Spatio-temporal analysis of team sports Joachim Gudmundsson

Size: px
Start display at page:

Download "Spatio-temporal analysis of team sports Joachim Gudmundsson"

Transcription

1 Spatio-temporal analysis of team sports Joachim Gudmundsson The University of Sydney Page 1

2 Team sport analysis Talk is partly based on: Joachim Gudmundsson and Michael Horton Spatio-Temporal Analysis of Team Sports ACM Computing Surveys, 50(2), 2017 Invasion sports: Two teams trying to score against each other. For example, football, American football, Australian football, ice hockey, handball, basketball, Spatio-temporal data as primary input. This talk will focus on algorithmic issues. The University of Sydney Page 2

3 Overview of major approaches The University of Sydney Page 3

4 Input data PLAYER NAME TEAM NAME MATCH FIXTURE HALF TIME Player X Position Player Y Position Bacary Sagna Arsenal Arsenal v Bolton First half Bacary Sagna Arsenal Arsenal v Bolton First half Bacary Sagna Arsenal Arsenal v Bolton First half Bacary Sagna Arsenal Arsenal v Bolton First half Bacary Sagna Arsenal Arsenal v Bolton First half Bacary Sagna Arsenal Arsenal v Bolton First half Bacary Sagna Arsenal Arsenal v Bolton First half Touch DIABY Abou Block BASHAM Chris Pass MCCANN Gavin Foul GARDNER Ricardo DENILSON Direct Free Kick Pass JAASKELAINEN Jussi Header CLICHY Gael Touch CLICHY Gael The University of Sydney Page 4

5 Input data The University of Sydney Page 5

6 History: Sports analysis Box scores for baseball started in the 1850s. Manual notation of football games started in the 1950s. Moneyball-era in baseball Similar development in basketball in the last 10 years Human observations are unreliable. Franks and Miller [1986] showed that expert observers recollection of significant match events is as low as 42%. Automated tracking of sport players started in the early 2000s. Nowadays a number of automatic tracking systems for football, ice hockey and basketball (not much in rugby, AFL and handball). The University of Sydney Page 6

7 Outline Playing area subdivision Dominant regions Applications Modelling player interaction as social networks Data mining Labelling Identifying formations and plays Trajectory analysis Sport-specific trajectory problems The University of Sydney Page 7

8 Playing area subdivision: Intensity maps First attempts to analyze trajectory data The University of Sydney Page 8

9 Playing area subdivision: Intensity maps And more The University of Sydney Page 9

10 Playing area subdivision: Dominant region A team s ability to control space is considered a key factor in the team s performance. Dominant region [Taki and Hasegawa 99] The dominant region of a player p is the region of the pitch that player p can reach before any other player. p Reach? The University of Sydney Page 10

11 Playing area subdivision: Dominant region Dominant region [Taki and Hasegawa 99] The dominant region of a player p is the region of the pitch that player p can reach before any other player. DR(p)={x d(x,p) d(x,q) for all q p} If d(, ) = Euclidean distance then Dominant region = Voronoi diagram [Descartes 1644] The University of Sydney Page 11

12 Playing area subdivision: Movement model [Taki and Hasegawa 99] Linear interpolation of acceleration in all directions. [Fujimura and Sugihara 05] Introduced a resistive force to decrease acceleration. The University of Sydney Page 12

13 Playing area subdivision: Movement models Simple way to model? The University of Sydney Page 13

14 Playing area subdivision: Movement model Movement model Circle model Ellipse model The University of Sydney Page 14

15 Playing area subdivision: Movement model Movement model A bisector in the ellipse model The University of Sydney Page 15

16 Playing area subdivision: Movement model Dominant region The University of Sydney Page 16

17 Playing area subdivision: Movement model Movement model Model: Turning cost + Euclidean distance The University of Sydney Page 17

18 Playing area subdivision: Movement model Movement model [Taki & Hasegawa 00] The University of Sydney Page 18 1

19 Playing area subdivision: Movement model Movement model [De Berg, Haverkort and Horton 17] The University of Sydney Page 19

20 Playing area subdivision: Movement model Open problem 1: Define a motion function that faithfully models player movement and is tractable for computation. The University of Sydney Page 20

21 Playing area subdivision: Passing evaluation A player p is open for a pass if there is some direction and (reasonable) speed that the ball can be passed, such that p can intercept the ball before any other player. The University of Sydney Page 21

22 Playing area subdivision: Passing evaluation Passability with a fixed pass speed (20m/s). The University of Sydney Page 22

23 Playing area subdivision: Passing evaluation The existing models for determining whether a player is open to receive a pass only consider passes made along the shortest path between passer and receiver and where the ball is moving at constant velocity. Open problem 2: Develop a more realistic model that allows for aerial passes, effects of ball-spin, and variable velocities. The University of Sydney Page 23

24 Playing area subdivision: Spatial Spatial pressure pressure of player [Taki et al. 96] Spatial pressure for a player p is related to the fraction P of the disk of radius r centred at p that lies within dominant region of opposing players, i.e. m(1-p)+(1-m)(1-d/d), where d distance between p and the ball D distance from p to point furthest from p on pitch m preset weight The University of Sydney Page 24

25 Playing area subdivision: Spatial Spatial pressure pressure of player The University of Sydney Page 25

26 Playing area subdivision: Spatial Spatial pressure pressure of player The University of Sydney Page 26

27 Playing area subdivision: Spatial Spatial pressure pressure of player The University of Sydney Page 27

28 Playing area subdivision: Spatial Spatial pressure pressure of player The University of Sydney Page 28

29 Playing area subdivision: Spatial Spatial pressure pressure of player The University of Sydney Page 29

30 Playing area subdivision: Spatial pressure The definition of spatial pressure is very simple. Open problem 3: Can a model that incorporates the direction the player is facing or the direction of pressuring opponents be devised and experimentally tested? The University of Sydney Page 30

