# Analytics Edge Project: Predicting the outcome of the 2014 World Cup

Save this PDF as:

Size: px
Start display at page:

Download "Analytics Edge Project: Predicting the outcome of the 2014 World Cup"

## Transcription

1 Analytics Edge Project: Predicting the outcome of the 2014 World Cup Virgile Galle and Ludovica Rizzo June 27, 2014 Contents 1 Project Presentation: the Soccer World Cup 2 2 Data Set 2 3 Models to predict the outcome of each game 4 4 Logistic Regression for outcome of Group Stage 6 5 Predictions for the 2014 World Cup in Brazil 8 6 Conclusion 9 1

2 1 Project Presentation: the Soccer World Cup The FIFA World Cup is an international association football competition contested by senior s men national teams. The championship has been awarded every four years since the inaugural tournament in 1930 (except in 1942 and 1946 where it was not held because of the Second World War). The current format (since 1998) of the tournament has two parts: a qualifying phase where teams compete among their continent and a final phase where the best 32 teams compete for the title. For our project we focus on the outcome of the final phase. The final tournament has two stages: Groups The 32 teams are split into 8 groups of 4 teams. In every group, all the teams play once against each other and the two best teams qualify for the following phase. The outcome of a game can be the victory of a team or a draw. Bracket 16 teams get out of the group phase and enter a bracket. In every round of the bracket the teams play each other just once, thus there can not be draw games. The next World Cup will be hosted by Brazil in June In our project, we use historical data (results from 1994 to 2010 world cups- 5 editions) to make predictions for the next World Cup. We tackle two type of questions: What will be the outcome of a single game (in the group and in the bracket phase)? In a more aggregate point of view, which teams will qualify for the bracket phase? We split our data set into a training set (results from 1994, 1998, 2002) and a validation test (2006, 2010). We then use our model to predict for the 2014 World Cup. 2 Data Set We collected data from the 5 last editions of the World Cup (1994 to 2010). In every world cup there are 64 games, thus our data set has 5 64 = 320 observations. This number is low compared to others data sets we used in class. We decided not to use more data for several reasons. First of all we believe that soccer has evolved a lot since the 80 s and past competitions are not representative of nowadays soccer. Secondly, before 1994 the FIFA Ranking didn t exist and this is a key feature for our models. For every edition we collected the results of every game and statistical information about every team involved. 2.1 Games In every World Cup, there are 48 group games and 16 bracket games. For every game we recorded the teams involved, the score, the outcome ( 1 if team 1 wins, 2 if team 2 wins and X if it is a draw) and the Stage (Group, or round in bracket). In figure 1, we reported two sample rows of our Games Results data set for Figure 1: Example of games results from

3 2.2 Teams For every team participating in a World Cup we collected the following data: Name of the team FIFA Ranking and FIFA points at the beginning of the competition FIFA (the international soccer association) makes a monthly ranking of national teams according to their performances. They give every team a score (computed with a fairly complicated weighted average of the team recent performances) and these scores are used to establish a ranking. We used the last score and ranking previous to the world cup. The way to compute the FIFA score changed over time, we thus scaled our FIFA points to be consistent Participation In a Row This variable counts the number of consecutive times the team participated in the final phase of the World Cup Average Goals Scored and Conceded (Avggf and Avgga) during the qualifying phase Playoff This is a binary variable that indicates if the team had to go throw a playoff to qualify for the World Cup. The three last variables are indicators of performance during the Qualifying phase. Qualification Zone This variable is a factor that represents the Qualification Zone of the team (Europe, South America, Asia, CONCAF (North and Central America), Africa) Host and Continent Binary variables that indicate if the team is hosting the competition or if it is in his continent. U20 Best performance in the last 4 years of the Under 20 youth team Figure 2: Example of team statistics for the 1998 World Cup In figure 2, we reported two sample rows of our Teams statistics data set for Models Using this data set we tackled two different problems: What is the issue of every game? We will answer this question in section 3. Our problem is split into two different sub problems. For the groups games there are 3 possible outcomes (one team wins or a draw). We will use a generalization of logistic regression (ordered logistic regression). For bracket s games, there are just two possible outcomes. We will use logistic regression to predict the winner. Which teams will qualify for the bracket phase? We will answer this question in section 4. For every group, we use logistic regression to predict which two teams are more likely to qualify for the bracket phase. In the two following sections, we will present the models we used and their performances on the train set and the validation test. In the last section we present our models predictions for the 2014 World Cup. 3

4 3 Models to predict the outcome of each game In this section, we tried to predict the outcome of each game in the group and bracket stage. We will first illustrate the shape of the data set we used and then present the models we used and their performances. 3.1 Data Set In order to predict the outcome of every game we merged our Games Results and our Teams Statistics data files. We first built a data frame where every row was a game and with the following columns: Name of Team 1 Name of Team 2 Outcome of the Game (1,X or 2) Statistics of Team 1 Statistics of Team 2 Our independent variables were the Statistics of the two teams and our dependent variable was the outcome of the game. When we first ran our models we realized that we needed symmetric data. Intuitively, if we ask our model what will be the outcome of a game where (T eam1 = F rance, T eam2 = Italy) or of a game where (T eam2 = Italy, T eam1 = F rance) it should give the same result. In the case of a logistic regression, this mathematically translates into the fact that the coefficients for the corresponding features of Team 1 and Team 2 have to have the same absolute value but opposite sign. Concretely, if the coefficient of FIFARanking.Team1 is 0.5 we want the coefficient of FIFARanking.Team2 to be 0.5. To achieve this symmetry we decided to replace the Statistics of the two teams by their difference. For example, if F IF ARanking.T eam1 = 3 and F IF ARanking.T eam2 = 10, we add the feature F IF ARanking = 3 10 = 7. The shape of our data frame becomes: Name of Team 1 Name of Team 2 Outcome of the Game (1,X or 2) Difference of Statistics We used different models to predict the outcome of every game: CART trees, Random Forests, SVM and logistic regression. We found that the best results were given by models of the logistic family. We will report in the two following paragraphs the best results we have. 3.2 Predicting the outcome of a game in the group As we explained above, in group games there are three different outcomes. We use the following notations: 1 means that Team 1 won, X represents a draw and 2 means that Team 2 won. In our data set these three classes are more or less equally populated: there are approximately 33% of 1, 33% of draws and 33% of 2. We use a generalization of the logistic regression called ordered logit. 4

