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The range of skills needed to interpret a volleyball set result for men and women 2

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The range of skills needed to interpret a volleyball set result for men and women

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The range of skills needed to interpret a volleyball set result for men and women 2

  1. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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
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  24. 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. 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. 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. 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. 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. 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. 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. 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. 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

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