yet he can tilt the match in favour of his side.

Before we mark our guard, we need to be sure about one fact: Match winners are not necessarily great batsmen; and great batsmen are not always match winners. There are batsmen who score big once in a blue moon and have a telling impact on the outcome of the game. Then, there are also batsmen who score consistently high, but their effect on the outcome of the game is marginal.

I developed an algorithm to statistically find out who the greatest is. I tried to minimize in every way my opinions affecting the end result with only the numbers having a say. Perhaps, the only place where my opinion lobbied the process was in choosing the importance of each type of records, as you would find later in the article.

The following records were taken into consideration in determining the greatest match winner.

- The percentage of man of the match awards in the total matches played
- The percentage of man of the series awards in the total series played
- The difference between the average of the batsman in wins and the average of his team in wins
- The percentage of centuries out of the total centuries that resulted in wins
- The percentage of centuries scored in the total number of wins for his team
- The number of times the batsman top scored for his team in wins as a percentage of total matches won.
- The number of times the batsman scored more than 40% of the team runs in wins as a percentage of the total wins.
- The amount of impact a batsman had in wins

**Man of the match awards**

The number of man of the match awards won is an excellent parameter that clearly elucidates the match-winning ability of a batsman, for it is the players who turn the tide in their team’s favour that are conferred with the award.

Tillakaratne Dilshan tops the list with 24 awards. Chris Gayle, to the surprise of few, is second in the list with 23 man of the match awards while AB de Villiers closely follows him with 22. Since, there is a large discrepancy in the number of matches played by each individual, let’s take the number of man of the match awards won as a percentage of the total matches played.

In this regard, Hashim Amla tops the table, winning man of the match awards in 13.91% of the games that he has been a part of. This way of analyzing makes more sense, since Amla has taken only 115 games to win 16 man of the match awards whereas Dilshan has won 24 awards in a whopping 314 matches.

Player | Matches | Awards | Percentage |
---|---|---|---|

HM Amla (SA) | 115 | 16 | 13.91 |

V Kohli (India) | 158 | 20 | 12.66 |

AB de Villiers (Afr/SA) | 187 | 22 | 11.76 |

Shakib Al Hasan (Ban) | 150 | 14 | 9.33 |

SR Watson (Aus) | 187 | 17 | 9.09 |

Mohammad Hafeez (Pak) | 161 | 14 | 8.69 |

CH Gayle (ICC/WI) | 269 | 23 | 8.55 |

TM Dilshan (SL) | 314 | 24 | 7.64 |

Shoaib Malik (Pak) | 219 | 16 | 7.31 |

MS Dhoni (Asia/India) | 262 | 19 | 7.25 |

WU Tharanga (Asia/SL) | 177 | 11 | 6.21 |

MN Samuels (WI) | 174 | 10 | 5.75 |

SK Raina (India) | 215 | 11 | 5.12 |

**Man of the series awards**

The number of man of the series awards won is also a scale that can calibrate a batsman’s ability to win matches. This award personifies one’s ability to put up consistent match winning performances throughout a series.

Chris Gayle leads the way with 7 awards but just as we did with the man of the match awards, let’s have a look at it as a percentage of the number of series played. In that regard, Amla tops the list once again, winning the award in 17.64% of the total number of series he has played in. Shakib Al Hasan is a distant second with 11.36%.

How many of them were due to his batting alone is a tough question to answer since he is a genuine all-rounder. Virat Kohli is only a couple of decimal points short in comparison to Shakib with 11.11%.

Player | Series | Awards | Percentage |
---|---|---|---|

HM Amla (SA) | 34 | 6 | 17.64 |

Shakib Al Hasan (Ban) | 44 | 5 | 11.36 |

V Kohli (India) | 36 | 4 | 11.11 |

CH Gayle (ICC/WI) | 64 | 7 | 10.94 |

MS Dhoni (Asia/India) | 60 | 6 | 10 |

AB de Villiers (Afr/SA) | 51 | 5 | 9.80 |

TM Dilshan (SL) | 76 | 6 | 7.89 |

RG Sharma (India) | 38 | 3 | 7.89 |

SR Watson (Aus) | 52 | 4 | 7.69 |

IR Bell (Eng) | 39 | 3 | 7.69 |

Mohammad Hafeez (Pak) | 44 | 3 | 6.82 |

Mushfiqur Rahim (Ban) | 44 | 3 | 6.82 |

Shoaib Malik (Pak) | 67 | 3 | 4.48 |

**Difference between player’s average and team’s average in wins**

A batsman’s contribution to wins cannot be better measured than by his average in victories. However, it is easier to score more runs when the rest of the batsmen do so and more difficult to do when everyone else fails. Hence, the discrepancy between a batsman’s average and that of his team in wins would tell us how much his team is dependent on him. The higher the average of a batsman and the lower the average of the team, the greater the match-winning ability of a particular batsman.

