Tag Archives: Ball Difference

Margin of Victory in a limited overs cricket match

IPL 2017 – At a Glance

Indian Premier League, 2017 – At a Glance

Results, Fixtures, Points and Ball Difference
Hm/Aw MI RPS SRH KKR KXIP DD GL RCB
MI 20pts,136bd -4 8 1 -3 10 3 1
RPS 1 18pts,49bd 0 -13 48 -43 1 43
SRH 10 -10 17pts,76bd 28 0 4 27 15
KKR -10 -6 12 16pts,171bd 21 22 -12 94
KXIP -29 6 -12 7 14pts,12bd 73 -4 33
DD -79 7 5 -3 22 12pts,-108bd 15 -5
GL 0 12 -13 -33 -13 -4 8pts,-41bd -9
RCB -9 -13 -31 -7 14 -39 7pts,-247bd
Key:

Home Team appears in rows
Away Team appears in columns
Fixture scheduled at 10:30 GMT | 16:00 Local
Fixture scheduled at 14:30 GMT | 20:00 Local
Home Win for Fixture scheduled at 10:30 GMT
Away Win for Fixture scheduled at 10:30 GMT
Home Win for Fixture scheduled at 14:30 GMT
Away Win for Fixture scheduled at 14:30 GMT
Number of points and Ball Difference appear diagonally
All underlined cells are links to iplt20.com

Points table using Ball Difference
Teams Mat Won Lost Tie NR Pts Ball Difference
MI 14 10 4 0 0 20 136
RPS 14 9 5 0 0 18 49
SRH 14 8 5 0 1 17 76
KKR 14 8 6 0 0 16 171
KXIP 14 7 7 0 0 14 12
DD 14 6 8 0 0 12 -108
GL 14 4 10 0 0 8 -41
RCB 14 3 10 0 1 7 -247

 

Largest Margin of Victory and Top Player Contributions

Largest Margin of Victory
# Match Summary Result Ball Difference
27 KKR 131(19.3 ovs); rcb 49(9.4 ovs) Kolkata Knight Riders won by 82 runs (with a difference of 94 balls) 94
45 MI 212/3(20 ovs); dd 66(13.4 ovs) Mumbai Indians won by 146 runs (with a difference of 77 balls) 79
36 dd 67(17.1 ovs); KXIP 68/0(7.5 ovs) Kings XI Punjab won by 10 wickets (with 73 balls remaining) 73
55 kxip 73(15.5 ovs); RPS 78/1(12 ovs) Rising Pune Supergiant won by 9 wickets (with 48 balls remaining) 48
9 DD 205/4(20 ovs); rps 108(16.1 ovs) Delhi Daredevils won by 97 runs (with a difference of 41 balls) 43
Most Valuable Players
Name Team Mts Runs Scored Balls Faced Balls Bowled Runs Conceded Wkts Cts St Run Outs Avg Contribution Total Contribution
DA Warner SRH 13 604 417 10 0.5 08.96 116.45
BA Stokes RPS 12 316 221 264 316 12 5 1.5 09.60 115.23
SP Narine KKR 14 214 120 306 371 10 4 08.00 112.03
AR Patel KXIP 14 227 162 288 362 15 7 0.5 07.82 109.55
GJ Maxwell KXIP 14 310 179 114 125 7 7 06.73 94.24
RV Uthappa KKR 12 386 230 7 6 1.0 06.59 79.07
Sandeep Sharma KXIP 13 7 6 288 398 17 4 06.03 78.38
Rashid Khan SRH 13 11 9 312 347 17 5 2.0 06.01 78.08
SK Raina GL 14 442 307 72 102 1 4 1.0 05.52 77.27
B Kumar SRH 13 4 4 308 358 25 4 0.5 05.90 76.64
Most Valuable Batsmen
Name Team Matches Runs Balls Avg Contribution Total Contribution
DA Warner SRH 13 604 417 08.83 114.82
RV Uthappa KKR 12 386 230 06.24 74.93
RA Tripathi RPS 12 388 257 06.00 71.96
G Gambhir KKR 14 454 355 04.99 69.80
S Dhawan SRH 13 468 363 05.20 67.63
HM Amla KXIP 10 420 288 06.69 66.90
CA Lynn KKR 5 285 153 13.15 65.77
RR Pant DD 14 366 221 04.68 65.47
KA Pollard MI 14 362 256 04.57 63.96
SK Raina GL 14 442 307 04.52 63.26
Most Valuable Bowlers
Name Team Matches Balls Runs Wickets Avg Contribution Total Contribution
Sandeep Sharma KXIP 13 288 398 17 05.88 76.40
B Kumar SRH 13 308 358 25 05.78 75.11
Rashid Khan SRH 13 312 347 17 05.69 73.96
CR Woakes KKR 13 264 386 17 05.21 67.67
AR Patel KXIP 14 288 362 15 04.80 67.21
JJ Bumrah MI 13 290 374 15 05.01 65.14
SP Narine KKR 14 306 371 10 04.65 65.03
Imran Tahir RPS 12 282 369 18 05.25 63.04
JD Unadkat RPS 10 227 279 21 06.28 62.83
Harbhajan Singh MI 11 246 266 8 05.51 60.64
Top Overall Performances
# Name Team Runs Scored Balls Faced Balls Bowled Runs Conceded Wkts Cts Sts Run Outs Player Contribution
37 DA Warner SRH 126 59 2 28.82
31 AJ Finch GL 72 34 1 28.70
3 CA Lynn KKR 93 41 0 25.17
46 SP Narine KKR 54 17 24 29 2 0 24.28
27 CR Woakes KKR 18 21 12 6 3 1 23.56
6 DA Warner SRH 76 45 0 23.07
36 MJ Guptill KXIP 50 27 0 21.65
9 SV Samson DD 102 63 2 21.19
39 BA Stokes RPS 103 63 24 36 0 0 20.70
27 C de Grandhomme KKR 0 2 10 4 3 0 20.57
Top Batting Performances
# Name Team Runs Balls Batting Contribution
31 AJ Finch GL 72 34 28.53
37 DA Warner SRH 126 59 28.51
3 CA Lynn KKR 93 41 25.17
6 DA Warner SRH 76 45 23.07
36 MJ Guptill KXIP 50 27 21.65
9 SV Samson DD 102 63 20.85
41 RA Tripathi RPS 93 52 20.19
19 M Vohra KXIP 95 50 19.71
8 AB de Villiers RCB 89 46 18.87
46 SP Narine KKR 54 17 18.76
Top Bowling Performances
# Name Team Balls Bowled Runs Conceded Wickets Bowling Contribution
27 CR Woakes KKR 12 6 3 22.29
27 C de Grandhomme KKR 10 4 3 20.57
36 Sandeep Sharma KXIP 24 20 4 19.70
27 NM Coulter-Nile KKR 18 21 3 19.10
27 UT Yadav KKR 18 15 1 15.78
55 SN Thakur RPS 24 19 3 14.69
34 LH Ferguson RPS 24 7 2 14.58
45 KV Sharma MI 22 11 3 11.55
19 B Kumar SRH 24 19 5 11.54
55 JD Unadkat RPS 18 12 2 11.17
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IPL 2016 – At a Glance

Indian Premier League, 2016 – At a Glance

Results, Fixtures, Points and Ball Difference
Hm/Aw GL RCB KKR SRH MI DD RPS KXIP
GL 18pts,-13bd 3 39 -33 13 -18 12 -20
RCB 45 16pts,105bd -7 15 -10 -7 3 49
KKR -14 -10 16pts,73bd 23 -7 35 53 7
SRH 6 7 -12 16pts,34bd 15 -13 -44 13
MI -2 12 12 -50 14pts,-19bd 37 -34 -20
DD -2 -13 12 0 2 14pts,-27bd -7 39
RPS -2 -8 -5 -7 -11 28 10pts,14bd 0
KXIP -16 -2 -19 -4 -20 2 8 8pts,-109bd
Key:

Home Team appears in rows
Away Team appears in columns
Fixture scheduled at 10:30 GMT | 16:00 Local
Fixture scheduled at 14:30 GMT | 20:00 Local
Home Win for Fixture scheduled at 10:30 GMT
Away Win for Fixture scheduled at 10:30 GMT
Home Win for Fixture scheduled at 14:30 GMT
Away Win for Fixture scheduled at 14:30 GMT
Number of points and Ball Difference appear diagonally
All underlined cells are links to iplt20.com

Points table using Ball Difference
Teams Mat Won Lost Tie NR Pts Ball Difference
GL 14 9 5 0 0 18 -13
RCB 14 8 6 0 0 16 105
KKR 14 8 6 0 0 16 73
SRH 14 8 6 0 0 16 34
MI 14 7 7 0 0 14 -19
DD 14 7 7 0 0 14 -27
RPS 14 5 9 0 0 10 14
KXIP 14 4 10 0 0 8 -109

 