31 Modelling team sports as social networks Understanding the interaction between players is one of the most important and complex problems in sports science. Numerous papers apply social network analysis to team sports. Passing network Transition network The University of Sydney Page 31

32 Modelling team sports as social networks Many properties of passing networks have been studied: Centrality Degree Betweenness Closeness Eigenvector centrality and Pagerank Clustering coefficients Density and heterogeneity Entropy, topological depth, Price-of-Anarchy The University of Sydney Page 32

33 Modelling team sports as social networks [Grund 12] Studied degree centrality on networks generated from 283k passes. Conclusion: High level of centralization decreases team performance. Open problem 4: A systematic study reviewing various centrality and clustering measures against predefined criteria, and on a large dataset would be a useful contribution to the field. The University of Sydney Page 33

34 Modelling team sports as social networks [Balkundi and Harrison 06] Density-performance hypothesis. More passes will make a team stronger. Open problem 5: The density-performance hypothesis suggests an interesting metric of team performance. Can this hypothesis be tested scientifically? The University of Sydney Page 34

35 Data mining: Labelling events Evaluate passes (good/bad) [Horton et al. 15] Identify teams (based on formation) [Bialkowski et al. 14] Predict rebounds (offensive/defensive team) [Maheswaran et al 12] The University of Sydney Page 35

36 Data mining: Labelling passes Examples of features: Area of receiving player s dominant region The net change in the area of receiving player s dominant region Total area of the team s dominant region The net change of the total area of the team s dominant region Passer Pressure Receiver Pressure Passer-Receiver Pressure Net Change The University of Sydney Page 36

37 Data mining: Labelling passes Extracted feature vectors from 2932 passes from four matches Pass examples were labelled by humans watching video of match Class imbalance: Class Rel. frequency Count Good OK Bad SVN classifier: Accuracy 90.8% which is similar to a human observer Features based on dominating region are among the most important [Horton et al. 15] The University of Sydney Page 37

38 Data mining: Labelling passes Our algorithms can with high accuracy give the following information: Number of good, ok or bad passes made by a player. The number of high risk vs low risk passes a player makes. A player s ability to execute a pass. The University of Sydney Page 38

39 Data mining: Role assignment to players Role swapping has been shown to be an effective attacking tactic. (Left defender swaps position with left midfielder during play) Given the position of the players and a formation which role has each player? Assignment problem (minimize sum) The University of Sydney Page 39

40 Data mining: Role assignment to players What if we have many different formations? The University of Sydney Page 40

41 Data mining: Identifying plays Given the movement of the players and a predefined play which role has each player? The University of Sydney Page 41

42 Data mining: Identifying plays What if we have many predefined plays? The University of Sydney Page 42

43 Trajectory analysis: Team sport perspective Currently not used much in team sports analysis. Hard to work with Not many available tools The University of Sydney Page 43

44 Trajectory analysis: Team sport perspective Given a set T={T 1,, T m } of trajectories. Typical queries: Given a query trajectory Q, report the nearest subtrajectory of a trajectory in T. [Restricted in time? Restricted to subset of trajectories?] T 1 Q [Driemel and Har-Peled 13, De Berg et al. 13, G and Smid 15] The University of Sydney Page 44

45 Trajectory analysis: Team sport perspective Given a set of query trajectories Q={Q 1,, Q k }, report the nearest set of k subtrajectories of k different trajectories in T. [Subtrajectories must be during same time interval. Restricted to subset of trajectories?] T 2 T 3 Q 2 Q 1 T 1 The University of Sydney Page 45

46 Trajectory analysis: Team sport perspective Subtrajectory clustering of large sets of trajectories? Current approaches are very slow. Distance measure between trajectories? The University of Sydney Page 46

47 Trajectory analysis: Team sport perspective Clustering of multiple subtrajectories occurring in the same time interval? The University of Sydney Page 47

48 Trajectory analysis: Team sport perspective One season in Premier League generates roughly 1 billion points. General questions: Can we sample the data? Can we use core sets for some simple query problems? Can we construct data structures that supports adding more data, without having to recompute them? Can we construct multi-purpose data structures? The University of Sydney Page 48

49 Summary Summary Sports analysis is a field that can benefit from tools and insights developed in many different fields, including geometric algorithms! Thank you! The University of Sydney Page 49

Using Spatio-Temporal Data To Create A Shot Probability Model

Using Spatio-Temporal Data To Create A Shot Probability Model Using Spatio-Temporal Data To Create A Shot Probability Model Eli Shayer, Ankit Goyal, Younes Bensouda Mourri June 2, 2016 1 Introduction Basketball is an invasion sport, which means that players move

More information

RUGBY is a dynamic, evasive, and highly possessionoriented

RUGBY is a dynamic, evasive, and highly possessionoriented VISUALIZING RUGBY GAME STYLES USING SOMS 1 Visualizing Rugby Game Styles Using Self-Organizing Maps Peter Lamb, Hayden Croft Abstract Rugby coaches and analysts often use notational data describing match

More information

Visual Traffic Jam Analysis Based on Trajectory Data

Visual Traffic Jam Analysis Based on Trajectory Data Visual Traffic Jam Analysis Based on Trajectory Data Zuchao Wang, Min Lu, Xiaoru Yuan, Peking University Junping Zhang, Fudan University Huub van de Wetering, Technische Universiteit Eindhoven Introduction

More information

ROSE-HULMAN INSTITUTE OF TECHNOLOGY Department of Mechanical Engineering. Mini-project 3 Tennis ball launcher

ROSE-HULMAN INSTITUTE OF TECHNOLOGY Department of Mechanical Engineering. Mini-project 3 Tennis ball launcher Mini-project 3 Tennis ball launcher Mini-Project 3 requires you to use MATLAB to model the trajectory of a tennis ball being shot from a tennis ball launcher to a player. The tennis ball trajectory model

