The document analyzes the range of skills needed to interpret volleyball set results for men and women. Performance data from top teams in Greek men's and women's volleyball leagues from 2013-2018 was collected. A 6-level scale was used to evaluate 12 variables for serves, attacks, blocks, and passes. Statistical analysis via MANOVA and discriminant analysis found differences in skills between winning/losing sets and set types for both men and women. Key discriminating skills were attacks winning points for most set types of both genders.
Complex 1 in male volleyball as a marcov chainSotiris Drikos
This document analyzes volleyball skills as a Markov chain. It examines passing and attacking patterns of three world champion volleyball teams at different age levels. The analysis found that:
1) Passing success rates did not consistently increase with higher passing ratings, suggesting issues with the rating scale.
2) Quick attacks were generally more important than slow attacks, though attacking certain zones was more important for different age groups.
3) Two conclusions about passing targets and setter attacking contradicted each other, requiring further examination.
Week3lecturemath221 120117081125-phpapp02Toni Gordon
This document provides a review for a week 3 quiz in a math 221 class. It covers topics like data collection, descriptive and inferential statistics, qualitative and quantitative data, different sampling methods, populations and samples, standard deviation and variance, regression equations, correlation coefficients, and how to create and interpret stem-and-leaf plots. Examples are provided for regression equations, using a multiple regression equation, and calculating relative frequencies from a stem-and-leaf plot. Students are encouraged to use online resources provided by the instructor on Facebook to further review these topics.
If you happen to like this powerpoint, you may contact me at flippedchannel@gmail.com
I offer some educational services like:
-powerpoint presentation maker
-grammarian
-content creator
-layout designer
Subscribe to our online platforms:
FlippED Channel (Youtube)
http://bit.ly/FlippEDChannel
LET in the NET (facebook)
http://bit.ly/LETndNET
This document discusses various topics related to tennis training and analysis, including serves, aerobic training, stabilization exercises, and analyzing length of rallies. It provides data on first serve percentages and success rates from different tournaments. It also examines which players tend to win longer rallies and discusses how work to rest ratios can vary depending on serving ability and surface speeds. Implications for training focus on developing tennis-specific endurance and stabilization.
Data mining and machine learning techniques like classification and clustering are increasingly being used to extract useful information from large datasets. Data mining helps provide better customer service and aids scientists in hypothesis formation by analyzing patterns in data from various sources like business transactions, sensor networks, and scientific experiments. Classification algorithms such as decision trees can be applied to datasets containing attributes for individuals and a target variable to predict, like credit worthiness, to build a predictive model. Clustering algorithms like K-means group unlabeled data into clusters without a predefined target variable to discover hidden patterns in the data.
Slides to accompany the Performance Prediction for Thoroughbred Racehorses by Jeff Seder, Founder & CEO of EQB.com
This presentation was delivered as the opening keynote at the inaugural HorseTech Conference on the 18th October 2017 hosted by the Royal Veterinary College London.
This document outlines a performer's testing and training plan to improve two priority areas over eight weeks. The performer identified weaknesses in power, speed, timing of runs, and strength through standardized and bespoke tests. Power and speed were selected as the two priority areas for improvement. The training plan sets measurable targets to increase the performer's vertical jump by 6cm and decrease their sprint time by 0.19 seconds. Justifications are provided explaining how improving power and speed will benefit the performer's selection opportunities and reduce injury risk, and help their team's attack.
This study examined the effects of a visual hitting system called Right View Pro (RVP) on bat velocity and batted-ball exit velocity in collegiate baseball players. 29 players were divided into 3 groups: weight training only, weight training with video analysis, weight training with video analysis and RVP analysis. Bat velocity and exit velocity were measured before and after. The results showed no significant differences in bat velocity or exit velocity between the groups. While RVP did not directly increase speed, the study concluded it may still provide indirect benefits to a hitter's mechanics. Further research is recommended using wood bats and examining RVP in conjunction with proven batting techniques.
Complex 1 in male volleyball as a marcov chainSotiris Drikos
This document analyzes volleyball skills as a Markov chain. It examines passing and attacking patterns of three world champion volleyball teams at different age levels. The analysis found that:
1) Passing success rates did not consistently increase with higher passing ratings, suggesting issues with the rating scale.
