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  Puchar Narodów Afryki 1972

# Drużyna SR W R P GF GA Diff Pts Pts/G W% ØGF ØGA
1. Kamerun 5 3 1 1 10 5 +5 10 2.00 60% 2.00 1.00
2. Republic of Congo 5 3 1 1 9 7 +2 10 2.00 60% 1.80 1.40
3. Republika Konga 5 1 2 1 9 11 -2 6 1.20 20% 1.80 2.00
4. Mali 5 0 3 1 11 11 0 5 1.00 20% 2.00 2.20
5. Maroko 3 0 3 0 3 3 0 3 1.00 0% 1.00 1.00
6. Kenia 3 0 2 1 3 4 -1 2 0.67 0% 1.00 1.33
7. Sudan 3 0 2 1 4 6 -2 2 0.67 0% 1.33 2.00
8. Togo 3 0 2 1 4 6 -2 2 0.67 0% 1.33 2.00


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Wikipedia

Niedziela 5. marzec 1972
   
Puchar Narodów Afryki1972 Kursy 1x2
Republic of Congo    3 - 2    Mali
   
Sobota 4. marzec 1972
   
Kamerun    5 - 2    Republika Konga
   
Czwartek 2. marzec 1972
   
Kamerun    0 - 1    Republic of Congo
Mali    4 - 3 AET Republika Konga
   
Wtorek 29. luty 1972
   
Republic of Congo    4 - 2    Sudan
Republika Konga    1 - 1    Maroko
   
Poniedziałek 28. luty 1972
   
Kamerun    1 - 1    Mali
Togo    1 - 1    Kenia
   
Niedziela 27. luty 1972
   
Maroko    1 - 1    Sudan
Republika Konga   2 - 0   Republic of Congo
   
Sobota 26. luty 1972
   
Kamerun    2 - 0    Togo
Mali    1 - 1    Kenia
   
Piątek 25. luty 1972
   
Republic of Congo    1 - 1    Maroko
Republika Konga    1 - 1    Sudan
   
Czwartek 24. luty 1972
   
Mali    3 - 3    Togo
   
środa 23. luty 1972
   
Kamerun    2 - 1    Kenia

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marzec 1972
luty 1972

Kamerun
Kenia
Mali
Maroko
Republic of Congo
Republika Konga
Sudan
Togo


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Official Site

Wikipedia

# Drużyna SR W R P GF GA Diff Pts Pts/G W% ØGF ØGA
1. Kamerun 5 3 1 1 10 5 +5 10 2.00 60% 2.00 1.00
2. Republic of Congo 5 3 1 1 9 7 +2 10 2.00 60% 1.80 1.40
3. Republika Konga 5 1 2 1 9 11 -2 6 1.20 20% 1.80 2.00
4. Mali 5 0 3 1 11 11 0 5 1.00 20% 2.00 2.20
5. Maroko 3 0 3 0 3 3 0 3 1.00 0% 1.00 1.00
6. Kenia 3 0 2 1 3 4 -1 2 0.67 0% 1.00 1.33
7. Sudan 3 0 2 1 4 6 -2 2 0.67 0% 1.33 2.00
8. Togo 3 0 2 1 4 6 -2 2 0.67 0% 1.33 2.00