More information

6 Motion in Two Dimensions BIGIDEA Write the Big Idea for this chapter.

6 Motion in Two Dimensions BIGIDEA Write the Big Idea for this chapter. 6 Motion in Two Dimensions BIGIDEA Write the Big Idea for this chapter. Use the What I Know column to list the things you know about the Big Idea. Then list the questions you have about the Big Idea in

More information

BASKETBALL PREDICTION ANALYSIS OF MARCH MADNESS GAMES CHRIS TSENG YIBO WANG

BASKETBALL PREDICTION ANALYSIS OF MARCH MADNESS GAMES CHRIS TSENG YIBO WANG BASKETBALL PREDICTION ANALYSIS OF MARCH MADNESS GAMES CHRIS TSENG YIBO WANG GOAL OF PROJECT The goal is to predict the winners between college men s basketball teams competing in the 2018 (NCAA) s March

More information

Players Movements and Team Shooting Performance: a Data Mining approach for Basketball.

Players Movements and Team Shooting Performance: a Data Mining approach for Basketball. Players Movements and Team Shooting Performance: a Data Mining approach for Basketball. arxiv:1805.02501v1 [stat.ap] 4 May 2018 Rodolfo Metulini 1 1 Department of Economics and Management, University of

More information

Practice Test: Vectors and Projectile Motion

Practice Test: Vectors and Projectile Motion ame: Practice Test: Vectors and Projectile Motion Part A: Multiple Choice [15 points] 1. A projectile is launched at an angle of 30 0 above the horizontal. eglecting air resistance, what are the projectile

More information

Figure 1: A hockey puck travels to the right in three different cases.

Figure 1: A hockey puck travels to the right in three different cases. Question (12) Average velocity and position (update) for three hockey pucks You view the motion of a hockey puck in a video and mark its location every. The resulting image for three different hockey pucks

More information

1 An object moves at a constant speed of 6 m/s. This means that the object:

1 An object moves at a constant speed of 6 m/s. This means that the object: Slide 1 / 57 1 n object moves at a constant speed of 6 m/s. This means that the object: Increases its speed by 6 m/s every second ecreases its speed by 6 m/s every second oesn t move Has a positive acceleration

More information

Title: Modeling Crossing Behavior of Drivers and Pedestrians at Uncontrolled Intersections and Mid-block Crossings

Title: Modeling Crossing Behavior of Drivers and Pedestrians at Uncontrolled Intersections and Mid-block Crossings Title: Modeling Crossing Behavior of Drivers and Pedestrians at Uncontrolled Intersections and Mid-block Crossings Objectives The goal of this study is to advance the state of the art in understanding

More information

Physics Final Exam Review Fall 2013

Physics Final Exam Review Fall 2013 Physics Final Exam Review Fall 2013 The lines on the graph represent displacement vectors for the route along which a person moves. Use the figure to answer problems 1 2. 1. What is the total distance

More information

There are many successful playing styles in world soccer

There are many successful playing styles in world soccer There are many successful playing styles in world soccer Allow the opponent to have a lot of ball possession Own team focusses on compact defending (D) Shutting down spaces Keeping thedefensiveshape Confront

More information

LINEAR MOTION. General Review

LINEAR MOTION. General Review LINEAR MOTION General Review 1. Velocity is to speed as displacement is to A. acceleration B. momentum C. time D. distance 1. Velocity is to speed as displacement is to A. acceleration B. momentum C. time

More information

Traffic circles. February 9, 2009

Traffic circles. February 9, 2009 Traffic circles February 9, 2009 Abstract The use of a traffic circle is a relatively common means of controlling traffic in an intersection. Smaller Traffic circles can be especially effective in routing

More information

Basketball data science

Basketball data science Basketball data science University of Brescia, Italy Vienna, April 13, 2018 paola.zuccolotto@unibs.it marica.manisera@unibs.it BDSports, a network of people interested in Sports Analytics http://bodai.unibs.it/bdsports/

More information

Y9 Curriculum Map: PE

Y9 Curriculum Map: PE 1 Autumn 1 (7 weeks) Basketball (7 lessons) Continue to develop the following key skills: passing, receiving, pivoting, To use the basic skills with consistency and accuracy in the game to outwit the opposition

More information

Opleiding Informatica

Opleiding Informatica Opleiding Informatica Analyzing the behaviour of players in soccer prior to a shot on target. Rose Browne Supervisors: Mitra Baratchi & Arie-Willem de Leeuw BACHELOR THESIS Leiden Institute of Advanced

More information

An Engineering Approach to Precision Ammunition Development. Justin Pierce Design Engineer Government and International Contracts ATK Sporting Group

An Engineering Approach to Precision Ammunition Development. Justin Pierce Design Engineer Government and International Contracts ATK Sporting Group An Engineering Approach to Precision Ammunition Development Justin Pierce Design Engineer Government and International Contracts ATK Sporting Group 1 Background Federal Premium extensive experience with

More information

1. downward 3. westward 2. upward 4. eastward

1. downward 3. westward 2. upward 4. eastward projectile review 1 Name 11-DEC-03 1. A baseball player throws a ball horizontally. Which statement best describes the ball's motion after it is thrown? [Neglect the effect of friction.] 1. Its vertical

More information

Evaluating and Classifying NBA Free Agents

Evaluating and Classifying NBA Free Agents Evaluating and Classifying NBA Free Agents Shanwei Yan In this project, I applied machine learning techniques to perform multiclass classification on free agents by using game statistics, which is useful

More information

TEACHER ANSWER KEY December 10, Projectile Review 1

TEACHER ANSWER KEY December 10, Projectile Review 1 Projectile Review 1 TEACHER ANSWER KEY December 10, 2004 4 1. A baseball player throws a ball horizontally. Which statement best describes the ball's motion after it is thrown? [Neglect the effect of friction.]

