Major League Baseball : Fantasy : Fantasy

Dear Rotoman:

While I have a great deal of respect for anyone that
compiles as much data for baseball fans and fantasy
addicts as you, I did want to point out some major
flaws with your projections for 2006 regarding
pitchers’ statistics. I am assuming you use some sort
of stat compiler or program you invented to project
season statistics. Of the pitchers you estimated
statistics for, you’ve projected only eight MLB
oversight? Colon finishes with 16, Oswalt and Santana
with 15 each, and Suppan, Sabathia, Lee, Prior and
Halladay with 14. I understand that its difficult to
pay individual attention to each pitcher, but there
must be a way to plug in the frequency of 20-game
winners, 19-game winners, 18-game winners, etc., into
whatever formula you are using to project stats. It’s
unrealistic to project such marginal stat lines, and
its a disservice to your fans to kick out these
figures like a robot, especially if you were being
intentionally conservative. SOMEBODY in the majors is
going to win at least 17 games. I’m willing to bet my
life savings on it. Wouldn’t you?

“Go for Broke”

Dear Go:

Absolutely, someone will win 18 games this year, but what good does it do to project the wrong guy to win 20 or 19 or 18 games?

It’s fun to model the whole year so it looks like the whole year (with 20 game winners and guys with 50+ homers) but it also means being wrong more often and by a larger amount, which doesn’t do anyone playing our game much good.

My projections are based on regressions of historical data modified by a few factors, the biggest one being age, combined with a different set of rate calculations, all of which are combined with a mechanical estimate of times at bat. I then go in and adjust based on probably changes in playing time and assessments of talent that don’t seem to be reflected in the projection. I call this tweaking, and it makes the boring regressions a little more lively and the overall correlation of the projections a good bit higher.

Then I scale everything so that the top 400 players projections are similar in total to the top 400 players actual stats. But the extremes in each stat just aren’t there, because the leaders in all categories usually change from year to year.

Thanks for writing,
Rotoman

Ps. I would not bet my life savings on the accumulation of any counting stat in a year when the Basic Agreement between the players and owners expires.

Rotoman!

You also have the MLB HR leader with 39 home runs.
When was the last time NOBODY hit 40 home runs?

“GFB”

Dear Go:

One of the byproducts of the system is that the AB of regular players are reduced about 10 percent off their usual peaks, with a similar reduction in the other stats. So all the guys who would project to 43 homers end up at 39 or 38.

The reason the AB get reduced is because about 10 percent of expected AB from year to year are lost to injury or other problems. This doesn’t happen evenly, but it happens consistently. There are two ways to handle this. One would be to ignore it and give all players their full measure of AB and stats. The other is to scale the pool of projected stats to the actual stats that will be produced by the pool.

I do the latter, because it gives a more accurate assessment of how groups of players perform on the year. The other looks better when everyone stays healthy, but since they never all do my method measures as more accurate.

Peter

## Major League Baseball : Rotoman’s Projections

Major League Baseball

MLB.com has been running my player projections for the past five years, usually a set at the end of February and an update just before the start of the season. This year they asked for more categories (doubles, caught stealing, among others) and for a set at the end of January, which I delivered. And then nothing. I was scheduled to deliver an update the first week of March, but in all the busy-ness of things didn’t get to it until last week, when I also finally asked my editor what happened to the first set of projections.

It turns out they’re being used in a game. And now for the first time the MLB.com Rotoman projections are posted at mlb.com, along with bid values for 4×4, 5×5, and mixed leagues. There will be an update March 29, for posterity’s (and late drafters’) sake.

## New Version of Patton \$ on Disk

A weekend of bug squishing led to the release of an updated and improved Patton \$ on Disk.

The program includes updated projections, bid prices from me and Alex Patton for 4×4 and Mike Fenger for 5×5. It is a great program for sorting lists and pricing players in the traditional 4×4 and 5×5 formats. The ease of updating projections and prices, the auction manager with bid values and all that make it useful for smaller mixed formats, too, but the pricing is not adjustable.

There is also an Excel worksheet with all the data available, and text and Word files will be out tonight.

The price: \$25.

## The Thomas George–Dollar Value Calculator

Fantasy Baseball – Dollar Value Calculator

With the Rototimes.com dollar value calculator skulking into the site’s pay section, hidden away like the Rotowire price calculator, an old standby moves on. The Thomas George is a venerable roto site with its own free calculator. It allocates percentages to hitters by percentage, which is not right unless you play in a league that auctions position by position, but a quick runthrough found it to be usefully accurate nonetheless. Maybe you can hack the percentages so that they treat all production equally.

## Forecasting 2006 — Tom M. Tango and Marcel the Monkey

The Hardball Times

It has been clear for years that the science of player projection is something of a scam. There is a finite amount of stuff we can know about a player’s performance the next year and a certain amount that is stochastic, random, unknowable. I’ve put the unknowable part at about 25 percent, based on various ways of measuring the accuracy of my expert projections.

This big random component means that the lens of a single season tells us only a little about about a player’s actual abilities. And while we use these small slices to tell us more about the player’s game, as a player ages his game changes. The measures that matter for a 25 year old are different for a 30 year old and different still for a 35 year old. The very smart Tom Tango set out to see how much of the potentially knowable 75 percent he could project using a very raw set of weighted averages building in regressive factors, and writes about it here.