-
Sezon Gier 1 x 2  ¦  1 2  ¦  Ponad 2.5 Poniżej 2.5 Ponad 1.5 Poniżej 1.5 Home Advantage
2023Group Stage36 36.1% 41.7% 22.2%  ¦  61.9% 38.1%  ¦  41.7% 58.3% 72.2% 27.8% 23.8%
2023Playoffy16 43.8% 37.5% 18.8%  ¦  56.3% 43.8%  ¦  25.0% 75.0% 62.5% 37.5% 12.5%
2021Group Stage36 41.7% 27.8% 30.6%  ¦  57.7% 42.3%  ¦  27.8% 72.2% 47.2% 52.8% 15.4%
2021Playoffy16 31.3% 43.8% 25.0%  ¦  50.0% 50.0%  ¦  31.3% 68.8% 56.3% 43.8% 0.0%
2019Group Stage36 41.7% 27.8% 30.6%  ¦  57.7% 42.3%  ¦  22.2% 77.8% 55.6% 44.4% 15.4%
2019Playoffy16 31.3% 31.3% 37.5%  ¦  43.8% 56.3%  ¦  37.5% 62.5% 56.3% 43.8% -12.5%
2017Group Stage24 37.5% 41.7% 20.8%  ¦  64.3% 35.7%  ¦  33.3% 66.7% 62.5% 37.5% 28.6%
2017Playoffy8 50.0% 25.0% 25.0%  ¦  50.0% 50.0%  ¦  25.0% 75.0% 62.5% 37.5% 0.0%
2015Group Stage24 12.5% 54.2% 33.3%  ¦  27.3% 72.7%  ¦  20.8% 79.2% 75.0% 25.0% -45.5%
2015Playoffy8 37.5% 37.5% 25.0%  ¦  62.5% 37.5%  ¦  62.5% 37.5% 75.0% 25.0% 25.0%
2013Group Stage24 33.3% 54.2% 12.5%  ¦  72.7% 27.3%  ¦  33.3% 66.7% 70.8% 29.2% 45.5%
2013Playoffy8 37.5% 37.5% 25.0%  ¦  62.5% 37.5%  ¦  37.5% 62.5% 75.0% 25.0% 25.0%
2012Group Stage24 54.2% 12.5% 33.3%  ¦  61.9% 38.1%  ¦  50.0% 50.0% 70.8% 29.2% 23.8%
2012Playoffy8 37.5% 37.5% 25.0%  ¦  62.5% 37.5%  ¦  25.0% 75.0% 62.5% 37.5% 25.0%
2010Group Stage21 47.6% 33.3% 19.0%  ¦  71.4% 28.6%  ¦  47.6% 52.4% 66.7% 33.3% 42.9%
2010Playoffy8 25.0% 37.5% 37.5%  ¦  37.5% 62.5%  ¦  25.0% 75.0% 37.5% 62.5% -25.0%
2008Group Stage24 54.2% 29.2% 16.7%  ¦  76.5% 23.5%  ¦  54.2% 45.8% 79.2% 20.8% 52.9%
2008Playoffy8 62.5% 12.5% 25.0%  ¦  62.5% 37.5%  ¦  75.0% 25.0% 75.0% 25.0% 25.0%
2006Group Stage24 54.2% 12.5% 33.3%  ¦  61.9% 38.1%  ¦  41.7% 58.3% 70.8% 29.2% 23.8%
2006Playoffy8 25.0% 37.5% 37.5%  ¦  50.0% 50.0%  ¦  37.5% 62.5% 50.0% 50.0% 0.0%
2004Group Stage24 41.7% 29.2% 29.2%  ¦  58.8% 41.2%  ¦  58.3% 41.7% 83.3% 16.7% 17.6%
2004Playoffy8 62.5% 25.0% 12.5%  ¦  87.5% 12.5%  ¦  62.5% 37.5% 87.5% 12.5% 75.0%
