Macro Vision June 13, 2018 2018 FIFA World Cup Russia : Who has the greatest chances of winning? During the next few weeks, billions will direct their eyes toward Russia, the country hosting the 2018 World Cup. As in past tournaments, we did an exercise to estimate which national teams will likely stand out. We built our model on results for each match in past World Cups, starting in 1994. To estimate the probability of victory by a team in a given match, we considered factors such as current quality of players, tradition and fan support. The World Cup has a first phase of accumulated points in which countries face each other off among groups of four and subsequent knockout rounds: Group of 16, Quarter-Finals, Semi-Finals and games for first and third places. Based on groups already drawn out for the first phase, we set up our own table with countries that, according to our model, should move on toward disputing the champion title. Factors in our model that determine chances of success In our view, there are at least three key factors that determine the success of a country in a World Cup: team quality during the competition, tradition and support from fans. We sought to build variables that represent these factors quantitatively and estimated an econometric model (Probit type) to calculate the likelihood of success for each team and match. We detailed these factors below: Current quality: It is possibly the most important factor for a team s performance during the championship. There are many measures to quantify quality, such as the market value of its players (more expensive teams often have the best players), each team s performance in recent games (or results in knockouts held in each continent: America Cup, UEFA, CAF, etc.). Each one of these variables certainly has relevance to predict the success of any one team. However, using simultaneously variables that measure similar factors tends to be counterproductive. To have a good model, we believe that less is more. Hence, we decided to infer the quality of a team using only its position in the FIFA ranking. This is a simple and straightforward variable that effectively summarizes the recent performance of participating teams, can be easily accessed and has a long history (unlike team market value, for instance). We tested this measure and it showed good correlation with results attained in past World Cups. Tradition: The weight of the jersey is a key factor in sports. Tradition represents the structural quality of a team across generations. Some countries have good soccer consistently over time. Even if they are not so strong at a given moment, their history warrants respect and fear. Hence, tradition is a complementary factor to current quality. To measure this effect, we found two variables that were proved significant in predicting team performance in World Cups: i) the ability to reach advanced phases of the tournament in past competitions; and ii) whether the country is European or Latin American. In other words, countries that went further in the past tend to go further this time. Also, European and Latin American nations perform better, on average, than countries from other continents. FIFA Ranking Germany 1 Brazil 2 Belgium 3 Portugal 4 Argentina 5 Switzerland 6 France 7 Spain 8 Poland 10 Peru 11 Denmark 12 England 13 Tunisia 14 Mexico 15 Colombia 16 Uruguay 17 Croatia 18 Iceland 22 Sweden 23 Costa Rica 25 Senegal 28 Serbia 35 Iran 36 Australia 40 Morocco 42 Egypt 46 Nigeria 47 Panama 55 Japan 60 South Korea 61 Russia 66 Saudi Arabia 70 (Apr 2018) Please refer to the last page of this report for important disclosures, analyst and additional information. Itaú Unibanco or its subsidiaries may do or seek to do business with companies covered in this research report. As a result, investors should be aware that the firm may have a conflict of interest that could affect the objectivity of this report. Investors should not consider this report as the single factor in making their investment decision.
Fans: Do countries that play at home get an extra boost? Results confirm that. One good example is the 2002 FIFA World Cup South Korea. Sweden and England just reached the final match when they hosted the event. And in six instances (nearly 1/3 of all), the host country won the championship. Fans tend to make a difference and the country playing at home often has an advantage. Thus, our model attributes more probability to host countries. We also tested a second effect, the host continent. I.e., a country may be helped if playing in its continent, given that fans don t need to travel so far. However, this variable has little significance. It did not bring relevant gain to our model and was thus discarded. Groups and orders in bracket order: This is not an actual variable in our model, but it is absolutely essential in any forecast. Group composition (and ranking) is important to the upcoming phases and may benefit/hurt teams as they are lucky/unlucky to face adversaries who are weaker/stronger. Group A Group B Russia Saudi Arabia Morocco Iran Egypt Uruguay Portugal Spain Group C Group D France Australia Argentina Iceland Peru Denmark Croatia Nigeria Group E Group F Costa Rica Serbia Germany Mexico Brazil Switzerland Sweden South Korea Group G Group H Belgium Panama Colombia Japan Tunisia England Poland Senegal Results: Base case The table below shows our model s results in its simplest version. We compared match by match the characteristics of the two teams who will be playing and estimated which one is more likely to move on to the next match. The probability of victory attributed to each national team is shown at its right. The favorites move on to knockout phases, without any huge surprise. Mexico and Serbia (which is in Brazil s group) are the more traditional teams that do not make it through the group phase. Despite a promising performance in the recent past, Iceland will also be left behind by its group peers Argentina and Croatia. In the Round of 16, other stronger teams fall behind, such as Colombia, Sweden and Portugal (defeated by Russia, who wins by a small margin in our model, benefited by its host status). Belgium and Russia are the only teams who have never won a Cup to reach the Quarter Finals, only to be eliminated by Brazil and France, respectively. There will be other tight races in this phase: Germany vs. England, Argentina vs. Spain. In the Semi-Finals, titans Germany and Argentina face each other off, while Brazil gets an opportunity of getting back at France, after being defeated in Quarter Finals in 1986, in the final match of 1998, and semi-final game in 2006. We believe that Brazil will get another chance of revenge in the final match against Germany. At each stage, forecasts become less certain, as victory probabilities of each team get more similar. But our calculations find Brazil has a slightly higher chance of winning. Along with our faith in our yellow jersey, our model allows us to expect Brazil to scream champion for the sixth time after a historic match on July 15. Page 2
Soccer is a box full of surprises : Alternative scenarios Our baseline scenario does not leave much room for surprises. In each match, team with the highest probability of winning moves onto next round. However, in many cases, our estimated chances for each side are quite similar. That is the case for the final match that we expect to happen between Germany and Brazil. Our forecasts are surrounded by a lot of uncertainty. After all, any modelling exercise simplifies reality, trying to summarize in a few dimensions a picture that is much more complex. Hence, we anticipate at least a few surprises in the results that will be coming from June 14 onward. Given the tournament s structure, the order in which teams are classified in the group stage is key to define what will happen in the Round of 16. And naturally, what happens in the knockout rounds defines the composition of the next stages. We thus held simulations to identify the more likely scenarios in each round, if there are deviations from our model. To that end, we identified points of greater uncertainty (those whose winning probability of each team is almost the same) and tossed a coin to decide who wins each of these matches. We did that for some levels of uncertainty (whenever the victory probability by one side is near 50%±x, for different values of x) and, in each case, we simulated an exhaustive number of scenarios that could be produced by such uncertainties, respecting the tournament s ordering criteria. Page 3
The tables below show the number of scenarios in which each national team arrives to a given phase in the tournament, in simulations with some levels of uncertainty. In columns further to the right, we show the frequency with which each team reaches one of the four final places. Darker colors represent where we have greater confidence in our results, given the estimated uncertainty. In the first table, when we ease the model to allow a toss of the coin in tighter matches (where the winning team s probability is lower than 60%), Brazil reaches the final game in 100% of our scenarios 1, but, in this case, we share the chances of earning the title with Argentina or Germany, which alternate in the final match 2. Disputing the 3 rd place, one of these teams faces (and defeats) Portugal in ¼ of the time; France 16% of the time, and Uruguay, Russia, Peru, Denmark and Croatia in other occasions. The arrival of a greater number of teams to the Round of 16 (and their