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  Africa Cup of Nations 1994

# Mannschaft SP S U N T+ T- Diff P P/M S% ØT+ ØT-
1. Nigeria 5 3 1 0 10 3 +7 12 2.40 80% 1.80 0.60
2. Côte d'Ivoire 5 3 0 1 12 7 +5 10 2.00 60% 2.40 1.20
3. Sambia 5 3 1 1 8 3 +5 10 2.00 60% 1.60 0.60
4. Ghana 3 2 0 1 3 2 +1 6 2.00 67% 1.00 0.67
5. Mali 5 2 0 3 4 8 -4 6 1.20 40% 0.80 1.60
6. Ägypten 3 1 1 1 4 1 +3 4 1.33 33% 1.33 0.33
7. Republik Kongo 3 1 1 1 2 3 -1 4 1.33 33% 0.67 1.00
8. Senegal 3 1 0 2 2 3 -1 3 1.00 33% 0.67 1.00
9. Tunisia 2 0 1 1 1 3 -2 1 0.50 0% 0.50 1.50
10. Sierra Leone 2 0 1 1 0 4 -4 1 0.50 0% 0.00 2.00
11. Guinea 2 0 0 2 1 3 -2 0 0.00 0% 0.50 1.50
12. Gabon 2 0 0 2 0 7 -7 0 0.00 0% 0.00 3.50


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Die offizielle Webseite

Wikipedia

Sonntag 10. April 1994
   
Africa Cup of Nations1994 Quoten 1x2
Côte d'Ivoire    3 - 1    Mali
Nigeria    2 - 1    Sambia
   
Mittwoch 6. April 1994
   
Nigeria    3 - 2 n.V. Côte d'Ivoire
Sambia    4 - 0    Mali
   
Sonntag 3. April 1994
   
Côte d'Ivoire    2 - 1    Ghana
Sambia    1 - 0    Senegal
   
Samstag 2. April 1994
   
Ägypten    0 - 1    Mali
Nigeria    2 - 0    Republik Kongo
   
Donnerstag 31. März 1994
   
Ghana    1 - 0    Senegal
Sambia    2 - 1    Côte d'Ivoire
   
Mittwoch 30. März 1994
   
Nigeria    0 - 0    Ägypten
Tunisia    1 - 1    Republik Kongo
   
Dienstag 29. März 1994
   
Senegal    2 - 1    Guinea
Sambia    0 - 0    Sierra Leone
   
Montag 28. März 1994
   
Ägypten    4 - 0    Gabon
Mali    0 - 1    Republik Kongo
   
Sonntag 27. März 1994
   
Ghana    1 - 0    Guinea
Côte d'Ivoire    4 - 0    Sierra Leone
   
Samstag 26. März 1994
   
Nigeria    3 - 0    Gabon
Tunisia    0 - 2    Mali

Mehr Ergebnisse


April 1994
März 1994

Ägypten
Côte d'Ivoire
Gabon
Ghana
Guinea
Mali
Nigeria
Republik Kongo
Sambia
Senegal
Sierra Leone
Tunisia


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1974
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Die offizielle Webseite

Wikipedia

# Mannschaft SP S U N T+ T- Diff P P/M S% ØT+ ØT-
1. Nigeria 5 3 1 0 10 3 +7 12 2.40 80% 1.80 0.60
2. Côte d'Ivoire 5 3 0 1 12 7 +5 10 2.00 60% 2.40 1.20
3. Sambia 5 3 1 1 8 3 +5 10 2.00 60% 1.60 0.60
4. Ghana 3 2 0 1 3 2 +1 6 2.00 67% 1.00 0.67
5. Mali 5 2 0 3 4 8 -4 6 1.20 40% 0.80 1.60
6. Ägypten 3 1 1 1 4 1 +3 4 1.33 33% 1.33 0.33
7. Republik Kongo 3 1 1 1 2 3 -1 4 1.33 33% 0.67 1.00
8. Senegal 3 1 0 2 2 3 -1 3 1.00 33% 0.67 1.00
9. Tunisia 2 0 1 1 1 3 -2 1 0.50 0% 0.50 1.50
10. Sierra Leone 2 0 1 1 0 4 -4 1 0.50 0% 0.00 2.00
11. Guinea 2 0 0 2 1 3 -2 0 0.00 0% 0.50 1.50
12. Gabon 2 0 0 2 0 7 -7 0 0.00 0% 0.00 3.50

