Quantitative Football
Football Quantitative Models and Products
C&I has R&D expertise and experience in football specific products. This section will give the overview of some of our main football targeted quantitative product groups that we developed in recent years.

Why Quantitative Approach?

Amount of available (football) performance data is rapidly increasing. With this increase, number of observed football measures, variables or attributes increases as well. Q-Ant line of products attempts to make use of data and large number of variables available and to produce simple and useful measure, to step away from use of raw data and extract information from it.

Data Driven and Information Driven

All our quantitative products are data driven, meaning that our methodologies, techniques and approach reveals hidden patterns in large datasets and creates algorithms that extract useful information. This way we’re moving from the concept of data value to the concept of information value.
How To Measure the Performance
Finding Patterns
We’re using various methods, with a base in regression techniques, neural networks, clustering, etc. Idea is that decisions of the professionals leave hidden pattern in the data space. Our techniques and methodologies reveal us those patterns and we're able to identify trends and divers of the specific decision trends.
Practical Solution
Calculation & Results
Scorecard is the resulting algorithm that contains variables, coefficients and functional relationship. In a simple form example, products of variables and coefficients is summed and result is the estimation value (e.g. transfer value).

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.

Football player transfer value
Quantifying the impact of individual player characteristic on the target estimation value.
Fair Transfer Value
Knowing the Value
Our idea is to provide formula that impartially evaluates football player value, i.e. fair transfer value at any time, regardless of transfer windows. Fair transfer value estimation is based on players performance.

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.

Player & Team Indicators
Quantify the impact of individual player characteristic on the target estimation value.
Quantifying the performance
Performance Indicators
Idea is to observe players not as a individual with name and surname, but as the individual comprised of certain set of performance indicators values. For performance indicators we use combination of several quantitative methods.

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.

Player Injury risk
Quantitative injury risk assessment and management.
Minimizing the risks
Injury Risk Management
Main idea is to provide experts with measures of injury risk assessment so they can manage it and put a risk at a acceptable level. Our techniques and methodologies can trace correlations between play and injury risk of a player.

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...

Team Win Potential
Quantify the impact of individual player characteristic on the target estimation value.
Its about the team not the names
Creating the optimal team
Idea is to observe player not as a original individual with name and surname, but as the individual comprised of certain set of performance indicators values. For performance indicators we use combination of several quantitative methods.

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.