Presentation In SAW 2018

1. 3rd AUEB Sports Analytics Workshop
Athens 26- 27 November 2018

2. The range of skills needed to interpret
a volleyball set result for men and
women.
Sotiris Drikos
Ph.D., AUEB Sports Analytics Group
The range of
skills needed to
interpret a
volleyball set
result for men
and women.
•Coaching
before analytics
with analytics
•Volleyball M-W
• The data
•Method
•Results
•Conclusions

3. The range of
skills needed to
interpret a
volleyball set
result for men
and women.
•Coaching
before analytics
with analytics
•Volleyball M-W
• The data
•Method
•Results
•Conclusions

4. • Coaching before analytics
• Decision making based on:
– Authority
– Coach’s instinct
– Test and error
• No accumulated data
• Analysis of a single match
• Evaluation of skills οn
positive/negative scale.
The range of
skills needed to
interpret a
volleyball set
result for men
and women.
•Coaching
before analytics
with analytics
•Volleyball M-W
• The data
•Method
•Results
•Conclusions

5. The range of
skills needed to
interpret a
volleyball set
result for men
and women.
•Coaching
before analytics
with analytics
•Volleyball M-W
• The data
•Method
•Results
•Conclusions
Early 90’s
Skills'
evaluation by
hand notation

6. The range of
skills needed to
interpret a
volleyball set
result for men
and women.
•Coaching
before analytics
with analytics
•Volleyball M-W
• The data
•Method
•Results
•Conclusions

7. The range of
skills needed to
interpret a
volleyball set
result for men
and women.
•Coaching
before analytics
with analytics
•Volleyball M-W
• The data
•Method
•Results
•Conclusions

8. The range of
skills needed to
interpret a
volleyball set
result for men
and women.
•Coaching
before analytics
with analytics
•Volleyball M-W
• The data
•Method
•Results
•Conclusions
Video
https://youtu.be/V
VAoNaz8WZk

9. The range of
skills needed to
interpret a
volleyball set
result for men
and women.
•Coaching
before analytics
with analytics
•Volleyball M-W
• The data
•Method
•Results
•Conclusions

10. • Simplify coaches’ search
• Allowing coaches to focus on a
smaller number of specific events.
• Data compression, interpretation and
prediction.
The range of
skills needed to
interpret a
volleyball set
result for men
and women.
•Coaching
before analytics
with analytics
•Volleyball M-W
• The data
•Method
•Results
•Conclusions

11. • Men’s and Women’s Volleyball.
• Similarities and differences.
– Skills
– Structure of the game
– Court’s dimensions
• Same structure of the game means
same important skills?
The range of
skills needed to
interpret a
volleyball set
result for men
and women.
•Coaching
before analytics
with analytics
•Volleyball M-W
• The data
•Method
•Results
•Conclusions Women
Video
https://youtu.be/8Y-xkIVud8o
Women
Video
https://youtu.be/9H6RXKz0SYA

12. • Comparison of tactical models and
performance indicators between
winning and losing teams.
– Difficulties
• Teams of different levels
• Sets with big score difference
• A team plays as well as they need to
win a specific opponent.
• Great score differences and teams
of different levels may bring bias in
our study.
The range of
skills needed to
interpret a
volleyball set
result for men
and women.
•Coaching
before analytics
with analytics
•Volleyball M-W
• The data
•Method
•Results
•Conclusions

13. The range of
skills needed to
interpret a
volleyball set
result for men
and women.
•Coaching
before analytics
with analytics
•Volleyball M-W
• The data
•Method
•Results
•Conclusions
• In order to highlight major skills of the
game, we select matches between
closely ranked teams (e.g. top teams
of a league) and sets with small
score difference (e.g. <5 points).
• A part of research has already been published

14. Performance data from the top 4 teams of R.S. in Greek
Men’s and Women’s Volleyleague from 2013-14 until
2017-18.
Primary recorded data
Men Women
Seasons 5
Matches 60 60
Sets 244 219
Serves 10.808 9.592
Passes 9.161 8.478
Attack 1 7.955 6.548
Attack 2 4.678 6.880
Block 5.027 3.402
Total 37.629 34.900
The range of
skills needed to
interpret a
volleyball set
result for men
and women.
•Coaching
before analytics
with analytics
•Volleyball M-W
• The data
•Method
•Results
•Conclusions

15. 12 variables
•Serve: Win, Lost (Swin, Serr)
•Attack 1: Win, errors & blocked (A1win, A1err,
A1blk)
•Attack 2: Win, errors & blocked (A2win, A2err,
A2blk)
•Pass: Precise =(Excellent + good), errors
(Pprecise, Perr)
•Block: kills/total points (Block)
•Opponents' unforced errors/ total points
(OppErr)
The range of
skills needed to
interpret a
volleyball set
result for men
and women.
•Coaching
before analytics
with analytics
•Volleyball M-W
• The data
•Method
•Results
•Conclusions

