Since my last update, i have implemented most of the attributes in Cycling - The board game.

Quick recap: the game is about getting to the finish line first. Each turn, each player rolls a dice (D6) and moves the amount of squares. The odds of the dice are rigged based on the player's strength (higher strength = better odds). Next to the dice roll the player can gain bonuses to move additional squares. These bonuses are based on their attributes (which can range from 1 - 20).

Now that i have implemented them, i have to check if they add fun to the game. Fun is of course hard to measure, but what i am aiming for mostly is balance between the different elements. For example, if one attribute is much more important than others, the others are irrelevant.

To test them, i left my computer running for a night and simulate over 600 different races with different AI players (with every time different attributes and strength). I now had a lot of data points to analyse with.

I decided to use a multiple regression analysis, where i analysed the impact of the strength and all the different attributes on the outcome of the race. This led to the following results:

R-squared: 

0.43 

Coefficient of different attributes

Conclusions

• This stuff really makes me feel like a nerd, which is nice ?
• The R squared means that more than half of the results are not explained by this model
• The fact that all attributes are negative is correct (the higher the attribute, the lower your total final time)
• The attribute luck has a different sign, which basically means that a lower attribute is better (although the coefficient is very small, so technically it means that it is not relevant)
• Strength is more important than all the different attributes
• Climbing, solo riding and group technique are most important
• Composure and determination hardly matter at all