Largest Margin of Victory and Top Player Contributions

Largest Margin of Victory
# Match Summary Result Ball Difference
45 rps 103/6(17.4 ovs); KKR 66/2(5 ovs) Kolkata Knight Riders won by 8 wickets (D/L method) (with 53 balls remaining) 53
37 SRH 177/3(20 ovs); mi 92(16.3 ovs) Sunrisers Hyderabad won by 85 runs (with a difference of 48 balls) 50
50 RCB 211/3(15 ovs); kxip 120/9(14 ovs) Royal Challengers Bangalore won by 82 runs (D/L method) (with a difference of 49 balls) 49
44 RCB 248/3(20 ovs); gl 104(18.4 ovs) Royal Challengers Bangalore won by 144 runs (with a difference of 45 balls) 45
22 srh 118/8(20 ovs); RPS 94/3(11 ovs) Rising Pune Supergiants won by 34 runs (D/L method) (with 42 balls remaining) 44
Most Valuable Players
Name Team Mts Runs Scored Balls Faced Balls Bowled Runs Conceded Wkts Cts St Run Outs Avg Contribution Total Contribution
DA Warner SRH 17 848 560 4 09.89 168.13
V Kohli RCB 16 973 640 6 13 6 1.0 09.21 147.42
AB de Villiers RCB 16 687 407 19 07.38 118.10
YK Pathan KKR 15 361 248 36 33 1 3 07.17 107.54
CH Morris DD 12 195 109 264 308 13 8 2.3 08.83 105.97
B Kumar SRH 17 43 27 396 490 23 6 1.0 06.06 103.02
SR Watson RCB 16 179 134 339 485 20 6 2.5 06.16 98.50
KH Pandya MI 12 237 124 187 236 6 2 0.5 07.75 93.00
S Dhawan SRH 17 501 429 5 1.0 05.32 90.49
RV Uthappa KKR 15 394 289 10 4 4.0 06.01 90.22
Most Valuable Batsmen
Name Team Matches Runs Balls Avg Contribution Total Contribution
DA Warner SRH 17 848 560 09.86 167.62
V Kohli RCB 16 973 640 09.12 145.94
AB de Villiers RCB 16 687 407 07.22 115.47
YK Pathan KKR 15 361 248 06.63 99.50
S Dhawan SRH 17 501 429 05.24 89.08
Q de Kock DD 13 445 327 06.49 84.42
RV Uthappa KKR 15 394 289 05.61 84.13
AJ Finch GL 13 393 299 06.20 80.65
G Gambhir KKR 15 501 411 05.23 78.47
RG Sharma MI 14 489 368 05.58 78.07
Most Valuable Bowlers
Name Team Matches Balls Runs Wickets Avg Contribution Total Contribution
B Kumar SRH 17 396 490 23 05.40 91.73
Mustafizur Rahman SRH 16 366 421 17 05.15 82.39
R Ashwin RPS 14 264 319 10 05.13 71.76
SR Watson RCB 16 339 485 20 04.44 70.99
YS Chahal RCB 13 295 401 21 05.39 70.06
Sandeep Sharma KXIP 14 300 366 15 04.89 68.52
DS Kulkarni GL 14 294 364 18 04.81 67.29
A Mishra DD 14 276 344 13 04.81 67.28
JJ Bumrah MI 14 312 406 15 04.76 66.65
MJ McClenaghan MI 14 320 436 17 04.66 65.23
Top Overall Performances
# Name Team Runs Scored Balls Faced Balls Bowled Runs Conceded Wkts Cts Sts Run Outs Player Contribution
45 YK Pathan KKR 37 18 0 40.35
50 V Kohli RCB 113 50 1 27.55
47 KH Pandya MI 86 37 13 15 2 0 0.5 25.26
44 AB de Villiers RCB 129 52 2 24.58
15 DA Warner SRH 74 48 0 24.45
12 DA Warner SRH 90 59 0 22.40
23 CH Morris DD 82 32 24 35 2 0 22.15
50 CH Gayle RCB 73 32 18 25 0 1 20.60
11 Q de Kock DD 108 51 0 19.50
51 SK Raina GL 53 36 2 19.29
Top Batting Performances
# Name Team Runs Balls Batting Contribution
45 YK Pathan KKR 37 18 40.35
50 V Kohli RCB 113 50 27.37
15 DA Warner SRH 74 48 24.45
44 AB de Villiers RCB 129 52 24.23
12 DA Warner SRH 90 59 22.40
47 KH Pandya MI 86 37 21.46
11 Q de Kock DD 108 51 19.50
51 SK Raina GL 53 36 18.95
8 G Gambhir KKR 90 60 18.53
1 AM Rahane RPS 66 42 18.19
Top Bowling Performances
# Name Team Balls Bowled Runs Conceded Wickets Bowling Contribution
49 AB Dinda RPS 24 20 3 15.57
22 AB Dinda RPS 24 23 3 14.55
22 MR Marsh RPS 24 14 2 14.02
7 A Mishra DD 18 11 4 13.56
49 A Zampa RPS 24 21 3 13.23
51 DR Smith GL 24 8 4 12.74
22 R Ashwin RPS 24 14 1 12.21
2 AD Russell KKR 18 24 3 11.04
37 A Nehra SRH 18 15 3 10.81
28 AR Patel KXIP 24 21 4 10.72

The Paramount Utility of the 10th Wicket

We observe. We measure. We try to be accurate. Assuming we have enough reliable observations we measure centrality of the data. The central value (or average) can be computed in a number of ways so we choose the most appropriate method. The central value need not be the most frequent observation. In fact it need not match any observation. Still it offers a benchmark which can be used beneficially.

Representing a whole set by a single value is bare bones modelling. Models are used to reduce an object. The object may or may not be complex. Complexity is stripped by focussing on essentials to understand what lies beneath. Earth is not a perfect sphere but its model will be spherical.

Of course we need more than a basic model. We observe a system; we understand its guiding principles; we try to find the chief causes that affect its core behaviour. And then we try to build a model that mimics most of whatever happens.

George Box said:

Now it would be very remarkable if any system existing in the real world could be exactly represented by any simple model. However, cunningly chosen parsimonious models often do provide remarkably useful approximations. For example, the law PV = RT relating pressure P, volume V and temperature T of an “ideal” gas via a constant R is not exactly true for any real gas, but it frequently provides a useful approximation and furthermore its structure is informative since it springs from a physical view of the behavior of gas molecules.

For such a model there is no need to ask the question “Is the model true?”. If “truth” is to be the “whole truth” the answer must be “No”. The only question of interest is “Is the model illuminating and useful?”.

The Relative Value Model to determine player contributions in a limited overs match is wrong. But is it useful?

The model is driven by:

  • Actual values only. No predictive element.
  • Most of the matches are equal. A select few are more equal. A bilateral between any two teams is equal. The same encounter in a World Cup tournament is more equal.
  • Total points for a match will be same irrespective of who played whom in an uniterrupted match. Fewer match points when matches interrupted.
  • A uniform margin of victory will determine the share for each team. Points will be shared equally in tied matches.
  • The difference in scoring rate of two teams and ‘averaged‘ scores from earlier matches determine whether batting unit contributed more towards margin of victory (or defeat) or bowling. This ‘advantage’ will be used to calculate the share of total team batting points out of total team points. In tight matches, whether low scoring or not, this advantage will be close to zero as both sides found it equally easy(or difficult) to score. In one sided matches, the entire credit may go to bowling or batting unit but usually it will be shared proportionately.
  • Team batting points will be divided amongst all batsmen based on runs scored and scoring rate when the team bats through an innings. Otherwise it will be based on runs scored.
  • Team bowling points will be determined using number of overs bowled, runs conceded, wickets taken and when the wickets were taken by each bowler. The value for wickets is higher if opposition is bowled out and even higher when it is bowled out early. Wickets are valued less when the top order bats till the end but a bit more when the lower order batsmen are facing the final delivery.

The current Relative Value model relies solely on information recorded in scorecards. The model is called Relative because Value of every performance is measured against remaining 21. It does not scan the scorecard against a set of tests to create an index by allocating weights to each measure. In future ball by ball data will also be used to determine Relative Value. This is required to measure runs scored or conceded based on match situation. When plenty of wickets fall too soon, the absolute number of runs added will matter and not the rate of scoring those runs. Which brings us to the topic of this post. The value of taking the 10th wicket vis-a-vis the price paid for not taking all ten.

The longer version played in two innings format is won by bowling out the opposition twice and scoring an extra run. It does not matter whether runs were scored quickly or not. Margin of victory is determined by extra runs scored or innings/wickets saved. A limited overs match, on the other hand, is won by scoring an extra run in fewest possible deliveries. It does not matter how many wickets were lost. For example a bonus point is awarded when a side batting second wins by more than 10 overs. Teams are willing to sacrifice wickets to achieve this objective.

A specialist batsman does not bowl but every bowler must bat. In the longer format bowlers do not have a choice because an innings will not come to an end until the 10th wicket falls (or the captain forfeits remaining wickets) but in the limited overs edition a typical tailender often does not have to face bowling when batsmen do their job well. McGrath had to face a delivery in about 25% ODI matches only.

Openers have an advantage. A bowler is restricted by the maximum overs allowed in a match. An opening batsman can bat through the innings without any restriction on number of balls faced. In general, the top 4 batsmen will get a chance to bat and it is their job to bat for bulk of the innings. Others may not get a chance and when they do, it should be for fewer overs. Best to send your best early.

Of course some teams choose to play their best batsman much lower. Think Brian Lara. Misbah-ul-Haq is a more recent case. I think Shaiman Anwar should bat higher for UAE. Does the team’s best batsman need protection? The answer is NO if it is understood that failure is certain but the odds of a longer innings are higher. In any case a team needs more than one good batsman. Anything may happen but playing the strongest quartet at the top means they are more likely to survive collectively as a unit despite individual failures.

The lower order batsmen usually bat towards the end of an innings and are expected to score quickly. It is a different responsibility when a lot of overs remain. An in-form bowler (or a pair) may have done bulk of the damage. The primary task at hand is to see off that spell even if scoring remains attritional for an extended period. When wickets remain in hand then lesser bowlers will have to bowl.

If a team gets bowled out for 120 it does not matter whether they got there in 40, 30 or 20 overs scoring at 3, 4 or 6 Runs Per Over. The effective run rate will be 2.4 in all these cases. On a difficult surface both sides will struggle when the contest is between near equals. Then there are times when bowlers earn bagful of wickets. This is about the times when wickets are gifted while trying to unnecessarily attack.

There is no value in re-iterating common knowledge. Here is a handy guide for a team keen on giving its entire XI a chance to demonstrate its batting prowess. If you are in a situation similar to Pakistan (4 wickets down after 3.1 overs) then the task for 5th wicket is to bat until 34th over. If a wicket falls earlier then the task in hand for 6th wicket partnership is to last until 39th over.

Pakistan scored 160 despite losing 4 wickets for 1 run. Bunch of wickets can fall at any point. England scored 123 after losing 4 wickets for 104 runs. Relative Value model does not use predictive element in allocating points at present. It will continue to use actual match data in future. Pulse, WARR & FIPS  map current match status with projections based on past data. These models are getting tested during CWC 2015 to understand whether match situation is read properly. The next step involves deriving raw data for the Relative Value model after each delivery to replace the current macro model with its micro version.

What is a good first innings total? A team may win after scoring 150 and lose even after scoring 300. FIPS projects team score after each delivery against an ‘average’ score based on past first innings data. It makes 3 projections after each delivery out of which 2 are shown in the chart: one favouring batting side and another favouring bowling team. The model projects that batting team will initially aim to score 300 and bowling side will try to restrict them under 200. Model will reveal revised projections as we fill in the actual runs scored and wickets fallen after each delivery. The projections do not assume uniform scoring rate throughout the innings.

England lost 3 wickets before 15th over and the run rate was slightly below par. At 57-3, we can assume that bowling side had an advantage. Model projects restricting England to 175 after 13.1 overs. At half way stage the 4th wicket partnership has made a mini recovery lifting projected score to about 200. Two more quick wickets reduce England to 104-5 after 26.2 overs. projected score is down to 175. The main difference between 13.1 & 26.2 is that we have double the actual data and lost 5 of Top 7 batsmen. 70 more runs in about 24 more overs is possible and a decent partnership may take England further.

The next 8 overs witnessed an exceptional collapse. An exception means it is an uncommon event. It is unlikely to happen often.