5 Ordered logistic regression The ordered logistic model allows to capture the fact that there are more than 2 possible outcomes and that these outcomes are ordered. For example, in our case, taking the perspective of Team 1 the outcomes are ordered in the following (increasing) way: 2 lose, X draw, 1 win. Let s call j [ 2, X, 1 ] the level and y the outcome. Let s say that y j if y [ 2, j ] (where the set is ordered). (For example t X y { 2, X }). Let x be the features of the model. In the classical logistic regression, the odds that y = 0 are Odds(y = 0) = e α β.x where α is an intercept and β is the vector of coefficients. The ordered logistic is built in the same way, the odds that y j are Odd(y j) = e αj β.x Let s remark that the coefficients β don t depend on the level but the intercept α j does. Significant variables We ran our model using the function clm in the package ordinal. The regression results are in table 3. We found that the significant variables were FIFARanking, PartInaRow and Continent. The signs of the coefficients make sense: a low FIFARanking difference means that Team 1 has higher chances to win (thus the coefficient has to be negative), a high PartInaRow difference means that Team 1 participated more often than Team 2 and statistically a team playing in his continent has more chances to win. Figure 3: Ordered logistic: significant variables Performances Here are the confusion matrix and the accuracy of our model. The baseline is a model that predict randomly with probability 33%. 2 X X Figure 4: Confusion matrix on train set Accuracy Train Baseline 33% Train Model 37% Test Baseline 33% Test Model 50% Figure 5: Accuracy We can remark that it is really hard to predict when a game is a draw. This makes sense on a intuitive point of view: there are a lot of games were we are confident that a team will win but we are never really confident that it will be a tie. If we consider only the games where we predict that the game is not a draw (first and last columns of the confusion matrix) the results are really satisfying. Looking just at a subset of games makes sense on a betting perspective, if our aim is to bet we can select just a subset of games and discard the others. 5

6 X Accuracy Train Baseline 33% Train Model 56% Test Baseline 33% Test Model 48% Figure 6: Confusion matrix on train set (games Figure 7: Accuracy (games where we do not predict where we do not predict X) X) 3.3 Predicting the outcome in a game in the bracket The main difficulty in predicting the outcome of a group s game is the possibility of a draw, that as we saw is really hard to predict. In this section we try to predict games in the bracket where the outcome is binary, our performances here a more satisfying than in the previous part. Model and results In this part, we use a classical logistic regression to estimate the outcome of a game. The significant variables are the same as in the previous part. Figure 8: Logistic regression for the bracket: significant variables Accuracy AUC Train Baseline 50% 50% Train Model 71% 78% Validation Baseline 50% 50% Validation Model 69% 70% Table 1: Accuracy and AUC Here the baseline is random and thus has an of 50%. We can see that the performance of our model is really satisfying, both the AUC and the accuracy strongly outperform the baseline. 4 Logistic Regression for outcome of Group Stage As we just noticed in Part 3, the ordered logistic has several assets. However it seems that it is very difficult to predict accurately when a draw occurs. Moreover, it seems that we have a very good accuracy in predicting games in the bracket since ties cannot happen. Hence the motivation of predicting the outcome of a group is the following : to predict entirely a World Cup, we do it in two stages : First, we can predict which two teams of each group will qualify for the final bracket and then the result of each game in the bracket. In order to predict accurately the outcome of a group, we are going to consider not each game by itself but only the features of every team that is in the group. 6

7 4.1 A New Database Since we don t want to use the games to predict the outcome of a group, we need a new data base using the features of each team described in Part 2 and the list of each group. For each World Cup, the database has 32 observations (24 for 1994) which corresponds to the number of teams. Every row of our database corresponds to a team in a World Cup. We report its name and its features, followed by the features of the 3 other teams in the same group and a binary variable that indicates whether the team qualifies for the bracket. We denote Team.1 Team.2 and Team.3 the other teams that are in the same group as Team. Here is the shape of our data set: Team (str) : Name of the team FIFApts (num) : The FIFA points normalized like in Part 2 PartInaRow (int) : The consecutive number of participation in the World Cup Team.1 (str) : Name of a team in Team s group FIFApts.1 (num) : The FIFA points also rescaled of Team.1 Qualifzone.1 (int) : The Qualifying zone of Team.1. In Part II, it was introduced as a string. Here we model it by integers ; SA (South America) is replaced by 1, AS (Asia) by -1 and all the other ones by 0 (this choice will be explained further down) Same 3 features for Team.2 and Team.3 OutGroup (int) : Binary variable. 1 if Team qualified for the final bracket, 0 if not. In this case,the train set is going to be every team participating in the 1994, 1998 and 2002 World Cups (88 observations). The validation set is the 2006 and 2010 World Cups (64 observations) and our test set Evaluation of performance of the model As in Part 3, we will evaluate the performance of our model comparing it to benchmarks. Away of measuring our model is the logloss measure. Since the output of our model are probabilities, minimizing the logloss helps us on understanding if our model is risky meaning that it gives high probability even if it s not sure. The formula of the logloss is the following: Logloss(ŷ) = 1 N N (y i log(ŷ i ) + (1 y i )log(1 ŷ i )) i=1 Since our model returns probabilities, then we say that the two teams of each group that have the highest probabilities will pass the group phase. 4.3 Baselines In order to compare our model to a benchmark, we propose two types of benchmarks. The first one is the random answer. Each team has 0.5 probability of qualifying for the bracket. The other one is a smarter baseline which takes into account the seeds (in terms of FIFA ranking). The first seed has 80% chance of qualifying, the second 60%, the third 40% and the last 20%. This corresponds to a rational behavior assuming that the FIFA ranking assesses properly the level of each team. The results are available in table 3 to compare with our model. 7

