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Performance analysis in sport contests

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Presenation in MSc programme "Athletic management" of Pelloponese Univers

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Performance analysis in sport contests

  1. 1. Sports Analytics Workshop Sotirios Drikos sdrikos@gmail.com www: sdrikos.com www: aueb-analytics.wixsite.com/sports
  2. 2. Performance Analysis in Sport contests.
  3. 3. 1901 1921 1924 1925 1928 1928 1931 1935 1960 1961 1961 1962 1964 1965 1967 Performance in mt 7.61 7.69 7.76 7.89 7.9 7.93 7.98 8.13 8.21 8.24 8.28 8.31 8.31 8.34 8.35 y = (0,0116 m/year)x + 7,61 R² = 0,9504 7.5 7.75 8 8.25 8.5 8.75 9 8,90 2012 WR in Long Jump until 1967
  4. 4. 1901 1921 1924 1925 1928 1928 1931 1935 1960 1961 1961 1962 1964 1965 1967 1968 1991 Performance in mt 7.61 7.69 7.76 7.89 7.9 7.93 7.98 8.13 8.21 8.24 8.28 8.31 8.31 8.34 8.35 8.9 8.95 y = (0,0116m/year)x + 7,6191 7.5 7.75 8 8.25 8.5 8.75 9 8,38 1967 WR in Long Jump
  5. 5. Contents • Sports Performance Analysis • Performance Indicators • Categorization of sport contests • Modeling of Sports Contests • Skill importance in Volleyball. List of Contents •Performance Analysis •Performance Indicators •Categorization of Sport contests •Modeling of sport contests •Skill importance in Men’s Volleyball
  6. 6. • SPA is the investigation of actual sports performance or performance in training. • Main reason for SPA is to develop an understanding of sports that can inform decision making  to enhance sport performance  to improve coaching • Also non coaching uses of SPA, e.g. media or judging. Performance Analysis of Sports •Concept •Aims •Procedure •Indicators
  7. 7. Athletes perform Performance analyzed Past results accounted for Coach plans practice Coach conducts practice Coach Observes Performance Analysis of Sports •Concept •Aims •Procedure •Indicators Coaching process
  8. 8. • Technical Evaluation • Tactical Evaluation • Analysis of movement • Coach & player education • Modeling using match analysis dtbase Performance Analysis of Sports •Concept •Aims •Procedure •Indicators
  9. 9. • Data gathering – During or after a performance • Analysis of data – During or after a performance • Communication of information depending on: – the relevant audience (athletes, coaches, judges, media) – the aim of analysis (athlete’s feedback, decision making, evaluation from judges) Performance Analysis of Sports •Concept •Aims •Procedure •Indicators
  10. 10. Performance indicators are variables that: – are valid measurements of important aspects of sport performance – can be described in an objective measurement procedure – have a known scale of measurement – are valid means of interpretation Performance Analysis of Sports •Concept •Aims •Procedure •Indicators
  11. 11. • Performance indicators express result or quality of performance • SPA is focused on results. Evaluation done on the basis of result and not of quality of movement. • Indicators are expressed in ratios for easier comparison across players, teams, champs. Performance Analysis of Sports •Performance Indicators •Technical Evaluation •Tactical Evaluation •Profile
  12. 12. • Concerned with how well skills are performed in sports • Positive/negative or winner/error ratios • All the skills are not black or white. • The degree of difficulty of situation is not taken into consideration • Ordinal Scales (usually 3 to 7 levels). – validity (More often face & content validity) – reliability(reproducibility of the measurement) • Important for practitioners (to monitor small but practically important changes) • For researchers (to quantify such changes in controlled trials with samples of reasonable size) – Pearson’s r, Chi-square, % error and Kappa test were used. Performance Analysis of Sports •Performance Indicators •Technical Evaluation •Tactical Evaluation •Profile
  13. 13. Nadal vs Federer • Nadal • 6 aces • Mean of Top8: 6 άσσοι • Mean recent matches: 8 aces • Federer • 12 aces • Mean of Top8: 6 άσσοι • Mean recent matches: 14 aces  Is the mean the proper measure of central tendency; • Nadal (6,6,6,6,6,6,20) •Federer (3,3,16,17,18,19,22) •Alternative mode, median. Performance Analysis of Sports •Performance Indicators •Technical Evaluation •Tactical Evaluation •Profile
  14. 14. Performance Analysis of Sports •Performance Indicators •Technical Evaluation •Tactical Evaluation •Profile • Evaluation of strategy and sport patterns • Match indicators • Skills placing (where, from/to) • Time of Skills • Decision making • Analysis of athletes’ movement.