Oooo fiesty one #cricket #billboard #australia #england #LOL 😂😂

A post shared by Jodie Sharp (@jodiesharp) on

Aussie fans wondered how England got in that tangle. A run fest making use of smaller boundaries was expected. There is no need to play a game if we can anticipate what happens next.

Australia started briskly. Lost an early wicket. Kept scoring at a rate healthier than par. Yet found themselves at 80-3 after 13.1 overs. Not much unlike England then. They continued to aim for batsman favoured ~300 instead of less than 200 favouring New Zealand. No 4th wicket partnership this time and soon it became 97-6 after 17.4 overs. Plenty of overs remain with Clarke and Haddin at crease. No doubts now that the match situation favoured bowling side. The projected score was under 150. A good partnership would certainly left Australia above 150 with 32 overs remaining. There was a partnership. It was for 10th wicket. Australia eventually bowled out for 151 with about 18 overs remaining.

There have been games where 10th wicket partnership rescued a side. There will be more such games in future. We can make very good projections about how a team will fare after losing bulk of the wickets. Projections get better once we have more data. Extra data comes in two forms: plenty of balls played without losing equivalent wickets OR plenty of wickets lost irrespective of balls played.

To differentiate between actuals and projections the very obvious rule must be kept in mind. Losing 9 wickets does not reduce the balls available resource. The fall of 10th wicket is the key event where remaining balls are forfeited. Try not to lose the first 9.

If the game halts suddenly then victorious team will be decided based on the match situation at the end of that delivery. The chasing team is expected to stay ahead of the par curve throughout the match. A typical successful above average score chase comprises of saving wickets in the middle overs and scoring just around or below required scoring rate and upping the tempo in the last lap. If sufficient wickets are in hand, it is expected that the team batting second will significantly increase the run rate once the target is in sight. It means the winning team stayed below the par curve for most of the match as part of a plan. A last ball six is sometimes sufficient to win the game even if scoring remains below par curve throughout the match.

McCullum scored 50 in 24 balls and took New Zealand to 78-2 after 7.4 overs, way above the par score. Fall of two more immediate wickets put them behind par. If the match stopped at that point, the game will be declared no result because 20 overs were not bowled. Since this is a low scoring game where more than 50% of target has been chased it is possible to call a winner within this blogspace. At 79-4 after 8.2 overs, Australia is ahead based on WARR and will be declared victorious if the game stops. This is based on the actual state of the match. The Pulse projection puts New Zealand firmly in command. With 6 wickets in hand, overs taken out of the equation and less than 75 runs remaining – an easy win with lots of balls to spare is projected. That is the projected state of the match.

PULSE allocates points out of 100 for both team after each delivery relying on projected scores. No projections required at the end of the match where points allocated by PULSE based on ball-by-ball data match the scorecard determined result values of Relative Value model.

I created the Pulse model based on my understanding of the game relying on fewest assumptions and recent historical data. One of my checks for the model is calling who is ahead during the game. I have my biases and I try not to conquer them. I rely on who is batting, who has overs left, recent form etc. The model does not care. It relies on runs remaining, wickets remaining, balls remaining and the recent historical trend. At 79-4 I told myself that the game will get interesting if Williamson departs. In other words I agree with the model even though we use different data.

5th wicket partnership secured the game. There was a wobble not unlike the Scotland match but it was too close to the target. Vettori departed at 145-7 and it still does not matter with only 7 runs needed. Model predicts a win for New Zealand with plenty of balls to spare and so did I.

Malinga fails to make it 5-in-5

Malinga fails to make it 5-in-5

World Cup 2007. Sri Lanka v South Africa. Chasing 210, South Africa sitting pretty at 206/5 after 44.4 overs. Malinga took 4-in-4 to leave them at 207-9 after 46.2 overs. Despite those 4 wickets, South Africa was ahead because WARR Par was 204. Sri Lanka needed the 10th wicket as Malinga tried to make if 5-in-5.

 46.3 Malinga to Langeveldt,no run,beaten Just kissed past the off stump. Tremendous nerves out there. Full and just outside off, yorker-length, Langeveldt pokes at it and gets beaten 

It did not happen. Eventually South Africa edged past Sri Lanka with 10 balls to spare. It was a tense finish to what should have been an easy one. Something similar although less dramatic happened in 23rd over bowled by Starc.

Williamson took a single on the second delivery. 146-7 with 6 needed. Next we get 2-in-2, both bowled by Starc making it 146-9. What if he made it 3-in-3?

A narrow 50.5-49.5 win for Australia, 7/28 by Starc would have fetched over 200 points relegating Boult’s 5/27 below 150 points. It is the magic of taking the 10th wicket. No need to guess what could have happened. The model was predicting a win for New Zealand until the hat trick delivery but updates values in favour of Australia once the improbable win is secured.

But the 10th wicket did not fall. Scoring a few runs is easier than taking a wicket. Wickets need to fall regularly while defending a low target. McCullum’s innings made the job easier for Williamson. New Zealand has stumbled twice while chasing an easy total. In these two cases the late clutter made the job tense without putting the chase in serious jeopardy. There will be other cases where a lot of wickets fall early and a late partnership secures a win with plenty of overs to spare.

A limited overs match is won by scoring an extra run in fewest possible deliveries. It does not matter how many wickets were lost. For example a bonus point is awarded when a side batting second wins by more than 10 overs. Teams are willing to sacrifice wickets to achieve this objective. Wickets play an important role during the match. The primary objective is reducing the run rate. Taking wickets is a good way to achieve that. Bowling out the opposition will limit the target. Bowling them out as early as possible is the best way to restrict runs. A side may not get bowled out. In general a team will score more runs if less than 5 wickets fall. Getting into the tail without getting the side out is helpful too. In a hypothetical scenario one team may set the target without losing any wicket. Side batting second can win the match while losing as many as 9 wickets. Wickets are secondary. It helps to take the 10th wicket which will happen only if the first 9 have fallen. Wickets are important. But it is extremely important to get the 10th wicket. It may decide whether a team wins or loses. Failing to do that and margin of victory gets decided by balls saved.

Relative Value model awards points by the size of victory. A narrow win or a loss conveys good performances by other team. The Relative Value of a very good performance can’t be significantly better than the rest in such matches.

How does one compare Starc’s figures with those by Boult?  Relative Value model evaluates these on the basis that it is a team game. The bowling performance that helps in bundling the opposition is more valuable. The bowling performance that sets up a win is more valuable. The bowling performance that sets up a very big win is even more valuable. Boult’s 5 wickets helped his team to bundle Australia in less than 32 overs. Starc’c 6 didn’t. His performance would have fetched far more points if he took the 7th or his team took the 10th.

This is about the Paramount Utility of the 10th Wicket!

ICC Cricket World Cup 2015

The home page of Relative Value Model’s ICC Cricket World Cup 2015. This page provides links to corresponding Tweet/YouTube video. A tweet will contain one or more charts. A YouTube video displays a chart updated after each ball. Tournament summary below the links.

Pool A

 Aus Bat Bowl

 Nzl Bat Bowl

 Slk Bat Bowl

 Bng Bat Bowl

 Eng Bat Bowl

 Afg Bat Bowl

 Sco Bat Bowl

Pool B

 Ind Bat Bowl

 Saf Bat Bowl

 Pak Bat Bowl

 Win Bat Bowl

 Ire Bat Bowl

 Zim Bat Bowl

 Uae

 

Match by Match Links

Final – Nzl 183(45); AUS 186/3(33.1)
2nd Semi-Final – AUS 328/7(50); Ind 233(46.5)
1st Semi-Final – Saf 281/5(43); NZL 299/6(42.5)                          Pulse Video
4th Quarter final – NZL 393/6(50); Win 250(30.3)                       Guptill 237*
3rd Quarter-Final – Pak 213(49.5); AUS 216/4(33.5)
2nd Quarter final – IND 302/6(50); Bng 193(45)
1st Quarter final – Slk 133(37.2); SAF 134/1(18)
42nd match, Pool B – Ire 237(50); PAK 241/3(46.1)
41st match, Pool B – Uae 175(47.4); WIN 176/4(30.3)
40th match, Pool A – Sco 130(25.4); AUS 133/3(15.2)
39th match, Pool B – Zim 287(48.5); IND 288/4(48.4)                Pulse Video
38th match, Pool A – Afg 111/7(36.2); ENG 101/1(18.1)
37th match, Pool A – Bng 288/7(50); NZL 290/7(48.5)               Pulse Video
36th match, Pool B – SAF 341/6(50); Uae 195(47.3)
35th match, Pool A – SLK 363/9(50); Sco 215(43.1)
34th match, Pool B – Ire 259(49); IND 260/2(36.5)
33rd match, Pool A – BNG 275/7(50); Eng 260(48.3)
32nd match, Pool A – AUS 376/9(50); Slk 312(46.2)
31st match, Pool A – Afg 186(47.4); NZL 188/4(36.1)
30th match, Pool B – IRE 331/8(50); Zim 326(49.3)
29th match, Pool B – PAK 222(46.4); Saf 202(33.3)
28th match, Pool B – Win 182(44.2); IND 185/6(39.1)
27th match, Pool A – Sco 318/8(50); BNG 322/4(48.1)
26th match, Pool A – AUS 417/6(50); Afg 142(37.3)
25th match, Pool B – PAK 339/6(50); Uae 210/8(50)
24th match, Pool B – SAF 411/4(50); Ire 210(45)
23rd match, Pool B – PAK 235/7(50); Zim 215(49.4)
22nd match, Pool A – Eng 309/6(50); SLK 312/1(47.2)
21st match, Pool B – Uae 102(31.3); IND 104/1(18.5)
20th match, Pool A – Aus 151(32.2); NZL 152/9(23.1)               Pulse Video
19th match, Pool B – SAF 408/5(50); Win 151(33.1)
18th match, Pool A – SLK 332/1(50); Bng 240(47)
17th match, Pool A – Sco 210(50); AFG 211/9(49.3)                  Pulse Video
16th match, Pool B – Uae 278/9(50); IRE 279/8(49.2)
15th match, Pool B – WIN 372/2(50); Zim 289(44.3)                  Gayle 215
14th match, Pool A – ENG 303/8(50); Sco 184(42.2)
13th match, Pool B – IND 307/7(50); Saf 177(40.2)
12th match, Pool A – Afg 232(49.4); SLK 236/6(48.2)
10th match, Pool B – WIN 310/6(50); Pak 160(39)
9th match, Pool A – Eng 123(33.2); NZL 125/2(12.2)
8th match, Pool B – Uae 285/7(50); ZIM 286/6(48)
7th match, Pool A – BNG 267(50); Afg 162(42.5)
6th match, Pool A – Sco 142(36.2); NZL 146/7(24.5)
5th match, Pool B – Win 304/7(50); IRE 307/6(45.5)
4th match, Pool B – IND 300/7(50); Pak 224(47)
3rd match, Pool B – SAF 339/4(50); Zim 277(48.2)
2nd match, Pool A – AUS 342/9(50); Eng 231(41.5)
1st match, Pool A – NZL 331/6(50); Slk 233(46.1)

* * *

The end of a World Cup marks the beginning of a new cycle. Seniors retire indicating no further appetite for plenty of bi-laterals until the next marquee event. Some of the established stars and the promising talent will get 4 more years to hone their skills for the next big stage. On the other hand, an exception like Tharindu Kaushal will make a debut at the quarterfinal stage. A veteran like Vettori may get recalled for one last hurrah despite lack of games leading into the tournament. A non-veteran like Grant Elliott gets another chance years after making the debut. Players like Boult and Mohit Sharma get selected on the basis of recent form instead of experience. In general, a team is likely to field a settled core of experienced players with depth in each department while incorporating a few talented inexperienced players to represent the nation at the World Cup.

