IPL 2018 - Examples of Poor Recruitment & Team Selection

14th April, 2018.

Email: Sportsanalyticsadvantage@gmail.com

Ten matches have now been completed in the 2018 IPL - the league table is starting to form, while both the supporters of teams and team management alike are starting to consider whether the big money spent at auction on certain players equated to good value.

In the first ten matches, we are able to draw some conclusions, and here is a range of data from the 2018 edition of the IPL so far:-

Batting RPW

24.82

Domestic Batting RPW

23.71

Overseas Batting RPW

26.67

Batting SR

135.21

Domestic Batting SR

126.63

Overseas Batting SR

150.37

Bowling RPW

26.93

Domestic Bowling RPW

25.50

Overseas Bowling RPW

28.82

Bowling RPO

8.39

Domestic Bowling RPO

8.40

Overseas Bowling RPO

8.37


So far in this year's IPL, we can see that overseas batsmen have done considerably better than their Indian counterparts, recording 26.67 runs per wicket (compared to 23.71) and striking at 150.37, as opposed to 126.63 for domestic players.

However, Indian bowlers, averaging 25.50 runs per wicket compared to 28.82 for overseas bowlers, have an edge, with almost identical economy rates.

Historically over the last couple of seasons in the IPL, as we discussed in our article here, overseas bowlers have had this similar economy rate to their domestic rivals, but a better average, so this year's 10 matches so far have flipped this on its head a little, while overseas batsmen had a marginally better average but a fair bit better strike rate than Indian batsmen.

With this in mind, and the other data discussed in that article, it is clear that overseas players are generally better than domestic players, as is absolutely logical.  On this basis, it's obviously a pre-requisite for any overseas player that they are better than the average player in their position - a concept which hasn't always been adhered to by T20 franchises.

As with any auction and draft, signings ranged from the sublime to the ridiculous, and while most previews and analysis of tournaments tend to focus on players to watch - those players who are perceived to be good value with future upside - I want to look at some of the signings that teams made during the auction, that our algorithm would perceive as mistakes.  

Such an exercise is pretty interesting, in that this analysis often points to reasons as to why teams are struggling - even just several players with negative expectation in a team can lead to a significant decrease in a team's expected win percentage, and as has been mentioned numerous times on this website, it is not only imperative that the correct players are signed at auctions and drafts (given expected conditions and opposition) but also that the correct players are chosen by the team management for any given match.  In addition, it's very easy to establish which players are world-class - we all know who the world-class players are, but understanding which players add negative value is much more different for anyone from the casual fan to commentators to team management.

Today's match between the Mumbai Indians and the Delhi Daredevils highlighted this fact.  Mumbai spinner Akila Dananjaya's data, according to our algorithm, is very poor indeed, and I'm amazed that he is picked up by T20 franchises, let alone IPL ones.  Opponents Delhi included Trent Boult and Mohammed Shami in their team, and both have much worse T20 data than red-ball, and it would appear that their recruitment, as well as selection, was affected by bias from other formats.  Both Boult and Dananjaya are overseas players, which as we've already discussed, should be better than the average player.  In both cases, this is extremely questionable.

Trent Boult's T20 data is much worse than his numbers in red-ball cricket...

Prior to the start of the 2018 IPL, my algorithm established expected batting and bowling data for all the players in the competition, and the following batsmen (to clarify, bowling all-rounders and bowlers were not included in this filter) had both a mean batting average and batting strike rate deviation of 0.95 or below, based on the mean IPL figures from 2017 (25.29 average, 133.36 strike rate) - in effect, our algorithm established they were at least 5% below the average batsmen for both batting average and batting strike rate:-



Expected IPL Batting Average

Batting Average Mean Deviation

Expected IPL Batting Strike Rate

Batting Strike Rate Mean Deviation

Expected IPL Bowling Average

Expected IPL Bowling Economy

Expected IPL Bowling Strike Rate










Mahipal Lomror

DOMESTIC

21.84

0.86

118.56

0.89

71.91

10.39

41.53

Sharad Lumba

DOMESTIC

20.93

0.83

102.74

0.77

N/A

N/A

N/A

Deepak Hooda

DOMESTIC

20.71

0.82

127.32

0.95

36.40

8.19

26.67

Naman Ojha

DOMESTIC

19.05

0.75

105.84

0.79

N/A

N/A

N/A

Dhruv Shorey

DOMESTIC

18.62

0.74

101.52

0.76

N/A

N/A

N/A

Rinku Singh

DOMESTIC

17.81

0.70

118.65

0.89

N/A

N/A

N/A


These players look to be pretty questionable signings - all six are domestic players (it would be horrific if an overseas batsman was included in this list, given the scarceness of that resource) but given the depth of the domestic talent pool in India, which is such that I can give the names of dozens of better batsmen not signed by IPL franchises, it's not a ringing endorsement of the domestic player recruitment that these players were signed either.

