We will create two ELO rating tables in football (soccer). The first will be an ELO table for England’s top 3 divisions from 2015 to present. The second will be an ELO table of every international team that has played since 1872 to the present.
Using R, we will calculate Elo ratings, a dynamic ranking system that updates team strength based on game results.
Extract match results for English leagues from 2015 to 2025. This function allows us to specify country, gender, season years, and league tiers. This takes 4-5 minutes.
pl_2015_25_results <- fb_match_results(
country = "ENG",
gender = "M",
season_end_year = 2015:2025,
tier = c("1st", "2nd", "3rd")
)
# Preview the data
head(pl_2015_25_results)## Competition_Name Gender Country Season_End_Year Round Wk Day
## 1 EFL Championship M ENG 2015 Championship 1 Fri
## 2 EFL Championship M ENG 2015 Championship 1 Sat
## 3 EFL Championship M ENG 2015 Championship 1 Sat
## 4 EFL Championship M ENG 2015 Championship 1 Sat
## 5 EFL Championship M ENG 2015 Championship 1 Sat
## 6 EFL Championship M ENG 2015 Championship 1 Sat
## Date Time Home HomeGoals Away AwayGoals Attendance
## 1 2014-08-08 19:45 Blackburn 1 Cardiff City 1 15625
## 2 2014-08-09 15:00 Watford 3 Bolton 0 15546
## 3 2014-08-09 15:00 Brighton 0 Sheffield Weds 1 26993
## 4 2014-08-09 15:00 Middlesbrough 2 Birmingham City 0 18371
## 5 2014-08-09 15:00 Brentford 1 Charlton Ath 1 9690
## 6 2014-08-09 15:00 Derby County 1 Rotherham Utd 0 30105
## Venue Referee Notes
## 1 Ewood Park Andy Madley
## 2 Vicarage Road Stadium Carl Boyeson
## 3 The American Express Community Stadium James Linington
## 4 Riverside Stadium Graham Salisbury
## 5 Griffin Park James Adcock
## 6 iPro Stadium Andrew D'Urso
## MatchURL
## 1 https://fbref.com/en/matches/e20ac0bc/Blackburn-Rovers-Cardiff-City-August-8-2014-Championship
## 2 https://fbref.com/en/matches/061a69cf/Watford-Bolton-Wanderers-August-9-2014-Championship
## 3 https://fbref.com/en/matches/3ff54b92/Brighton-and-Hove-Albion-Sheffield-Wednesday-August-9-2014-Championship
## 4 https://fbref.com/en/matches/625cda2d/Middlesbrough-Birmingham-City-August-9-2014-Championship
## 5 https://fbref.com/en/matches/62cfd9a7/Brentford-Charlton-Athletic-August-9-2014-Championship
## 6 https://fbref.com/en/matches/720fee88/Derby-County-Rotherham-United-August-9-2014-Championship
## Home_xG Away_xG
## 1 NA NA
## 2 NA NA
## 3 NA NA
## 4 NA NA
## 5 NA NA
## 6 NA NAClean the data set by removing unnecessary columns and drop rows with missing values.
pl_2015_25_results_clean <- pl_2015_25_results %>%
select(-Round, -Notes, -Attendance, -Gender, -Day, -Venue, -Referee, -MatchURL) %>%
drop_na()
# Preview cleaned data
head(pl_2015_25_results_clean)## Competition_Name Country Season_End_Year Wk Date Time Home
## 1 EFL Championship ENG 2019 1 2018-08-03 20:00 Reading
## 2 EFL Championship ENG 2019 1 2018-08-04 15:00 Ipswich Town
## 3 EFL Championship ENG 2019 1 2018-08-04 15:00 Preston
## 4 EFL Championship ENG 2019 1 2018-08-04 15:00 West Brom
## 5 EFL Championship ENG 2019 1 2018-08-04 15:00 Brentford
## 6 EFL Championship ENG 2019 1 2018-08-04 15:00 Millwall
## HomeGoals Away AwayGoals Home_xG Away_xG
## 1 1 Derby County 2 0.9 0.7
## 2 2 Blackburn 2 0.6 1.8
## 3 1 QPR 0 0.6 0.5
## 4 1 Bolton 2 1.5 1.2
## 5 5 Rotherham Utd 1 2.5 0.6
## 6 2 Middlesbrough 2 2.2 1.8Prepare the data for Elo ratings by selecting the columns Week (Wk), Home, Away, HomeGoals, and AwayGoals. We create a new column ‘Result’ that represents the match outcome, with 1 for a home win, 0 for an away win, and 0.5 for a draw.
elo_data <- pl_2015_25_results_clean %>%
select(Wk, Home, Away, HomeGoals, AwayGoals) %>%
mutate(Result = case_when(
HomeGoals > AwayGoals ~ 1, # Home team wins
HomeGoals < AwayGoals ~ 0, # Away team wins
TRUE ~ 0.5 # Draw
))
# Finalise the data format for Elo calculation
elo_data_final <- elo_data %>%
select(Wk, Home, Away, Result) %>%
mutate(Wk = as.numeric(Wk))
# Preview prepared data
head(elo_data_final)## Wk Home Away Result
## 1 1 Reading Derby County 0.0
## 2 1 Ipswich Town Blackburn 0.5
## 3 1 Preston QPR 1.0
## 4 1 West Brom Bolton 0.0
## 5 1 Brentford Rotherham Utd 1.0
## 6 1 Millwall Middlesbrough 0.5Calculate Elo ratings using a K-factor of 50. The Elo function requires initial ratings (init) and an adjustment factor (k). Here we use 1500 as the initial Elo rating for each team.