-
Saison Matchs 1 x 2  ¦  1 2  ¦  Über 2.5 Unter 2.5 Über 1.5 Unter 1.5 Heimspiel
2023Divisionphase36 36.1% 41.7% 22.2%  ¦  61.9% 38.1%  ¦  41.7% 58.3% 72.2% 27.8% 23.8%
2023Playoffs16 43.8% 37.5% 18.8%  ¦  56.3% 43.8%  ¦  25.0% 75.0% 62.5% 37.5% 12.5%
2021Divisionphase36 41.7% 27.8% 30.6%  ¦  57.7% 42.3%  ¦  27.8% 72.2% 47.2% 52.8% 15.4%
2021Playoffs16 31.3% 43.8% 25.0%  ¦  50.0% 50.0%  ¦  31.3% 68.8% 56.3% 43.8% 0.0%
2019Divisionphase36 41.7% 27.8% 30.6%  ¦  57.7% 42.3%  ¦  22.2% 77.8% 55.6% 44.4% 15.4%
2019Playoffs16 31.3% 31.3% 37.5%  ¦  43.8% 56.3%  ¦  37.5% 62.5% 56.3% 43.8% -12.5%
2017Divisionphase24 37.5% 41.7% 20.8%  ¦  64.3% 35.7%  ¦  33.3% 66.7% 62.5% 37.5% 28.6%
2017Playoffs8 50.0% 25.0% 25.0%  ¦  50.0% 50.0%  ¦  25.0% 75.0% 62.5% 37.5% 0.0%
2015Divisionphase24 12.5% 54.2% 33.3%  ¦  27.3% 72.7%  ¦  20.8% 79.2% 75.0% 25.0% -45.5%
2015Playoffs8 37.5% 37.5% 25.0%  ¦  62.5% 37.5%  ¦  62.5% 37.5% 75.0% 25.0% 25.0%
2013Divisionphase24 33.3% 54.2% 12.5%  ¦  72.7% 27.3%  ¦  33.3% 66.7% 70.8% 29.2% 45.5%
2013Playoffs8 37.5% 37.5% 25.0%  ¦  62.5% 37.5%  ¦  37.5% 62.5% 75.0% 25.0% 25.0%
2012Divisionphase24 54.2% 12.5% 33.3%  ¦  61.9% 38.1%  ¦  50.0% 50.0% 70.8% 29.2% 23.8%
2012Playoffs8 37.5% 37.5% 25.0%  ¦  62.5% 37.5%  ¦  25.0% 75.0% 62.5% 37.5% 25.0%
2010Divisionphase21 47.6% 33.3% 19.0%  ¦  71.4% 28.6%  ¦  47.6% 52.4% 66.7% 33.3% 42.9%
2010Playoffs8 25.0% 37.5% 37.5%  ¦  37.5% 62.5%  ¦  25.0% 75.0% 37.5% 62.5% -25.0%
2008Divisionphase24 54.2% 29.2% 16.7%  ¦  76.5% 23.5%  ¦  54.2% 45.8% 79.2% 20.8% 52.9%
2008Playoffs8 62.5% 12.5% 25.0%  ¦  62.5% 37.5%  ¦  75.0% 25.0% 75.0% 25.0% 25.0%
2006Divisionphase24 54.2% 12.5% 33.3%  ¦  61.9% 38.1%  ¦  41.7% 58.3% 70.8% 29.2% 23.8%
2006Playoffs8 25.0% 37.5% 37.5%  ¦  50.0% 50.0%  ¦  37.5% 62.5% 50.0% 50.0% 0.0%
2004Divisionphase24 41.7% 29.2% 29.2%  ¦  58.8% 41.2%  ¦  58.3% 41.7% 83.3% 16.7% 17.6%
2004Playoffs8 62.5% 25.0% 12.5%  ¦  87.5% 12.5%  ¦  62.5% 37.5% 87.5% 12.5% 75.0%
2000Divisionphase24 33.3% 37.5% 29.2%  ¦  53.3% 46.7%  ¦  29.2% 70.8% 62.5% 37.5% 6.7%
2000Playoffs8 37.5% 37.5% 25.0%  ¦  62.5% 37.5%  ¦  50.0% 50.0% 75.0% 25.0% 25.0%