8 4.4 Logistic Model After having tried CART Tree, Random Forests, SVMs and Logistic Regression, it appears that the last one has better performances. One major issue for all models was the fact that our data base is actually asymmetric like we noticed in Part 3. We know that all coefficients corresponding to all features of Team.1,.2 and.3 should be the same. In order to recover this, we permuted the columns and created several logistic models, saved their coefficients and averaged them. With this method, we find exactly the same coefficients. Notice here that we didn t take the difference like in Part 3. Another remark is that we found here that all coefficients for the Qualifzones were the same except for Asia and South America, hence the decision to change the nature of the variable Qualifzone. The coefficients we obtain are in Table 2 Variables Coefficient FIFApts PartInaRow FIFApts Qualifzone FIFApts Qualifzone FIFApts Qualifzone Intercept Table 2: Coefficients of the Logistic Regressions All the signs of coefficients are easily interpretable. For instance, the more FIFApts Team has, the more likely it has to qualify and this make sense. Another interpretation to make is that teams of South America are more likely to qualify which is also a well known fact in the Soccer world. 4.5 Results The results concerning the prediction of the outcome of Groups are summarized in Table 3. Our model works very well compared to the random baseline and better than the smarter baseline in most of the cases and for all performance measures. In addition, it has another asset that is not outlined here : it predicts differently than the baseline. Sometimes it says that seeds lose when it s not the case, but it also know when a non easily predictable team qualifies which can be a real help while betting against bookmaker for instance. Set Predictions Accuracy Logloss Train Random Baseline Train Smart Baseline Train Logistic Validation Random Baseline Validation Smart Baseline Validation Logistic Table 3: Results of the outcome of the Groups 5 Predictions for the 2014 World Cup in Brazil To summarize, in the last two sections we presented three models: a first model that predicts the outcome of a game in the groups stage, a model that predicts the outcome of a game in the bracket phase and a model that 8

9 predicts which teams, in every group, will qualify for the bracket phase. We will now apply our three models to make predictions for the 2014 World Cup. We have two different ways to predict the outcome of the group phase: 1. We can use the model in section 4 that gives the probability that every team is qualified. We will denote this method OutGroup 2. We can use the ordered logistic model in section 3 and compute the expected number of points earned by every team in the group phase. In every group, the two teams with the greatest number of points qualify. We will denote this method Games. In table 6 in the appendix, we present the results of our two models. We can see that they agree most of time. They disagree on groups that are hard to predict, where the teams performances are really close to each other. We then used the predictions in the groups to build a bracket. Since our two models don t always agree we have two different brackets for the Round of 16 phase. Starting from these two brackets we use the logistic regression of section 3 to predict the outcome of every game. Our results are in in the trees 4 and 7 in the appendix. For every game, we wrote in bold font the name of the expected winner and we reported the probability of this outcome. We can see that our models agree starting from the semifinals. 6 Conclusion To summarize our work, we used historical data of soccer World Cup to build models to predict the outcome of the 2014 World Cup in Brazil. We tried to answer two different type of questions. First of all, what will be the outcome of every single game (in the groups or in the Bracket phase)? Secondly, in a more aggregate way, which teams will pass the group phase and qualify for the bracket? We found that the best performing models were part of the logistic regression family. The significant variables that are able to predict a team s performances are the FIFA Ranking, the Number of consecutive participation to the competition, if the team is playing in its own continent and the qualification zone it comes from. We realized that predicting games in the group phase is way harder than in the bracket phase because of the possibility of draws, we then used aggregated features to predict which teams will qualify for the bracket instead of trying the predict every single game of the bracket. On a betting perspective, we realized that we are almost never confident that a game will be a draw, but we can be really confident that one team will win. Thus, if we were to bet, we would use just games where we predict that one team is going to win. Finally, we made predictions for the 2014 World Cup and we can t wait to see if we are right! 9

10 Appendix Table 4: Bracket predicted starting with the method OutGroup Brazil 97% Netherlands Greece England 63% Brazil 95% England Brazil 79% Germany Switzerland 67% Iran Germany 90% Algeria Spain 68% Mexico Switzerland Germany 89% Spain Italy 63% Brazil 74% Argentina Brazil Italy 60% Colombia Italy Argentina 71% Argentina 87% France Argentina 85% USA Belgium USA 69% 10

11 Table 5: Bracket predicted with the method Games Brazil 92% Chile Brazil 90% Uruguay Colombia Uruguay 57% Brazil 79% Germany Ecuador 70% Bosnia Ecuador Germany 84% Germany 90% Russia Brazil 74% Argentina Brazil Spain 68% Mexico Spain Italy 63% Italy 81% Greece Italy Argentina 71% Argentina 87% France Argentina 85% Portugal Belgium Portugal 68% 11

12 Table 6: Ranking in the groups predict by our two models A OutGroups Prob Games Pts 1 Brazil 97.3% Brazil Mexico 57.1% Mexico Croatia 39.7% Croatia Cameroon 26.8% Cameroon 1.7 B OutGroups Prob Games Pts 1 Spain 93% Spain Netherlands 65.6% Chile Chile 64.2% Netherlands Australia 31.3% Australia 1.5 C OutGroups Prob Games Pts 1 Greece 60.4% Colombia Colombia 48.1% Greece Ivory Coast 35.4% Ivory Coast Japan 31.6% Japan 2.5 D OutGroups Prob Games Pts 1 Italy 90.9% Italy England 52.4% Uruguay Uruguay 45.3% England Costa Rica 36.4% Costa Rica 1.7 E OutGroups Prob Games Pts 1 Switzerland 53.7% Ecuador France 49.8% France Honduras 35.7% Switzerland Ecuador 27.6% Honduras 2.6 F OutGroups Prob Games Pts 1 Argentina 84.5% Argentina Iran 40.5% Bosnia Bosnia 27.9% Iran Nigeria 20.2% Nigeria 2.5 G OutGroups Prob Games Pts 1 Germany 96.6% Germany USA 68.2% Portugal Portugal 63.1% USA Ghana 39.7% Ghana 1.9 H OutGroups Prob Games Pts 1 Belgium 63% Belgium Algeria 61% Russia South Korea 47.5% Algeria Russia 39.4% South Korea

