# A Network-Assisted Approach to Predicting Passing Distributions

Save this PDF as:

Size: px
Start display at page:

## Transcription

1 A Network-Assisted Approach to Predicting Passing Distributions Angelica Perez Stanford University Jade Huang Stanford University Abstract We introduce an approach of using a linear regression model plus stochastic gradient descent to predict the passing distribution of a soccer team based on data from the UEFA Champions League. Features are derived from player-specific statistics, team-specific history, and network measures while weights are learned to predict the number of passes between each pair of players on a team. Our linear regression model with features and SGD achieved a 25.27% improvement in the average loss from our baseline model of only average past passing networks. 1 Introduction The passing network of a team can reveal much about game strategy who the key player or key players are, whether a team had a hard time against its opponent and focused mainly on defense or if it was completely dominating its opponent and was rocking the offense. Furthermore, the passing network changes based on the opposing team team A will play differently when faced with inferior team B than when faced with superior team C. Given the history of passing networks of team A and another team B, predicting the passing networks of both teams ahead of time has a number of implications. We can uncover the different patterns of how a team plays another team of a certain difficulty. We can predict the layout of future games, winning more bets, and perhaps giving teams a leg up on their competitors by giving them clues on how their competitor may play based on their playing history. 2 Problem Given a match between two teams A and B, each of their starting lineups, and each team s passing history comprised of t past passing distributions P D (A) = {pd (A) 1, pd (A) 2,, pd (A) t } and P D (B) = {pd (B) 1, pd (B) 2,, pd (B) t } along with additional team and player statistical data, our model seeks to predict new passing networks pd (A) t+1 and pd(b) t+1 for each team. We use a linear regression model to learn feature weights using stochastic gradient descent that are then used to predict the edge weight, or number of passes, between each pair of nodes, or teammates, in match t + 1. The features are comprised of a variety of UEFA data and attempt to encapsulate the necessary team and player characteristics to predict edge weights, such as team rankings, average passes between a pair of players, and average betweenness of a player. We carefully choose features that accurately describe a pair of player s historical relationship on the field, the typical involvement of an individual player amongst his team, and, equally as important, a team s strategy and passing history, including how a team s style of play (i.e. heavy midfield or light offense) may change against a specific opposing team. 3 Related Work (Pena et al., 2012) uncover how players perform on a team by analyzing centrality measures such as closeness, betweenness, PageRank, and clustering. The authors found that through betweenness, one can uncover which players are more involved. If the network is very complete with most edges be-

2 tween most or all players, one can say the team is very well-connected. (Grund, 2012) expands upon (Pena et al., 2012) with two hypotheses that he confirms in his paper: 1. increased interaction intensity leads to increased team performance and 2. increased centralization of interaction in teams leads to decreased team performance. Using the mean degree (volume of play), variance degree (diversity of play), and harmonic mean of the mean and variance degree of the player passing network, (Cintia et al., 2015) achieved 53% accuracy in predicting match outcomes. Building upon (Pena et al., 2012), centrality measures, especially betweenness centrality, can be used as features in predicting edges between players. A player with a higher centrality measure perhaps will receive more passes on average. Features used by (Cintia et al., 2015) and (Grund, 2012) such as mean degree, variance degree, interaction intensity, centralization of interaction are indicative of teamspecific patterns and can be used in our model when predicting edges. Due to limitation of data from FIFA, (Pena et al., 2012) computed passing networks by dividing the number of passes by the total number of plays played by each team, lacking a per-game analysis which could be indicative since a team most likely does not play the same way with all opponents. Similarly, (Cintia et al., 2015) and (Grund, 2012) fail to acknowledge how team dynamics will perform in relation to the dynamics of an opponent. (Grund, 2012) defines the most centralized network as the one where most interactions involve the same two individuals. However, such a definition can be further expanded to include cases such as where all members of a team pass to one central player equally. 4 Model 4.1 Baseline For a baseline model, the average of past passing networks during the group stage is used to predict the passing networks during the round of 16 stage. Thus, during training, for every player to player combination in each team for which there exists a pass, the total number of passes over six games is calculated during the group stage and averaged over the number of games. The average values calculated during training are used to predict the number of passes for each team in each game in the round of 16 of the season. For example, if in team X, player 1 passed to player 9 an average of five times during the group stage, this baseline model would predict that player 1 would pass to player 9 five times when playing some team in the round of 16. This is a naive baseline that does not take into account the specific opponent during each of the past games during the group stage, but merely generalizes, ignoring any idiosyncrasies which may arise when facing a higher ranked or lower ranked opponent. 5 Algorithm To build upon the baseline, a linear regression model was constructed to include features that are indicative of the amount of passes between two players and which would learn weights which would correspond how descriptive a feature is in relation to the amount of passes between two players. 5.1 Features With guidance from Tim Althoff, we postulated the following are key ingredients leading to how often two certain players pass: Average number of past passes between two players, a continuous value that is precomputed before the process of training and learning weights and averaged over the group stage. As with the baseline, the average of all passes between two players for each player for each team over all past games, where the notion of past is experimented with later on, is taken. Whether two players are of the same position, firing 1 if the condition is true and 0 otherwise. The motivation for this feature is that based on analyzing the data, certain positions pass to each other very often, such as from defender to midfielder, while others never pass to each other, such as goalkeeper to goalkeeper. Please see 1 for a bar graph showing the number of passes made between each position in

