Answers to fantasy baseball questions (and much more) since 1996
Some member of the vast ESPN network, bigger than Disneyland, reported that a dude (not a star) will be promoted to the majors. This was based on anonymous sources. I’m not making any decisions about anything based on this nonsense.
Are we out of our freakin’ minds? Anonymous sources for player transactions involving fodder? Yeesh.
The Patton $ Evaluator is Windows software that lets you sort through last year’s stats, apply your 2011 fantasy team rosters, and analyze just what the hell happened, good or bad.
It’s free and we hope serves as in introduction to the Patton $ Projector, which will be released on February 5, with roto prices from Alex Patton, Mike Fenger and me, as well as my championship player projections.
To download and install the Windows program, right click here. Then click the option Save Linked File, or Save Target File. The file will download.
To install, rename the file setup.msi. (This is a security measure. Windows won’t allow you to download an executable file.)
Double-click to launch the installer, and follow the instructions.
Have fun!
Over the winter I came up with the idea of posting a Tout Wars Leaderboard at toutwars.com. The idea would be to assign an entry fee for each team, and then award prizes at the end of the season reflecting the payout, based on the number of teams in the league (since we have 12 in AL, 13 in NL, and have had anywhere from 12 to 17 in the Mixed league).
One sensitive issue was the prevailing ethos when the first 12 years of Tout Wars were played, which said that, “Second place is first loser.” I know that there were times when, dealt a tough hand, I made the high-risk high-reward (if only) trade rather than grind into battle for fourth place, the way I would have if there was money at stake. I’m certain others played this way, too, so these retroactive results are imposed from above. They don’t reflect how behavior might have been changed if this way of measuring was known when the games were played. In spite of this limitation, I forged forward.
Choosing a $100 entry fee was easy. It’s round, easy to average, like an index. Done.
Deciding how to pay out fairly was more complicated. I’m not going to go into all the round and around I did, but I decided to pay out to the top 33 percent of the teams in each league. In 12 and 13 team leagues this means the top four teams are paid. In 14-16 team leagues five teams are paid. In 17-19 team leagues 6 teams are paid. This seemed in keeping with the original roto rules of Top 4 being paid in a 12 team league.
But the standard roto payout is the somewhat awkward 50-25-15-10, which not only isn’t linear, but it doesn’t scale to the larger-sized league payouts. Since the idea was to compare across leagues, the payout reflecting the difficulty of prevailing, it seemed to me to be a good practice to have the percentages be fairly consistent as they scale up. So I made a simple equation, assigning X to the last money spot and then doubling the value of X for each higher spot. For a 12 team league this looks like: 1x+2x+4x+ 8x. In this case x = 1200/15 = 80. The payouts go:
Fourth=$80
Third=$160
Second=$320
First=$640
Sweet. The same process was used in the larger leagues, all of which–I think–does a fairly good job of representing the value we take away from our standard roto leagues, which were the models for Tout Wars. But this isn’t the only way to do this.
I’ve played (and play) in leagues with a more graduated payout. That is, last place may get nothing, and each place up the standings earns a little something more than the one below it. This is more like the way real baseball works. It isn’t all or nothing between fourth and fifth, but a graduated scale reflecting success in winning and managing costs. It would certainly be instructive if we could see the profil/loss statements for the actual teams in addition to the won/lost records. A graduated payout changes the way teams play, since there isn’t the same desperation to get into the money, but there is incentive to win more money.
I’ve played in leagues with no salary cap, where you could spend as much or as little as you wanted (and deemed prudent), in order to win a percentage of the pool. This, I found, turned out to be less interesting than (I think, because we never got that game together) )if we’d played for fixed amounts for first, second, third and fourth, with no salary cap, and any extra money that was spent was paid to those who finished fifth, sixth, seventh and so on, down to next to last.
I’ve also played in winner take all games, and others that paid bonuses for rare events, like no-hitters and hitting for the cycle, or allowed the roster to carry over into the post season, for a second pool. These payouts weren’t major enough to change regular season play, but if they were incentives might be skewed and teams might play differently. The point is that designing the payout structure of your league (which pertains even if you don’t play for actual money) reflects decisions you’re making about how you value winning, being the runner up, the importance of participation all season long, and a host of other things that make your game fun or more or less so.