I am going to compare the performance of a player during these 6-9 games (that have a higher recall value) against his own performances between CWC11 and CWC15 with a cut-off of 20 ODIs. A chart for batting and bowling per team where all-rounders get included in both.

Let us begin with UAE. This Associate team did not feature a single player with an experience of 20 ODIs between 2011 and 2015. I am in favour of more ODIs for plenty of lower ranked teams throughout the cricket season and not limited to the festive season. In an earlier post about 28 team World Cup, I recommended that top 8 test nations, in groups of 2, should separately host 5 teams each alternately over two home seasons. Associates to feature in a few official ODIs while participating in host nation’s domestic tournaments. Back to UAE. Well no comparative charts for UAE because we do not have a decent sample to compare their performances. Is it a co-incidence that they not only failed to win any match but also earned the wooden spoon in performance measures?

Scotland was the other team with no wins. At least they had a core of 4 experienced players. We begin with Majid Haq and Berrington in their bowling hat. Note: Click on the tweet to view a larger image.

All World Cup matches are given higher weight. Hence a player performing at par will appear to have put in an above average performance at CWC. Majid Haq bowled in 21 matches before the World Cup with good consistency indicated by a very thin blue box. Not unexpectedly, against stronger opposition, he was not consistent. But overall in the 4 matches where he got an opportunity, he has improved. If you are keen to find out how Josh Davey, Alastair Evans, Iain Wardlaw and Rob Taylor performed, you need to click on ‘Sco’ link under Pool A in the table at the top of this post.

Preston Mommsen improved on all four averages: Q1, Q3, Arithmetic Average and Geometric Average. His consistency improved too evident by a much thinner blue box. On other hand Calum Macleod, expected to be the lead batsmen, had a very bad series: 48 runs in 63 balls over 6 innings. Majid Haq also improved with the bat but from a very low base.

Captain Mohammad Nabi performed below expectations. Dawlat Zadran improved considerably. Mirwais Ashraf improved well in the two matches he played but his presence on the right hand side of chart indicates that he was not an automatic choice despite the 20+ ODIs played. Again, if you are interested in the performances of Shapoor Zadran and Hamid Hassan then click on the ‘Afg’ link under Pool A above.

Samiullah Shenwari had a very good series with the bat and was the top performer for Afghanistan along with bowler Shapoor Zadran. Mohammad Nabi was expected to be the leader with both bat and ball but disappointed. Mirwais Ashraf improved well in the two matches he played.

Zimbabwe also won a solitary game against the lowest performing team in its pool. They feature at the bottom on all performance measures as a collective bowling unit. Individually, Chatara and Sean Williams improved well from a low base. Even less was expected off Chigumbura and Masakadza. They ended up performing even worse at the World Cup or in other words they were taken apart by top nations.

Brendan Taylor was one of the top performers at the group stage justifying his place as the top batsman for Zimbabwe. Sean Williams was expected to give him good support and he did so admirably and makes the Top 5 in the Relative Value Model’s list of all-rounders.

Ireland beat 2 test playing nations to finish fifth in Pool B above Zimbabwe. None of the experienced bowlers performed above expectation during the six group matches. All-rounder Kevin O’Brien was the best amongst 3 bowlers with 20+ ODI games before the World Cup. Alex Cusack, John Mooney & Andy McBrine are not featured here due to insufficient games.

Kevin O’Brien and Paul Stirling were expected to be the best with bat for Ireland. Both scored at run a ball but either side of 150 runs in 6 Innings is not sufficient. Ed Joyce scored an 80 and a 100 in wins over West Indies and Zimbabwe but completely failed against South Africa and India. Porterfield’s best was against losses to Pakistan and India. He scored in double digits but 4 of these were under 40. These two could have been more consistent. Batting primarily at #6, Gary Wilson’s best knock was in the win against UAE. Also a cameo in the high scoring thriller with Zimbabwe. Collectively it was a mixed bag. They did well to win 3 matches but would have backed themselves to do better in the other 3 at least to minimise the margin of loss.

England failed to qualify for knockouts. Enough has appeared in print and online. Suffice to say Relative Value Model ranked Finn, Jordan and Tredwell higher than Broad but he was the only automatic selection. Anderson was expected to be the pick of the bowlers but he had a series to forget.

One would expect more than 4 batsmen to have played over 20 ODIs before the major tournament but England’s selection policy is not under discussion here. Let us move on to the quarter-finalists.

Bangladesh qualified at the expense of England. The top 3 bowlers expected to do well lifted their performance especially Mortaza followed by Rubel Hossain. Shakib consistently ranks amongst the top players in the Relative Value Model’s unpublished measures. He maintained his level but the expectations were higher.

Shakib was expected to be the best batsman too. He raised his level a bit but not as much as Mushfiqur. Most notable mention goes to Mahmudullah who was the least ranked batsman for Bangladesh which is evident from his right most appearance in the chart. Tamim was expected to do much more at the top of the innings. Anamul & Nasir Hossain did not make much of the two chances they got.

Jerome Taylor did not play enough ODIs to qualify for these comparisons. Kemar Roach was the top performer in CWC 2011 for West Indies and expected to lead the attack based on his performances before the World Cup. He seriously underperformed in the three matches he bowled. Jason Holder did extremely well. Andre Russell too was good as an all-rounder. Chris Gayle, the bowler, was a surprise.

Chris Gayle, the batsman, was ranked very low. Barring the double hundred, he performed along those limited expectations. None of the batsmen raised the performance levels at this event but managed to win big when they could to ensure that Ireland did not take their place in the knockouts.

Tharindu Kaushal was handed a debut at the quarterfinal stage which shows that Sri Lanka could not field a settled core for a variety of reasons. None of the bowlers had an outstanding series and relatively Malinga and Angelo Mathews were better than others.

On the other hand, Kunar Sangakkara had an OUTSTANDING series with the bat. Dilshan did well but not as well as he did in the previous World Cup. Angelo Mathews was decent in his supporting role. Jayawardene would have liked to match Kumar especially against the stronger opposition. Thirimanne  was inconsistent. Tharanga and Chandimal got a chance to bat only once each.

Pakistan was handicapped. Only three bowlers had played over 20 ODIs and their case was very different from England. Shahid Afridi was outstanding in the 2011 World Cup but failed this time. Irfan justified his place as top bowler for Pakistan but it was Wahab Riaz who contributed at a high level consistently in all the matches.

Misbah-ul-Haq scored 350 runs in 466 balls. Any Absolute Value Model will mark down such slow batting efforts. Relative Value Model evaluates each innings in the context of the match. Such performance in high scoring games will not get many points but it is different when both teams struggle to reach 250 in completed innings. According to Relative Value Model, Misbah-ul-Haq raised his game during the tournament and was the star batsman for Pakistan. Sarfraz Ahmed did very well in the 3 matches he got a chance to play unlike Nasir Jamshed who had the worst series. Afridi disappointed with the bat too and Shehzad could have done better.

Before the tournament I had expected a semi-final lineup of Aus vs Ind and SA vs NZ. It turned out that way by accident. I expected Australia and South Africa to top their respective pools. Neither did. In fact, South Africa lost thrice in the tournament.

Dale Steyn was expected to be the top bowler for South Africa. He will still be the go to bowler for defending 12 runs in the last over. It was disappointing that he had an ordinary series in the light of this strong performances between the two world cups. Morkel raised his game. imran Tahir was the pick of the bowlers.

de Villiers had an outstanding series. Hashim Amla had a very ordinary series. de Kock was inconsistent. du Plessis and Miller performed above expectations perhaps due to failure at the top giving them more chances to bat. Hashim Amla is the top performer in an ODI once every 5 times. de Villiers is the top performer in an ODI once every 5 times. When one outperforms, generally the other is not too far behind. The underperforming duo of Amla & Steyn could be the reason why South Africa lost 3 of the 8 matches played.

India won 7 games in a row dismissing the opposition every time. Shami and Yadav exceeded themselves. Shami was the most consistent bowler picking wickets at either end of the innings. Ashwin did the same role that Tahif performed for South Africa. Mohit Sharma is not listed due to insufficient games but he too excelled in his role of the first change bowler. Compared to these 4, Jadeja was the weak link. But he too on an average performed near his par.

Shikhar Dhawan, Dhoni, Rohit Sharma and Raina performed above expectation with bat. Kohli was expected to be the top batsman for India. He had a disappointing series like Amla. He kept getting the starts but it was unlike him to not convert some of them to become the top performer in the match. Just like Amla and de Villiers he too finishes as the top player once every 5 games. Before the tournament, I expected India to win one of the 2 opening games, finish second in the group and win the quarter-final. The odds of beating the hosts were low but a semi-final finish gave a good account of the team. That India performed very well as a unit, without heroics from an individual, was a pleasant surprise. The semi-final finish after that performance was underwhelming.

New Zealand won 8 out of 8 at home. Boult and Vettori did not play enough games hence they are not part of the comparison. Southee had an outstanding tournament in 2011. He had a relatively quiet series this time. Corey Anderson was admirable as fourth bowler after the first three had done sufficient damage.

Brendan McCullum can get a very good start. But he fails often. He was not expected to the top performer for New Zealand. Kane Williamson, Corey Anderson and Ross Taylor were the best batsmen coming into the tournament. None of them had an outstanding series. It was compensated by McCullum, Guptill and Elliott. While Indians performed as a unit without outstanding effort from any player, the Kiwis won their matches on the back of 3 exceptional bowlers and 3 outstanding batsmen.

