Okay, so first, for each vehicle type, compute the inverse weight as 1 / points. Normalize these weights to convert them into probabilities summing to 100%. This is done by dividing each weight by the total sum of all weights. Ensure the probabilities sum exactly to 100% by adjusting the last probability. For each vehicle type, compute the absolute difference between John's probability and the city's frequency and then sum all these differences. Subtract the total difference from 100% to calculate the match percentage: Match Percentage = 100 - Σ|P(Jhon) - P(City)|. If the match percentage is negative, set it to 0.

Here is an example: Points: [1, 3, 5, 6] City Frequencies: [58.82%, 19.61%, 11.76%, 9.81%] | Weight=[1, 1/3 ,1/5 ,1/6] | Sum of weights = 1.7 | Probabilities= [1/1.7, 0.33/1.7, 0.2/1.7, (1/6)/1.7]*100= | Probabilities = [58.82, 19.61, 11.76, 9.81] | In this case the sum is already 100%, so no adjustment is needed. | Differences= |58.82 - 58.82| + |19.61 - 19.61| + |11.76 - 11.76| + |9.81 - 9.81| = 0 | Match Percentage = 100 - 0 = 100.00%

Fixed

The "match percentage" in this context measures how closely John’s point-based probability distribution aligns with the actual frequency distribution of vehicle types in the city. The purpose is to assess how well John's point system predicts the city's actual vehicle distribution. A high match percentage means the point system effectively captures real-world probabilities, while a low percentage suggests it deviates significantly, potentially making the game less representative of actual trends.

I think i fixed it

Thank you! I think I solved the problem.