Finding the Best Batting Order

In my last post, I explained how I used Monte Carlo simulation to find the 9 players that should be in the lineup. In this post, I will show how I found the best batting order using these 9 players..

There are

9*8*7*6*5*4*3*2*1 = 362,880

different batting orders that can be made up from 9 players. So finding the particular batting order that is the best would appear to be difficult but not impossible.

I wrote a program to find these 362,880 batting orders and analyze them using my Monte Carlo simulation.

Here are the 9 players that I suggested should be in the lineup in the last post.

Player    

1        

2        

3        

6        

7        

10     

11      

12    
14    

For all 362,880 batting orders, I ran 10 trials of 100 seasons of 20 games to find all of the sets that had an average of 6.5 runs or more per game. I found 51 potential batting orders to examine in more detail.

Then I conducted a runoff, based on the average number of runs per game, for these 51 batting orders using the program that I discussed in the last post. Again, I ran a simulation with 500 trials of 500 seasons of 20 games.

I found the winner of the runoff was the following batting order.

Player  

2         

1       

3       

10     

11    
12 

7   

6   

14 

On-base average is valued for the leadoff hitter.  Then slugging percentage is valued for the 2, 3 and 4 hitters.  Players with low slugging percentages are relegated to the bottom of the batting order.  .