2000Group Stage24 33.3% 37.5% 29.2%  ¦  53.3% 46.7%  ¦  29.2% 70.8% 62.5% 37.5% 6.7%
2000Playoffy8 37.5% 37.5% 25.0%  ¦  62.5% 37.5%  ¦  50.0% 50.0% 75.0% 25.0% 25.0%
1998Group Stage24 50.0% 20.8% 29.2%  ¦  63.2% 36.8%  ¦  58.3% 41.7% 75.0% 25.0% 26.3%
1998Playoffy8 12.5% 50.0% 37.5%  ¦  25.0% 75.0%  ¦  25.0% 75.0% 75.0% 25.0% -50.0%
1996Sezon 29 51.7% 13.8% 34.5%  ¦  60.0% 40.0%  ¦  51.7% 48.3% 79.3% 20.7% 20.0%
1994Sezon 20 65.0% 20.0% 15.0%  ¦  82.4% 17.6%  ¦  50.0% 50.0% 65.0% 35.0% 64.7%
1992Sezon 20 60.0% 30.0% 10.0%  ¦  85.7% 14.3%  ¦  35.0% 65.0% 50.0% 50.0% 71.4%
1990Sezon 16 87.5% 12.5% 0.0%  ¦  100.0% 0.0%  ¦  25.0% 75.0% 50.0% 50.0% 100.0%
1988Sezon 16 37.5% 56.3% 6.3%  ¦  87.5% 12.5%  ¦  12.5% 87.5% 50.0% 50.0% 75.0%
1986Sezon 16 68.8% 25.0% 6.3%  ¦  91.7% 8.3%  ¦  25.0% 75.0% 50.0% 50.0% 83.3%
1984Sezon 16 56.3% 31.3% 12.5%  ¦  83.3% 16.7%  ¦  56.3% 43.8% 68.8% 31.3% 66.7%
1982Sezon 16 56.3% 43.8% 0.0%  ¦  100.0% 0.0%  ¦  37.5% 62.5% 62.5% 37.5% 100.0%
1980Sezon 16 68.8% 31.3% 0.0%  ¦  100.0% 0.0%  ¦  37.5% 62.5% 56.3% 43.8% 100.0%
1978Sezon 16 68.8% 31.3% 0.0%  ¦  100.0% 0.0%  ¦  43.8% 56.3% 75.0% 25.0% 100.0%
1976Sezon 18 61.1% 33.3% 5.6%  ¦  91.7% 8.3%  ¦  55.6% 44.4% 88.9% 11.1% 83.3%
1974Sezon 17 76.5% 17.6% 5.9%  ¦  92.9% 7.1%  ¦  64.7% 35.3% 88.2% 11.8% 85.7%
1972Sezon 16 37.5% 56.3% 6.3%  ¦  87.5% 12.5%  ¦  37.5% 62.5% 93.8% 6.3% 75.0%
1970Sezon 16 62.5% 31.3% 6.3%  ¦  92.3% 7.7%  ¦  50.0% 50.0% 81.3% 18.8% 84.6%
1968Sezon 16 81.3% 12.5% 6.3%  ¦  86.7% 13.3%  ¦  81.3% 18.8% 81.3% 18.8% 73.3%
1965Sezon 8 75.0% 25.0% 0.0%  ¦  85.7% 14.3%  ¦  75.0% 25.0% 75.0% 25.0% 71.4%
1963Sezon 8 62.5% 25.0% 12.5%  ¦  83.3% 16.7%  ¦  75.0% 25.0% 100.0% 0.0% 66.7%
1962Sezon 4 75.0% 25.0% 0.0%  ¦  100.0% 0.0%  ¦  100.0% 0.0% 100.0% 0.0% 100.0%
1959Sezon 3 100.0% 0.0% 0.0%  ¦  100.0% 0.0%  ¦  66.7% 33.3% 66.7% 33.3% 100.0%
1957Sezon 2 50.0% 0.0% 50.0%  ¦  50.0% 50.0%  ¦  100.0% 0.0% 100.0% 0.0% 0.0%