In that aspect, the great MS Dhoni takes the top spot, with a difference of 26.79. He averages 72.83 in wins while his team averages 46.04 in all the games he has been a part of. Amla is second in the list averaging 71.1 in wins while his teammates average 49.22.

Player | Batsman’s avg | Team’s avg | Difference |
---|---|---|---|

MS Dhoni (Asia/India) | 72.83 | 46.04 | 26.79 |

HM Amla (SA) | 71.1 | 49.22 | 21.88 |

V Kohli (India) | 68.74 | 47.25 | 21.49 |

AB de Villiers (Afr/SA) | 66.53 | 47.39 | 19.14 |

Shakib Al Hasan (Ban) | 50.02 | 35.51 | 14.51 |

JP Duminy (SA) | 59.24 | 46.42 | 12.82 |

LRPL Taylor (NZ) | 54.58 | 41.93 | 12.65 |

AD Mathews (SL) | 52.74 | 40.81 | 11.93 |

TM Dilshan (SL) | 50.25 | 38.5 | 11.75 |

CH Gayle (ICC/WI) | 52.09 | 40.46 | 11.63 |

**Minimum of 50 wins*

**Top Scores in victories**

The number of times a batsman has top scored for his team in victories also serves as a unit by which a batsman’s match-winning ability can be measured. Dilshan is on top with 35 top scores for his team in victories while Gayle finds himself one short and De Villiers four short.

But if we go by the percentage as we did before, the Jamaican rockets to number one, top scoring in 31.77% of the games his team has won when he has played. Tamim Iqbal, to many people’s surprise, is at second place top scoring in 30.35% of the wins. The bearded South African is separated from Tamim by a whisker as he has top scored in 30.14% of the wins. With the kind of top-quality batsmen that South Africa boast of, Amla’s record is truly incredible.

Player | No. of top scores | Wins | Percentage |
---|---|---|---|

CH Gayle (WI) | 34 | 107 | 31.77 |

Tamim Iqbal (Ban) | 17 | 56 | 30.35 |

HM Amla (SA) | 22 | 73 | 30.14 |

V Kohli (India) | 27 | 95 | 28.42 |

IR Bell (Eng) | 18 | 69 | 26.08 |

AB de Villiers (SA) | 30 | 116 | 25.86 |

TM Dilshan (SL) | 35 | 164 | 21.34 |

Mohammad Hafeez (Pak) | 17 | 80 | 21.25 |

MJ Guptill (NZ) | 12 | 57 | 21.05 |

WU Tharanga (SL) | 18 | 93 | 19.35 |

**Contribution to team’s total in wins**

One of the shortcomings in the above method is that the degree of contribution to the team cannot be precisely known. For instance, a batsman may have top-scored for his team while another batsman could have found himself just a run short of the top score. Hence, since the two batsmen are only separated by a few runs (in this case just one), the contributions of the two batsmen is more or less equal.

So, to find out the number of times a batsman has contributed more towards the team’s victory, let’s gauge the amount of match-winning innings that contributed more than 40% to the total. The runs scored through extras have been ignored here.

Player | Contribution more than 40% | Number of wins | Custom |
---|---|---|---|

CH Gayle (WI) | 23 | 107 | 21.50 |

Tamim Iqbal (Ban) | 9 | 56 | 16.07 |

V Kohli (India) | 15 | 95 | 15.78 |

HM Amla (SA) | 11 | 73 | 15.06 |

WU Tharanga (SL) | 12 | 93 | 12.90 |

IR Bell (Eng) | 8 | 69 | 11.59 |

AB de Villiers (SA) | 13 | 116 | 11.20 |

TM Dilshan (SL) | 18 | 164 | 10.97 |

MJ Guptill (NZ) | 6 | 57 | 10.52 |

RG Sharma (India) | 8 | 78 | 10.25 |

**Minimum of 50 wins*

The king of sixes, Gayle tops the list yet again scoring more than 40% of the team’s runs in 21.5% of the wins. The Bangladeshi opener surprises once again having scored more than 40% of the runs in 16.07% of the wins. Kohli finds himself at third place while Amla is pushed to the fourth position.