elo_ratings <- elo(elo_data_final, init = 1500, k = 50, history = TRUE)
# Display Elo ratings
print(elo_ratings)##
## Elo Ratings For 55 Players Playing 6205 Games
##
## Player Rating Games Win Draw Loss Lag
## 1 Manchester City 1934 275 209 34 32 8
## 2 Liverpool 1878 275 181 60 34 8
## 3 Arsenal 1806 275 153 51 71 8
## 4 Newcastle Utd 1728 275 100 70 105 8
## 5 Aston Villa 1725 245 101 55 89 0
## 6 Crystal Palace 1715 275 84 76 115 8
## 7 Tottenham 1706 275 144 47 84 8
## 8 West Ham 1686 275 98 63 114 8
## 9 Chelsea 1685 275 136 65 74 8
## 10 Manchester Utd 1653 275 143 62 70 8
## 11 Brighton 1640 275 82 89 104 8
## 12 Brentford 1602 260 106 68 86 0
## 13 Sheffield Utd 1596 264 106 54 104 0
## 14 Everton 1568 275 92 67 116 8
## 15 Bournemouth 1564 291 107 66 118 0
## 16 QPR 1534 288 97 67 124 0
## 17 Fulham 1533 253 93 57 103 0
## 18 Luton Town 1522 234 82 59 93 0
## 19 Wigan Athletic 1518 138 38 42 58 0
## 20 Blackburn 1509 288 106 72 110 0
## 21 Wolves 1502 237 82 55 100 8
## 22 Leicester City 1489 283 121 56 106 0
## 23 Leeds United 1489 264 120 57 87 0
## 24 Stoke City 1488 326 98 93 135 0
## 25 Ipswich Town 1478 101 33 32 36 0
## 26 Burnley 1471 286 97 79 110 0
## 27 Oxford United 1471 12 3 5 4 34
## 28 Watford 1467 302 101 74 127 0
## 29 Huddersfield 1466 306 83 84 139 0
## 30 Southampton 1464 283 84 71 128 0
## 31 Middlesbrough 1461 288 118 68 102 0
## 32 Swansea City 1459 326 119 88 119 0
## 33 Millwall 1448 288 99 89 100 0
## 34 Wycombe 1442 46 11 10 25 0
## 35 Norwich City 1439 272 108 63 101 0
## 36 Sunderland 1437 104 43 24 37 0
## 37 West Brom 1435 318 118 94 106 0
## 38 Coventry City 1435 196 69 58 69 0
## 39 Nott'ham Forest 1434 269 92 82 95 0
## 40 Sheffield Weds 1419 196 62 49 85 0
## 41 Preston 1412 288 106 74 108 0
## 42 Barnsley 1403 138 41 34 63 0
## 43 Cardiff City 1398 280 97 60 123 0
## 44 Portsmouth 1390 12 1 5 6 34
## 45 Derby County 1388 196 66 54 76 0
## 46 Plymouth Argyle 1367 58 16 15 27 0
## 47 Bristol City 1351 288 101 73 114 0
## 48 Hull City 1349 242 79 63 100 0
## 49 Blackpool 1347 92 27 23 42 0
## 50 Charlton Ath 1336 46 12 12 22 0
## 51 P'borough Utd 1327 46 9 10 27 0
## 52 Rotherham Utd 1309 184 35 54 95 0
## 53 Reading 1300 230 70 60 100 0
## 54 Birmingham City 1294 276 77 82 117 0
## 55 Bolton 1235 45 8 8 29 0Extract the Elo ratings history to see how ratings change over time. Convert the Elo history data to a data frame.
