tag:blogger.com,1999:blog-227138112024-03-13T14:54:40.636+01:00Pappus' plane - cricket statsMy writings on and analyses of cricket statistics.David Barryhttp://www.blogger.com/profile/08378763233797445502noreply@blogger.comBlogger103125tag:blogger.com,1999:blog-22713811.post-23946404599701179082014-03-01T12:51:00.000+01:002014-03-01T12:51:13.085+01:00Highest scores<p>Recently I got thinking <a href="http://pappubahry.livejournal.com/584538.html">about Flappy Bird scores</a>, and noted that they should be similar to cricket scores in the way they're distributed.</p>
<p>Translating the problem to cricket, the question is, "What is the relationship between a batsman's highest score, his average, and the number of innings he's batted in?" A follow-up question is how well such a formula predicts, say, the average based on the innings and highest score. I expected there to be quite a lot of scatter (assessing a batsman on just one innings!), but the correlation ended up being pretty decent. This isn't a particularly useful correlation, but I found it fun to play with.</p>
<p>Given the average, the probability that a batsman makes a score less than x is roughly (1 - exp(-x/avg)). Treating each innings as independent of the others, the probability that all N of a batsman's innings are less than x is therefore (1 - exp(-x/avg))<sup>N</sup>.</p>
<p>A natural "expected" highest score, given the batsman's average, would be the value x such that the probability that all innings are less than x is 1/2. Calling this value HS, we have</p>
<p>(1 - exp(-HS/avg))<sup>N</sup> = 1/2,</p>
<p>which is the basic relation between highest score, average, and number of innings batted.</p>
<p>Solving for HS (i.e., what highest score do we expect, given average and innings batted?):</p>
<p>HS = -avg * ln(1 - 0.5<sup>1/N</sup>).</p>
<p>Solving for avg (given only highest score and innings batted, what do we think the batsman's average is?):</p>
<p>avg = -HS / ln(1 - 0.5<sup>1/N</sup>).</p>
<p>Using the latter formula as a predictor works surprisingly (to me) well:</p>
<p><img src="http://i44.photobucket.com/albums/f35/pappubahry/cricket/avg_from_HS_innings.png"></p>
<p>The weird qualification of 50 dismissals rather than innings is because I began by including all abtsmen with at least 2 dismissals, and at sample sizes this small, I figured dismissals would be useful to avoid not-out irregularities. (The R-squared is actually a bit higher, about 0.78, in that expanded sample.)</p>
<p>I doubt I'll ever use this formula, but Perhaps the most amusing individual result is Shane Warne: based on his average and innings batted, his "predicted" high score is... 98! So he didn't quite deserve that hundred after all.</p>
<p>Full table, straight off Statsguru – I was so lazy that I didn't even turn off the ICC game.</p>
<pre> predict one based
real on the other
Player Inns NO HS avg HS avg
DG Bradman (Aus) 80 10 334 99.9 475.0 70.3
H Sutcliffe (Eng) 84 9 194 60.7 291.6 40.4
KF Barrington (Eng) 131 15 256 58.7 307.7 48.8
ED Weekes (WI) 81 5 207 58.6 279.3 43.4
WR Hammond (Eng) 140 16 336 58.5 310.4 63.3
KC Sangakkara (SL) 209 17 319 58.1 331.6 55.9
GS Sobers (WI) 160 21 365 57.8 314.5 67.0
JB Hobbs (Eng) 102 7 211 56.9 284.4 42.2
CL Walcott (WI) 74 7 220 56.7 265.0 47.1
L Hutton (Eng) 138 15 364 56.7 300.1 68.7
JH Kallis (ICC/SA) 280 40 224 55.4 332.4 37.3
GS Chappell (Aus) 151 19 247 53.9 290.1 45.9
AD Nourse (SA) 62 7 231 53.8 242.1 51.3
SR Tendulkar (India) 329 33 248 53.8 331.5 40.2
BC Lara (ICC/WI) 232 6 400 52.9 307.5 68.8
Javed Miandad (Pak) 189 21 280 52.6 294.9 49.9
R Dravid (ICC/India) 286 32 270 52.3 315.1 44.8
Mohammad Yousuf (Pak) 156 12 223 52.3 283.3 41.2
AB de Villiers (SA) 152 16 278 52.3 281.9 51.6
S Chanderpaul (WI) 261 45 203 51.9 308.1 34.2
RT Ponting (Aus) 287 29 257 51.9 312.5 42.6
HM Amla (SA) 130 11 311 51.6 270.0 59.4
A Flower (Zim) 112 19 232 51.5 262.2 45.6
MEK Hussey (Aus) 137 16 195 51.5 272.5 36.9
Younis Khan (Pak) 158 14 313 51.4 279.1 57.6
SM Gavaskar (India) 214 16 236 51.1 293.1 41.2
SR Waugh (Aus) 260 46 200 51.1 302.7 33.7
MJ Clarke (Aus) 178 19 329 50.8 282.0 59.3
ML Hayden (Aus) 184 14 380 50.7 283.2 68.1
AR Border (Aus) 265 44 205 50.6 300.7 34.5
DPMD Jayawardene (SL) 240 15 374 50.3 294.2 63.9
IVA Richards (WI) 182 12 291 50.2 279.9 52.2
DCS Compton (Eng) 131 15 278 50.1 262.5 53.0
Inzamam-ul-Haq (ICC/Pak) 200 22 329 49.6 281.1 58.1
FMM Worrell (WI) 87 9 261 49.5 239.3 54.0
V Sehwag (ICC/India) 180 6 319 49.3 274.4 57.4
B Mitchell (SA) 80 9 189 48.9 232.3 39.8
TT Samaraweera (SL) 132 20 231 48.8 256.1 44.0
Misbah-ul-Haq (Pak) 80 14 161 48.8 231.7 33.9
GC Smith (ICC/SA) 203 13 277 48.7 276.8 48.8
RN Harvey (Aus) 137 10 205 48.4 256.0 38.8
KD Walters (Aus) 125 14 250 48.3 250.8 48.1
SJ McCabe (Aus) 62 5 232 48.2 216.9 51.6
ER Dexter (Eng) 102 8 205 47.9 239.2 41.0
G Boycott (Eng) 193 23 246 47.7 268.7 43.7
VS Hazare (India) 52 6 164 47.7 206.1 37.9
EH Hendren (Eng) 83 9 205 47.6 228.1 42.8
AC Gilchrist (Aus) 137 20 204 47.6 251.8 38.6
SM Nurse (WI) 54 1 258 47.6 207.6 59.1
RB Kanhai (WI) 137 6 256 47.5 251.4 48.4
KP Pietersen (Eng) 181 8 227 47.3 263.2 40.8
WM Lawry (Aus) 123 12 210 47.2 244.3 40.5
LRPL Taylor (NZ) 98 9 217 46.9 232.6 43.8
RB Simpson (Aus) 111 7 311 46.8 237.8 61.2
PBH May (Eng) 106 9 285 46.8 235.4 56.6
CH Lloyd (WI) 175 14 242 46.7 258.2 43.7
AL Hassett (Aus) 69 3 198 46.6 214.4 43.0
DM Jones (Aus) 89 11 216 46.6 226.2 44.5
AN Cook (Eng) 183 10 294 46.5 259.4 52.7
AR Morris (Aus) 79 3 206 46.5 220.3 43.5
IJL Trott (Eng) 87 6 226 46.5 224.7 46.7
DR Martyn (Aus) 109 14 165 46.4 234.7 32.6
DL Amiss (Eng) 88 10 262 46.3 224.5 54.0
AD Mathews (SL) 62 12 157 46.2 207.7 34.9
M Leyland (Eng) 65 5 187 46.1 209.4 41.1
WM Woodfull (Aus) 54 4 161 46.0 200.6 36.9
VVS Laxman (India) 225 34 281 46.0 265.9 48.6
EJ Barlow (SA) 57 2 201 45.7 202.0 45.5
NC O'Neill (Aus) 69 8 181 45.6 209.8 39.3
Saeed Anwar (Pak) 91 2 188 45.5 222.2 38.5
IR Bell (Eng) 170 22 235 45.4 250.0 42.7
MD Crowe (NZ) 131 11 299 45.4 237.9 57.0
JL Langer (Aus) 182 12 250 45.3 252.3 44.9
G Kirsten (SA) 176 15 275 45.3 250.7 49.6
CC Hunte (WI) 78 6 260 45.1 213.0 55.0
SM Katich (Aus) 99 6 157 45.0 223.6 31.6
M Azharuddin (India) 147 9 199 45.0 241.3 37.1
Zaheer Abbas (Pak) 124 11 274 44.8 232.4 52.8
MH Richardson (NZ) 65 3 145 44.8 203.5 31.9
CG Greenidge (WI) 185 16 226 44.7 249.9 40.4
GP Thorpe (Eng) 179 28 200 44.7 248.1 36.0
GM Turner (NZ) 73 6 259 44.6 208.1 55.6
AI Kallicharran (WI) 109 10 187 44.4 224.9 36.9
RB Richardson (WI) 146 12 194 44.4 237.6 36.2
TW Graveney (Eng) 123 13 258 44.4 230.0 49.8
Shoaib Mohammad (Pak) 68 7 203 44.3 203.6 44.2
AH Jones (NZ) 74 8 186 44.3 207.0 39.8
DI Gower (Eng) 204 18 215 44.3 251.6 37.8
DJ Cullinan (SA) 115 12 275 44.2 226.1 53.8
G Gambhir (India) 96 5 206 44.2 218.0 41.7
MC Cowdrey (Eng) 188 15 182 44.1 246.9 32.5
Hanif Mohammad (Pak) 97 8 337 44.0 217.5 68.2
ME Trescothick (Eng) 143 10 219 43.8 233.5 41.1
Saleem Malik (Pak) 154 22 237 43.7 236.2 43.8
RA Smith (Eng) 112 15 175 43.7 222.2 34.4
EAB Rowan (SA) 50 5 236 43.7 187.1 55.1
DC Boon (Aus) 190 20 200 43.7 245.1 35.6
JH Edrich (Eng) 127 9 310 43.5 227.0 59.5
MA Taylor (Aus) 186 13 334 43.5 243.3 59.7
IR Redpath (Aus) 120 11 171 43.5 224.1 33.2
BF Butcher (WI) 78 6 209 43.1 203.8 44.2
PA de Silva (SL) 159 11 267 43.0 233.7 49.1
DA Warner (Aus) 54 3 180 42.9 187.0 41.3
HP Tillakaratne (SL) 131 25 204 42.9 224.8 38.9
MJ Slater (Aus) 131 7 219 42.8 224.6 41.8
C Washbrook (Eng) 66 6 195 42.8 195.3 42.7
GA Gooch (Eng) 215 6 333 42.6 244.4 58.0
M Amarnath (India) 113 10 138 42.5 216.6 27.1
RC Fredericks (WI) 109 7 169 42.5 215.0 33.4
IM Chappell (Aus) 136 10 196 42.4 224.1 37.1
JB Stollmeyer (WI) 56 5 160 42.3 186.2 36.4
DL Haynes (WI) 202 25 184 42.3 240.1 32.4
PR Umrigar (India) 94 8 223 42.2 207.4 45.4
SC Ganguly (India) 188 17 239 42.2 236.4 42.6
DB Vengsarkar (India) 185 22 166 42.1 235.5 29.7
NS Sidhu (India) 78 2 201 42.1 199.2 42.5
DJ McGlew (SA) 64 6 255 42.1 190.6 56.3
CH Gayle (WI) 174 9 333 42.0 232.2 60.2
HH Gibbs (SA) 154 7 228 42.0 226.8 42.2
GR Viswanath (India) 155 10 222 41.9 226.9 41.0
ME Waugh (Aus) 209 17 153 41.8 238.8 26.8
CG Macartney (Aus) 55 4 170 41.8 183.0 38.8
AG Prince (SA) 104 16 162 41.6 208.8 32.3
MP Vaughan (Eng) 147 9 197 41.4 222.1 36.8
JC Adams (WI) 90 17 208 41.3 200.9 42.7
GN Yallop (Aus) 70 3 268 41.1 190.0 58.0
GRJ Matthews (Aus) 53 8 130 41.1 178.4 29.9
KC Wessels (Aus/SA) 71 3 179 41.0 190.0 38.6
TM Dilshan (SL) 145 11 193 41.0 219.1 36.1
AJ Strauss (Eng) 178 6 177 40.9 227.1 31.9
PH Parfitt (Eng) 52 6 131 40.9 176.9 30.3
MJ Prior (Eng) 116 20 131 40.8 209.2 25.6
HW Taylor (SA) 76 4 176 40.8 191.7 37.4
LEG Ames (Eng) 72 12 149 40.6 188.5 32.1
PD Collingwood (Eng) 115 10 206 40.6 207.4 40.3
W Bardsley (Aus) 66 5 193 40.5 184.6 42.3
AW Greig (Eng) 93 4 148 40.4 198.2 30.2
Saeed Ahmed (Pak) 78 4 172 40.4 191.0 36.4
B Sutcliffe (NZ) 76 8 230 40.1 188.5 48.9
ST Jayasuriya (SL) 188 14 340 40.1 224.6 60.7
BL D'Oliveira (Eng) 70 8 158 40.1 185.1 34.2
SP Fleming (NZ) 189 10 274 40.1 224.7 48.8
RR Sarwan (WI) 154 8 291 40.0 216.3 53.8
WJ Edrich (Eng) 63 2 219 40.0 180.6 48.5
KWR Fletcher (Eng) 96 14 216 39.9 196.9 43.8
HA Gomes (WI) 91 11 143 39.6 193.4 29.3
AJ Stewart (Eng) 235 21 190 39.5 230.4 32.6
BM McMillan (SA) 62 12 113 39.4 177.1 25.1
CC McDonald (Aus) 83 4 170 39.3 188.3 35.5
DN Sardesai (India) 55 4 212 39.2 171.8 48.4
C Hill (Aus) 89 2 191 39.2 190.5 39.3
Mushtaq Mohammad (Pak) 100 7 201 39.2 194.9 40.4
Azhar Ali (Pak) 60 4 157 39.1 174.8 35.1
VL Manjrekar (India) 92 10 189 39.1 191.4 38.6
VT Trumper (Aus) 89 8 214 39.0 189.7 44.0
MS Atapattu (SL) 156 15 249 39.0 211.4 46.0
AP Gurusinha (SL) 70 7 143 38.9 179.8 31.0
Majid Khan (Pak) 106 5 167 38.9 195.9 33.2
Asif Iqbal (Pak) 99 7 175 38.9 192.9 35.2
MS Dhoni (India) 130 15 224 38.8 203.0 42.8
Taufeeq Umar (Pak) 81 5 236 38.7 184.5 49.5
WW Armstrong (Aus) 84 10 159 38.7 185.7 33.1
CD McMillan (NZ) 91 10 142 38.5 187.7 29.1
PJP Burge (Aus) 68 8 181 38.2 175.2 39.4
Mudassar Nazar (Pak) 116 8 231 38.1 195.1 45.1
BB McCullum (NZ) 145 8 302 38.1 203.6 56.5
Shakib Al Hasan (Ban) 65 5 144 38.0 172.6 31.7
JG Wright (NZ) 148 7 185 37.8 202.9 34.5
Imran Khan (Pak) 126 25 136 37.7 196.2 26.1
MA Atherton (Eng) 212 7 185 37.7 215.8 32.3
Ijaz Ahmed (Pak) 92 4 211 37.7 184.3 43.1
JV Coney (NZ) 85 14 174 37.6 180.8 36.2
PE Richardson (Eng) 56 1 126 37.5 164.8 28.6
KR Stackpole (Aus) 80 5 207 37.4 177.9 43.6
KJ Hughes (Aus) 124 6 213 37.4 194.1 41.0
ND McKenzie (SA) 94 7 226 37.4 183.7 46.0
AN Petersen (SA) 52 3 182 37.3 161.3 42.1
N Hussain (Eng) 171 16 207 37.2 204.9 37.6
SV Manjrekar (India) 61 6 218 37.1 166.5 48.6
Mohsin Khan (Pak) 79 6 200 37.1 175.9 42.2
NJ Astle (NZ) 137 10 222 37.0 195.8 42.0
KR Miller (Aus) 87 7 147 37.0 178.8 30.4
Tamim Iqbal (Ban) 62 0 151 36.6 164.6 33.6
CL Hooper (WI) 173 15 233 36.5 201.3 42.2
WJ Cronje (SA) 111 9 135 36.4 184.9 26.6
KS Williamson (NZ) 56 2 135 36.4 160.0 30.7
SR Watson (Aus) 95 3 176 36.3 178.9 35.7
JDP Oram (NZ) 59 10 133 36.3 161.6 29.9
Wasim Raja (Pak) 92 14 125 36.2 176.9 25.6
AJ Lamb (Eng) 139 10 142 36.1 191.4 26.8
FE Woolley (Eng) 98 7 154 36.1 178.7 31.1
Sadiq Mohammad (Pak) 74 2 166 35.8 167.4 35.5
AL Logie (WI) 78 9 130 35.8 169.2 27.5
RJ Shastri (India) 121 14 206 35.8 184.9 39.9
A Ranatunga (SL) 155 12 135 35.7 193.2 24.9
JN Rhodes (SA) 80 9 117 35.7 169.5 24.6
CG Borde (India) 97 11 177 35.6 176.0 35.8
MW Gatting (Eng) 138 14 207 35.6 188.3 39.1
MN Samuels (WI) 90 6 260 35.5 172.9 53.4
BJ Haddin (Aus) 94 9 169 35.5 174.4 34.4
JA Rudolph (SA) 83 9 222 35.4 169.7 46.4
Aamer Sohail (Pak) 83 3 205 35.3 169.0 42.8
GM Ritchie (Aus) 53 5 146 35.2 152.9 33.6
MAK Pataudi (India) 83 3 203 34.9 167.2 42.4
JP Crawley (Eng) 61 9 156 34.6 155.2 34.8
MA Butcher (Eng) 131 7 173 34.6 181.3 33.0
TL Goddard (SA) 78 5 112 34.5 162.9 23.7
TW Hayward (Eng) 60 2 137 34.5 153.9 30.7
W Jaffer (India) 58 1 212 34.1 151.2 47.8
GS Blewett (Aus) 79 4 214 34.0 161.3 45.1
Mohammad Hafeez (Pak) 70 6 196 34.0 156.9 42.4
WR Endean (SA) 52 4 162 34.0 146.8 37.5
Yuvraj Singh (India) 62 6 169 33.9 152.6 37.6
AP Sheahan (Aus) 53 6 127 33.9 147.3 29.2
AC MacLaren (Eng) 61 4 140 33.9 151.8 31.2
IT Botham (Eng) 161 6 208 33.5 182.8 38.2
CL Cairns (NZ) 104 5 158 33.5 168.1 31.5
KD Mackay (Aus) 52 7 89 33.5 144.8 20.6
Yashpal Sharma (India) 59 11 140 33.5 148.8 31.5
Shoaib Malik (Pak) 54 6 148 33.5 145.9 33.9
AC Hudson (SA) 63 3 163 33.5 151.0 36.1
DW Randall (Eng) 79 5 174 33.4 158.2 36.7
JR Reid (NZ) 108 5 142 33.3 168.1 28.1
GR Marsh (Aus) 93 7 138 33.2 162.7 28.1
WW Hinds (WI) 80 1 213 33.0 156.9 44.8
L Klusener (SA) 69 11 174 32.9 151.3 37.8
APE Knott (Eng) 149 15 135 32.8 176.0 25.1
M Prabhakar (India) 58 9 120 32.7 144.7 27.1
NT Paranavitana (SL) 60 5 111 32.6 145.5 24.9
P Roy (India) 79 4 173 32.6 154.3 36.5
CJ Tavare (Eng) 56 2 149 32.5 142.9 33.9
GP Howarth (NZ) 83 5 147 32.4 155.4 30.7
Mushfiqur Rahim (Ban) 71 4 200 32.4 150.3 43.2
SL Campbell (WI) 93 4 208 32.4 158.8 42.4
SM Pollock (SA) 156 39 111 32.3 175.1 20.5
BE Congdon (NZ) 114 7 176 32.2 164.5 34.5
JM Parks (Eng) 68 7 108 32.2 147.7 23.5
MS Sinclair (NZ) 56 5 214 32.1 141.0 48.7
Imran Farhat (Pak) 77 2 128 32.0 150.9 27.1
PJL Dujon (WI) 115 11 139 31.9 163.4 27.2
Rameez Raja (Pak) 94 5 122 31.8 156.4 24.8
GM Wood (Aus) 112 6 172 31.8 162.0 33.8
BA Young (NZ) 68 4 267 31.8 145.9 58.2
A Flintoff (Eng/ICC) 130 9 167 31.8 166.4 31.9
RES Wyatt (Eng) 64 6 149 31.7 143.6 32.9
MJK Smith (Eng) 78 6 121 31.6 149.5 25.6
NJ Contractor (India) 52 1 108 31.6 136.6 25.0
CPS Chauhan (India) 68 2 97 31.6 144.9 21.1
MH Mankad (India) 72 5 231 31.5 146.3 49.7
DJ Bravo (WI) 71 1 113 31.4 145.6 24.4
GA Hick (Eng) 114 6 178 31.3 159.9 34.9
HAPW Jayawardene (SL) 77 11 154 31.2 147.2 32.7
MG Burgess (NZ) 92 6 119 31.2 152.6 24.3
GT Dowling (NZ) 77 3 239 31.2 146.9 50.7
FM Engineer (India) 87 3 121 31.1 150.3 25.0
AL Wadekar (India) 71 3 143 31.1 144.0 30.9
N Kapil Dev (India) 184 15 163 31.1 173.4 29.2
Habibul Bashar (Ban) 99 1 113 30.9 153.3 22.8
Kamran Akmal (Pak) 92 6 158 30.8 150.6 32.3
JT Tyldesley (Eng) 55 1 138 30.8 134.7 31.5
KLT Arthurton (WI) 50 5 157 30.7 131.6 36.6
ML Jaisimha (India) 71 4 129 30.7 142.2 27.8
MJ Greatbatch (NZ) 71 5 146 30.6 141.9 31.5
BA Edgar (NZ) 68 4 161 30.6 140.4 35.1
Salman Butt (Pak) 62 0 122 30.5 137.0 27.1
JHB Waite (SA) 86 7 134 30.4 146.9 27.8
T Taibu (Zim) 54 3 153 30.3 132.2 35.1
MV Boucher (ICC/SA) 206 24 125 30.3 172.6 21.9
RA McLean (SA) 73 3 142 30.3 141.2 30.5
MA Noble (Aus) 73 7 133 30.3 141.0 28.5
BF Hastings (NZ) 56 6 117 30.2 132.8 26.6
W Rhodes (Eng) 98 21 179 30.2 149.6 36.1
HH Dippenaar (SA) 62 5 177 30.1 135.6 39.3
DL Vettori (ICC/NZ) 173 23 140 30.1 166.2 25.4
AD Gaekwad (India) 70 4 201 30.1 138.9 43.5
K Srikkanth (India) 72 3 123 29.9 138.9 26.5
AW Nourse (SA) 83 8 111 29.8 142.6 23.2
TE Bailey (Eng) 91 14 134 29.7 145.2 27.5
MJ Guptill (NZ) 59 1 189 29.6 131.8 42.5
GW Flower (Zim) 123 6 201 29.5 153.1 38.8
GJ Whittall (Zim) 82 7 203 29.4 140.6 42.5
Imtiaz Ahmed (Pak) 72 1 209 29.3 136.1 45.0
RS Mahanama (SL) 89 1 225 29.3 142.2 46.3
Rashid Latif (Pak) 57 9 150 28.8 127.0 34.0
Abdul Razzaq (Pak) 77 9 134 28.6 134.9 28.4
J Darling (Aus) 60 2 178 28.6 127.6 39.9
Moin Khan (Pak) 104 8 137 28.6 143.2 27.3
KG Viljoen (SA) 50 2 124 28.4 121.8 28.9
MJ Horne (NZ) 65 2 157 28.4 129.0 34.5
RD Jacobs (WI) 112 21 118 28.3 144.0 23.2
RP Arnold (SL) 69 4 123 28.0 129.0 26.7
IA Healy (Aus) 182 23 161 27.4 152.6 28.9
MR Ramprakash (Eng) 92 6 154 27.3 133.7 31.5
D Ramdin (WI) 95 13 166 27.3 134.2 33.7
ADR Campbell (Zim) 109 4 103 27.2 137.7 20.4
Sir RJ Hadlee (NZ) 134 19 151 27.2 143.1 28.7
RC Russell (Eng) 86 16 128 27.1 130.8 26.5
KR Rutherford (NZ) 99 8 107 27.1 134.5 21.6
SMH Kirmani (India) 124 22 102 27.0 140.3 19.7
SV Carlisle (Zim) 66 6 118 26.9 122.7 25.9
H Masakadza (Zim) 50 2 119 26.9 115.3 27.8
P Willey (Eng) 50 6 102 26.9 115.3 23.8
J Dyson (Aus) 58 7 127 26.6 118.1 28.6
PR Reiffel (Aus) 50 14 79 26.5 113.7 18.4
RW Marsh (Aus) 150 13 132 26.5 142.6 24.5
AC Parore (NZ) 128 19 110 26.3 137.2 21.1
JJ Crowe (NZ) 65 4 128 26.2 119.3 28.2
RS Kaluwitharana (SL) 78 4 132 26.1 123.5 27.9
G Miller (Eng) 51 4 98 25.8 111.1 22.8
D Ganga (WI) 86 2 135 25.7 124.0 28.0
KS More (India) 64 14 73 25.7 116.4 16.1
RG Nadkarni (India) 67 12 122 25.7 117.6 26.7
IDS Smith (NZ) 88 17 173 25.6 123.9 35.7
DA Allen (Eng) 51 15 88 25.5 109.8 20.4
MW Tate (Eng) 52 5 100 25.5 110.2 23.1
N Boje (SA) 62 10 85 25.2 113.5 18.9
SA Durani (India) 50 2 104 25.0 107.3 24.3
DS Smith (WI) 58 2 108 24.7 109.5 24.4
AK Davidson (Aus) 61 7 80 24.6 110.2 17.8
GS Ramchand (India) 53 5 109 24.6 106.8 25.1
JM Parker (NZ) 63 2 121 24.6 110.8 26.8
SE Gregory (Aus) 100 7 201 24.5 122.0 40.4
C White (Eng) 50 7 121 24.5 104.8 28.2
R Benaud (Aus) 97 7 122 24.5 120.9 24.7
V Pollard (NZ) 59 7 116 24.3 108.3 26.1
WPUJC Vaas (SL) 162 35 100 24.3 132.7 18.3
DJ Richardson (SA) 64 8 109 24.3 109.9 24.1
SCJ Broad (Eng) 95 12 169 24.2 119.2 34.3