3 Figure 1: Number of passes made between each position in a game between APOEL FC and Paris Saint-Germain. Average number of passes between two positions on a team, a continuous value. The motivation is that perhaps on a team, two given positions generally pass to each other some similar amount across all games. The average betweenness centrality of both players respectively averaged over games during the group stage. As mentioned in related work, betweenness centrality can indicate the importance of a player, of how often the player is a cog in between passes. a game between APOEL FC and Paris Saint- Germain. Similarly, we also have a feature for whether two players are of different positions. Whether team A s rank is higher than team B s rank, firing 1 if true and 0 otherwise. The motivation is that perhaps players may pass more or less depending on whether the opponent team is ranked higher or lower. Whether team A has won against a similar team as team B in the past, firing 1 if true and 0 otherwise. In every subsequent stage, teams play teams that they haven t played in previous stages thus there is no previously existing data stored for team combinations at testing time. Thus, it may be helpful to find a team C that is similar to a team B in team A s play history and use the outcome of that match in determining if team A has a good chance of winning against team B. Whether team A s average pass completion rate is higher than team B s pass completion rate, firing 1 if true and 0 otherwise. The average is taken of pass completion rates across past games ignoring specific opponents. Similarly we also have features for whether team A s average passes attempted and average passes failed are higher than team B s pass completion rate. In analyzing the data, a trend was observed where 63.5% of the time the team with a higher pass completion rate and was also the team that lost the match. Also, 62.5% of the time the team with a higher number of passes attempted was also the team that lost the match. Average percentage of passes completed for two specific players averaged over percentage of passes completed during the group stage, a continuous value. The intuition is perhaps it is more likely for two players to pass if their percentage of passes completed is higher. 5.2 Evaluation We utilized the squared loss to capture the accuracy of a prediction of number of passes between two players in comparison to the actual number of passes between two players. The score φ(x) w is considered the prediction while y represents the actual number of passes. Loss squared = (predicted actual) 2 = (φ(x) w y) 2 To evaluate our model as a whole, we took the average of the loss over all passes between players. In the below equation, T represents the total number of passes between players over all teams and games during the round of 16 stage. Loss model = 1 T 5.3 Learning Weights i=t (φ(x) w y) 2 (1) i=0 Our linear regression model learns weights using stochastic gradient descent. The objective is to minimize our squared loss, thus with every new training example, i.e. number of passes between two players, we update our weights with the following equation:

4 w w η w Loss w w η2(φ(x) w y)φ(x) where η is the step size controlling the rate of descent. 5.4 Data Data used includes team passing distributions, tactical lineups, and squad lists provided by the UEFA Champions League press kits for the Group, Round of 16, Quarter-finals, and Semifinal stages of the season. Team rankings were taken from the UEFA rankings for club competitions for the season. We implemented Python and bash scripts to parse this set information for a total of 124 games for 32 teams, with a total of 26, 358 passes between 3, 428 players over 248 networks with an average of players per team and passes per team. For each team for each game in each stage, passing distributions included number of passes completed between all players as well as the total number of passes completed and attempted while squad lists included player names and positions. 6 Results 6.1 Baseline The baseline model has an average loss of 15.00, which was calculated by summing up the individual loss for each player to player pass and dividing by the total number of passes during the round of 16 stage. For the predicted number of passes to differ from the actual number of passes by on average can be explained by the generalization made when averaging over all past passes. In addition, the games played during the Round of 16 have teams playing other teams that they did not play during the Group stage. Thus, the baseline is utilizing data that while generalizes how much players will pass to each other on average, fails to capture any changes in style a team may implement in the face of different opponents. Feature Set Loss Average Passing Networks Same Position Difference in Team Rank Mean Degree Betweenness of Receiving Player Average Pass Completion % of Passing Player Table 1: Best-performing feature set in Shared Weights Model. The average loss for the set is The losses listed are the increase in average loss when the specified feature is removed from the set. 6.2 Linear Regression Model Using shared model weights Experimenting with various subsets of features proved to be very interesting. The features that computed average passes between two players, difference in team rank, whether two players are the same or different position lowered the loss consistently no matter which additional features were added to their sets. Intuitively, these three features have a lot to do with how many times two players will pass to each other. The average passes between two players is arguably the most indicative feature since that is the precise value we are attempting to predict. Difference in team rank is a good indicator of which team will possess the ball for the majority of the game and by how much, so for a team that is far better than their opponent, the feature accurately raises edge weights across the board for the entire team. Examining if two players are in the same position also accurately raises edge weights as it is more frequent for two such players to pass to each other due to sheer proximity on the field. The average pass completion percentage for the passing player in a pair of teammates slightly reduced the loss consistently as well. It makes sense that a higher completion percentage will increase a player s out-edges. We observed interesting outcomes during the use of combinations of three features: average betweenness of the passing player, average betweenness of the receiving player, and the mean degree of a team per match. Contrary to our hypothesis, average betweenness of the passing player performed better