Doherty featured in a single match not picking any wickets. Starc was expected to be the top performer for Australia. He improved beyond expectation during this series. Johnson was expected to the consistent performer just behind Starc. He was consistent and very very good. Faulkner was expected to put in solid efforts maintaining consistency at a lower level. He was inconsistent but excellent in the games where he performed. Watson was below par but it did not matter. Maxwell was OK with the ball in his role as an all-rounder.

Watson, Clarke and Warner were expected to lead the batting. Stability was found only after moving Watson down the order to make way for the star performer Steven Smith. Clarke was not consistent but Warner had a good series. Finch was alright compared to his par but Maxwell made hay when the sun shone. Australia was clinical in the knock out games to take the title.

Relative Value Model relies on aggregate performances. It is difficult to separate performance from luck in a single match but as the sample size increases one can get a better understanding of the relative performance levels of an individual. Just as change is the only constant, failure is the only certainty. Hence performances are evaluated using 4 different averages accounting for abject failures. Which brings us to the summary for this World Cup:

👍 Steven Smith, Mitchell Starc (Australia)
👍 Brendan McCullum, Trent Boult (New Zealand)
👎 Virat Kohli (India)
👍 Mohammad Shami (India)
👎 Hashim Amla, Dale Steyn (South Africa)
👍 Misbah-ul-Haq, Wahab Riaz (Pakistan)
👍 Kumar Sangakkara (Sri Lanka)
👍 Jason Holder (West Indies)
👍 Mahmadullah, Mashrafe Mortaza (Bangladesh)
👎 James Anderson (England)
👍 Brendan Taylor, Sean Williams (Zimbabwe)

In other words, England and South Africa should be disappointed. India may rue the missed opportunity. All other teams should be elated/satisfied depending on the stage they reached.

A Review of Cricket World Cup 2011

Margin of Victory
Points allocated for a Match
Projected Scores while First Innings in progress
Wicket Adjusted Run Rate (WARR)
Pulse – Tracking team points
Top performances in a match
Most Valuable Players
Analysing Performances within a Team
World XI

I discovered Ball Difference as the uniform margin of victory while watching the opening game of Cricket World Cup 2011. A lot has happened since. I tried to publicise the theory, explaining the simplicity and benefits to anyone willing to lend me an ear. Later I built a custom database to implement this theory while adding complexity to build a normalised player performance model. Now I am not interested in explaining or promoting Ball Difference. Hence no further posts in this blog or comments elsewhere. Ball Difference is the fundamental building block of the Relative Value Player Performance Model for limited overs cricket. It will get a mention with regards to explaining other measures but not otherwise.

This post is a curtain raiser for the forthcoming Cricket World Cup 2015. The format for Cricket World Cup 2011 was identical and thus reviewing the team and individual performances will also explain the proprietary charts that will be used during the live tournament coverage on this site (and Twitter).

Margin of Victory

I maintained a hand-made table in March 2011 about the largest margins of victory in that edition which can be found here. That table looked like this:

# Gp TeamA RunsA OversA TeamB RunsB OversB Ball Difference Comments
2 A Ken 69 23.5 NZ 72/0 8 252 New Zealand won by 10 wickets (with 252 balls remaining)
19 B Ban 58 18.5 WI 59/1 12.2 226 West Indies won by 9 wickets (with 226 balls remaining)

A simple bar chart can capture this information.

CWC 2011- Margin of Victory

CWC 2011- Margin of Victory

The margin of victory appears in blue on the left hand side of the yellow bar. Matches are sorted by the size of victory with narrowest margin appearing at the bottom. Match summary is also shown for every match above that yellow bar. For example the highest margin of victory was recorded in the second match between Kenya and New Zealand. Its match summary Ken 69(23.5); NZL 72/0(8) describes the runs scored, wickets lost and overs needed for both teams. New Zealand won the match hence its three letter short country code is capitalised. At the other end, we have the tied match between India and England – IND 338(49.5); ENG 338/8(50) where short country codes are capitalised for both teams.

This time I will not maintain the alternate points table comparing Net Run Rate and Ball Difference. Please see Wikipedia entry for NRR which mentions three other alternatives. As stated during the introduction I will henceforth use Ball Difference strictly in my own models only. It will not be mentioned otherwise.

The chart can be used to understand various types of matches. One team can totally outplay another either by bowling them out cheaply followed by a quick chase – Bng 58(18.5); WIN 59/1(12.2) OR post a big total and bundle out the opposition – SAF 284/8(50); Bng 78(28). At the other end we can have very close matches where both teams find it easy to bat – IND 338(49.5); ENG 338/8(50) OR easy to bowl – ENG 171(45.4); Saf 165(47.4). Points are allocated to each player based on the ease/complexity in batting/bowling along with the margin of victory.

Points allocated for a Match

The first chart shows points allocated to each player for batting, bowling and fielding. An earlier post describes how to read this chart.

A 10wkt, 63 ball win for Sri Lanka in quarterfinals.

A 10wkt, 63 ball win for Sri Lanka in quarterfinals.

How good is the first innings total of 230? At the group stage England defended 243 against West Indies – ENG 243(48.4); Win 225(44.4) and lost a close match to Bangladesh after scoring 225 – Eng 225(49.4); BNG 227/8(49). It means that generally a first innings total below 200 is below par and greater than 300 is above par but whether it will lead to victory depends purely on how the second innings pans out. In this match Sri Lanka posted a 10 wicket win. It will be a massive win in the longer format but in the limited overs edition chasing 230 in 30 overs, even if wickets are lost, should be treated as a bigger win. Dilshan and Tharanga scored at less than 6 runs per over yet saved more than 10 overs for a very comfortable win. The batting unit gets more points (511 against 333) and the 10 wicket win means all the batting points get shared only between openers. Dilshan scored more runs consuming fewer deliveries than Tharanga to earn more batting points. Trott and Morgan earn the most batting points for England followed by the bowling points earned by Mendis for his economical spell.

Let us look at another comfortable win by Sri Lanka inspired by bowlers.

Sri Lankan bowlers script a comfortable win

Sri Lankan bowlers script a comfortable win

265 is merely a decent first innings effort but bowling the opposition out for 153 is splendid bowling effort. It means more points for Murali’s 4/25 than Sangakkara’s hundred. Next we have another 10 wicket win at quarter finals stage which can be attributed to bowlers.

Pakistan outplayed West Indies

Pakistan outplayed West Indies

Mohammad Hafiz bowled economically in a supporting capacity and then led the opening act scoring nearly at run-a-ball. Shahid Afridi was the top bowler with 4 scalps.

Projected Scores while First Innings in progress

Team batting second has an advantage in pacing their chase. Team batting first sometimes loses too many wickets in pursuit of extra runs. In other words, bowling team can restrict first innings total in last 20 overs even after conceding too many runs in the first 30. During group stage South Africa restricted Indian batting after a frenetic start.

India was on course to score in excess of 320 but failed to even bat 300 balls

India was on course to score in excess of 320 but failed to even bat 300 balls

FIPS stands for First Innings Projected Score. Based on the scoring pattern, a par value is determined which is plotted as the thin straight-ish line. Worm is a familiar curve illustrating ball-by-ball scoring pattern. The worm is coloured Red if Team batting first is ahead of par value or Blue if the bowling side is deemed ahead. After each delivery, based on runs scored and wickets lost till that point, two projections are made. The red curve is the projected final score if Team 1 bats above average that point onwards till the end of innings. The blue curve shows how Team 2 can restrict scoring through an above average performance.

Before the first ball is bowled, Team 1 will aim to score above 300 and Team 2 will try to restrict them below 200. Indian openers opened up after the first two overs making the most of power play overs. At the end of 15th over, the projected score was in excess of 350 which assumes that Indian batsmen maintain an above average tempo for 35 more overs, At that stage South Africa could expect to keep India under 265 by doing a much better job in remaining 35. After 30th, India had lost only 1 wicket to reach 197. Could they double this score? Not according to this model which projected only 350 runs (not 400) if the batting continued to be above average in the last 15 overs too. The projection for South Africa was 277 at this stage and it could work with quick wickets only. 9 more overs passed before the second wicket partnership was broken. After 39th India, after scoring 258, was projected to reach 359 while South Africa faced an above 300 score even if it could put a very good 11 over spell.

India lost 3 wickets in the next two overs. But losing 4 wickets with only 9 more overs to go does not make much difference to projections. India was still expected to cross 350 and South Africa wasn’t expected to keep them below 300. 2 more wickets were lost in next 4 overs. At 288/6 with 30 deliveries to bat, South Africa still faced an above 300 score. In the next 3 overs, 7 runs were scored for the loss of 2 more wickets. At 295/8, does one expect a team to score 5 more runs in 12 more deliveries? India added one more run and lost the last 2 wickets in the next 4 deliveries to finish at 296. South Africa bowled remarkably well in the last 11 overs or Indians failed to bat sensibly or both.

The projected scores are just that: projections. Broadcasters typically make a projection based on current run rate, 6 an over, 8 an over or 10 an over which are all in favour of batting side. FIPS model churns out three projections after each delivery: an average projection (not shown in the chart) similar to current run rate, a higher projection in favour of batting side (shown in red) and lower projection in favour of bowling side (shown in blue). During the World Cup 2015, I will publish these projections typically after 15th, 30th and 40th over whenever possible.

Another instance of India failing to bat 50 overs after a sound start

Another instance of India failing to bat 50 overs after a sound start

Albeit less dramatic, a similar pattern can be found in the tied match played earlier between India and England. India scored 292/3 after 45 overs which generally does not end at 338/10. It shows that bowling side can, and sometimes does, make amends. Hence a need for a projection in favour of bowling side.

Another custom chart is coming up to review England’s chase.

Wicket Adjusted Run Rate (WARR)

This chart was discussed at length in an earlier post.

England's largely successful chase of 338

England’s largely successful chase of 338

The thin straight-ish line called ‘Target (ball-by-ball)’ specifies an expected score after each delivery for successfully chasing the target. Blue flat line indicates the final target while red line is the target score to reach after minimum overs. The runs scored worm is blue when chasing team is ahead and red otherwise. At the 40 over mark, England did very well to lose only 2 wickets while staying close or ahead of required score. England were favourites to win the match at that stage. Yet staying close to a mammoth required score and keeping up with it right up to finish line are two different things. Indian bowlers started pulling back the match with some quick wickets. After 49th, England needed 14 to win with 2 wickets in hand. India still marginally ahead. But 9 runs were scored in the first three deliveries. One bad delivery could result in a win for England. India did not conceded any boundary but 4 more runs in last 3 balls meant that the match ended in a tie.