SC Williams (WI) 52 3 128 24.1 104.4 29.6
NR Mongia (India) 68 8 152 24.0 110.3 33.1
Mohammad Ashraful (Ban) 119 5 190 24.0 123.6 36.9
GO Jones (Eng) 53 4 100 23.9 103.8 23.0
G Giffen (Aus) 53 0 161 23.4 101.4 37.1
R Illingworth (Eng) 90 11 113 23.2 113.2 23.2
AC Bannerman (Aus) 50 2 94 23.1 98.9 21.9
CC Lewis (Eng) 51 3 117 23.0 99.1 27.2
DL Murray (WI) 96 9 91 22.9 113.0 18.4
JM Brearley (Eng) 66 3 91 22.9 104.4 19.9
DD Ebrahim (Zim) 55 1 94 22.7 99.3 21.5
WAS Oldfield (Aus) 80 17 65 22.7 107.7 13.7
S Madan Lal (India) 62 16 74 22.7 101.9 16.4
Wasim Akram (Pak) 147 19 257 22.6 121.3 48.0
JE Emburey (Eng) 96 20 75 22.5 111.2 15.2
MG Johnson (Aus) 87 14 123 22.4 108.4 25.4
CB Wishart (Zim) 50 1 114 22.4 96.0 26.6
HH Streak (Zim) 107 18 127 22.4 112.7 25.2
FJ Titmus (Eng) 76 11 84 22.3 104.8 17.9
Intikhab Alam (Pak) 77 10 138 22.3 105.0 29.3
GP Swann (Eng) 76 14 85 22.1 103.9 18.1
Javed Omar (Ban) 80 2 119 22.1 104.8 25.0
DJG Sammy (WI) 63 2 106 21.7 97.9 23.5
KJ Wadsworth (NZ) 51 4 80 21.5 92.5 18.6
KD Ghavri (India) 57 14 86 21.2 93.7 19.5
RR Lindwall (Aus) 84 13 118 21.2 101.6 24.6
AF Giles (Eng) 81 13 59 20.9 99.5 12.4
DN Patel (NZ) 66 8 99 20.7 94.3 21.7
AFA Lilley (Eng) 52 8 84 20.5 88.7 19.4
TG Evans (Eng) 133 14 104 20.5 107.8 19.8
JG Bracewell (NZ) 60 11 110 20.4 91.2 24.6
BR Taylor (NZ) 50 6 124 20.4 87.4 28.9
S Abid Ali (India) 53 3 81 20.4 88.4 18.6
B Lee (Aus) 90 18 64 20.2 98.1 13.1
H Trumble (Aus) 57 14 70 19.8 87.4 15.9
HDPK Dharmasena (SL) 51 7 62 19.7 84.9 14.4
B Yardley (Aus) 54 4 74 19.6 85.3 17.0
Khaled Mashud (Ban) 84 10 103 19.0 91.4 21.5
TG Southee (NZ) 50 5 77 19.0 81.4 18.0
MD Marshall (WI) 107 11 92 18.9 95.1 18.2
JN Gillespie (Aus) 93 28 201 18.7 91.8 41.0
Mohammad Rafique (Ban) 63 6 111 18.6 83.8 24.6
IWG Johnson (Aus) 66 12 77 18.5 84.4 16.9
Harbhajan Singh (India) 142 22 115 18.4 97.7 21.6
J Briggs (Eng) 50 5 121 18.1 77.6 28.2
DG Cork (Eng) 56 8 59 18.0 79.2 13.4
A Kumble (India) 173 32 110 17.8 98.1 19.9
Sarfraz Nawaz (Pak) 72 13 90 17.7 82.3 19.4
PH Edmonds (Eng) 65 15 64 17.5 79.6 14.1
SK Warne (Aus) 199 17 99 17.3 98.1 17.5
JJ Kelly (Aus) 56 17 46 17.0 74.9 10.5
HJ Tayfield (SA) 60 9 75 16.9 75.5 16.8
MG Hughes (Aus) 70 8 72 16.6 76.9 15.6
Nasim-ul-Ghani (Pak) 50 5 101 16.6 71.1 23.6
BL Cairns (NZ) 65 8 64 16.3 74.0 14.1
RW Taylor (Eng) 83 12 97 16.3 78.0 20.3
GF Lawson (Aus) 68 12 74 16.0 73.3 16.1
Wasim Bari (Pak) 112 26 85 15.9 80.8 16.7
WW Hall (WI) 66 14 50 15.7 71.8 11.0
JM Blackham (Aus) 62 11 74 15.7 70.5 16.4
Abdul Qadir (Pak) 77 11 61 15.6 73.5 12.9
DR Pringle (Eng) 50 4 63 15.1 64.7 14.7
ATW Grout (Aus) 67 8 74 15.1 69.0 16.2
AME Roberts (WI) 62 11 68 14.9 67.2 15.1
CM Old (Eng) 66 9 65 14.8 67.6 14.2
PAJ DeFreitas (Eng) 68 5 88 14.8 68.0 19.2
SB Doull (NZ) 50 11 46 14.6 62.6 10.7
Saqlain Mushtaq (Pak) 78 14 101 14.5 68.5 21.4
RO Collinge (NZ) 50 13 68 14.4 61.7 15.9
DW Steyn (SA) 89 21 76 14.3 69.2 15.6
PM Siddle (Aus) 76 11 51 14.2 67.0 10.8
J Srinath (India) 92 21 76 14.2 69.5 15.5
VA Holder (WI) 59 11 42 14.2 63.2 9.4
Fazal Mahmood (Pak) 50 6 60 14.1 60.4 14.0
JC Laker (Eng) 63 15 63 14.1 63.6 14.0
CV Grimmett (Aus) 50 10 50 13.9 59.7 11.7
FS Trueman (Eng) 85 14 39 13.8 66.5 8.1
MA Holding (WI) 76 10 73 13.8 64.8 15.5
GAR Lock (Eng) 63 9 89 13.7 62.0 19.7
DK Lillee (Aus) 90 24 73 13.7 66.8 15.0
JA Snow (Eng) 71 14 73 13.5 62.7 15.8
GR Dilley (Eng) 58 19 56 13.4 59.2 12.6
Iqbal Qasim (Pak) 57 15 56 13.1 57.7 12.7
HMRKB Herath (SL) 70 15 80 13.0 59.9 17.3
Mashrafe Mortaza (Ban) 67 5 79 12.9 58.8 17.3
JR Thomson (Aus) 73 20 49 12.8 59.7 10.5
AV Bedser (Eng) 71 15 79 12.8 59.1 17.0
D Gough (Eng) 86 18 65 12.6 60.6 13.5
J Garner (WI) 68 14 60 12.4 57.1 13.1
CEL Ambrose (WI) 145 29 53 12.4 66.3 9.9
M Morkel (SA) 65 10 40 12.3 55.8 8.8
GD McKenzie (Aus) 89 12 76 12.3 59.6 15.6
CJ McDermott (Aus) 90 13 42 12.2 59.4 8.6
IR Bishop (WI) 63 11 48 12.2 54.9 10.6
Z Khan (India) 127 24 75 12.0 62.3 14.4
SJ Harmison (Eng/ICC) 86 23 49 11.8 56.9 10.2
Mushtaq Ahmed (Pak) 72 16 59 11.7 54.4 12.7
S Venkataraghavan (India) 76 12 64 11.7 54.9 13.6
M Muralitharan (ICC/SL) 164 56 67 11.7 63.8 12.3
AA Mallett (Aus) 50 13 43 11.6 49.8 10.0
Mohammad Sami (Pak) 56 14 49 11.6 51.0 11.1
DL Underwood (Eng) 116 35 45 11.6 59.2 8.8
RC Motz (NZ) 56 3 60 11.5 50.8 13.6
RGD Willis (Eng) 128 55 28 11.5 60.0 5.4
EAS Prasanna (India) 84 20 37 11.5 55.1 7.7
JB Statham (Eng) 87 28 38 11.4 55.3 7.9
AA Donald (SA) 94 33 37 10.7 52.5 7.5
MS Kasprowicz (Aus) 54 12 25 10.6 46.2 5.7
AR Caddick (Eng) 95 12 49 10.4 51.1 10.0
JM Anderson (Eng) 127 47 34 10.4 54.0 6.5
Waqar Younis (Pak) 120 21 45 10.2 52.6 8.7
Shahadat Hossain (Ban) 65 17 40 10.2 46.3 8.8
Shoaib Akhtar (Pak) 67 13 47 10.1 46.1 10.3
Umar Gul (Pak) 67 9 65 9.9 45.5 14.2
M Ntini (SA) 116 45 32 9.8 50.4 6.2
RM Hogg (Aus) 58 13 52 9.8 43.2 11.7
GP Wickramasinghe (SL) 64 5 51 9.4 42.6 11.3
I Sharma (India) 81 28 31 9.2 43.9 6.5
PR Adams (SA) 55 15 35 9.0 39.4 8.0
BS Bedi (India) 101 28 50 9.0 44.8 10.0
EJ Chatfield (NZ) 54 33 21 8.6 37.4 4.8
M Dillon (WI) 68 3 43 8.4 38.7 9.4
DK Morrison (NZ) 71 26 42 8.4 39.0 9.1
S Ramadhin (WI) 58 14 44 8.2 36.3 9.9
CD Collymore (WI) 52 27 16 7.9 34.1 3.7
DBL Powell (WI) 57 5 36 7.8 34.5 8.2
CA Walsh (WI) 185 61 30 7.5 42.1 5.4
ARC Fraser (Eng) 67 15 32 7.5 34.1 7.0
GD McGrath (Aus) 138 51 61 7.4 39.0 11.5
MJ Hoggard (Eng) 92 27 38 7.3 35.6 7.8
Danish Kaneria (Pak) 84 33 29 7.1 33.9 6.0
LR Gibbs (WI) 109 39 25 7.0 35.3 4.9
FH Edwards (WI) 88 28 30 6.6 31.8 6.2
TM Alderman (Aus) 53 22 26 6.5 28.4 6.0
DE Malcolm (Eng) 58 19 29 6.1 26.8 6.5
PCR Tufnell (Eng) 59 29 22 5.1 22.7 4.9
MS Panesar (Eng) 68 23 26 4.9 22.4 5.7
AL Valentine (WI) 51 21 14 4.7 20.2 3.3
BS Chandrasekhar (India) 80 39 22 4.1 19.3 4.6
CS Martin (NZ) 104 52 12 2.4 11.8 2.4</pre>David Barryhttp://www.blogger.com/profile/08378763233797445502noreply@blogger.com10tag:blogger.com,1999:blog-22713811.post-56478554092927678842014-01-16T12:10:00.001+01:002014-01-16T12:10:36.055+01:00Some women's T20 record lists<p>The other day, Avi Singh tweeted:
<blockquote class="twitter-tweet" lang="en"><p><a href="https://twitter.com/RafNicholson">@RafNicholson</a> From this match today <a href="http://t.co/1TwKWX1n6Q">http://t.co/1TwKWX1n6Q</a> do you know if Auckland's 189 & <a href="https://twitter.com/sjmac7">@sjmac7</a>'s 131* are women's domestic T20 records?</p>— Avi Singh (@MannerOfSpeakin) <a href="https://twitter.com/MannerOfSpeakin/statuses/421511196034859008">January 10, 2014</a></blockquote>
<script async src="//platform.twitter.com/widgets.js" charset="utf-8"></script></p>
<p>As best I can tell, McGlashan's score is a women's T20 record, but the team total is definitely not a world record.</p>
<p>I am not aware of any site that compiles record lists of women's List A or T20 cricket, so I have filled a small part of that gap with some tables at the end of this post. The stats are in some cases not complete. There are many gaps in CricketArchive's database – preliminary rounds of at least one tournament in India are completely missing; I ran into matches in both England and South Africa where only very partial scores were available, and in these cases I did not add them to my own database; I ran into matches in Australia and England where some players' names were not recorded (one might have taken five wickets in an innings!); there are as far as I can see no West Indies domestic games classified as WT20 ("wtt") by CricketArchive – for reasons unknown to me (design? accident? they are slowly systematising their classifications and haven't reached the West Indies yet?), the WI domestic tournaments have their matches classified as miscellaneous.</p>
<p>The above paragraph is not intended to be an exhaustive list of the gaps in the data – they just happen to be the cases I consciously noticed, and in some cases I just tweaked my code that was parsing the scorecards, so that I was blissfully unaware of any subsequent gaps of that type.</p>
<p>On a much more minor note, while most of the matches played by the batsmen in the tables below have the balls-faced recorded, there are a couple (literally 2 for Charlotte Edwards) where this isn't the case, and such innings are ignored when calculating the strike rate.</p>
<p>The 'wtt' columns in some tables below refer to the CricketArchive match ID's. I don't have the scorecard URL's handy, but you can go to CA's <a href="http://cricketarchive.com/cgi-bin/ask_the_scorecard_oracle.cgi">scorecard Oracle</a>, and enter, e.g., wtt1264 as the match ID, and that will bring up the scorecard showing McGlashan's 131 not out. My hope is that the values I give in the tables below will become obsolete as West Indies (and other?) domestic games are re-classified by CA as wtt.</p>
<p>CA doesn't present record lists for women's domestic matches, but they do show the stats for individual players (useful for me since it allowed me to check my results). In light of this, I think that it shouldn't be too hard for CA to add women's T20 and List A to the list of their stats options, and I would encourage anyone interested to lobby CA on that front. I don't intend to maintain my own database regularly, nor to update the tables in this post. I am willing to compile record lists for women's List A if there is demand for it.</p>
<p><b>Leading run-scorers</b>
<pre>name mat inns no hs runs avg sr
<a href="http://cricketarchive.com/Archive/Players/8/8457/8457.html">CM Edwards</a> 109 105 15 88 3043 33.8 112.7
<a href="http://cricketarchive.com/Archive/Players/45/45550/45550.html">LJ Poulton</a> 101 100 9 103* 2692 29.6 117.7
<a href="http://cricketarchive.com/Archive/Players/88/88260/88260.html">SJ Taylor</a> 101 95 18 77 2592 33.7 110.3
<a href="http://cricketarchive.com/Archive/Players/45/45878/45878.html">SW Bates</a> 102 102 10 78* 2513 27.3 101.2
<a href="http://cricketarchive.com/Archive/Players/250/250897/250897.html">MM Lanning</a> 88 88 12 79 2455 32.3 116.9
<a href="http://cricketarchive.com/Archive/Players/44/44360/44360.html">AJ Blackwell</a> 113 101 25 99* 2356 31.0 106.7
<a href="http://cricketarchive.com/Archive/Players/44/44475/44475.html">SJ McGlashan</a> 92 89 18 131* 1979 27.9 113.4
<a href="http://cricketarchive.com/Archive/Players/142/142817/142817.html">JE Cameron</a> 101 91 17 68* 1960 26.5 111.8
<a href="http://cricketarchive.com/Archive/Players/73/73098/73098.html">AE Satterthwaite</a> 97 88 15 80* 1754 24.0 95.6
<a href="http://cricketarchive.com/Archive/Players/17/17152/17152.html">M Raj</a> 70 63 25 85* 1729 45.5 99.2</pre>
<b>Highest averages (qual. 20 innings)</b>
<pre>name mat inns no hs runs avg sr
<a href="http://cricketarchive.com/Archive/Players/4/4759/4759.html">KL Rolton</a> 36 33 10 96* 1153 50.1 104.9
<a href="http://cricketarchive.com/Archive/Players/17/17152/17152.html">M Raj</a> 70 63 25 85* 1729 45.5 99.2
<a href="http://cricketarchive.com/Archive/Players/90/90922/90922.html">MD Thirush Kamini</a> 24 23 5 63 783 43.5 85.7
<a href="http://cricketarchive.com/Archive/Players/149/149696/149696.html">FC Wilson</a> 24 23 5 99 739 41.1 120.4
<a href="http://cricketarchive.com/Archive/Players/317/317603/317603.html">SR Taylor</a> 58 56 11 90 1612 35.8 100.4
<a href="http://cricketarchive.com/Archive/Players/10/10561/10561.html">KV Jain</a> 28 28 12 53* 569 35.6 79.2
<a href="http://cricketarchive.com/Archive/Players/60/60743/60743.html">M Kanojia</a> 43 39 14 57* 849 34.0 80.6
<a href="http://cricketarchive.com/Archive/Players/8/8457/8457.html">CM Edwards</a> 109 105 15 88 3043 33.8 112.7
<a href="http://cricketarchive.com/Archive/Players/88/88260/88260.html">SJ Taylor</a> 101 95 18 77 2592 33.7 110.3
<a href="http://cricketarchive.com/Archive/Players/73/73099/73099.html">SFM Devine</a> 63 61 14 99* 1571 33.4 121.0</pre>
<b>Highest strike rates (qual. 20 innings with balls-faced recorded)</b>
<pre>name mat inns no hs runs avg sr
<a href="http://cricketarchive.com/Archive/Players/317/317605/317605.html">DJS Dottin</a> 61 60 13 112* 1120 23.8 136.1
<a href="http://cricketarchive.com/Archive/Players/328/328787/328787.html">AC Smith</a> 49 49 4 89 1049 23.3 127.1
<a href="http://cricketarchive.com/Archive/Players/8/8463/8463.html">SC Taylor</a> 49 46 10 76* 1196 33.2 124.8
<a href="http://cricketarchive.com/Archive/Players/73/73099/73099.html">SFM Devine</a> 63 61 14 99* 1571 33.4 121.0
<a href="http://cricketarchive.com/Archive/Players/149/149696/149696.html">FC Wilson</a> 24 23 5 99 739 41.1 120.4
<a href="http://cricketarchive.com/Archive/Players/113/113054/113054.html">SE Rowe</a> 47 32 12 67 647 32.4 119.0
<a href="http://cricketarchive.com/Archive/Players/45/45550/45550.html">LJ Poulton</a> 101 100 9 103* 2692 29.6 117.7
<a href="http://cricketarchive.com/Archive/Players/250/250897/250897.html">MM Lanning</a> 88 88 12 79 2455 32.3 116.9
<a href="http://cricketarchive.com/Archive/Players/317/317607/317607.html">SC King</a> 52 48 11 81 672 18.2 116.6
<a href="http://cricketarchive.com/Archive/Players/187/187174/187174.html">AJ Healy</a> 92 84 13 90 1554 21.9 116.5
</pre>
<b>Centuries</b>
<pre>name score for against year wtt
<a href="http://cricketarchive.com/Archive/Players/44/44475/44475.html">SJ McGlashan</a> 131* Auckland Northern Districts 2014 1264
<a href="http://cricketarchive.com/Archive/Players/60/60249/60249.html">SA Fritz</a> 116* South Africa Netherlands 2010 437
<a href="http://cricketarchive.com/Archive/Players/60/60642/60642.html">RA Birch</a> 113* Devon Dorset 2013 1139
<a href="http://cricketarchive.com/Archive/Players/317/317605/317605.html">DJS Dottin</a> 112* West Indies South Africa 2010 356
<a href="http://cricketarchive.com/Archive/Players/228/228177/228177.html">HC Knight</a> 111 Berkshire Devon 2012 913
<a href="http://cricketarchive.com/Archive/Players/228/228251/228251.html">EL Marriott</a> 109* Leicestershire Oxfordshire 2011 629
<a href="http://cricketarchive.com/Archive/Players/113/113874/113874.html">A Shrubsole</a> 109* Somerset Wales 2013 1175
<a href="http://cricketarchive.com/Archive/Players/73/73092/73092.html">RH Priest</a> 106* Central Districts Otago 2012 812
<a href="http://cricketarchive.com/Archive/Players/228/228177/228177.html">HC Knight</a> 106* Berkshire Wales 2012 891
<a href="http://cricketarchive.com/Archive/Players/21/21203/21203.html">MJ Bulow</a> 103 Queensland Tasmania 2011 698
<a href="http://cricketarchive.com/Archive/Players/45/45550/45550.html">LJ Poulton</a> 103* New South Wales Australian Capital Territory 2011 720
<a href="http://cricketarchive.com/Archive/Players/113/113029/113029.html">DN Wyatt</a> 102 Nottinghamshire Yorkshire 2013 1189
<a href="http://cricketarchive.com/Archive/Players/4/4755/4755.html">LM Keightley</a> 101* Wiltshire Buckinghamshire 2010 375
<a href="http://cricketarchive.com/Archive/Players/113/113029/113029.html">DN Wyatt</a> 100* Staffordshire Lancashire 2012 886
</pre>
<b>Leading wicket-takers</b>
<pre>name mat balls runs wkts bb avg econ
<a href="http://cricketarchive.com/Archive/Players/8/8086/8086.html">LC Sthalekar</a> 99 2106 1908 110 5/11 17.3 5.44
<a href="http://cricketarchive.com/Archive/Players/88/88054/88054.html">HL Colvin</a> 82 1771 1513 93 4/9 16.3 5.13
<a href="http://cricketarchive.com/Archive/Players/150/150521/150521.html">SEA Ruck</a> 102 2121 1885 87 3/9 21.7 5.33
<a href="http://cricketarchive.com/Archive/Players/187/187173/187173.html">EA Perry</a> 90 1869 1707 85 4/20 20.1 5.48
<a href="http://cricketarchive.com/Archive/Players/115/115584/115584.html">D Hazell</a> 72 1647 1423 81 4/12 17.6 5.18
<a href="http://cricketarchive.com/Archive/Players/73/73689/73689.html">LR Doolan</a> 91 1768 1732 80 4/10 21.7 5.88
<a href="http://cricketarchive.com/Archive/Players/12/12302/12302.html">S Nitschke</a> 68 1450 1329 79 4/21 16.8 5.50
<a href="http://cricketarchive.com/Archive/Players/44/44418/44418.html">NJ Browne</a> 91 1757 1544 77 4/15 20.1 5.27
<a href="http://cricketarchive.com/Archive/Players/372/372495/372495.html">SJ Coyte</a> 71 1423 1430 74 4/5 19.3 6.03
<a href="http://cricketarchive.com/Archive/Players/326/326626/326626.html">EA Osborne</a> 81 1627 1484 71 4/13 20.9 5.47</pre>
<b>Lowest averages (qual. 20 wickets)</b>
<pre>name mat balls runs wkts bb avg econ
<a href="http://cricketarchive.com/Archive/Players/122/122592/122592.html">J Turner</a> 18 373 246 27 4/8 9.1 3.96
<a href="http://cricketarchive.com/Archive/Players/383/383882/383882.html">A Maitra</a> 17 378 214 20 3/6 10.7 3.40
<a href="http://cricketarchive.com/Archive/Players/416/416069/416069.html">A Upendran</a> 35 618 432 40 4/12 10.8 4.19
<a href="http://cricketarchive.com/Archive/Players/125/125022/125022.html">N Buley</a> 16 328 274 25 4/11 11.0 5.01
<a href="http://cricketarchive.com/Archive/Players/10/10489/10489.html">S Dabir</a> 32 680 527 46 4/13 11.5 4.65
<a href="http://cricketarchive.com/Archive/Players/159/159564/159564.html">S Asha</a> 24 540 394 34 4/7 11.6 4.38
<a href="http://cricketarchive.com/Archive/Players/140/140437/140437.html">A Kaur</a> 31 525 342 28 5/6 12.2 3.91