  15. 15. Performance Analysis of Sports •Performance Indicators •Technical Evaluation •Tactical Evaluation •Profile • Mapping of the playing surface (Scatter diagrams). • 4W are necessary: Who/where/what/when • Winners/Losers Models – Problem: Interaction between competitors • Solution: Criteria like classification of the competitors (e.g. 1-8 vs 9- 16) – Ranking where exists (Tennis, Beach Volley, Squash, Table tennis etc) – Problem: Matches with big score differences • Solution: Only Ambivalent matches – Problem: Multicollinearity • Solution: Principal components analysis
  16. 16. 3c 3b 3d 3a 1 9 2 65 87 4 Performance Analysis of Sports •Performance Indicators •Technical Evaluation •Tactical Evaluation •Profile
  17. 17. A5 A4 A3 A4 A1 B5 B4 B3 B4 B1 C5 C4 C3 C2 C1 D3D5 D1 E3E5 E1 6 0 A3 A2 B3 B2 Performance Analysis of Sports •Performance Indicators •Technical Evaluation •Tactical Evaluation •Profile
  18. 18. 4 3 2 7 8 9 K R IS 5 1665 61 Performance Analysis of Sports •Performance Indicators •Technical Evaluation •Tactical Evaluation •Profile
  19. 19. Performance Analysis of Sports •Performance Indicators •Technical Evaluation •Tactical Evaluation •Profile • Performance indicators are not stable variables (e.g. anthropometrics). Many sources of variability in SPA. • Performance profile is collection of indicators that together characterize the typical performance. • Multiple matches – How many? – How recent? – The greater the dtbase, the more accurate the model but less sensitive to changes of playing patterns.
  20. 20. Performance Analysis of Sports •Performance Indicators •Technical Evaluation •Tactical Evaluation •Profile • Interpretation of result – Comparing profiles – Athletes in different championships – Teams in different periods • Important skills • Crucial moments of a contest (momentum) • Modeling of sport performance • Simulation of a contest – Prediction of a result esp. in Net Wall games.
  21. 21. •Categorization of Sport contests •Sport Contests •Invasion games •Net Wall games •Striking Fielding games • Proper criterion for games categorization. – Structure of the Contests • Games with similar structure • Common performance indicators • Interaction of opponents during match in the same way. • Similar systems of observation & spatiotemporal recording
  22. 22. • Time dependent • Common playing area. Invasion • Score dependent • Innings dependent • Different playing area • Change offense/defense per period •Categorization of Sport contests •Sport Contests •Invasion games •Net Wall games •Striking Fielding games
  23. 23. Formal games Net / Wall games Invasion Games Striking/ Fielding Games Score Dependent Time Dependent Innings Dependent •Categorization of Sport contests •Sport Contests •Invasion games •Net Wall games •Striking Fielding games
  24. 24. Invasion Games Goal Throwing Games Try Scoring Games Goal Striking Games Basketball Handball Rugby/ Football Soccer HockeyWaterpolo •Categorization of Sport contests •Sport Contests •Invasion games •Net Wall games •Striking Fielding games
  25. 25. Net / Wall games Net Games Wall Games No Volley Games Bounce and Volley Games No Bounce Games Bounce and Volley Games Squash Table Tennis Tennis Volleyball Badminton •Categorization of Sport contests •Sport Contests •Invasion games •Net Wall games •Striking Fielding games
  26. 26. Striking and Fielding Games Wicket games Base Running Games Cricket Baseball Softball •Categorization of Sport contests •Sport Contests •Invasion games •Net Wall games •Striking Fielding games
  27. 27. • Modeling in Net/Wall games is easier because of hierarchical structure. – In Invasion games it is more complex because of unexpected change of ball’s possession and the unstable chronological sequential order of the tactical actions. • Each action starts with a serve. • No draw. Every action has a winner and a loser. • Outcomes & Sport tactics can be carefully analyzed on the basis of probability. Modeling of sport games •Winner profile •Simulation •Tennis Volleyball •Shot response/ sequence profile •Table tennis •Volleyball •Transition matrix •Skills importance