Sri Lanka comfortably beats England

Sri Lanka comfortably beats England

Dilshan & Tharanga played watchfully for the first 7 overs but kept up the scoring rate after that to remain comfortably ahead of the target curve progressively increasing the margin between where they should be and where they were. This match ended with only 10 overs remaining. In the other quarterfinal, Pakistan finished the job much earlier by staying way way ahead of a modest target.

Pakistan wins by about 30 overs

Pakistan wins by about 30 overs

Now let us look at a failed chase.

England defends 171 against South Africa

England defends 171 against South Africa

Early wickets are the key to defend a low score. But South Africa started confidently by staying above the par curve in the opening partnership. England took 3 wickets by 20th over to ensure South Africa did not run away with the match. At 30 over mark, South Africa were still favourites but 4 wickets for not many runs in the next 10 overs swung the pendulum in England’s favour. Partnership for 8th wicket seemed to do the job but England struck at the right time and ensured that the tail did not wag to win by 6 runs.

Which brings us to the final chart for a single match.

Pulse – Tracking team points

This topic was covered in the most recent post. England were well below Par in the first innings handing over the points advantage to South Africa.

England defends a low total

England defends a low total

It was unlikely to win the match by bowling economically for 50 overs. Taking 10 South African wickets was the more likely way of overhauling first innings points deficit. Even after the fall of 9th wicket, South Africa had plenty of overs to reach the target. England’s margin of victory could be a few runs at best but South Africa would win the match, for the loss of 9 wickets, with overs to spare registering a comfortable win by balls remaining. Hence the difference between red and blue curves is bridged only towards the very end when England snuffed out the tail quickly.

3rd wicket partnership for England bridges the points gap efficiently

3rd wicket partnership for England bridges the points gap efficiently

In the high scoring match, England fell behind significantly after the first innings by conceding too many runs. This time the chase was near perfect and points deficit was steadily eroded. Around 40th over, we see a sharp spike narrowing the gap in points which means that England became favourites to win for the first time in that match. India increased the gap by breaking that partnership and taking a few more wickets to retain ascendency. But bulk of the mammoth target was already chipped. England stayed in the hunt with timely hits over the rope to get really close. The red & blue curve are too close in the 50th over and the match deservedly ended in a tie with both teams ending with 50 points apiece.

Canada manage to keep Australia quiet for 20 overs

Canada manage to keep Australia quiet for 20 overs

Australia thrashed Canada. Australia was ahead at the end of first innings. Canada did reasonably well for the first 20 overs. They could not break the opening partnership but the runs were checked which meant that the points gap narrowed slightly. Of course upset was never a possibility. Watson picked up scoring between 20th and 30th. Points deficit was too large when Watson got out and last rites were performed in the next 5 overs.

Top performances in a match

I would like to watch my team win with plenty to spare. A top performance is one that lacks drama. Bowling first, bowlers should keep opposition very quiet preferably bowling them out. Then top order should ensure that target is reached without fuss, Otherwise bat for 50 overs to score over 300 and win by 100 runs or more. That is why the top performances in the next 3 charts will invariably be from one-sided matches.

Dilshan and Tharanga made hay when the sun was shining

Dilshan and Tharanga made hay when the sun was shining

The first and third top performance is from the same match – a 10 wicket win inspired by openers. Watson’s 94 is sandwiched which was part of the chase against Canada discussed in earlier section. Dilshan makes 4 appearances and Tharanga is in the fray thrice making them ideal openers. Sehwag’s 175 against Bangladesh is 6th in the chart because India’s margin of victory was not significant. According to this Relative Value model, runs were cheaper in the opening game reducing the impact of the rapidly scored big hundred.

Ensure that opposition is a) bowled out & b) bowled out in fewest possible overs

Ensure that opposition is a) bowled out & b) bowled out in fewest possible overs

All the top bowling performances are from the matches where one side was bowled out for a very small total and the other side failed to do the same with ball in hand. Bennett’s 4/16 was part of Ken 69(23.5); NZL 72/0(8). Benn’s 4/18 resulted in Bng 58(18.5); WIN 59/1(12.2). Peterson’s 4/12 came in SAF 284/8(50); Bng 78(28). A top bowling performance is one where a player not only does better than teammates but also the opposition while the team’s objective of bowling out the opposition is collectively achieved.

It is possible to top the chart with bat OR ball - but it is easier to achieve it by contributing with bat AND ball

It is possible to top the chart with bat OR ball – but it is easier to achieve it by contributing with bat AND ball

Hafeez was the star performer in the one-sided quarterfinal win against West Indies. Peterson’s lusty blows to score 22* off 9 deliveries came in the match against Bangladesh mentioned earlier. Dilshan contributed with bat and ball in the big win against Zimbabwe – SLK 327/6(50); Zim 188(39). Bennett and Benn made it to the top without contributing with bat. If Sehwag’s 175(140) is of interest, or the hundred by Strauss in the tied match, then check the margin of victory. In this Relative Value model, a big hundred is valued more when neither your team mates nor the opposition lineup scores comparable runs alone or collectively.

Most Valuable Players

Next we look at overall performances during the entire tournament. I like the league format where all teams play the same number of matches. In such cases we can simply add the points earned during the entire tournament. If a player misses any match it will be ignored since an equal number of opportunities were available to all, some just failed to grab them. In CWC 2011, two teams played 9 matches, two other played 8, another four played 7 matches and remaining 6 played six league games. We will disregard the number of matches and simply add all points. Those who feature at the top may have played more matches. May be they deserved to because some of those top performances ensured additional opportunities.

CWC11 - Most Valuable Batsmen

CWC11 – Most Valuable Batsmen

No surprises with Dilshan & Tharanga at the top. They ensured that Lankans got a solid start and matches were won with plenty to spare. These two are followed by the top order batsmen from leading test nations. Relative Value model expects a team to play its best batsmen for maximum overs. If they do their job then lower order batsmen will not get enough chances to score. Since limited overs matches are different from the long format, we do not have to worry about equal opportunities. A team is likely to win more if the top order does its job more often.

CWC 11 - Most Valuable Bowlers

CWC 11 – Most Valuable Bowlers

Unlike top batsmen, top bowlers must make way for lesser bowlers in the team. So this list is not dominated by a certain type of bowler. Shahid Afridi had an outstanding series especially with the ball in the subcontinent. India needed Zaheer Khan to perform which he did admirably.

CWC11 - Most Valuable Players

CWC11 – Most Valuable Players

All-rounders are expected to lead this chart as they get to contribute with both bat and ball. Afridi scored more bowling points than the batting points earned by Dilshan. But Dilshan compensated better with his bowling. Hence we find Dilshan ahead of Afridi. Yuvraj was the man of the tournament but India’s overall record was patchy. Sri Lanka eased into the knock out rounds while India lost to South Africa and tied with England during group stage. Both Pakistan and Sri Lanka recorded emphatic 10 wicket wins at the quarter final stage. India won the world cup by winning the three crucial knock out matches but Sri Lanka was the form team. The figures across all the world cup editions based on this Relative Value model confirm that CWC 2011 was the only instance where the team that won the title failed to earn maximum team points. Runners up Sri Lanka were ahead of India. Yuvraj is not far behind Afridi but he played an extra match. Yuvraj along with Zaheer were the chief architects for India; not the celebrated top order.

Next we look at the same three charts by superimposing their averages. An earlier post about Quartiles, Geometric Mean and Arithmetic Mean can be found here. The chart used is called CandleVolume (or BoxWhisker) that generally depicts the traded volume of a stock along with OHLC (Open, High, Low & Close) prices for the day. Open and Close values form the body of candlestick (or box) while High and Low appear as upper and lower shadows (or whiskers). Q3 (the third quartile) is the high whisker indicating that 75% performances were below this value. Q1 (the first quartile) is the low whisker and 25% performances (roughly 2 matches if we assume that top nations played 8 matches each) were below that value. Top of the dark black box indicates the Arithmetic Mean which is the generally understood Average Value. One strong performances masks other failures when we look at this average in isolation. Geometric Mean is a different type of average which will aid us in recognising consistency and its value will be the one along the bottom of the dark black box.

CWC11- Averages for Top Batsmen

CWC11- Averages for Top Batsmen

Tharanga misses out in this revised table. Most of his points were scored in a few matches. He scored two hundreds but also got dismissed for single digit scores thrice and once under 20. The Geometric Mean for his batting scores was not high enough to warrant a place in this selection. Yuvraj, Guptill, McCullum, Hafeez, Dhoni, etc miss out for the same reason. Instead we have Ashish Bagai from Canada, representing a weak side, who managed to put consistent performances without any scintillating effort in scoring over 300 batting points in 6 league matches. Sehwag and Mahela both made the cut but notice how wide the black box is. The higher the height of the black box, greater is the propensity of few good scores masking ordinary performances. Compare this with Sangakkara. The whiskers are neither above 120 nor below 60. It means roughly twice he scored more than 120 points. In addition he registered less than 60 points very few times. Such a narrow band of values certainly helps in getting the Arithmetic and Geometric Mean closer.

Is such a consistency a very good thing if a player does not cash in once getting set? The bottom of Dilshan’s black box (or G) is above the top of Kumar’s box (or µ) which means he may not be consistent enough within his zone but his zone is well above others. It is a bit like complaining about the wide disparity between Bradman’s G (=50) and µ(=90). That gap of nearly 40 is higher than the career average of most players. It is best to evaluate all four averages, Q1, Q3, G and µ together to get a feel for combined performances across the tournament.

CWC11- Averages for Top Bowlers

CWC11- Averages for Top Bowlers

A similar pattern can be observed between top 2 players in this chart too. Afridi did very very well in a few matches and failed in some others. Zaheer was more consistent throughout the tournament without any stand-out effort. Zaheer’s box isn’t tall at all but Afridi’s taller box is placed above his. Kemar Roach was the least consistent bowler in this selection.

CWC11- Averages for All-rounders

CWC11- Averages for All-rounders

Very few players passed the standard I had set to qualify as an all-rounder. It was partly done to discard those who already made a mark with bat or ball alone. This selection looks at 10-over bowlers who can bat a bit better than others.

Analysing Performances within a Team

Now we will look at the same information but compare players within a team. We will expect top order batsmen to do well and the middle order to make amends whenever they get a chance. It means we expect a good team’s top order batsmen to score more batting points than its middle order. If that does not happen then the concerned team needs to find a better top order. Relative Value model should be used to compare a specific type of player with another. It is designed to measure each player’s contribution not adjust/inflate contributions from certain positions to compensate lack of opportunities.

CWC11 - Who did how much for India

CWC11 – Who did how much for India

This chart is presented by typical batting position while mentioning the number of matches played by each member. Sehwag, Tendulkar and Gambhir were alright but none of them fared as well as Zaheer the bowler. Yuvraj was the most valuable player which is stating the obvious. A Ball Difference of 32 means that India won a match with about 5 overs to spare.