16. Skill/
Level
Serve Attack 1,2 Block Pass
6 Ace
(point)
Win-kill
(point)
Win-kill
(point)
Excellent pass. All
options for attack
without adjustments
for the setter
5
Over
The ball to the serving
team
The ball to the attacking
team with good conditions
or to the defending team
with bad conditions
The ball to the blocking
team with good conditions
or to the attacking team
with bad conditions
Good Pass.
All options for attack
4 One option for attack
for the receiving team
The ball to the attacking or
defending
team with medium
conditions
The ball to the blocking or
attacking
team with medium
conditions
Two options for attack
from the sidelines
3 Two options for attack
for the receiving team
The ball to the attacking
team with bad conditions or
to the defending team with
good conditions
The ball to the blocking
team with bad conditions or
to the attacking team with
good conditions
One option for attack
or attack out of the
system
2
All options for the
attack on the receiving
team
Stuffed by a Kill block
(lost point)
Error on the net
(Incorrect touch of the net,
lost point)
Overpass
The ball was passed
directly to the serving
team court.
1 Error
(lost point)
Error
(lost point)
Error
(lost point)
Error
(lost point)
The range of
skills needed to
interpret a
volleyball set
result for men
and women.
•Coaching
before analytics
with analytics
•Volleyball M-W
• The data
•Method
•Results
•Conclusions
Evaluation scale consists of 6 levels.

17. • Reliability of the data collection and entry was
checked in 20% of the sample with a test-retest
procedure with a 2-week interval by an expert in
evaluation and recording of volleyball performance
skills and as accepted value of Adjusted Κ Cohen was
set .80.
• Per skill Adjusted Κ Cohen: for Serve was .83, for
Attack1 was .89, for attack 2 was .88, for block was .83
and for pass was. 82.
The range of
skills needed to
interpret a
volleyball set
result for men
and women.
•Coaching
before analytics
with analytics
•Volleyball M-W
• The data
•Method
•Results
•Conclusions

18. • Set categorization was accomplished through k-means
cluster analysis.
The range of
skills needed to
interpret a
volleyball set
result for men
and women.
•Coaching
before analytics
with analytics
•Volleyball M-W
• The data
•Method
•Results
•Conclusions
Sets Clustering
Men Women
Balanced 2-3 points 2-3 points
Semi- balanced 4-7 points 4-7 points
Unbalanced ≥8 points ≥8 points
Ambivalent Minimum difference (2 points)

19. • Basic statistical assumptions were tested and met
• No multicollinearity between variables.
Correlations were all <|.5|.
• Μ.ΑN.Ο.VA. 2(set outcomes)X3(set types) and
discriminant analysis.
• Aim is to determine:
• differences among types of sets, and types of
result and their interaction
• Skills which classify the data successfully.
The range of
skills needed to
interpret a
volleyball set
result for men
and women.
•Coaching
before analytics
with analytics
•Volleyball M-W
• The data
•Method
•Results
•Conclusions

20. Sets Clustering
The range of
skills needed to
interpret a
volleyball set
result for men
and women.
•Coaching
before analytics
with analytics
•Volleyball M-W
• The data
•Method
•Results
•Conclusions

21. Sets Clustering
The range of
skills needed to
interpret a
volleyball set
result for men
and women.
•Coaching
before analytics
with analytics
•Volleyball M-W
• The data
•Method
•Results
•Conclusions

22. MANOVA
The range of
skills needed to
interpret a
volleyball set
result for men
and women.
•Coaching
before analytics
with analytics
•Volleyball M-W
• The data
•Method
•Results
•Conclusions
Multivariate Tests(c)
Effect Value F Hypothesis df Error df Sig.
Partial Eta
Squared
Intercept Pillai's Trace
,996
27601,687
(a)
4,000 429,000 ,000 ,996
Wilks' Lambda
,004
27601,687
(a)
4,000 429,000 ,000 ,996
Hotelling's Trace
257,358
27601,687
(a)
4,000 429,000 ,000 ,996
Roy's Largest Root
257,358
27601,687
(a)
4,000 429,000 ,000 ,996
Typeofresult Pillai's Trace ,830 522,736(a) 4,000 429,000 ,000 ,830
Wilks' Lambda ,170 522,736(a) 4,000 429,000 ,000 ,830
Hotelling's Trace 4,874 522,736(a) 4,000 429,000 ,000 ,830
Roy's Largest Root 4,874 522,736(a) 4,000 429,000 ,000 ,830
Type_of_set Pillai's Trace ,015 ,790 8,000 860,000 ,612 ,007
Wilks' Lambda ,985 ,789(a) 8,000 858,000 ,612 ,007
Hotelling's Trace ,015 ,788 8,000 856,000 ,613 ,007
Roy's Largest Root ,012 1,318(b) 4,000 430,000 ,263 ,012
Typeofresult *
Type_of_set
Pillai's Trace ,640 50,616 8,000 860,000 ,000 ,320
Wilks' Lambda ,368 69,518(a) 8,000 858,000 ,000 ,393
Hotelling's Trace 1,694 90,619 8,000 856,000 ,000 ,459
Roy's Largest Root
1,680 180,634(b) 4,000 430,000 ,000 ,627
a Exact statistic
b The statistic is an upper bound on F that yields a lower bound on the significance level.
c Design: Intercept+Typeofresult+Type_of_set+Typeofresult * Type_of_set
WOMEN

