How Net Run Rate is Calculated in a Tournament - The Cric ...

Well, this is an easy question isn’t it? Whoever hit the Most Sixes is the Greatest. Duh. That player hit more Sixes than anyone ever, how can they not be the greatest? Let’s have a look who it is…

1. Shahid Afridi – 351 Sixes in 398 Matches
2. CH Gayle – 314 Sixes in 289 Matches
3. ST Jayasuriya – 270 Sixes in 445 Matches
4. MS Dhoni – 224 Sixes in 341 Matches
5. RG Sharma – 218 Sixes in 206 Matches
6. AB de Villiers – 204 Sixes in 228 Matches
7. BB McCullum – 200 Sixes in 260 Matches
8. SR Tendulkar – 195 Sixes in 463 Matches
9. SC Ganguly – 190 Sixes in 311 Matches
10. EJG Morgan – 189 Sixes in 222 Matches
11. MJ Guptill – 164 Sixes in 169 Matches
12. RT Ponting – 162 Sixes in 375 Matches
13. Yuvraj Singh – 155 Sixes in 304 Matches
14. CL Cairns – 153 Sixes in 215 Matches
15. AC Gilchrist – 149 Sixes in 287 Matches

Shahid Afridi comfortably leads this category, although Gayle could overtake him if he has a good world cup. All the big names are all there; de Villiers, McCullum, Sachin, Ponting. I wouldn’t say there are too many surprises in this list.
So there you have it Shahid Afridi is the greatest six hitt-, wait hang on a minute. Ganguly has played 90 more matches, yet only scored one more six than Morgan… Is he really a greater six hitter than Morgan?
What if I considered how many sixes were hit per innings? And also how many balls it took per six, to consider the batsmen who come in lower down the order and don’t face as many balls per innings. Yes ok that could be a good idea, but wait does that mean I need to do more analysis? Yes? Ugh alright then, here we go.
Let’s have a look at Sixes per innings, with a minimum of 30 sixes hit:

1. SO Hetmyer – 1.50 Sixes per innings
2. HH Pandya – 1.24 Sixes per innings
3. AD Russell – 1.23 Sixes per innings
4. CJ Anderson – 1.22 Sixes per innings
5. MP Stoinis – 1.16 Sixes per innings
6. KA Pollard – 1.16 Sixes per innings
7. CH Gayle – 1.11 Sixes per innings
8. Rizwan Cheema – 1.09 Sixes per innings
9. RG Sharma – 1.09 Sixes per innings
10. JC Buttler – 1.08 Sixes per innings
11. YK Pathan – 1.05 Sixes per innings
12. Najibullah Zadran – 1.02 Sixes per innings
13. MJ Guptill – 0.99 Sixes per innings
14. R Powell – 0.97 Sixes per innings
15. GJ Maxwell – 0.97 Sixes per innings

Well this measure is definitely better. All these players are well-known for their big hitting, and Hetmyer especially has burst onto the scene and really shown his batting prowess. Maybe you’d expect to see de Villiers (17th with 0.94) and McCullum (25th with 0.88) in the top fifteen to name a few but they are both still up there, and it is worth mentioning that they were much better than just ‘power players’ like the names above. For what it's worth, Rahul Dravid takes bottom spot, 196th with a measly 0.13 sixes per innings.
Now let’s look at Balls per Six, which will better accommodate the lower order batsmen who have less balls to construct an innings, again minimum 30 sixes hit:

1. AD Russell – 14.2 Balls per Six
2. YK Pathan – 16.6 Balls per Six
3. HH Pandya – 17.4 Balls per Six
4. CJ Anderson – 18.5 Balls per Six
5. Rizwan Cheema – 19.6 Balls per Six
6. Shahid Afridi – 19.6 Balls per Six
7. DJG Sammy – 22.3 Balls per Six
8. KA Pollard – 22.4 Balls per Six
9. SO Hetmyer – 22.3 Balls per Six
10. BL Cairns – 23.0 Balls per Six
11. NLTC Perera – 23.9 Balls per Six
12. JC Buttler – 25.2 Balls per Six
13. GJ Maxwell – 25.4 Balls per Six
14. DR Smith – 26.3 Balls per Six
15. R Powell – 26.9 Balls per Six