1998Divisionphase24 50.0% 20.8% 29.2%  ¦  63.2% 36.8%  ¦  58.3% 41.7% 75.0% 25.0% 26.3%
1998Playoffs8 12.5% 50.0% 37.5%  ¦  25.0% 75.0%  ¦  25.0% 75.0% 75.0% 25.0% -50.0%
1996Saison29 51.7% 13.8% 34.5%  ¦  60.0% 40.0%  ¦  51.7% 48.3% 79.3% 20.7% 20.0%
1994Saison20 65.0% 20.0% 15.0%  ¦  82.4% 17.6%  ¦  50.0% 50.0% 65.0% 35.0% 64.7%
1992Saison20 60.0% 30.0% 10.0%  ¦  85.7% 14.3%  ¦  35.0% 65.0% 50.0% 50.0% 71.4%
1990Saison16 87.5% 12.5% 0.0%  ¦  100.0% 0.0%  ¦  25.0% 75.0% 50.0% 50.0% 100.0%
1988Saison16 37.5% 56.3% 6.3%  ¦  87.5% 12.5%  ¦  12.5% 87.5% 50.0% 50.0% 75.0%
1986Saison16 68.8% 25.0% 6.3%  ¦  91.7% 8.3%  ¦  25.0% 75.0% 50.0% 50.0% 83.3%
1984Saison16 56.3% 31.3% 12.5%  ¦  83.3% 16.7%  ¦  56.3% 43.8% 68.8% 31.3% 66.7%
1982Saison16 56.3% 43.8% 0.0%  ¦  100.0% 0.0%  ¦  37.5% 62.5% 62.5% 37.5% 100.0%
1980Saison16 68.8% 31.3% 0.0%  ¦  100.0% 0.0%  ¦  37.5% 62.5% 56.3% 43.8% 100.0%
1978Saison16 68.8% 31.3% 0.0%  ¦  100.0% 0.0%  ¦  43.8% 56.3% 75.0% 25.0% 100.0%
1976Saison18 61.1% 33.3% 5.6%  ¦  91.7% 8.3%  ¦  55.6% 44.4% 88.9% 11.1% 83.3%
1974Saison17 76.5% 17.6% 5.9%  ¦  92.9% 7.1%  ¦  64.7% 35.3% 88.2% 11.8% 85.7%
1972Saison16 37.5% 56.3% 6.3%  ¦  87.5% 12.5%  ¦  37.5% 62.5% 93.8% 6.3% 75.0%
1970Saison16 62.5% 31.3% 6.3%  ¦  92.3% 7.7%  ¦  50.0% 50.0% 81.3% 18.8% 84.6%
1968Saison16 81.3% 12.5% 6.3%  ¦  86.7% 13.3%  ¦  81.3% 18.8% 81.3% 18.8% 73.3%
1965Saison8 75.0% 25.0% 0.0%  ¦  85.7% 14.3%  ¦  75.0% 25.0% 75.0% 25.0% 71.4%
1963Saison8 62.5% 25.0% 12.5%  ¦  83.3% 16.7%  ¦  75.0% 25.0% 100.0% 0.0% 66.7%
1962Saison4 75.0% 25.0% 0.0%  ¦  100.0% 0.0%  ¦  100.0% 0.0% 100.0% 0.0% 100.0%
1959Saison3 100.0% 0.0% 0.0%  ¦  100.0% 0.0%  ¦  66.7% 33.3% 66.7% 33.3% 100.0%
1957Saison2 50.0% 0.0% 50.0%  ¦  50.0% 50.0%  ¦  100.0% 0.0% 100.0% 0.0% 0.0%

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AnnaBet Powerratings

Aktualisiert 2025-04-12 11:44:13
#Mannschaft Powerrating
1. Senegal 1352
2. Tunisia 1267
3. Ägypten 1249
4. Côte d'Ivoire 1182
5. Mali 1177
6. Nigeria 1101
7. Gabon 1052
8. Republik Kongo 1032
9. Ghana 1031
10. Sambia 1018
11. Guinea 1017
12. Sierra Leone 907


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.