Even though it must have been easier for both Gayle and Tamim to have scored the bulk of the runs for their respective teams in victories, Kohli’s and Amla’s feats are exceptional since they have come playing for teams that boast of some of the greatest names in modern-day cricket. To be able to steal the show while playing alongside other great players testifies their match-winning capability.

At the same time, this shows how much the team has depended on both Gayle and Tamim to win matches for their corresponding teams. Only match winners have the ability to carry the entire team solely on their shoulders.

**Percentage of centuries that resulted in victories**

Centuries are often match winning. Hence, computing the number of times a team wins the match after a batsman scores a century can be a good measure to identify the greatest match-winner. Kohli, in this regard, has the most outstanding record with only 2 of his centuries coming in a losing cause. Amla, too, has only 2 centuries resulting in defeats but has scored a couple of centuries less than the Indian Test skipper.

Player | 100s | No. of Wins | Percentage |
---|---|---|---|

V Kohli (India) | 22 | 20 | 90.90 |

HM Amla (SA) | 20 | 18 | 90 |

AB de Villiers (SA) | 20 | 17 | 85 |

WU Tharanga (SL) | 13 | 11 | 84.61 |

TM Dilshan (SL) | 22 | 18 | 81.81 |

CH Gayle (WI) | 22 | 13 | 59.09 |

LRPL Taylor (NZ) | 13 | 6 | 46.15 |

**Minimum 10 centuries*

**Percentage of wins in which a batsman scored a century**

A relatively similar factor that would help us determine the greatest is to find the number of victorious matches in which a batsman has scored a century.

Player | Matches won | 100s | Percentage |
---|---|---|---|

HM Amla (SA) | 73 | 18 | 24.65 |

V Kohli (India) | 95 | 20 | 21.05 |

AB de Villiers (Afr/SA) | 117 | 17 | 14.52 |

TM Dilshan (SL) | 167 | 18 | 10.77 |

CH Gayle (ICC/WI) | 109 | 13 | 11.92 |

WU Tharanga (Asia/SL) | 94 | 11 | 11.70 |

**Minimum 10 centuries*

According to the above table, the South African opening batsman has scored a century in 24.65% of his team’s victories which clearly makes him stand out. Kohli has done the same in 21.05% of the wins while AB de Villiers is a distant third having scored a century in 14.52% of the wins.

**The impact in wins**

This is by far the best way to analyse the match-winning ability of a batsman. As aforementioned, a great batsman is not necessarily a great match winner. A team should depend on a batsman to a great extent for him to be termed a match winner. So how do you find out how much a team depends on a batsman?

If a batsman has a very high average in wins, that means he has contributed a lot to his team’s wins. But there are batsmen who have had higher averages in defeats too. What this signifies is that, despite the gargantuan numbers, their innings didn’t have much impact in the outcome of the game.

So the difference between average in wins and average in defeats would give you the impact a batsman has in his team, for a higher average in wins and lower average in defeats would give an idea as to how much the team has been reliant on a batsman’s performance. But let’s not make it too simple.

Another factor to consider is how the team as a whole has fared. Averaging higher as a batsman when the team averages higher cannot be considered a trait of a match winner. Lahiru Thirimanne’s average of 42.53 in wins, for instance, might seem decent. But the fact that his team averages 45.21 in wins makes his gains less significant.

So, a more efficient way of calculating the impact is by finding the difference between the average of the batsman and the team in wins and defeats separately and finding the difference between the values obtained for both wins and defeats.