There isn’t a right or wrong way in the end, but whatever decisions you make will inevitably reflect the values you bring to the game and the value of winning (or not). That’s pretty darn interesting, if you ask me, and relates to whole lot of things we do outside of our fantasy sports pursuits.
I participated in my first auction last night, the Cardrunners League, and because we’re using CBSsports.com to run the league, you can easily get a chart with the projected stats for each team. I did that and learned that according to the CBS projections my hitting is mediocre (uh-oh, and they’re not as negative on Grady Sizemore as they probably should be) and my pitching is pretty good. Overall, it looks like 75 points or so for my team, which I’ll take.
But then I found on the CBSsports.com site, a story by Al Melchior and a widget that lets you graphically compare the CBSsports projections and the Accuscore projections. The differences are striking, and a good reminder that projections give you a very limited amount of information.
You can find out more about my draft at Patton and Co, in the Kevin Gregg discussion.
One of the ultimate oddball formats: 20 teams, Mixed, Draft, Yahoo Rules.
I came in third in year one, and have been near or in the basement ever since. I can say that this is one of the smartest groups of players I know. So this list may be of help, if you draft 5×5.
Every ballpark, every year, every dimension, every park factor. The new Seamheads Ballpark Database promises to be an invaluable resource.
Having just finished and released my projections for the Patton $ Online Software product I’m thinking about the accuracy and usefulness of projections more than usual (and I usually think about this subject a lot).
Those of us who make projections want our projections to be the most accurate, but it turns out that measuring a set of projections versus what actually happened is a complicated business. Just how complicated becomes clear if you read the first two parts of Tom Tango’s analysis of five different projection systems from 2007-2010.
But you don’t have to, Tom says you can skip those parts, and you’ll still appreciate the results, which show that CHONE was probably the best projection system in recent years, but that it wasn’t much better than Marcel, which Tango invented as a simple baseline projection that could be measured against more sophisticated systems to evaluate them. If they don’t do better, they aren’t adding value.
The question is how much value any of the systems is adding. The answer depends on what you’re looking for, but the assertion by one of the commenters that accurate projections probably matter most to fantasy players rubs me that raw way. As the survey results show, using projections to value players for your fantasy league isn’t going to get you very far. The margin of error for each projection is far wider than the range of projections from all the various sources.
Different projection systems incorporate different aspects of baseball analysis. My projections use complex regression analysis of previous performance, filtered first by age, and then by my tweaking.
Other systems use other inputs. PECOTA draws on similar player/career arcs to project into the future, for instance, while ZIPS and CHONE incorporate some of the newer stats to establish complex systems of regressing outlying performance to the mean.
I have my doubts how far such empirical formulation will take us toward the grail of accurate projections, the ball hasn’t moved much in recent years despite lots of new data, but all the work is necessary to tease out what real information there is to be found in the numbers. Tango’s report and the many comments that follow it are invaluable for showing what the challenges are, and perhaps eventually suggesting a way forward.
Anyone who plays roto knows that what you pay for your players can be just as important as who was on your team. The fantasy game is one of markets, and the winner’s objective is always to get as many players as possible that the market undervalued. How do you know a player was undervalued? At the end of the season he’s earned more than you paid for him.
The funny thing is that despite the importance of what guys cost, once Jerry Heath sold his legendary stat service back in the mid-90s, nobody kept track of what players actually cost each year. Nobody, that is, until I started collecting and publishing that info in the Fantasy Baseball Guide (ON SALE NOW) six or seven years ago.
Right now I’m putting the polish on the stats and projections that are going into the Patton $ Online product we sell (free trial going on now at software.askrotoman.com) and I came across the Cost and Price Scans for Eric Bedard, and do they tell a story:
In 2006, we paid $8 for Bedard, and he went out and earned us $17.
So, in 2007, we paid $19 for Bedard, and he went out and earned us $29.
So, in 2008, we paid $30 for Bedard, and he fell off the edge, earning $7.
But in 2009, we paid $19 because he was Eric Bedard, and he bounced back to earn $12 and lose us money.