5 Figure 2: Predicted passing distribution for Juventus during the Round of 16 when playing against Borussia Dortmund. Figure 3: Actual passing distribution for Juventus during the Round of 16 when playing against Borussia Dortmund. than average betweenness of the receiving player and the use of both features in the same set performed even worse than using only one of them. We expected the combination of both to produce the least amount of loss since both players centrality in the lineup seems important to the frequency of the two connecting. Additionally, we found that average betweenness of the passing player consistently reduces loss when used in a feature set that does not include the other two features, but using both average betweenness of the receiving player and mean degree in a feature set without average betweenness of the passing player has an even greater positive impact on the loss. We hypothesized that mean degree would have less of an impact on the magnitude of edge weights since it does not really capture the relationship between two players or the team s strategy against a specific opponent. Moreover, other features in our experiments, such as difference in team rank and whether a team has won against a similar opponent in the past, seem to be better indicators of the number of passes an entire team will make against a certain opponent. Yet, our best-performing set of features includes mean degree, which only performs well with betweenness centrality of the receiving player. Figures 2 and 3 above depict the predicted and actual passing distributions of the team Juventus against Borussia Dortmund during the Round of 16 stage. Our predicted network generally has more edges than the actual network, as it is more likely to predict a real value that is non-zero than zero due to the nature of our scoring function. What is exciting is that our predicted network was able to capture the concentrated movement along the defensive line of Evra, Chiellini, and Bonucci. This defensive line is not always so concentrated, as can be seen in a game against Malmo FF during the group stage (figure 4 in the Appendix), where the passing concentration is more heavily weighted in the midfield and more spread out, thus for our features and weights to be able to tease this out is promising. 6.3 Using team-specific model weights While one set of weights performed better overall than using team-specific weights, we noted many differences in the performance of features. For example, in the case of the boolean features of same position and different position between the receiving and passing players, same position worked best without different position in the single-weight model, but the pair worked best together in the teamspecific weights model. Using both features most likely works well in the team-specific weights model because it is capturing the position-to-position passing style of each team, which does vary. An aggres-

6 Feature Set Loss Average Passes Per Position Same Position Different Position Difference in Team Rank Betweenness of Passing Player Average Pass Completion % of Receiving Player Table 2: Best-performing feature set in Team-Specific Weights Model. The average loss for the set is The losses listed are the increase in average loss when the specified feature is removed from the set. sive team may have more frequent long passes from the defense or midfield to the offense, while a patient team may pass a lot between midfielders while waiting for an attacking opportunity to present itself. Interestingly enough, the feature used in the baseline, average passes from one player to another, is not part of the best-performing set for this model depicted in table 2. The teammate relationship reduced the loss by the largest amount and was best indicated by the pair of features same position and different position. Individual passing player features performed decently in this model: betweenness of passing player and average pass completion percentage of receiving player. However, we expected these features to work best with the opposite player in the teammate pair with betweenness of the receiving player and average pass completion percentage of the passing player. The features characterizing team dynamics, average passes per position and difference in team rank, significantly lowered the loss. Average passes per position determines where on the field a team prefers to play, so it performed best when trained on teamspecific weights as expected. Difference in team rank performed well too, but not as well as in the shared-weights model perhaps because each set of team weights has fewer games to train on than the 96 games of the group stage that were used to trained the shared weights in the previous model and difference in team rank is applicable to all teams. The continuous feature of mean degree and boolean feature indicating whether a team won against a similar team did not perform as well as Model Avg.Loss Baseline Shared Weights Team-Specific Weights Table 3: Total loss for each model. expected. Both of these features we believed to have the potential to capture a team s unique style of play and strategy, but perhaps the model needs more training matches for these features have a more positive impact. However, adding additional training matches could possible have a negative effect on the model due to the fact that a team s strategy can change a lot over time. These are experiments we can explore in the future. 6.4 Additional analysis The superior performance of the shared-weights model is surprising because one would think that using team-specific weights would cater more to the idiosyncrasies of each team, but it seems that certain features are generally of the same importance to each team. Also, by using shared weights, the weights are updated far more often than if we use team-specific weights. So perhaps, it is a bit of both phenomenons coming into play. Experiments with varying training iterations showed little change in either models results, while modifying the learning rate produced drastic changes. The optimal values for these parameters resulted in 2 training iterations and a learning rate of We attempted two different training and test sets. First we tried training on the 96 Group Stage matches and testing on the 16 Round of 16 matches, which are the results seen in tables 1, 2, and 3. We also experimented with training on the 112 matches of the Group Stage and Round of 16 and tested on the 8 Quarter Final matches, which significantly increased loss in both the shared-weights and teamspecific weights models. We believe this occurred not only because the test set is cut in half, but the nature of the quarter finals matches is more competitive making it more difficult for certain features to pick up on discrepancies between team strategy in the later stages of the tournament.

7 7 Problems Encountered 7.1 Parsing Data While we were blessed with a large amount of pergame, per-player, and per-team analysis from the UEFA Champions League, we had to process the data, which was all in pdf form, into parse-able formats such as csv ourselves. 7.2 Feature Engineering Not all features attempted were successful in lowering average loss. Not all the methods in which we attempted to implement our features were successful, sometimes seemingly intuitive but resulting in massive average loss and skyrocketing weights. We made choices of whether to precompute values for a given set beforehand, sort of simulating a genie knowing values beforehand, or to keep running averages, which is seemingly more realistic and chronological. For example, on matchday 1, a team does not know its average passing network for the entire tournament. 8 Future Direction As mentioned before, team strategies change over time, so it would be interesting compare our results when we expand our training and dev set to more stages or even train on one season and attempt to predict games in the next season. Similar to (Pena et al., 2012), we simplified the constantly changing landscape of the soccer field to a static network which was represented by set tactical lineups. It is indeed a limitation of a network to represent a static state, especially since our aim was chiefly to predict the edges between player nodes. In addition, we also simplified positions to four values: goalkeepers, midfielders, defenders, and forwards. This fails to capture that positions have much more depth: such as defensive forwards, or defenders acting as midfielders. It would be interesting to have various networks representing different points in the game and to take into consideration how different positions evolve throughout a game or per team. Angelica: Problem statement, creating and running experiments, results, analysis of results and visualizations Jade: Crawling data, parsing data, coding up algorithm and features, abstract, introduction, related work, model, evaluation, visualizations 10 Shared Project between CS224W and CS221 We are sharing the data that we parsed for this project for our final project for CS221. References [Cintia et al.2015] Cintia, P., Rinzivillo, S., Pappalardo, L A network-based approach to evaluate the performance of football teams Machine Learning and Data Mining for Sports Analytics workshop (MLSA 15). ECML/PKDD conference [Grund 2012] Grund, T. U Network Structure and Team Performance: The Case of English Premier League Soccer Teams. Social Networks 34.4 (2012): [Pena et al.2012] Pena, J. L., Touchette, H A network theory analysis of football strategies C. Clanet (ed.), Sports Physics: Proc Euromech Physics of Sports Conference, p , Éditions de l École Polytechnique, Palaiseau, (ISBN ). 9 Contributions