elo_history <- as.data.frame(elo_ratings$history)
# View the first few rows of the Elo history data
head(elo_history)## 1.Rating 2.Rating 3.Rating 4.Rating 5.Rating 6.Rating 7.Rating
## Arsenal 1600 1585.615 1553.928 1704.192 1719.923 1695.005 1760.973
## Aston Villa 1425 1543.024 1592.921 1535.785 1604.769 1587.307 1554.698
## Barnsley 1500 1467.908 1450.242 1399.721 1386.820 1373.346 1358.200
## Birmingham City 1575 1560.219 1479.738 1531.675 1421.356 1418.099 1414.562
## Blackburn 1550 1546.947 1643.895 1568.497 1498.271 1421.484 1550.088
## Blackpool 1525 1464.424 1419.537 1453.454 1420.890 1467.963 1410.489
## 8.Rating 9.Rating 10.Rating 11.Rating 12.Rating 13.Rating
## Arsenal 1684.439 1726.343 1722.672 1647.961 1588.898 1673.163
## Aston Villa 1646.374 1583.115 1518.517 1466.819 1475.389 1628.895
## Barnsley 1385.314 1375.533 1346.896 1338.174 1365.160 1336.916
## Birmingham City 1443.390 1461.738 1420.490 1452.179 1407.751 1422.711
## Blackburn 1434.356 1376.043 1433.573 1374.379 1457.893 1448.959
## Blackpool 1462.607 1462.419 1428.096 1425.497 1392.462 1431.453
## 14.Rating 15.Rating 16.Rating 17.Rating 18.Rating 19.Rating
## Arsenal 1664.890 1640.909 1700.343 1678.849 1736.077 1681.802
## Aston Villa 1574.165 1657.289 1669.632 1634.536 1616.657 1579.532
## Barnsley 1284.329 1311.236 1320.023 1323.317 1315.690 1368.846
## Birmingham City 1514.089 1500.027 1463.363 1447.053 1383.653 1393.917
## Blackburn 1436.890 1576.684 1530.403 1559.215 1549.902 1479.540
## Blackpool 1491.017 1505.480 1448.445 1481.990 1510.757 1480.419
## 20.Rating 21.Rating 22.Rating 23.Rating 24.Rating 25.Rating
## Arsenal 1690.974 1755.008 1609.249 1604.205 1750.767 1721.599
## Aston Villa 1620.902 1636.581 1531.588 1599.251 1597.809 1562.246
## Barnsley 1326.773 1358.108 1396.369 1418.275 1408.706 1359.983
## Birmingham City 1464.455 1395.969 1353.374 1360.793 1383.115 1336.359
## Blackburn 1512.105 1508.874 1458.127 1472.370 1358.808 1328.701
## Blackpool 1420.714 1362.520 1341.278 1371.148 1354.254 1327.381
## 26.Rating 27.Rating 28.Rating 29.Rating 30.Rating 31.Rating
## Arsenal 1827.446 1798.656 1815.294 1793.891 1779.948 1677.787
## Aston Villa 1558.438 1545.311 1588.393 1555.055 1582.537 1582.517
## Barnsley 1329.246 1343.453 1311.377 1318.962 1314.232 1338.981
## Birmingham City 1270.525 1322.940 1333.041 1319.021 1385.037 1466.652
## Blackburn 1428.332 1464.790 1515.753 1526.662 1378.865 1329.357
## Blackpool 1378.357 1386.818 1412.440 1406.702 1417.960 1407.418
## 32.Rating 33.Rating 34.Rating 35.Rating 36.Rating 37.Rating
## Arsenal 1682.448 1589.701 1731.939 1743.464 1715.915 1667.063
## Aston Villa 1494.239 1521.745 1559.335 1592.413 1615.781 1729.223
## Barnsley 1380.631 1419.245 1499.288 1553.937 1506.892 1497.230
## Birmingham City 1436.078 1492.118 1383.912 1313.232 1340.569 1303.212
## Blackburn 1329.048 1399.428 1415.391 1438.283 1363.723 1370.072
## Blackpool 1368.815 1407.044 1356.047 1408.122 1402.713 1457.597
## 38.Rating 39.Rating 40.Rating 41.Rating 42.Rating 43.Rating
## Arsenal 1805.924 1805.924 1805.924 1805.924 1805.924 1805.924
## Aston Villa 1747.852 1754.074 1760.580 1764.924 1770.459 1772.994
## Barnsley 1501.815 1467.741 1504.811 1466.275 1443.754 1431.673
## Birmingham City 1293.022 1343.249 1454.254 1427.099 1353.169 1377.663
## Blackburn 1408.905 1270.293 1274.818 1347.244 1402.733 1419.926
## Blackpool 1436.743 1382.584 1352.631 1331.311 1341.193 1353.353
## 44.Rating 45.Rating 46.Rating 1.Games 2.Games 3.Games 4.Games
## Arsenal 1805.924 1805.924 1805.924 8 16 24 32
## Aston Villa 1781.231 1767.030 1724.741 7 14 21 28
## Barnsley 1403.036 1376.002 1402.581 3 6 9 12
## Birmingham City 1386.894 1332.461 1293.990 6 12 18 24
## Blackburn 1438.656 1417.091 1508.732 7 14 21 28
## Blackpool 1393.264 1341.336 1347.258 2 4 6 8
## 5.Games 6.Games 7.Games 8.Games 9.Games 10.Games 11.Games
## Arsenal 40 48 56 64 72 79 86
## Aston Villa 35 42 49 56 63 69 75
## Barnsley 15 18 21 24 27 30 33
## Birmingham City 30 36 42 48 54 60 66
## Blackburn 35 42 49 56 63 70 77
## Blackpool 10 12 14 16 18 20 22
## 12.Games 13.Games 14.Games 15.Games 16.Games 17.Games 18.Games
## Arsenal 93 100 107 114 121 128 135
## Aston