  28. 28. • Skilled actions refer to the ability of the action to reach a positive outcome. • I.i.d. Assumption – A team/or a player is not influenced: • if the previous point was won or lost (independent)- independence- • If the current point is of particular importance, eg. Match point (identical distribution)- stationarity-. • Is i.i.d. assumption a logical one for net/wall games? – Hot hand or strike – Back to the wall. Modeling of sport games •Winner profile •Simulation •Tennis Volleyball •Shot response/ sequence profile •Table tennis •Volleyball •Transition matrix •Skills importance
  29. 29. • Stochastic (Markov) processes:2fold investigation: – Scoring Structure: Winner/error profile – Sport tactic: shot response/sequence profile • Use of the outcome – the winning of a point- as the unit of the probability analysis. • Use of Markov chain on the calculation of probabilities to win a game, a set or a match. Modeling of sport games •Winner profile •Simulation •Tennis Volleyball •Shot response/ sequence profile •Table tennis •Volleyball •Transition matrix •Skills importance
  30. 30. SRV + - Tennis 1-p p p= player A wins when serving 1-p= player A loses when serving Modeling of sport games •Winner profile •Simulation •Tennis Volleyball •Shot response/ sequence profile •Table tennis •Volleyball •Transition matrix •Skills importance
  31. 31. SRV + - Tennis 1-p p p= player A wins when serving 1-p= player A loses when serving Modeling of sport games •Winner profile •Simulation •Tennis Volleyball •Shot response/ sequence profile •Table tennis •Volleyball •Transition matrix •Skills importance
  32. 32. p Tennis/ one game 0-0 1-0 2-0 3-0 4-0 3-1 4-1 3-2 4-2 2-1 2-2 2-3 3-3 4-3 5-3 4-4 adv game adv deuce game 3-4 3-52-4 1-1 0-1 0-2 0-3 0-4 1-3 1-4 1-2 1-p p= player A wins when serving 1-p= player A loses when serving p 1-p p 1-p p 1-p p 1-p p 1-p p 1-p p 1-p p 1-p p 1-p p 1-p p 1-p p 1-p p 1-p p 1-p p 1-p p 1-p p 1-p p 1-p p 1-p p 1-p Modeling of sport games •Winner profile •Simulation •Tennis Volleyball •Shot response/ sequence profile •Table tennis •Volleyball •Transition matrix •Skills importance
  33. 33. 0-0 1-0 2-0 3-0 4-0 5-0 6-0 1-1 2-1 2-2 2-3 3-3 4-3 4-4 4-5 4-6 5-3 6-2 5-4 6-4 5-5 3-1 4-1 5-1 6-1 3-2 4-2 5-2 6-2 1-2 1-3 1-4 2-4 3-4 1-5 2-5 2-6 3-5 3-6 1-6 0-1 0-2 0-3 0-4 0-5 0-6 p= player A wins when serving 1-p= player A loses when serving q= player B wins when serving 1-q= player B loses when serving Tennis / Set p 1-p p 1-p p 1-p p 1-p p 1-p p 1-p p 1-p p 1-p p 1-p p 1-p p 1-p p 1-p p 1-p p 1-p p 1-p p 1-p p 1-p Modeling of sport games •Winner profile •Simulation •Tennis Volleyball •Shot response/ sequence profile •Table tennis •Volleyball •Transition matrix •Skills importance q q 1-q q 1-q q 1-q 1-q q 1-q q 1-q q 1-q q 1-q q 1-q q 1-q q 1-q q 1-q q 1-q q 1-q q 1-q q 1-q q 1-q q 1-q
  34. 34. SRV Team A + - SRV Team B + - Volleyball p= team A wins when serving 1-p=team A loses when serving q= team B wins when serving 1-q= team B loses when serving 1-p p 1-q q Modeling of sport games •Winner profile •Simulation •Tennis Volleyball •Shot response/ sequence profile •Table tennis •Volleyball •Transition matrix •Skills importance
  35. 35. SRV Team A + - SRV Team B + - Volleyball p= team A wins when serving 1-p=team A loses when serving q= team B wins when serving 1-q= team B loses when serving 1-p p 1-q q Modeling of sport games •Winner profile •Simulation •Tennis Volleyball •Shot response/ sequence profile •Table tennis •Volleyball •Transition matrix •Skills importance