8 Figure 4: Actual passing distribution for Juventus during the Group stage when playing against Malmo FF, a lower-ranked team than Juventus. Note how the strong defensive line seen when playing Borussia Dortmund is not present here, but rather the passes are more spread out.

### PREDICTING the outcomes of sporting events

CS 229 FINAL PROJECT, AUTUMN 2014 1 Predicting National Basketball Association Winners Jasper Lin, Logan Short, and Vishnu Sundaresan Abstract We used National Basketball Associations box scores from 1991-1998

### Predicting the Total Number of Points Scored in NFL Games

Predicting the Total Number of Points Scored in NFL Games Max Flores (mflores7@stanford.edu), Ajay Sohmshetty (ajay14@stanford.edu) CS 229 Fall 2014 1 Introduction Predicting the outcome of National Football

### 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

### 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

### 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

### Ranking Cricket Teams through Runs and Wickets

Ranking Cricket Teams through Runs and Wickets Ali Daud 1 and Faqir Muhammad 1 Department of Computer Science & Software Engineering, IIU, Islamabad 44000, Pakistan Department of Business Administration,

### FROM THE ARTICLE: MOMENT OF ORGANIZATION FROM VALENCIA CF (Edition 62, Tactical-Football)

SYSTEMIC DRILL FROM THE ARTICLE: MOMENT OF ORGANIZATION FROM VALENCIA CF (Edition 62, al-football) Name: Manuel Torres Pericás Physical Education Teacher Bachelor CAFE (Specialty Football) Football manager

### 1. OVERVIEW OF METHOD

1. OVERVIEW OF METHOD The method used to compute tennis rankings for Iowa girls high school tennis http://ighs-tennis.com/ is based on the Elo rating system (section 1.1) as adopted by the World Chess

### Why Small Sided Games?

Why Small Sided Games? by Tom Goodman US Youth National Director of Coaching WHY SMALL-SIDED? Because we want our young soccer players to touch and play the ball more often and become more skillful with

### Mini Soccer What Game Format and Development Model is Best?

Mini Soccer What Game Format and Development Model is Best? A Study by the Sports University of Cologne Copyright, 1996 Dale Carnegie & Associates, Inc. The Study. Conducted by the German Football Association,

### Spatio-temporal analysis of team sports Joachim Gudmundsson

Spatio-temporal analysis of team sports Joachim Gudmundsson The University of Sydney Page 1 Team sport analysis Talk is partly based on: Joachim Gudmundsson and Michael Horton Spatio-Temporal Analysis

### Title: 4-Way-Stop Wait-Time Prediction Group members (1): David Held

Title: 4-Way-Stop Wait-Time Prediction Group members (1): David Held As part of my research in Sebastian Thrun's autonomous driving team, my goal is to predict the wait-time for a car at a 4-way intersection.

### The Progression from 4v4 to 11v11

The Progression from 4v4 to 11v11 The 4v4 game is the smallest model of soccer that still includes all the qualities found in the bigger game. The shape of the team is a smaller version of what is found

### An Analysis of Factors Contributing to Wins in the National Hockey League

International Journal of Sports Science 2014, 4(3): 84-90 DOI: 10.5923/j.sports.20140403.02 An Analysis of Factors Contributing to Wins in the National Hockey League Joe Roith, Rhonda Magel * Department

### Motion Control of a Bipedal Walking Robot

Motion Control of a Bipedal Walking Robot Lai Wei Ying, Tang Howe Hing, Mohamed bin Hussein Faculty of Mechanical Engineering Universiti Teknologi Malaysia, 81310 UTM Skudai, Johor, Malaysia. Wylai2@live.my

### Game Management in Soccer

Game Management in Soccer Game management in soccer can be defined as a team s ability to recognize important phases and adapt to changing circumstances which play out through the course of a match. Teams

### Should bonus points be included in the Six Nations Championship?

Should bonus points be included in the Six Nations Championship? Niven Winchester Joint Program on the Science and Policy of Global Change Massachusetts Institute of Technology 77 Massachusetts Avenue,

### Average Runs per inning,

Home Team Scoring Advantage in the First Inning Largely Due to Time By David W. Smith Presented June 26, 2015 SABR45, Chicago, Illinois Throughout baseball history, the home team has scored significantly

### Kelsey Schroeder and Roberto Argüello June 3, 2016 MCS 100 Final Project Paper Predicting the Winner of The Masters Abstract This paper presents a

Kelsey Schroeder and Roberto Argüello June 3, 2016 MCS 100 Final Project Paper Predicting the Winner of The Masters Abstract This paper presents a new way of predicting who will finish at the top of the

### Chapter 4 (sort of): How Universal Are Turing Machines? CS105: Great Insights in Computer Science

Chapter 4 (sort of): How Universal Are Turing Machines? CS105: Great Insights in Computer Science Some Probability Theory If event has probability p, 1/p tries before it happens (on average). If n distinct