Villa 81 87 93 99 105 111 117
## Barnsley 36 39 42 45 48 51 54
## Birmingham City 72 78 84 90 96 102 108
## Blackburn 84 90 96 102 108 114 120
## Blackpool 24 26 28 30 32 34 36
## 19.Games 20.Games 21.Games 22.Games 23.Games 24.Games 25.Games
## Arsenal 142 149 156 163 170 177 184
## Aston Villa 123 129 135 141 147 153 159
## Barnsley 57 60 63 66 69 72 75
## Birmingham City 114 120 126 132 138 144 150
## Blackburn 126 132 138 144 150 156 162
## Blackpool 38 40 42 44 46 48 50
## 26.Games 27.Games 28.Games 29.Games 30.Games 31.Games 32.Games
## Arsenal 191 198 205 212 219 226 233
## Aston Villa 165 171 177 183 189 195 201
## Barnsley 78 81 84 87 90 93 96
## Birmingham City 156 162 168 174 180 186 192
## Blackburn 168 174 180 186 192 198 204
## Blackpool 52 54 56 58 60 62 64
## 33.Games 34.Games 35.Games 36.Games 37.Games 38.Games 39.Games
## Arsenal 240 247 254 261 268 275 275
## Aston Villa 207 213 219 225 231 237 238
## Barnsley 99 102 105 108 111 114 117
## Birmingham City 198 204 210 216 222 228 234
## Blackburn 210 216 222 228 234 240 246
## Blackpool 66 68 70 72 74 76 78
## 40.Games 41.Games 42.Games 43.Games 44.Games 45.Games 46.Games
## Arsenal 275 275 275 275 275 275 275
## Aston Villa 239 240 241 242 243 244 245
## Barnsley 120 123 126 129 132 135 138
## Birmingham City 240 246 252 258 264 270 276
## Blackburn 252 258 264 270 276 282 288
## Blackpool 80 82 84 86 88 90 92
## 1.Lag 2.Lag 3.Lag 4.Lag 5.Lag 6.Lag 7.Lag 8.Lag 9.Lag 10.Lag
## Arsenal 0 0 0 0 0 0 0 0 0 0
## Aston Villa 0 0 0 0 0 0 0 0 0 0
## Barnsley 0 0 0 0 0 0 0 0 0 0
## Birmingham City 0 0 0 0 0 0 0 0 0 0
## Blackburn 0 0 0 0 0 0 0 0 0 0
## Blackpool 0 0 0 0 0 0 0 0 0 0
## 11.Lag 12.Lag 13.Lag 14.Lag 15.Lag 16.Lag 17.Lag 18.Lag 19.Lag
## Arsenal 0 0 0 0 0 0 0 0 0
## Aston Villa 0 0 0 0 0 0 0 0 0
## Barnsley 0 0 0 0 0 0 0 0 0
## Birmingham City 0 0 0 0 0 0 0 0 0
## Blackburn 0 0 0 0 0 0 0 0 0
## Blackpool 0 0 0 0 0 0 0 0 0
## 20.Lag 21.Lag 22.Lag 23.Lag 24.Lag 25.Lag 26.Lag 27.Lag 28.Lag
## Arsenal 0 0 0 0 0 0 0 0 0
## Aston Villa 0 0 0 0 0 0 0 0 0
## Barnsley 0 0 0 0 0 0 0 0 0
## Birmingham City 0 0 0 0 0 0 0 0 0
## Blackburn 0 0 0 0 0 0 0 0 0
## Blackpool 0 0 0 0 0 0 0 0 0
## 29.Lag 30.Lag 31.Lag 32.Lag 33.Lag 34.Lag 35.Lag 36.Lag 37.Lag
## Arsenal 0 0 0 0 0 0 0 0 0
## Aston Villa 0 0 0 0 0 0 0 0 0
## Barnsley 0 0 0 0 0 0 0 0 0
## Birmingham City 0 0 0 0 0 0 0 0 0
## Blackburn 0 0 0 0 0 0 0 0 0
## Blackpool 0 0 0 0 0 0 0 0 0
## 38.Lag 39.Lag 40.Lag 41.Lag 42.Lag 43.Lag 44.Lag 45.Lag 46.Lag
## Arsenal 0 1 2 3 4 5 6 7 8
## Aston Villa 0 0 0 0 0 0 0 0 0
## Barnsley 0 0 0 0 0 0 0 0 0
## Birmingham City 0 0 0 0 0 0 0 0 0
## Blackburn 0 0 0 0 0 0 0 0 0
## Blackpool 0 0 0 0 0 0 0 0 0Now that we have computed Elo ratings for English league matches, let’s apply similar calculations to international matches. We’ll read in a dataset of all historical international matches from 1872 and prepare it for Elo calculations.
Load the international matches dataset. This data should contain details of match scores, and we will select relevant columns and create a Result column for Elo calculations. NOTE: Ensure that ‘results.csv’ is in your working directory or provide the full path to the file.
results <- read_csv("results.csv")## Rows: 47777 Columns: 9
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (5): home_team, away_team, tournament, city, country
## dbl (2): home_score, away_score
## lgl (1): neutral
## date (1): date
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.# Preview the loaded data
head(results)## # A tibble: 6 × 9
## date home_team away_team home_score away_score tournament city country
## <date> <chr> <chr> <dbl> <dbl> <chr> <chr> <chr>
## 1 1872-11-30 Scotland England 0 0 Friendly Glasg… Scotla…
## 2 1873-03-08 England Scotland 4 2 Friendly London England
## 3 1874-03-07 Scotland England 2 1 Friendly Glasg… Scotla…
## 4 1875-03-06 England Scotland 2 2 Friendly London England
## 5 1876-03-04 Scotland England 3 0 Friendly Glasg… Scotla…
## 6 1876-03-25 Scotland Wales 4 0 Friendly Glasg… Scotla…
## # ℹ 1 more variable: neutral <lgl>Clean the international matches dataset by removing columns that are not required for Elo calculations.
