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

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

  1. 1. 3rd AUEB Sports Analytics Workshop Athens 26- 27 November 2018
  2. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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

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