This list is very similar to the Sixes per Innings list, as it should be, but lower order batsman like Russell, Sammy and Perera rise up the list, while good batsman who face a lot of balls, like Gayle, Sharma and Guptill fall down the list. Amid all the change there is one constant. In last again is Dravid with 363.9 Balls per Six, which is impressive in how high that is.
So it seems that Russell and Pandya, who are in the top 3 for both measures are the greatest so that settles that then. Thanks for readi-. Oh hang on again, I’ve just realised something.
Let’s have a look at this graph here, posted by chance by a random (most likely very handsome) redditor. The graph is for INDIVIDUAL innings, and shows that average balls needed to hit a six has fallen from 131 balls in 2000 to 52 balls in 2019 (*up to CWC 2019), and therefore sixes hit in an average individual innings has increased from 0.24 sixes per individual innings in 2000 to 0.60 in 2019. If you take this further back the values for 1980 and 1990 for Sixes per Innings is 0.14 and 0.27 respectively and for Balls per Six for 1980 and 1990 is 433 and 203 respectively. Therefore you can deduce that Sixes are hit roughly 4 times more often in 2019 compared to 1990.
This very obviously shows that sixes are hit way more often nowadays than previously. It is therefore no surprise that 13 of the top 15 players in the second list and 12 of the top 15 in the third list have played at least one game in the period 2015-2019 which is generally recognised as a period of rising strike rates and massive scores. Here’s a thought, what if I weighted every Six hit in a year by the yearly average and arrive at a number where you could easily differentiate between players.
(This paragraph is a boring formula, scroll down if you don’t want to read.) What I have done, for both the previous Balls and Innings measures, is taken that specific value for a given year and divided by the average figure for that year (so Morgan in 2019 hit 2.38 sixes per innings divided by the average 0.6 gives 3.93). Then take that value and multiply it by innings played in the yea total innings played. (So for Morgan, it would be 3.93*(8/207) = 0.15). You do this for all years that the batsmen played and Sum all the values. The final value for Morgan is 2.08. This means that if an average batsman played exactly the same number of innings in those years as Morgan, then Morgan would hit 2.08 times more Sixes than the average batsman in this period. The exact same calculation is done for Balls per Six, and Morgan’s value here is 1.67, which makes sense, as he is likely to face more balls per innings than the average batsman.
So this should take years into perspective, and looking at Weighted Sixes per innings first, let’s look who came out on top:

1. IVA Richards – 3.64
2. BL Cairns – 3.21
3. Rizwan Cheema – 3.19
4. CG Greenidge – 3.13
5. KA Pollard – 3.11
6. YK Pathan – 3.09
7. CH Gayle – 3.08
8. AD Russell – 3.02
9. SO Hetmyer – 2.97
10. Shahid Afridi – 2.96
11. RL Powell – 2.94
12. JM Kemp – 2.93
13. A Flintoff – 2.88
14. CL Cairns – 2.73
15. HH Pandya – 2.67

This gives a better unbiased look, as at least three batsmen played in each decade from the 1970s. Viv, as everyone knows, was a monster. He hit more than 3.5x more sixes than his average peer and leads the field by a distance. The big hitters of today are still in the top 15, but have been relegated down somewhat, while big hitters of yesteryear like Flintoff, Cairns and Greenidge make an appearance. Now lets check out Weighted Balls per Six, which will most likely reward lower order batsmen who faced less balls:

1. BL Cairns – 12.10
2. Shahid Afridi – 5.42
3. YK Pathan – 5.30
4. AD Russell – 5.21
5. IT Botham – 4.76
6. Wasim Akram – 4.71
7. IVA Richards – 4.56
8. Rizwan Cheema – 4.41
9. Kapil Dev – 4.29
10. RL Powell – 4.16
11. HH Pandya – 3.78
12. KA Pollard – 3.60
13. DJG Sammy – 3.56
14. DR Smith – 3.53
15. CJ Anderson – 3.50

