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KickForm predictions are based on a statisticaꦗl model for predictions of football matches, developed by Physics professor Andreas Heuer. To develop its forecasts, KickForm used this procedure as a foundation since it was proven to be especially accurate. Compared to the odds of bookmakers, predictions according to t꧒his model were proven to be even better than those of bookmakers.
The result of a footജball match is predicted using the following seven steps:
Step 1: Determining the Home Advantage
You can calculate the Hoꦛme Advantage by using the below formula:
home adva💛ntage = c1 (home advantage of the last three years) + c2 (home advantage of the currenꦡt season)
On average, Home teams tend to score higher (Statistics: 1.66 goals for Home and 1.20 for Away).
Step 2: Calculating the Number of Goals per Match
Goals per match on average,ꦛ ta𒀰king into account all teams.
Typically, 3 goals are scored in a football match. However, for the sake of precision, the number of goals has decreased over time and now lies at 2.8.
Step 3: Calculating the Performance Level and Expected Goal Difference
Use the below formulas to calculaꦰte the performance level and expected goal differen✨ce of a match:
Performance level = c1 X1 + c2 X2 + c3 X3 + c4 X4
X1 = mean go🧸alscoring difference (GCD) of the pr𓆉evious season, weighting of the last three years (0.5, 0.35, 0.15)
X2 = goalscoring difference of the current season
X3 = current fitness value (mean goalscoring di𝓀fference, weighted with a decreasing exponential function)
X4 = logarithmised market value
Goal scoring opportunities are much more informative for the purpose of forecasts than goals. Good teams display a slightly better conversion of chances. The prediction becomes a lot more accurate if the goalscoring opportunities of the current and the past season as well as the market value are taken into account. By doing so, correlations of up to 0.67 are made, resulting in a 67% rate of correct predictions.
Step 4: Determining the Exceptionality of Promoted Teams
The performance of promoted teams is stunningly well-predetermined. Obvious deviations from the lower half of the table (goal difference: -13 +/- 8) are therefore quite rare.
Step 5: Calculating the Expected Amount of Goals
For every match the total amount of expected 🍸goals is similar; however, there are high-performing teams that score more goals than average.
amount of goals = c1 X𒁃1 + c2 X2🀅 + mean goals per match
X1 = total of goalscoring opportunities in the past with identical weightin🎃g-parameters of the last 3 years
X2 = effective total of goalscori🍨ng opportunities in the current season. Here, the tota♛l of goalscoring opportunities of all teams is subtracted so that the total of goalscoring opportunities as compared to the average is determined.
Step 6: Calculate the expected goals
Proportion of the calc𒅌ulated goal differꦅence and the total goals for the respective match.
Step 7: Matchday Weighting Factor
Weighting ൲factor for the respective matchday or for stage of the season.
Example: Dortmund against Schalke
Prediction after using the KickForm Football Formula™: 1.429:1.022
Obviously, no mat♎ch results in 1.429:1.022 - thisไ is just the average. With the help of the Poisson Distribution, we can calculate these figures for the distribution of 100% using a row of results for each team.
The Poisson Formula itself is as follows: P(x; μ) = (e-μ) (μx) / x!
The followin💛g Poisson Distribution has been calculated from the above example:
| Goals | 0 | 1 | 2 | 3 | 4 | 5 |
| Dortmund | 23.95 % | 34.23 % | 24.46 % | 11.65 % | 4.16 % | 1.19 % |
| Schalke | 35.99 % | 36.78 % | 18.79 % | 6.40 % | 1.64 % | 0.33 % |
This example shows that:
Dortmund has a 23.95% probability of scoring no goals, a 34.23% probability of scoring one goal and a 24.46% probability of scoring two go𝓰als.
Schalke, on the other hand, has a 35.99% probability of scoring no goals, a 36.78 % probability of scoring🔯 one goal and a 18.79 % chance of scoring two goals.
The most likely result, therefore, is 1:1.
This result will occur with a probability 𝓡of 12.59%.
KickForm even goes a step further and offers each user the possibility to customise the Formula by using a different weighting according to his or her personal predictions. In this way, football fans can, even without a great in-depth knowledge of mathematics, develop their own forecasts on a scientific basis. Registered users can choose factors such as market value, possession, Home advantage, favourite team 🉐or Away weakness. Like this, anyone can create their own Formula and become a betting expert.
Example: Weighting Factor Home Advantage
You are of the opinion that Hoꦕme teams (crucially) score more goals. You give tಌhe factor Home advantage more weighting, e.g. + 0.6. Consequently, the result for Dortmund Schalke now amounts to 2.029:1.022, and so the most likely result changes from 1:1 to 2:1.