### Revisiting the Hot Hand Theory with Free Throw Data in a Multivariate Framework

Calhoun: The NPS Institutional Archive DSpace Repository Faculty and Researchers Faculty and Researchers Collection 2010 Revisiting the Hot Hand Theory with Free Throw Data in a Multivariate Framework

### Changes in speed and efficiency in the front crawl swimming technique at 100m track

Proceeding 10th INSHS International Christmas Sport Scientific Conference, 4-5 December 2015. International Network of Sport and Health Science. Szombathely, Hungary Changes in speed and efficiency in

### A Portfolio of Winning. Football Betting Strategies

A Portfolio of Winning Football Betting Strategies Contents Lay The Draw Revisited Overs/Unders 2.5 Goals Rating Method La Liga Specials Early Season Profits Http://www.scoringforprofit.com 1 1. Lay The

### Analysis of the offensive teamwork intensity in elite female basketball

Original Article Analysis of the offensive teamwork intensity in elite female basketball BORIS BAZANOV 1, INDREK RANNAMA Institute of Health Sciences and Sports, Tallinn University, Tallinn, Estonia ABSTRACT

### Building the Playing Style Concepts

Building the Playing Style Concepts Style Of Play - Specific Players 1, 2 or 3 touch maximum - minimizing the number of touches improves the speed of play. Keep the game simple - do not force situations,

### Analysis of performance at the 2007 Cricket World Cup

Analysis of performance at the 2007 Cricket World Cup Petersen, C., Pyne, D.B., Portus, M.R., Cordy, J. and Dawson, B Cricket Australia, Department of Physiology, Australian Institute of Sport, Human Movement,

### Using Computer Vision and Machine Learning to Automatically Classify NFL Game Film and Develop a Player Tracking System

Using Computer Vision and Machine Learning to Automatically Classify NFL Game Film and Develop a Player Tracking System Omar Ajmeri omarajmeri@gmail.com Ali Shah ali@alternative.ly Paper Track: Other Sports

### U15 Small Sided Competition Guidelines

U15 Small Sided Competition Guidelines Age and developmentally appropriate lacrosse competition for boys playing in the U15 Age Classification Our Vision We envision a future that offers people everywhere

### Benefits in effective scouting:

Why Scout? This scouting information will prepare the coach to create a realistic training environment that specifically is designed to compete against the opponent. Professional scouting is a very meticulous

### The Rise in Infield Hits

The Rise in Infield Hits Parker Phillips Harry Simon December 10, 2014 Abstract For the project, we looked at infield hits in major league baseball. Our first question was whether or not infield hits have

### Figure 1. Winning percentage when leading by indicated margin after each inning,

The 7 th Inning Is The Key By David W. Smith Presented June, 7 SABR47, New York, New York It is now nearly universal for teams with a 9 th inning lead of three runs or fewer (the definition of a save situation

### JOSE MOURINHO'S TACTICS

JOSE MOURINHO'S TACTICS Jose Mourinho has used many different formations during his coaching career, in order to get the best out of his players characteristics. The most popular have been the 4-4-2 with

### STUDY BACKGROUND. Trends in NCAA Student-Athlete Gambling Behaviors and Attitudes. Executive Summary

STUDY BACKGROUND Trends in NCAA Student-Athlete Gambling Behaviors and Attitudes Executive Summary November 2017 Overall rates of gambling among NCAA men have decreased. Fifty-five percent of men in the

### 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

### Licensed Coaches Event The England DNA: In the Grassroots game

Licensed Coaches Event The England DNA: In the Grassroots game The England DNA: In The Grassroots Game Following its launch in December 2014, this FA Licensed Coaches Club Event is designed to introduce

### LOCOMOTION CONTROL CYCLES ADAPTED FOR DISABILITIES IN HEXAPOD ROBOTS

LOCOMOTION CONTROL CYCLES ADAPTED FOR DISABILITIES IN HEXAPOD ROBOTS GARY B. PARKER and INGO CYLIAX Department of Computer Science, Indiana University, Bloomington, IN 47405 gaparker@cs.indiana.edu, cyliax@cs.indiana.edu

### ROSEMARY 2D Simulation Team Description Paper

ROSEMARY 2D Simulation Team Description Paper Safreni Candra Sari 1, R.Priyo Hartono Adji 1, Galih Hermawan 1, Eni Rahmayanti 1, 1 Digital Media and Game Technology Lab. School of Electrical Engineering

### Genetic Algorithm Optimized Gravity Based RoboCup Soccer Team

Genetic Algorithm Optimized Gravity Based RoboCup Soccer Team Tory Harter Advisor: Dr. Jon Denning Taylor University July 28, 2015 ABSTRACT This report describes how we use genetic algorithms to optimize

### arxiv: v3 [stat.ap] 26 Sep 2017

Quantifying the relation between performance and success in soccer Luca Pappalardo 1,2, Paolo Cintia 2 1 Department of Computer Science, University of Pisa, Italy 2 ISTI-CNR, Pisa, Italy lpappalardo@di.unipi.it,

### Navigate to the golf data folder and make it your working directory. Load the data by typing

Golf Analysis 1.1 Introduction In a round, golfers have a number of choices to make. For a particular shot, is it better to use the longest club available to try to reach the green, or would it be better

### 18 th UEFA Course For Coach Educators April 2010 Coverciano/Italy

18 th UEFA Course For Coach Educators 12-16 April 2010 Coverciano/Italy UEFA CHAMPIONS LEAGUE THE TRENDS Image name & copyright Presentation by Andy Roxburgh, UEFA Technical Director UCL ANALYSIS WHY?

