Analysis: Wasting Batting Resources in T20s


5th November, 2017.

A team sport played by individuals.  Or perhaps, an individual sport played by teams.  Either description is likely to be pretty true of cricket, and both are continually demonstrated by analysis of individual innings, particularly in limited overs matches.

In these limited overs matches, players frequently either pay no regard to the match situation - their team's scores with relation to either some sort of par score, when batting first, or to the total required to win when batting second.  

There are a number of reasons why this could be the case - firstly, a player could be keener to play for their average than for their team, and this is something that shouldn't be ruled out.  If a player was asked if they'd rather get a century and lose, or score a duck but win, it's certain that there wouldn't be unanimous vote.  Alternatively the player may lack the mindset to correctly pace an innings, or finally, they may not have the skill-set required to score at the rate required - in effect, they don't have a high strike rate in their 'locker'.

With this in mind, I thought it would be interesting to assess every individual innings in the last completed set of each of the major domestic T20 leagues throughout the world, as well as T20 internationals, to work out the worst relative batting performances from players in around the last 12 months.  It's vital that players, and teams, understand the effect of wasting resources, with even a 4 (10) innings costing a team in excess of 8 runs in the average T20 match.  Each ball, for example, is 0.83% of a team's batting resources, so a 10 ball innings is 8.3% of a team's innings.

Firstly, I calculated the 'Innings v Match Run Deficit' figure for innings, relating how many runs a player scored, and their resources used, with the total runs in the completed match.  

For example, in the 2017 IPL, Virat Kohli scored 64 runs from 50 balls for the Royal Challengers Bangalore against the Gujarat Lions.  He used 41.67% of his team's innings resources to score these 64 runs, but in the match 240 balls were bowled which generated 405 runs.  Therefore, in the match (405/240) = 1.6875 runs were scored per ball, so in these 50 balls, Kohli's match par was (50*1.6875) = 84.38.  Given that he scored just 64, his innings would be considered 20.38 (84.38-64) runs below par, and he'd therefore have an innings v match run deficit of -20.38.

I then calculated the 'Innings v Tournament Run Deficit' figure for innings, which instead related how many runs a player scored, and their resources used, with the run rate of the tournament in that year.

Using the Kohli example again, in the IPL 2017, 1.33 runs per ball were scored on average, and this would reflect that his innings expectation over the average IPL innings would be (50*1.33) = 66.5.  Therefore his innings would be considered just 2.50 (66.5-64) runs below par using the average IPL innings metric, and he'd therefore have an innings v tournament run deficit of -2.50.

Making use of both measures is beneficial and also slightly fairer, primarily because it gives more credit to a player who bats first and scores reasonably compared to the tournament mean (likely to be used by teams to set par scores) but poorly compared to the match mean - obviously it's easier to pace an innings chasing than batting first.

In total, 293 innings in the last running of each major domestic T20 event, and T20 internationals in 2017, had a run deficit of -8 or worse across both metrics, which were then averaged to give us a mean run deficit figure.  The worst 20 innings in the sample (sorted by mean run deficit) are listed below:-

