Application of Six Sigma on the Cricket Field. This is a sample case study to demonstrate the application of Six Sigma methodology and tools for process improvement.

2. Business Case
18
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Mike’s Batting Average (runs scored per completed
innings)
Matches
Lost
Matches
Won
Mike is the best batsman in the team and the probability of
winning a match is higher when he bats well
Improving Mike’s consistency with the bat
will help the team win more matches.
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3. The average number of runs scored per innings by
Mike was 32.5 for the last 50 matches (Jan to Dec
2011) against a benchmark of 40 runs per innings.
This has had a negative impact on the match results
as the team has won only 36% of the matches
played (Jan to Dec 2011).
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LSL Mike’s
Average
40
32.5
The objective of the project is to
improve Mike’s batting average and
consistency

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Process Capability
Mean 32.52
Median 15
Standard Deviation 39.47
Count 50
Defect 36
DPMO 720000
Sigma (Zst) 0.92
Project CTQ Number of runs scored by Mike in each completed innings
Defect Definition Any complete innings with a score of less than 40 runs
would be a defect

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What should be the desired outcome?
Mike scoring 40
runs or more in
half the
matches played
will be a
significant
improvement
(p-value: 0.001)

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What are the causes
affecting Mike’s batting
performance?

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Let’s look at the defective innings
(Innings where Mike got out for less than 40 runs)
 In 30 out of 36 defective
innings (83%), Mike was
dismissed for less than 20
runs
 Once Mike crosses 20
runs, the probability of
playing a long innings is
high (only 6 dismissals
between 20 & 40 out of 50
completed innings)
 Why is Mike getting out so
often before scoring 20
runs?

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How is Mike getting dismissed?
Pareto Charts of dismissal types:
Innings Score < 20 runs Innings Score > 20 runs
 “Caught Behind” is the major cause for dismissal when Mike scores less than 20 runs: 50%
compared to 10% when he scores more than 20 runs
 Why is Mike getting caught by the keeper & the slip fielders so often at the start of his
innings?
 Team statistician Dave was asked to provide Mike’s shot selection data

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Why is Mike getting “Caught Behind”?
Analysis based on Mike’s shot selection data provided by Dave
Pareto Chart of Type of Stroke Pareto Chart of Attacking Shot Played
 67% caught behind dismissals at the start of his innings were while playing
attacking strokes
 Attacking shots contributing to most Caught Behind dismissals are Hook, Pull
and Upper Cut

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Hook, Pull & Upper Cut played by Mike
Box Plot of Strike Rate (p-value: 0.000)
2-proportion test
Statistics 0-25 Balls 25+ Balls
Totals shots played 28 117
Number of dismissals 10 4
% Defective 35.71% 3.42%
90% CI (20.82, 53.00) (1.18, 7.65)
P-value 0.000
Balls per dismissal 2.80 29.25
 On an average, Mike plays 25 balls
to score 20 runs
 We tested the success of Hook,
Pull & Upper Cuts during the first
25 balls played by Mike in
comparison to shots played after
25 balls
 The Strike Rate for 25+ balls is
almost double compared to first 25
balls (strike rate is number of runs
scored per ball)
 Compared to 10 dismissals out of
28 attempts in the first 25 balls,
Mike got out just 4 times in 117
attempts after playing 25 balls
Mike needs to avoid playing
the high risk shots (hook, pull
& upper cut) in the initial
stages of his innings

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What are the other
causes affecting
Mike’s batting
performance?

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Cause & Effect Diagram
Mike’s Batting Performance

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Potential Causes (Xs)
Critical
Not Critical
Causes shortlisted as critical in brainstorming session
with project team and chief batting coach

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Impact of Batting Innings &
Pitch Conditions on Mike’s
Performance

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 There is no difference in Mike’s
performance while batting first or
chasing a target
 Mike performs better on flat pitches
compared to Green pitches
 What can he do to score more runs
while batting on Green pitches?
Pitch Conditions Flat Green
Number of Innings 24 26
Number of defectives 14 22
% Defective 58.33 84.62
95% CI
(36.64,
77.89)
(65.13,
95.64)
P-value 0.059
Batting Innings First Second
Number of Innings 23 27
Number of defectives 16 20
% Defective 69.57 74.07
95% CI
(47.08,
86.79)
(53.72,
88.89)
P-value 0.761
Impact of Batting Innings & Pitch
Conditions

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What is affecting Mike’s batting on
Green pitches?
P-value: 0.031
 While batting on Green pitches, Mike bats
better when he is not required to open
the batting and face the new ball bowlers
(p-value: 0.03)
 His average at 4th position is 52.5
compared to 12.1 while opening the
batting on Green pitches

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Impact of Bowling Style on Mike’s Batting
Chi-Square % Defective Test for Bowling Style
 “Number Tested” is
no. of balls played
 “Defectives” are no.
of false or risky shots
that did or could lead
to a dismissal
 There are differences among the % defectives at
the 0.05 level of significance (p-value: 0.000)
 Mike plays more risky / false shots while playing
Left Arm Seam Bowlers (21.48%)

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How can Mike improve
his batting against left
arm seam bowlers?

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We asked the Chief Batting Coach and the Chairman
of the Cricket Academy to analyze his batting video
footage against Left Arm Seam Bowlers
 Mike plays Left Arm
Seam with a Closed
Stance (Figure 1.1)
 The orthodox stance
blocks him before he
begins playing a shot
& he ends up playing
around the front pad
 Mike also keeps his
backswing too
straight & thus he
plays across the line
& ends up too chest
on
Figure 1.1
 Mike needs to play
Left Arm Seam with
an Open Stance
(Figure 1.2) & Wider
Back-lift
 With an open stance,
he can align himself
up to the angle that
the ball is arriving at
 For a wider back-lift,
he needs to pick the
bat up over the off
stump or 1st slip area
rather than over the
middle stumpFigure 1.2
Analysis Suggestions

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Conclusions & Action Plan
Root Cause Description Improvement Plan Responsibility
Batting Style Hook, Pull and Upper Cut are
shots contributing to maximum
dismissals at the start of Mike's
innings
Mike needs to avoid playing these
high risk shots in the initial stages of
his innings (first 20-25 balls)
Mike, Batting Coach,
Team Captain
Pitch Conditions Mike’s batting average on Green
Pitches is significantly lower
compared to his performance
on Flat pitches. The problem is
facing the new ball at the start
of his innings on Green pitches
that suit seam bowling. He does
well batting lower down the
order on Green pitches
(average: 52.5)
Suggestion: Mike should be sent at 4th
position in matches played on Green
pitches
Action Plan: Continuously changing
the batting order based on pitch
conditions would negatively impact
other players. As his batting is
consistent at all positions on Flat
pitches, Mike would bat at number 4
in all matches irrespective of pitch
conditions
Team Captain,
Batting Coach
Bowling Style Mike tends to play more false /
risky shots while facing left arm
seam bowlers
Mike needs to play Left Arm Seam
bowling with an open stance & wider
back-lift. Mandatory inclusion of left
arm net bowlers for team practice
sessions
Mike, Batting Coach,
Team Manager

23. contact@xserve.in

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Results
LSL: 40
Mean: 32.52
Median: 15.00
LSL: 40
Mean: 49.32
Median: 46.50

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Improvement
Metric Measure Improve
Mean 32.52 49.32
Median 15.00 46.50
Standard Deviation 39.47 42.51
Innings Played 50 28
Defective Innings 36 12
DPMO 72000 428571
Sigma (Zst) 0.92 1.68
% Innings < 40 Runs (LSL)

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Benefits
% Matches Won by the Team