Lance Cairns wins this by an absolute country mile. He hit over 12 times more Sixes than his average peer. To show his incredible ability: in 1983, 145 sixes were hit in 37,464 balls (258 balls per six). Lance hit 21 sixes in 259 balls at a frankly ridiculous rate of 13 balls per six, 21 times better than the average. Looking at the unweighted Sixes per ball, Lance is 10th and was the only player in the top 15 to play in the 70s and 80s and only Shahid Afridi played any ODI cricket before 2003. As predicted, the lower order batsman come to the fore in this table, and the big hitters of today still make an appearance.
Who is the Greatest Six hitter of all time? You could argue Afridi, or Russell, or Viv, but for me it has to be Lance Cairns. He hit 41 sixes in 65 innings, back when people didn’t even know what a boundary was. He is 2nd behind Viv in Weighted Sixes per innings despite facing less than 15 balls per average innings. Upon further research, I also found out he named his bat ‘Excalibur’.. I mean come on, if you don’t love him even more now, then you never will.
Thanks for reading, I actually very much enjoyed making this, so I hope you enjoyed reading this. If you have any specific queries about what I could've done a bit different, please comment down below and if you're interested in finding a value for a specific player and where they rank, comment their name and I should be able to tell you their scores :)

What is the DLS?
The Duckworth-Lewis-Stern or DLS method (as it is now known) is a mathematical system employed to calculate target scores and reach outcomes in rain-shortened limited-overs matches. Devised by English statisticians Frank Duckworth and Tony Lewis and originally named after them, it was first used in 1997. Australian academic Steve Stern updated the formula, becoming its custodian ahead of the 2015 World Cup; his name was added to the title.Neither the ARR nor the MPO methods were able to factor the match situation into their calculations, failing to take into account the wickets a team had left. The DLS method addresses this issue, considering both wickets and overs as resources and revising the target based on the availability of those resources. At the start of an innings, a team has 100% of its resources — 50 overs and 10 wickets — available. The DLS method expresses the balls and wickets remaining at any point as a percentage. How much is a wicket or a ball worth in percentage terms? This is calculated according to a formula which takes into account the scoring pattern in international matches, derived from analysis of data (ODI and T20, men and women) from a sliding four-year window. On the first of July every year, a new year’s worth of data is added; so the DLS evolves as scoring trends do.
The rate at which resources deplete is not constant over the course of an innings: the curve is exponential, with that resource percentage falling faster as more wickets are lost and more balls are consumed.
The DLS methods sets targets (and decides outcomes) by calculating how many runs teams should score (and would have scored) if the resources available to both sides were equal. To calculate a target, the formula may simply be expressed thus: Team 2’s par score = Team 1’s score x (Team 2’s resources/Team 1’s resources). In international cricket, the resource values (which are not publicly available) are obtained from a computer programme.
The DLS method also allows for the fact that a team batting before a rain interruption would have batted differently had it known the game was going to be truncated. Of course, the weighting of wickets and overs is based on a formula, and there can be no universally perfect weightage, simply because the method cannot make qualitative measurements of individual batting abilities. It was long felt that under the D-L method, teams chasing big totals were better off keeping wickets in hand when rain was around the corner even if it meant scoring at a lower rate. Steve Stern felt he had improved on the D-L method in this regard by adjusting the formula to reflect changing realities in high-scoring ODIs and T20 matches.
An older version of the DL method (called the D-L Standard Edition), meant to be used where computers are not available, applies pre-calculated resource values off a chart. Where upward revisions are required (when the first innings is interrupted), a quantity called the G50 — the average total score in a 50-over innings — is used as reference. For matches involving ICC full member nations, G50 is currently fixed at 245. However, the Standard Edition is not used in international cricket.
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Yesterday I had a great chat with IndYeah777 about simple methods to analyse players consistency, and ability to pass certain scoring milestones. For those interested they can see the original conversation here.
Personally I prefer things that anyone with a few minutes and stats guru themselves could calculate to more over the top analyses, although the latter has tempted me a great deal of times in the past. The benefits of more simplified analyses though, with clearly defined metrics, far outweigh the greater probative value of more complex analyses. This isn't just limited to more people being able to see and critique the method, but also allowing them to be more approachable to a wider audience.
In this instance my concern is with using something well known in cricket circles, the conversion rate. This is a measure of how often a batsman goes from scoring 50 to 100, this being in terms of a simple formula 100s/(50s+100s). The reason that this is 50s+100s on the bottom here is that when a batsman passes 100, that score only counts in the 100s column, not the 50s column as well. It's also why someone like Bradman can have 29 centuries and only 13 50s.
Now, from this base we can consider something that is well known within cricket statistics, but not well described by most stats, this being that players tend to settle into their innings, and that this is different for each player. You can see this with players like Kohli who is generally not great until he gets going, then can flay opposition.
We can also use something else known within cricket, that scores tend to following something called a geometric distribution. This distribution represents the probability of a number of failures before a success (or vice versa), in this instance of course "failures" are balls where a wicket doesn't occur, and "successes" are wickets. Of course as we're talking about batsmen I doubt they'd consider it that way around, but I'd rather keep the language to the same as used in that wikipedia article for those interested in further reading. There is a subtly we need to consider about this, but first we're going to take two assumptions to keep this as simple as possible:

1. That balls is approximately proportional to runs (that is, they have a fairly consistent strike rate over their innings).
2. Most players face enough balls that we can use the more convenient exponential distribution.

The first means that we can just consider runs and it shouldn't be too much of a problem, and the second means we can use the continuous analogue to the geometric function. Now, there are cases where neither of these are appropriate, but for our purposes this should be sufficient.
Now, there is a subtly we need to keep in mind, the geometric/exponential distribution assumes that the probability of getting out each ball is the same. This is funnily enough the exact thing we're trying to probe though, how this changes. Why use these then? Because we can make what we'll call "the model batsman". Essentially, we can predict the chance of passing these milestones for a batsman of a given average, and compare with what players actually manage. In effect we're interested in how much better or worse than the model batsmen each player is.
How can we use this then, well, we should first define the data we'll use:

1. Total innings a player has batted
2. Number of times they've scored 25+
3. Number of 50s
4. Number of 100s

This gives us three distinct regions we can probe. There were a number of ways to do this, but the one that seemed most appropriate was focusing on how well they converted from one score to the next.
It's also worth noting that exponential and geometric distributions are "memoryless", that is, the chance of scoring 30 more runs when you're on 50 should be the same as when you're on 3. This is a property we can take advantage of. Essentially we can compare how well players go from one score to another from the above.
With those previous three we can define three separate new conversion rates:

1. Past 25 runs: Number of 25+ Scores / Total Innings
2. 25s converted to 50+: Number of 50s + 100s / Number of 25+ Scores
3. 50s converted to 100+: Number of 100s / Number of 50s + 100s

It's worth noting that this final one is simply the standard conversion rate. Using these definitions and comparing it to what average that would relate to for the "model batsman", we can find the following for all batsmen with at least 10 centuries:

Players	Ave.	Ins	25+%	50+%	100+%	25+ Ave.	50+ Ave.	100+ Ave.
DG Bradman (AUS)	99.94	80	71.25%	73.68%	69.05%	73.75	81.86	134.99
GA Headley (WI)	60.83	40	55.00%	68.18%	66.67%	41.81	65.27	123.31
H Sutcliffe (ENG)	60.73	84	69.05%	67.24%	41.03%	67.49	62.99	56.11
SPD Smith (AUS)	59.66	104	59.62%	66.13%	48.78%	48.33	60.45	69.65
KF Barrington (ENG)	58.67	131	61.07%	68.75%	36.36%	50.69	66.72	49.42
ED Weekes (WI)	58.61	81	65.43%	64.15%	44.12%	58.94	56.31	61.10
WR Hammond (ENG)	58.45	140	62.86%	52.27%	47.83%	53.84	38.53	67.78
GS Sobers (WI)	57.78	160	60.63%	57.73%	46.43%	49.95	45.50	65.16
KC Sangakkara (SL)	57.40	233	58.37%	66.18%	42.22%	46.43	60.55	57.98
JB Hobbs (ENG)	56.94	102	66.67%	63.24%	34.88%	61.65	54.54	47.47
CL Walcott (WI)	56.68	74	56.76%	69.05%	51.72%	44.13	67.49	75.84
L Hutton (ENG)	56.67	138	60.87%	61.90%	36.54%	50.35	52.12	49.66
JH Kallis (SA)	55.37	280	57.14%	64.38%	43.69%	44.67	56.76	60.38
GS Chappell (AUS)	53.86	151	55.63%	65.48%	43.64%	42.62	59.03	60.29
SR Tendulkar (INDIA)	53.78	329	54.41%	66.48%	42.86%	41.07	61.23	59.01
JE Root (ENG)	53.76	110	57.27%	71.43%	28.89%	44.85	74.30	40.26
BC Lara (WI)	52.88	232	56.03%	63.08%	41.46%	43.16	54.25	56.79
CA Pujara (INDIA)	52.65	85	56.47%	58.33%	46.43%	43.74	46.38	65.16
Javed Miandad (PAK)	52.57	189	57.14%	61.11%	34.85%	44.67	50.76	47.43
R Dravid (INDIA)	52.31	286	58.04%	59.64%	36.36%	45.95	48.36	49.42
Mohammad Yousuf (PAK)	52.29	156	54.49%	67.06%	42.11%	41.17	62.56	57.80
Younis Khan (PAK)	52.05	213	55.40%	56.78%	50.75%	42.32	44.17	73.71
RT Ponting (AUS)	51.85	287	54.01%	66.45%	39.81%	40.58	61.17	54.27
A Flower (ZIM)	51.54	112	53.57%	65.00%	30.77%	40.05	58.03	42.42
MEK Hussey (AUS)	51.52	137	59.85%	58.54%	39.58%	48.70	46.68	53.95
S Chanderpaul (WI)	51.37	280	55.71%	61.54%	31.25%	42.73	51.49	42.98
KS Williamson (NZ)	51.16	110	52.73%	72.41%	40.48%	39.06	77.45	55.28
SM Gavaskar (INDIA)	51.12	214	52.80%	69.91%	43.04%	39.14	69.84	59.30
SR Waugh (AUS)	51.06	260	51.54%	61.19%	39.02%	37.71	50.90	53.13
ML Hayden (AUS)	50.73	184	58.70%	54.63%	50.85%	46.92	41.35	73.92
AR Border (AUS)	50.56	265	54.72%	62.07%	30.00%	41.45	52.41	41.52
AB de Villiers (SA)	50.46	176	59.66%	57.14%	35.00%	48.40	44.67	47.62
IVA Richards (WI)	50.23	182	56.59%	66.99%	34.78%	43.91	62.40	47.34
DCS Compton (ENG)	50.06	131	54.96%	62.50%	37.78%	41.76	53.19	51.36
HM Amla (SA)	49.91	186	56.99%	59.43%	42.86%	44.45	48.04	59.01
DPMD Jayawardene (SL)	49.84	252	54.37%	61.31%	40.48%	41.02	51.10	55.28
Inzamam-ul-Haq (PAK)	49.60	200	54.00%	65.74%	35.21%	40.57	59.60	47.90
V Kohli (INDIA)	49.55	101	50.50%	60.78%	54.84%	36.58	50.21	83.22
V Sehwag (ICC/INDIA)	49.34	180	56.11%	54.46%	41.82%	43.26	41.13	57.35
MJ Clarke (AUS)	49.10	198	46.97%	59.14%	50.91%	33.08	47.59	74.05
TT Samaraweera (SL)	48.76	132	49.24%	67.69%	31.82%	35.29	64.07	43.66
RN Harvey (AUS)	48.41	137	52.55%	62.50%	46.67%	38.86	53.19	65.60
KD Walters (AUS)	48.26	125	53.60%	71.64%	31.25%	40.08	74.96	42.98
GC Smith (SA)	48.25	205	56.59%	56.03%	41.54%	43.90	43.16	56.91
DA Warner (AUS)	47.94	123	56.10%	63.77%	45.45%	43.24	55.56	63.41