More information

A Computational Assessment of Gas Jets in a Bubbly Co-Flow 1

A Computational Assessment of Gas Jets in a Bubbly Co-Flow 1 A Computational Assessment of Gas Jets in a Bubbly Co-Flow 1 Melissa Fronzeo*, 1 Michael Kinzel 1 The Pennsylvania State University, University Park, PA, USA Abstract In this effort, Computational Fluid

More information

A Novel Approach to Predicting the Results of NBA Matches

A Novel Approach to Predicting the Results of NBA Matches A Novel Approach to Predicting the Results of NBA Matches Omid Aryan Stanford University aryano@stanford.edu Ali Reza Sharafat Stanford University sharafat@stanford.edu Abstract The current paper presents

More information

knn & Naïve Bayes Hongning Wang

knn & Naïve Bayes Hongning Wang knn & Naïve Bayes Hongning Wang CS@UVa Today s lecture Instance-based classifiers k nearest neighbors Non-parametric learning algorithm Model-based classifiers Naïve Bayes classifier A generative model

More information

Coaching Players Ages 17 to Adult

Coaching Players Ages 17 to Adult Coaching Players Ages 17 to Adult This section provides you with a twelve-week training program for players ages 17 through Adult. Each practice features four fun and progressive drills and culminates

More information

a. Determine the sprinter's constant acceleration during the first 2 seconds. b. Determine the sprinters velocity after 2 seconds have elapsed.

a. Determine the sprinter's constant acceleration during the first 2 seconds. b. Determine the sprinters velocity after 2 seconds have elapsed. AP Physics 1 FR Practice Kinematics 1d 1 The first meters of a 100-meter dash are covered in 2 seconds by a sprinter who starts from rest and accelerates with a constant acceleration. The remaining 90

More information

The International Coaches Association Advanced Passing Drills and Games

The International Coaches Association Advanced Passing Drills and Games The International Coaches Association Advanced Passing Drills and Games Manchester United Passing Drill Italian Passing Awareness Drill Liverpool Passing Game Juventus Reverse Passing Drill Burnley FC

More information

Original Article. Dependence of Football Repulsion on the Pressure Within This Sport

Original Article. Dependence of Football Repulsion on the Pressure Within This Sport Journal of Physical Education and Sport (JPES), 16(2), Art 69, pp. 452-458, 2016 online ISSN: 2247-806X; p-issn: 2247 8051; ISSN - L = 2247-8051 JPES Original Article Dependence of Football Repulsion on

More information

TECHNICAL STUDY 2 with ProZone

TECHNICAL STUDY 2 with ProZone A comparative performance analysis of games played on artificial (Football Turf) and grass from the evaluation of UEFA Champions League and UEFA Cup. Introduction Following on from our initial technical

More information

Penalty Corners in Field Hockey: A guide to success. Peter Laird* & Polly Sutherland**

Penalty Corners in Field Hockey: A guide to success. Peter Laird* & Polly Sutherland** Penalty Corners in Field Hockey: A guide to success Peter Laird* & Polly Sutherland** *4 Seton Place, Edinburgh, EH9 2JT **39/5 West Bryson Road, Edinburgh, EH11 1BQ. Abstract The purpose of this study

More information

Bézier Curves and Splines

Bézier Curves and Splines CS-C3100 Computer Graphics Bézier Curves and Splines Majority of slides from Frédo Durand vectorportal.com CS-C3100 Fall 2016 Lehtinen Before We Begin Anything on your mind concerning Assignment 1? Linux

More information

TOPIC: Playing Out of the Back in a 1:4:4:2 Formation By Anthony Latronica and

TOPIC: Playing Out of the Back in a 1:4:4:2 Formation By Anthony Latronica and TOPIC: Playing Out of the Back in a 1:4:4:2 Formation By Anthony Latronica and www.soccerspecific.com Activity #1- Warm-Up: Players are organized in a 18X44 yd grid (size of penalty box) as shown below.

More information

Pedestrian Dynamics: Models of Pedestrian Behaviour

Pedestrian Dynamics: Models of Pedestrian Behaviour Pedestrian Dynamics: Models of Pedestrian Behaviour John Ward 19 th January 2006 Contents Macro-scale sketch plan model Micro-scale agent based model for pedestrian movement Development of JPed Results

More information

Analysis and modeling of pedestrian flows in railway stations

Analysis and modeling of pedestrian flows in railway stations SBB-Beirat Technologie, Methoden und Prozesse Analysis and modeling of pedestrian flows in railway stations Flurin Hänseler, Transport and Mobility Lab, EPFL December 4, 2013 1/24 Pedestrian flows in train

More information

Chapter 5 DATA COLLECTION FOR TRANSPORTATION SAFETY STUDIES

Chapter 5 DATA COLLECTION FOR TRANSPORTATION SAFETY STUDIES Chapter 5 DATA COLLECTION FOR TRANSPORTATION SAFETY STUDIES 5.1 PURPOSE (1) The purpose of the Traffic Safety Studies chapter is to provide guidance on the data collection requirements for conducting a

More information

FUTURE SOCCER PRO. Grassroots ebook Vol.2 U10. Love.Learn.Play

FUTURE SOCCER PRO. Grassroots ebook Vol.2 U10. Love.Learn.Play U10 The player must be in the driving seat, coaches are there to provide the map that keeps them driving (Mark Parsons Future Soccer PRO Founder) U10 This ebook is to support the coaches currently working

More information

QUESTION 1. Sketch graphs (on the axes below) to show: (1) the horizontal speed v x of the ball versus time, for the duration of its flight;

QUESTION 1. Sketch graphs (on the axes below) to show: (1) the horizontal speed v x of the ball versus time, for the duration of its flight; QUESTION 1 A ball is thrown horizontally from a cliff with a speed of 10 ms -1 shown in the diagram at right. Neglecting the effect of air resistance and taking gravitational acceleration to be g +9.8ms

More information

EVALUATION OF METHODOLOGIES FOR THE DESIGN AND ANALYSIS OF FREEWAY WEAVING SECTIONS. Alexander Skabardonis 1 and Eleni Christofa 2

EVALUATION OF METHODOLOGIES FOR THE DESIGN AND ANALYSIS OF FREEWAY WEAVING SECTIONS. Alexander Skabardonis 1 and Eleni Christofa 2 EVALUATION OF METHODOLOGIES FOR THE DESIGN AND ANALYSIS OF FREEWAY WEAVING SECTIONS Alexander Skabardonis 1 and Eleni Christofa 2 1: University of California, Berkeley, Institute of Transportation Studies,

More information

Northern SC U12 Playing Formats 8v8 (7 field players + 1 GK)

Northern SC U12 Playing Formats 8v8 (7 field players + 1 GK) Northern SC U12 Playing Formats 8v8 (7 field players + 1 ) This document outlines guidelines for increasing the consistency of playing formations and terminology we use with U12 players. As players of

More information

Overview. 2 Module 13: Advanced Data Processing

Overview. 2 Module 13: Advanced Data Processing 2 Module 13: Advanced Data Processing Overview This section of the course covers advanced data processing when profiling. We will discuss the removal of the fairly gross effects of ship heave and talk

More information

Technical Handbook (Booklet 3 of 3)

Technical Handbook (Booklet 3 of 3) Alberta Soccer Association U12 Development Soccer 8v8 Technical Handbook (Booklet 3 of 3) This handbook contains a series of exercises to be used a guide to assist with U12 development soccer. Acknowledgements

More information

Physics 2204 Worksheet 6.5: Graphical Analysis of Non- Uniform Motion D-T GRAPH OF NON-UNIFORM MOTION (ACCELERATING) :

Physics 2204 Worksheet 6.5: Graphical Analysis of Non- Uniform Motion D-T GRAPH OF NON-UNIFORM MOTION (ACCELERATING) : Physics 2204 Worksheet 6.5: Graphical Analysis of Non- Uniform Motion D-T GRAPH OF NON-UNIFORM MOTION (ACCELERATING) : The d-t graph for uniformly Accelerated motion is definitely not the same as a d-t

More information

Understanding Games by Playing Games An Illustrative Example of Canada s PlaySport Program

Understanding Games by Playing Games An Illustrative Example of Canada s PlaySport Program Understanding Games by Playing Games An Illustrative Example of Canada s PlaySport Program Presented at: Teaching Sport and Physical Education for Understanding Melbourne, Australia December 12 th, 11:30

More information

Fatigue in soccer: NEW APPROACHES AND CONCEPTS. SPAIN PERSPECTIVE. Carlos Lago-Peñas University of Vigo, SPAIN

Fatigue in soccer: NEW APPROACHES AND CONCEPTS. SPAIN PERSPECTIVE. Carlos Lago-Peñas University of Vigo, SPAIN Fatigue in soccer: NEW APPROACHES AND CONCEPTS. SPAIN PERSPECTIVE Carlos Lago-Peñas University of Vigo, SPAIN Today s talk Fatigue in soccer. Traditional concepts and approaches The influence of situational

More information

Filtering Procedures for Sensor Data in Basketball

Filtering Procedures for Sensor Data in Basketball Filtering Procedures for Sensor Data in Basketball Rodolfo Metulini 1 1 Department of Economics and Management, University of Brescia arxiv:1806.10412v1 [stat.ap] 27 Jun 2018 Please cite as: Metulini,

More information

Projectiles Shot up at an Angle

Projectiles Shot up at an Angle Projectile Motion Notes: continued Projectiles Shot up at an Angle Think about a cannonball shot up at an angle, or a football punt kicked into the air, or a pop-fly thrown into the air. When a projectile

More information

Cricket umpire assistance and ball tracking system using a single smartphone camera

Cricket umpire assistance and ball tracking system using a single smartphone camera 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 Cricket umpire assistance and ball tracking system using a single smartphone camera Udit Arora

More information

STEM SPORTS.

STEM SPORTS. STEM SPORTS Mr. B. Pezzuto Email: bpezzuto@revere.mec.edu 781-388-7520 ext: 54217 Staying Connected to STEM Sports Twitter: @SBA_Pezzuto Instagram: SBA_Pezzuto Classroom Policies Grading Policy: Tests:

More information

Coach central defenders to deal with crosses in the final third

Coach central defenders to deal with crosses in the final third Coach central defenders to deal with crosses in the final third Age: 14-Adult Number of players: 14 (13+GK) Difficulty: Medium Area/Time: 1/3 Field (25-35 mins.) Drill objective (s): 1. Develop understanding

More information

Last First Date Per SETTLE LAB: Speed AND Velocity (pp for help) SPEED. Variables. Variables

Last First Date Per SETTLE LAB: Speed AND Velocity (pp for help) SPEED. Variables. Variables DISTANCE Last First Date Per SETTLE LAB: Speed AND Velocity (pp108-111 for help) Pre-Activity NOTES 1. What is speed? SPEED 5-4 - 3-2 - 1 2. What is the formula used to calculate average speed? 3. Calculate

More information

Northern SC U6 Playing Format 3v3

Northern SC U6 Playing Format 3v3 Northern SC U6 Playing Format 3v3 This document outlines guidelines for increasing the consistency of playing formations and terminology we use with U6 players. As players of this age may have different

More information

Decision Trees. Nicholas Ruozzi University of Texas at Dallas. Based on the slides of Vibhav Gogate and David Sontag

Decision Trees. Nicholas Ruozzi University of Texas at Dallas. Based on the slides of Vibhav Gogate and David Sontag Decision Trees Nicholas Ruozzi University of Texas at Dallas Based on the slides of Vibhav Gogate and David Sontag Announcements Course TA: Hao Xiong Office hours: Friday 2pm-4pm in ECSS2.104A1 First homework

More information

SOCCER DRILLS AND PRACTICE PLANS

SOCCER DRILLS AND PRACTICE PLANS SOCCER DRILLS AND PRACTICE PLANS VOLUME 2 1 SOCCER DRILLS AND PRACTICE PLANS VOLUME 2 by Coach Steve Parker SOCCER DRILLS AND PRACTICE PLANS VOLUME 2 2 Copyright Notice The material enclosed is copyrighted.

More information

Original Article. Pressure dependence of handball repulsion within this sport

Original Article. Pressure dependence of handball repulsion within this sport Journal of Physical Education and Sport (JPES), 16 Supplement issue 2, Art 172, pp. 1078-1083, 2016 online ISSN: 2247-806X; p-issn: 2247 8051; ISSN - L = 2247-8051 JPES Original Article Pressure dependence

More information

Spring 2010 Coaching Sessions U14

Spring 2010 Coaching Sessions U14 Spring 2010 Coaching Sessions U14 Coaching Ages 13 to 14 Years This section provides you with a eight week training program for children ages 13 through 14 years. Each practice features four fun and progressive

More information

Introduction. Level 1

Introduction. Level 1 Introduction Game Analysis is the second teaching and learning resource in the Science Through Sport series. The series is designed to reinforce scientific and mathematical principles using sport science

More information

WHEN TO RUSH A BEHIND IN AUSTRALIAN RULES FOOTBALL: A DYNAMIC PROGRAMMING APPROACH

WHEN TO RUSH A BEHIND IN AUSTRALIAN RULES FOOTBALL: A DYNAMIC PROGRAMMING APPROACH WHEN TO RUSH A BEHIND IN AUSTRALIAN RULES FOOTBALL: A DYNAMIC PROGRAMMING APPROACH 8.0. Abstract In Australian rules football, points are scored when the ball passes over the goal line. Six points are

More information

An experimental validation of a robust controller on the VAIMOS autonomous sailboat. Fabrice LE BARS

An experimental validation of a robust controller on the VAIMOS autonomous sailboat. Fabrice LE BARS An experimental validation of a robust controller on the VAIMOS autonomous sailboat Fabrice LE BARS Outline Introduction VAIMOS, an autonomous sailboat for oceanography Autonomy / control Theoretical validation

More information

Physics-Based Modeling of Pass Probabilities in Soccer

Physics-Based Modeling of Pass Probabilities in Soccer Physics-Based Modeling of Pass Probabilities in Soccer William Spearman, Austin Basye, Greg Dick, Ryan Hotovy, and Paul Pop Hudl, Lincoln, NE 68508 Email: william.spearman@hudl.com Abstract In this paper,

More information

Spring/Summer Session

Spring/Summer Session Spring/Summer Session Development Path U12+ C1 U12 + aged teams U11 Soccer The real game U9/10 Academy United In Development Recreational Content Sessions Structure of training 4 technical to one technical

More information

A Network-Assisted Approach to Predicting Passing Distributions

A Network-Assisted Approach to Predicting Passing Distributions A Network-Assisted Approach to Predicting Passing Distributions Angelica Perez Stanford University pereza77@stanford.edu Jade Huang Stanford University jayebird@stanford.edu Abstract We introduce an approach

More information

AGE GROUP/PROGRAM: U14 TOWN WEEK # 1

AGE GROUP/PROGRAM: U14 TOWN WEEK # 1 AGE GROUP/PROGRAM: U14 TOWN WEEK # 1 THEME: AWARENESS/ARGENTINA Improve 1 st touch Speed of play Playing with the head up Players on their toes, balanced and communicating. Good 1st touch out of the body

More information

Introduction to Pattern Recognition

Introduction to Pattern Recognition Introduction to Pattern Recognition Jason Corso SUNY at Buffalo 12 January 2009 J. Corso (SUNY at Buffalo) Introduction to Pattern Recognition 12 January 2009 1 / 28 Pattern Recognition By Example Example:

More information

ENHANCED PARKWAY STUDY: PHASE 2 CONTINUOUS FLOW INTERSECTIONS. Final Report

ENHANCED PARKWAY STUDY: PHASE 2 CONTINUOUS FLOW INTERSECTIONS. Final Report Preparedby: ENHANCED PARKWAY STUDY: PHASE 2 CONTINUOUS FLOW INTERSECTIONS Final Report Prepared for Maricopa County Department of Transportation Prepared by TABLE OF CONTENTS Page EXECUTIVE SUMMARY ES-1

More information

Attacking and defending neural networks. HU Xiaolin ( 胡晓林 ) Department of Computer Science and Technology Tsinghua University, Beijing, China

Attacking and defending neural networks. HU Xiaolin ( 胡晓林 ) Department of Computer Science and Technology Tsinghua University, Beijing, China Attacking and defending neural networks HU Xiaolin ( 胡晓林 ) Department of Computer Science and Technology Tsinghua University, Beijing, China Outline Background Attacking methods Defending methods 2 AI

More information

During the Push What kind of motion does the puck have at this time? Is it speeding up, slowing down, not moving, or moving at a steady speed?

During the Push What kind of motion does the puck have at this time? Is it speeding up, slowing down, not moving, or moving at a steady speed? Elaborate The teacher is giving a demonstration with a hover puck. Make the requested predictions before using your observations to check if you were correct or if you need to make changes.. Predict how

More information

CHAPTER 10: LINEAR KINEMATICS OF HUMAN MOVEMENT

CHAPTER 10: LINEAR KINEMATICS OF HUMAN MOVEMENT CHAPTER 10: LINEAR KINEMATICS OF HUMAN MOVEMENT 1. Vector mechanics apply to which of the following? A. displacement B. velocity C. speed D. both displacement and velocity 2. If velocity is constant, then

More information

HOMEWORK BOOKLET DEVELOPMENT NAME: FORM: TEACHER:

HOMEWORK BOOKLET DEVELOPMENT NAME: FORM: TEACHER: HOMEWORK BOOKLET DEVELOPMENT NAME: FORM: TEACHER: There are seventeen laws of association football that must be followed. Each of these laws covers a different aspect of the game. This section will cover

More information

Defend deep to counter-attack

Defend deep to counter-attack Defend deep to counter-attack Age: 14 - Adult Number of players: 18 (two teams of 9) Difficulty: Medium Area/Time: 80 x 50 yards (30-45 mins.) Drill objective (s): 1. Learn how to regain possession in

More information

Age Group/Program: U14 Town Week # 1 Theme: Awareness/argentina Session Goals: Coaching Points: Understand Your Audience: Improve 1 st touch Speed of play Playing with the head up Players on their toes,

More information

QUESTION 1. Sketch graphs (on the axes below) to show: (1) the horizontal speed v x of the ball versus time, for the duration of its flight;

QUESTION 1. Sketch graphs (on the axes below) to show: (1) the horizontal speed v x of the ball versus time, for the duration of its flight; QUESTION 1 A ball is thrown horizontally from a cliff with a speed of 10 ms -1 shown in the diagram at right. Neglecting the effect of air resistance and taking gravitational acceleration to be g = +9.8ms

More information

2010 ACHPER HEALTH & PHYSICAL EDUCATION CONFERENCE

2010 ACHPER HEALTH & PHYSICAL EDUCATION CONFERENCE 2010 ACHPER HEALTH & PHYSICAL EDUCATION CONFERENCE Monash University, Clayton Thursday 25th November Minor Games for Developing Strategy in Invasion Sports Presenter: Ray Breed High Performance Coach North

More information

EF 151 Exam #2 - Spring, 2016 Page 1 of 6

EF 151 Exam #2 - Spring, 2016 Page 1 of 6 EF 151 Exam #2 - Spring, 2016 Page 1 of 6 Name: Section: Instructions: Sit in assigned seat; failure to sit in assigned seat results in a 0 for the exam. Put name and section on your exam. Put seating

More information

DIFFERENCES BETWEEN THE WINNING AND DEFEATED FEMALE HANDBALL TEAMS IN RELATION TO THE TYPE AND DURATION OF ATTACKS

DIFFERENCES BETWEEN THE WINNING AND DEFEATED FEMALE HANDBALL TEAMS IN RELATION TO THE TYPE AND DURATION OF ATTACKS DIFFERENCES BETWEEN THE WINNING AND DEFEATED FEMALE HANDBALL TEAMS IN RELATION TO THE TYPE AND DURATION OF ATTACKS Katarina OHNJEC, Dinko VULETA, Lidija BOJIĆ-ĆAĆIĆ Faculty of Kinesiology, University of

More information

PREDICTING THE NCAA BASKETBALL TOURNAMENT WITH MACHINE LEARNING. The Ringer/Getty Images

PREDICTING THE NCAA BASKETBALL TOURNAMENT WITH MACHINE LEARNING. The Ringer/Getty Images PREDICTING THE NCAA BASKETBALL TOURNAMENT WITH MACHINE LEARNING A N D R E W L E V A N D O S K I A N D J O N A T H A N L O B O The Ringer/Getty Images THE TOURNAMENT MARCH MADNESS 68 teams (4 play-in games)

More information

*This is a Recreational and Developmental league. The goal is to have fun and introduce them to soccer. WE DO NOT KEEP SCORE AT THIS AGE.

*This is a Recreational and Developmental league. The goal is to have fun and introduce them to soccer. WE DO NOT KEEP SCORE AT THIS AGE. U4, U5, & U6 Program Players on the field: 4v4 with NO goalies Roster Size: 6 8 Ball size: 3 Practices: no longer than 30 minutes per week Substitutions: Both Teams substitute at stoppage of play. Player

More information

SCIENTIFIC COMMITTEE SEVENTH REGULAR SESSION August 2011 Pohnpei, Federated States of Micronesia

SCIENTIFIC COMMITTEE SEVENTH REGULAR SESSION August 2011 Pohnpei, Federated States of Micronesia SCIENTIFIC COMMITTEE SEVENTH REGULAR SESSION 9-17 August 2011 Pohnpei, Federated States of Micronesia CPUE of skipjack for the Japanese offshore pole and line using GPS and catch data WCPFC-SC7-2011/SA-WP-09

More information

Hockey Scholar Curriculum Guide

Hockey Scholar Curriculum Guide Hockey Scholar Curriculum Guide NHL Future Goals Hockey Scholar Your local NHL team has made it all the way to the Stanley Cup Final and now you just need to win 4 games to bring home the cup! You ve been

More information

UNDER 17 TECHNICAL CURRICULUM TABLE OF CONTENTS

UNDER 17 TECHNICAL CURRICULUM TABLE OF CONTENTS Team Canada Program of Excellence High Performance Program UNDER 17 TECHNICAL CURRICULUM TABLE OF CONTENTS INTRODUCTION...1 NATIONAL TEAM SELECTION CRITERIA...2 Positional Characteristics...3 Player Selection...3

More information

SOFTWARE. Sesam user course. 12 May 2016 HydroD Hydrostatics & Stability. Ungraded SAFER, SMARTER, GREENER DNV GL 2016

SOFTWARE. Sesam user course. 12 May 2016 HydroD Hydrostatics & Stability. Ungraded SAFER, SMARTER, GREENER DNV GL 2016 SOFTWARE Sesam user course DNV GL 1 SAFER, SMARTER, GREENER Scope of presentation Describe features & commands for performing a hydrostatic analysis, and their concepts Analysis setup Code-checking Reporting

More information

Game Theory (MBA 217) Final Paper. Chow Heavy Industries Ty Chow Kenny Miller Simiso Nzima Scott Winder

Game Theory (MBA 217) Final Paper. Chow Heavy Industries Ty Chow Kenny Miller Simiso Nzima Scott Winder Game Theory (MBA 217) Final Paper Chow Heavy Industries Ty Chow Kenny Miller Simiso Nzima Scott Winder Introduction The end of a basketball game is when legends are made or hearts are broken. It is what

More information

In this session we look at developing teams ability to defend as a unit.

In this session we look at developing teams ability to defend as a unit. Defending as a unit In this session we look at developing teams ability to defend as a unit. What you tell your players the session is about: Working hard together to win possession. Pressing, supporting

More information

The Rules of The Game

The Rules of The Game The Rules of The Game Before getting started, take a few minutes to read the rules. They will help you to understand the game and be a better player. Introduction TsegBall is a non-contact, co-ed sport

More information

Combined impacts of configurational and compositional properties of street network on vehicular flow

Combined impacts of configurational and compositional properties of street network on vehicular flow Combined impacts of configurational and compositional properties of street network on vehicular flow Yu Zhuang Tongji University, Shanghai, China arch-urban@163.com Xiaoyu Song Tongji University, Shanghai,

More information

Weekly Practice Schedule:

Weekly Practice Schedule: Coaching Ages 13 to 14 Years This section provides you with a twelve-week training program for children ages 13 through 14 years. Each practice features four fun and progressive drills and culminates with

More information

GLOBAL PREMIER SOCCER

GLOBAL PREMIER SOCCER GLOBAL PREMIER SOCCER GPS 204 CURRICULUM FUTSAL VACATION PROGRAM u - u4 GPS CURRICULUM METHODOLOGY U4 - U8 JUNIORS U9-U2 JUNIOR PREMIER U3-U8 PREMIER 8+ PREMIER Player Evaluation & Assessment FUN-Damental

More information

Riverboat Simulator Activity Sheet

Riverboat Simulator Activity Sheet Riverboat Simulator Activity Sheet Purpose: The purpose of this activity is to analyze the relationship between the two vector components of motion for a river boat as it travels across a river in the

More information

CAAD CTF 2018 Rules June 21, 2018 Version 1.1

CAAD CTF 2018 Rules June 21, 2018 Version 1.1 CAAD CTF 2018 Rules June 21, 2018 Version 1.1 The organizer will invite 5 teams to participate CAAD CTF 2018. We will have it in Las Vegas on Aug. 10 th, 2018. The rules details are below: 1. Each team

More information

PHYSICS 12 NAME: Kinematics and Projectiles Review

PHYSICS 12 NAME: Kinematics and Projectiles Review NAME: Kinematics and Projectiles Review (1-3) A ball is thrown into the air, following the path shown in the diagram. At 1, the ball has just left the thrower s hand. At 5, the ball is at its original

More information

Advanced PMA Capabilities for MCM

Advanced PMA Capabilities for MCM Advanced PMA Capabilities for MCM Shorten the sensor-to-shooter timeline New sensor technology deployed on off-board underwater systems provides navies with improved imagery and data for the purposes of

More information

Analysis of Curling Team Strategy and Tactics using Curling Informatics

Analysis of Curling Team Strategy and Tactics using Curling Informatics Hiromu Otani 1, Fumito Masui 1, Kohsuke Hirata 1, Hitoshi Yanagi 2,3 and Michal Ptaszynski 1 1 Department of Computer Science, Kitami Institute of Technology, 165, Kouen-cho, Kitami, Japan 2 Common Course,

More information

Football Pass Prediction using Player Locations

Football Pass Prediction using Player Locations Football Pass Prediction using Player Locations Philippe Fournier-Viger 1, Tianbiao Liu 2, and Jerry Chun-Wei Lin 3 1 School of Natural Sciences and Humanities, Harbin Institute of Technology (Shenzhen),

More information

Soccer Manual. Rules, Regulations, & Training Information.

Soccer Manual. Rules, Regulations, & Training Information. www.soky.org Soccer Manual Rules, Regulations, & Training Information All Special Olympics Kentucky Soccer games and tournaments should be run according to FIFA rules. Any modifications to those rules

More information

1 st Team and / or Preferred Academy Drills

1 st Team and / or Preferred Academy Drills st Team and / or Preferred Academy Drills Drill Drill Drill 0m 7 v in the playing area, players for each team on the outside. The purpose of the game is to score by playing a wall pass with a player on

More information

Reading Time: 15 minutes Writing Time: 1 hour 30 minutes. Structure of Book. Number of questions to be answered. Number of modules to be answered

Reading Time: 15 minutes Writing Time: 1 hour 30 minutes. Structure of Book. Number of questions to be answered. Number of modules to be answered Reading Time: 15 minutes Writing Time: 1 hour 30 minutes Letter Student Number: Structure of Book Section A - Core Section B - Modules Number of questions Number of questions to be answered Number of marks

More information

REVIEW : KINEMATICS

REVIEW : KINEMATICS 1 REVIEW 5-4-16: KINEMATICS Kinematics-Defining Motion 1 A student on her way to school walks four blocks east, three blocks north, and another four blocks east, as shown in the diagram. Compared to the

More information

Motion Graphing Packet

Motion Graphing Packet Name: Motion Graphing Packet This packet covers two types of motion graphs Distance vs. Time Graphs Velocity vs. Time Graphs Describing the motion of an object is occasionally hard to do with words. Sometimes

More information

Soccer Manual. Rules, Regulations, & Training Information.

Soccer Manual. Rules, Regulations, & Training Information. www.soky.org Soccer Manual Rules, Regulations, & Training Information All Special Olympics Kentucky Soccer games and tournaments should be run according to FIFA rules. Any modifications to those rules

More information