2) Quick attacks were generally more important than slow attacks, though attacking certain zones was more important for different age groups.
3) Two conclusions about passing targets and setter attacking contradicted each other, requiring further examination.
Week3lecturemath221 120117081125-phpapp02Toni Gordon
This document provides a review for a week 3 quiz in a math 221 class. It covers topics like data collection, descriptive and inferential statistics, qualitative and quantitative data, different sampling methods, populations and samples, standard deviation and variance, regression equations, correlation coefficients, and how to create and interpret stem-and-leaf plots. Examples are provided for regression equations, using a multiple regression equation, and calculating relative frequencies from a stem-and-leaf plot. Students are encouraged to use online resources provided by the instructor on Facebook to further review these topics.
If you happen to like this powerpoint, you may contact me at flippedchannel@gmail.com
I offer some educational services like:
-powerpoint presentation maker
-grammarian
-content creator
-layout designer
Subscribe to our online platforms:
FlippED Channel (Youtube)
http://bit.ly/FlippEDChannel
LET in the NET (facebook)
http://bit.ly/LETndNET
This document discusses various topics related to tennis training and analysis, including serves, aerobic training, stabilization exercises, and analyzing length of rallies. It provides data on first serve percentages and success rates from different tournaments. It also examines which players tend to win longer rallies and discusses how work to rest ratios can vary depending on serving ability and surface speeds. Implications for training focus on developing tennis-specific endurance and stabilization.
Data mining and machine learning techniques like classification and clustering are increasingly being used to extract useful information from large datasets. Data mining helps provide better customer service and aids scientists in hypothesis formation by analyzing patterns in data from various sources like business transactions, sensor networks, and scientific experiments. Classification algorithms such as decision trees can be applied to datasets containing attributes for individuals and a target variable to predict, like credit worthiness, to build a predictive model. Clustering algorithms like K-means group unlabeled data into clusters without a predefined target variable to discover hidden patterns in the data.
Slides to accompany the Performance Prediction for Thoroughbred Racehorses by Jeff Seder, Founder & CEO of EQB.com
This presentation was delivered as the opening keynote at the inaugural HorseTech Conference on the 18th October 2017 hosted by the Royal Veterinary College London.
This document outlines a performer's testing and training plan to improve two priority areas over eight weeks. The performer identified weaknesses in power, speed, timing of runs, and strength through standardized and bespoke tests. Power and speed were selected as the two priority areas for improvement. The training plan sets measurable targets to increase the performer's vertical jump by 6cm and decrease their sprint time by 0.19 seconds. Justifications are provided explaining how improving power and speed will benefit the performer's selection opportunities and reduce injury risk, and help their team's attack.
This study examined the effects of a visual hitting system called Right View Pro (RVP) on bat velocity and batted-ball exit velocity in collegiate baseball players. 29 players were divided into 3 groups: weight training only, weight training with video analysis, weight training with video analysis and RVP analysis. Bat velocity and exit velocity were measured before and after. The results showed no significant differences in bat velocity or exit velocity between the groups. While RVP did not directly increase speed, the study concluded it may still provide indirect benefits to a hitter's mechanics. Further research is recommended using wood bats and examining RVP in conjunction with proven batting techniques.
Analysis of different parameters in game of cricketShrawan Arya
The document analyzes different parameters in cricket through statistical analysis using Microsoft Excel and SPSS. It contains the introduction, objectives, literature review, and four hypotheses to compare different cricket parameters. The first hypothesis analyzes the relationship between a player's average strike rate in ODI and Test matches. Statistical tests like t-test and F-test are applied on data of two players, Virender Sehwag and Sachin Tendulkar, to compare their strike rates in the two formats. The results show Sehwag's average strike rate is higher but more varied in ODIs, while Tendulkar's data is still being analyzed.