23. MANOVA
The range of
skills needed to
interpret a
volleyball set
result for men
and women.
•Coaching
before analytics
with analytics
•Volleyball M-W
• The data
•Method
•Results
•Conclusions
MEN
Multivariate Tests(c)
Effect Value F Hypothesis df Error df Sig.
Partial Eta
Squared
Intercept Pillai's Trace
,996
27601,687
(a)
4,000 429,000 ,000 ,996
Wilks' Lambda
,004
27601,687
(a)
4,000 429,000 ,000 ,996
Hotelling's Trace
257,358
27601,687
(a)
4,000 429,000 ,000 ,996
Roy's Largest Root
257,358
27601,687
(a)
4,000 429,000 ,000 ,996
Typeofresult Pillai's Trace ,830 522,736(a) 4,000 429,000 ,000 ,830
Wilks' Lambda ,170 522,736(a) 4,000 429,000 ,000 ,830
Hotelling's Trace 4,874 522,736(a) 4,000 429,000 ,000 ,830
Roy's Largest Root 4,874 522,736(a) 4,000 429,000 ,000 ,830
Type_of_set Pillai's Trace ,015 ,790 8,000 860,000 ,612 ,007
Wilks' Lambda ,985 ,789(a) 8,000 858,000 ,612 ,007
Hotelling's Trace ,015 ,788 8,000 856,000 ,613 ,007
Roy's Largest Root ,012 1,318(b) 4,000 430,000 ,263 ,012
Typeofresult *
Type_of_set
Pillai's Trace ,640 50,616 8,000 860,000 ,000 ,320
Wilks' Lambda ,368 69,518(a) 8,000 858,000 ,000 ,393
Hotelling's Trace 1,694 90,619 8,000 856,000 ,000 ,459
Roy's Largest Root
1,680 180,634(b) 4,000 430,000 ,000 ,627
a Exact statistic
b The statistic is an upper bound on F that yields a lower bound on the significance level.
c Design: Intercept+Typeofresult+Type_of_set+Typeofresult * Type_of_set

24. **
***
The range of
skills needed to
interpret a
volleyball set
result for men
and women.
•Coaching
before analytics
with analytics
•Volleyball M-W
• The data
•Method
•Results
•Conclusions *** p<.001
** p<.01
* p<.05

25. **
**
The range of
skills needed to
interpret a
volleyball set
result for men
and women.
•Coaching
before analytics
with analytics
•Volleyball M-W
• The data
•Method
•Results
•Conclusions *** p<.001
** p<.01
* p<.05

26. ***
***
***
***
***
*** *
*
*
The range of
skills needed to
interpret a
volleyball set
result for men
and women.
•Coaching
before analytics
with analytics
•Volleyball M-W
• The data
•Method
•Results
•Conclusions
*
*** p<.001
** p<.01
* p<.05

27. ***
***
***
***
**
***
**
***
***
***
The range of
skills needed to
interpret a
volleyball set
result for men
and women.
•Coaching
before analytics
with analytics
•Volleyball M-W
• The data
•Method
•Results
•Conclusions *** p<.001
** p<.01
* p<.05

28. *
*
The range of
skills needed to
interpret a
volleyball set
result for men
and women.
•Coaching
before analytics
with analytics
•Volleyball M-W
• The data
•Method
•Results
•Conclusions *** p<.001
** p<.01
* p<.05

29. **
**
The range of
skills needed to
interpret a
volleyball set
result for men
and women.
•Coaching
before analytics
with analytics
•Volleyball M-W
• The data
•Method
•Results
•Conclusions *** p<.001
** p<.01
* p<.05

30. ***
*** **
*
*
* *
The range of
skills needed to
interpret a
volleyball set
result for men
and women.
•Coaching
before analytics
with analytics
•Volleyball M-W
• The data
•Method
•Results
•Conclusions
*
*** p<.001
** p<.01
* p<.05