Player

Tournament

Team

Opponent

Runs

Balls Faced

Match Balls Faced

Match Runs

Innings v Match Run Deficit

Innings v Tournament Run Deficit

Mean Run Deficit












DA Hendricks

South Africa CSA

Lions

Warriors

38

48

229

305

-25.93

-23.92

-24.93

Abdul Mazid

BPL

Khulna

Dhaka

21

36

228

317

-29.05

-20.76

-24.91

AI Ross

Big Bash

Heat

Sixers

17

28

240

334

-21.97

-20.24

-21.10

C Munro

Super Smash

Auckland

Wellington

38

44

236

313

-20.36

-21.84

-21.10

MQ Adams

South Africa CSA

Titans

Cobras

17

29

233

283

-18.22

-20.41

-19.32

Mandeep Singh

IPL

Bangalore

Punjab

28

34

207

298

-20.95

-17.22

-19.08

JTA Burnham

Blast

Durham

Derbyshire

34

39

237

325

-19.48

-18.65

-19.07

MHW Papps

Super Smash

Wellington

Central

29

35

240

330

-19.13

-18.25

-18.69

MS Dhoni

IPL

Pune

Gujarat

26

33

238

328

-19.48

-17.89

-18.68

KS Williamson

T20I

New Zealand

Bangladesh

60

57

240

361

-25.74

-11.40

-18.57

BAC Howell

Blast

Gloucestershire

Glamorgan

27

33

240

327

-17.96

-17.55

-17.76

MC Henriques

Blast

Surrey

Somerset

26

32

230

315

-17.83

-17.20

-17.51

LLL Sesele

South Africa CSA

Knights

Warriors

2

17

186

187

-15.09

-19.93

-17.51

MJ Guptill

CPL

Warriors

Tridents

27

33

235

319

-17.80

-16.89

-17.34

MJ Santner

Blast

Worcestershire

Warwickshire

12

20

240

375

-19.25

-15.00

-17.13

DM de Silva

T20I

Sri Lanka

South Africa

19

27

239

339

-19.30

-14.82

-17.06

KS WIlliamson

CPL

Tridents

St Lucia Stars

46

44

212

325

-21.45

-12.52

-16.99

SR Hain

Blast

Warwickshire

Derbyshire

34

35

240

374

-20.54

-13.25

-16.90

DJ Bell-Drummond

Blast

Kent

Sussex

5

15

168

263

-18.48

-15.25

-16.87

TM Alsop

Blast

Hampshire

Kent

32

34

224

338

-19.30

-13.90

-16.60


It is evident to see here, looking at the runs vs balls faced, that a player scoring fewer runs than balls faced is a huge negative to a team - only Kane Williamson (60 off 57, and 46 off 44) played innings listed above with more runs than balls faced, and the inclusion of those innings was due to the huge amounts of his team's batting resources used where barely a run a ball was scored.  Certainly, scoring fewer runs than balls faced after 20 runs of an innings in particular (usage of 16% of a batting team's resources or greater) should be avoided at all costs.

Mentioning Williamson here neatly moves me onto a further point - there were a number of players who regularly played innings with negative contributions.  In fact, the New Zealander played the most innings in the sample (six innings) with deficits of -8 or worse across both metrics, one more innings than all of Chris Gayle, Eoin Morgan and countryman Martin Guptill.

The players who player three or more innings with deficits of -8 or worse across both metrics are listed below:-

6 - Kane Williamson
5 - Chris Gayle, Eoin Morgan, Martin Guptill.
4 - Colin Munro, Moises Henriques.
3 - Alex Ross, Cameron Delport, Jacques Rudolph, Marlon Samuels, Mohammad Hafeez, Riki Wessels, Shikhar Dhawan, Shaun Marsh, Shahriar Nafees, Travis Head.

Gayle's propensity to start innings slowly is well documented, and while he frequently does well after slow starts, he also has got out after starting slowly a number of times, therefore leaving his team effectively playing catch-up on a large scale.  Furthermore, the West Indian's strike rate this year has declined markedly - he's scored 793 runs from 632 balls (strike rate 125.47) and if this downward movement continues, expect further innings from Gayle in this manner.  Eoin Morgan's considerable issues in T20 have been well documented many times in my previous articles, although I am surprised to see Martin Guptill playing so many negative innings.

Alex Ross is an interesting player to discuss briefly, with the Brisbane Heat batsman playing three innings of -8 deficits or greater across both metrics in just seven completed Big Bash innings, and his -8 deficit ratio was by far the highest.  Jacques Rudolph and Shahriar Nafees (who also scored 35 off 34 yesterday in a match where each ball was worth 1.33 runs - an innings which wasn't included), who both played in just one domestic league, also had a high percentage of negative innings.

Clearly, most of the innings listed above came via at least 20 balls faced, but there were also some hugely negative innings where batsmen faced fewer than 20 balls, and the worst ten are listed below:-

Player

Tournament

Team

Opponent

Runs

Balls Faced

Match Balls Faced

Match Runs

Innings v Match Run Deficit

Innings v Tournament Run Deficit

Mean Run Deficit












LLL Sesele

South Africa CSA

Knights

Warriors

2

17

186

187

-15.09

-19.93

-17.51

DJ Bell-Drummond

Blast

Kent

Sussex

5

15

168

263

-18.48

-15.25

-16.87

DL Lloyd

Blast

Glamorgan

Sussex

3

13

240

378

-17.48

-14.55

-16.01

G Gambhir

IPL

Kolkata

Punjab

8

18

240

320

-16.00

-15.94

-15.97

JB Lintott

Blast

Hampshire

Somerset

8

18

231

280

-13.82

-16.30

-15.06

BL D’Oliveira

Blast

Worcestershire

Yorkshire

15

19

240

429

-18.96

-10.65

-14.81

P Mustard

Blast

Gloucestershire

Hampshire

3

12

184

295

-16.24

-13.20

-14.72

EJG Morgan

PSL

Peshawar

Karachi

3

13

239

339

-15.44

-12.99

-14.21

PD Trego

Blast

Somerset

Surrey

10

17

240

358

-15.36

-12.95

-14.15

KR Mayers

CPL

St Lucia Stars

Tridents

8

16

212

325

-16.53

-11.68

-14.10



Interestingly, six of these innings came in the T20 Blast, a competition which has two notable dynamics.  Firstly, it is a competition which is extremely high scoring on average - the average ball faced was worth 1.35 runs in 2017 - and also it has the most diluted quality, given the fact that it has 18 teams competing in it.  Given that there are many players in the competition who wouldn't be close to getting contracts with overseas clubs, plus the demand to score quickly in the tournament (with high average scores), it is absolutely logical that a high proportion of negative innings are in this competition, although ironically, several of the players listed above are not necessarily bad T20 batsmen generally.

Summarising, similar to the first table which showed the most negative innings in major T20 matches throughout the world, the data above should give players and clubs a solid idea of the type of innings which should be avoided by players, and furthermore, the implications in terms of the runs these type of innings costs teams overall.


Hopefully this case study has given you insight into the data that Sports Analytics Advantage can offer cricket franchises around the world in formulating draft or auction plans - please feel free to enquire for bespoke draft and auction strategies via sportsanalyticsadvantage@gmail.com.
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