<a href="http://cricketarchive.com/Archive/Players/870/870738/870738.html">AN Davidson-Richards</a> 26 408 378 30 4/13 12.6 5.56
<a href="http://cricketarchive.com/Archive/Players/416/416135/416135.html">P Jagtap</a> 24 505 346 27 3/11 12.8 4.11
<a href="http://cricketarchive.com/Archive/Players/381/381210/381210.html">M Chaudhary</a> 23 486 310 24 3/11 12.9 3.83</pre>
<b>Lowest economy rates (qual. 80 overs)</b>
<pre>name mat balls runs wkts bb avg econ
<a href="http://cricketarchive.com/Archive/Players/383/383841/383841.html">Shivangiraj Sin</a> 25 550 349 18 2/1 19.4 3.81
<a href="http://cricketarchive.com/Archive/Players/381/381210/381210.html">M Chaudhary</a> 23 486 310 24 3/11 12.9 3.83
<a href="http://cricketarchive.com/Archive/Players/140/140437/140437.html">A Kaur</a> 31 525 342 28 5/6 12.2 3.91
<a href="http://cricketarchive.com/Archive/Players/90/90923/90923.html">SN Pradhan</a> 32 535 357 20 3/30 17.9 4.00
<a href="http://cricketarchive.com/Archive/Players/416/416135/416135.html">P Jagtap</a> 24 505 346 27 3/11 12.8 4.11
<a href="http://cricketarchive.com/Archive/Players/8/8465/8465.html">N David</a> 33 696 477 27 4/8 17.7 4.11
<a href="http://cricketarchive.com/Archive/Players/10/10437/10437.html">P Dimri</a> 25 570 392 30 3/9 13.1 4.13
<a href="http://cricketarchive.com/Archive/Players/10/10466/10466.html">J Goswami</a> 60 1246 862 59 5/11 14.6 4.15
<a href="http://cricketarchive.com/Archive/Players/416/416069/416069.html">A Upendran</a> 35 618 432 40 4/12 10.8 4.19
<a href="http://cricketarchive.com/Archive/Players/159/159733/159733.html">M Shalini</a> 34 530 381 23 3/7 16.6 4.31
</pre>
<b>Five wickets in an innings</b>
<pre>name figures for against year wtt
<a href="http://cricketarchive.com/Archive/Players/910/910124/910124.html">DH Kaur</a> 6/3 Punjab Jammu and Kashmir 2009 281
<a href="http://cricketarchive.com/Archive/Players/159/159534/159534.html">NM Vijeylakshmi</a> 6/7 Tamil Nadu Kerala 2011 500
<a href="http://cricketarchive.com/Archive/Players/73/73098/73098.html">AE Satterthwaite</a> 6/17 New Zealand England 2007 25
<a href="http://cricketarchive.com/Archive/Players/1162/1162255/1162255.html">H Swan</a> 5/6 Hertfordshire Cambridgeshire and Huntingdonshire 2011 677
<a href="http://cricketarchive.com/Archive/Players/60/60251/60251.html">APC Kilowan</a> 5/8 West Coast Central 2008 58
<a href="http://cricketarchive.com/Archive/Players/75/75192/75192.html">Asmavia Iqbal</a> 5/8 Pakistan Nottinghamshire 2009 168
<a href="http://cricketarchive.com/Archive/Players/61/61254/61254.html">A Mohammed</a> 5/10 West Indies South Africa 2009 224
<a href="http://cricketarchive.com/Archive/Players/8/8086/8086.html">LC Sthalekar</a> 5/11 New South Wales Australian Capital Territory 2011 573
? 5/14 Durham Cumbria 2011 621
<a href="http://cricketarchive.com/Archive/Players/45/45878/45878.html">SW Bates</a> 5/15 Otago Canterbury 2011 560
<a href="http://cricketarchive.com/Archive/Players/90/90703/90703.html">P Roy</a> 5/16 India Pakistan 2009 177
<a href="http://cricketarchive.com/Archive/Players/300/300133/300133.html">KA Levick</a> 5/16 Yorkshire Staffordshire 2011 648
<a href="http://cricketarchive.com/Archive/Players/66/66744/66744.html">EAJ Richardson</a> 5/17 Central Districts Canterbury 2011 502
<a href="http://cricketarchive.com/Archive/Players/45/45551/45551.html">CA Anneveld</a> 5/22 Australian Capital Territory South Australia 2011 700
</pre>
<b>Highest team totals</b>
<pre>team opponent total year wtt
Somerset Wales 3/255 2013 1175
Ireland Bedfordshire 1/218 2012 876
Derbyshire Oxfordshire 0/207 2011 658
South Africa Netherlands 1/205 2010 437
Staffordshire Lancashire 0/202 2012 886
Kent Essex 8/199 2011 642
Berkshire Wales 2/199 2013 1162
Kent Surrey 3/198 2010 386
Maharashtra Mumbai 3/194 2013 1084
Berkshire Devon 5/192 2012 913</pre></p>David Barryhttp://www.blogger.com/profile/08378763233797445502noreply@blogger.com0tag:blogger.com,1999:blog-22713811.post-49087065062371498592012-04-14T10:37:00.000+02:002012-04-14T10:38:46.927+02:00Balls faced errors<a href="http://www.sportstats.com.au/bloghome.html">Charles Davis</a>, in his blog post titled "Balls Faced Blues" (31 March 2012), reports on errors in the previously published scorecards to <a href="http://www.cricketarchive.co.uk/Archive/Scorecards/44/44537.html">this Test</a> between the West Indies and Australia in 1983/4. He writes:<br /><br /><blockquote>A full re-score confirms Border’s runs and the sequence of strokes, yet the balls faced figures as published seem to be quite wrong. Border is given 314 balls for his 98* and 269 for the 100*. The figures I get are 283 and 285 balls, respectively. Viv Richards’ figures for his 76 are also very doubtful: I get 130 balls not 188. There are other discrepancies. Using the revised figures, the balls faced now reconcile with the bowling figures.</blockquote><br />I had never thought to compare the balls faced with the total overs in an innings. I was surprised to find that substantial disagreements are quite common. Some discrepancies are to be expected – a single off a no-ball used to be an extra ball but not a sundry; many wides can come from one ball; a wide used to be counted as a ball faced in Australia; there have been plenty of 5- and 7-ball overs which won't show up in the team totals.<br /><br />But still. Below is a list of innings where the discrepancy is at least 20 balls. It's enormous. I've defined "discrepancy" as the sum of the balls faced, minus the total number of balls in the innings, minus the extras. I had originally planned to manually check all of these anomalies, but I couldn't be bothered. I've made notes on a few of the more egregious cases.<br /><br />I didn't output the dates of the Tests when making this table. The quickest way to pull up the scorecard is probably via CricketArchive's <a href="http://www.cricketarchive.com/cgi-bin/ask_the_scorecard_oracle.cgi">Scorecard Oracle</a>. To bring up Test 104, enter t104 as the 'match id'. The Test that Davis talked about is t983 – you can see that all three innings of that match are on the list, as are two innings from the fourth Test of that series.<br /><br />The following list is based on the CricketArchive scorecards as they were a few years ago. Probably some errors have been ironed out since. (I've also omitted one scorecard which had obviously flipped the balls-faced and minutes; hopefully it will be corrected in the coming days.)<br /><br /><pre>Test no. Home Away inns sum BF inns balls nb wd discrepancy<br />104 England Australia 4 310 330 0 0 20<br />180 Australia England 4 832 805 0 0 27<br />290 Australia India 3 368 399 1 0 32<br />303 England Australia 1 277 253 0 0 24<br />470 New Zealand England 2 377 437 0 0 60 Judging by the minutes, <br /> I think Bolton faced ~60 <br /> more balls than is given<br />537 Australia England 1 715 693 0 2 20<br />598 Australia England 1 883 861 1 0 21<br />613 South Africa Australia 2 736 763 0 0 27<br />628 Australia India 4 618 590 0 0 28<br />673 South Africa Australia 3 779 728 20 0 31<br />703 India England 1 458 508 0 0 50 Minutes make sense. <br /> Probably Venkat faced more.<br />708 India England 3 522 636 3 0 117 Minutes make sense. <br /> Probably Denness faced more.<br />730 Australia New Zealand 3 740 717 2 0 21<br />731 West Indies England 2 798 736 16 0 46<br />748 Australia England 3 645 729 11 1 96 Wisden says Titmus batted for <br /> 200 minutes, not 108; probably <br /> faced a lot more. Old too?<br />757 New Zealand England 3 401 461 12 0 72<br />835 India West Indies 1 812 822 13 0 23<br />835 India West Indies 2 928 966 8 0 46<br />855 India Australia 2 831 783 23 0 25<br />855 India Australia 3 657 682 3 0 28<br />858 India Australia 1 932 866 24 0 42<br />858 India Australia 2 628 639 19 0 30<br />859 India Australia 2 787 754 8 4 21<br />863 India Pakistan 1 593 551 4 0 38<br />865 India Pakistan 3 399 419 5 0 25<br />869 India Pakistan 3 441 400 17 0 24<br />876 Pakistan Australia 2 664 713 10 0 59<br />876 Pakistan Australia 3 521 540 2 1 22<br />878 Pakistan Australia 1 1302 1266 3 0 33<br />878 Pakistan Australia 2 724 756 15 0 47<br />889 Australia New Zealand 3 316 289 5 2 20<br />891 Australia New Zealand 1 799 729 6 3 61<br />908 England Australia 3 658 626 7 1 24<br />914 India England 2 955 961 21 4 31<br />918 India England 2 917 935 12 0 30<br />922 New Zealand Australia 1 762 696 11 4 51<br />924 New Zealand Australia 4 138 178 0 0 40<br />937 Pakistan Australia 3 494 540 8 0 54<br />970 Australia Pakistan 1 722 734 4 4 20<br />983 West Indies Australia 1 605 553 17 0 35<br />983 West Indies Australia 2 815 756 1 2 56<br />983 West Indies Australia 3 634 673 14 1 54<br />985 Sri Lanka New Zealand 2 1079 1050 6 2 21<br />987 West Indies Australia 1 563 581 10 0 28<br />987 West Indies Australia 2 850 874 1 0 25<br />993 England West Indies 3 639 579 0 0 60<br />998 Pakistan New Zealand 1 392 448 0 0 56<br />998 Pakistan New Zealand 2 530 560 2 0 32<br />998 Pakistan New Zealand 3 441 498 4 1 62<br />998 Pakistan New Zealand 4 347 391 0 0 44<br />1000 Pakistan New Zealand 1 674 651 1 0 22<br />1000 Pakistan New Zealand 3 378 337 3 0 38<br />1010 New Zealand Pakistan 2 1002 882 6 0 114 Probably Wright faced a lot less.<br />1033 Australia India 2 930 890 17 0 23<br />1045 West Indies England 2 635 646 40 0 51<br />1052 India Australia 2 600 566 6 0 28<br />1052 India Australia 3 273 294 1 0 22<br />1058 Pakistan West Indies 2 566 661 2 1 98 Rameez faced much more.<br />1065 Australia England 2 598 564 2 2 30<br />1072 New Zealand West Indies 3 382 423 8 0 49<br />1087 Australia New Zealand 4 577 552 4 0 21<br />1097 West Indies Pakistan 3 546 563 14 0 31<br />1118 West Indies India 1 618 632 22 0 36<br />1118 West Indies India 2 667 671 22 0 26<br />1118 West Indies India 3 543 585 13 0 55<br />1118 West Indies India 4 271 288 3 0 20<br />1152 Pakistan New Zealand 2 782 750 12 0 20<br />1163 Australia England 1 509 400 5 1 103 Atherton faced much less.<br />1170 West Indies Australia 3 584 547 4 0 33<br />1180 Australia India 2 836 732 11 0 93 Probably Jones faced much less.<br />1186 Australia India 3 719 681 6 0 32<br />1195 Sri Lanka Australia 3 715 690 5 0 20<br />1220 West Indies Pakistan 1 222 230 11 2 21<br />1232 Sri Lanka South Africa 4 592 672 15 1 96 Probably Rhodes faced much more.<br />1237 Pakistan Zimbabwe 1 903 930 1 0 28<br />1238 Australia New Zealand 1 608 633 2 0 27<br />1239 Sri Lanka West Indies 3 102 133 1 0 32<br />1247 India Sri Lanka 1 350 383 0 0 33<br />1247 India Sri Lanka 3 608 627 1 0 20<br />1262 England New Zealand 1 855 879 2 1 27<br />1266 England South Africa 1 543 554 18 1 30<br />1268 Pakistan Australia 2 558 523 4 0 31<br />1278 India West Indies 1 855 856 14 5 20<br />1278 India West Indies 2 767 771 17 1 22<br />1282 South Africa New Zealand 3 643 667 6 1 31<br />1291 New Zealand South Africa 1 663 633 0 0 30<br />1293 New Zealand Sri Lanka 2 887 834 15 0 38<br />1325 West Indies New Zealand 1 369 372 17 1 21<br />1325 West Indies New Zealand 2 949 987 12 0 50<br />1415 Sri Lanka New Zealand 1 665 632 9 1 23</pre>David Barryhttp://www.blogger.com/profile/08378763233797445502noreply@blogger.com3tag:blogger.com,1999:blog-22713811.post-37526510007228260132012-03-30T12:57:00.003+02:002012-04-01T06:25:32.724+02:00On the batting h-indexKartikeya Date has <a href="http://cricketingview.blogspot.com/2012/03/alternative-batting-statistic-for.html">written</a> a post applying the <a href="http://en.wikipedia.org/wiki/H-index">h-index</a>, originally designed as a measure of academics' research impact, to batsmen. The basic idea isn't original (see, e.g., <a href="http://horadecubitus.wordpress.com/2011/11/28/an-h-index-for-test-cricket-batsmen/">this blog post</a> with a related h-based stat; that post also cites a paper by Gangan Prathap which I can't read because it costs 35 euros to download), but Kartikeya's implementation seems to be novel.<br /><br />The definition of h-index is this: it is the largest integer h such that the batsman has made h scores of at least h. Combining Test and ODI cricket, Sachin Tendulkar has an h-index of 100, since he's made 100 scores of at least 100.<br /><br />Kartikeya correctly notes that a batsman's h-index will tend to rise over the course of his career, and it can never decrease – if after 30 innings, he has 12 scores of at least 12, then he will never again have less than 12 scores of at least 12. The question then arises as to how to normalise the h-index, and Kartikeya proposes h^2 / innings, which "is a measure of consistency and quality without either privileging or penalizing a player for length of career".<br /><br />I was initially very sceptical that such a normalisation could work. But it does, somehow. I'll split this post into two parts: in the first, I'll talk about why h^2 / innings is a decent metric over a range of different career lengths; in the second, I'll describe what I would do with an h-index, and show that my method doesn't give much (if any) improvement on h^2 / innings.<br /><br /><b>h^2 / innings</b><br /><br />For ease of doing the maths, let's assume that a batsman's scores are exponentially distributed, so that the probability of a score greater than n is exp(-n / avg). This assumption is not particularly good for low scores, but it'll do for now. <br /><br />For a career of N innings, the expectation value is for N * exp(-n / avg) scores of above n. Now we can write an equation to define what this idealised batsman's h-index will be:<br /><br />N * exp(-h / avg) = h.<br /><br />This sort of equation doesn't have a solution in terms of elementary functions, but it does using the ProductLog function that you can use in Wolfram Alpha. In any case, if you hold avg constant, and write h as a function of career length N, and square h, then this is the graph that you get:<br /><br /><img alt="y = mx" title="y = mx" src="http://i44.photobucket.com/albums/f35/pappubahry/cricket/h2vsinnings.png"><br /><br />That is remarkably close to a straight line. I don't know whether there's a good mathematical reason why it should be so close to linear, or whether it's just a coincidence. In any case, it shows that dividing h^2 by the number of innings will give you something roughly constant over a career, assuming an idealised batsman with constant ability.<br /><br />It can't <i>always</i> be near-linear though. Obviously someone who's only batted in one innings can have an h-index of at most 1, and then h^2 / innings will also be 1. So early in a career, this won't be a reliable measure (though it does have the possibly desirable feature of preventing someone with an excellent start to his career showing up near the top of the all-time list after 20 innings). This is clearer if we plot h^2 / innings against innings, done here for an idealised batsman who averages 40:<br /><br /><img alt="y does not equal m" title="y does not equal m" src="http://i44.photobucket.com/albums/f35/pappubahry/cricket/h2oninningsvsinnings.png"><br /><br />There are two points to make here. The first is that for career lengths between about 50 innings and 300 innings, h^2 / innings doesn't vary too much: its maximum in this case is at about 14.7, its minimum over that range about 13. The second point is that this level of variability may be undesirable. An increase of 1.5 in h^2 / innings could send someone from being ranked 50th all-time to being ranked 23rd.<br /><br /><b>What I would do with the h-index</b><br /><br />So let's instead go back to that equation, and solve instead for avg. This is straightforward:<br /><br />avg = h / ln(N/h).<br /><br />Essentially, then, for a given number of innings N, we are associating an h-index with some sort of effective average, which I'll call an h-average (it won't be defined if h = N). I think it's useful to do this, because the h-average is on the same scale as the regular average, which we're all familiar with. An h-average of 50 is very high; Chris Martin has an h-average of 1.8 (or at least he did whenever I last updated my database, in December 2010). A table of h- and regular averages is at the end of this post.