  36. 36. 0-0 1-0 2-0 3-0 4-0 5-0 4-1 3-1 4-1 3-2 2-1 3-1 4-1 3-2 2-2 3-2 2-3 1-1 2-1 3-1 4-1 3-2 2-2 3-2 2-3 1-2 2-2 3-2 2-3 1-3 2-3 1-4 0-1 1-1 1-2 1-3 1-4 2-3 2-2 2-3 3-2 2-1 3-1 4-1 3-2 2-2 3-2 2-3 0-2 1-2 2-2 3-2 2-3 1-3 2-3 1-4 0-3 1-3 2-3 1-4 0-4 1-4 0-5 p 1-p p 1-p p 1-p p 1-p p 1-p q 1-q q 1-q q 1-q 1-q q Volleyball/ Set p= team A wins when serving 1-p=team A loses when serving q= team B wins when serving 1-q= team B loses when serving Modeling of sport games •Winner profile •Simulation •Tennis Volleyball •Shot response/ sequence profile •Table tennis •Volleyball •Transition matrix •Skills importance Rot Team A SRV Team B SRV 6 45% 42% 5 20% 28% 4 40% 34% 3 29% 32% 2 42% 35% 1 41% 39% Volleyball: 6 rotations/ 6 different probs to win when serving
  37. 37. • Shot selection behaviors and outcomes for racket sports • Sequence profile and outcomes for Volleyball • Unit of analysis is the shot behavior / sequence of specific skills that leads to the outcome • Probs of the analysis are obtained from data collected from observation. • Game is described by Categorical states strictly defined by individual skills. • Creation of a transition matrix. Elements of the matrix express the probability of moving from one state (skill) to another state (skill) and to a +/-point. Modeling of sport games •Winner profile •Simulation •Tennis Volleyball •Shot response/ sequence profile •Table tennis •Volleyball •Transition matrix •Skills importance
  38. 38. Modeling of sport games •Winner profile •Simulation •Tennis Volleyball •Shot response/ sequence profile •Table tennis •Volleyball •Transition matrix •Skills importance Game action Stroke position Stroke Direction Stroke technique Receive Forehand Short Forehand Topspin Neutral Backhand Long Forehand Drive Offense Pivot Short backhand Smash Defense Backhand turn Long Backhand Flip Control Close to body Chop Net Ball Chopping Short… 9 Serve Outcome Point/ fault Next rally In alternative order between the two payers Stroke
  39. 39. Modeling of sport games •Winner profile •Simulation •Tennis Volleyball •Shot response/ sequence profile •Table tennis •Volleyball •Transition matrix •Skills importance Game action Stroke position Stroke Direction Stroke technique Receive Forehand Short Forehand Topspin Neutral Backhand Long Forehand Drive Offense Pivot Short backhand Smash Defense Backhand turn Long Backhand Flip Control Close to body Chop Net Ball Chopping Short… 9 Serve Outcome Point/ fault Next rally In alternative order between the two payers Stroke
  40. 40. Modeling of sport games •Winner profile •Simulation •Tennis Volleyball •Shot response/ sequence profile •Table tennis •Volleyball •Transition matrix •Skills importance F_A F_B B_A B_B Pv_A Pv_B Bt_A Bt_B P_A P_B F_A 56% 8% 20% 16% F_B 68% 12% 16% 4% B_A 32% 5% 47% 16% B_B 50% 37% 6% 6% Pv_A 28% 50% 6% 16% Pv_B Bt_A Bt_B Stroke position Forehand Backhand Pivot Backhand turn Point
  41. 41. Pass Set/ Attack 1 C + - Serve Direct Attack Block Free Ball Dig Set/ Attack 2 C + - C + - Modeling of sport games •Shot response/ sequence profile •Table tennis •Volleyball •Transition matrix •Skills importance
  42. 42. Pass Set/ Attack 1 C + - Serve C + - Direct Attack Block Free Ball Dig Set/ Attack 2 C + - Modeling of sport games •Sequence profile •Volleyball
  43. 43. 59X62 Modeling of sport games •Winner profile •Simulation •Tennis Volleyball •Shot response/ sequence profile •Table tennis •Volleyball •Transition matrix •Skills importance
  44. 44. Method of analysis • Most recent world Champion in Men (POLAND, 2014) • Transition Matrix 59 X 62. Last two columns are terminal moves (point + or point- ) for the team under study. • is the probability for a skill to end up in a point after two subsequent game moves. Skills Importance in Men’s Volleyball. •Data •Method of analysis •Results •Conclusions •Suggestions )|()|int()|int( 112 1,1 1 itktktt n kk itti SYSYPSYpoYPSYpoYPP         iP