Class imbalance problems frequently occur in real-world tasks, and conventional deep learning algorithms are well known for performance degradation on imbalanced training datasets. To mitigate this problem, many approaches have aimed to balance among given classes by re-weighting or re-sampling training samples. These re-balancing methods increase the impact of minority classes and reduce the influence of majority classes on the output of models. However, the extracted representations may be of poor quality owing to the limited number of minority samples. To handle this restriction, several methods have been developed that increase the representations of minority samples by leveraging the features of the majority samples. Despite extensive recent studies, no deep analysis has been conducted on determination of classes to be augmented and strength of augmentation has been conducted. In this study, we first investigate the correlation between the degree of augmentation and class-wise performance, and find that the proper degree of augmentation must be allocated for each class to mitigate class imbalance problems. Motivated by this finding, we propose a simple and efficient novel curriculum, which is designed to find the appropriate per-class strength of data augmentation, called CUDA: CUrriculum of Data Augmentation for long-tailed recognition. CUDA can simply be integrated into existing long-tailed recognition methods. We present the results of experiments showing that CUDA effectively achieves better generalization performance compared to the state-of-the-art method on various imbalanced datasets such as CIFAR-100-LT, ImageNet-LT, and iNaturalist 2018.
The document provides a review of key concepts for a statistics final exam, including how to calculate regression equations and lines, probabilities using normal and binomial distributions, hypothesis testing, and other statistical analyses. It includes examples of problems and questions that may appear on the exam.
This document provides an introduction to biostatistics and descriptive statistics concepts. It defines key terms like data, variables, populations, samples, and measurement scales. It also discusses measures of central tendency like mean, median and mode. Measures of dispersion such as range, variance, standard deviation and coefficient of variation are introduced. Finally, the document discusses frequency distributions, histograms, percentiles, quartiles, and box plots as ways to summarize and visualize data distributions. Examples are provided throughout to illustrate statistical concepts.
The document provides information about an upcoming Week 3 Quiz in statistics. It defines key terms like descriptive vs inferential statistics, levels of measurement, and differences between sample and population. It also gives examples of questions that may appear on the quiz related to representing and analyzing data through tables, charts, measures of center and variation. Additional resources are provided on descriptive vs inferential statistics. The document emphasizes understanding differences between common statistical concepts before taking the quiz.
Enhanced data collection methods can help uncover the true extent of child abuse and neglect. This includes Integrated Data Systems from various sources (e.g., schools, healthcare providers, social services) to identify patterns and potential cases of abuse and neglect.
Discovering Digital Process Twins for What-if Analysis: a Process Mining Appr...Marlon Dumas
This webinar discusses the limitations of traditional approaches for business process simulation based on had-crafted model with restrictive assumptions. It shows how process mining techniques can be assembled together to discover high-fidelity digital twins of end-to-end processes from event data.
We are pleased to share with you the latest VCOSA statistical report on the cotton and yarn industry for the month of May 2024.
Starting from January 2024, the full weekly and monthly reports will only be available for free to VCOSA members. To access the complete weekly report with figures, charts, and detailed analysis of the cotton fiber market in the past week, interested parties are kindly requested to contact VCOSA to subscribe to the newsletter.
Analysis of different parameters in game of cricketShrawan Arya
The document analyzes different parameters in cricket through statistical analysis using Microsoft Excel and SPSS. It contains the introduction, objectives, literature review, and four hypotheses to compare different cricket parameters. The first hypothesis analyzes the relationship between a player's average strike rate in ODI and Test matches. Statistical tests like t-test and F-test are applied on data of two players, Virender Sehwag and Sachin Tendulkar, to compare their strike rates in the two formats. The results show Sehwag's average strike rate is higher but more varied in ODIs, while Tendulkar's data is still being analyzed.
Class imbalance problems frequently occur in real-world tasks, and conventional deep learning algorithms are well known for performance degradation on imbalanced training datasets. To mitigate this problem, many approaches have aimed to balance among given classes by re-weighting or re-sampling training samples. These re-balancing methods increase the impact of minority classes and reduce the influence of majority classes on the output of models. However, the extracted representations may be of poor quality owing to the limited number of minority samples. To handle this restriction, several methods have been developed that increase the representations of minority samples by leveraging the features of the majority samples. Despite extensive recent studies, no deep analysis has been conducted on determination of classes to be augmented and strength of augmentation has been conducted. In this study, we first investigate the correlation between the degree of augmentation and class-wise performance, and find that the proper degree of augmentation must be allocated for each class to mitigate class imbalance problems. Motivated by this finding, we propose a simple and efficient novel curriculum, which is designed to find the appropriate per-class strength of data augmentation, called CUDA: CUrriculum of Data Augmentation for long-tailed recognition. CUDA can simply be integrated into existing long-tailed recognition methods. We present the results of experiments showing that CUDA effectively achieves better generalization performance compared to the state-of-the-art method on various imbalanced datasets such as CIFAR-100-LT, ImageNet-LT, and iNaturalist 2018.