G Boycott (ENG)	47.72	193	55.44%	59.81%	34.38%	42.38	48.64	46.82
AC Gilchrist (AUS)	47.60	137	52.55%	59.72%	39.53%	38.86	48.49	53.88
RB Kanhai (WI)	47.53	137	59.85%	52.44%	34.88%	48.70	38.72	47.47
KP Pietersen (ENG)	47.28	181	56.35%	56.86%	39.66%	43.59	44.28	54.05
WM Lawry (AUS)	47.15	123	52.85%	61.54%	32.50%	39.19	51.49	44.48
LRPL Taylor (NZ)	47.10	146	52.05%	56.58%	37.21%	38.29	43.89	50.57
RB Simpson (AUS)	46.81	111	55.86%	59.68%	27.03%	42.92	48.42	38.21
Azhar Ali (PAK)	46.78	116	52.59%	65.57%	35.00%	38.89	59.24	47.62
PBH May (ENG)	46.77	106	56.60%	58.33%	37.14%	43.92	46.38	50.48
CH Lloyd (WI)	46.67	175	54.29%	61.05%	32.76%	40.92	50.66	44.80
Misbah-ul-Haq (PAK)	46.62	132	55.30%	67.12%	20.41%	42.20	62.71	31.46
AL Hassett (AUS)	46.56	69	56.52%	53.85%	47.62%	43.81	40.38	67.39
DM Jones (AUS)	46.55	89	47.19%	59.52%	44.00%	33.29	48.18	60.90
AR Morris (AUS)	46.48	79	55.70%	54.55%	50.00%	42.71	41.24	72.13
DR Martyn (AUS)	46.37	109	55.05%	60.00%	36.11%	41.87	48.94	49.08
AN Cook (ENG)	46.33	266	50.38%	64.18%	36.05%	36.46	56.37	49.00
DL Amiss (ENG)	46.30	88	42.05%	59.46%	50.00%	28.85	48.08	72.13
VVS Laxman (INDIA)	45.97	225	52.44%	61.86%	23.29%	38.73	52.05	34.31
Saeed Anwar (PAK)	45.52	91	53.85%	73.47%	30.56%	40.38	81.08	42.17
MD Crowe (NZ)	45.36	131	49.62%	53.85%	48.57%	35.67	40.38	69.23
G Kirsten (SA)	45.27	176	50.00%	62.50%	38.18%	36.06	53.19	51.93
JL Langer (AUS)	45.27	182	54.40%	53.54%	43.40%	41.05	40.01	59.89
M Azharuddin (INDIA)	45.03	147	51.70%	56.58%	51.16%	37.89	43.89	74.60
SM Katich (AUS)	45.03	99	56.57%	62.50%	28.57%	43.87	53.19	39.91
Zaheer Abbas (PAK)	44.79	124	46.77%	55.17%	37.50%	32.90	42.03	50.97
CG Greenidge (WI)	44.72	185	51.35%	55.79%	35.85%	37.51	42.83	48.73
GP Thorpe (ENG)	44.66	179	49.72%	61.80%	29.09%	35.77	51.94	40.49
AI Kallicharran (WI)	44.43	109	51.38%	58.93%	36.36%	37.53	47.27	49.42
RB Richardson (WI)	44.39	146	56.16%	52.44%	37.21%	43.33	38.72	50.57
TW Graveney (ENG)	44.38	123	52.85%	47.69%	35.48%	39.19	33.76	48.25
DI Gower (ENG)	44.25	204	54.41%	51.35%	31.58%	41.07	37.51	43.37
DJ Cullinan (SA)	44.21	115	52.17%	56.67%	41.18%	38.42	44.01	56.35
MC Cowdrey (ENG)	44.06	188	51.06%	62.50%	36.67%	37.19	53.19	49.83
Hanif Mohammad (PAK)	43.98	97	45.36%	61.36%	44.44%	31.62	51.19	61.65
ME Trescothick (ENG)	43.79	143	51.75%	58.11%	32.56%	37.94	46.05	44.55
Saleem Malik (PAK)	43.69	154	50.00%	57.14%	34.09%	36.06	44.67	46.46
DC Boon (AUS)	43.65	190	50.53%	55.21%	39.62%	36.62	42.08	54.00
JH Edrich (ENG)	43.54	127	54.33%	52.17%	33.33%	40.97	38.42	45.51
MA Taylor (AUS)	43.49	186	52.69%	60.20%	32.20%	39.01	49.26	44.12
PA de Silva (SL)	42.97	159	47.17%	56.00%	47.62%	33.27	43.11	67.39
HP Tillakaratne (SL)	42.87	131	43.51%	54.39%	35.48%	30.04	41.04	48.25
MJ Slater (AUS)	42.83	131	50.38%	53.03%	40.00%	36.46	39.41	54.56
IR Bell (ENG)	42.69	205	47.80%	69.39%	32.35%	33.87	68.40	44.30
GA Gooch (ENG)	42.58	215	53.02%	57.89%	30.30%	39.40	45.74	41.87
M Amarnath (INDIA)	42.50	113	55.75%	55.56%	31.43%	42.78	42.53	43.19
IM Chappell (AUS)	42.42	136	52.94%	55.56%	35.00%	39.30	42.53	47.62