CWC11 - Who did how much for Sri Lanka

CWC11 – Who did how much for Sri Lanka

Dilshan stands tall as the batsman consistently supported by Sangakkara and occasionally in match winning starts by Tharanga. Murali and Malinga take the honours in bowling. The average winning margin was double that of India. Sri Lanka won by more than 10 overs on an average. The only problem is that they lost the final even though they have more tournament points (>7000) compared to India (<7000).

CWC11 - Who did how much for India - this time with averages

CWC11 – Who did how much for India – this time with averages

Now we look at the same information but with Quartiles and Averages superimposed. We find that Ashwin did well in the two matches he played. Kohli and Dhoni were not good enough overall though it is common knowledge that Dhoni played one good knock against Sri Lanka when the stakes were highest.

During CWC 2015, I will upload only the quartile charts for a team as it combines all the relevant information. Most of us despise formulae, resent averages, detest charts and dislike multiple axes. If a picture is worth a 1000 words then a chart should be good enough for few tens. These will appear on my Twitter handle as part of my whispers in the cul-de-sac while others trumpet their services in the marketplace.

World XI

I did a similar exercise earlier so I will spare the details. My selection criterion was simple: no more than one player from any team. I chose Ashish Bagai over Shakib or Kevin O’Brien for the eleventh spot. Other 10 selected themselves.

CWC 2011 - Fantasy Team

CWC 2011 – Fantasy Team

ODI Pulse Chart: Tracking team points during a match

In a Pulse chart, Team batting first is depicted in red and other team is plotted in blue. A worm tracks the progress of a match by plotting the team score after each delivery against a black PAR value. Worm is shaded red or blue based on the team deemed ahead of the Par value after that delivery. PAR value for first innings is determined from the average scoring rate for a 50 over match. The second innings PAR is determined by the TARGET set by team batting first, overs played and wickets lost (see Wicket Adjusted Run Rate Chart for details).

Overs played are displayed on X-axis, Runs Scored on the Primary Y-axis (left side) and Total Team Points on the Secondary Y-axis (right side).

Batting first, England lost top order wickets early and regularly and as a result stayed below the average scoring rate throughout the match. Some big spikes in the blue points curve show the Australian dominance. The fifth wicket partnership between Morgan and Buttler reduced the gap between red and blue curves. A sharp increase in Australian Points at the fall of 5th wicket indicates that the partnership was good for England but not long enough. 6th and 7th wicket partnerships just about restored the parity when England reached 224/7 after 46.5 overs. At that point, England could have scored 20-25 more runs in the remaining 19 balls. 3 more wickets in next 6 balls resulted in Australia leading 26.2-23.8 at innings break.

The first innings score against an overall average score is indicative at best. 400+ score may not be enough to win while a below 200 score is more than sufficient sometimes. England needed some early wickets and/or a sedate Australian start but neither happened. England’s best chance was at 71/2 after 12.4 overs when Australia was marginally ahead 33.5-31.6 on points but the 3rd wicket partnership prospered.

Even though Australia lost a few more wickets the scoring remained quick enough to stay well above the Par curve increasing the gap between these two teams. At 165/3 (31 ov), the gap increased to 45.8-39.3 and even after two quick wickets the gap widened to 56.8-40.7 at 219/5 (38 ov). England stalled while Australia accelerated.

61 ball win for Australia

61 ball win for Australia

2 more wickets fell before the finish line which were immaterial because the scoring was very quick. Australia won by 61 balls which amounts to a very comfortable 58.8-41.2 win. The quick wickets that England had to take after first 15 overs of Australian innings did not materialise and the game ended in a thumping victory for Australians.

Player Performance Analysis for 2014 edition of Champions League Twenty 20

CLT2014-16: bt 113(19.4 ovs); HOB 117/4(18.2 ovs)

CLT2014-16: bt 113(19.4 ovs); HOB 117/4(18.2 ovs)

This is a Champions League T20 “Who Did How Much?” post on the lines of the introductory post published during IPL 2014. All the key points about understanding the chart which depicts player batting, bowling & fielding performance in a T20 match are covered in that post.

CLT2014-01 - csk 157/4(20 ovs); KKR 159/7(19 ovs)

CLT2014-01 – csk 157/4(20 ovs); KKR 159/7(19 ovs)

The first match of this tournament was won by KKR with 6 balls in hand. This post will summarise all player contributions through a single image. Successful 160 odd runs chase is deemed as a match won by batting unit. So KKR gets more batting points (27.36) compared to bowling points (24.76). Russell’s 58 in 25 balls is worth 13.8 points which is more than half the batting points available. He also earns nominal 0.5 points (out of the team total of 24.76 points) for his single over that cost his team 12 runs. Sunil Narine was the best bowler and overall the next best performer who kept CSK quiet by conceding only 9 runs. Nehra picked up 3 early wickets and a late one but did not get enough support. Despite appearing on losing side his spell of 4 wkts conceding 21 is worth nearly the same (8.56) as Narine (8.61). That is the purpose of this analysis – an attempt to quantify the contribution in each discipline by all the players such that the total adds up to 100 points for a completed match. Ryan ten Doeschate who kept KKR in the hunt with 51 runs off 41 balls is fourth in the list. Chawla and Dhoni are not too far behind with nearly 8 points apiece. At the other end, Pandey does not get any point for his duck which is fairly obvious. Later we will find out how slow batting and expensive bowing also results in NIL points.

CLT2014-02 - hob 144/6(20 ovs); KXIP 146/5(17.4 ovs)

CLT2014-02 – hob 144/6(20 ovs); KXIP 146/5(17.4 ovs)

This is a comfortable win with 14 balls remaining which results in nearly 56-44 split. Perera is the top performer with nearly the same number of points as Russell (from Match 1). But this is a balanced performance with bat and ball. He picked up 2 wkts for 17 runs in his 3 overs. At 77/5 after 10.5 overs, his team was in trouble but his assured 35 in 20 balls in partnership with Bailey resulted in a comfortable win. He gets about 9 points for his batting compared to 11 by Maxwell (43 in 25). Akshar Patel’s spell of 4-20-1 is worth 6.5 points which is nearly equal to 6.7 points for Bailey who scored 34 runs in 27 balls. This is an example of determining near equal contributions from two independent disciplines.

CLT2014-03 - NK 206/5(20 ovs); cob 44/2(7.2 ovs)

CLT2014-03 – NK 206/5(20 ovs); cob 44/2(7.2 ovs)

Third match was interrupted by rain. Williamson’s 101(49) is worth nearly 57% of the total batting points for his team. Cobra’s needed about 3 runs an over when the tie was resolved using D/L. The loss by 33 runs at that stage results in a big victory for Knights who earned nearly 72 points. Losing team bowled entire quota of overs but faced fewer overs. Hence bulk of the 28 points are allocated to bowling side. A similar split in overs faced vs overs bowled results in far more batting points for the Knights compared to bowling. Proportionally reducing the total number of points for an interrupted match will reduce the anomaly of awarding far too many points to a single player. Only 44 balls were bowled during the chase. Kuggeleijn’s sole over costing 11 runs despite a wicket is worth NOTHING because others restricted the scoring to 32 runs in 38 balls. This is an example of how a bowler is not assured of any points as a result of conceding too many runs.

CLT2014-04 - dol 164/7(20 ovs); PER 165/4(20 ovs)

CLT2014-04 – dol 164/7(20 ovs); PER 165/4(20 ovs)

Fourth match went the distance with the winning stroke scored off the last delivery. It means the Ball Difference is 0 which is the boundary condition. That condition means that, irrespective of scores, the batting and bowling units always get exactly half the nearly 50 points. Scorchers get an extra 0.15 points out of 100 for scoring that extra winning run after facing equal number of deliveries. 3 players earned more than 8 points. Marsh was declared the Man of the Match for scoring 40(26) and above average bowling spell of 3-21-0. Zondo appears on the losing side despite scoring 63(50). Frylinck scored quick 15(6) during death overs and completed his quota of 4 overs conceding about par 32 runs while picking up a wicket.

CLT2014-05 - bt 174/6(20 ovs); KXIP 178/6(19.4 ovs)

CLT2014-05 – bt 174/6(20 ovs); KXIP 178/6(19.4 ovs)

In the fifth match, Punjab posted their second win which was much closer this time with only 2 balls in hand. It means that points are split around 51-49 mark and no player manages to get 10 or more points. Miller was awarded MotM but quantitatively he is only the 5th best performer. Tridents did very well to score 174 on the back of two strong batting performances but their death bowling was poor. Patel did extremely well to score 20 in the penultimate over swinging the match in favour of Kings XI. He also bowled his entire quota which fetches him a nominal 2.5 bowling points. In a match where no single player stands out, his collective batting/bowling figures make him the best performer on the winning side. Unlike Patel, Mendis bowled well to finish his quota but the death bowling undid his effort. Note 0 batting points for Chigumbura 3(5) and Bailey 7(11) – an example of below par above-zero scores that still result in no batting points.

CLT2014-06 - cob 184/6(20 ovs); HOB 186/4(19 ovs)

CLT2014-06 – cob 184/6(20 ovs); HOB 186/4(19 ovs)

Hurricanes won with an over to spare in the sixth match thanks to Blizzard. The match would have been closer had Blizzard departed in 18th over after his 50. But the Philander no-ball which spared him was punished by his SIX on the free-hit that turned the tide. His 78(48) was ably supported by Dunk who scored 54(35). Engelbrecht bowled well and Philander batted well for Cobras. Peterson chipped in with a decent all-round performance for the losing side.

CLT2014-07 - lio 151/7(20 ovs); KKR 153/6(19.3 ovs)

CLT2014-07 – lio 151/7(20 ovs); KKR 153/6(19.3 ovs)

KKR were 100/0 and cruising, were then reduced to 147/6 but hung on to win by 3 balls. Lions did not field well which means the assured knock by Shehzad earning about 12 points was wasted. Gambhir batted well at the top of the chase but it was Narine who followed his 9/1 in the first match with 9/3 in this one. Quantitatively his superb bowling performance is only as good as Gambhir’s batting. In the limited overs format, the best bowler does not have the liberty of bowling more than 20% overs. Hence the total points available to a very good bowler gets shared by 4 others especially if the collective bowling effort is not decisive. A batsman in form can bat until the end with no restriction on number of balls faced. Therefore batsmen score higher number of points with an exceptional performance. The opposite is true in Test matches. Generally 4 bowlers share available bowling points while earning some points for their batting. A very good spell will earn plenty of bowling points if the number of overs bowled is not restricted.

