Tradezilla cont. (page 2 of 3)
Calculating Trade Value (Second Attempt)
So, our first attempt to define trade value was:
(What the player should be paid) minus (What the player is getting paid) |
If you're getting $10,000,000 of value from a $5,000,000 player, then you are very happy to have him on the team. And any other team would also be happy to have him. So he has a lot of trade value.
But the above algorithm doesn't always make sense. For starters, most free agents are getting paid exactly what Baseball Mogul thinks they should be getting paid. This may change some if the player's skills improve or decline over the course of a contract. But in general, players that make it to free agency have very little trade value by this method.
This doesn't make sense to baseball fans. We know that players like Hideki Matsui and Mariano Rivera have trade value. Even though they are free agents earning large salaries, you still couldn't pry them away from George Steinbrenner without offering something in return.
The primary reason for this is scarcity. Mariano Rivera might be getting paid $10.5 million. But if you lost Mo and had that money to spend, you couldn't replace him. Even at 36, he has the best cut fastball in the history of the game, and he's still perhaps the league's best closer.
So, the original Baseball Mogul Trade AI incorporated a player's talent and scarcity to assess trade value. This made for a very challenging "artificial GM". But even in Baseball Mogul 2006 there were still some holes that could be exploited. For example, the Trade AI placed value on minor league players that sometimes outweighed their eventual value. Since players develop and different rates and sometimes even change positions, it was hard to find the right balance.
Brute Force Calculations
So, in Baseball Mogul 2006, the Trade AI was essentially the 'player evaluator' with some extra rules added on top. As more people played Baseball Mogul over the last 9 years, they would send us their feedback and we would incorporate it into the game.
Some customer might tell us that Baseball Mogul puts too much value on strong defensive middle infielders. In other words, after playing the game for hundreds of simulated seasons, they would report that it was possible to trade a marginal slick-fielding weak-hitting shortstop for a much more valuable players. Others would report
We took all of this feedback and added it to the game. Essentially, we were using brute force testing to improve our algorithm. Thousands of customers would play the game and report things that didn't seem realistic. And we would add a rule to the algorithm.
It's not an easy process. We're trying to make a computer as smart as a human General Manager. And it's just one part of a much more complex baseball computer game. User feedback was improving the AI. But only a little bit at a time.
Next page: A Different Kind of Brute Force
Footnotes
Although we use some different algorithms for assessing "replacement level", the logic above is essentially the same.