<br /><br />I expected that, for batsmen with 50 or more Test innings, the h-average would give a nicer all-time ranking than h^2 / innings. But while there was a bit of shuffling, overall the two measures are very closely correlated:<br /><br /><img alt="Hours of work and I get nothing but a straight line...." title="Hours of work and I get nothing but a straight line...." src="http://i44.photobucket.com/albums/f35/pappubahry/cricket/h2oninnsvh-average.png"><br /><br />It is interesting to see how these h measures correlate with the regular batting average. Relaxing the qualification to just 20 Test innings, here are the two scatterplots (first the h-average, then h^2 / innings):<br /><br /><img alt="Not sure what the deal is with Bradman's relatively low h-average: his century rate is dead on what you'd expect." title="Not sure what the deal is with Bradman's relatively low h-average: his century rate is dead on what you'd expect." src="http://i44.photobucket.com/albums/f35/pappubahry/cricket/h-averagevaverage.png"><br /><br /><img alt="With that pesky Ryder outlier out of the way, the title can return to the top of the plot." title="With that pesky Ryder outlier out of the way, the title can return to the top of the plot." src="http://i44.photobucket.com/albums/f35/pappubahry/cricket/h2oninningsvaverage.png"><br /><br />The wild non-Bradman outlier on the first plot is Jesse Ryder, who at the time of my last database update had an h-index of 20 after just 25 innings. In the second scatterplot, you can see that his (and a bunch of other high-h-average players) h^2 / innings is suppressed because of his low number of innings. But correlations are quite good – given a player's average, his h-average doesn't tell you that much extra information about his batting, and vice versa.<br /><br />I don't think I have a strong conclusion. The h-index rewards consistent scoring, and while consistency may be useful, I'm not sure that it's better or worse than scoring the same amount of total runs but with less consistency.<br /><br />(<b>Edit 1 April</b>: I ignored not-outs, because they're annoying. You get slightly better agreement between the h-average and runs-per-innings than between the h-average and the regular average.)<br /><br />Anyway, here is a nice big table. Qualification of 50 Test innings; stats current to late 2010.<br /><pre>name mat inns no runs h avg h^2/inns h-avg<br />DG Bradman 52 80 10 6996 44 99.9 24.2 73.6<br />JB Hobbs 61 102 7 5410 47 56.9 21.7 60.7<br />KF Barrington 82 131 15 6806 53 58.7 21.4 58.6<br />H Sutcliffe 54 84 9 4555 41 60.7 20.0 57.2<br />IVA Richards 121 182 12 8540 60 50.2 19.8 54.1<br />SR Tendulkar 174 284 30 14366 73 56.6 18.8 53.7<br />ED Weekes 48 81 5 4455 39 58.6 18.8 53.4<br />L Hutton 79 138 15 6971 52 56.7 19.6 53.3<br />SM Gavaskar 125 214 16 10122 63 51.1 18.5 51.5<br />KC Sangakkara 94 156 12 8244 54 57.3 18.7 50.9<br />RS Dravid 146 251 29 11920 67 53.7 17.9 50.7<br />RT Ponting 149 253 28 12220 67 54.3 17.7 50.4<br />SM Katich 55 97 6 4186 42 46.0 18.2 50.2<br />KD Walters 74 125 14 5357 48 48.3 18.4 50.2<br />BC Lara 130 230 6 11912 64 53.2 17.8 50.0<br />CL Walcott 44 74 7 3798 36 56.7 17.5 50.0<br />JH Kallis 141 239 35 11366 65 55.7 17.7 49.9<br />Mohammad Yousuf 90 156 12 7530 53 52.3 18.0 49.1<br />GS Chappell 87 151 19 7110 52 53.9 17.9 48.8<br />EH Hendren 51 83 9 3525 38 47.6 17.4 48.6<br />AD Nourse 34 62 7 2960 32 53.8 16.5 48.4<br />JB Stollmeyer 32 56 5 2159 30 42.3 16.1 48.1<br />AL Hassett 43 69 3 3073 34 46.6 16.8 48.0<br />B Mitchell 42 80 9 3471 37 48.9 17.1 48.0<br />S Chanderpaul 129 219 34 9063 61 49.0 17.0 47.7<br />DPMD Jayawardene 116 190 13 9527 57 53.8 17.1 47.3<br />Inzamam-ul-Haq 119 198 22 8829 58 50.2 17.0 47.2<br />HM Amla 48 85 7 3647 38 46.8 17.0 47.2<br />A Flower 63 112 19 4794 44 51.5 17.3 47.1<br />EJ Barlow 30 57 2 2516 30 45.7 15.8 46.7<br />CC Hunte 44 78 6 3245 36 45.1 16.6 46.6<br />GS Sobers 93 160 21 8032 52 57.8 16.9 46.3<br />Javed Miandad 124 189 21 8832 56 52.6 16.6 46.0<br />Saeed Anwar 55 91 2 4052 39 45.5 16.7 46.0<br />IR Redpath 66 120 11 4737 45 43.5 16.9 45.9<br />SR Waugh 168 260 46 10927 64 51.1 15.8 45.7<br />VVS Laxman 117 192 30 7707 56 47.6 16.3 45.4<br />Younis Khan 65 116 8 5440 44 50.4 16.7 45.4<br />G Boycott 108 193 23 8114 56 47.7 16.2 45.3<br />MH Richardson 38 65 3 2776 32 44.8 15.8 45.2<br />MEK Hussey 56 97 12 4420 40 52.0 16.5 45.2<br />AR Border 156 265 44 11174 64 50.6 15.5 45.0<br />ER Dexter 62 102 8 4502 41 47.9 16.5 45.0<br />GC Smith 87 152 9 7302 50 51.1 16.4 45.0<br />MC Cowdrey 114 188 15 7624 55 44.1 16.1 44.7<br />SJ McCabe 39 62 5 2748 31 48.2 15.5 44.7<br />ML Hayden 102 182 14 8437 54 50.2 16.0 44.4<br />CH Lloyd 110 175 14 7515 53 46.7 16.1 44.4<br />V Sehwag 83 142 6 7467 48 54.9 16.2 44.3<br />RC Fredericks 59 109 7 4334 42 42.5 16.2 44.0<br />DCS Compton 78 131 15 5807 46 50.1 16.2 44.0<br />ME Waugh 128 209 17 8029 57 41.8 15.5 43.9<br />AH Jones 39 74 8 2922 34 44.3 15.6 43.7<br />NS Sidhu 51 78 2 3202 35 42.1 15.7 43.7<br />MA Taylor 104 186 13 7525 54 43.5 15.7 43.7<br />AN Cook 62 111 7 4814 42 46.3 15.9 43.2<br />PBH May 66 106 9 4537 41 46.8 15.9 43.2<br />WR Hammond 85 140 16 7249 47 58.5 15.8 43.1<br />AR Morris 46 79 3 3533 35 46.5 15.5 43.0<br />RA Smith 62 112 15 4236 42 43.7 15.8 42.8<br />WM Lawry 67 123 12 5234 44 47.2 15.7 42.8<br />AC Gilchrist 95 135 20 5475 46 47.6 15.7 42.7<br />G Kirsten 101 176 15 7289 52 45.3 15.4 42.6<br />GR Viswanath 91 155 10 6080 49 41.9 15.5 42.5<br />AW Greig 58 93 4 3599 38 40.4 15.5 42.5<br />M Amarnath 69 113 10 4378 42 42.5 15.6 42.4<br />AB de Villiers 63 108 13 4540 41 47.8 15.6 42.3<br />TT Samaraweera 63 98 17 4395 39 54.3 15.5 42.3<br />KP Pietersen 68 119 6 5576 43 49.3 15.5 42.2<br />GP Thorpe 100 179 28 6744 52 44.7 15.1 42.1<br />DI Gower 117 204 18 8231 55 44.3 14.8 42.0<br />DR Martyn 67 109 14 4406 41 46.4 15.4 41.9<br />M Leyland 41 65 5 2764 31 46.1 14.8 41.9<br />NC O'Neill 42 69 8 2779 32 45.6 14.8 41.6<br />GA Gooch 118 215 6 8900 56 42.6 14.6 41.6<br />GM Turner 41 73 6 2991 33 44.6 14.9 41.6<br />RR Sarwan 83 146 8 5759 47 41.7 15.1 41.5<br />M Azharuddin 99 147 9 6215 47 45.0 15.0 41.2<br />RB Simpson 62 111 7 4869 41 46.8 15.1 41.2<br />MJ Clarke 65 106 12 4596 40 48.9 15.1 41.0<br />Majid Khan 63 106 5 3931 40 38.9 15.1 41.0<br />C Washbrook 37 66 6 2569 31 42.8 14.6 41.0<br />CG Greenidge 108 185 16 7558 52 44.7 14.6 41.0<br />Saeed Ahmed 41 78 4 2991 34 40.4 14.8 40.9<br />BF Butcher 44 78 6 3104 34 43.1 14.8 40.9<br />DL Haynes 116 202 25 7487 54 42.3 14.4 40.9<br />MA Atherton 115 212 7 7728 55 37.7 14.3 40.8<br />IR Bell 59 102 11 4007 39 44.0 14.9 40.6<br />ME Trescothick 76 143 10 5825 46 43.8 14.8 40.6<br />JL Langer 104 180 12 7674 51 45.7 14.5 40.4<br />RN Harvey 79 137 10 6149 45 48.4 14.8 40.4<br />RB Kanhai 79 137 6 6227 45 47.5 14.8 40.4<br />PE Richardson 34 56 1 2061 28 37.5 14.0 40.4<br />SP Fleming 111 189 10 7172 52 40.1 14.3 40.3<br />KC Wessels 40 71 3 2788 32 41.0 14.4 40.2<br />AI Kallicharran 66 109 10 4399 40 44.4 14.7 39.9<br />RB Richardson 86 146 12 5949 46 44.4 14.5 39.8<br />Taufeeq Umar 27 50 2 1866 26 38.9 13.5 39.8<br />DJ Cullinan 70 115 12 4554 41 44.2 14.6 39.8<br />HH Gibbs 90 154 7 6167 47 42.0 14.3 39.6<br />G Gambhir 36 64 5 2992 30 50.7 14.1 39.6<br />DB Vengsarkar 116 185 22 6868 51 42.1 14.1 39.6<br />WJ Cronje 68 111 9 3714 40 36.4 14.4 39.2<br />SC Ganguly 113 188 17 7212 51 42.2 13.8 39.1<br />SV Manjrekar 37 61 6 2043 29 37.1 13.8 39.0<br />WM Woodfull 35 54 4 2300 27 46.0 13.5 39.0<br />MS Dhoni 51 77 9 2746 33 40.4 14.1 38.9<br />AJ Strauss 79 143 6 5888 45 43.0 14.2 38.9<br />DC Boon 107 190 20 7422 51 43.7 13.7 38.8<br />MJ Slater 74 131 7 5312 43 42.8 14.1 38.6<br />CH Gayle 91 159 6 6374 47 41.7 13.9 38.6<br />FMM Worrell 51 87 9 3860 35 49.5 14.1 38.4<br />TL Goddard 41 78 5 2516 33 34.5 14.0 38.4<br />CC McDonald 47 83 4 3107 34 39.3 13.9 38.1<br />BL D'Oliveira 44 70 8 2484 31 40.1 13.7 38.1<br />JH Edrich 77 127 9 5138 42 43.5 13.9 38.0<br />A Ranatunga 93 155 12 5105 46 35.7 13.7 37.9<br />Mohsin Khan 48 79 6 2709 33 37.1 13.8 37.8<br />N Hussain 96 171 16 5764 48 37.2 13.5 37.8<br />VS Hazare 30 52 6 2192 26 47.7 13.0 37.5<br />KD Mackay 37 52 7 1507 26 33.5 13.0 37.5<br />IM Chappell 75 136 10 5345 43 42.4 13.6 37.3<br />TW Graveney 79 123 13 4882 41 44.4 13.7 37.3<br />HW Taylor 42 76 4 2936 32 40.8 13.5 37.0<br />B Sutcliffe 42 76 8 2727 32 40.1 13.5 37.0<br />MD Crowe 77 131 11 5444 42 45.4 13.5 36.9<br />AJ Stewart 133 235 21 8463 54 39.5 12.4 36.7<br />Shoaib Mohammad 45 68 7 2705 30 44.3 13.2 36.7<br />JM Parks 46 68 7 1962 30 32.2 13.2 36.7<br />CPS Chauhan 40 68 2 2084 30 31.6 13.2 36.7<br />Saleem Malik 103 154 22 5768 45 43.7 13.1 36.6<br />EAB Rowan 26 50 5 1965 25 43.7 12.5 36.1<br />FE Woolley 64 98 7 3283 36 36.1 13.2 35.9<br />SM Nurse 29 54 1 2523 26 47.6 12.5 35.6<br />NJ Astle 81 137 10 4702 42 37.0 12.9 35.5<br />PD Collingwood 65 111 10 4222 38 41.8 13.0 35.4<br />Rameez Raja 57 94 5 2833 35 31.8 13.0 35.4<br />ND McKenzie 58 94 7 3253 35 37.4 13.0 35.4<br />GN Yallop 39 70 3 2756 30 41.1 12.9 35.4<br />Zaheer Abbas 78 124 11 5062 40 44.8 12.9 35.4<br />KJ Hughes 70 124 6 4415 40 37.4 12.9 35.4<br />DM Jones 52 89 11 3631 34 46.6 13.0 35.3<br />Mushtaq Mohammad 57 100 7 3643 36 39.2 13.0 35.2<br />ST Jayasuriya 110 188 14 6973 48 40.1 12.3 35.2<br />LRPL Taylor 28 51 1 2077 25 41.5 12.3 35.1<br />MP Vaughan 82 147 9 5719 43 41.4 12.6 35.0<br />JG Wright 82 148 7 5334 43 37.8 12.5 34.8<br />CG Macartney 35 55 4 2131 26 41.8 12.3 34.7<br />JR Reid 58 108 5 3428 37 33.3 12.7 34.5<br />WJ Edrich 39 63 2 2440 28 40.0 12.4 34.5<br />Hanif Mohammad 55 97 8 3915 35 44.0 12.6 34.3<br />PA de Silva 93 159 11 6361 44 43.0 12.2 34.2<br />PH Parfitt 37 52 6 1882 25 40.9 12.0 34.1<br />KR Miller 55 87 7 2958 33 37.0 12.5 34.0<br />DJ McGlew 34 64 6 2440 28 42.1 12.3 33.9<br />HP Tillakaratne 83 131 25 4545 40 42.9 12.2 33.7<br />MA Butcher 71 131 7 4288 40 34.6 12.2 33.7<br />Asif Iqbal 58 99 7 3575 35 38.9 12.4 33.7<br />TM Dilshan 66 105 11 3990 36 42.4 12.3 33.6<br />AL Logie 52 78 9 2470 31 35.8 12.3 33.6<br />Aamer Sohail 47 83 3 2823 32 35.3 12.3 33.6<br />Nawab of Pataudi 46 83 3 2793 32 34.9 12.3 33.6<br />PR Umrigar 59 94 8 3631 34 42.2 12.3 33.4<br />CL Hooper 102 173 15 5762 45 36.5 11.7 33.4<br />GRJ Matthews 33 53 8 1849 25 41.1 11.8 33.3<br />AP Sheahan 31 53 6 1594 25 33.9 11.8 33.3<br />C Hill 49 89 2 3412 33 39.2 12.2 33.3<br />Sadiq Mohammad 41 74 2 2579 30 35.8 12.2 33.2<br />WW Armstrong 50 84 10 2863 32 38.7 12.2 33.2<br />MJ Prior 37 57 11 1923 26 41.8 11.9 33.1<br />AP Gurusinha 41 70 7 2452 29 38.9 12.0 32.9<br />KWR Fletcher 59 96 14 3272 34 39.9 12.0 32.8<br />JN Rhodes 52 80 9 2532 31 35.7 12.0 32.7<br />W Bardsley 41 66 5 2469 28 40.5 11.9 32.7<br />HA Gomes 60 91 11 3171 33 39.6 12.0 32.5<br />Shoaib Malik 32 54 6 1606 25 33.5 11.6 32.5<br />DJ Bravo 40 71 1 2200 29 31.4 11.8 32.4<br />VL Manjrekar 55 92 10 3208 33 39.1 11.8 32.2<br />CL Cairns 62 104 5 3320 35 33.5 11.8 32.1<br />AJ Lamb 79 139 10 4656 40 36.1 11.5 32.1<br />FM Engineer 46 87 3 2611 32 31.1 11.8 32.0<br />AC Hudson 35 63 3 2007 27 33.5 11.6 31.9<br />APE Knott 95 149 15 4389 41 32.8 11.3 31.8<br />DN Sardesai 30 55 4 2001 25 39.2 11.4 31.7<br />PJP Burge 42 68 8 2290 28 38.2 11.5 31.6<br />AG Prince 59 94 15 3435 33 43.5 11.6 31.5<br />GP Howarth 47 83 5 2531 31 32.4 11.6 31.5<br />MA Noble 42 73 7 1997 29 30.3 11.5 31.4<br />MJK Smith 50 78 6 2278 30 31.6 11.5 31.4<br />A Flintoff 78 128 9 3795 38 31.9 11.3 31.3<br />BE Congdon 61 114 7 3448 36 32.2 11.4 31.2<br />TW Hayward 35 60 2 1999 26 34.5 11.3 31.1<br />MW Tate 39 52 5 1198 24 25.5 11.1 31.0<br />NJ Contractor 31 52 1 1611 24 31.6 11.1 31.0<br />MW Gatting 79 138 14 4409 39 35.6 11.0 30.9<br />DL Vettori 102 155 22 4045 41 30.4 10.8 30.8<br />MJ Horne 35 65 2 1788 27 28.4 11.2 30.7<br />CD McMillan 55 91 10 3116 32 38.5 11.3 30.6<br />CG Borde 55 97 11 3061 33 35.6 11.2 30.6<br />KR Stackpole 43 80 5 2807 30 37.4 11.3 30.6<br />Imran Farhat 39 75 2 2327 29 31.9 11.2 30.5<br />MG Burgess 50 92 6 2684 32 31.2 11.1 30.3<br />Wasim Raja 57 92 14 2821 32 36.2 11.1 30.3<br />GM Ritchie 30 53 5 1690 24 35.2 10.9 30.3<br />SV Carlisle 37 66 6 1615 27 26.9 11.0 30.2<br />ML Jaisimha 39 71 4 2056 28 30.7 11.0 30.1<br />AL Wadekar 37 71 3 2113 28 31.1 11.0 30.1<br />MV Boucher 135 190 22 5223 44 31.1 10.2 30.1<br />Habibul Bashar 50 99 1 3026 33 30.9 11.0 30.0<br />IT Botham 102 161 6 5200 41 33.5 10.4 30.0<br />BM McMillan 38 62 12 1968 26 39.4 10.9 29.9<br />HH Dippenaar 38 62 5 1718 26 30.1 10.9 29.9<br />Salman Butt 33 62 0 1889 26 30.5 10.9 29.9<br />DL Amiss 50 88 10 3612 31 46.3 10.9 29.7<br />M Prabhakar 39 58 9 1600 25 32.7 10.8 29.7<br />GT Dowling 39 77 3 2306 29 31.2 10.9 29.7<br />RJ Shastri 80 121 14 3830 36 35.8 10.7 29.7<br />GA Hick 65 114 6 3383 35 31.3 10.7 29.6<br />Kapil Dev 131 184 15 5248 43 31.1 10.0 29.6<br />AW Nourse 45 83 8 2234 30 29.8 10.8 29.5<br />VT Trumper 48 89 8 3163 31 39.0 10.8 29.4<br />MS Atapattu 90 156 15 5502 40 39.0 10.3 29.4<br />JA Rudolph 35 63 7 2028 26 36.2 10.7 29.4<br />GW Flower 67 123 6 3457 36 29.5 10.5 29.3<br />BA Young 35 68 4 2034 27 31.8 10.7 29.2<br />Mudassar Nazar 76 116 8 4114 35 38.1 10.6 29.2<br />GS Blewett 46 79 4 2552 29 34.0 10.6 28.9<br />P Roy 43 79 4 2442 29 32.6 10.6 28.9<br />RES Wyatt 40 64 6 1839 26 31.7 10.6 28.9<br />JV Coney 52 85 14 2668 30 37.6 10.6 28.8<br />WW Hinds 45 80 1 2608 29 33.0 10.5 28.6<br />GM Wood 59 112 6 3374 34 31.8 10.3 28.5<br />Ijaz Ahmed 60 92 4 3315 31 37.7 10.4 28.5<br />JHB Waite 50 86 7 2405 30 30.4 10.5 28.5<br />AD Gaekwad 40 70 4 1985 27 30.1 10.4 28.3<br />CJ Tavaré 31 56 2 1755 24 32.5 10.3 28.3<br />GR Marsh 50 93 7 2854 31 33.2 10.3 28.2<br />SL Campbell 52 93 4 2882 31 32.4 10.3 28.2<br />BB McCullum 55 93 6 3232 31 37.1 10.3 28.2<br />Yuvraj Singh 34 52 6 1639 23 35.6 10.2 28.2<br />WR Endean 28 52 4 1630 23 34.0 10.2 28.2<br />AC MacLaren 35 61 4 1931 25 33.9 10.2 28.0<br />JP Crawley 37 61 9 1800 25 34.6 10.2 28.0<br />MJ Greatbatch 41 71 5 2021 27 30.6 10.3 27.9<br />RW Marsh 96 150 13 3633 38 26.5 9.6 27.7<br />ADR Campbell 60 109 4 2858 33 27.2 10.0 27.6<br />LEG Ames 47 72 12 2434 27 40.6 10.1 27.5<br />Imran Khan 88 126 25 3807 35 37.7 9.7 27.3<br />JC Adams 54 90 17 3012 30 41.3 10.0 27.3<br />J Dyson 30 58 7 1359 24 26.6 9.9 27.2<br />BA Edgar 39 68 4 1958 26 30.6 9.9 27.0<br />SM Pollock 108 156 39 3781 38 32.3 9.3 26.9<br />KG Viljoen 27 50 2 1365 22 28.4 9.7 26.8<br />Kamran Akmal 53 92 6 2648 30 30.8 9.8 26.8<br />JDP Oram 33 59 10 1780 24 36.3 9.8 26.7<br />Yashpal Sharma 37 59 11 1606 24 33.5 9.8 26.7<br />RP Arnold 44 69 4 1821 26 28.0 9.8 26.6<br />PJL Dujon 81 115 11 3322 33 31.9 9.5 26.4<br />IA Healy 119 182 23 4356 40 27.4 8.8 26.4<br />GJ Whittall 46 82 7 2207 28 29.4 9.6 26.1<br />RS Mahanama 52 89 1 2576 29 29.3 9.4 25.9<br />MS Sinclair 33 56 5 1635 23 32.1 9.4 25.8<br />DS Smith 32 56 2 1370 23 25.4 9.4 25.8<br />K Srikkanth 43 72 3 2062 26 29.9 9.4 25.5<br />Imtiaz Ahmed 41 72 1 2079 26 29.3 9.4 25.5<br />RS Kaluwitharana 49 78 4 1933 27 26.1 9.3 25.5<br />TE Bailey 61 91 14 2290 29 29.7 9.2 25.4<br />Rashid Latif 37 57 9 1381 23 28.8 9.3 25.3<br />RA McLean 40 73 3 2120 26 30.3 9.3 25.2<br />MR Ramprakash 52 92 6 2350 29 27.3 9.1 25.1<br />GO Jones 34 53 4 1172 22 23.9 9.1 25.0<br />G Giffen 31 53 0 1238 22 23.4 9.1 25.0<br />RC Russell 54 86 16 1897 28 27.1 9.1 25.0<br />SMH Kirmani 88 124 22 2759 33 27.0 8.8 24.9<br />W Jaffer 31 58 1 1944 23 34.1 9.1 24.9<br />L Klusener 49 69 11 1906 25 32.9 9.1 24.6<br />AC Parore 78 128 19 2865 33 26.3 8.5 24.3<br />Moin Khan 69 104 8 2741 30 28.6 8.7 24.1<br />JT Tyldesley 31 55 1 1661 22 30.8 8.8 24.0<br />J Darling 34 60 2 1657 23 28.6 8.8 24.0<br />W Rhodes 58 98 21 2325 29 30.2 8.6 23.8<br />CC Lewis 32 51 3 1105 21 23.0 8.6 23.7<br />G Miller 34 51 4 1213 21 25.8 8.6 23.7<br />KR Rutherford 56 99 8 2465 29 27.1 8.5 23.6<br />SE Gregory 58 100 7 2282 29 24.5 8.4 23.4<br />D Ramdin 42 73 8 1482 25 22.8 8.6 23.3<br />N Boje 43 62 10 1312 23 25.2 8.5 23.2<br />SC Williams 31 52 3 1183 21 24.1 8.5 23.2<br />Javed Omar 40 80 2 1720 26 22.1 8.5 23.1<br />MN Samuels 29 53 4 1408 21 28.7 8.3 22.7<br />GS Ramchand 33 53 5 1180 21 24.6 8.3 22.7<br />R Benaud 63 97 7 2201 28 24.5 8.1 22.5<br />KS More 49 64 14 1285 23 25.7 8.3 22.5<br />R Illingworth 61 90 11 1836 27 23.2 8.1 22.4<br />Abdur Razzaq 46 77 9 1946 25 28.6 8.1 22.2<br />JJ Crowe 39 65 4 1601 23 26.2 8.1 22.1<br />KLT Arthurton 33 50 5 1382 20 30.7 8.0 21.8<br />SA Durani 29 50 2 1202 20 25.0 8.0 21.8<br />D Ganga 48 86 2 2160 26 25.7 7.9 21.7<br />DW Randall 47 79 5 2470 25 33.4 7.9 21.7<br />AK Davidson 44 61 7 1328 22 24.6 7.9 21.6<br />RG Nadkarni 41 67 12 1414 23 25.7 7.9 21.5<br />WAS Oldfield 54 80 17 1427 25 22.7 7.8 21.5<br />RD Jacobs 65 112 21 2577 29 28.3 7.5 21.5<br />BF Hastings 31 56 6 1510 21 30.2 7.9 21.4<br />IDS Smith 63 88 17 1815 26 25.6 7.7 21.3<br />DL Murray 62 96 9 1993 27 22.9 7.6 21.3<br />RJ Hadlee 86 134 19 3124 31 27.2 7.2 21.2<br />Wasim Akram 104 147 19 2898 32 22.6 7.0 21.0<br />JM Parker 36 63 2 1498 22 24.6 7.7 20.9<br />WPUJC Vaas 111 162 35 3089 33 24.3 6.7 20.7<br />DJ Richardson 42 64 8 1359 22 24.3 7.6 20.6<br />S Abid Ali 29 53 3 1018 20 20.4 7.5 20.5<br />V Pollard 32 59 7 1266 21 24.3 7.5 20.3<br />MH Mankad 44 72 5 2109 23 31.5 7.3 20.2<br />B Yardley 33 54 4 978 20 19.6 7.4 20.1<br />MG Johnson 39 54 8 1030 20 22.4 7.4 20.1<br />JM Brearley 39 66 3 1442 22 22.9 7.3 20.0<br />JE Emburey 64 96 20 1713 26 22.5 7.0 19.9<br />DD Ebrahim 29 55 1 1226 20 22.7 7.3 19.8<br />AF Giles 