D Elgar (SA)	42.30	67	43.28%	62.07%	55.56%	29.85	52.41	85.06
DL Haynes (WI)	42.29	202	49.50%	57.00%	31.58%	35.55	44.47	43.37
PR Umrigar (INDIA)	42.22	94	51.06%	54.17%	46.15%	37.19	40.77	64.66
CH Gayle (WI)	42.18	182	53.30%	53.61%	28.85%	39.72	40.09	40.21
SC Ganguly (INDIA)	42.17	188	54.26%	50.00%	31.37%	40.88	36.06	43.13
DB Vengsarkar (INDIA)	42.13	185	49.73%	56.52%	32.69%	35.78	43.81	44.72
HH Gibbs (SA)	41.95	154	50.65%	51.28%	35.00%	36.75	37.43	47.62
GR Viswanath (INDIA)	41.93	155	52.90%	59.76%	28.57%	39.26	48.55	39.91
ME Waugh (AUS)	41.81	209	53.11%	60.36%	29.85%	39.50	49.52	41.35
AG Prince (SA)	41.64	104	42.31%	50.00%	50.00%	29.06	36.06	72.13
MP Vaughan (ENG)	41.44	147	48.98%	50.00%	50.00%	35.02	36.06	72.13
TM Dilshan (SL)	40.98	145	49.66%	54.17%	41.03%	35.71	40.77	56.11
AJ Strauss (ENG)	40.91	178	53.37%	50.53%	43.75%	39.81	36.62	60.48
PD Collingwood (ENG)	40.56	115	50.43%	51.72%	33.33%	36.52	37.92	45.51
ST Jayasuriya (SL)	40.07	188	45.21%	52.94%	31.11%	31.49	39.30	42.82
RR Sarwan (WI)	40.01	154	47.40%	63.01%	32.61%	33.48	54.13	44.61
AJ Stewart (ENG)	39.54	235	52.34%	48.78%	25.00%	38.61	34.82	36.06
Asad Shafiq (PAK)	39.21	95	45.26%	65.12%	35.71%	31.53	58.27	48.56
Mushtaq Mohammad (PAK)	39.17	100	48.00%	60.42%	34.48%	34.06	49.61	46.96
MS Atapattu (SL)	39.02	156	41.67%	50.77%	48.48%	28.55	36.87	69.06
Asif Iqbal (PAK)	38.85	99	49.49%	46.94%	47.83%	35.54	33.05	67.78
BB McCullum (NZ)	38.64	176	44.32%	55.13%	27.91%	30.72	41.98	39.17
Mudassar Nazar (PAK)	38.09	116	46.55%	50.00%	37.04%	32.69	36.06	50.33
JG Wright (NZ)	37.82	148	50.00%	47.30%	34.29%	36.06	33.39	46.70
MA Atherton (ENG)	37.69	212	49.53%	59.05%	25.81%	35.58	47.45	36.91
Ijaz Ahmed (PAK)	37.67	92	36.96%	70.59%	50.00%	25.11	71.77	72.13
N Hussain (ENG)	37.18	171	42.69%	64.38%	29.79%	29.37	56.77	41.28
NJ Astle (NZ)	37.02	137	45.26%	56.45%	31.43%	31.53	43.72	43.19
CL Hooper (WI)	36.46	173	46.24%	50.00%	32.50%	32.41	36.06	44.48
AJ Lamb (ENG)	36.09	139	46.04%	50.00%	43.75%	32.23	36.06	60.48
RJ Shastri (INDIA)	35.79	121	38.84%	48.94%	47.83%	26.43	34.98	67.78
MW Gatting (ENG)	35.55	138	43.48%	51.67%	32.26%	30.01	37.85	44.19
IT Botham (ENG)	33.54	161	45.96%	48.65%	38.89%	32.16	34.69	52.94

Now, I could go through and discuss particular examples at length, but I feel it's probably better to let others go through and see what they can find for themselves. The only example I really want to note is Joe Root, where this reveals interestingly that he has an interesting spike in his ability to convert 25s to 50s with an effective average in that region around 74. In this sense it could be argued that it's not that he's bad at converting 50s as such, but rather that he's just really good at making them compared to his batting otherwise.
It'd also note that these averages aren't "their average score" during that time, but rather what their rate of converting between those scores would be for the "model batsman".
There are also some cases where players averages are lower in all three than their actual average (e.g. Dravid). This is an example of something called Simpson's Paradox.