### Pierce 0. Measuring How NBA Players Were Paid in the Season Based on Previous Season Play

Pierce 0 Measuring How NBA Players Were Paid in the 2011-2012 Season Based on Previous Season Play Alex Pierce Economics 499: Senior Research Seminar Dr. John Deal May 14th, 2014 Pierce 1 Abstract This

### Basic organization of the training field

Burke Athletic Club u4 & 5 Training Scheme Phase I Introduction to Soccer 1v1 Legend The purpose of this training program is to allow the u4 and 5 s the opportunity to learn some basic ideas and experience

### 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:

### We can use a 2 2 array to show all four situations that can arise in a single play of this game, and the results of each situation, as follows:

Two-Person Games Game theory was developed, starting in the 1940 s, as a model of situations of conflict. Such situations and interactions will be called games and they have participants who are called

### Topic: Creating Goal-Scoring Opportunities. By Greg Maas, State Technical Director, Utah Youth Soccer Association.

Topic: Creating Goal-Scoring Opportunities. By Greg Maas, State Technical Director, Utah Youth Soccer Association. Emphasis: To teach technical and tactical considerations in creating goal-scoring opportunities.

### Developing Game Intelligence for 11- years- old football players. 1 st Simplified Game. Maintaining Ball Possession 3 on 1

Simplified Games 3 on 3 The simplified game is still an ideal framework for discovering, understanding, and resolving specific game-related problems for 7-a-side football. 1 st Simplified Game Maintaining

### Fit to Be Tied: The Incentive Effects of Overtime Rules in Professional Hockey

Fit to Be Tied: The Incentive Effects of Overtime Rules in Professional Hockey Jason Abrevaya Department of Economics, Purdue University 43 West State St., West Lafayette, IN 4797-256 This version: May

### OXSY 2016 Team Description

OXSY 2016 Team Description Sebastian Marian, Dorin Luca, Bogdan Sarac, Ovidiu Cotarlea OXygen-SYstems laboratory, Str. Constantin Noica, Bl. 5, Sc. C, Ap. 36, C.P. 550169, Sibiu, ROMANIA Email: (sebastian.marian@oxsy.ro)

### LEE COUNTY WOMEN S TENNIS LEAGUE

In order for the Lee County Women s Tennis League to successfully promote and equitably manage 2,500+ members and give all players an opportunity to play competitive tennis, it is essential to implement

### Queue analysis for the toll station of the Öresund fixed link. Pontus Matstoms *

Queue analysis for the toll station of the Öresund fixed link Pontus Matstoms * Abstract A new simulation model for queue and capacity analysis of a toll station is presented. The model and its software

### DIFFERENTIATED ANALYSIS OF OFFENSIVE ACTIONS BY FOOTBALL PLAYERS IN SELECTED MATCHES FROM THE EURO 2008

Pol. J. Sport Tourism 2013, 20, 188-193 DOI: 10.248/pjst-2013-001 188 DIFFERENTIATED ANALYSIS OF OFFENSIVE ACTIONS BY FOOTBALL PLAYERS IN SELECTED MATCHES FROM THE EURO 2008 1 1 TOMASZ BURACZEWSKI, LESZEK

### Predictors for Winning in Men s Professional Tennis

Predictors for Winning in Men s Professional Tennis Abstract In this project, we use logistic regression, combined with AIC and BIC criteria, to find an optimal model in R for predicting the outcome of

### PHYSICAL EDUCATION PERFORMANCE ASSESSMENT SUPPORT MATERIAL VOLLEYBALL

PHYSICAL EDUCATION PERFORMANCE ASSESSMENT SUPPORT MATERIAL VOLLEYBALL IMPORTANT INFORMATION School Curriculum and Standards Authority, 2017 This document apart from any third party copyright material contained

### Analysis of Shear Lag in Steel Angle Connectors

University of New Hampshire University of New Hampshire Scholars' Repository Honors Theses and Capstones Student Scholarship Spring 2013 Analysis of Shear Lag in Steel Angle Connectors Benjamin Sawyer

### INTERNATIONAL CHAMPIONS CUP

INTERNATIONAL CHAMPIONS CUP PRESENTED BY HEINEKEN 2017 PRESS KIT Juventus F.C. Paris Saint- Germain F.C. Manchester United F.C. FC Barcelona Manchester City F.C. Tottenham Hotspur F.C. AS Roma press@releventsports.com

### Team Advancement. 7.1 Overview Pre-Qualifying Teams Teams Competing at Regional Events...3

7 Team Advancement 7.1 Overview...3 7.2 Pre-Qualifying Teams...3 7.3 Teams Competing at Regional Events...3 7.3.1 Qualifying Awards at Regional Events...3 7.3.2 Winning Alliance at Regional Events...3

### Article Title: Exploring the right spaces to penetrate and goalscoring

Article Title: Exploring the right spaces to penetrate and goalscoring scenarios By Jed C. Davies We want to dominate the game between our midfield and our attack, and we want to dominate the game [in

### Predicting Season-Long Baseball Statistics. By: Brandon Liu and Bryan McLellan

Stanford CS 221 Predicting Season-Long Baseball Statistics By: Brandon Liu and Bryan McLellan Task Definition Though handwritten baseball scorecards have become obsolete, baseball is at its core a statistical

### Modelling and Simulation of Environmental Disturbances

Modelling and Simulation of Environmental Disturbances (Module 5) Dr Tristan Perez Centre for Complex Dynamic Systems and Control (CDSC) Prof. Thor I Fossen Department of Engineering Cybernetics 18/09/2007

### ScienceDirect. Rebounding strategies in basketball

Available online at www.sciencedirect.com ScienceDirect Procedia Engineering 72 ( 2014 ) 823 828 The 2014 conference of the International Sports Engineering Association Rebounding strategies in basketball

### OptaPro Post-Match Pack /16. Juventus - Barcelona. Champions League Season 2014/15, Matchday 13. Saturday, 06 June :45

OptaPro Post-Match Pack - 2015/16 Juventus - Barcelona Champions League Season 2014/15, Matchday 13 Saturday, 06 June 2015-19:45 Juventus - Barcelona 1-3 (0-1) 9 Á. Morata 85' 10 C. Tévez 11 Neymar 9 L.