54 81 13 1421 24 20.9 7.1 19.7<br />P Willey 26 50 6 1184 19 26.9 7.2 19.6<br />PR Reiffel 35 50 14 955 19 26.5 7.2 19.6<br />C White 30 50 7 1052 19 24.5 7.2 19.6<br />CB Wishart 27 50 1 1098 19 22.4 7.2 19.6<br />NR Mongia 44 68 8 1442 22 24.0 7.1 19.5<br />DG Cork 37 56 8 864 20 18.0 7.1 19.4<br />HDPK Dharmasena 31 51 7 868 19 19.7 7.1 19.2<br />KJ Wadsworth 33 51 4 1010 19 21.5 7.1 19.2<br />AFA Lilley 35 52 8 903 19 20.5 6.9 18.9<br />SK Warne 144 197 17 3142 33 17.5 5.5 18.5<br />Mohammad Ashraful 55 107 4 2306 26 22.4 6.3 18.4<br />JG Bracewell 41 60 11 1001 20 20.4 6.7 18.2<br />TG Evans 91 133 14 2439 28 20.5 5.9 18.0<br />Khaled Mashud 44 84 10 1409 23 19.0 6.3 17.8<br />RR Lindwall 61 84 13 1502 23 21.2 6.3 17.8<br />Madan Lal 39 62 16 1042 20 22.7 6.5 17.7<br />BR Taylor 30 50 6 898 18 20.4 6.5 17.6<br />MG Hughes 53 70 8 1032 21 16.6 6.3 17.4<br />MD Marshall 81 107 11 1810 25 18.9 5.8 17.2<br />HH Streak 65 107 18 1990 25 22.4 5.8 17.2<br />B Lee 75 88 18 1447 23 20.7 6.0 17.1<br />DN Patel 37 66 8 1200 20 20.7 6.1 16.8<br />HJ Tayfield 37 60 9 862 19 16.9 6.0 16.5<br />FJ Titmus 53 76 11 1449 21 22.3 5.8 16.3<br />Intikhab Alam 47 77 10 1493 21 22.3 5.7 16.2<br />Mohammad Rafique 33 63 6 1059 19 18.6 5.7 15.9<br />AC Bannerman 28 50 2 1108 17 23.1 5.8 15.8<br />J Briggs 33 50 5 815 17 18.1 5.8 15.8<br />KD Ghavri 39 57 14 913 18 21.2 5.7 15.6<br />Sarfraz Nawaz 55 72 13 1045 20 17.7 5.6 15.6<br />Harbhajan Singh 90 124 21 1915 25 18.6 5.0 15.6<br />DA Allen 39 51 15 918 17 25.5 5.7 15.5<br />BL Cairns 43 65 8 928 19 16.3 5.6 15.4<br />A Kumble 132 173 32 2506 28 17.8 4.5 15.4<br />RW Taylor 57 83 12 1156 21 16.3 5.3 15.3<br />WW Hall 48 66 14 818 19 15.7 5.5 15.3<br />H Trumble 32 57 14 851 17 19.8 5.1 14.1<br />SB Doull 32 50 11 570 16 14.6 5.1 14.0<br />PH Edmonds 51 65 15 875 18 17.5 5.0 14.0<br />IWG Johnson 45 66 12 1000 18 18.5 4.9 13.9<br />FS Trueman 67 85 14 981 20 13.8 4.7 13.8<br />ATW Grout 51 67 8 890 18 15.1 4.8 13.7<br />JM Blackham 35 62 11 800 17 15.7 4.7 13.1<br />AME Roberts 47 62 11 762 17 14.9 4.7 13.1<br />JN Gillespie 71 93 28 1218 20 18.7 4.3 13.0<br />GAR Lock 49 63 9 742 17 13.7 4.6 13.0<br />JJ Kelly 36 56 17 664 16 17.0 4.6 12.8<br />Wasim Bari 81 112 26 1366 21 15.9 3.9 12.5<br />CM Old 46 66 9 845 17 14.8 4.4 12.5<br />DR Pringle 30 50 4 695 15 15.1 4.5 12.5<br />Fazal Mahmood 34 50 6 620 15 14.1 4.5 12.5<br />Nasim-ul-Ghani 29 50 5 747 15 16.6 4.5 12.5<br />Abdul Qadir 67 77 11 1029 18 15.6 4.2 12.4<br />PAJ DeFreitas 44 68 5 934 17 14.8 4.3 12.3<br />VA Holder 40 59 11 682 16 14.2 4.3 12.3<br />CJ McDermott 71 90 13 940 19 12.2 4.0 12.2<br />J Srinath 67 92 21 1009 19 14.2 3.9 12.0<br />AV Bedser 51 71 15 714 17 12.8 4.1 11.9<br />JR Thomson 51 73 20 679 17 12.8 4.0 11.7<br />Z Khan 76 100 22 995 19 12.8 3.6 11.4<br />RC Motz 32 56 3 612 15 11.5 4.0 11.4<br />MA Holding 60 76 10 910 17 13.8 3.8 11.4<br />S Venkataraghavan 57 76 12 748 17 11.7 3.8 11.4<br />Iqbal Qasim 50 57 15 549 15 13.1 3.9 11.2<br />DK Lillee 70 90 24 905 18 13.7 3.6 11.2<br />CV Grimmett 37 50 10 557 14 13.9 3.9 11.0<br />CEL Ambrose 98 145 29 1439 21 12.4 3.0 10.9<br />JA Snow 49 71 14 772 16 13.5 3.6 10.7<br />EAS Prasanna 49 84 20 735 17 11.5 3.4 10.6<br />D Gough 58 86 18 855 17 12.6 3.4 10.5<br />JC Laker 46 63 15 676 15 14.1 3.6 10.5<br />GP Wickramasinghe 40 64 5 555 15 9.4 3.5 10.3<br />GD McKenzie 60 89 12 945 17 12.3 3.2 10.3<br />Saqlain Mushtaq 49 78 14 927 16 14.5 3.3 10.1<br />Mashrafe Mortaza 36 67 5 797 15 12.9 3.4 10.0<br />J Garner 58 68 14 672 15 12.4 3.3 9.9<br />GF Lawson 46 68 12 894 15 16.0 3.3 9.9<br />M Dillon 38 68 3 549 15 8.4 3.3 9.9<br />AR Caddick 62 95 12 861 17 10.4 3.0 9.9<br />RO Collinge 35 50 13 533 13 14.4 3.4 9.7<br />SJ Harmison 62 84 23 742 16 12.2 3.0 9.6<br />Mushtaq Ahmed 52 72 16 656 15 11.7 3.1 9.6<br />IR Bishop 43 63 11 632 14 12.2 3.1 9.3<br />MS Kasprowicz 38 54 12 445 13 10.6 3.1 9.1<br />Mohammad Sami 35 54 14 475 13 11.9 3.1 9.1<br />DW Steyn 43 54 13 577 13 14.1 3.1 9.1<br />Shahadat Hossain 29 55 15 394 13 9.9 3.1 9.0<br />Shoaib Akhtar 46 67 13 544 14 10.1 2.9 8.9<br />M Muralitharan 132 162 56 1259 19 11.9 2.2 8.9<br />DBL Powell 37 57 5 407 13 7.8 3.0 8.8<br />Waqar Younis 87 120 21 1010 17 10.2 2.4 8.7<br />GR Dilley 41 58 19 521 13 13.4 2.9 8.7<br />AA Mallett 38 50 13 430 12 11.6 2.9 8.4<br />M Ntini 101 116 45 699 16 9.8 2.2 8.1<br />DL Underwood 86 116 35 937 16 11.6 2.2 8.1<br />PR Adams 45 55 15 360 12 9.0 2.6 7.9<br />JB Statham 70 87 28 675 14 11.4 2.3 7.7<br />S Ramadhin 43 58 14 361 12 8.2 2.5 7.6<br />RM Hogg 38 58 13 439 12 9.8 2.5 7.6<br />JM Anderson 54 72 31 513 13 12.5 2.3 7.6<br />AA Donald 72 94 33 652 14 10.7 2.1 7.4<br />BS Bedi 67 101 28 656 14 9.0 1.9 7.1<br />RGD Willis 90 128 55 840 15 11.5 1.8 7.0<br />DK Morrison 48 71 26 379 12 8.4 2.0 6.8<br />LR Gibbs 79 109 39 488 13 7.0 1.6 6.1<br />MJ Hoggard 67 92 27 473 12 7.3 1.6 5.9<br />DE Malcolm 40 58 19 236 10 6.1 1.7 5.7<br />GD McGrath 123 136 51 639 13 7.5 1.2 5.5<br />CA Walsh 132 185 61 936 14 7.5 1.1 5.4<br />Danish Kaneria 61 84 33 360 11 7.1 1.4 5.4<br />ARC Fraser 46 67 15 388 10 7.5 1.5 5.3<br />FH Edwards 43 69 21 248 10 5.2 1.4 5.2<br />MS Panesar 39 51 17 187 8 5.5 1.3 4.3<br />CD Collymore 30 52 27 197 8 7.9 1.2 4.3<br />TM Alderman 41 53 22 203 8 6.5 1.2 4.2<br />AL Valentine 36 51 21 141 7 4.7 1.0 3.5<br />EJ Chatfield 43 54 33 180 7 8.6 0.9 3.4<br />PCR Tufnell 42 59 29 153 7 5.1 0.8 3.3<br />BS Chandrasekhar 58 80 39 167 6 4.1 0.5 2.3<br />CS Martin 59 85 44 97 5 2.4 0.3 1.8</pre>David Barryhttp://www.blogger.com/profile/08378763233797445502noreply@blogger.com6tag:blogger.com,1999:blog-22713811.post-90415047428122546262012-01-08T06:52:00.002+01:002012-01-08T07:22:08.337+01:00Note on the probability of centuryThis is mostly for myself so that I have something to refer to in future. In the <a href="http://pappubahry.blogspot.com/2011/12/duck-to-century-ratios.html">previous post</a> I skipped some algebra and gave a formula for the probability of a century, given a hazard function which is 3/(avg+3) on nought and 1/(avg+3) for all scores greater than nought.<br /><br />The algebra I originally posted was slightly wrong (I have corrected it in the second edit), but could have been simplified. The probability of a score of at least 100 is [avg/(avg+3)] * [(avg+2)/(avg+3)]<sup>99</sup>. It is a little bit annoying having such a high exponent. Re-write (avg+2)/(avg+3) as [1-1/(avg+3)]. Then from a definition of the exponential function, raising that to the power of 99 is approximately equal to exp[-99/(avg+3)]. <br /><br />So, the probability of a century is [avg/(avg+3)] * exp[-99/(avg+3)].<br /><br />Empirically, the simple exp(-100/avg) is a better predictor of the fraction of a batsman's innings that are centuries. If we exclude not-outs below 100, then it is much of a muchness between those two and exp(-99/avg).David Barryhttp://www.blogger.com/profile/08378763233797445502noreply@blogger.com0tag:blogger.com,1999:blog-22713811.post-29138656731305139392011-12-26T06:38:00.004+01:002012-01-08T06:15:19.477+01:00Duck to century ratios<a href="https://twitter.com/#!/RicFinlay/status/151157970586181632">Ric Finlay on Twitter</a>:<br /><br /><blockquote>SMarsh Oz's 1319th Test duck. 747 Oz centuries. Oz 1.77 ducks per century Ind 2.01 ducks per century. Bangladesh 9 ducks/100!</blockquote><br /><br />As a bit of pointless fun, let's pretend that all representatives of each country are equally good batsmen (a terrible assumption, obviously, but perhaps the tail-enders' ducks and top-order centuries will roughly cancel out in the right ratios) and apply the hazard function mentioned at the end of <a href="http://pappubahry.blogspot.com/2010/12/are-some-batsmen-nervous-starters.html">this post</a>.<br /><br />This hazard function is the simplest extension of the constant-hazard model, the latter saying, effectively, that a batsman always has his eye in. In the "new" model, the batsman is three times more likely to get out on nought than to get out when on some other score, but gets his eye in as soon as he's off the mark. When studying the proportion of ducks, this is an important change to make, because the constant-hazard model will under-estimate the number of ducks.<br /><br />The algebra is boring and I will skip it. The probability of a duck is 3/(avg + 3), and the probability of a score less than 100 is 3/(avg + 3) + {1 - [(avg+2)/(avg+3)]<sup>98</sup>} * avg/(avg + 3). Subtract the latter expression from 1 to get the probability of scoring a century.<br /><br />Throw it into an Excel formula, and you find that a duck:century ratio of 9 corresponds to an average of about 21.1, a ratio of 2.01 corresponds to an average of about 30.1, and a ratio of 1.77 corresponds to an average of about 31.1.<br /><br /><a href="http://stats.espncricinfo.com/ci/engine/stats/index.html?class=1;filter=advanced;groupby=team;orderby=batting_average;template=results;type=batting">The actual averages</a> are 20.6, 31.5, and 32.3 respectively.<br /><br />(<b>Edit</b>: Fixed the exponent in the probability for a score less than 100.)<br /><br />(<b>Edit 2, 8/1/2011</b>: The earlier edit fixed the typo in my transcription from my handwritten work to the blog, but the handwritten work was wrong. The exponent should be 99, not 98. The corresponding averages to the three duck:century ratios should be about 21.3, 30.4, and 31.4 respectively.)David Barryhttp://www.blogger.com/profile/08378763233797445502noreply@blogger.com0tag:blogger.com,1999:blog-22713811.post-62583604941140408232011-02-08T07:09:00.002+01:002011-02-08T07:14:57.685+01:00The World Cup group stageRuss has done some ODI ratings and <a href="http://deggles.csoft.net/post.php?postid=1544">looked ahead</a> to the group stage of the World Cup. His main point is that it is more likely than a side from outside the "top eight" will make the quarters. (I need to use scare quotes, because Bangladesh are actually ahead of the West Indies in the ICC ODI rankings now, though not in Russ's rankings.)<br /><br />But there's more to the group stage than seeing if Bangladesh will beat the Windies (I'm writing off Ireland – they were easily beaten by Bangladesh in the one series they've played in Asia). In the quarter-finals it'll be A1 v B4, A2 v B3, etc., so finishing top of the group will probably mean an easy quarter-final. I'm not sure if this'll be enough to keep us interested, but it is a real effect.<br /><br />Using Russ's ratings (so don't blame me for India being easily below Australia and South Africa) and assuming that the "top eight" sides make the quarters in rating order, I ran 100000 simulations of the knockout stage. These are the probabilities of overall victory than I get:<br /><pre>Aus 0.28 SA 0.28<br />SL 0.13 Ind 0.18<br />Pak 0.04 Eng 0.08<br />NZ 0.01 WI 0.01</pre><br />Now suppose that Australia, through bad luck, loses its games to SL, Pak, and NZ, and so finishes fourth in group A, thus playing South Africa in the quarters:<br /><pre>SL 0.22 SA 0.20<br />Pak 0.05 Ind 0.21<br />NZ 0.01 Eng 0.11<br />Aus 0.19 WI 0.01</pre><br />Sri Lanka went from having less than half Australia's chance at overall success, to having a better chance than Australia. Obviously South Africa takes quite a hit in this scenario as well.<br /><br />What if we only swap India and South Africa?<br /><pre>Aus 0.28 Ind 0.21<br />SL 0.13 SA 0.25<br />Pak 0.04 Eng 0.08<br />NZ 0.01 WI 0.01</pre><br />A little change. <br /><br />These ideas should motivate the teams to play hard in the group stage – there won't be any really dead rubbers against the major sides (except maybe at the very end). Of course the winning team will still have to win three straight games to take the Cup, so my suspicion is that from a fan's perspective, this won't add much excitement to the group matches.David Barryhttp://www.blogger.com/profile/08378763233797445502noreply@blogger.com4tag:blogger.com,1999:blog-22713811.post-50502056869107725652011-01-21T13:21:00.002+01:002011-01-21T14:08:52.101+01:00Conversions from 50 to 100<a href="http://cricketingview.blogspot.com/2011/01/samit-patel-and-englands-discriminatory.html">In comments</a> at Kartikeya's blog there was a little aside about Samit Patel's conversion rate – he has only 10 first-class centuries, despite reaching fifty 34 times. Kartikeya said that such a low conversion rate was typical of players who bat at 6 or 7, and gave the example of VVS Laxman.<br /><br />(From the few scorecards I've checked, Patel seems to often bat at 4 for Notts.)<br /><br />The breakdown of Laxman's record is indeed <a href="http://stats.espncricinfo.com/ci/engine/player/30750.html?class=1;template=results;type=batting">stark</a>: batting at 6, he averages 51 and has made 5 centuries having reached fifty 25 times; batting at 3, he averages 47 and has made 4 centuries having reached fifty 10 times.<br /><br />The obvious question is, is this typical? This seems like as good an excuse as any to use the aside mentioned at the bottom of <a href="http://pappubahry.blogspot.com/2010/12/are-some-batsmen-nervous-starters.html">this post</a>. In that basic model, batsmen effectively bat like they average 2 runs more per innings once they get off the mark. So, their conversion rate from 50 to 100 should be, on average, exp(-50/(avg+2)). <br /><br />Here is a scatterplot of actual conversion rate against expected conversion rate, for all batsmen who've scored 2000 runs batting at positions 1-4:<br /><br /><img alt="So it looks like I haven't given up blogging just yet...." title="So it looks like I haven't given up blogging just yet...." src="http://i44.photobucket.com/albums/f35/pappubahry/cricket/conversionrate1-4.png"><br /><br />The red line is y=x. There are 79 batsmen above the line and 75 below, so the model seems pretty decent. <br /><br />Now here is the same scatterplot for batsmen at positions 6-7:<br /><br /><img alt="I still have one more spreadsheet waiting to turn into a blog post as well." title="I still have one more spreadsheet waiting to turn into a blog post as well." src="http://i44.photobucket.com/albums/f35/pappubahry/cricket/conversionrate6-7.png"><br /><br />Only 6 of the 27 batsmen are above expectation, presumably because they're left stranded or have to start hitting out with 9 wickets down. Laxman is the point (0.406, 0.179). He's on the bottom edge of the scatter, so his very low conversion rate somewhat unusual, even for lower-middle order batsmen. The regression line forced through the origin is y = 0.82x.<br /><br />Returning to the "purer" sample of top-order batsmen, we can ask whether conversion from 50 to 100 is a skill. Using the same method as in the post I linked to earlier, we can treat "scoring a century, having reached fifty" as a binomial random variable, which happens with probability p = exp(-50/(avg+2)). If a batsman has reached fifty N times, then we can calculate z = (actual number of hundreds - Np) / sqrt[Np(1-p)]. If "scoring a century, having reached fifty" is a skill separate from the batting average, then we'd expect the standard deviation of z's to be greater than 1.<br /><br />As it happens, the standard deviation is 0.93. Perhaps that'd increase a little bit if you treated not-out innings between 50 and 99 properly. But it looks to me like most of the variation in conversion rates between top-order players is down to differences in their general batting ability (as measured by their average) and random luck.David Barryhttp://www.blogger.com/profile/08378763233797445502noreply@blogger.com3tag:blogger.com,1999:blog-22713811.post-77373290589146271462010-12-29T13:58:00.002+01:002010-12-29T14:04:29.521+01:00Update on no-ball ratesIt's been a few years since I wrote <a href="http://pappubahry.blogspot.com/2008/01/no-no-balls.html">this</a>, noting the sharp decline in the no-ball rate in ODI's since the start of Twenty20. The trends don't appear to be a blip, as we now have three more years of data to look at, and the evidence is pretty solid:<br /><br /><img alt="This time I haven't got funny French dates on the x-axis." title="This time I haven't got funny French dates on the x-axis." src="http://i44.photobucket.com/albums/f35/pappubahry/cricket/nbrateodi.png"><br /><br />The drop-off is also quite visible, though not as dramatic, in Tests:<br /><br /><img alt="No prizes for guessing when the front-foot law was introduced." title="No prizes for guessing when the front-foot law was introduced." src="http://i44.photobucket.com/albums/f35/pappubahry/cricket/nbratetest.png"><br /><br />In less than a week, my almost month-long holiday from work will be over, so the recent burst of activity on this blog will probably end. I'll have a couple more blog posts in the next few days, and then I'll probably go back to just being a commenter around the place.David Barryhttp://www.blogger.com/profile/08378763233797445502noreply@blogger.com0tag:blogger.com,1999:blog-22713811.post-37354938852820508142010-12-15T15:27:00.004+01:002010-12-15T16:33:49.880+01:0018th century statisticsThe <a href="http://acscricket.com/">ACS</a> seems to have settled on a list of 'great' matches from the 18th century, starting from 1772, considered equivalent to first-class for statistical purposes. CricketArchive now lists these matches as first-class, starting with <a href="http://www.cricketarchive.co.uk/Archive/Scorecards/0/4.html">Hampshire v England</a>.