The document provides a review of key concepts for a statistics final exam, including how to calculate regression equations and lines, probabilities using normal and binomial distributions, hypothesis testing, and other statistical analyses. It includes examples of problems and questions that may appear on the exam.
This document provides an introduction to biostatistics and descriptive statistics concepts. It defines key terms like data, variables, populations, samples, and measurement scales. It also discusses measures of central tendency like mean, median and mode. Measures of dispersion such as range, variance, standard deviation and coefficient of variation are introduced. Finally, the document discusses frequency distributions, histograms, percentiles, quartiles, and box plots as ways to summarize and visualize data distributions. Examples are provided throughout to illustrate statistical concepts.
The document provides information about an upcoming Week 3 Quiz in statistics. It defines key terms like descriptive vs inferential statistics, levels of measurement, and differences between sample and population. It also gives examples of questions that may appear on the quiz related to representing and analyzing data through tables, charts, measures of center and variation. Additional resources are provided on descriptive vs inferential statistics. The document emphasizes understanding differences between common statistical concepts before taking the quiz.
Enhanced data collection methods can help uncover the true extent of child abuse and neglect. This includes Integrated Data Systems from various sources (e.g., schools, healthcare providers, social services) to identify patterns and potential cases of abuse and neglect.
Discovering Digital Process Twins for What-if Analysis: a Process Mining Appr...Marlon Dumas
This webinar discusses the limitations of traditional approaches for business process simulation based on had-crafted model with restrictive assumptions. It shows how process mining techniques can be assembled together to discover high-fidelity digital twins of end-to-end processes from event data.
We are pleased to share with you the latest VCOSA statistical report on the cotton and yarn industry for the month of May 2024.
Starting from January 2024, the full weekly and monthly reports will only be available for free to VCOSA members. To access the complete weekly report with figures, charts, and detailed analysis of the cotton fiber market in the past week, interested parties are kindly requested to contact VCOSA to subscribe to the newsletter.
Overview IFM June 2024 Consumer Confidence INDEX Report.pdf
The range of skills needed to interpret a volleyball set result for men and women 2
1. The range of skills needed to interpret
a volleyball set result for men and
women.
Sotiris Drikos1, Leonidas Karaiskos, Vasileios Manasis1
1 AUEB Sports Analytics Group
The range of
skills needed to
interpret a
volleyball set
result for men
and women.
•Volleyball M-W
• The data
•Method
•Results
•Conclusions
2. • 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.
•Volleyball M-W
• The data
•Method
•Results
•Conclusions
3. • 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.
•Volleyball M-W
• The data
•Method
•Results
•Conclusions
4. The range of
skills needed to
interpret a
volleyball set
result for men
and women.
•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).
5. 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.
•Volleyball M-W
• The data
•Method
•Results
•Conclusions
6. Evaluation scale consists of 6 levels.
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.
•Volleyball M-W
• The data
•Method
•Results
•Conclusions
7. 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.
•Volleyball M-W
• The data
•Method
•Results
•Conclusions
8. • 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.
•Volleyball M-W
• The data
•Method
•Results
•Conclusions
9. • Set categorization was accomplished through k-means
cluster analysis.
The range of
skills needed to
interpret a
volleyball set
result for men
and women.
•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)
10. • 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.
•Volleyball M-W
• The data
•Method
•Results
•Conclusions
11. Sets Clustering
The range of
skills needed to
interpret a
volleyball set
result for men
and women.
•Volleyball M-W
• The data
•Method
•Results
•Conclusions
12. Sets Clustering
The range of
skills needed to
interpret a
volleyball set
result for men
and women.