13 Table 7: Bracket predicted after the Group Phase Brazil 92% Chile Brazil 90% Uruguay Colombia Uruguay 57% Brazil 79% Germany France 69% Nigeria France Germany 79% Germany 89% Algeria Brazil 74% Argentina Brazil Netherlands Mexico 82% Mexico 66% Costa Rica Costa Rica 60% Greece Mexico Argentina 90% Argentina 88% Switzerland Argentina 85% USA Belgium USA 87% 13

### 3:00 p.m.: B1. Spain vs. B2 Netherlands at Arena Fonte Nova, Salvador

Thursday, June 12 4:00 p.m.: A1. Brazil vs. A2. Croatia at Arena Corinthians, São Paulo Friday, June 13 12:00 p.m.: A3. Mexico vs. A4. Cameroon at Estadio das Dunas, Natal 3:00 p.m.: B1. Spain vs. B2 Netherlands

### CIES Football Observatory. World Cup 2014 Scenario

CIES Football Observatory 2014 Scenario Scenario Methodology The CIES Football Observatory has analysed the career trajectory of squad members from the last four finalists and is now able to identify some

### 2014 FIFA WORLD CUP TV BROADCAST SCHEDULE

2014 WORLD CUP TV BROADCAST SCHEDULE Week 1 WK 1 Day 1 Day 2 Day 3 Day 4 12-Jun 13-Jun 14-Jun 15-Jun 2/25 3/25 4/25 1/32 2/32 19:00 2 20:15 20:40 21:10 2010 WC Review Game 2: Mexico vs. Cameroon Game 5:

### 2013 SCHOLARSHIP EXAMINATION PRACTICAL SECTION

2013 SCHOLARSHIP EXAMINATION PRACTICAL SECTION DEPARTMENT COURSE TITLE TIME ALLOWED NUMBER OF QUESTIONS IN PAPER NUMBER OF QUESTIONS TO BE ANSWERED GENERAL INSTRUCTIONS SPECIAL INSTRUCTIONS CALCULATORS

### executive summary permanent tsb

2014 permanent tsb executive summary The permanent tsb research continues to highlight the lack of mobility in the banking sector relative to the previous research in January 2014. Only 1-in-10 (11%) banking

### What can econometrics tell us about World Cup performance? May 2010

What can econometrics tell us about World Cup performance? May 2010 What can econometrics tell us about World Cup performance? Executive summary Econometrics is the standard technique used in explaining

### FIVB WORLD LEAGUE PREVIEWS WEEK 1 JUNE 17-19, 2016

Pool A1: Australia Belgium (17 June) Belgium have won all of their four World League matches against Australia, all in group 2 in the 2014 edition. The Belgian team won twice by 3-1 and twice in straight

### Appendix 1: Full Country Rankings

Appendix 1: Full Country Rankings Below please find the complete rankings of all 75 markets considered in the analysis. Rankings are broken into overall rankings and subsector rankings. Overall Renewable

### FIVB WORLD LEAGUE PREVIEWS WEEK 1 JUNE 17-19, 2016

Pool A2: Netherlands Slovakia (17 June) Netherlands and Slovakia will meet for the first time in the World League. These nations most recently clashed in a competitive match at the 2007 European Championship,

### Jan Vecer Tomoyuki Ichiba Mladen Laudanovic. Harvard University, September 29, 2007

Sports Sports Tomoyuki Ichiba Mladen Laudanovic Department of Statistics, Columbia University, http://www.stat.columbia.edu/ vecer Harvard University, September 29, 2007 Abstract Sports In this talk we

### Football Bets Explained:

Football Bets Explained: How to cash-in on ALL football matches without ever picking a winning team www.worldcup2010bet.co.uk The World Cup is the biggest sporting event on the planet and South Africa

### Tble Home Team Visiting Team Home Team Visiting Team Home Team Visiting Team. Open Qualification Group A. 1st Round 2nd Round 3rd Round

Open Qualification Group A 14th WORLD BRIDGE GAMES OPEN SERIES Group A 1 Netherlands Mexico Chile Netherlands Netherlands Denmark 2 Saudi Arabia Bosnia & Hrzna Switzerland Jordan Spain Germany 3 Korea

### A June 2015 The Economic Pulse

Global @dvisor The Economic Pulse of the World Citizens in 24 Countries Assess the Current State of their Country s Economy for a Total Global Perspective The Economic Pulse These are the findings of the

### Tourism Statistical Yearly Report 2012

Tourism Statistical Yearly Report 2012 The Costa Rica Tourism Board is pleased to present the Tourism Statistical Yearly Report 2012. The information has been collected and organized by officers of Subproceso

### Predicting the World Cup. Dr Christopher Watts Centre for Research in Social Simulation University of Surrey

Predicting the World Cup Dr Christopher Watts Centre for Research in Social Simulation University of Surrey Possible Techniques Tactics / Formation (4-4-2, 3-5-1 etc.) Space, movement and constraints Data

### A March 2016 The Economic Pulse

Global @dvisor The Economic Pulse of the World Citizens in 25 Countries Assess the Current State of their Country s Economy for a Total Global Perspective The Economic Pulse These are the findings of the