results_clean <- results %>%
select(-tournament, -city, -country, -neutral)
# Preview the cleaned data
head(results_clean)## # A tibble: 6 × 5
## date home_team away_team home_score away_score
## <date> <chr> <chr> <dbl> <dbl>
## 1 1872-11-30 Scotland England 0 0
## 2 1873-03-08 England Scotland 4 2
## 3 1874-03-07 Scotland England 2 1
## 4 1875-03-06 England Scotland 2 2
## 5 1876-03-04 Scotland England 3 0
## 6 1876-03-25 Scotland Wales 4 0Prepare the data for Elo by creating a Result column, where: 1 indicates a home team win, 0 indicates an away team win, and 0.5 indicates a draw.
elo_data_international <- results_clean %>%
mutate(Result = case_when(
home_score > away_score ~ 1, # Home team wins
home_score < away_score ~ 0, # Away team wins
TRUE ~ 0.5 # Draw
))
# Select the relevant columns for Elo calculations
elo_data_final_international <- elo_data_international %>%
select(date, home_team, away_team, Result) %>%
mutate(date = as.numeric(as.Date(date, "%Y-%m-%d")))
# Preview the prepared international match data
head(elo_data_final_international)## # A tibble: 6 × 4
## date home_team away_team Result
## <dbl> <chr> <chr> <dbl>
## 1 -35460 Scotland England 0.5
## 2 -35362 England Scotland 1
## 3 -34998 Scotland England 1
## 4 -34634 England Scotland 0.5
## 5 -34270 Scotland England 1
## 6 -34249 Scotland Wales 1Calculate Elo ratings for international matches using the same K-factor and initial rating as before.
elo_ratings_international <- elo(elo_data_final_international, init = 1500, k = 50, history = TRUE)
# Display the international Elo ratings
print(elo_ratings_international)##
## Elo Ratings For 336 Players Playing 47777 Games
##
## Player Rating Games Win Draw Loss Lag
## 1 Spain 2154 762 447 176 139 0
## 2 Argentina 2130 1051 578 256 217 0
## 3 Colombia 2055 622 251 172 199 0
## 4 France 2041 912 462 193 257 1
## 5 Germany 2024 1012 587 211 214 1
## 6 Brazil 1994 1044 665 212 167 0
## 7 Portugal 1993 679 339 155 185 0
## 8 Italy 1979 868 462 241 165 1
## 9 England 1972 1074 612 257 205 2
## 10 Netherlands 1959 855 439 192 224 1
## 11 Croatia 1946 380 199 102 79 0
## 12 Japan 1942 784 381 162 241 0
## 13 Austria 1921 843 355 181 307 2
## 14 Belgium 1921 831 368 177 286 1
## 15 Denmark 1914 854 391 180 283 0
## 16 Iran 1902 598 341 141 116 0
## 17 Ecuador 1887 575 176 148 251 0
## 18 Uruguay 1884 952 420 233 299 0
## 19 Turkey 1883 626 248 150 228 1
## 20 South Korea 1869 994 530 249 215 0
## 21 Basque Country 1869 58 38 11 9 532
## 22 Greece 1865 613 233 152 228 2
## 23 Morocco 1857 587 281 167 139 0
## 24 Switzerland 1852 866 308 196 362 0
## 25 Hungary 1850 989 465 218 306 1
## 26 Serbia 1850 330 149 85 96 0
## 27 Canada 1843 446 169 100 177 0
## 28 Slovenia 1842 300 110 79 111 2
## 29 Mexico 1841 978 500 224 254 0
## 30 Uzbekistan 1831 338 163 67 108 0
## 31 Poland 1829 875 376 221 278 0
## 32 Russia 1822 727 372 192 163 12
## 33 Ukraine 1820 322 144 92 86 1
## 34 Jersey 1820 232 152 19 61 156
## 35 Senegal 1817 615 282 165 168 0
## 36 Czech Republic 1817 353 186 71 96 1
## 37 Norway 1813 852 316 194 342 2
## 38 Australia 1810 565 287 120 158 0
## 39 Venezuela 1810 444 112 99 233 0
## 40 Sweden 1797 1079 533 230 316 1
## 41 Catalonia 1795 48 19 12 17 684
## 42 Algeria 1791 590 269 157 164 1
## 43 Paraguay 1787 768 268 204 296 0
## 44 Egypt 1785 731 368 176 187 0
## 45 United States 1782 762 337 159 266 0
## 46 Wales 1779 710 228 160 322 1
## 47 Slovakia 1776 351 142 84 125 1
## 48 Peru 1775 699 229 171 299 0
## 49 German DR 1770 298 140 66 92 6758
## 50 Georgia 1766 291 94 61 136 1
## 51 Romania 1754 749 327 193 229 0
## 52 Iraq 1753 632 293 183 156 0
## 53 Ivory Coast 