31. ***
***
***
***
***
***
***
*
***
*
The range of
skills needed to
interpret a
volleyball set
result for men
and women.
•Coaching
before analytics
with analytics
•Volleyball M-W
• The data
•Method
•Results
•Conclusions
***
*** p<.001
** p<.01
* p<.05

32. Discriminant Analysis
Structure coefficients >|.3|
Ambivalent Balanced Semi-
balanced
Unbalanced
MEN
A1win A1win A1win A1win
A1err
Classification
results
72% 74% 90,4% 98,8%
Women
A1win A1win A1win A1win
A2win A2win A2win A2win
Opperr A1blk
Classification
results
67,3% 66,4% 86% 96,1%
The range of
skills needed to
interpret a
volleyball set
result for men
and women.
•Coaching
before analytics
with analytics
•Volleyball M-W
• The data
•Method
•Results
•Conclusions

33. The range of
skills needed to
interpret a
volleyball set
result for men
and women.
•Coaching
before analytics
with analytics
•Volleyball M-W
• The data
•Method
•Results
•Conclusions
WOMEN

34. The range of
skills needed to
interpret a
volleyball set
result for men
and women.
•Coaching
before analytics
with analytics
•Volleyball M-W
• The data
•Method
•Results
•Conclusions
WOMEN

35. The range of
skills needed to
interpret a
volleyball set
result for men
and women.
•Coaching
before analytics
with analytics
•Volleyball M-W
• The data
•Method
•Results
•Conclusions
WOMEN

36. The range of
skills needed to
interpret a
volleyball set
result for men
and women.
•Coaching
before analytics
with analytics
•Volleyball M-W
• The data
•Method
•Results
•Conclusions
WOMEN

37. The range of
skills needed to
interpret a
volleyball set
result for men
and women.
•Coaching
before analytics
with analytics
•Volleyball M-W
• The data
•Method
•Results
•Conclusions
WOMEN

38. The range of
skills needed to
interpret a
volleyball set
result for men
and women.
•Coaching
before analytics
with analytics
•Volleyball M-W
• The data
•Method
•Results
•Conclusions
MEN

39. The range of
skills needed to
interpret a
volleyball set
result for men
and women.
•Coaching
before analytics
with analytics
•Volleyball M-W
• The data
•Method
•Results
•Conclusions
MEN

40. The range of
skills needed to
interpret a
volleyball set
result for men
and women.
•Coaching
before analytics
with analytics
•Volleyball M-W
• The data
•Method
•Results
•Conclusions
MEN

41. The range of
skills needed to
interpret a
volleyball set
result for men
and women.
•Coaching
before analytics
with analytics
•Volleyball M-W
• The data
•Method
•Results
•Conclusions
MEN

42. The range of
skills needed to
interpret a
volleyball set
result for men
and women.
•Coaching
before analytics
with analytics
•Volleyball M-W
• The data
•Method
•Results
•Conclusions
MEN

43. The range of
skills needed to
interpret a
volleyball set
result for men
and women.
•Coaching
before analytics
with analytics
•Volleyball M-W
• The data
•Method
•Results
•Conclusions
For Women and Men: In a typical Vb set, as the
score difference gets smaller, the range of critical
factors that differ significantly statistically
between winning and losing teams gets narrow.
For Men and Women: In a typical Vb set, as the
score difference gets smaller, the % of correct
classification gets smaller, too.

44. The range of
skills needed to
interpret a
volleyball set
result for men
and women.
•Coaching
before analytics
with analytics
•Volleyball M-W
• The data
•Method
•Results
•Conclusions
For Women: Effectiveness of attack 1 and attack
2 are the best discriminant factors between
winning and losing for all types of sets.
Successful attack 1and 2 can predict the 81% of
variance for the type of result for a typical
volleyball set.
For Men: Attack after pass (Attack 1) is the best
discriminant factor between winning and losing
teams for all types of set. The correct
classification reaches 84% for a typical
volleyball set.

45. The range of
skills needed to
interpret a
volleyball set
result for men
and women.
•Coaching
before analytics
with analytics
•Volleyball M-W
• The data
•Method
•Results
•Conclusions
The interpretation of the result
in a typical volleyball set and in all types of sets
(ambivalent, balanced, semi-balanced,
unbalanced) is easier in men’s Volleyball than
women’s, even with fewer important skills in the
equation.

46. Thank you for your
attention!
The range of
skills needed to
interpret a
volleyball set
result for men
and women.
•Coaching
before analytics
with analytics
•Volleyball M-W
• The data
•Method
•Results
•Conclusions
•The End