### EVOLVING HEXAPOD GAITS USING A CYCLIC GENETIC ALGORITHM

Evolving Hexapod Gaits Using a Cyclic Genetic Algorithm Page 1 of 7 EVOLVING HEXAPOD GAITS USING A CYCLIC GENETIC ALGORITHM GARY B. PARKER, DAVID W. BRAUN, AND INGO CYLIAX Department of Computer Science

### N.I.S.L. Player Development Guide. Under 8 Under 10 Created by the NISL Technical Committee for the benefit of the NISL Member Clubs

N.I.S.L. Player Development Guide Under 8 Under 10 Created by the NISL Technical Committee for the benefit of the NISL Member Clubs I. INTRODUCTION A. Principles of coaching 1. Know who you are coaching

### Live by the Three, Die by the Three? The Price of Risk in the NBA

Live by the Three, Die by the Three? The Price of Risk in the NBA Matthew Goldman, UCSD Dept. of Economics, @mattrgoldman Justin M. Rao, Microsoft Research, @justinmrao Shoot a 2 or a 3? Determining the

Grand Slam Tennis Computer Game (Version 2010.3) Table of Contents 1. Introduction - What is the grand slam tennis program? 2 2. Options - What are the available playing options? 3 3. History - How has

### FUT-K Team Description Paper 2016

FUT-K Team Description Paper 2016 Kosuke Onda and Teruya Yamanishi Department of Management Information Science, Fukui University of Technology Gakuen, Fukui 910 8505, Japan Abstract. This paper describes

### Citation for published version (APA): Canudas Romo, V. (2003). Decomposition Methods in Demography Groningen: s.n.

University of Groningen Decomposition Methods in Demography Canudas Romo, Vladimir IMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite from it. Please

### THE PLAYING PATTERN OF WORLD S TOP SINGLE BADMINTON PLAYERS

THE PLAYING PATTERN OF WORLD S TOP SINGLE BADMINTON PLAYERS Yuen-Ming Tong and Youlian Hong Department of Sports Science and Physical Education, The Chinese University of Hong Kong, Hong Kong SAR A total

### Johnston - Urbandale Soccer Club U8. Coaching Curriculum

Johnston - Urbandale Soccer Club U8 Coaching Curriculum JUSC U8 Theme: Look After the Ball. U8 Topic Finder Click topic and jump there. Coaching Introduction and Overview U8 Coaches and their players and

### U15 Small Sided Competition Guidelines

U15 Small Sided Competition Guidelines Age and developmentally appropriate lacrosse competition for girls playing in the U15 Age Classification Our Vision We envision a future that offers people everywhere

### Topic: Winning Possession in Midfield and Transitioning Quickly to Support the Attack by Mick McDermott and SoccerSpecific.com

Topic: Winning Possession in Midfield and Transitioning Quickly to Support the Attack by Mick McDermott and SoccerSpecific.com Warm Up: The squad of players is split into 4 groups and positioned as shown

### Golf Ball Impact: Material Characterization and Transient Simulation

12 th International LS-DYNA Users Conference Blast/Impact(1) Golf Ball Impact: Material Characterization and Transient Simulation Xiaohu Liu, David Quinn and Jorgen Bergström Veryst Engineering, LLC 47A

### ECO 199 GAMES OF STRATEGY Spring Term 2004 Precept Materials for Week 3 February 16, 17

ECO 199 GAMES OF STRATEGY Spring Term 2004 Precept Materials for Week 3 February 16, 17 Illustration of Rollback in a Decision Problem, and Dynamic Games of Competition Here we discuss an example whose

### Finite Element Analysis of an Aluminium Bike Frame

Finite Element Analysis of an Aluminium Bike Frame Word Count: 1484 1 1. Introduction Each new bike model must pass a series of structural tests before being released for public retail. The purpose of

### the fa coaching futsal level 1 core techniques (1/6)

core techniques (1/6) Control Control to secure the ball Control to change the direction of play Control the ball on the players safe side Control using different surfaces of the body Control to keep the

Design 101: Capacity Issues Part 2 March 7, 2012 Presentation Outline Part 2 Geometry and Capacity Choosing a Capacity Analysis Method Modeling differences Capacity Delay Limitations Variation / Uncertainty

### The Importance of Pressing in Modern Football Why the English Game Must Keep Up

The Importance of Pressing in Modern Football Why the English Game Must Keep Up Posted on October 2, 2013 As football s ever evolving state enters a new phase, pressing has quickly become one of the most

### System of Play Position Numbers and Player Profiles

System of Play Position Numbers and Player Profiles System of Play 1-4-3-3 Playing System 1-4-3-3 3 s 3 s Back Back 4 Backs 1 System of Play 1-4-3-3 Back Back POSITION PROFILE TECHNICAL.