Last year, we paid $8 for Bedard, and he didn’t pitch. What are we going to pay him this year?
Alex Patton says $5 right now. Mike Fenger says $4. I say he looks like a reserve to me, a guy who is worth controlling, but not such a good bet to spend money on, though if he’s having a good spring I’d pay a few dollars for his talented arm. The problem is that if he’s having a good spring his price is going to go back up to $8 or even more come opening day.
The point is that we tend to pay the most talented players as if they’re going to have as good a season as we can imagine, even if they’ve let us down recently. Bedard’s price went up lockstep with his earnings in 2007 and 2008, but when the injuries grabbed hold of him the air didn’t rush out of expectations. We kept bidding him up, hoping he’d get healthy again and the little discount we thought we were getting for his iffy health would become a big one.
The problem with this is that we’re actually still investing top dollar in a fragile economy. Over the past five years we’ve spent $81 on Bedard and he’s earned back $65. That’s not a disaster, but it isn’t a winning strategy either. You want to pay up for the guys who are going up before they go up, like Bedard in 2006 and 2007, and try to avoid the guys who are at their peak with nowhere to go but down, as the oft-injured Bedard has proved the last three years.
It isn’t always easy and it’s a call all of us get wrong more than we’d like, but it is the single most important mental adjustment you can make. Paying for last year’s stats, especially from players without a serious track record of success and health, is often a losing game.
Based on this story by Yahoo’s fine Jeff Passan, Voros McCracken leads a Defense Independent Life.
Like much writing on the internet, this story is probably twice as long as it should be, and because of repetition suffers from a sentimentality that makes me less sympathetic than I might be. But it is a good and sad story, and helps explain that whole Voros thing that always gets folks worked up, and puts a human face on it, too.
I do think that it isn’t reasonable to expect to make a living from thinking about baseball, or, for instance, inventing a game like Rotisserie. It could happen, but more than likely won’t. Them’s the breaks.
UPDATE: A story in Slate today looks at efforts to discover Moneyball-like efficiencies in soccer stats. Curiously, these efforts are led by Billy Beane, and the story ends noting that Voros is working on soccer these days. But the real insight is that while efforts to decode baseball are largely open source, the push into soccer (which has no meaningful collective “sabermetrics”) are being led by proprietary interests, just as Voros’ revolutionary insight was made in public, and his work life these days for a European soccer club is private.
For much of my long adult life, Murray Chass wrote about baseball for the New York Times, my hometown paper. His old-school ways provoked the enmity of bloggers and sabermetricians and a few years ago the Times chose not to continue to employ him. But thankfully Murray soldiers on, because despite his myopia about the numbers of baseball, he is a fine prose stylist with a well-stocked rolodex of baseball contacts. His voice is of value, even if he’s not au courant.
I’m writing this because of a recent Chass post on his website (at which he writes short articles about things that interest him twice weekly while abjuring blogs) about the relationship between the Hall of Fame Ballot, which was due last Friday, and stats like Wins Above Replacement, which try to objectify a player’s value to his team. You can read Murray Chass’s blog post, er, article here.
I don’t read Murray Chass’s site regularly, and in fact came to this story via Tom Tango’s The Book Blog, where Tom tried to answer some of Murray’s questions about WAR the other day. Interestingly, his post provoked an avalanche of debate at Baseball Think Factory about whether Tom’s tone was inclusive or condescending.
Tom says he was trying to be helpful. Murray says he thought Tom was trying to be helpful. Case closed. But the lengthy discussion reveals lots about the issues. We love baseball because it’s a game played by humans, in all their variety, that excites us because of the skill of the players.
But we also love baseball because it is a game played outdoors in warm weather. Baseball provides spectacle to fan and family member who couldn’t care less alike about the actual game, but enjoys the experience visiting the ballpark provides.
And some of us love baseball because it is a closed statistical system, that allows us to munch and crunch the numbers in many clever ways to discover things that may not be directly related to describing the humans who play the games, but does give us insight into the way the game works.
I think Murray Chass is wrong about bloggers and sabermetricians, but I think bloggers and sabermetricians are wrong about Murray Chass. We need all the voices who know anything at all about baseball contributing if we’re to get our analysis and history right.