Since there are different scoring metrics for various positions, I chose to analyze goalkeepers because their range of variables would be limited to mainly saves rather than a mix of defensive and offensive stats. As a result, I collected 45 different goalkeeper equations that had 43 variables in total. By using Linear Algebra, I put these equations into matrix form so I could solve for each variable simultaneously. If each action was continually rated the same (i.e. an aerial won is always 10 points), then this system of equations should come out evenly, but my findings were not non-logical. The chart shows some of the player actions’ value that I solved for, and it clearly shows that when I put garbage in, I got garbage out. Positive actions like successful passes were awarded a high negative score and negative actions like conceding goals were awarded positive scores, making it apparent that the data source is flawed.

Granted, the Match Center does state that the “computation of the Audi Player Index score is proprietary information and as such, some scoring metrics are not listed.” Since MLS and Audi are hiding data from us, I decided to perform a several regressions, where in each iteration I removed statistically insignificant data. The yellow player actions reveal statistically significant events (p value < .05). These results are imperfect because there is influential data that MLS and Audi are keeping to themselves, but these coefficients can provide some insight into a rough estimate of what the modifiers really are. The significant player actions make sense in terms of positive actions are awarded points while negative actions deduct points with the exception of a ball collected save being worth -55 API points. This regression reveals that a goal conceded from inside the box has a value of -73, which is reasonable considering that Tornaghi had a score of -225 for “conceding” three goals inside the box (-75 per goal). Performing the regression may have closed in on some of the API’s rating system, but without data for the entire 86 component dataset, it cannot be solved.