User can have only values for the variables in scorecard formula and enter them for any player, for any referring period and scorecard will produce estimation for player (transfer value) for the date to which input values relate. In its operational form, scorecard formula has more complex structure and larger number of variables, conditions and functional dependencies. In order to feed formula with data and to be able to use calculations, it is useful to integrate formula into clients IT (BI) system or to have custom interface.
Models aim to quantify the impact of individual player characteristic on the target estimation value.
The methodology is based on the idea that player's characteristics attract buyers, and therefore, to estimate each player value it is necessary to determine the prices of these individual characteristics, i.e. their implicit prices, but also how their particular combination affects valuation.
We can combine expert knowledge of football professionals and quantify performance measures that experts find useful. Player indices can be extended to “team indices” and this is opening the path to the measures of the team cohesion. Our further research on transfer value and player indices led to method of calculation of team winning potential, that takes into account particular formation and lineups of both teams. Our methodology can be perceived as the valuable know-how that can produce customized indices for football decision support systems.
There are 3 main question that we want to provide answer for:
What is the risk for the player towards specific injury?
How long will recovery of the injured player last?
How long will it take for the player to reach pre-injury performance level?
All calculations are completely data driven. We use a combination of methodologies like regression analysis, neural networks, clustering...
We can combine expert knowledge of football professionals an quantify performance measures that experts find useful.
To estimate match outcome probabilities, we use specific set of quantitative models on a historical performance data. Model output is probability of "Home win", "Draw/Tie" and "Away win". Our team winning potential takes into account particular formation and lineups of both teams. As team is influenced by the set of individual player characteristics, this is opening the path to the measures of the team cohesion. Results can be used in scouting efforts, as they point to the optimal characteristics that team could benefit form and player with the desirable set of the characteristics can be searched for.