1735 618 313 159 146 0
## 54 Jordan 1734 456 170 126 160 0
## 55 Costa Rica 1733 702 309 170 223 0
## 56 Scotland 1729 835 392 181 262 0
## 57 Panama 1720 514 170 125 219 0
## 58 Albania 1718 382 104 81 197 1
## 59 Czechoslovakia 1716 502 226 114 162 6322
## 60 Qatar 1710 615 256 148 211 0
## 61 Tunisia 1709 654 284 183 187 0
## 62 Chile 1708 818 308 176 334 0
## 63 Yugoslavia 1701 483 223 98 162 4611
## 64 Andalusia 1697 13 8 4 1 1652
## 65 Cameroon 1692 598 263 186 149 1
## 66 Occitania 1687 33 19 6 8 1361
## 67 Guernsey 1687 240 145 25 70 157
## 68 Jamaica 1686 638 246 149 243 1
## 69 Republic of Ireland 1681 616 235 173 208 2
## 70 Northern Cyprus 1680 34 20 7 7 819
## 71 Bolivia 1678 515 114 116 285 0
## 72 DR Congo 1678 502 194 141 167 0
## 73 New Zealand 1675 400 166 72 162 1
## 74 Ynys Môn 1666 65 35 11 19 156
## 75 Mali 1664 554 226 145 183 0
## 76 Iceland 1663 505 155 99 251 1
## 77 Nigeria 1657 634 297 182 155 4
## 78 Isle of Wight 1656 48 26 7 15 157
## 79 Angola 1654 400 138 140 122 0
## 80 Kernow 1652 12 7 2 3 155
## 81 North Macedonia 1652 290 90 73 127 2
## 82 Saudi Arabia 1647 710 338 164 208 0
## 83 Isle of Man 1645 53 31 4 18 157
## 84 South Africa 1640 460 204 140 116 0
## 85 United Arab Emirates 1638 592 239 145 208 0
## 86 Burkina Faso 1636 442 152 119 171 2
## 87 Iraqi Kurdistan 1635 27 13 8 6 1129
## 88 Padania 1634 44 30 8 6 666
## 89 Corsica 1623 9 4 4 1 978
## 90 Oman 1620 520 199 128 193 0
## 91 Kosovo 1619 88 32 20 36 0
## 92 Abkhazia 1618 32 14 13 5 666
## 93 County of Nice 1617 9 6 1 2 1275
## 94 Bahrain 1616 564 201 152 211 0
## 95 Northern Ireland 1616 691 179 157 355 0
## 96 Kárpátalja 1611 11 5 5 1 819
## 97 Finland 1608 798 209 163 426 2
## 98 Honduras 1604 600 238 156 206 1
## 99 Rhodes 1603 18 11 2 5 2059
## 100 Haiti 1603 492 208 106 178 1
## 101 Equatorial Guinea 1598 162 48 35 79 1
## 102 Israel 1595 484 174 113 197 1
## 103 Guatemala 1591 534 192 136 206 0
## 104 Cape Verde 1590 218 82 49 87 0
## 105 Bulgaria 1588 719 258 184 277 0
## 106 Yorkshire 1587 7 5 1 1 673
## 107 Montenegro 1584 154 51 39 64 1
## 108 Thailand 1583 842 324 202 316 7
## 109 Galicia 1581 10 4 3 3 1143
## 110 Artsakh 1577 11 6 2 3 666
## 111 Ellan Vannin 1572 22 11 5 6 825
## 112 Guinea 1571 526 196 148 182 0
## 113 Trinidad and Tobago 1562 729 316 156 257 1
## 114 Arameans Suryoye 1561 10 5 2 3 1482
## 115 Gabon 1556 390 134 112 144 0
## 116 Mozambique 1552 359 113 94 152 1
## 117 Bosnia and Herzegovina 1546 267 98 60 109 1
## 118 Maule Sur 1546 2 2 0 0 304
## 119 Cascadia 1546 7 4 1 2 680
## 120 Canary Islands 1543 4 3 0 1 2783
## 121 Brittany 1542 12 6 3 3 1662
## 122 Ghana 1542 658 308 172 178 0
## 123 Panjab 1540 19 9 3 7 562
## 124 Réunion 1536 124 53 22 49 144
## 125 Belarus 1536 299 88 76 135 0
## 126 Azerbaijan 1534 294 59 75 160 1
## 127 Silesia 1532 8 3 2 3 2997
## 128 Comoros 1531 130 29 31 70 0
## 129 Biafra 1528 3 2 0 1 326
## 130 Asturias 1528 1 1 0 0 15565
## 131 Luxembourg 1526 452 48 61 343 0
## 132 Sudan 1526 450 149 111 190 0
## 133 Kazakhstan 1525 252 66 57 129 2
## 134 Chameria 1525 7 3 2 2 168
## 135 Zambia 1525 782 355 201 226 0
## 136 Malaysia 1525 818 318 188 312 1
## 137 Syria 1524 468 165 117 186 8
## 138 Martinique 1522 325 145 83 97 0
## 139 Surrey 1521 1 1 0 0 842
## 140 Parishes of Jersey 1521 3 2 0 1 672
## 141 China PR 1520 700 344 152 204 0
## 142 Donetsk PR 1518 6 2 2 2 920
## 143 Felvidék 1517 12 5 2 5 967
## 144 Moldova 1516 283 57 66 160 2
## 145 Menorca 1516 25 11 6 8 157
## 146 Libya 1514 372 139 85 148 4
## 147 Elba Island 1514 1 1 0 0 295
## 148 Armenia 1514 257 63 54 140 2
## 149 Nicaragua 1513 208 49 24 135 1
## 150 Indonesia 