### Correlation Analysis Between SoccerGame World Ranking and Player League Distribution

Sport and Art (): -0, 0 DOI: 0.89/saj.0.000 http://www.hrpub.org Correlation Analysis Between SoccerGame World Ranking and Player League Distribution Sandgren Evelina, Karlsson Mia, Ji-Guo Yu * Sports

### Global AML Resource Map Over 2000 AML professionals

www.pwc.co.uk Global AML Resource Map Over 2000 AML professionals January 2016 Global AML Resources: Europe France Italy Jersey / Guernsey 8 Ireland 1 Portugal 7 Luxembourg 5 United Kingdom 1 50 11 Spain

### A Beautiful Game. Vince Ganzberg Indiana Youth Soccer Director of Education

A Beautiful Game Vince Ganzberg Indiana Youth Soccer Director of Education This past fall I purchased a book entitled, A Beautiful Game by Tom Watt. The book shares untold stories of some of soccer s best

### 41 T Korea, Rep. 52.3. 42 T Netherlands 51.4. 43 T Japan 51.1. 44 E Bulgaria 51.1. 45 T Argentina 50.8. 46 T Czech Republic 50.4. 47 T Greece 50.

Overall Results Climate Change Performance Index 2012 Table 1 Rank Country Score** Partial Score Tendency Trend Level Policy 1* Rank Country Score** Partial Score Tendency Trend Level Policy 21 - Egypt***

### Energy Briefing: Global Crude Oil Demand & Supply

Energy Briefing: Global Crude Oil Demand & Supply November 6, 215 Dr. Edward Yardeni 516-972-7683 eyardeni@ Debbie Johnson 48-664-1333 djohnson@ Please visit our sites at www. blog. thinking outside the

### International Mains Voltages Edition 01/97

International Mains Voltages Edition 01/97 0921 271X / 0197 The international low voltages used in industry, business and domestically. Listed in continents and countries. Status April 1996 The following

### 2015 Growth in data center employment continues but the workforce is changing

Published in Conjunction with MARKET BRIEFING GLOBAL DATA CENTER EMPLOYMENT 2015 2015 Growth in data center employment continues but the workforce is changing Globally, the number of people working in

### INOMICS. Job Market Report Worldwide Overview

INOMICS Job Market Report 2014 Worldwide Overview 1 Table of Contents I. Methodology 03 II. Key Findings 04 III. Job Market Outlook 05 1. Profile of Respondents 05 a. Demographics 05 Age Gender Country

Logix5000 Clock Update Tool V2.00.36. 1 Overview Logix5000 Clock Update Tool 1. 1. What is is it? it? 2. 2. How will it it help me? 3. 3. How do do I I use it? it? 4. 4. When can I I get get it? it? 2

### Green Survey Key Findings

GfK PURCHASING POWER INTERNATIONAL 1 Agenda 1. Europe 3 2. Americas 45 3. Asia & Near East 54 4. Afrika 66 5. Australia 68 6. Overview of countries and available levels 70 2 2 EUROPE 4 GfK

### World Consumer Income and Expenditure Patterns

World Consumer Income and Expenditure Patterns 2014 14th edi tion Euromonitor International Ltd. 60-61 Britton Street, EC1M 5UX TableTypeID: 30010; ITtableID: 22914 Income Algeria Income Algeria Income

### The big pay turnaround: Eurozone recovering, emerging markets falter in 2015

The big pay turnaround: Eurozone recovering, emerging markets falter in 2015 Global salary rises up compared to last year But workers in key emerging markets will experience real wage cuts Increase in

### Is UEFA Right? Measuring Competitivenes of Domestic Football Leagues

Is UEFA Right? Measuring Competitivenes of Domestic Football Leagues Soumodip Sarkar Associate Professor, Universidade de Evora, Portugal and Director of Center of Management and Economic Studies- Universidade

### Beating the MLB Moneyline

Beating the MLB Moneyline Leland Chen llxchen@stanford.edu Andrew He andu@stanford.edu 1 Abstract Sports forecasting is a challenging task that has similarities to stock market prediction, requiring time-series

### FDI performance and potential rankings. Astrit Sulstarova Division on Investment and Enterprise UNCTAD

FDI performance and potential rankings Astrit Sulstarova Division on Investment and Enterprise UNCTAD FDI perfomance index The Inward FDI Performance Index ranks countries by the FDI they receive relative

### INFLATION BY THE DECADES: 1950s

BY THE DECADES: 1950s BY: STEVE H. HANKE & NICHOLAS KRUS JOHNS HOPKINS INSTITUTE FOR APPLIED ECONOMICS, GLOBAL HEALTH & THE STUDY OF BUSINESS ENTERPRISE BY THE DECADES:1950S KEY FINDINGS Data was extremely

### List of tables. I. World Trade Developments

List of tables I. World Trade Developments 1. Overview Table I.1 Growth in the volume of world merchandise exports and production, 2010-2014 39 Table I.2 Growth in the volume of world merchandise trade

### Research Notes. «Economic analysis: neat tools applied to dirty data, Economists view: And the world champion is...

Research Notes Economists view: And the world champion is... UBS Wealth Management Research / 21.04.2006 The question of who will be the next world champion is a topic of endless discussion among the countless

### PANDUIT Physical Layer Infrastructure Management. EMC Smarts Integration Module

PANDUIT Physical Layer Infrastructure Management EMC Smarts Integration Module SM About PANDUIT A World Class Developer PANDUIT is a world class developer and provider of leading edge solutions that help

### KINGDOM OF CAMBODIA NATION RELIGION KING 3

KINGDOM OF CAMBODIA NATION RELIGION KING 3 TOURISM STATISTICS REPORT February 2016 MINISTRY OF TOURISM Statistics and Tourism Information Department No. A3, Street 169, Sangkat Veal Vong, Khan 7 Makara,

### Betting Guide. www.sportsbet.co.za. BET ONline AND MOBI: Betting in running on all matches Spreads in Running on all Matches Exotics available

