Player Impact and the Distribution of This Impact Across a team: Quick Summary

Player Impact and the Distribution of This Impact Across a team: Quick Summary
Image Credit: Riot Games
Written by: BigTime
-
24/10/2021

The EMEA LCQ went just as quickly as it came; it was a treat. We saw fantastic games, brilliant plays, and Team Liquid clawing their way into Champions. 


During the course of the tournament, I decided to track and calculate two stats that have not been used in VALORANT before. These are a players tournament KAST percent and a team’s Impact distribution coefficient.


KAST Percent: KAST is an acronym that stands for Kills, Assists, Survived, and Traded. A player is counted as having an impact in a round if they manage one of those 4 things. The KAST percent is then calculated by dividing the total rounds where a player had an impact by the total number of rounds played.


Impact Distribution Coefficient: This is a modified version of the Gini coefficient from economics. The calculation gives teams a value between 0 and 1, with 0 representing absolute equality in terms of impact, and 1 representing absolute inequality. Teams have been ranked 1-8 with rank 1 being the most even and rank 8 being the most uneven.


Before we dive in, this article is a quick summary of my work on this topic. For a more in-depth read on this topic, you can download the paper I have written HERE. If you are at all interested in the deeper application of these statistics or deepening your understanding of player impact at the top level of VALORANT, I highly recommend you check it out.


KAST PERCENT


Below we can see every player’s KAST percentage from the EMEA LCQ. Unsurprisingly, the players on better performing teams saw better KAST percentages.

 

PLAYER

TEAM

KAST %

RANK

Jamppi

Liquid

79.09%

1

Brave

SMB

76.06%

2

Turko

SMB

75.53%

3

Nivera

Liquid

75.45%

4

L1NK

Liquid

75.45%

5

ScreaM

Liquid

75.00%

6

Leo

Guild

74.45%

7

AvovA

G2

73.68%

8

soulcas

Liquid

72.73%

9

nukkye

G2

71.93%

10

koldamenta

G2

71.93%

11

qRaxs

Futbolist

71.56%

12

MOJJ

Futbolist

71.56%

13

qw1

Futbolist

71.09%

14

russ

SMB

70.21%

15

Izzy

SMB

70.21%

16

Sasuke

Futbolist

69.67%

17

glovee

Oxygen

68.99%

18

Avez

Anubis

68.67%

19

draken

Guild

68.54%

20

Sayf

Guild

68.22%

21

Yacine

Guild

67.60%

22

Paura

SMB

67.55%

23

m1tez

Oxygen

67.44%

24

STERBEN

Futbolist

67.30%

25

bonkar

Guild

67.29%

26

XiSTOU

Oxygen

66.67%

27

Toronto

Oxygen

65.89%

28

zeddy

OBG

65.22%

29

Shalaby

Anubis

63.86%

30

Mixwell

G2

63.16%

31

keloqz

G2

62.72%

32

Unity

Oxygen

62.02%

33

chrollo

Anubis

60.24%

34

fr0st

Anubis

60.24%

35

Minse

OBG

59.78%

36

Coffee

OBG

59.78%

37

Sp1ke

OBG

57.61%

38

zizox

Anubis

56.06%

39

hugeon

OBG

52.17%

40

Tuna

Anubis

47.06%

41

 

 

TEAM IDC


Next, we can look at team’s IDC. We can see how each team ranks in order of evenness of distribution of impact. The impact is measured using the KAST percentages shown above.

 

TEAM

TOURNAMENT IDC

RANK

Futbolist

0.01187584345

1

Liquid

0.01395908544

2

Guild

0.01764176418

3

Oxygen

0.018735363

4

SMB

0.02485207101

5

G2

0.03575989783

6

OBG

0.03837638376

7

Anubis

0.06152401169

8

 

 

While these numbers are relatively small, the differences between the numbers represent important differences in impact distribution. 


When we compare this IDC data to each team's round win percentage during the tournament we get this table.

 

TEAM

WIN %

IDC

G2

51.75%

0.03575989783

Anubis

36.14%

0.06152401169

Futbolist

48.34%

0.01187584345

Oxygen

48.06%

0.018735363

SMB

51.60%

0.02485207101

Guild

52.02%

0.01764176418

Liquid

59.09%

0.01395908544

OBG

32.61%

0.03837638376

 

From these two data sets, we get a correlation coefficient of -0.7233 that is statistically significant at the 5% level. This means that as teams IDC increases (becomes more uneven), their round win percentage decreases.


Unequal KAST in Rounds


I also studied the rounds where one team has more players providing impact than another. Out of a total of 736 rounds, one team had more players providing impact than the other in 663 rounds. Out of these 663 uneven rounds, the team had more players providing impact won 622 times, meaning that a team with more impact won 93.82% of the time.


Once again, if any of these statistics are interesting to you and you’d like to read more, you can see my full paper HERE.