<br /><br />If any of the few remaining readers that I have were here three years ago, they might remember a series of posts on 19th-century first-class cricket in England. The biggest challenge was estimating bowling averages for the very early scorecards, which don't credit catches to the bowler, and don't record the number of runs conceded by the bowler. The method is described in <a href="http://pappubahry.blogspot.com/2008/01/1800s-first-class-cricket-in-england_28.html">this post</a>, with results (with funny +/- values!) <a href="http://pappubahry.blogspot.com/2008/01/1800s-first-class-cricket-in-england_29.html">here</a>.<br /><br />Over the last few days I've dug up my old code and run the estimations on the "new" first-class matches, and added these numbers to the 19th-century estimations where applicable.<br /><br />Things to remember:<br /><br />- The error in the bowling average "should" be about 8%, in the sense that about 68% of the true averages should be within 8% of the estimated figure. But in the testing runs I did a few years ago, very high wicket-takers seemed unusually likely to get anomalous estimates of their average (out to around 15%). <br /><br />- The exercise is even more speculative for these very early matches. The training data is from 1854-1863, and I'm here using it to find stats in games played more than 80 years before that.<br /><br />- Nevertheless, they can't really be too far wrong.<br /><br />- The estimates of wickets, unlike bowling averages, can be atrocious. They are all almost certainly under-estimates, by seemingly random amounts.<br /><br />- I only consider matches where the dismissals are all known, and it was eleven-a-side. (So the number of matches played is slightly lower than what you'll see on CricketArchive.)<br /><br />Here are the leading wicket-takers (at least 150 estimated wickets) of players who began their career in the 18th century, ordered by estimated bowling average (their batting stats are also there):<br /><pre> batting est. bowling<br />name start end mats inns n.o. runs avg wkts runs avg<br />D Harris 1789 1798 72 124 42 467 5.7 481.7 4985.1 10.3<br />John Wells 1789 1815 138 256 19 2927 12.4 551.3 6134.9 11.1<br />T Boxall 1790 1803 81 147 28 822 6.9 465.7 5251.4 11.3<br />T Walker 1789 1810 166 315 19 5757 19.4 486.0 5488.7 11.3<br />T Lord 1790 1815 55 100 15 831 9.8 217.0 2495.4 11.5<br />Lord F Beauclerk 1791 1825 124 229 19 5259 25.0 577.5 7047.1 12.2<br />R Purchase 1773 1803 109 199 18 1820 10.1 373.8 4572.2 12.2<br />E Stevens 1773 1789 71 126 37 694 7.8 496.0 6112.4 12.3<br />W Beldham 1789 1821 178 328 18 6709 21.6 376.0 4704.5 12.5<br />J Hammond 1790 1816 114 206 13 3741 19.4 244.3 3098.8 12.7<br />R Clifford 1789 1792 68 131 7 1484 12.0 343.5 4396.1 12.8<br />T Brett 1773 1778 24 43 11 231 7.2 154.4 2180.9 14.1<br />W Fennex 1790 1816 82 155 14 1881 13.3 220.6 3239.0 14.7<br />W Bullen 1789 1797 108 207 43 1697 10.3 289.5 4271.4 14.8<br />R Nyren 1774 1786 41 76 11 824 12.7 162.2 2459.1 15.2</pre><br />My knowledge of 18th-century cricket is pretty close to zero, so I had to look most of these players up. The <i>Who's Who of Cricketers</i> says of David Harris: <i>The greatest bowler of the Hambledon Club, it is impossible to gauge his real success owing to lack of bowling analyses.</i> Well now we have a bit of a gauge.<br /><br />When I did the estimates for the 19th century, there were players (such as Alfred Mynn) who played in many matches with their bowling analyses recorded, and in many without. These players serve as a useful check on the method – if their estimated average was similar to their actual average in matches with known figures, then that adds to the confidence we can put in the estimates.<br /><br />For the 18th century, that's not possible. We instead have to add another link in the chain – take players who played in both centuries, whose 19th-century estimates we're pretty confident about because of the Mynn-like players from later on. For whatever that's worth, we can look at Wells, Walker, Beauclerk, Beldham, and Hammond, and make the comparison.<br /><pre>player 18th C 19th C<br />Wells 10.9 11.4<br />Walker 11.2 12.0<br />Beauclerk 11.3 12.6<br />Beldham 12.5 12.4<br />Hammond 12.9 12.1</pre><br />I don't think I'd want to draw too many conclusions from such a small sample, but it at least passes a sanity check.<br /><br />In case anyone's interested in batting stats, here they are (qual. 2000 runs):<br /><pre> batting est. bowling<br />name start end mats inns n.o. runs avg wkts runs avg<br />Lord F Beauclerk 1791 1825 124 229 19 5259 25.0 577.5 7047.1 12.2<br />R Robinson 1792 1819 102 196 15 3992 22.1 46.6 1142.7 24.5<br />W Beldham 1789 1821 178 328 18 6709 21.6 376.0 4704.5 12.5<br />T Walker 1789 1810 166 315 19 5757 19.4 486.0 5488.7 11.3<br />J Hammond 1790 1816 114 206 13 3741 19.4 244.3 3098.8 12.7<br />J Aylward 1773 1797 99 194 6 3611 19.2 5.0 103.8 20.8<br />H Walker 1789 1802 93 171 4 2518 15.1 0.0 0.0 0.0<br />J Small sen 1773 1798 100 189 8 2724 15.0 7.1 158.1 22.4<br />J Ring 1789 1796 83 164 10 2088 13.6 2.5 48.3 19.3<br />J Small jun 1789 1810 134 252 13 3216 13.5 0.0 0.0 0.0<br />A Freemantle 1789 1810 125 235 28 2674 12.9 0.0 0.0 0.0<br />John Wells 1789 1815 138 256 19 2927 12.4 551.3 6134.9 11.1<br />Earl of Winchilsea 1789 1804 124 235 10 2048 9.1 6.5 112.5 17.2</pre>David Barryhttp://www.blogger.com/profile/08378763233797445502noreply@blogger.com1tag:blogger.com,1999:blog-22713811.post-29398075933048539302010-12-03T10:51:00.002+01:002010-12-03T12:02:31.991+01:00Are some batsmen nervous starters?Probably. But the ability to get off the mark seems to be determined by how good a batsman is overall. There is of course variation between batsmen in the percentage of ducks they make, but no more than would be expected by random chance.<br /><br />The starting point is to work out what the relationship is between a batsman's average and the percentage of innings that are ducks. (Ideally I would exclude scores of nought not-out from this analysis, but I did everything with Statsguru because it's easier. This won't make much of a difference.)<br /><br />I took all batsmen with at least 20 Test innings against top-eight sides and put them into 'buckets' – the first bucket had batsmen who averaged less than 10, the second averaged 10-19.99, the third 20-20.99, etc., up to 50-59.99.<br /><br />Then for each bucket, I sum up the number of ducks and divide by the number of innings to get the percentage of ducks. I also find the overall average of all the batsmen in the bucket.<br /><br />Now, as discussed in <a href="http://pappubahry.blogspot.com/2008/04/getting-your-eye-in.html">this old post</a>, the probability H(x) of getting out on a particular score x is related to an 'effective average' µ(x) by µ(x) = 1/H(x) - 1.<br /><br />Since we will be plotting against the overall average, it makes sense to use the effective average on nought rather than the percentage of ducks. The result is a lovely linear plot:<br /><br /><img alt="Using buckets makes the plot so clean." title="Using buckets makes the plot so clean." src="http://i44.photobucket.com/albums/f35/pappubahry/cricket/effavgonnoughtvavg.png"><br /><br />Note that the problem of nought not-out innings is particularly acute for the first data point, which is full of people who batted at number 11. These innings make it look like the batsmen were better at getting off the mark than they really were, thus improving their apparent effective average. The regression line has been forced through the origin, both because logically it should do so, and so that the problem of the nought not-outs is reduced.<br /><br />By a wonderful quirk, the effective average on zero is (on average) one third of the overall average. This makes the algebra relatively easy (details left as an exercise): a batsman's expected fraction of ducks is 3/(3 + avg).<br /><br />What I then did was, for each individual batsman, calculate the number of binomial standard deviations his actual number of ducks was from his expected number of ducks.<br /><br />As an example, consider Shane Warne. Average 17.65, so expected duck fraction 3/(3 + 17.65) = 0.145. He played 194 innings, which gives an expected number of ducks of 28.18. Warne actually made 34 ducks. A standard deviation for a binomial random variable is sqrt[N*p*(1-p)] = sqrt(194*0.145*0.855) = 4.9. Warne's number of ducks is therefore (34 - 28.18) / 4.9 = 1.2 standard deviations above expected.<br /><br />If getting off the mark is a particular skill that some players are better at than others, independent of their overall batting abilities, then the standard deviation of the standard deviations should be greater than 1. If the only two factors going into the number of ducks are the overall batting average and random luck, then the sd of sd's should be 1.<br /><br />The sd of sd's for all the batsmen who average more than 10 is 0.98, pretty close to 1.<br /><br />(The breakdown by bucket goes like this. 0-9.99: 1.16 (but remember the problem of nought not-outs). 10-19.99: 0.82. 20-29.99: 1.01. 30-39.99: 0.98. 40-49.99: 1.04. 50-59.99: 1.07.)<br /><br />By contrast, if you assume that there is no distribution of skill whatsoever in getting off the mark, and just assume that everyone (from Chris Martin to Sachin Tendulkar) gets off zero with equal probability (0.0917 in this sample), then the sd of sd's is 1.34, much greater than 1.<br /><br />So my conclusion is that if someone seems to make an unusually large number of ducks, then he's almost certainly just unlucky.<br /><br /><i>Mathematical aside: Usually when I need to model the distribution of a batsman's scores, I use the geometric or exponential distribution. One level more advanced than this would be to have the hazard function take on a particular value at zero, and then a constant for scores greater than or equal to 1.<br /><br />Using the above result, such a hazard function is this:<br /><br />H(0) = 3/(avg + 3), H(n) = 1/(avg + 3) for n > 0.</i>David Barryhttp://www.blogger.com/profile/08378763233797445502noreply@blogger.com2tag:blogger.com,1999:blog-22713811.post-60456627246331347682010-07-31T04:42:00.002+02:002010-07-31T04:46:11.716+02:00My database<a href="http://sites.google.com/site/cricketdatabase/">Tests</a>, <a href="http://sites.google.com/site/cricketdatabase/first-class">first-class matches</a>. Have at it.<br /><br />Post any questions you have in comments, or by email. I have no idea how easy it is for someone else to make sense of what I've done.David Barryhttp://www.blogger.com/profile/08378763233797445502noreply@blogger.com2tag:blogger.com,1999:blog-22713811.post-85725950802053935692010-07-18T11:44:00.002+02:002010-07-18T12:12:35.687+02:00The co-efficient of variationGabriel Rogers <a href="http://blogs.cricinfo.com/itfigures/archives/2010/06/achieving_the_right_consistenc.php">debuted at It Figures</a> with a post on batsmen's consistency. The main tool he used was the co-efficient of variation – the standard deviation divided by the mean. In general I think this is OK, but there is a problem with including players with short careers in the analysis.<br /><br />The problem is that shorter careers might tend to have lower CV's. (I haven't checked this empirically.) To show this I'll play with exponential random variables. The distribution of a batsman's scores is reasonably close to an exponential distribution, so the results below should apply to real batsmen.<br /><br />I generated 10000 "careers" of 2 innings, 10000 careers of 3 innings, 10000 careers of 4 innings, and so on. For each career length, I calculated the average CV. This is a graph of the results:<br /><br /><img alt="I wonder if I'm even on anyone's feed readers anymore." title="I wonder if I'm even on anyone's feed readers anymore." src="http://i44.photobucket.com/albums/f35/pappubahry/cricket/cvsamplesize.png"><br /><br />(I've used the "N-1" version of the standard deviation here.)<br /><br />The theoretical CV for an exponential distribution is 1 (the standard deviation equals the mean; for real cricketers the typical CV is about 1.05, because the distribution is skewed by lots of ducks and low scores, and occasional very big scores), and you can see that for moderately large careers, this is true – the average CV for a 50-innings career is about 0.98. But for short careers the CV's are noticeably less than 1. For a two-innings career, I think the expectation of the CV is 1/sqrt(2). <br /><br />My guess is that, if this effect carries over to real cricketers, then the trend shown in Figure 1 of the linked blog post is actually stronger than it looks – batsmen with shorter careers tend to be worse and have lower averages, so there'll be disproportionately many dots in the lower-left part of the scatterplot.<br /><br />Of course I could check this myself, but I am pretty lazy with stats these days, as evidenced by the very long break in posting here!David Barryhttp://www.blogger.com/profile/08378763233797445502noreply@blogger.com3tag:blogger.com,1999:blog-22713811.post-74614655049541713232009-03-22T00:51:00.002+01:002009-03-22T00:58:31.862+01:00New cricket stats analysis blogWhile I'm being lazy and not doing any real updates (I am getting closer to writing all the readme files etc. for my database), you can get your stats fix from <a href="http://cricketanalysis.com/">this blog</a>, which has been around for a few months and is generally very good. It is likely that some of the new ideas will need to be improved over time, but they are very valuable first step, especially on declaration stategies (on this, Elliot Tonkes has done some academic work on this, but I don't know if it's been published) and wins above replacement for ODI's.<br /><br />Do check it out.David Barryhttp://www.blogger.com/profile/08378763233797445502noreply@blogger.com2tag:blogger.com,1999:blog-22713811.post-14146710429526436462009-03-15T11:33:00.002+01:002009-03-15T11:48:37.032+01:00Batted ball speedHey look, I have this thing called a blog, maybe I should update it.<br /><br />I should update my profile – I'm no longer a student (sort of, my Masters thesis is under assessment), having got out of my PhD and started a 9-5 job, which is in geostatistics, if anyone is wondering.<br /><br />I've been very slack with cricket stats lately, with work and chess taking up more of my time. Anyway, <a href="http://www.insidethebook.com/ee/index.php/site/comments/everything_you_wanted_to_know_about_the_science_of_the_bat_ball_collision/">this</a> is a long and interesting discussion about a baseball hitting a baseball bat. The very counter-intuitive result is that the batted ball speed doesn't depend on the grip, as long as the ball hits the "meat" of the bat. The same is true for cricket (<a href="http://www.physics.usyd.edu.au/~cross/cricket.html">this page</a> gives a few physicsy aspects of cricket). All that matters for batted ball speed is the speed of the bat at impact.<br /><br />On a different topic, I have been thinking about putting my cricket database online. It wouldn't be pretty, and any professional coders out there may be horrified at my code, but there are enough of you out there with good ideas that I think it would be worthwhile. In the long term, I would like there to be a cricket equivalent of <a href="http://www.retrosheet.org/">Retrosheet</a>, which now has over 50 years' worth of play-by-play data for Major League Baseball.<br /><br />I'm sort of thinking out loud on this at the moment. My database uses CricketArchive player and match ID's, so if they changed their numbering system (which they did a few years ago), that'd screw things up. I might exchange emails with them and see what comes out of it. <br /><br />Feel free to share your thoughts on this.David Barryhttp://www.blogger.com/profile/08378763233797445502noreply@blogger.com11tag:blogger.com,1999:blog-22713811.post-23751215405617724862009-01-04T02:38:00.003+01:002009-01-04T02:41:40.452+01:00FormRuss in a post <a href="http://deggles.csoft.net/post.php?postid=1366">here</a> suggests a way of measuring form (go and read that post, it's full of goodies). The broad question is, if there are two batsmen with the same true talent, and one has better recent form, will that batsman tend to average more in his next innings?