Thoughts?

xERA is the expected number of earned runs given up below what the number of earned runs given up would be for the average pitcher under the same number of IP.

If the average ERA is 3.81, and Lincecum is projected to have a 2.85 ERA in 218 IP, his xERA is (avgERA-playerERA)*IP/9=(3.81-2.85)*218/9=23.25.

I appreciate the answers. Yes, I had read part IV of the how-it-works, however, I wasn’t quite able to catch onto it. Now, I think I do understand somewhat better.

You take the first 225 (or starting amount) players, and assume for the moment that they are the top players. Then you take the SD and positional adjustments from that, and carry those over to value the entire player population. After that, you sort by value the top 225 players, and then get the new SD / positional adjustments from those 225… continue revaluing and readjusting the SD and positional adjustments, until the top 225 matches any previous iteration. Is this correct?

I may have other questions about this in the future, and I appreciate the offer! I will need to try out this system while reworking the site during the offseason. Thank you for all the explanations you’ve offered about the process!

When SGP computes how far teams are separated in the standings, it is basically figuring a team-level standard deviation. If everyone has 14 hitters, then the team-level SD in a stat should approximate the player-level SD.

The difference, as you point out, is that team-level SDs can be distorted by certain strategies (streaming, punting, etc.) Typically it won’t make a big difference, though.

1. Take the first 225 players on the list and build standard deviations and positional adjustments.

2. Use those SDs and adjustments to value all of the players in the list.

3. Sort the list from high to low values.

4a. If the players are ordered the same as they were in Step 1, stop. We’ve found the optimal values.

4b. If the players are ordered the same as they have been on any previous iteration, stop. This means there is more than one set of players that could be “optimal.” All of the “optimal” sets should be virtually identical, so we just display this one.

4c. Otherwise, save this list so we can compare it with future iterations, and go back to Step 1.

I’m assuming you’ve already seen this:

http://www.lastplayerpicked.com/how-the-price-guide-works-part-iv-iterations/

Feel free to email me if you still have questions.

I appreciate the response.

Would you be willing to share more about how the iterations are done? As the script is PHP I’m assuming the code is related to that, although I have no idea how to make such an algorithm in PHP.

I’ll be up front in stating that I’m trying to do something of a replication. I’m working with a friend on his fantasy team, and next draft is the first time I’ll be there with him (I just started helping him during this season because he has been busy with work). I’ve been putting together a PHP/MySQL site that tracks players and projections based upon the methodology that he has been using. However, his is something closer to a SGP model, as points awarded to the players are based upon how much they would accomplish relative to a team which finished 3rd in every category. I prefer the replacement level method myself. However, my only method would be to go through and pick the top players by hand (225 — 25 active x 9 teams).

Now, I don’t really have a problem with doing that. Although I wouldn’t be 100% efficient in my selections, neither would the league be (so what I chose as the top 225 players would probably have an equal or perhaps slightly better composite projection than the actual 225 starting players). However, I would prefer to have it automated via script and hopefully at least a bit more accurate than my own eye.

The site is to be set up on an Ubuntu MySQL server on the laptop, so that we will still be able to access it, add players to teams, constantly view the updated player lists and team financial reports, etc. Otherwise, I would simply run the report from the Price Guide if we only needed a list of player values.

One other question I had – How well does a standard deviation in a category correlate to gains in that category? For instance, in a league where the the standard deviations for R and RBI are 11.6 and 14.4 respectively, does this difference in the players themselves generally also lead to 11.6 increased R being worth an equal amount as 14.4 increased RBI (not counting any outside circumstances such as a team punting R, for instance)? Would we say that they are roughly equal, or that a deviation in one category should be weighted more heavily than that in another?

Once again, thank you. I appreciate any information you are willing to and can share.