Last week's solution can be found here.
Soccer, Football, Fußball, whatever you call it, it’s the world’s game. This week’s challenge uses a dataset from the popular game franchise Fifa 18, which contains players from across the globe. In the game, each player is assigned a score for each attribute category, a preferred position, and an overall rating from 0-99. The rating is calculated by multiplying a player’s scores by coefficients for various attributes which are most critical to the position he plays. The score is rounded to the nearest whole number (e.g. 1.5-2.49 = 2).
Use the Attribute References table to calculate each player’s overall rating at each position. All relevant Attribute References should be used when calculating a rating for a position. *This is a simplified version of the positional calculations which does not factor in chemistry, international ratings, and other in-game factors. Results will not match the Overall Rating in the dataset.
For a tougher challenge, recreate the attribute reference table from the Rating Coefficients list. (note that the sweeper GK has the same attributes as a standard GK in addition to the unique sweeper coefficients). If you want to factor in the basic chemistry and International Reputation into the calculations, use the Int Rep table and apply a +5 boost to the categories listed in the Basic Chemistry table. *These calculations will not match the actual Overall Rating as some in-game factors are not included.
My results are off due to rounding, but I like my approach. It parses the field names and applies the rounding at the level described in the instructions. Wish this was a basketball challenge as I could use the help filling out my bracket!