1513 710 274 140 296 0
## 151 Crimea 1510 5 3 0 2 3009
## 152 New Caledonia 1510 259 133 33 93 5
## 153 North Korea 1509 382 161 104 117 0
## 154 Suriname 1508 381 167 88 126 0
## 155 Fiji 1507 257 115 44 98 5
## 156 Central Spain 1503 2 1 0 1 15509
## 157 Tamil Eelam 1502 24 12 0 12 150
## 158 Mapuche 1502 2 1 0 1 305
## 159 Zanzibar 1502 205 41 41 123 877
## 160 Franconia 1500 3 2 0 1 649
## 161 Matabeleland 1498 8 3 2 3 326
## 162 Estonia 1498 534 132 118 284 1
## 163 Sealand 1498 12 4 5 3 1438
## 164 Benin 1497 308 78 75 155 0
## 165 South Ossetia 1495 16 5 3 8 568
## 166 Saugeais 1495 1 0 1 0 1635
## 167 Botswana 1495 319 88 89 142 0
## 168 Shetland 1495 55 21 7 27 157
## 169 Togo 1494 435 137 110 188 1
## 170 Palestine 1493 229 66 63 100 0
## 171 Délvidék 1492 2 1 0 1 1095
## 172 Uganda 1491 670 278 180 212 0
## 173 United Koreans in Japan 1490 12 3 6 3 819
## 174 Tanzania 1489 580 160 167 253 0
## 175 Western Sahara 1488 5 2 0 3 1858
## 176 West Papua 1488 1 0 1 0 264
## 177 Madrid 1487 1 0 0 1 1652
## 178 Kyrgyzstan 1485 181 56 29 96 0
## 179 Burma 1485 7 2 2 3 12876
## 180 Western Armenia 1485 16 5 4 7 665
## 181 Gozo 1484 11 4 0 7 157
## 182 Rwanda 1482 268 84 73 111 0
## 183 Kenya 1482 690 263 169 258 1
## 184 Luhansk PR 1481 6 2 1 3 568
## 185 Székely Land 1481 27 9 3 15 167
## 186 Mauritania 1481 255 49 64 142 0
## 187 Malta 1480 444 61 75 308 2
## 188 Cilento 1478 1 0 0 1 1948
## 189 Greenland 1476 76 25 10 41 157
## 190 Guadeloupe 1475 264 111 47 106 0
## 191 Ryūkyū 1474 1 0 0 1 1045
## 192 Tahiti 1473 239 129 31 79 4
## 193 Yoruba Nation 1473 2 0 1 1 328
## 194 Somaliland 1473 6 2 1 3 682
## 195 Seborga 1473 1 0 0 1 1438
## 196 Kuwait 1468 665 282 171 212 0
## 197 Gambia 1468 253 68 72 113 1
## 198 Republic of St. Pauli 1467 4 1 1 2 3111
## 199 El Salvador 1465 596 195 133 268 2
## 200 North Vietnam 1464 24 7 3 14 10962
## 201 Niue 1464 2 0 0 2 8308
## 202 Åland Islands 1462 51 21 8 22 157
## 203 South Yemen 1461 3 0 0 3 8452
## 204 Hmong 1458 3 1 0 2 150
## 205 Kabylia 1457 6 1 2 3 819
## 206 Zimbabwe 1457 477 187 132 158 1
## 207 Guinea-Bissau 1456 178 51 41 86 0
## 208 Tajikistan 1456 188 70 41 77 9
## 209 Malaya 1455 2 0 0 2 12875
## 210 Romani people 1454 7 2 0 5 1144
## 211 Chechnya 1454 3 0 1 2 3170
## 212 Madagascar 1452 308 113 72 123 1
## 213 Aymara 1452 2 0 0 2 304
## 214 Cuba 1452 384 135 88 161 1
## 215 Curaçao 1449 367 135 95 137 1
## 216 Sápmi 1447 29 12 2 15 155
## 217 Dominican Republic 1446 143 57 21 65 0
## 218 Provence 1443 19 6 1 12 1691
## 219 Burundi 1442 207 63 50 94 2
## 220 Western Isles 1442 32 13 3 16 157
## 221 Monaco 1441 24 8 6 10 986
## 222 Saarland 1437 9 1 2 6 13108
## 223 Cyprus 1435 410 77 68 265 0
## 224 Ticino 1432 2 0 0 2 151
## 225 Manchukuo 1431 3 0 0 3 14213
## 226 Sierra Leone 1430 290 87 75 128 0
## 227 Găgăuzia 1428 3 0 1 2 3012
## 228 Palau 1427 2 0 0 2 4945
## 229 Mayotte 1422 38 11 11 16 148
## 230 Malawi 1419 631 215 178 238 0
## 231 Latvia 1417 446 127 106 213 2
## 232 Solomon Islands 1416 205 88 35 82 5
## 233 Two Sicilies 1416 6 1 0 5 167
## 234 Vietnam 1415 282 115 58 109 3
## 235 Namibia 1414 267 67 76 124 1
## 236 Lithuania 1413 417 110 79 228 0
## 237 Lebanon 1410 321 87 88 146 9
## 238 Central African Republic 1407 128 27 27 74 0
## 239 Vanuatu 1406 205 66 27 112 3
## 240 Sark 1406 4 0 0 4 3754
## 241 Barawa 1403 12 3 0 9 680
## 242 Raetia 1398 26 6 5 15 151
## 243 Gotland 1397 30 11 4 15 952
## 244 French Guiana 1393 177 54 38 85 1
## 245 Vietnam Republic 1387 195 82 37 76 10046
## 246 Ambazonia 1387 6 0 1 5 2910
## 247 Vatican City 1386 7 0 3 4 686
## 248 Congo 1385 403 135 98 170 0
## 249 Yemen DPR 1381 25 