Match 8 : CSK 242/6(20 ovs); dol 188(20 ovs)

CLT2014-08 - CSK 242/6(20 ovs); dol 188(20 ovs)

CLT2014-08 – CSK 242/6(20 ovs); dol 188(20 ovs)

Raina scored 90 in 43 balls in a big 26 ball win which results in a nearly 61-38 match in favour of CSK. Raina’s batting performance is deemed worth 25% of total team contribution which meane he gets 15.38 points. How does it compare with Jadeja’s 40(14) who scored 44.44% of his runs but in 33.33% of deliveries faced by Raina? Well it has to be worth at least 44.44% points of the batting points i.e. 6.83 with a bonus for scoring even quicker. That bonus is worth about 10% resulting in 8.31 batting points for Jadeja. Mohit Sharma’s spell of 4-41-4 is deemed better than McCullum’s 49(29). Mohit outscores Brendon 7.2 – 6.76. Bravo’s all-round effort places him between these two. Delport’s brisk 34(9) at the top of chase is worth 17% of his total team contribution. Since his side fell sufficiently short, Delport’s total batting points (6.69) are lower despiteJadeja contributing a lower 14% towards his team.

Match 9: HOB 178/3(20 ovs); nk 92(16.4 ovs)

CLT2014-09 - HOB 178/3(20 ovs); nk 92(16.4 ovs)

CLT2014-09 – HOB 178/3(20 ovs); nk 92(16.4 ovs)

This is a comprehensive win for Hurricanes where both the batting and bowling units contribute towards the final winning margin – an above par score of 178 batting first followed by an exceptional bowling performance to bowl the opposition for 92. This match provides a nice opportunity to compare player specific values. Styris pipping Hilfenhaus – how can it be explained? Hilfenhaus took 3 wickets (at 2.4, 2.5 & 6.4 ov) and conceded only 14 runs after bowling his full quota. That is worth 20% of team performance resulting in a very high score of 12.9 points. Styris was required to both bat and bowl. With winners outperforming in either discipline, Styris is deemed to have faced better bowling and batting. Only Styris and Southee contributed meaningfully fo Knights. 7 of the 11 batsmen get NIL points as Watling and Mitchell join them in scoring batting points. Paine 43(34) on winning side and Styris 37(27) on losing side are comparable. Paine scored 6 more runs facing 7 more deliveries while facing inferior bowling attack. Quantifying – Styris beats Paine 8.90 – 6.69. On the bowling front, Styris, Sodhi & Southee completed their quota with similar above par figures whereas Kuggeleijn and Boult brothers underperformed. This time we will compare Styris using Bollinger’s figures. Styris did not take any wicket conceding 7 runs an over in a match where winners scored above 8. Bollinger picked three wickets off the last 4 balls of his spell conceding over 8 runs against an inferior batting side. Numerically Bollinger leads Styris 5.37 – 4.23. Effectively 2.4-22-3 is not deemed significantly better than 4-28-0 taking match context into account. Mennie’s 3-10-2 is also rated above Doherty’s 4-17-2 by 8.07 – 7.58.

Match 10: per 151/7(20 ovs); KKR 153/7(19.4 ovs)

CLT2014-10: per 151/7(20 ovs); KKR 153/7(19.4 ovs)

CLT2014-10: per 151/7(20 ovs); KKR 153/7(19.4 ovs)

Man of the Match was awarded to Kuldeep Yadav for his spell of 4-24-3. Narine picked 4 wkts for 31 runs. Conventionally 4 wickets is ranked higher than 3 wickets. But here we rank Yadav(8.55) ahead of Narine(6.88) because his spell was more economical and Narine got bulk of his wickets at the death unlike Yadav who picked his wickets after deliveries 8.6, 11.5 & 16.1 ov.

Match 11 was abandoned.

Match 12: bt 174/8(20 ovs); COB 174/5(20 ovs)

CLT2014-12: bt 174/8(20 ovs); COB 174/5(20 ovs)

CLT2014-12: bt 174/8(20 ovs); COB 174/5(20 ovs)

This tied match was won by Cobras who settled it in superover. Cobras were cruising before digging a hole when Engelbrecht scored 11 of 12 in the last over to tie the match. He followed this by protecting 11 runs in superover. Carter was the top performer with 111(68). Requiring 3 to win off the last ball, Engelbrecht faced Emrit. He failed to hit a boundary. Reifer collected the ball and threw it to keeper’s end who failed to collect and Tridents failed to win the match. Carter would have been the deserving player of the match if Dowrich effected the run out which was awarded to Engelbrecht. Man of the Match is generally, not always, awarded to one of the top performers from the winning team. This analysis identifies the top performer based on numbers alone.

Match 13: KXIP 215/5(20 ovs); nk 95(15.2 ovs)

CLT2014-13: KXIP 215/5(20 ovs); nk 95(15.2 ovs)

CLT2014-13: KXIP 215/5(20 ovs); nk 95(15.2 ovs)

Wins keep getting bigger. Kings XI won the match improving on Hurricanes. Batting first they scored way above par and defended 215 by bowling the opposition for 95. This means they won by nearly 80-20 margin. Sehwag, Miller and Vohra benefit the most. Sehwag’s 52(37) is slowest among these three rated 14%. Miller with 40(18) was the fastest of these three but scored fewest runs for his 16% contribution. Vohra was part of the opening century stand with Viru where Punjab overhauled the eventual Knights score in the 9th over itself. The final margin of 71 balls is largely due to Vohra’s 65(32) for which he earns a huge chunk of team points. This model rates such 65 higher than another 100 when scored in context.

Match 14: LIO 164/5(20 ovs); dol 148/9(20 ovs)

CLT2014-14 - LIO 164/5(20 ovs); dol 148/9(20 ovs)

CLT2014-14 – LIO 164/5(20 ovs); dol 148/9(20 ovs)

Lions should have won this match with plenty to spare when they had restricted Dolphins to 93/9 after 15.4 overs. For the 10th wicket, Subrayen contributed 1 run facing 7 out of remaining 26 balls. If we exclude 3 extras in the partnership, then Frylinck scored 51 in 19 deliveries. His allround contribution with bat and ball is worth 31% of his team points. In other words he earned 21.15 points; higher than Vohra’s contribution with bat alone.

Match 15: CSK 155/6(20 ovs); per 142/7(20 ovs)

CLT2014-15 : CSK 155/6(20 ovs); per 142/7(20 ovs)

CLT2014-15: CSK 155/6(20 ovs); per 142/7(20 ovs)

Batting first Jadeja scored 14 runs off the last 3 deliveries. Chennai Super Kings eventually won by 13 runs. That explains the reason for a Ball Difference of 3 despite the margin of 13 runs. CSK scored 66 off the 155 in the last 4 overs. Since Ball Difference takes into account the actual rate of scoring where ball-by-ball data is available, a late surge after a slow start will result in a lower margin of victory.

Match 16: bt 113(19.4 ovs); HOB 117/4(18.2 ovs)

CLT2014-16: bt 113(19.4 ovs); HOB 117/4(18.2 ovs)

CLT2014-16: bt 113(19.4 ovs); HOB 117/4(18.2 ovs)

This was a low scoring match which means bowlers will get more points for checking the run rate. Shoaib Malik contriutes with bat, ball along with a catch to become the top performer. Carter’s brisk 42(34) knock, in context of a low scoring match, is worth close to 10 points. Hilfenhaus gets more bowling points than Doherty due to better economy and picking both his wickets before all four by Doherty.

Match 17: cob 135(18.3 ovs); KXIP 139/3(18.1 ovs)

CLT2014-17: cob 135(18.3 ovs); KXIP 139/3(18.1 ovs)

CLT2014-17: cob 135(18.3 ovs); KXIP 139/3(18.1 ovs)

Once again bowlers check the scoring and gain more points collectively. Saha contributes with bat and glove to become the top performer. Peterson’e 2/19 in a losing cause is rated above 3/15 by Patel (on winning side) because he took his wickets in first 10 overs unlike Patel.

Match 18: KKR 187/2(20 ovs); dol 151/8(20 ovs)

CLT2014-18: KKR 187/2(20 ovs); dol 151/8(20 ovs)

CLT2014-18: KKR 187/2(20 ovs); dol 151/8(20 ovs)

A tall score by KKR batting first and a relatively high score by Dolphins chasing vis-a-vis lower winning scores in the two earlier matches indicate that batting side should get more points. Uthappa and Pandey scored most of the runs batting first to pick up nearly 30 of the available 31 points for the side. Phehlukwayo’s 37(18) is the best effort on losing side.

Match 19: lio 124/6(20 ovs); PER 130/7(19 ovs)

CLT2014-19: lio 124/6(20 ovs); PER 130/7(19 ovs)

CLT2014-19: lio 124/6(20 ovs); PER 130/7(19 ovs)

Two allround efforts, one from each side, stand out. 63(38) by Marsh and 69(55) by Nasim are on either side of 14 batting points but Marsh raced ahead overall due to better bowling figures.

Match 20: nk 135/8(19 ovs); BT 138/4(18.4 ovs)

CLT2014-20: nk 135/8(19 ovs); BT 138/4(18.4 ovs)

CLT2014-20: nk 135/8(19 ovs); BT 138/4(18.4 ovs)

Last group game was interrupted but both sides played the same number of overs. Target was nominally incremented but Tridents managed to win with 2 balls to spare. Bowlers ahead again but not by much. 47(47) by Devcich is not rated much higher than 29(19) by Watling. Munaweera’s economical bowling in second half is worth more than his batting cameo at the top.

Semi-final 1: hob 140/6(20 ovs); KKR 141/3(19.1 ovs)

CLT2014-SF1: hob 140/6(20 ovs); KKR 141/3(19.1 ovs)

CLT2014-SF1: hob 140/6(20 ovs); KKR 141/3(19.1 ovs)

KKR won another match after restricting the opposition to a manageble score. Kallis and Pandey ensured that the chase did not go wrong. Pathan sneaks between these two by virtue of his decent spell with ball. Shoaib Malik and Gulbis scored plenty of points in defeat.

Semi-final 2: CSK 182/7(20 ovs); kxip 117(18.2 ovs)

CLT2014-SF2: CSK 182/7(20 ovs); kxip 117(18.2 ovs)

CLT2014-SF2: CSK 182/7(20 ovs); kxip 117(18.2 ovs)

In the other semi-final, CSK scored above par while batting and then restricted the scoring below par while bowling. The net contribution is only marginally in favour of batting side. It is a big win with roughly 64-36 split. Bravo picks up plenty of points while batting for 67(39) but his bowling spell of 1-12-0 is worth only 0.005 points.