Looking at all-rounders, the following players were rated by our algorithm as having a mean deviation of 1.00 or below for all of their batting and bowling metrics - in effect, they were below the average batsman and bowler in all four mean deviation metrics:-



Expected IPL Batting Average

Batting Average Mean Deviation

Expected IPL Batting Strike Rate

Batting Strike Rate Mean Deviation

Expected IPL Bowling Average

Bowling Average Mean Deviation

Expected IPL Bowling Economy

Bowling Economy Mean Deviation











Hardik Pandya

DOMESTIC

22.71

0.90

131.90

0.99

32.78

0.86

8.44

0.98

Stuart Binny

DOMESTIC

23.54

0.93

128.78

0.97

36.30

0.78

8.53

0.97

Gurkeerat Singh

DOMESTIC

17.00

0.67

116.91

0.88

36.50

0.78

8.53

0.97

Tom Curran

OVERSEAS

16.48

0.65

128.67

0.96

37.80

0.75

8.53

0.97

Shreyas Gopal

DOMESTIC

16.14

0.64

115.53

0.87

44.20

0.64

8.76

0.95

Yuvraj Singh

DOMESTIC

24.25

0.96

119.66

0.90

43.58

0.65

8.77

0.95

Chaitanya Bishnoi

DOMESTIC

16.00

0.63

91.78

0.69

33.58

0.84

8.89

0.93

Rahul Tripathi

DOMESTIC

22.87

0.90

132.07

0.99

36.50

0.78

8.92

0.93

Sachin Baby

DOMESTIC

24.40

0.96

130.31

0.98

64.73

0.44

9.85

0.84

Akshdeep Nath

DOMESTIC

18.27

0.72

113.69

0.85

28.55

0.99

10.07

0.82


This list of below-average all-rounders is fascinating.  We now see the addition of overseas players (replacement player Tom Curran) while some of the domestic players on the list may also of a surprise to some readers, with Hardik Pandya (he looks much better at 50 over cricket than T20 currently) and the declining Yuvraj Singh among the high-profile players in this below-average all-rounder list.

While Hardik Pandya has upside, his numbers in T20 are far from world-class...

Finally, we can establish a list of bowlers who had a mean deviation of 0.95 or below, with our algorithm establishing that they were at least 5% below the average bowler for both bowling average and bowling economy rate (it's worth noting that some bowlers did not have a big enough data sample for my algorithm to assess, such as Shivam Mavi):-



Expected IPL Batting Average

Batting Average Mean Deviation

Expected IPL Batting Strike Rate

Batting Strike Rate Mean Deviation

Expected IPL Bowling Average

Bowling Average Mean Deviation

Expected IPL Bowling Economy

Bowling Economy Mean Deviation











Pradeep Sangwan

DOMESTIC

13.57

0.54

102.48

0.77

37.78

0.75

8.69

0.95

Harshal Patel

DOMESTIC

13.11

0.52

148.14

1.11

31.64

0.90

8.72

0.95

Shardul Thakur

DOMESTIC

4.52

0.18

96.06

0.72

34.23

0.83

8.80

0.94

Dushmantha Chameera

OVERSEAS

10.26

0.41

92.29

0.69

38.68

0.73

8.82

0.94

Akila Dananjaya

OVERSEAS

12.12

0.48

92.56

0.69

71.24

0.40

8.97

0.92

Mitchell McClenaghan

OVERSEAS

5.55

0.22

127.57

0.96

31.90

0.89

9.03

0.92

Anureet Singh

DOMESTIC

10.54

0.42

96.22

0.72

40.08

0.71

9.08

0.91

Sayan Ghosh

DOMESTIC

N/A


N/A


30.23

0.94

9.34

0.89

Basil Thampi

DOMESTIC

10.57

0.42

131.92

0.99

36.03

0.79

9.42

0.88

Abhishek Sharma

DOMESTIC

N/A


N/A


33.22

0.85

9.60

0.86

Trent Boult

OVERSEAS

4.65

0.18

61.83

0.46

32.11

0.88

9.63

0.86

Mohammed Shami

DOMESTIC

8.33

0.33

142.79

1.07

39.80

0.71

9.90

0.84


The dozen players here contain four overseas bowlers (the aforementioned Boult and Dananjaya, as well as Boult's countryman, Mitchell McClenaghan, and Dananjaya's compatriot, Dushmantha Chameera), while the remainder of the list are domestic bowlers, with generally very poor bowling economy rates.  However, several of the domestic players have international honours, which illustrate that national team selectors also have issues picking their best squads.

Of the entire list of below-average batsmen, all-rounders and bowlers, players from the Delhi Daredevils (seven players) dominate the list, with Rajasthan Royals (six) and Mumbai Indians (five) following.  Interestingly, this trio of franchises currently comprise the bottom three of the league table.  Impressively, Royal Challengers Bangalore did not have a single player in the list.

Now that we have established a comprehensive list of below-average players, it is interesting to see how they have fared so far in IPL 2018 - have their performances so far justified my low pre-tournament expectations of them?

Player

Team

Completed Innings

Runs

Balls Faced

Balls Bowled

Runs

Wickets









Overall

Overall

8

141

151

280

450

13









Chaltanya Bishnoi

Chennai Super Kings

0

0

0

0

0

0

Dhruv Shorey

Chennai Super Kings

0

0

0

0

0

0

Shardul Thakur

Chennai Super Kings

0

0

0

24

37

1

Abhishek Sharma

Delhi Daredevils

0

0

0

0

0

0

Gurkeerat Singh

Delhi Daredevils

0

0

0

0

0

0

Harshal Patel

Delhi Daredevils

0

0

0

0

0

0

Mohammed Shami

Delhi Daredevils

0

0

0

59

91

2

Naman Ojha

Delhi Daredevils

0

0

0

0

0

0

Sayan Ghosh

Delhi Daredevils

0

0

0

0

0

0

Trent Boult

Delhi Daredevils

0

0

0

65

99

4

Akshdeep Nath

Kings XI Punjab

0

0

0

0

0

0

Yuvraj Singh

Kings XI Punjab

2

16

26

0

0

0

Rinku Singh

Kolkata Knight Riders

2

8

10

0

0

0

Tom Curran

Kolkata Knight Riders

0

2

5

18

39

2

Akila Dananjaya

Mumbai Indians

0

4

5

24

47

0

Hardik Pandya

Mumbai Indians

1

24

23

36

56

3

Mitchell McClenaghan

Mumbai Indians

0

0

0

24

44

1

Pradeep Sangwan

Mumbai Indians

1

0

4

12

19

0

Sharad Lumba

Mumbai Indians

0

0

0

0

0

0

Anureet Singh

Rajasthan Royals

0

0

0

0

0

0

Dushmantha Chameera

Rajasthan Royals

0

0

0

0

0

0

Mahipal Lomror

Rajasthan Royals

0

0

0

0

0

0

Rahui Tripathi

Rajasthan Royals

1

32

26

0

0

0

Shreyas Gopal

Rajasthan Royals

1

18

18

18

18

0

Stuart Binny

Rajasthan Royals

0

0

0

0

0

0

Basil Thampi

Sunrisers Hyderabad

0

0

0

0

0

0

Deepak Hooda

Sunrisers Hyderabad

0

37

34

0

0

0

Sachin Baby

Sunrisers Hyderabad

0

0

0

0

0

0


So far, these players have recorded the following combined data:-

Batting Average: 17.63
Batting Strike Rate: 93.38
Bowling Average: 34.62
Bowling Economy Rate: 9.64

Evidently, the combined data for these players is considerably worse than the average IPL data for this year (batting average 24.82, strike rate 135.21, bowling average 26.39, bowling economy 8.39) and while a number of these players have not been included in teams so far, players in this list who have been picked by franchises have hardly covered themselves in glory.

Such situations need not be an issue with quality recruitment which focuses on data analysis.  If this article has given you insight into the data that Sports Analytics Advantage can offer cricket franchises around the world in formulating draft or auction plans, please feel free to enquire for bespoke draft and auction strategies via sportsanalyticsadvantage@gmail.com.

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