•Volleyball M-W
• The data
•Method
•Results
•Conclusions
70 49
13. MANOVA
The range of
skills needed to
interpret a
volleyball set
result for men
and women.
•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
14. Multivariate Tests(c)
Effect Value F Hypothesis df Error df Sig.
Partial Eta
Squared
Intercept Pillai's Trace ,992 4651,633(a) 12,000 471,000 ,000 ,992
Wilks' Lambda ,008 4651,633(a) 12,000 471,000 ,000 ,992
Hotelling's Trace 118,513 4651,633(a) 12,000 471,000 ,000 ,992
Roy's Largest Root 118,513 4651,633(a) 12,000 471,000 ,000 ,992
Typeofresult Pillai's Trace ,667 78,706(a) 12,000 471,000 ,000 ,667
Wilks' Lambda ,333 78,706(a) 12,000 471,000 ,000 ,667
Hotelling's Trace 2,005 78,706(a) 12,000 471,000 ,000 ,667
Roy's Largest Root 2,005 78,706(a) 12,000 471,000 ,000 ,667
Typeofset Pillai's Trace ,038 ,768 24,000 944,000 ,779 ,019
Wilks' Lambda ,962 ,767(a) 24,000 942,000 ,781 ,019
Hotelling's Trace ,039 ,765 24,000 940,000 ,783 ,019
Roy's Largest Root ,022 ,875(b) 12,000 472,000 ,572 ,022
Typeofresult * Typeofset Pillai's Trace ,401 9,864 24,000 944,000 ,000 ,201
Wilks' Lambda ,604 11,252(a) 24,000 942,000 ,000 ,223
Hotelling's Trace ,647 12,673 24,000 940,000 ,000 ,244
Roy's Largest Root ,634 24,935(b) 12,000 472,000 ,000 ,388
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+Typeofset+Typeofresult * Typeofset
MANOVA
The range of
skills needed to
interpret a
volleyball set
result for men
and women.
•Volleyball M-W
• The data
•Method
•Results
•Conclusions
MEN
15. **
***
The range of
skills needed to
interpret a
volleyball set
result for men
and women.
•Volleyball M-W
• The data
•Method
•Results
•Conclusions
*** p<.001
** p<.01
* p<.05
16. **
**
The range of
skills needed to
interpret a
volleyball set
result for men
and women.
•Volleyball M-W
• The data
•Method
•Results
•Conclusions
*** p<.001
** p<.01
* p<.05
18. ***
***
***
***
***
*** *
*
*
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
20. *
*
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
21. **
**
The range of
skills needed to
interpret a
volleyball set
result for men
and women.
•Volleyball M-W
• The data
•Method
•Results
•Conclusions
*** p<.001
** p<.01
* p<.05
22. ***
*** **
*
*
* *
The range of
skills needed to
interpret a
volleyball set
result for men
and women.
•Volleyball M-W
• The data
•Method
•Results
•Conclusions
*
*** p<.001
** p<.01
* p<.05
24. 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.
•Volleyball M-W
• The data
•Method
•Results
•Conclusions
25. The range of
skills needed to
interpret a
volleyball set
result for men
and women.
•Volleyball M-W
• The data
•Method
•Results
•Conclusions
WOMEN
26. The range of
skills needed to
interpret a
volleyball set
result for men
and women.
•Volleyball M-W
• The data
•Method
•Results
•Conclusions
MEN
27. The range of
skills needed to
interpret a
volleyball set
result for men
and women.
•Volleyball M-W
• The data
•Method
•Results
•Conclusions
WOMENMEN
28. The range of
skills needed to
interpret a
volleyball set
result for men
and women.
•Volleyball M-W
• The data
•Method
•Results
•Conclusions
WOMENMEN
29. The range of
skills needed to
interpret a
volleyball set
result for men
and women.
•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.
30. The range of
skills needed to
interpret a
volleyball set
result for men
and women.
•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 1 and 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.
31. The range of
skills needed to
interpret a
volleyball set
result for men
and women.
•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.
32. Thank you for your
attention!
Αt your disposal for clarifications or questions.
The range of
skills needed to
interpret a
volleyball set
result for men
and women.
•Volleyball M-W
• The data
•Method
•Results
•Conclusions
•The End