possible victories in later phases) contributes to widen the realm of results. But different possibilities of bracket orders also contribute in this direction. This point is shown by the cases of Russia and Uruguay, which have chance to advance if any one of them places first in Group A and faces Portugal, but are immediately eliminated by Spain if they place second in their group. The same goes for Denmark, France and Peru: the second place in group C is run over by Argentina, while the team that places first has a chance to beat the second place from group D. First group of simulations: Results set randomly when probability of the winner is <60% Group A B C D E F G H Team Round of Quarterfinalfinals play-off Semi- 3rd place 16 Final 4th 3rd 2nd Winner Uruguay 100% 50% 15% 15% 0% 15% 0% 0% 0% Russia 100% 50% 15% 15% 0% 15% 0% 0% 0% Egypt 0% 0% 0% 0% 0% 0% 0% 0% 0% Saudi Arabia 0% 0% 0% 0% 0% 0% 0% 0% 0% France 66% 33% 16% 16% 0% 16% 0% 0% 0% Peru 67% 25% 13% 13% 0% 13% 0% 0% 0% Denmark 67% 25% 13% 13% 0% 13% 0% 0% 0% Australia 0% 0% 0% 0% 0% 0% 0% 0% 0% Spain 100% 50% 0% 0% 0% 0% 0% 0% 0% Portugal 100% 50% 25% 25% 0% 25% 0% 0% 0% Morocco 0% 0% 0% 0% 0% 0% 0% 0% 0% Iran 0% 0% 0% 0% 0% 0% 0% 0% 0% Argentina 100% 100% 100% 50% 50% 0% 49% 25% 25% Croatia 50% 16% 4% 4% 0% 4% 0% 0% 0% Iceland 50% 0% 0% 0% 0% 0% 0% 0% 0% Nigeria 0% 0% 0% 0% 0% 0% 0% 0% 0% Brazil 100% 100% 100% 0% 100% 0% 0% 50% 50% Switzerland 100% 0% 0% 0% 0% 0% 0% 0% 0% Serbia 0% 0% 0% 0% 0% 0% 0% 0% 0% Costa Rica 0% 0% 0% 0% 0% 0% 0% 0% 0% Germany 100% 100% 100% 50% 50% 0% 51% 25% 25% Sweden 51% 0% 0% 0% 0% 0% 0% 0% 0% Mexico 49% 0% 0% 0% 0% 0% 0% 0% 0% South Korea 0% 0% 0% 0% 0% 0% 0% 0% 0% Belgium 100% 100% 0% 0% 0% 0% 0% 0% 0% England 100% 100% 0% 0% 0% 0% 0% 0% 0% Tunisia 0% 0% 0% 0% 0% 0% 0% 0% 0% Panama 0% 0% 0% 0% 0% 0% 0% 0% 0% Poland 100% 0% 0% 0% 0% 0% 0% 0% 0% Colombia 100% 0% 0% 0% 0% 0% 0% 0% 0% Senegal 0% 0% 0% 0% 0% 0% 0% 0% 0% Japan 0% 0% 0% 0% 0% 0% 0% 0% 0% 1 This is the case because, under such conditions, the first game played by Brazil to be decided by a coin toss (probability of winning is lower than 60%) would be the final match. 2 As we toss a coin to define the result of a match whenever there is uncertainty, by definition, observed frequencies are multiples of ½. Page 4
In the table below, we eased the model further and attributed random results to all matches when the winning probability of one side is lower than 70%. Naturally, the model becomes less useful because predictions become more random. Nevertheless, they may serve as a guide to those who are more skeptical of our (humble) soccer forecasts. In that case, the Brazilian squad is still the favorite, getting to the final match 87% of the time and winning in a little less than in half of all scenarios, but gives up its place in the semi-final match to Belgium a few times. Germany and Argentina continue to be the main competitors to the championship trophy. In cases when Belgium beats Brazil in the same-final match, the European country has non-negligible chances of getting its first world championship. Second group of simulations: Results set randomly when probability of the winner is <70% Group A B C D E F G H Team Round of Quarterfinalfinals play-off Semi- 3rd place 16 Final 4th 3rd 2nd Winner Uruguay 100% 51% 14% 13% 1% 12% 2% 1% 0% Russia 100% 51% 14% 13% 1% 12% 2% 1% 0% Egypt 0% 0% 0% 0% 0% 0% 0% 0% 0% Saudi Arabia 0% 0% 0% 0% 0% 0% 0% 0% 0% France 68% 25% 12% 11% 1% 10% 1% 1% 0% Peru 66% 18% 9% 9% 1% 8% 1% 1% 0% Denmark 67% 18% 8% 8% 1% 7% 1% 1% 0% Australia 0% 0% 0% 0% 0% 0% 0% 0% 0% Spain 100% 49% 29% 26% 3% 16% 11% 2% 0% Portugal 100% 50% 13% 12% 1% 10% 2% 1% 0% Morocco 0% 0% 0% 0% 0% 0% 0% 0% 0% Iran 0% 0% 0% 0% 0% 0% 0% 0% 0% Argentina 100% 100% 88% 44% 44% 5% 39% 21% 23% Croatia 57% 28% 11% 11% 0% 11% 0% 0% 0% Iceland 34% 11% 1% 1% 0% 1% 0% 0% 0% Nigeria 8% 0% 0% 0% 0% 0% 0% 0% 0% Brazil 100% 100% 87% 0% 87% 0% 0% 41% 46% Switzerland 59% 0% 0% 0% 0% 0% 0% 0% 0% Serbia 33% 0% 0% 0% 0% 0% 0% 0% 0% Costa Rica 8% 0% 0% 0% 0% 0% 0% 0% 0% Germany 100% 100% 75% 33% 42% 1% 31% 20% 22% Sweden 50% 0% 0% 0% 0% 0% 0% 0% 0% Mexico 50% 0% 0% 0% 0% 0% 0% 0% 0% South Korea 0% 0% 0% 0% 0% 0% 0% 0% 0% Belgium 100% 49% 26% 12% 13% 5% 7% 6% 7% England 100% 50% 12% 6% 6% 3% 3% 5% 1% Tunisia 0% 0% 0% 0% 0% 0% 0% 0% 0% Panama 0% 0% 0% 0% 0% 0% 0% 0% 0% Poland 100% 50% 0% 0% 0% 0% 0% 0% 0% Colombia 100% 50% 0% 0% 0% 0% 0% 0% 0% Senegal 0% 0% 0% 0% 0% 0% 0% 0% 0% Japan 0% 0% 0% 0% 0% 0% 0% 0% 0% When we reach the end of the group stage in a couple weeks, we will update our forecasts with the odds we estimate for the victory of each team that makes it to the knockout stage. May the best win! Pedro Renault Page 5
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