Phone to bet: 0860 510 520 BET ONline AND MOBI: www.sportsbet.co.za 2010 Betting Guide 0 % TAX On Sport Bets! Betting in running on all matches Spreads in Running on all Matches Exotics available 1 World

Brochure More information from http://www.researchandmarkets.com/reports/3278449/ The 2016 World Forecasts of Hand-Operated Date, Sealing, or Numbering Stamps; Devices for Printing or Embossing Labels;

### GLOBAL DATA CENTER INVESTMENT 2013

2013 CENSUS REPORT: Global Data Center Investment 2013 GLOBAL DATA CENTER INVESTMENT 2013 2013 - Healthy Growth in Data Center Investment Globally Globally, the data center industry has continued to maintain

### Bangladesh Visa fees for foreign nationals

Bangladesh Visa fees for foreign nationals No. All fees in US \$ 1. Afghanistan 5.00 5.00 10.00 2. Albania 2.00 2.00 3.00 3. Algeria 1.00 1.00 2.00 4. Angola 11.00 11.00 22.00 5. Argentina 21.00 21.00 42.00

### SuccessFactors Employee Central: Cloud Core HR Introduction, Overview, and Roadmap Update Joachim Foerderer, SAP AG

Orange County Convention Center Orlando, Florida June 3-5, 2014 SuccessFactors Employee Central: Cloud Core HR Introduction, Overview, and Roadmap Update Joachim Foerderer, SAP AG SESSION CODE: 1812 Cloud

### Total of 280 fatal cases in Europe and EFTA countries and in the rest of the world have been reported up to date.

ECDC DAILY UPDATE Pandemic (H1N1) 2009 Update 28 October 2009, 09:00 hours CEST Main developments in past 24 hours Total of 280 fatal cases in Europe and EFTA countries and 5 658 in the rest of the world

### The Ultimate Guide to Creating Successful Sports Betting Systems from SportsBettingChamp.com

The Ultimate Guide to Creating Successful Sports Betting Systems from SportsBettingChamp.com Here s my definitive guide on how YOU can create your own successful sports betting systems! What sport? Before

### Region Country AT&T Direct Access Code(s) HelpLine Number. Telstra: 1 800 881 011 Optus: 1 800 551 155

Mondelēz International HelpLine Numbers March 22, 2013 There are many ways to report a concern or suspected misconduct, including discussing it with your supervisor, your supervisor s supervisor, another

### Fall 2015 International Student Enrollment

Fall 2015 International Student Enrollment Prepared by The Office of International Affairs Nova Southeastern University Nova Southeastern University International Student Statistics Fall 2015 International

### International Statistical Institute, 56th Session, 2007: Phil Everson

Teaching Regression using American Football Scores Everson, Phil Swarthmore College Department of Mathematics and Statistics 5 College Avenue Swarthmore, PA198, USA E-mail: peverso1@swarthmore.edu 1. Introduction

### Computer Sciences Corporation Accelerates Innovations to Capture New Business Opportunities

CUSTOMER CASE STUDY Accelerates Innovations to Capture New Business Opportunities Executive Summary CUSTOMER NAME INDUSTRY Service Provider BUSINESS CHALLENGES Identify new, high-growth service models

### High Income Fund, Inc.[NYSE: AWF] (the "Fund") today released its monthly portfolio update as of June 30, 2013.

News Release FOR IMMEDIATE RELEASE Contacts: Shareholder Contact: 1-800-221-5672 AllianceBernstein Global High Income Fund RELEASES MONTHLY PORTFOLIO UPDATE New York, NY, July 30, 2013 - AllianceBernstein

### One billion digital TV households

One billion digital TV households About 455 million digital homes were added around the world between end-2010 and end-2014, according to a new report from Digital TV Research. This took the digital TV

### GLOBAL. 2014 Country Well-Being Rankings. D Social (% thriving) E Financial (% thriving) F Community (% thriving) G Physical (% thriving)

0 0 GLOBAL 0 Country Rankings 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 : >0.0% 0.% 0.0% 0.% 0.0% 0.% 0.0% 0.0% A Country s global rank B in three or more elements of well-being C (% thriving) D (% thriving) E

### Data Modeling & Bureau Scoring Experian for CreditChex

Data Modeling & Bureau Scoring Experian for CreditChex Karachi Nov. 29 th 2007 Experian Decision Analytics Credit Services Help clients with data and services to make business critical decisions in credit

### Chapter 4A: World Opinion on Terrorism

1 Pew Global Attitudes Project, Spring 2007 Now I m going to read you a list of things that may be problems in our country. As I read each one, please tell me if you think it is a very big problem, a moderately

### FÉDÉRATION INTERNATIONALE DE FOOTBALL ASSOCIATION. Football

FÉDÉRATION INTERNATIONALE DE FOOTBALL ASSOCIATION Football A. EVENTS (2) Men s Event (1) Women s Event (1) 16-team tournament 12-team tournament B. ATHLETES QUOTA 1. Total Quota for Football: Qualification

### FedEx International Priority. FedEx International Economy 3

SERVICES AND RATES FedEx International Solutions for your business Whether you are shipping documents to meet a deadline, saving money on a regular shipment or moving freight, FedEx offers a suite of transportation

### 2015 Country RepTrak The World s Most Reputable Countries

2015 Country RepTrak The World s Most Reputable Countries July 2015 The World s View on Countries: An Online Study of the Reputation of 55 Countries RepTrak is a registered trademark of Reputation Institute.