Hopefully, these kinds of statistical roundups are interesting to the community and I will continue to make them for more regions


@BigTimeVAL1

 

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Player Impact and the Distribution of This Impact Across a team: Quick Summary

Player Impact and the Distribution of This Impact Across a team: Quick Summary
Image Credit: Riot Games
Written by: BigTime
-
24/10/2021

The EMEA LCQ went just as quickly as it came; it was a treat. We saw fantastic games, brilliant plays, and Team Liquid clawing their way into Champions. 


During the course of the tournament, I decided to track and calculate two stats that have not been used in VALORANT before. These are a players tournament KAST percent and a team’s Impact distribution coefficient.


KAST Percent: KAST is an acronym that stands for Kills, Assists, Survived, and Traded. A player is counted as having an impact in a round if they manage one of those 4 things. The KAST percent is then calculated by dividing the total rounds where a player had an impact by the total number of rounds played.


Impact Distribution Coefficient: This is a modified version of the Gini coefficient from economics. The calculation gives teams a value between 0 and 1, with 0 representing absolute equality in terms of impact, and 1 representing absolute inequality. Teams have been ranked 1-8 with rank 1 being the most even and rank 8 being the most uneven.


Before we dive in, this article is a quick summary of my work on this topic. For a more in-depth read on this topic, you can download the paper I have written HERE. If you are at all interested in the deeper application of these statistics or deepening your understanding of player impact at the top level of VALORANT, I highly recommend you check it out.


KAST PERCENT


Below we can see every player’s KAST percentage from the EMEA LCQ. Unsurprisingly, the players on better performing teams saw better KAST percentages.

 

PLAYER

TEAM

KAST %

RANK

Jamppi

Liquid

79.09%

1

Brave

SMB

76.06%

2

Turko

SMB

75.53%

3

Nivera

Liquid

75.45%

4

L1NK

Liquid

75.45%

5

ScreaM

Liquid

75.00%

6

Leo

Guild

74.45%

7

AvovA

G2

73.68%

8

soulcas

Liquid

72.73%

9

nukkye

G2

71.93%

10

koldamenta

G2

71.93%

11

qRaxs

Futbolist

71.56%

12

MOJJ

Futbolist

71.56%

13

qw1

Futbolist

71.09%

14

russ

SMB

70.21%

15

Izzy

SMB

70.21%

16

Sasuke

Futbolist

69.67%

17

glovee

Oxygen

68.99%

18

Avez

Anubis

68.67%

19

draken

Guild

68.54%

20

Sayf

Guild

68.22%

21

Yacine

Guild

67.60%

22

Paura

SMB

67.55%

23

m1tez

Oxygen

67.44%

24

STERBEN

Futbolist

67.30%

25

bonkar

Guild

67.29%

26

XiSTOU

Oxygen

66.67%

27

Toronto

Oxygen

65.89%

28

zeddy

OBG

65.22%

29

Shalaby

Anubis

63.86%

30

Mixwell

G2

63.16%

31

keloqz

G2

62.72%

32

Unity

Oxygen

62.02%

33

chrollo

Anubis

60.24%

34

fr0st

Anubis

60.24%

35

Minse

OBG

59.78%

36

Coffee

OBG

59.78%

37

Sp1ke

OBG

57.61%

38

zizox

Anubis

56.06%

39

hugeon

OBG

52.17%

40

Tuna

Anubis

47.06%

41

 

 

TEAM IDC


Next, we can look at team’s IDC. We can see how each team ranks in order of evenness of distribution of impact. The impact is measured using the KAST percentages shown above.

 

TEAM

TOURNAMENT IDC

RANK

Futbolist

0.01187584345

1

Liquid

0.01395908544

2

Guild

0.01764176418

3

Oxygen

0.018735363

4

SMB

0.02485207101

5

G2

0.03575989783

6

OBG

0.03837638376

7

Anubis

0.06152401169

8

 

 

While these numbers are relatively small, the differences between the numbers represent important differences in impact distribution. 


When we compare this IDC data to each team's round win percentage during the tournament we get this table.

 

TEAM

WIN %

IDC

G2

51.75%

0.03575989783

Anubis

36.14%

0.06152401169

Futbolist

48.34%

0.01187584345

Oxygen

48.06%

0.018735363

SMB

51.60%

0.02485207101

Guild

52.02%

0.01764176418

Liquid

59.09%

0.01395908544

OBG

32.61%

0.03837638376

 

From these two data sets, we get a correlation coefficient of -0.7233 that is statistically significant at the 5% level. This means that as teams IDC increases (becomes more uneven), their round win percentage decreases.


Unequal KAST in Rounds


I also studied the rounds where one team has more players providing impact than another. Out of a total of 736 rounds, one team had more players providing impact than the other in 663 rounds. Out of these 663 uneven rounds, the team had more players providing impact won 622 times, meaning that a team with more impact won 93.82% of the time.


Once again, if any of these statistics are interesting to you and you’d like to read more, you can see my full paper HERE.


Hopefully, these kinds of statistical roundups are interesting to the community and I will continue to make them for more regions


@BigTimeVAL1

 

Comments
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