Player | Batsman’s avg in wins | Team’s avg in wins | Batsman’s avg in defeats | Teams’ average in defeats | Difference in avg in wins | Difference in avg in defeats | Difference between wins and defeats |
---|---|---|---|---|---|---|---|

JP Duminy (SA) | 59.24 | 46.42 | 20.06 | 23.15 | 12.82 | -3.09 | 15.91 |

MS Dhoni (Asia/India) | 72.83 | 46.04 | 34.23 | 22.69 | 26.79 | 11.54 | 15.25 |

HM Amla (SA) | 71.1 | 49.22 | 30.27 | 23.08 | 21.88 | 7.19 | 14.69 |

V Kohli (India) | 68.74 | 47.25 | 31.19 | 23.71 | 21.49 | 7.48 | 14.01 |

MJ Guptill (NZ) | 51.4 | 41.05 | 20.46 | 21.29 | 10.35 | -0.83 | 11.18 |

MN Samuels (WI) | 52.62 | 41.11 | 23.34 | 21.41 | 11.51 | 1.93 | 9.58 |

Shakib Al Hasan (Ban) | 50.02 | 35.51 | 26.41 | 20.23 | 14.51 | 6.18 | 8.33 |

EJG Morgan (Eng/Ire) | 52.51 | 43.01 | 26.12 | 23.23 | 9.5 | 2.89 | 6.61 |

AB de Villiers (Afr/SA) | 66.53 | 47.39 | 36.3 | 23.07 | 19.14 | 13.23 | 5.91 |

TM Dilshan (SL) | 50.25 | 38.5 | 29.19 | 22.23 | 11.75 | 6.96 | 4.79 |

According to the table, you can clearly see that Duminy’s average has been lower than that of the team in defeats. But, he has averaged 12.82 higher in victories. The obvious inference that can be made is that his performance has had a major impact in the outcome of a match.

On the other hand, it can be argued that South Africa have performed badly in defeats and Duminy has fared worse. So, is it right to penalize a batsman for doing well in both defeats and wins equally? However, we are in the quest to find the greatest match winner and not the best batsman. So, if a team is going to perform well when a batsman performs well and is going to stutter when the batsman struggles, then that is a clear indication of the match winning ability of the batsman.

It is quite startling to note that De Villiers has managed to perform far better than his team in both defeats and wins. Even though we could deduce his meticulousness in batting, this clearly shows that he has not been a deciding factor when it comes to the outcome of a game.

**Finding the winner**

Thus far, we have looked at 8 different factors that govern a batsman’s match-winning ability. An easy way of finding out the winner is to add up everything and finding out who has a higher number.

But not all 8 factors are equal. For instance, a match winner might not necessarily be a hoarder of centuries. Dhoni, easily one of the best match winners India has got, has only nine centuries to his name.

At the same time, the impact a batsman makes – as we calculated above – can have a profound influence on the result of a match.

So, to find the weighted average match-winning ability of a batsman, let’s first find out the mean of each record and then compute the percentage of deviation.

One common observation you would have made in the above lists is that only a handful of batsmen have appeared in all of those lists. The research was done with 31 batsmen with some making some lists and some failing.

So, it is important that we find the mean value of all those 31 batsmen. Since the list is long, I am including only the mean value in this article.

MOM (%) | 8.71425% |
---|---|

MOS (%) | 9.242407% |

Difference between avg in wins | 6.61258065 |

Top scores in victories (%) | 15.8563198% |

Contribution to winning score (%) | 7.700475852% |

Centuries resulting in wins (%) | 76.7982% |

Wins in which a century was scored (%) | 10.021324% |

Impact | 2.63032 |

Now, we know the average numbers, so let’s compute the percentage of deviation from the mean value for each batsman. The percentage of deviation is calculated by dividing a batsman’s record by the mean value and multiplying the answer by 100.

**The percentage of deviation of the top 10 batsmen**

Players | Impact | Contribution | Top scores | 100s resulting in wins | 100s in total wins | MOS | MOM | AVG diff. in wins |
---|---|---|---|---|---|---|---|---|

HM Amla | 558.49 | 195.68 | 190.06 | 117.19 | 246.05 | 190.94 | 159.66 | 330.88 |

V Kohli | 532.63 | 205.05 | 179.24 | 118.37 | 210.08 | 120.22 | 145.26 | 324.99 |

MS Dhoni | 579.78 | 18.16 | 75.50 | 0.00 | 41.01 | 108.20 | 83.22 | 405.14 |

JP Duminy | 604.87 | 29.51 | 71.67 | 0.00 | 45.36 | 0.00 | 0.00 | 193.87 |

AB de Villiers | 224.69 | 145.54 | 163.10 | 110.68 | 144.99 | 106.08 | 135.01 | 289.45 |