### Football Match Winner Prediction

Football Match Winner Prediction Kushal Gevaria 1, Harshal Sanghavi 2, Saurabh Vaidya 3, Prof. Khushali Deulkar 4 Department of Computer Engineering, Dwarkadas J. Sanghvi College of Engineering, Mumbai,

### THE ICSID CASELOAD STATISTICS (ISSUE 2016 1)

THE ICSID CASELOAD STATISTICS (ISSUE 06 ) The ICSID Caseload Statistics (Issue 06 ) This issue of the ICSID Caseload Statistics updates the profile of the ICSID caseload, historically and for the calendar

### High Income Fund, Inc.[NYSE: AWF] (the "Fund") today released its monthly portfolio update as of January 31, 2013.

News Release FOR IMMEDIATE RELEASE Contacts: Shareholder Contact: 1-800-221-5672 AllianceBernstein Global High Income Fund RELEASES MONTHLY PORTFOLIO UPDATE New York, NY, February 26, 2013 - AllianceBernstein

### Carnegie Mellon University Office of International Education Admissions Statistics for Summer and Fall 2015

Carnegie Mellon University Admissions Statistics for and Fall 2015 New International Students and Fall 2015 Undergraduate 344 15.2% Master's 1599 70.6% Doctorate 167 7.4% Exchange 73 3.2% 81 3.6% Total

I. World trade developments The value of world merchandise exports increased by 20 per cent in 2011 while exports of commercial services grew by 11 per cent. Key developments in 2011: a snapshot Trade

### Seemingly Irrelevant Events Affect Economic Perceptions and Expectations: The FIFA World Cup 2006 as a Natural Experiment

DISCUSSION PAPER SERIES IZA DP No. 2275 Seemingly Irrelevant Events Affect Economic Perceptions and Expectations: The FIFA World Cup 2006 as a Natural Experiment Thomas Dohmen Armin Falk David Huffman

### The basic unit in matrix algebra is a matrix, generally expressed as: a 11 a 12. a 13 A = a 21 a 22 a 23

(copyright by Scott M Lynch, February 2003) Brief Matrix Algebra Review (Soc 504) Matrix algebra is a form of mathematics that allows compact notation for, and mathematical manipulation of, high-dimensional

### Digital TV Research. http://www.marketresearch.com/digital-tv- Research-v3873/ Publisher Sample

Digital TV Research http://www.marketresearch.com/digital-tv- Research-v3873/ Publisher Sample Phone: 800.298.5699 (US) or +1.240.747.3093 or +1.240.747.3093 (Int'l) Hours: Monday - Thursday: 5:30am -

### Byder velkommen til debat om Korruption i Sport

& Byder velkommen til debat om Korruption i Sport journalist Paneldeltagere: Declan Hill Forfatter til bogen Det Aftalte Spil, undersøgende og Ph.d. fra Oxford England Henrik Brandt Direktør for Idrættens

### Making Sense of the Mayhem: Machine Learning and March Madness

Making Sense of the Mayhem: Machine Learning and March Madness Alex Tran and Adam Ginzberg Stanford University atran3@stanford.edu ginzberg@stanford.edu I. Introduction III. Model The goal of our research

### Contact Centers Worldwide

A Contact Centers Worldwide Country Tel.no. Supported lang. Contact Center Albania Algeria 852 665 00 +46 10 71 66160 Angola 89900 +34 91 339 2121 (Port) and Portuguese +34 913394044 +34 913394023 (Por)

### INTERNATIONAL MBA IE BUSINESS SCHOOL

INTERNATIONAL MBA IE BUSINESS SCHOOL CAREER REPORT 2014 2013-2014 GRADUATES www.ie.edu/international-mba INTERNATIONAL MBA CAREER REPORT 2013-2014 GRADUATES CONTENTS I. Facts and Trends II. Profile of

### Statistical Foundations: Measures of Location and Central Tendency and Summation and Expectation

Statistical Foundations: and Central Tendency and and Lecture 4 September 5, 2006 Psychology 790 Lecture #4-9/05/2006 Slide 1 of 26 Today s Lecture Today s Lecture Where this Fits central tendency/location

### Foreign players in football teams

CIES Football Observatory Monthly Report Issue no. 12 - February 2016 Foreign players in football teams Drs Raffaele Poli, Loïc Ravenel and Roger Besson 1. Introduction The proportion of foreign players

### Differences in the Developmental Needs of Managers at Multiple Levels

1 Differences in the Developmental Needs of Managers at Multiple Levels Ross DePinto, MBA Jennifer J. Deal, Ph.D. Center for Creative Leadership 2 Outline Introduction Background Research questions The

### THE BLEISURE REPORT 2014 BRIDGESTREET.COM

THE BLEISURE REPORT 2014 BRIDGESTREET.COM THE BLEISURE REPORT 2014 BridgeStreet Global Hospitality s Bleisure Study, a recent survey of 640 international guests. EXECUTIVE SUMMARY Travel habits The majority

### Behavioral Finance: FIFA World Cup Expectations and Stock Market Success

Behavioral Finance: FIFA World Cup Expectations and Stock Market Success By Jeff Janousek Abstract Behavioral finance, defined as the combination of behavioral and cognitive psychology theory with economics

### INCU Update and Its Role in International Trade Facilitation

International Network of Customs Universities (INCU) INCU Update and Its Role in International Trade Facilitation Dr. Mikhail Kashubsky WCO PICARD Conference 2014 Puebla, Mexico 17-19 September 2014 Current

### High Income Fund, Inc.[NYSE: AWF] (the "Fund") today released its monthly portfolio update as of April 30, 2013.

News Release FOR IMMEDIATE RELEASE Contacts: Shareholder Contact: 1-800-221-5672 AllianceBernstein Global High Income Fund RELEASES MONTHLY PORTFOLIO UPDATE New York, NY, May 28, 2013 - AllianceBernstein

### DuchenneConnect. www.duchenneconnect.org

DuchenneConnect www.duchenneconnect.org 1 What is DuchenneConnect? Web based patient self report registry to link the resources and needs of the Duchenne/Becker muscular dystrophy community, including:

### Ch. 13.2: Mathematical Expectation

Ch. 13.2: Mathematical Expectation Random Variables Very often, we are interested in sample spaces in which the outcomes are distinct real numbers. For example, in the experiment of rolling two dice, we