Naciśnij na wybranej kolumnie w celu posortowania danych
Mouseover percentages (%) to see value odds

AnnaBet Power Ratings

Updated 2025-03-21 09:51:27
#Drużyna Rating
1. Maroko 1540
2. Mali 1189
3. Kamerun 1126
4. Republika Konga 1032
5. Sudan 1032
6. Togo 988
7. Kenia 976
8. Republic of Congo 882


Our ratings are currently calculated from games played after 1.1.2000

In football our ratings are similar to World Football Elo Ratings but we have tuned up the formula. For example when goal difference is low or game is tied and we have shots on goal statistics available for the game, we’ll then analyze the shots ratio to have some effect on the ratings. For example if a game was tied 1-1 but home team outshoot away team by 10-2 you might say the home team was the better team despite the result.

In ice hockey the ratings are similar but we have taken account the higher number of goals scored and “home” team (first mentioned team) line change advantage. For example if ice hockey game Sweden – Finland was played at Finland but Sweden had the line change advantage it is then taken account when calculating ratings.

Some examples how ratings are adjusted after each game

In the beginning each team has starting rating of 1000 points. After each game played the sum of points change is 0: if home team gets +20 points then away team gets -20 pts deducted. Amount is always based on the weight/importance of the tournament: in friendlies teams get much less points than in World Cup finals.

Two equal teams meet: winner gets some decent points and loser looses the same amount. Example +20 / -20.
Heavy favorite (much higher rating) wins by few goals: gets only few points because it was very expected result. Your points rises very slowly by beating much poorer teams than you. Example +3 / -3.
Heavy favorite ties a game: favorite loses small amount of points because it was expected that the team should win, the opponent get some points. Example -3 / +3 points.
Heavy favorite loses a game: loses lots of rating points, winner gets lots of points. Example -40 / +40.

Sample Winning Expectancies

Difference
in Ratings
Higher
Rated
Lower
Rated
0 0.500 0.500
10 0.514 0.486
20 0.529 0.471
30 0.543 0.457
40 0.557 0.443
50 0.571 0.429
60 0.585 0.415
70 0.599 0.401
80 0.613 0.387
90 0.627 0.373
100 0.640 0.360
110 0.653 0.347
120 0.666 0.334
130 0.679 0.321
140 0.691 0.309
150 0.703 0.297
160 0.715 0.285
170 0.727 0.273
180 0.738 0.262
190 0.749 0.251
200 0.760 0.240

Table by Eloratings.net

Why are Power Ratings better than winning percentage or league table?

Let’s say we have 2 teams whose performance we are analyzing: Finland and Sweden. Both teams have played 8 games and Finland has 6 wins and 2 losses, Sweden 5 wins and 3 losses. You might say Finland is the better team based on that info? What if Finland has won 4 games against poor teams, 2 against mediocre and lost 2 against better teams. Sweden on the other hand had win 3 games against better teams, 2 against mediocre and then 3 narrow losses against mediocre teams. Putting it that way, you might not believe Finland should be a favorite here after all. Would our Power Ratings tell you the exactly same thing:

Finland starting rating 1000:

1. game 4-0 win against poor team +10 pts (1010)
2. game 3-1 win against poor team +6 pts (1016)
3. game 0-2 loss against better team -10 pts (1006)
4. game 4-3 win against mediocre team +15 pts (1021)
5. game 5-3 win against mediocre team +18 pts (1039)
6. game 3-5 loss against better team -10 pts (1029)
7. game 2-0 win against poor team +6 pts (1035)
8. game 5-2 win against poor team +8 pts (1043)
Current rating 1043

Sweden starting rating 1000:

1. game 3-2 win against better team +25 pts (1025)
2. game 2-3 loss against mediocre team -12 pts (1013)
3. game 4-2 win against better team +30 pts (1043)
4. game 3-0 win against mediocre team +20 pts (1063)
5. game 3-5 loss against mediocre team -15 pts (1048)
6. game 3-4 loss against mediocre team -12 pts (1036)
7. game 4-1 win against better team +35 pts (1071)
8. game 3-1 win against mediocre team +16 pts (1087)
Current rating 1087

These are just rough examples for you to get the idea.

Finland vs Sweden Power Ratings: 1043 – 1087, ratings difference 44 and by looking at the table above you can see that this game should be about Finland 46% winning chance and Sweden 54%. Note that home advantage is usually about 100 points so 46%-54% would be only at neutral venue.

It is not always about how many games you have won but rather which teams and by how many goals that tells more about your true Power. But still remember these are only computer calculated estimations and does not take account real world situations like injuries, weather etc. Also note Power Ratings being much less accurate when teams have a big difference between number of games played and/or quality/diversity of tournaments where they have played.

For example in ice hockey USA and Canada plays only few friendly matches before major tournaments and European teams plays a lots of smaller tournaments - and also playing many games against couple of selected opponents only. Smaller tournaments and friendly matches makes of course smaller changes to Power Ratings than major tournaments but when you play a lot of smaller games it can add up.