CH Gayle | 141.43 | 279.14 | 200.40 | 76.94 | 120.11 | 118.34 | 98.12 | 175.88 |

MJ Guptill | 425.04 | 136.70 | 132.77 | 0.00 | 89.10 | 0.00 | 0.00 | 156.52 |

Shakib Al Hasan | 316.69 | 76.39 | 102.02 | 0.00 | 74.47 | 122.95 | 107.10 | 219.43 |

MN Samuels | 364.21 | 102.52 | 107.88 | 0.00 | 78.78 | 0.00 | 65.95 | 174.06 |

TM Dilshan | 182.11 | 142.53 | 134.59 | 106.54 | 109.52 | 85.42 | 87.71 | 177.69 |

**Note that certain players have a 0 deviation, since the concerned record was too low to be reckoned as a sufficient sample space.*

Now that we have the percentage of deviation, let’s assign different weightages to different records according to their importance and find the weighted average of the percentages.

As mentioned in the beginning of this long article, this is the only place I let my opinion lobby the results.

Below is the list of records in the descending order of importance to compute the match winning ability of a batsman. I have allocated a number starting from 8 in the descending order to each record to find out the weightage each record would have in the final value to be calculated.

Record | Index | Weightage |
---|---|---|

Impact | 8 | 22.22 |

Contribution | 7 | 19.44 |

Top Scores | 6 | 16.66 |

MOS | 5 | 13.88 |

MOM | 4 | 11.11 |

Diff. in avg in wins | 3 | 8.33 |

Centuries resulting in wins | 2 | 5.55 |

Centuries scored in total wins | 1 | 2.77 |

Your priority list might vary, and if you want to find out who the greatest match winner is according to your list, all you need to do is find the product of each record and their weightage and divide by 100. Then add the result for each record to find the weighted average.

So, according to my list, here is what I got.

Player | Impact | Contribution | Top score | 100s resulting in wins | 100s in total wins | MOS | MOM | Diff, in avg in wins | Weighted average |
---|---|---|---|---|---|---|---|---|---|

HM Amla | 124.09 | 38.04 | 31.67 | 6.51 | 6.83 | 26.51 | 17.73 | 27.57 | 278.99 |

V Kohli | 118.35 | 39.86 | 29.87 | 6.57 | 5.83 | 16.69 | 16.13 | 27.08 | 260.42 |

MS Dhoni | 128.82 | 3.53 | 12.58 | 0 | 1.13 | 15.02 | 9.24 | 33.76 | 204.11 |

JP Duminy | 134.40 | 5.73 | 11.94 | 0 | 1.25 | 0 | 0 | 16.15 | 169.50 |

AB de Villiers | 49.92 | 28.29 | 27.18 | 6.14 | 4.02 | 14.73 | 15.01 | 24.12 | 169.43 |

CH Gayle | 31.42 | 54.27 | 33.39 | 4.27 | 3.33 | 16.43 | 10.90 | 14.65 | 168.70 |

MJ Guptill | 94.44 | 26.57 | 22.12 | 0 | 2.47 | 0 | 0 | 13.04 | 158.66 |

Shakib Al Hasan | 70.36 | 14.85 | 17.01 | 0 | 2.06 | 17.07 | 11.90 | 18.28 | 151.55 |

MN Samuels | 80.92 | 19.93 | 17.98 | 0 | 2.18 | 0 | 7.32 | 14.50 | 142.86 |

TM Dilshan | 40.4 | 27.71 | 22.43 | 5.91 | 3.04 | 11.86 | 9.74 | 14.80 | 135.98 |

To the surprise of many including myself, the greatest match-winner in ODIs among current batsmen is Amla.

There might be arguments saying that Amla is not as aggressive as the rest, despite him striking at around 90 runs per 100 balls. There is a tendency among fans to confuse between match-winners and finishers. It can be justified to an extent that humans have a stronger memory of the latest happenings than the earlier ones. But, remember a run-a-ball 100 at the top of the innings lays the main foundation for a victory even if a finisher seals the game with a 15-ball 30.

**NOTE: I didn’t filter out records based on venues or oppositions since in my methodology, I tried to separate a batsman from the rest of the team. So if a batsman is going to do far better than his teammates, then that makes him a clear winner. Hence, even if a batsman is going to outplay his teammates in a game against a minnow team, then that is a profound barometer of his match-winning ability as the rest of his team failed to shine against the same opposition. **

**A match-winner for a particular team might not be the same for another. A batsman’s match-winning ability is relative to his team only.**

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