Category Archives: Statistics

AnnaBet Power Ratings guide

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.

Purchase Statistics Pro to see national soccer and ice hockey teams power ratings and value odds in our head-to-head statistics

Example screenshot in our h2h / team comparison page Poland vs France:

Click here to buy Statistics Pro from our web store

Our Power Rating pages

Jokerit not winning outside their home town in previous 10 playoffs away games

By using our fantastic Statistics Pro -service. We noticed that Finnish SM-liiga team Jokerit from Helsinki haven’t won their last 10 away games in SM-liiga playoffs outside Helsinki. Only wins as an away team has been against local team HIFK. Here is a screenshot of our stats filter (Statistics Pro) showing only playoffs games in the head-to-head statistics:

Last time Lukko’s odds was 3.77 at PinnacleSports and they won 5-2. At the time of writing this article the best odds for tonight’s Lukko – Jokerit game are:

Click here to compare Lukko vs Jokerit

Of course you can do much more than showing just playoffs games. You can get statistics only for certain period (first, second, third or all overtime games), or filter games by 1×2 odds. Choose small odds to see games where team has been clear favorite and big odds when playing as clear underdog. When you choose to view statistics for 1. period only, you’ll also see odds comparison for 1. period bets! In soccer you’ll also get odds for 2nd half too.

Edit: Game result was Lukko – Jokerit 2-1 so this was Jokerit’s 11th loss at away from home town in playoffs! Nice winning odds for “underdog” Lukko again (3.44).

Soccer goal probabilities: Poisson vs actual distribution

How does Poisson distribution work in football (soccer) goal probability calculations? In this article we have research data from the following European leagues in our soccer database:

Leagues: English Premier League, English Championship, Italian Serie A, Spanish Primera División and French Ligue 1
All the results we have till 9th of March 2013. Regular season games and regular playtime only, awarded games not included (note: we might not have all the awarded games info stored into our database).
Home goal average = 1.5042
Away goal average = 1.0639
n
= 39 996 games

HOME team goal distribution POISSON vs ACTUAL

AWAY team goal distribution POISSON vs ACTUAL

Poisson results here are quite accurately showing goal probabilities per team – except for the high number of goals for away team (which has much lower goal average).

Final score calculation MATH vs ACTUAL

Mathematically we can calculate each possible final score probability by multiplying each home goal probability by each away goal probability. Here you can see the results. Math (Poisson) works quite well for most of the scores but not all:

Mathematical model differs most at 0-0, 0-1 and 1-2 results. Notice that real world tie (draw) probability is higher than what is mathematically calculated:

*Math percentages calculated using each possible goal combination – not just 0 -> 3 goals as shown in the Final score table.

Poisson Distribution is a simple predictive model that doesn’t allow for a lot of factors. The system is of greatest benefit over a long period of time – using it for a whole season’s worth of games, rather than one-off matches.

PoissoNed! calculator

There is an easy way to calculate a great variety of soccer goal probabilities (for example: correct score, asian handicaps, total goals, team totals) from goal average values using PoissoNed! online calculator which has also lots of adjustable parameters like tie probability multiplier or adjusting (“forcing”) the results to certain home win – tie – away win % distribution:

See some of the results here (open the calculator from the link above to see all the probabilities). Value odds (decimal odds format) are next to the percentages. They are also available in UK and US odds.

See also this guide: Poisson Distribution for Soccer Betting by PinnacleSports

 

Statistics Tool

Our pure amazing sports statistics (betting) tool finally available in public! Too much features to list here (you should try the demo!) but I give you some examples: “show me games of team A vs team B in NHL playoffs where team A was heavy favorite with 1×2 odds under 1.5 and team B underdog with odds 3.00+ and also show me all these games results and statistics as -1.5 handicap for the team A!”

“Show me the 1st half 1×2% distribution where Arsenal was <1.40 odds heavy favorite at home and Stoke 4.50+ underdog at away and show me the current 1st half 1×2 odds comparison too!”

Basketball example: “show me how New York Knicks has played as heavy favorite with -9.5 handicap!”