<br /><br />To start with, Russ weights more recent innings more heavily than earlier innings. Specifically, the k-th innings for a batsmen with N innings in his career is weighted 0.95<sup>N-k</sup>. (This is, I believe, similar to what the ICC rankings do.)<br /><br />Now, one problem with assessing "form" statistically is that a batsman will usually play a series against one team, followed by a series against another, etc. Since one of those two opposition teams can have a much stronger bowling attack than the other, what may appear to be good form and bad form may simply be a result of playing against weak bowlers and then strong bowlers. So, for everything I do in this post, I'll adjust the batting averages by the quality of the attack, as explained <a href="http://pappubahry.blogspot.com/2007/12/modified-batting-averages.html">here</a>.<br /><br />So, when I talk about a regular average, I really mean an adjusted average. When I talk about a <i>weighted</i> average, I'll mean Russ's weighting by how recent the innings was (each innings also being adjusted for the quality of the attack).<br /><br />Before continuing about form, I'll just look at the weighted average as a predictive tool. For all batsmen with at least 50 innings, I calculated career-to-date averages and weighted averages, as well as a 10-innings moving average. Then, from the 11th innings of each batsman's career, I calculated the absolute difference between his next innings and each of those three measures. (If the innings was a not-out, I used the not-out score as the absolute difference for each measure.) Then I averaged these errors. I did the same for all batsmen, and then found the "average average" error. The regular average was the best predictor, about 1% better than the weighted average, and 4% better than the moving average. The weighted average becomes more accurate if the 0.95 in the formula is increased towards 1, but it is always worse than the regular average.<br /><br />So, as a measure of true talent of a batsman, I'll use the regular average rather than the weighted average.<br /><br />Now to the question of defining form. Russ does this by taking a weighted log average. Defining R<sub>i</sub> as the runs scored in the i-th innings, and w<sub>i</sub> as the weight of that innings, this weighted log average is:<br /><pre><br /> / SUM w<sub>i</sub> log(R<sub>i</sub>) \<br />exp | ------------- |<br /> \ SUM w<sub>i</sub> / <br /></pre><br />I've actually modified this a bit. If the i-th innings is a not-out, I didn't include it in the sum in the denominator. I hope this isn't too great a crime against statistics.<br /><br />The measure of form is then the ratio of the weighted log average to the weighted average. Now, if scores are distributed exponentially, then this ratio is about 0.56 (well, it is with equally weighted innings at least). If a batsman makes the same score every innings (and gets out!), the ratio is 1. If a batsman recently has one big score and a bunch of little scores, the ratio is down towards 0.3. So, good form is a high ratio, bad form is a low ratio.<br /><br />Because I exclude not-outs in the denominator, it's possible to get ratios greater than 1. I'm not really sure how to interpret these, but let's carry on anyway.<br /><br />Russ's hypotheses are (I hope I've got this right):<br /><br />a) If there are two batsmen with a similar average, one with a typical ratio and one with a low ratio, then the one with the low ratio will tend to average more in his next innings. The logic here is that the batsman with the low ratio is capable of larger scores, whereas the other batsman is just not so good.<br /><br />b) Given two batsmen with the same average, one with a high ratio will tend to do better in his next innings than one with a typical ratio.<br /><br />Both of these are correct, somewhat to my surprise. I went through all batsmen, and for each innings (after the tenth in their career), calculated the career-to-date average, and the ratio-to-date, and binned them as in the table below. I then calculated the overall average for each bin.<br /><br />Ratios are down the left-hand side, averages across the top. The figures are the low end of the bin. So, e.g., the '5' means that the bin is for averages 5 to 9.99, the '10' is for averages 10-14.99, etc. Only bins with at least 50 innings are shown; bold is used for at least 100 innings.<br /><pre><br />r/a 5 10 15 20 25 30 35 40 45 50 55<br />0.35 <b>23.4 28.4 28.7 40.8</b> 41.3 <br />0.40 <b>12.6 18.8 21.4 31.7 30.5 34.9 40.3 40.7</b> 55.3 <br />0.45 7.4 <b>12.1 15.9 20.9 26.7 30.1 34.0 46.9 39.9 50.2</b><br />0.50 <b>9.6 11.1 17.6 22.2 26.4 31.7 36.3 34.9 43.5 46.0</b> <br />0.55 <b>8.4 11.9 16.5 21.0 26.1 31.6 35.2 40.1 42.6 53.3</b> <br />0.60 <b>7.6 12.2 18.3 24.4 27.9 33.5 38.2 42.7 46.6</b> 58.0 40.4<br />0.65 <b>7.8 12.3 18.1 24.7 28.0 33.4 39.2 43.3 48.7 48.1</b> 46.6<br />0.70 <b>8.2 12.4 19.3 25.7 27.5 34.4 40.4 46.1 44.0</b> 56.7 <br />0.75 <b>9.3 15.3 17.4 23.1 29.3 38.9 41.0 47.4 51.8 50.1</b> <br />0.80 9.8 <b>12.0 17.7</b> 24.4 <b>30.5 35.1 48.2 54.2 51.8</b> 58.9 <br />0.85 15.0 22.1 25.8 38.8 40.5 44.6 <b>51.3</b> 61.9 46.8 <br />0.90 15.6 26.7 42.1 42.6 59.2 54.1 <br />0.95 46.5<br />1.00 16.6 35.9 32.5 44.7 53.9 <b>68.5</b> 73.2 <b>62.9</b></pre><br />When the ratio is very low, the batsman does indeed tend to average much more in his next innings. (Since I've used regular averages to define the true talent, the top row may be full of players early in their career. I'm not sure.) Going down each column, the minimum is usually somewhere around 0.5 to 0.6, which seems to correspond to the 0.56 that you'd expect from the exponential distribution. <br /><br />Really good recent form seems to give a 20% boost and sometimes more. This is a lot more than I had expected.<br /><br />(My thinking on this issue seems to have been confused — in my last post I said that Johnson was good because he kept getting starts, which is consistent with this analysis.)David Barryhttp://www.blogger.com/profile/08378763233797445502noreply@blogger.com14tag:blogger.com,1999:blog-22713811.post-69671982514176697902008-12-20T04:32:00.002+01:002008-12-20T04:37:47.965+01:00Johnson's battingI've only seen Johnson bat once (at the Gabba against NZ), and he looked very good. Looking through his Test scores to date, he's only been dismissed in single figures in a quarter of his innings. If you assume an exponential distribution of scores, that is consistent with an average of about 35. Currently he's averaging low 20's.<br /><br />So, there are two main possibilities — he's been lucky to make so many starts, or he's been unlucky to get out for 20odd so often. Batting at number 10 probably isn't helping him.<br /><br />I'd like to see him moved up to number 8. At worst, he'll be about as good as Lee. At best, I think he could be a very good bowling all-rounder.<br /><br />Sorry for the lack of posting lately. I'm trying to finish off my Masters thesis (transferred down to PhD), I will start a proper job in February, and I'm spending much of my spare time playing and studying chess.David Barryhttp://www.blogger.com/profile/08378763233797445502noreply@blogger.com8tag:blogger.com,1999:blog-22713811.post-75178851465412516822008-11-15T00:08:00.002+01:002008-11-15T00:21:07.522+01:00Can Dan Cullen bowl a doosra?I ask because I saw him bowl balls spinning from leg to off during the warmups last night (the warmups was the only time I was in line with the bowler). Some of them I picked as leg breaks, but others I didn't, making me wonder if he can bowl a doosra now. It's equally possible that I wasn't paying close enough attention to his hand.<br /><br />Other notes from last night's game:<br /><br />- What would be a good name for a team which includes Michael Dighton, Dan Marsh, and Ryan Harris? I know, <i>All Stars</i>. <br /><br />- Brendan Drew was bowling nice little outswingers in the warmups.<br /><br />- When Xavier Doherty was fielding at long off a few metres from me, I resisted the temptation to tell him that he's got the worst first-class average of any bowler ever. It's only just <a href="http://stats.cricinfo.com/ci/content/records/283308.html">false</a>. With the qualification of 5000 balls, the only people with worse averages are a guy who only played for Oxford University, Sachin Tendulkar, and Stuart Saunders, who at least averaged 25 with the bat for Tassie. But Doherty's pretty good at limited-overs cricket. Weird.<br /><br />- When Magoffin (I like Magoffin — he was the only guy who stuck in my memory from the Queensland Academy side I saw play India in 2003/4) was bowling to Sean Marsh, Gilchrist set a 7-2 field. It was a disgustingly negative tactic. Captains have a responsibility to make sure that the game is entertaining for the spectators. It's because of people like Gilchrist that people are turning away from Twenty20 cricket.David Barryhttp://www.blogger.com/profile/08378763233797445502noreply@blogger.com8tag:blogger.com,1999:blog-22713811.post-66221269257944162412008-10-21T02:16:00.002+02:002008-10-21T02:23:25.320+02:00KeepersI've got what I think is an <a href="http://blogs.cricinfo.com/itfigures/archives/2008/10/analysing_wicketkeepers_by_bye.php">interesting post</a> up at it Figures. It ranks wicket-keepers by the rate at which they let byes through, adjusted by country. It doesn't work for keepers who kept up to the stumps to fast bowling a lot (i.e., keepers from the olden days), but I'm happy with how well it works for modern keepers. <a href="http://content-aus.cricinfo.com/ci/content/story/374726.html">Here</a> is the full list (qualification: 20 Tests) for those who want to just see the results.<br /><br />I must admit I hadn't heard of some of those guys near the top. Kirmani rang a bell, but Tamhane was new to me. I was happy to see in his Cricinfo profile that Wally Grout compared Tamhane to Don Tallon. It's unfortunate that Tallon (and Grout, and the rest) kept up to the stumps so often. It makes cross-era keeper comparisons difficult. (You can't just go by the prevailing rate of byes in world cricket at the time, otherwise modern keeper-batsmen would come up as the equal of Knott, Taylor, etc.).<br /><br />Ideally, we'd be able to take this one step further and get a well-founded measure of a keeper-batsman. But the main difference between a good keeper and a bad one is the number of dismissals effected, and it would be close to impossible to get accurate estimates on, e.g., how many dismissals Knott would have had if he'd kept for Pakistan in the 1990's.David Barryhttp://www.blogger.com/profile/08378763233797445502noreply@blogger.com11tag:blogger.com,1999:blog-22713811.post-54914358865323339542008-10-08T12:26:00.000+02:002008-10-08T12:47:49.794+02:00Free hitsWe're on the eve of what should be another fantastic battle of Test cricket, but this evening I finally got around to a simple study of the effect of the free hit rule in the IPL. The IPL's the only ball-by-ball database I have, otherwise I'd see if the results are the same in ODI's as well.<br /><br />Anyway, I went through the tournament and found the run rate on the ball immediately following a no-ball (there might be some non-front-foot no-balls in there, but whatever...). The result was 140 runs off 82 balls, a run rate of 10.2 per over.<br /><br />To work out how many runs you'd expect to have been scored if it wasn't a free hit, I used the overall run rates by over (actually I did a bit of smoothing first, fitting quadratic trends to the first six overs and remaining 14 overs). The graph looks like this:<br /><br /><img alt="Feel free to suggest a caption." title="Feel free to suggest a caption." src="http://i44.photobucket.com/albums/f35/pappubahry/cricket/iplrrbyover.png"><br /><br />So, e.g., if there was a free hit in the 10th over, you'd assume that if it was a normal ball, 7.0/6 = 1.17 runs would have been scored off that ball. Doing this for each of the free hits, you end up with an expected score of 111 runs coming off those free hits, a run rate of 8.1 per over. So the batsmen are scoring more than usual off the free hits, but not by a lot. An extra third of a run per free hit, on average.<br /><br />It is a nice check that the average run rate in the 20th over (when you're batting like they're all free hits anyway) was 10.1.<br /><br />Presumably free hits in ODI's also go at about 10/over (or 1.7 per ball), so proportionally speaking, the punishment is greater in the 50-over form of the game.<br /><br />That graph above is pretty interesting. There's a bit of noise, but the end of the fielding restrictions is very clear. It's interesting that the acceleration is gradual.<br /><br />The batsmen are circumspect (relatively speaking) in the first over, and the effect certainly muhc larger than just the first over being just bowled by the best bowler (no-one had an economy rate as low as 5.2 in the IPL). Perhaps it is worth opening with your "fifth" bowler. I don't know.David Barryhttp://www.blogger.com/profile/08378763233797445502noreply@blogger.com16tag:blogger.com,1999:blog-22713811.post-56047657700636245782008-09-28T11:10:00.000+02:002008-09-28T11:50:17.893+02:00Queensland v KolkataIn a post some time back, <a href="http://nestaquin.wordpress.com/">Nestaquin</a> told me that the Kolkata Knight Riders would be playing some games in Brisbane, and that I should go and report on what I see. Today they played the first of three double-headers against Queensland, and this gives me an easy way to end the neglect of this blog. The games were played at Allan Border Field, which is a lovely ground which doesn't have enough matches to warrant over-corporatisation — there's still a white picket fence, a hill, and trees.<br /><br />The first Kolkata player I saw was Ajit Agarkar (at least I'm 90% sure it was him — I only saw him for a second or two), who was going for a jog as I was walking to the ground. But unfortunately he didn't play, as the coach John Buchanan brought his squad here to give the young guys some experience.<br /><br />Boy do they need it. They batted first in the morning game. I had a pen and notepad, and decided to keep a tally of balls where the batsman was beaten (i.e., played and missed, miscued, hit on pad). I don't know of a good word to describe this, so I'll borrow the French football term <i>occasion</i> (which in football means a scoring opportunity). In 120 balls plus a few wides, there were 58 of these <i>occasions</i>, which makes one every other ball. The batsmen, mostly sub-first-class standard, were completely unable to cope with the pace and bounce of the quick bowlers. Then Chris Simpson came on to bowl some offies, and they weren't all that good against him either.<br /><br />Sharma (not sure of his first name) gave up 12 <i>occas</i> on his way to 5 runs, and Patel gave up 10 on his way to 3 runs.<br /><br />They crawled their way to 8/79 from their 20 overs. The only three batsmen to make double-figures were Michael Buchanan (John's son; he's been a fringe player for Queensland) with 16, their other opener Prashantha (I think that was his name — I didn't see a match programme, haven't seen a scorecard, and had to go by the ground announcer), who was steady-ish but slow and who scored 12 before getting out in the 8th over, and Adam Hollioake. I didn't even know he was still playing, but Cricinfo tells me that while he retired from first-class cricket in 2004, he kept playing T20's till 2007. Anyway, he looked pretty overweight, but he looked a class above most of his teammates.<br /><br />There really wasn't much positive to take out of the batting. Even with only four fielders inside the circle, they were incapable of nudging singles. Symonds bowled two consecutive maidens with the field set deep! <br /><br />I couldn't be bothered taking notes after that.<br /><br />Their bowlers in the morning game were no better than the batsmen. Too short, too full, too much width, .... They might have been a bit unlucky, in the sense that every time the batsmen swung at the ball it found the middle of the bat, but it was still carnage. The Queensland openers whacked 80 runs in about 7 overs, and then they decided to keep playing, with a revised target of 180. They made it with about four overs to spare, with seven wickets in hand. Symonds failed, scoring just 2, skying a catch to long on with his first real swing of the bat.<br /><br />I stuck around for the afternoon game, and I'm glad I did. Queensland batted first (shuffling their batting order), and this time the KKR bowlers stuck to a much better length. Short balls got to the head rather than the lower chest. And they had more luck, as batsmen started miscuing. Chaudry (I think) took three wickets. Queensland kept batting aggressively but lost wickets. Siddarth Kaul was one of the bowlers. Iqbal Abdullah bowled some left-arm orthodox darters and looked OK. I can't remember the others. They brought on a legspinner who struggled to find his length for a while, which meant that most of his spell was bad — when you only get four overs, there's not much time to get your rhythm. Queensland got to 8/96 after 12 and finished all out 129.