5 3 17 6943
## 250 Western Australia 1379 32 11 3 18 8882
## 251 Ethiopia 1374 399 140 92 167 0
## 252 Guyana 1372 300 94 61 145 0
## 253 East Timor 1372 4 0 0 4 392
## 254 Western Samoa 1369 6 0 0 6 8720
## 255 Micronesia 1364 7 1 0 6 3750
## 256 Faroe Islands 1363 272 49 36 187 2
## 257 Papua New Guinea 1359 147 48 26 73 5
## 258 Orkney 1359 24 6 1 17 157
## 259 Singapore 1357 676 211 137 328 46
## 260 India 1357 522 189 116 217 3
## 261 Darfur 1356 7 0 0 7 1483
## 262 Lesotho 1351 285 51 91 143 0
## 263 Saint Barthélemy 1350 10 4 1 5 208
## 264 Saare County 1347 35 5 5 25 952
## 265 Bermuda 1347 184 64 32 88 0
## 266 Hitra 1339 19 4 1 14 654
## 267 Saint Pierre and Miquelon 1339 7 0 0 7 1789
## 268 Saint Lucia 1338 223 79 33 111 1
## 269 Chagos Islands 1335 16 3 2 11 562
## 270 Saint Helena 1333 8 0 0 8 157
## 271 Turkmenistan 1333 169 59 32 78 46
## 272 Puerto Rico 1331 152 37 24 91 1
## 273 Liberia 1322 289 74 67 148 1
## 274 Niger 1319 244 54 58 132 0
## 275 Saint Vincent and the Grenadines 1310 220 79 48 93 2
## 276 Eswatini 1305 266 55 79 132 1
## 277 Saint Kitts and Nevis 1303 213 85 41 87 0
## 278 Belize 1302 111 28 19 64 0
## 279 Wallis Islands and Futuna 1300 24 5 0 19 5674
## 280 Kiribati 1299 11 0 1 10 2004
## 281 Afghanistan 1296 137 35 32 70 46
## 282 Chad 1290 126 26 30 70 0
## 283 Hong Kong 1288 429 132 92 205 0
## 284 Samoa 1277 63 22 4 37 3
## 285 Philippines 1269 331 85 48 198 9
## 286 Montserrat 1267 62 16 7 39 2
## 287 Gibraltar 1262 145 37 18 90 2
## 288 Tibet 1262 16 0 2 14 151
## 289 Yemen 1262 255 48 51 156 46
## 290 Grenada 1258 247 80 55 112 1
## 291 Mauritius 1254 318 91 70 157 11
## 292 South Sudan 1251 69 11 13 45 0
## 293 Sint Maarten 1251 54 17 8 29 1
## 294 Frøya 1249 33 7 2 24 157
## 295 Saint Martin 1241 60 21 5 34 1
## 296 Tuvalu 1235 39 8 4 27 111
## 297 Andorra 1231 212 13 27 172 2
## 298 Bonaire 1227 36 13 6 17 2
## 299 Falkland Islands 1226 33 6 1 26 157
## 300 Dominica 1216 205 51 42 112 0
## 301 Barbados 1205 292 93 64 135 0
## 302 Eritrea 1191 71 11 16 44 599
## 303 Myanmar 1179 492 204 86 202 2
## 304 Taiwan 1171 226 62 29 135 4
## 305 Cook Islands 1169 53 11 4 38 11
## 306 Aruba 1168 142 28 29 85 1
## 307 São Tomé and Príncipe 1165 65 8 10 47 48
## 308 Antigua and Barbuda 1164 225 71 41 113 0
## 309 Cayman Islands 1160 110 23 20 67 0
## 310 Liechtenstein 1149 243 18 32 193 2
## 311 Nepal 1146 257 61 44 152 46
## 312 Bangladesh 1144 293 76 62 155 9
## 313 Djibouti 1144 105 7 10 88 48
## 314 Maldives 1127 220 61 37 122 126
## 315 Alderney 1118 135 5 2 128 337
## 316 Bahamas 1098 61 14 10 37 0
## 317 Tonga 1095 68 16 6 46 8
## 318 Cambodia 1083 285 62 39 184 0
## 319 Turks and Caicos Islands 1058 50 11 5 34 0
## 320 American Samoa 1051 53 4 2 47 11
## 321 Mongolia 1049 99 25 9 65 7
## 322 Seychelles 1043 170 26 30 114 46
## 323 Brunei 1042 128 21 10 97 7
## 324 Pakistan 1020 239 50 41 148 46
## 325 Somalia 1020 116 10 13 93 47
## 326 Northern Mariana Islands 1018 30 4 3 23 60
## 327 San Marino 988 209 2 9 198 2
## 328 British Virgin Islands 981 113 18 20 75 3
## 329 Sri Lanka 980 237 44 46 147 2
## 330 Guam 975 115 23 10 82 60
## 331 Laos 974 212 41 31 140 126
## 332 United States Virgin Islands 960 74 8 11 55 3
## 333 Anguilla 959 83 5 9 69 3
## 334 Bhutan 940 103 11 6 86 9
## 335 Macau 916 141 21 16 104 7
## 336 Timor-Leste 898 70 7 4 59 7To focus on teams with significant match experience, filter for teams that have played more than 40 games. Extract ratings from the Elo calculation and filter by game count.
ratings_filtered <- elo_ratings_international[["ratings"]] %>%
filter(Games > 40)
# View the filtered ratings
view(ratings_filtered)