  45. 45. Method of analysis Measure • Importance score ( ). Measure of impact & uncertainty for a skill (Fellingham & Reese, 2004). )|( )|( yPV yPE I i i i  Posterior mean Standard deviation Skills Importance in Men’s Volleyball. •Data •Method of analysis •Results •Conclusions •Suggestions iI
  46. 46. Method of analysis Assumptions • 1st assumption: Scoring for each skill is i.i.d. • 2nd assumption: Patterns are first order Markov chains. )|int()|int( 121 ittitti SYpoYPSYpoYPP       Skills Importance in Men’s Volleyball. •Data •Method of analysis •Results •Conclusions •Suggestions
  47. 47. • Simple multinomial model to estimate transition & success probabilities • For each skill we assume multinomial likelihood )|( 1 itktik SYSYP    ik i y ikMk ninininininii yyyyf   ),,,...|,,,...( 2,1,,12,1,,1 Skills Importance in Men’s Volleyball. •Data •Method of analysis •Results •Conclusions •Suggestions Method of analysis Model ik
  48. 48. Method of analysis Prior distribution • We use a conjugate Dirichlet prior distribution of the type • Prior estimations from expert coaches. Low weight to experts/coaches opinion. Multiply 0.1X Ni, (10% additional of data points). • All success probabilities & importance scores were calculated using a Monte Carlo scheme of 10.000 iterations.     1 )|(    k i kMk ii Af Skills Importance in Men’s Volleyball. •Data •Method of analysis •Results •Conclusions •Suggestions
  49. 49. Skill Importance score Success Probability QMR Pass in Float 5 (1) 27.6 0.581 Pass in Jump 6 (2) 27.4 0.589 Pass in Float 6 (3) 27.2 0.569 Pass in Jump 5 (4) 27.0 0.593 Pass in Jump 4 (5) 24.9 0.548 Pass in Float 4 (6) 22.8 0.539 Attack 1 MF quick (7)21.9 0.704 Srv Float 3 (8) 17.8 0.332 Attack 1 LS quick (9) 17.2 0.557 ………………………………………………..………………………………… Attack 2 MB quick (22) 10.2 0.717 1.320 Attack 2 MF quick (31) 7.5 0.738 1.258 Attack 1 STR (41) 4.8 0.722 2.093 Skills Importance in Men’s Volleyball. •Data •Method of analysis •Results •Conclusions •Suggestions 0.05 0.95 M Q QMR Q M    0.05 0.95 M Q QMR Q M   
  50. 50. Skills Importance in Men’s Volleyball. •Data •Method of analysis •Results •Conclusions •Suggestions 0.05 0.95 M Q QMR Q M    0.05 0.95 M Q QMR Q M    •Extreme values •Variety of the data • Quantile Mid range Ratio •QMR= •Values >1.2 indicates negative skewness •Sample size small and posterior variance high •High importance but not often execution MQ QM   95,0 05,0
  51. 51. • Introduction of a new supplementary index (QMR) to estimate Volleyball skills. • Serve: • is a disadvantage for top level’s men volleyball. Increased difficulty of serve does not connect linearly to the outcome. • Pass: – The penalty for overpass (level 2) is higher than for the pass off the net (level 3). – Pass level 6 has no advantage compared to pass level 5 • Setting & attack in complex 1: – Quick tempo is more important than high tempo. – Importance of back row attack – All levels of organized attack 1 have higher importance score than attacks 2. • Setting & attack in complex 2: – Set out of system is most important. – Quick tempo better than high tempo sets Skills Importance in Men’s Volleyball. •Data •Method of analysis •Results •Conclusions •Suggestions
  52. 52. for coaches • Serve absolute hard or safe. No errors without aces. • Complex 1 (side out point) is very important. • Complex 2 (break point): – Better preparation of unpredictable situations (attack out of system). Skills Importance in Men’s Volleyball. •Data •Method of analysis •Results •Conclusions •Suggestions
  53. 53. • Improvement of model – Use of past data of team’s performance as prior information. – Standardized team profile. – On line use. – Indications for coaches’ decisions during match. for researchers Skills Importance in Men’s Volleyball. •Data •Method of analysis •Results •Conclusions •Suggestions
  54. 54. Thank you for your attention! Skills Importance in Men’s Volleyball. •Data •Method of analysis •Results •Conclusions •Suggestions •The End

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