<br /><br />Michael Buchanan led the chase with 49 from 31. The batsmen at the other end were not so good, and when Buchanan was dismissed they were 4/80ish. They looked ready to collapse, but the lower-order kept hanging on, Hollioake again making double figures. I wasn't taking notes or tallying, but my impression was that they were much more solid, making contact with the ball and middling it a lot more often.<br /><br />They needed 29 off the last two, and Daniel Doran bowled the nineteenth over (I think it was the nineteenth...). Doran must have good figures in grade cricket, because he keeps getting picked for Queensland, despite averaging over 70 with the ball in the last two years. Anyway, one of the KKR tailenders hit him for 16 runs in three balls (all straight hits I think), and they then needed 11 off the last over with one wicket in hand.<br /><br />Grant Sullivan bowled it — he had looked sharp but lacked control and Buchanan had hit him around earlier. His first ball was short and gave too much width, and Kaul managed to get some bat on it and got a boundary to third man. 7 off 5. Dot ball. Third ball was good length, Kaul hits it over long off for 6, and momentarily thinks that he's won the match, but then realises that scores are tied. Field comes in. Dot ball. Dot ball. Last ball, defended to Andrew Symonds, they run, Symonds throws and misses, Kolkata win by one wicket.<br /><br />The KKR guys were all happy with a win.David Barryhttp://www.blogger.com/profile/08378763233797445502noreply@blogger.com8tag:blogger.com,1999:blog-22713811.post-24675387482304830702008-09-07T11:14:00.001+02:002008-09-07T11:14:44.717+02:00Simulating one-day cricket and batting ordersI spent some of today reading through <a href="http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6VC5-4DW8VR6-4&_user=331728&_rdoc=1&_fmt=&_orig=search&_sort=d&view=c&_version=1&_urlVersion=0&_userid=331728&md5=0237a750feaba9f9d4b9332c685c3543">this paper</a> (you'll probably need a University subscription to read that) by Swartz et al. It's called 'Optimal batting orders in one-day cricket', and it is useful because it gives a way of simulating one-day innings.<br /><br />(The paper itself looks at the Indian batting order in the 2003 World Cup. The best they came up with went Dravid, Tendulkar, Ganguly, Sehwag, Mongia, Y Singh, Khan, Kaif, H Singh, Agarkar, Srinath. Their second-best lineup swapped Dravid and Ganguly, sent Kaif to 7. They reckon it would have done better by about 6 runs, on average, than their actual lineup for the World Cup final. Not many matches are won and lost by less than six runs, but I suppose you want to squeeze out every run you can. It's interesting that the simulations reckoned that Kaif was best left to come in and slog at the death. A full run of their simulations takes a long time — there are a <i>lot</i> of batting lineups to go through, even when you do clever tricks and make the search much smaller. But they say that it would be much quicker if you had only a limited number of options, such as during a match when you've lost a couple of wickets. Using a computer to find the optimal batting order based on the situation of the game meshes well with <a href="http://www.thewisdencricketer.com/blog/?p=157">Rob Smyth's belief</a> that batting orders in one-day should be fluid.)<br /><br />The way they do it is to work out 'baseline' characteristics for each batsman in the team. That is, they get the probability that a batsman will play a dot ball, score a single, a 2, a 3, a 4, a 6, or get out. But they don't just take their overall career numbers, they take into account the match situation when they batted.<br /><br />So, given the number of wickets fallen w, balls bowled b, Duckworth-Lewis percentage resources used R(w,b), what they actually did was fit parameters to a loglinear model that looks like this (the subscript k denotes what happened on the ball, so k = 0 is a dot, etc.; the subscript j refers to the jth batsman):<br /><br />log(q<sub>jwbk</sub>) = μ<sub>jk</sub> + α<sub>k</sub>*w/9 + β<sub>k</sub>*b/299 + θ<sub>k</sub>*R(w,b)/100.<br /><br />The μ<sub>jk</sub>'s give the baseline probabilities for each type of ball-result (dot, single, etc.) for each batsman at the start of the innings. (Well, not directly probabilities — the probabilities p<sub>jk</sub> are given by p<sub>jk</sub> = q<sub>jk</sub> / &Sigma<sub>k</sub> q<sub>jk</sub>.) <br /><br />The other parameters (α, β, θ) describe how the probabilities change as the game situation changes. It is assumed that all batsmen change in the same way.<br /><br />So that's all well and good. You throw the paramaters into the computer, generate a bunch of random numbers and you end up with 50 overs of simulated cricket. The results are pretty close to what real cricket scores are, at least at the team level. I tried running the same algorithm and one of the openers scored a double-century on the second run, so it's probably not perfect, but on the average it seems to do a good job. (I'm getting a slightly higher result for the team average — 253 against 250 — than the authors of the study did. Some minor bug in my code somewhere, I guess.)<br /><br />Anyway, I think that this paper could be useful for me. What I want to do is see how to properly assess batting average and strike rate. So what I hope to do is get the relevant parameters for batsmen as a whole in the 2000's. Unfortunately, I don't have ball-by-ball ODI data, so I'm going to have to estimate it somehow. I've asked S Rajesh for the overall numbers (i.e., total dot balls, singles, 2's, etc.), and hopefully with some fiddling I'll get the weird loglinear parameters to match them.<br /><br />The α, β, and θ parameters I'll leave unchanged. Hopefully India's batsmen from 1998 to 2003 (the period that the study looked at) are representative of how batsmen generally change over the course of an innings.<br /><br />Then, once I've got a good simulator of an average batting lineup against average bowling, I'll be able to vary the parameters of one of the batsmen, tweaking average and strike rate (indirectly — I'll be tweaking probability of dismissal on each ball, and probability of each type of scoring shot). Then you see what effect this has on the average team score. So it'll be like <a href="http://pappubahry.blogspot.com/2008/08/averages-and-strike-rates-in-odis.html">the post below</a>, only accurate. Hopefully.David Barryhttp://www.blogger.com/profile/08378763233797445502noreply@blogger.com0tag:blogger.com,1999:blog-22713811.post-46013462540455132662008-08-25T11:49:00.001+02:002008-08-25T11:53:33.375+02:00Averages and strike rates in ODI'sRating batsmen in one-day cricket is difficult because both average and strike rate are important, and it's not clear how they should be weighted.<br /><br />I can't see a theoretical solution (multiplying the two measures might be good enough, but it seems arbitrary), and I think that the actual answer will come from simulations. In this post, I show some results from some woefully inaccurate simulations. But hopefully even though the total scores were below what they should be, the equivalences of various averages and strike rates should be reasonably accurate.<br /><br />Here's what I did. I took the overall average and strike rate since 2000 for each batting position (I think using the top eight teams). I ran a largish number (20000) simulations to get what the "average" total score is, and it turned out to be 208 (I told you it was inaccurate...).<br /><br />Then, I replaced one of the openers with a batsman with an average of 1, strike rate 50, then average 2, strike rate 50, average 3, strike rate 50, and so on, doing 20000 simulations each time to get the average total score. I did this until I had a grid of average total scores for strike rates from 50 to 130, and averages from 1 to 60. Then I made contour plots with curves of equal value.<br /><br />The simulations assumed a constant run rate and exponentially distributed scores. Not realistic, but it was straightforward to do and avoided doing ball-by-ball simulations.<br /><br />There's a bit of noise in the results. Here's the contour plot for openers:<br /><br /><img title="I couldn't be bothered" alt="I couldn't be bothered" src="http://i44.photobucket.com/albums/f35/pappubahry/cricket/avgsrcurvesopener.png"><br /><br />The number 3:<br /><br /><img title="getting the sizes" alt="getting the sizes" src="http://i44.photobucket.com/albums/f35/pappubahry/cricket/avgsrcurvesno3.png"><br /><br />The number 4:<br /><br /><img title="the same each time." alt="the same each time." src="http://i44.photobucket.com/albums/f35/pappubahry/cricket/avgsrcurvesno4.png"><br /><br />Because I was exceedingly lazy, the contours in the separate plots may not correspond to the same total team scores. But you will agree that the pictures are colourful.<br /><br />Now for some numbers. In each of the following little tables, the rows are equivalent. So, an opener with an average of 50 and a strike rate of 73 is worth the same as an opener with an average of 25 and a strike rate of 101. According to the simulations, at least.<br /><br />Making an average score of 210, as an opener:<br /><pre><br />avg sr<br />50 73<br />45 74<br />40 76<br />35 80<br />30 87<br />25 101</pre><br />210, number 3:<br /><pre><br />avg sr<br />50 69<br />45 71<br />40 73<br />35 76<br />30 82<br />25 96</pre><br />210, number 4:<br /><pre><br />avg sr<br />50 72<br />45 75<br />40 77<br />35 82<br />30 90<br />25 110</pre><br />I didn't get past number 4. I'll do the rest tomorrow.<br /><br />I imagine that the curves would change with more accurate simulations, but this is at least a start.David Barryhttp://www.blogger.com/profile/08378763233797445502noreply@blogger.com6tag:blogger.com,1999:blog-22713811.post-47499428749935488032008-08-24T06:10:00.002+02:002008-08-25T01:36:35.759+02:00Non-boundary strike ratesNear the end of the discussion <a href="http://blogs.cricinfo.com/itfigures/archives/2008/08/the_odi_bowling_average_is.php">here</a>, there's a comment from me about the changing nature of the way runs are scored in ODI cricket. Most of it, of course, is coming from boundaries, which are much more common today. But it is interesting that there's been no real changes in the rate of non-boundary scoring since 1990.<br /><br />Here's a graph showing the yearly overall "non-boundary strike rate", that is the runs that are actually run, divided by the number of balls not hit to the boundary (times 100). Top eight sides only. (There's some missing boundary data, especially before 1990. The actual non-boundary strike rates for these years are lower than those in the graph.)<br /><br /><img alt="Nice little up-down pattern from 1973 to 1983" title="Nice little up-down pattern from 1973 to 1983" src="http://i44.photobucket.com/albums/f35/pappubahry/cricket/runsr.png"><br /><br />(I've called it "run sr" in the graph, run as in running.)<br /><br />I would have thought that batsmen today are more adept at "milking" bowlers, but the non-boundary strike rate has never got above 3 runs per over for any long period.<br /><br />If you suppose that there are three types of balls:<br /><br />- good balls that can't be scored off<br />- OK balls that can be worked around<br />- bad balls that can be hit to the boundary<br /><br />then <s>it seems that batsmen these days are able to hit the OK balls to the boundary more often, but can't do much about the good balls</s>. (<b>Edit</b>: No, wait, that's not right. They're getting better at milking the good balls at the same rate as they're getting better at hitting the OK balls for four. Roughly. The constant non-boundary strike rate with an increased frequency of boundaries means that the percentage of dot balls is getting lower.)<br /><br />At a team level (now including all teams, since 2000, but I've forgotten what I did with Kenya, probably I discarded them because they play against minnows too often):<br /><pre><br />team sr run sr<br />Australia 83.6 50.6<br />Sri Lanka 75.5 47.4<br />South Africa 78.3 47.0<br />Pakistan 77.3 46.7<br />India 78.8 45.7<br />England 74.4 45.4<br />New Zealand 75.2 44.7<br />West Indies 74.2 43.9<br />Zimbabwe 67.0 43.3<br />Bangladesh 63.4 38.6</pre><br />Bangladesh are really bad at working the ball around for singles, etc. There's a clear gap between Australia and Sri Lanka, then a gradual progression down through to Zimbabwe. Then there's a huge dropoff to Bangladesh.<br /><br />India are a bit of an anomaly, with their high overall strike rate coming more heavily from boundaries than the other teams. It's probably not just a factor of their grounds — opposition teams in India have the highest non-boundary strike rate of away teams anywhere. <br /><br />Top eight sides since 2000, individuals, average at least 30, at least 1000 runs.<br /><pre><br />Player mats inns runs avg sr run sr diff<br />JN Rhodes 71 66 1994 41.5 86.1 59.7 26.3<br />DS Lehmann 44 39 1219 42.0 78.8 56.0 22.8<br />MEK Hussey 77 60 2079 54.7 85.6 55.3 30.4<br />L Klusener 91 73 1592 33.9 87.6 54.9 32.7<br />A Symonds 157 133 4300 40.2 94.1 54.1 40.1<br />MJ Clarke 117 105 3486 42.5 81.0 53.9 27.2<br />MS Dhoni 101 91 3064 44.4 89.0 53.6 35.5<br />SR Waugh 46 38 1134 40.5 79.9 52.6 27.2<br />PD Collingwood 124 114 2956 31.1 74.6 51.5 23.1<br />RP Arnold 136 123 2984 32.8 72.1 51.5 20.6</pre><br />Symonds and Dhoni are best known for their hitting, but they're good at the less flashy stuff as well.<br /><br />Unhonourable mention goes to Chris Gayle: overall strike rate of 80.3, non-boundary strike rate of 37.3, lowest of all the players who made the qualification. Lazy.David Barryhttp://www.blogger.com/profile/08378763233797445502noreply@blogger.com4tag:blogger.com,1999:blog-22713811.post-32362307627345068302008-08-13T11:56:00.002+02:002008-08-13T11:57:31.574+02:00Bowlers as they get more experiencedThere was a comment from Gary Naylor <a href="http://www.thewisdencricketer.com/blog/?p=127#comments">here</a> saying that Monty Panesar should improve on his average of 32 as his career goes on, since he'll learn more about how to bowl.<br /><br />I'm not convinced. I took all spinners with 149 or more wickets since WWII, and found split their wickets into which Test it was in their careers, so I could find the overall average in debut Tests, second Tests, third Tests, etc. <br /><br />To get rid of some noise I actually took a five-Test moving average, so the first data point in the graph below is the overall average in the spinners' first to fifth Tests, the next the average in their second to sixth Tests, etc.<br /><br />Also I weighted wickets by the average of the batsmen dismissed.<br /><br /><img alt="The down-then-up shape is more dramatic if you do a ten-Test moving average." title="The down-then-up shape is more dramatic if you do a ten-Test moving average." src="http://i44.photobucket.com/albums/f35/pappubahry/cricket/experiencespinners.png"><br /><br />Note that there's a bit of a selection effect going on. I'm only looking at spinners who were good enough to play enough Tests to take 149+ wickets. Towards the right-hand end of the graph this is also a factor — if you imagine it continuing further on out, you'd eventually just be plotting Warne, Murali, and Kumble. There are, for those interested, 24 bowlers going into Tests 1-36, then 23 in Test 37, 22 in Tests 38-43, 20 in Test 44, 19 in Tests 45 and 46, and 18 in Tests 47-49.<br /><br />I don't know how much I want to read into the graph, though I'm happy in saying that spinners improve after their first ten Tests or so. After that there may or may not be a trend — batsmen working them out? Certainly there's no strong evidence that Panesar will improve significantly (he's played 33 Tests), though of course it's possible.<br /><br />Here's the corresponding graph for pacemen:<br /><br /><img alt="R^2 of 0.38 with a linear fit" title="R^2 of 0.38 with a linear fit" src="http://i44.photobucket.com/albums/f35/pappubahry/cricket/experiencepacemen.png"><br /><br />There are 46 bowlers going into all of those data points. There's a downward trend — pacemen tend to get better with experience, at least for a few dozen Tests.<br /><br />One thing to try in future is age rather than Test experience. The people who've done this sort of analysis in baseball say that age is a better thing to use than Major League experience. But of course baseball is not cricket, so I'm not sure what will come out of it.David Barryhttp://www.blogger.com/profile/08378763233797445502noreply@blogger.com2