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Reflections on Sport and mathematics
The value of graphical presentation of data: an example from cricket

Iain Skinner, School of Electrical Engineering, The University of New South Wales

I recently found an elegant, instructive example of how some simple mathematics, combined with the power of a small computer, allows a very intelligible graphical presentation of information that is conventionally presented in long, barely comprehensible tables of numbers. Given that finding ways to interpret numerical information is an important function of mathematics, and given that the subject of the example was cricket, I thought there may be wider interest in this instructive example.

In cricket there are three numbers frequently used as summary statistics to indicate the quality of a bowler's performance. The first, and most frequently quoted, is the average (ave), which is defined as the total runs scored off, divided by the number of wickets taken by, the bowler. It is often termed the cost of a wicket, and, in general, the lower the average, the better the bowler. The second number is the bowler's strike rate (SR). It measures how often a bowler captures a wicket and is defined as the total number of deliveries bowled divided by the number of wickets taken. Again, the lower the better. The third number characterizing a bowler is the economy rate (ER). There are several definitions of this. (The definitive source of cricket records, the annual edition of Wisden Cricketers' Almanack, does not calculate one at all.) The one used here (consistent with the definition of Kimber (1993)) is the number of runs conceded per hundred balls bowled. Once again, the smaller the number, the better is the bowling.

By tradition (and cricket is a very traditional game) cumulative bowling figures are gathered for a specified period of time (season, career, etc.), and then ordered and tabulated, necessarily in a small font so that the most numbers can be arranged on the least number of pages. A glance at a typical set (e.g. Tables 1 and 2) of bowling figures shows that it is not immediately clear who has performed best. If the table were ordered by average, as is often the case, the better strike rates would remain obscure. Providing a second table, while helpful, does not solve the difficulty, for comparison across tables is not easy. An improvement in presenting a comparative summary of bowling performances was explained by Kimber (1993).

Table 1. The statistics for the bowlers noted in Figure 1 (those with at least 25 wickets in the
1996-97 Australian first-class cricket season. [Source: Cricinfo]

Key

Balls
Wkts
Ave
SR
ER
JA

J. Angel

1697
31
22.10
54.7
40.4
AB

A. Bichel

1474
30
21.67
49.1
44.1
IB

I. Bishop

1482
25
29.04
59.3
49.0
AD

A. Dale

2755
42
22.07
65.6
33.6
IH

I. Harvey

1904
35
27.57
54.4
50.7
BJ

B. Julian

2076
35
25.94
59.3
43.7
MK

M. Kasprowicz

2873
48
25.54
59.9
42.7
JM

J. Marquet

1763
25
41.12
70.5
58.3
GM

G. McGrath

1417
29
19.55
48.9
40.0
PM

P. McIntyre

2973
35
40.37
84.9
47.5
BM

B. McNamara

1490
33
22.45
45.2
49.7
CM

C. Miller

2607
32
35.72
81.5
43.8
TM

T. Moody

2148
38
24.37
56.5
43.1
MR

M. Ridgway

1832
28
34.93
65.4
53.4
DS

D. Saker

2573
32
37.81
80.4
47.0
SW

S. Warne

1892
27
29.44
70.1
42.0
SY

S. Young

2236
35
31.31
63.9
49.0

Table 2. The statistics for the bowlers noted in Figure 2 (those with at least 70 test wickets for
Australia since World War I (as at 1 May, 1998). [Source: Cricinfo]

Key

Balls
Wkts
Ave
SR
ER
TA

T Alderman

10 181
170
27.15
59.9
45.3
RB

R. Benaud

19 108
248
27.03
77.0
35.1
AC

A. Connolly

7 818
102
29.22
76.6
38.1
AD

A. Davidson

11 587
186
20.53
62.3
33.0
GD

G. Dymock

5 545
78
27.12
71.1
38.1
JG

J. Gleeson

8 853
93
36.20
95.2
38.0
JMG

J. Gregory

5 582
85
31.15
65.7
47.4
CG

C. Grimmett

14 513
216
24.21
67.2
36.0
NH

N. Hawke

6 974
91
29.41
76.6
38.4
RH

R. Hogg

7 633
123
28.47
62.1
45.9
MH

M. Hughes

12 285
212
28.38
57.9
49.0
HI

H. Ironmonger

4 695
74
17.97
63.4
28.3
IJ

I. Johnson

8 780
109
29.19
80.6
36.2
WJ

W. Johnston

11 048
160
23.91
69.1
34.6
GL

G. Lawson

11 118
180
30.56
61.8
49.5
DL

D. Lillee

18 467
355
23.92
52.0
46.0
RL

R. Lindwall

13 650
228
23.03
59.9
38.5
AAM

A. Mailey

6 119
99
33.91
61.8
54.9
AM

A. Mallett

9 990
132
29.84
75.7
39.4
TM

T. May

6 577
75
34.74
87.7
39.6
CM

C. McDermott

16 586
291
28.63
57.0
50.2
GM

G. McGrath

8 849
166
23.49
53.3
44.1
GMc

G. McKenzie

17 681
246
29.78
71.9
41.4
KM

K. Miller

10 461
170
22.97
61.5
37.3