### Introducing GlobalStar Travel Management

Introducing GlobalStar Travel Management GlobalStar is a worldwide travel management company owned and managed by local entrepreneurs. In total over 80 market leading enterprises, representing over US\$13

### Global Downturn s Heavy Toll

Global Downturn s Heavy Toll Bruce Stokes Director, Global Economic Attitudes Pew Research Center June 2013 Spring 2013 Pew Global Attitudes Survey Economic Sentiment 2 Crisis Soured Economic Views % Say

### The Future Demographic

The Future Demographic Global Population Trends and Forecasts to 2030 3rd edition Euromonitor In ter na tional Ltd, 60-61 Britton Street, Lon don EC1M 5UX Euromonitor International 2012 www.euromonitor.com

### UNHCR, United Nations High Commissioner for Refugees

Belgium 22 Jul 1953 r 08 Apr 1969 a Belize 27 Jun 1990 a 27 Jun 1990 a Benin 04 Apr 1962 s 06 Jul 1970 a Bolivia 09 Feb 1982 a 09 Feb 1982 a Bosnia and Herzegovina 01 Sep 1993 s 01 Sep 1993 s Botswana

### THE ICSID CASELOAD STATISTICS (ISSUE 2015-1)

THE ICSID CASELOAD STATISTICS (ISSUE 05-) The ICSID Caseload Statistics (Issue 05-) This issue of the ICSID Caseload Statistics updates the profile of the ICSID caseload, historically and for the calendar

### Building on 55 GW of experience. Track record as of 31 December 2012

Building on 55 GW of experience Track record as of 31 December 2012 Proven technology Proven technology. For Vestas, it is more than a saying it is something we live by. With more than 30 years in the

### Puerto Rico 2016 SERVICES AND RATES

SERVICES AND RATES FedEx International Solutions for your business Whether you are shipping documents to meet a deadline, saving money on a regular shipment or moving freight, FedEx offers a suite of transportation

### Contact. Kantar Worldpanel, Korea / tel / High Definition Inspiration

Contact Kantar Worldpanel, Korea / tel 02-3779-4496 / e-mail website.kr@kantarworldpanel.com High Definition Inspiration Contact Kantar Worldpanel, Korea 02-3779-4496 website.kr@kantarworldpanel.com twitter.com/kwp_kr

### The Role of Banks in Global Mergers and Acquisitions by James R. Barth, Triphon Phumiwasana, and Keven Yost *

The Role of Banks in Global Mergers and Acquisitions by James R. Barth, Triphon Phumiwasana, and Keven Yost * There has been substantial consolidation among firms in many industries in countries around

### YTD 2015-27 CS AWARDS IN AMERICAS

YTD 2015-27 CS AWARDS IN AMERICAS Argentina Bolivia Brazil Frontline Customer Service Team of the Year, All Industries (Bronze) Customer Service Department of the Year, Airlines, Distribution & Transportation

### DIRECT MARKETING STRATEGY. Web & Software Development Services

DIRECT MARKETING STRATEGY Web & Software Development Services Document created by : Rakesh Negi Business Development & Market Research Consultant New Delhi, India Skype: rakesh_negi1 MSN: rakesh_negi1@hotmail.com

### Cisco Phone Systems Telemarketing Script Cold Call

Cisco Phone Systems Telemarketing Script Cold Call 1. Locate Contact: Name listed Owner General Manager / Office Manager Chief BDM (Business Decision Maker) Note: Avoid talking to IT since this is not

### The face of consistent global performance

Building safety & security global simplified accounts The face of consistent global performance Delivering enterprise-wide safety and security solutions. With more than 500 offices worldwide Johnson Controls

### Foreign Taxes Paid and Foreign Source Income INTECH Global Income Managed Volatility Fund

Income INTECH Global Income Managed Volatility Fund Australia 0.0066 0.0375 Austria 0.0045 0.0014 Belgium 0.0461 0.0138 Bermuda 0.0000 0.0059 Canada 0.0919 0.0275 Cayman Islands 0.0000 0.0044 China 0.0000

### The World Market for Medical, Surgical, or Laboratory Sterilizers: A 2013 Global Trade Perspective

Brochure More information from http://www.researchandmarkets.com/reports/2389480/ The World Market for Medical, Surgical, or Laboratory Sterilizers: A 2013 Global Trade Perspective Description: This report

### ISO 26000 status. Staffan Söderberg October 20, 2015 Vice Chair Post Publication Organization ISO 26000

ISO 26000 status Staffan Söderberg October 20, 2015 staffan.soderberg@amap.se www.amap.se Vice Chair Post Publication Organization ISO 26000 Some basic information: ISO ISO is 165 national members (75%

### MANUAL GAME ARRANGEMENTS

MANUAL OF GAME ARRANGEMENTS Prepared for: Southwest Lawn Bowls Association Materials to compile this handbook Were taken from previous work of Tournament Directors all over the U.S. and are considered

### Does the Dividend Yield Predict International Equity Returns?

Does the Dividend Yield Predict International Equity Returns? Navid K. Choudhury 1 Spring 2003 Abstract The use of the dividend yield as a forecaster for stock market returns is examined by focusing on

### Among the 34 OECD countries, Belgium performed above the OECD average in each of

BELGIUM ***Note- Some results for Belgium published today (3 December 2013) in the PISA 2012 international reports are in need of revision due to a technical error. An erratum is available from the PISA

### Report authors 2. Introduction 3. Match schedule 4. Report notes 5. Top-line coverage and audience summary 6. Executive summary 7

Contents Report authors 2 Introduction 3 Match schedule 4 Report notes 5 Top-line coverage and audience summary 6 Executive summary 7 Key market summaries 8 In-home data sources and methodology 10 Global

### Senate Committee: Education and Employment. QUESTION ON NOTICE Budget Estimates 2015-2016

Senate Committee: Education and Employment QUESTION ON NOTICE Budget Estimates 2015-2016 Outcome: Higher Education Research and International Department of Education and Training Question No. SQ15-000549