### Case Study: How Misinterpreting Probabilities Can Cost You the Game. Kurt W. Rotthoff Seton Hall University Stillman School of Business

Case Study: How Misinterpreting Probabilities Can Cost You the Game Kurt W. Rotthoff Seton Hall University Stillman School of Business Abstract: Using data to make future decisions can increase the odds

### Johnston - Urbandale Soccer Club U9-U10. Coaching Curriculum

Johnston - Urbandale Soccer Club U9-U10 Coaching Curriculum JUSC U9/U10 Theme: Slow Down! U9-U10 Topic Finder Click topic and jump there. Coaching Areas of Focus Game Thoughts #1 Dribbling to Create Space

### DEVELOPMENT OF A DATABASE AND DECISION SUPPORT SYSTEM FOR PERFORMANCE EVALUATION OF SOCCER PLAYERS. Suat Kasap 1*, Nihat Kasap 2

DEVELOPMENT OF A DATABASE AND DECISION SUPPORT SYSTEM FOR PERFORMANCE EVALUATION OF SOCCER PLAYERS Suat Kasap 1*, Nihat Kasap 2 1 Department of Industrial Engineering, Çankaya University, Ögretmenler Caddesi,

### Match Analysis of a Women's Volleyball Championship Game

University of Tennessee, Knoxville Trace: Tennessee Research and Creative Exchange University of Tennessee Honors Thesis Projects University of Tennessee Honors Program 12-2016 Match Analysis of a Women's

### THE EFFICIENCY MODEL OF SOCCER PLAYER S ACTIONS IN COOPERATION WITH OTHER TEAM PLAYERS AT THE FIFA WORLD CUP

2008, vol. 9 (1), 56 61 THE EFFICIENCY MODEL OF SOCCER PLAYER S ACTIONS IN COOPERATION WITH OTHER TEAM PLAYERS AT THE FIFA WORLD CUP DOI: 10.2478/v10038-008-0002-y Andrzej Szwarc Śniadecki University School

### Hydrodynamic analysis of submersible robot

International Journal of Advanced Research and Development ISSN: 2455-4030, Impact Factor: RJIF 5.24 www.advancedjournal.com Volume 1; Issue 9; September 2016; Page No. 20-24 Hydrodynamic analysis of submersible

### Evolving Gaits for the Lynxmotion Hexapod II Robot

Evolving Gaits for the Lynxmotion Hexapod II Robot DAVID TOTH Computer Science, Worcester Polytechnic Institute Worcester, MA 01609-2280, USA toth@cs.wpi.edu, http://www.cs.wpi.edu/~toth and GARY PARKER

### DESINGNING A DECISION-SUPPORT TOOL FOR GAME ANALYSIS USING BIG DATA SD

G E O R G E M A S O N U N I V E R S ITY SPORTS ANALYTICS DESINGNING A DECISION-SUPPORT TOOL FOR GAME ANALYSIS USING BIG DATA SD P R E P A R E D B Y : S A R A H A L M U J A H E D N I C O L E O N G O R J

### Building up from the back central defenders and midfielders basic cooperation

Age:15-Adult Building up from the back central defenders and midfielders basic cooperation No Players: 14 (13+Gk) Difficulty: Medium/Hard Area/Time: 2/3 Field (25mins) Drill objective (s): 1. Role of midfield

### SOCCER STUDY GUIDE. Soccer is played in 132 countries all over the world. It is the world s number one sport, played for both fun and fitness.

Page 1 of 6 HISTORY: SOCCER STUDY GUIDE Soccer is the fasted growing sport in the world, and the most popular. Attempts have been made to trace soccer, known worldwide as football, to Greek and Roman origins.

### Determination of the Design Load for Structural Safety Assessment against Gas Explosion in Offshore Topside

Determination of the Design Load for Structural Safety Assessment against Gas Explosion in Offshore Topside Migyeong Kim a, Gyusung Kim a, *, Jongjin Jung a and Wooseung Sim a a Advanced Technology Institute,

### Is Home-Field Advantage Driven by the Fans? Evidence from Across the Ocean. Anne Anders 1 John E. Walker Department of Economics Clemson University

Is Home-Field Advantage Driven by the Fans? Evidence from Across the Ocean Anne Anders 1 John E. Walker Department of Economics Clemson University Kurt W. Rotthoff Stillman School of Business Seton Hall

### LEE COUNTY WOMEN S TENNIS LEAGUE

In order for the Lee County Women s Tennis League to successfully promote and equitably manage 2,500+ members and give all players an opportunity to play competitive tennis, it is essential to implement

### The impact of human capital accounting on the efficiency of English professional football clubs

MPRA Munich Personal RePEc Archive The impact of human capital accounting on the efficiency of English professional football clubs Anna Goshunova Institute of Finance and Economics, KFU 17 February 2013

### Hockey Decathlon A COMPETITION FOR BEGINNERS. Horst Wein. Page 1. International Educational Management Systems

Hockey Decathlon A COMPETITION FOR BEGINNERS by Horst Wein Page 1 1. THE TUNNEL One player passes the ball, alternately with his front and reverse stick, through the tunnel formed by the legs of a second

### BASIC FUTSAL COACHING PREPARATION

BASIC FUTSAL COACHING PREPARATION I. Basics Futsal The priority in Futsal is to motivate players in an environment that is conducive to learning. The more pleasure kids derive from their participation,

### International Journal of Computer Science in Sport

International Journal of Computer Science in Sport Volume 16, Issue 1, 2017 Journal homepage: http://iacss.org/index.php?id=30 DOI: 10.1515/ijcss-2017-0003 Network structure of UEFA Champions League teams: