# Ratings of European and South American Football Leagues Based on Glicko-2
with Modifications

1st Andrei Shelopugin  Independent Researcher
<EMAIL_ADDRESS>2nd Alexander Sirotkin  HSE University
Saint Petersburg, Russia
<EMAIL_ADDRESS>
###### Abstract

One of the key problems in the field of soccer analytics is predicting how a
player performance changes when transitioning from one league to another. One
potential solution to address this issue lies in the evaluation of the
respective league strength.

This article endeavors to compute club ratings of the first and second
European and South American leagues. In order to calculate these ratings, the
authors have designed the Glicko-2 rating system-based approach, which
overcomes some Glicko-2 limitations. Particularly, the authors took into
consideration the probability of the draw, the home-field advantage, and the
property of teams to become stronger or weaker following their league
transitions. Furthermore, authors have constructed a predictive model for
forecasting match results based on the number of goals scored in previous
matches. The metrics utilized in the analysis reveal that the Glicko-2 based
approach exhibits a marginally superior level of accuracy when compared to the
commonly used Poisson regression-based approach. In addition, Glicko-2 based
ratings offer greater interpretability and can find application in various
analytics tasks, such as predicting soccer player metrics for forthcoming
seasons or the detailed analysis of a player performance in preceding matches.

The implementation of the approach is available on
github.com/andreyshelopugin/GlickoSoccer.

###### Index Terms:

glicko-2, rating systems, sports analytics, machine learning.

##  I Introduction

###  I-A Player Selection Problem

One of the main challenges faced by football managers, scouts, and analysts is
the assessment of players’ abilities. This problem can be addressed through
video analysis of players game or by analysis of players performance metrics.
In the latter scenario, it becomes imperative for an analyst to understand the
resistance level of different championships for optimal decision-making
process for players hiring.

###  I-B Problem of Matches Outcomes Prediction

It is necessary to create a ranking model for predicting matches outcomes.
This ranking model affords us the capability to simulate forthcoming match
outcomes, thereby assisting football managers in strategic transfer planning.
Moreover, our ranking model can also be used to impose constraints on a draw
simulator for final tournaments, enhancing group parity, and subsequently
elevating spectator engagement.

###  I-C Rating UEFA Problem

The UEFA rating is the most acknowledged system in soccer, therefore we
consider its weak spots. Firstly, it neglects to consider the strength of
opponents, treating victories over favorites and weaker teams equally.
Additionally, it places significant emphasis on past performance, which can
impact a team’s current points and the results of a draw round. In the case of
the performance drop, strong opponents would be selected for a weakened team,
hindering points accumulation for the latter. Essentially, the UEFA rating
system employs the same weight coefficients for matches, irrespective of the
season or year, thus failing to accurately reflect teams’ current strengths.

In addition to the aforementioned issues, there exist several less significant
drawbacks associated with the UEFA rating system. For instance, the rating
points lack a robust mathematical foundation, rendering the UEFA rating
challenging to interpret.

This rating system lacks the capability to effectively assess the strength of
a national league. The UEFA rating of a country is determined solely by the
performance of its strongest teams. Therefore, we have no information
concerning the performance of weaker teams in the first league, as well as the
second and lower divisions.

##  II Matches outcomes prediction based on gradient boosting

###  II-A Matches Outcomes Prediction Idea

Initially we have designed a model for predicting matches outcomes. Our
approach leverages a widely recognized concept for forecasting football match
results, involving the prediction of the number of goals each team will score
against their respective opponents. Subsequently, we used the assumption that
the goals in football conform to a Poisson distribution. This assumption
enables the utilization of the probability mass function associated with a
Skellam distribution:

$p(k,\mu_{1},\mu_{2})=exp(-(\mu_{1}+\mu_{2}))(\frac{\mu_{1}}{\mu_{2}})^{\frac{k}{2}}I_{k}(2\sqrt{\mu_{1}\mu_{2}})$
(1)

In the formula $I_{k}$ is the modified Bessel function, $k$ is a score
difference in football match. So, if $k>0$, the value of the probability mass
function equals the probability of the first team winning, for $k=0$ we
received the draw probability, and finally if $k<0$, we get the probability of
the second team winning. Parameters $\mu_{1}$ and $\mu_{2}$ are the means of
two Poisson-distributed random variables. In our case we use goals predictions
as $\mu_{1}$ and $\mu_{2}$.

###  II-B Implementation of gradient boosting based model

Gradient boosting models have demonstrated impressive performance when applied
to tabular data. Consequently, we have constructed a machine learning model
utilizing the LightGBM framework to predict the goal count for both competing
teams. We also experimented with the CatBoost model; however, it yielded
inferior results. The goals in football conform to the Poisson distribution,
therefore we set LightGBM parameter ”objective” to ”poisson”.

The algorithm was trained using the following features, with each row in the
dataset corresponding to a participant in a match:

\- a team plays at home (true/false);

\- a match happens during pandemic (true/false);

\- the average number of goals in the last 5, 10, 20, 30 matches (numeric); \-
the average number of team opponent missed goals in the last 5, 10, 20, 30
matches (numeric);

\- a tournament, e.g. Italian Serie A, Champions League, Coppa Italia
(categorical);

\- a league of team/opponent, e.g. Italian Serie A (categorical);

\- the average/median number of goals scored at home (if a team plays at home
in the current match) or away (if a team plays away in the current match) in
the last 5, 10, 20, 30 matches (numeric);

\- the average/median number of goals missed by the opponent team when playing
away(if a team plays at home in the current match) or at home field (if a team
plays away in the current match) in the last 5, 10, 20, 30 matches (numeric).

###  II-C Data

The data was collected from flashscore.com. The data consists of the matches
of Europe and South America first and second leagues, matches of national
cups, and international tournaments such as Champions League or Copa
Libertadores. It’s important to note that matches involving teams from lower-
ranking leagues, specifically those in the third division and below, have been
deliberately excluded. This includes instances such as national cup matches
featuring third-tier teams. Additionally, matches decided by forfeit victories
have also been omitted from the dataset. The dataset contains approximately
366 000 matches played between 2010/2011 and 2022/2023 seasons.

###  II-D Train Test Split

The model was trained using match data from the 2010/2011 through the
2020/2021 seasons, while matches that took place during the 2021/2022 and
2022/2023 seasons were set aside and selected for use as a test set.

##  III Matches outcomes prediction based on Glicko-2 with modifications

###  III-A Rating Systems

Today there are a lot of rating systems designed for predicting matches
outcomes: Elo [1], Glicko [2], TrueSkill [3]. Nevertheless, it is necessary
that opponents are placed on a level playing field, otherwise, these models
tend to exhibit suboptimal performance. This inherent limitation makes these
models inapplicable for effectively forecasting outcomes in team sports like
football, basketball, or ice hockey. Notably, existing rating systems neglect
crucial factors such as home team advantage, player injuries, player
transfers, and more.

In this article we propose the model for evaluating the strength of the
European football Leagues. Although some models calculate ratings of football
Leagues, such as Club Elo [4] and UEFA rating [5], existing approaches suffer
serious drawbacks.

###  III-B Glicko-2

To address the aforementioned challenges, we propose a model that leverages
the Glicko-2 algorithm, specifically tailored for football applications.

The Glicko-2 model has various applications across various domains in both
sports and computer science, including designing rating systems for chess [6]
and online games [7], volleyball [8], programming competitions [9], obstacle
courses races and other multiplayer competitions [9]. Besides sports, Glicko-2
is widely used for many other purposes, for example, online courses evaluation
[10], the evaluation of the discriminator and generator performance in
generative models [11] and the comparison of different machine learning
benchmarks [12].

Glicko-2 represents the strength of each team as a prob- ability distribution.
Initially, each team is associated with a normal prior distribution. As match
outcomes accumulate, teams are approximated to a posterior distribution. When
the team wins its rating increases. This increase is influenced by two key
factors: firstly, the difference in ratings, resulting in a more substantial
increase when a poor-rated team wins, and secondly, rating deviations, which
lead to rapid ratings adjustments when there is limited historical match data
available for the opponents.

###  III-C Draw Probability

The initial challenge pertains to the potential occurrence of a draw in
football matches. The mathematical expectation formulas utilized in Glicko-2
do not facilitate the assessment of draw probabilities. The draw probability
is influenced not solely by the disparity in skill levels between competing
teams but also by the scoring tendencies of the teams involved. For instance,
the probability of a draw would be marginally higher in a match featuring two
teams with a history of low-scoring performances.

The original Glicko-2 version calculates win probability in this way:

$E(\mu,\mu_{j},\phi_{j})=\frac{1}{1+exp(-g(\phi_{j})(\mu-\mu_{j}))}$ (2)

We have modified it in the following way:

$E(\mu,\mu_{j},\phi_{j},d,s)=\frac{exp(g(\phi_{j})(\mu-\mu_{j}))}{1+exp(g(\phi_{j})(\mu-\mu_{j}))+exp(d+s)}$
(3)

We have added two parameters into the original formula: $s$ represents the
draw probability derived from the LightGBM based model and $d$ serves as a
hyperparameter responsible for adjusting draw outcomes.

It is necessary to clarify the introduced term adjusting draw outcomes. We
have used the assumption that the goals in football conform to a Poisson
distribution. However the assumption is not entirely precise. A significant
issue arises from the underestimation of the frequency of low-scoring matches
[13], resulting in a bias in our draw predictions. Consequently, we have
incorporated a hyperparameter denoted as $d$ in (3) to address this issue.

###  III-D Home Team Advantage

Another aspect to take into account is the advantage of the home team, which
is influenced by the support of their fans and the logistical challenges faced
by the visiting team. Consequently, due to the diminished impact of the home
team advantage during the pandemic, we conducted separate training for the
home team constant for the period from March 2020 to June 2021.

The original Glicko-2 version updates rating in this way:

$\mu^{{}^{\prime}}=\mu+\phi^{{}^{\prime}2}g(\phi_{j})(s_{j}-E(\mu,\mu_{j},\phi_{j}))$
(4)

We have modified it for a home team (with modifications from (3)):

$\mu^{{}^{\prime}}=\mu+\phi^{{}^{\prime}2}g(\phi_{j})(s_{j}-E(\mu+h,\mu_{j},\phi_{j},d,s))$
(5)

The parameter $h$ will be replaced by $h_{p}$ during the pandemic. The
parameters $h$ and $h_{p}$ are positive numbers representing the home field
advantage. Also we have imposed the following restriction:

$h>h_{p}$ (6)

###  III-E Rating Initialization

The Glicko-2 model takes into consideration only the difference of the team
ratings. To enhance its accuracy, we can introduce a novel parameter known as
the ”initial league rating,” which characterizes the average rating within a
league. This parameter does not influence the National Championship match
outcomes prediction but proves useful for comparing the opponents from diverse
national leagues, particularly in international Cups. It effectively prevents
underfitting, ensuring more robust comparisons.

However, this approach can inadvertently lead to rating inflation. The initial
league rating represents roughly the average league rating. As we mentioned
earlier, our dataset includes data starting from the year 2010. Now, let’s
imagine we have a team whose earliest match in our dataset dates back to the
year 2015 in the second league. This implies that the team played in weaker
leagues before 2015, which means its rating should be below the average rating
of the second league.

To address this issue, we propose a solution: penalizing the initial league
rating for teams coming from lower-ranked leagues, thereby setting them below
the average league rating. As a result, we update team ratings before each
season, and the rating initialization process unfolds as follows:

$r(\mu,\phi,\sigma)=\begin{cases}r(\mu_{init}+\mu_{new},\phi,\sigma)&\text{if
team was promoted,}\\\ r(\mu_{init},\phi,\sigma)&\text{otherwise.}\end{cases}$
(7)

The parameter $\mu_{new}$ should be negative.

###  III-F Mid-Season

The fact that the team rosters can change during the transfer window cannot be
overlooked. To address this issue, we lever- age the rating deviation
parameter from the Glicko-2 model. At the beginning of a tournament, ratings
are notably influenced by every win or loss, but as more data accumulates,
these ratings undergo fewer deviations. We update this parameter at the
beginning of every season.

Furthermore, we introduce a parameter that accounts for the propensity of
teams to either strengthen or weaken during transitions between leagues. For
instance, when a team is promoted to the Premier League or relegated to the
Second League, its rating experiences slight adjustments. This parameter
enables us to capture the tendency of different national leagues to launch an
active transfer campaign after a league change.

Consequently, we update team ratings prior to the commencement of every season
using the following formula:

$r(\mu,\phi,\sigma)=\begin{cases}r(\mu+\mu_{l},\phi+\phi_{s},\sigma)&\text{if
team changed league,}\\\
r(\mu,\phi+\phi_{s},\sigma)&\text{otherwise.}\end{cases}$ (8)

The parameter $\phi_{s}$ should be positive to introduce a degree of
uncertainty. The parameter $\mu_{l}$ should be positive for promoted teams and
negative for relegated because usually the club budget increases after
promotion and decreases after relegation.

The other factor that can lead to rating inflation is the fact that the number
of teams changes because of changes in league rules etc. Thus, we normalize
all ratings after each season to prevent the inflation. Hence, the average
rating of all teams should be constant.

###  III-G Training Model

It’s essential to highlight that we conduct training for all the parameters
described across the entire league. As a result, each league possesses a
distinct set of parameters, which includes $\mu_{init}$, $\mu_{new}$,
$\mu_{l}$, $\phi_{s}$, $h$, $h_{p}$. Furthermore, we need to optimize the
initial values $\phi$, $\sigma$, and $d$ from (3).

These parameters are trained through the minimization of the log loss function
associated with match outcomes, enabling us to compute predictions.

While it would be theoretically desirable to assign a unique set of parameters
to each team, considering factors such as stadium capacity, distance between
opponent cities, and other complex variables that influence the actual home-
field advantage, this approach tends to result in model overfitting.

###  III-H Results

We have opted for the log loss metric as the criterion for assessing the
quality of outcome predictions. We computed the loss functions on the test
set, consisting of 60,091 matches. Below, you’ll find a comparison of the
models’ performance.

TABLE I: Model comparison Method | Log Loss
---|---
Catboost based | 0.5931
LightGBM based | 0.5896
Original Glicko-2 | 0.5949
Glicko-2 with modifications | 0.5832

The calculated ratings are presented in the appendix. Results are relevant for
the summer of 2023.

The implementation of approach is available on
github.com/andreyshelopugin/GlickoSoccer.

###  III-I Interpretation of ratings

Club ratings offer a high level of interpretability. The table II provides
estimations of the probabilities of a win, draw and loss based on the
percentage difference in team ratings.

TABLE II: Interpretation of Ratings Ratings difference | Win | Draw | Loss
---|---|---|---
0 | 35.7 | 28.6 | 35.7
10 | 37.1 | 28.5 | 34.5
20 | 38.5 | 28.2 | 33.3
50 | 42.5 | 27.8 | 29.7
100 | 49.9 | 25.7 | 24.3
200 | 63.8 | 20.5 | 15.7
500 | 90.5 | 6.0 | 3.5
800 | 98.1 | 1.2 | 0.7

It’s important to note that these probabilities are approximate. We took
average parameters $\phi_{j}$ and $s$ from equation (3). These parameters can
vary from one league to another.

##  IV Example of Application League Ratings in the Task of Players Metrics
Prediction

Consider, for instance, our objective to predict various player metrics for
the upcoming season, such as the number of goals a specific player will score.
To make accurate predictions, we can take into account player age, position,
previous performance, number of goals in the previous season and other
relevant features. However, a significant challenge arises when a player
undergoes transfers, moving to a different club or league during their career.
How can we effectively inform the model about such transitions?

One possible solution involves introducing a categorical feature, such as
”player transfers from Denmark Superliga to France Ligue 1”. Nonetheless, this
approach encounters the problem of dimensionality – there are relatively few
transfers from the Denmark Superliga to France Ligue 1, making it challenging
for the model to learn effectively. A more viable approach, and one that
aligns with our method, is to generate numeric features. For example, we can
create a feature that quantifies the difference in ratings between the Denmark
Superliga and France Ligue 1. This approach enables the model to account for
league transitions more effectively.

Additionally, we can enhance the precision of player performance analysis,
particularly within the context of a single team. For instance, we can
calculate the average rating of the opposing teams faced by each player. This
fine-grained analysis helps differentiate player performances, as several
players may exhibit similar statistics, but the strength of the opponent teams
they encounter could vary significantly.

##  V Problems

The primary limitation of the proposed approach stems from its sole reliance
on match outcomes for calculations. Consequently, this approach assumes that
the strength of teams remains relatively stable throughout the season.
However, second league teams frequently experience significant roster changes
as the season progresses.

The other challenge is the poor international matches history of teams outside
the top 40 ranking UEFA. This constraint may lead to an underfitting issue, as
the model lacks sufficient data for these teams. Furthermore, it’s not
feasible to compare clubs from Europe with those from South America due to the
scarcity of match history between teams from these two regions.

##  VI Conclusion

Despite the constraints mentioned earlier, we have developed a cost-effective
model that relies only on publicly accessible data. Both methods exhibit
comparable log losses; nevertheless, the Glicko-2 based model offers the
advantage of utilizing the computed ratings in a wide range of analytical
tasks.

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##  Appendix A Tables with ratings

TABLE III: Ratings of the best European clubs # | team | rating | league
---|---|---|---
1 | Manchester City | 2237.7 | England. First
2 | Bayern Munich | 2175.0 | Germany. First
3 | FC Porto | 2135.9 | Portugal. First
4 | Real Madrid | 2125.4 | Spain. First
5 | Napoli | 2121.7 | Italy. First
6 | Inter | 2116.4 | Italy. First
7 | Liverpool | 2111.2 | England. First
8 | Arsenal | 2101.2 | England. First
9 | Manchester Utd | 2099.5 | England. First
10 | RB Leipzig | 2096.4 | Germany. First
11 | Benfica Portugal | 2093.1 | Portugal. First
12 | Barcelona | 2078.6 | Spain. First
13 | Salzburg | 2078.2 | Austria. First
14 | Dortmund | 2077.2 | Germany. First
15 | Paris SG | 2068.2 | France. First
16 | AC Milan | 2047.5 | Italy. First
17 | Newcastle | 2044.3 | England. First
18 | Atl. Madrid | 2039.6 | Spain. First
19 | Juventus | 2033.9 | Italy. First
20 | Crvena zvezda | 2023.9 | Serbia. First
21 | PSV | 2022.3 | Netherlands. First
22 | Celtic | 2015.7 | Scotland. First
23 | Sporting CP | 2015.4 | Portugal. First
24 | Feyenoord | 2014.1 | Netherlands. First
25 | Rangers Scotland | 2010.9 | Scotland. First
26 | Lens | 2006.4 | France. First
27 | Zenit | 2006.2 | Russia. First
28 | Shakhtar Donetsk | 2004.7 | Ukraine. First
29 | Young Boys | 2003.6 | Switzerland. First
30 | Union Berlin | 2001.2 | Germany. First
31 | Lazio | 2000.3 | Italy. First
32 | Freiburg | 1999.8 | Germany. First
33 | Real Sociedad | 1995.0 | Spain. First
34 | Tottenham | 1991.5 | England. First
35 | Ajax | 1991.2 | Netherlands. First
36 | Brighton | 1990.1 | England. First
37 | AS Roma | 1988.2 | Italy. First
38 | Rakow | 1982.7 | Poland. First
39 | Fenerbahce | 1978.0 | Turkey. First
40 | Chelsea | 1976.6 | England. First
41 | Atalanta | 1973.5 | Italy. First
42 | Bayer Leverkusen | 1973.0 | Germany. First
43 | Eintracht Frankfurt | 1968.0 | Germany. First
44 | Braga | 1967.6 | Portugal. First
45 | Aston Villa | 1967.5 | England. First
46 | Royale Union SG | 1963.4 | Belgium. First
47 | Marseille | 1962.1 | France. First
48 | Brentford | 1958.7 | England. First
49 | Villarreal | 1958.4 | Spain. First
50 | Galatasaray | 1955.9 | Turkey. First

TABLE IV: Ratings of the best South American clubs # | team | rating | league
---|---|---|---
1 | Palmeiras | 1961.0 | Brazil. First
2 | Flamengo RJ | 1883.1 | Brazil. First
3 | Atletico-MG | 1849.4 | Brazil. First
4 | River Plate Argentina | 1842.7 | Argentina. First
5 | Athletico-PR | 1826.8 | Brazil. First
6 | Ind. del Valle | 1823.2 | Ecuador. First
7 | Fluminense | 1821.5 | Brazil. First
8 | Libertad Asuncion | 1815.7 | Paraguay. First
9 | Internacional | 1810.3 | Brazil. First
10 | Sao Paulo | 1809.9 | Brazil. First
11 | Cerro Porteno | 1793.6 | Paraguay. First
12 | Defensa y Justicia | 1787.2 | Argentina. First
13 | Fortaleza Brazil | 1782.6 | Brazil. First
14 | Botafogo RJ | 1780.7 | Brazil. First
15 | Talleres Cordoba | 1775.5 | Argentina. First
16 | Nacional Uruguay | 1772.8 | Uruguay. First
17 | LDU Quito | 1749.1 | Ecuador. First
18 | Boca Juniors | 1748.4 | Argentina. First
19 | Olimpia Asuncion | 1747.6 | Paraguay. First
20 | Estudiantes L.P. | 1745.8 | Argentina. First
21 | Bragantino | 1742.5 | Brazil. First
22 | Gremio | 1740.8 | Brazil. First
23 | Corinthians | 1737.5 | Brazil. First
24 | San Lorenzo Argentina | 1736.6 | Argentina. First
25 | Millonarios | 1732.7 | Colombia. First
26 | Newells Old Boys | 1728.4 | Argentina. First
27 | Racing Club | 1723.9 | Argentina. First
28 | Atl. Nacional | 1718.7 | Colombia. First
29 | Belgrano | 1717.7 | Argentina. First
30 | Argentinos Jrs | 1712.4 | Argentina. First
31 | Rosario Central | 1704.5 | Argentina. First
32 | America MG | 1704.4 | Brazil. First
33 | Cruzeiro | 1701.2 | Brazil. First
34 | Atletico GO | 1699.1 | Brazil. Second
35 | Sport Recife | 1697.7 | Brazil. Second
36 | Barcelona SC | 1694.3 | Ecuador. First
37 | Ceara | 1688.8 | Brazil. Second
38 | Penarol | 1688.2 | Uruguay. First
39 | Godoy Cruz | 1685.2 | Argentina. First
40 | Goias | 1681.7 | Brazil. First
41 | Nacional Asuncion | 1681.1 | Paraguay. First
42 | Guarani Paraguay | 1681.0 | Paraguay. First
43 | Cuiaba | 1680.3 | Brazil. First
44 | Lanus | 1680.2 | Argentina. First
45 | Tigre | 1677.0 | Argentina. First
46 | Sportivo Trinidense | 1676.8 | Paraguay. First
47 | Santos | 1674.6 | Brazil. First
48 | U. Catolica Ecuador | 1667.5 | Ecuador. First
49 | Aucas | 1667.0 | Ecuador. First
50 | Bahia | 1664.5 | Brazil. First
TABLE V: Average European league ratings of top 5 teams # | league | rating | # | league | rating
---|---|---|---|---|---
1 | England. First | 2118.8 | 50 | Lithuania. First | 1661.7
2 | Germany. First | 2069.9 | 51 | North Macedonia. First | 1651.8
3 | Italy. First | 2064.0 | 52 | Portugal. Second | 1646.9
4 | Spain. First | 2039.4 | 53 | Kosovo. First | 1642.1
5 | Portugal. First | 2000.7 | 54 | Luxembourg. First | 1640.6
6 | France. First | 1981.4 | 55 | Estonia. First | 1637.8
7 | Netherlands. First | 1965.8 | 56 | Moldova. First | 1636.9
8 | Belgium. First | 1923.3 | 57 | Malta. First | 1622.7
9 | Turkey. First | 1911.9 | 58 | Austria. Second | 1619.6
10 | Russia. First | 1906.5 | 59 | Denmark. Second | 1616.9
11 | Austria. First | 1899.3 | 60 | Faroe Islands. First | 1615.1
12 | Switzerland. First | 1893.4 | 61 | Albania. First | 1614.2
13 | Poland. First | 1890.7 | 62 | Gibraltar. First | 1612.3
14 | Sweden. First | 1883.9 | 63 | Greece. Second | 1611.2
15 | Ukraine. First | 1878.1 | 64 | Netherlands. Second | 1602.1
16 | Greece. First | 1868.8 | 65 | Sweden. Second | 1587.9
17 | Denmark. First | 1852.3 | 66 | Belgium. Second | 1581.6
18 | Czech Republic. First | 1832.4 | 67 | Romania. Second | 1577.1
19 | Scotland. First | 1826.2 | 68 | Montenegro. First | 1562.7
20 | Israel. First | 1817.1 | 69 | Hungary. Second | 1557.3
21 | Serbia. First | 1816.6 | 70 | Wales. First | 1556.8
22 | Bulgaria. First | 1809.7 | 71 | Israel. Second | 1553.7
23 | Norway. First | 1808.1 | 72 | Scotland. Second | 1532.3
24 | Romania. First | 1807.6 | 73 | Croatia. Second | 1525.0
25 | Croatia. First | 1807.2 | 74 | Ukraine. Second | 1519.7
26 | Cyprus. First | 1795.4 | 75 | Slovenia. Second | 1517.7
27 | Germany. Second | 1785.3 | 76 | Norway. Second | 1516.7
28 | Latvia. First | 1767.9 | 77 | Slovakia. Second | 1490.9
29 | Italy. Second | 1767.6 | 78 | Kazakhstan. Second | 1477.3
30 | England. Second | 1766.0 | 79 | Serbia. Second | 1473.3
31 | Spain. Second | 1763.3 | 80 | North Macedonia. Second | 1466.5
32 | Slovenia. First | 1749.0 | 81 | Bulgaria. Second | 1460.6
33 | Slovakia. First | 1748.5 | 82 | Georgia. Second | 1456.8
34 | Belarus. First | 1735.6 | 83 | Bosnia And Herzegovina. Second | 1456.6
35 | Armenia. First | 1732.3 | 84 | Czech Republic. Second | 1453.2
36 | Hungary. First | 1727.1 | 85 | Andorra. First | 1443.9
37 | Georgia. First | 1726.1 | 86 | Armenia. Second | 1443.3
38 | Bosnia And Herzegovina. First | 1724.8 | 87 | Cyprus. Second | 1436.2
39 | Finland. First | 1721.9 | 88 | Finland. Second | 1430.4
40 | France. Second | 1721.9 | 89 | Ireland. Second | 1427.2
41 | Northern Ireland. First | 1706.5 | 90 | San Marino. First | 1412.1
42 | Azerbaijan. First | 1704.0 | 91 | Belarus. Second | 1409.0
43 | Switzerland. Second | 1700.7 | 92 | Albania. Second | 1374.1
44 | Ireland. First | 1698.4 | 93 | Latvia. Second | 1365.0
45 | Turkey. Second | 1689.6 | 94 | Iceland. Second | 1362.8
46 | Russia. Second | 1684.8 | 95 | Malta. Second | 1327.4
47 | Kazakhstan. First | 1683.2 | 96 | Lithuania. Second | 1316.7
48 | Poland. Second | 1671.4 | 97 | Estonia. Second | 1304.2
49 | Iceland. First | 1669.8 | 98 | Northern Ireland. Second | 1276.8
TABLE VI: Average South American league ratings of top 5 teams # | league | rating
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1 | Brazil. First | 1868.4
2 | Argentina. First | 1779.9
3 | Paraguay. First | 1743.8
4 | Ecuador. First | 1720.2
5 | Brazil. Second | 1676.0
6 | Uruguay. First | 1673.1
7 | Colombia. First | 1665.6
8 | Chile. First | 1605.5
9 | Argentina. Second | 1604.4
10 | Peru. First | 1592.5
11 | Venezuela. First | 1557.8
12 | Bolivia. First | 1556.2