DIL: The Voros McCracken Story

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.

2009 Luckiest and Unluckiest Hitters and Pitchers

Tristan Cockcroft for ESPN.com

I missed this story by the esteemed Tristan Cockcroft in February, and mention it now only because despite his consumer warning at the start (a low BABIP doesn’t necessarily mean that a hitter has been unlucky), and because of his interesting use of Expected BABIP, I have some concerns.

1) Tristan’s Expected BABIP is calculated without regard for a pitcher’s defense or a batter’s speed. No wonder Jarrod Washburn had a low BABIP last year in Seattle (as Tristan points out), he was pitching in front of a dee that turned hits into outs. Sticking with Seattle, isn’t it clear from Ichiro’s career BABIP that his expected BABIP, calculated from the components of his AB, is wrong? In this context, what use is the expected BABIP? Maybe some, but since it tells us less than it promises, it seems a little dangerous.

2) Component stats are useful tools, but they are subject to random variation, too. Just because you’re measuring the type of hit by a batter or a pitcher doesn’t mean that the results will hew to the expected number of hits and outs. A small sample is a small sample, and there will be error. How much and in which direction is impossible to say, which is a good reason not to count on players regressing to the mean based on expected BABIP.

3) But they do. Robert Sikon, at Fantasybaseballtrademarket.com, did some studies looking at 2008 BABIP and determined whether unlucky players improved the next year and lucky players batting averages declined. He reports that 64 percent of unlucky hitters improved the next year, and 90 percent of lucky hitters declined.

4) In 2008, Chris Dutton and Peter Bendix published at the hardballtimes.com an improved version of Expected BABIP. This was improved over Dave Studeman’s original formula, which was a rather simplistic Line Drive Percentage + .120. Dutton and Bendix ran regression analysis on years of data to determine which inputs were relevant and they claim their formula explains 39 percent of the variance in BABIP. They don’t publish the formula in this paper, however, so I don’t know how it has stood up, and can’t personally test it.  They do have a online tool to calculate xBABIP, which Derek Carty wrote about last year, but you have to enter the info by hand.

I think this BABIP work is really important and I’m glad smart people are working on solving it, but it seems worthwhile to point out that all conclusions are somewhat tentative at this point. We’re still working out how much genuine info is found in these data, and how much it will help us improve our projections, for isn’t that its real value?

John Burnson’s The Graphical Player

I am a big fan of John Burnson’s Heater Magazine, a weekly pdf of baseball stats and analysis that makes the Sports Weakly baseball stats pages look like the Weekly Reader.

John sent me a copy of his annual book, The Graphical Player, in January, when it came out. I glanced at it then, but I was busy and it ended up on a shelf and I didn’t write about it then, which is too bad. Like Heater Magazine, the Graphical Player is crammed full of information. John is evolving a set of graphical rules for presenting data that makes it increasingly useful and understandable, and helps put a player’s skills in the context of his team and of the game as a whole.

This is not a book to use to look up a fact, though there are plenty of those in here. This is a book to browse through, to hunt for patterns in, to savor as a baseball fan the way a gourmand might taste a sauce. The good news, even at this somewhat frantic moment, is that much of the information in the Graphical Player will still pertain after the season starts. If you want to see if a player has historically been a slow starter, this book has graphs that show that he has been or hasn’t. Once you get used to the way the information is presented, this sort of research is a pleasure. The data and its context are presented as a picture.

Other features of note: John asked three writers who follow prospects to name their 60 top rookies for this year. He has compiled their rankings and notes for these 111 ROY-eligible players, with their stats (presented in a very useful format) for the last three years. This is a very helpful survey of this year’s top prospects, though it does omit my decidedly dark horse candidate Thomas Neale (who didn’t make The Guide, which shows just how dark a horse Neale is).

I also think, as documentary, that the team profile pages in the back of the book are full of useful information. They won’t surprise readers of Heater, but as with much of the book, once you get past the sheer data density you’ll be surprised how satisfying it is to see a chart of who played what position the most each month for each team. And the charts that compare each team’s production in different categories to the league average spark only ideas thus far, but clearly they help us understand what was going on. This is a new way to experience this data, and an invigorating one.

I’ve only scratched the surface of the types of information included in the Graphical Player. Some is of help analyzing baseball, while other stuff is geared totally to fantasy players. I don’t want to be grandiose, but it is an amazing accomplishment.

UPDATE: So I posted the above glowing review only to find out that the only copy of the book you can buy at Amazon currently costs $91. It’s worth every penny, of course, but that’s a little steep. It seems the Graphical Player is also sold out at Acta Publishing, the company that published it. Barnes and Noble doesn’t have it. I’ll tell you what, I’ll sell my copy to the first bidder for $75. And in the meantime, I hope this means that John Burnson sold out his print run and made a small fortune.

Forecaster and Handbook are out!

I got my copy of the Baseball Forecaster about 10 days ago, but closing the magazine meant not cracking it, even though I’ve got a short bit in it (which happened to run here first, about WHIP v. WH/9), until now.

Ron’s lead essay is very smart. It’s about how wrong we are about players, year after year, and he wonders why we pursue exacting but nearly always wrong projections. Then he comes up with something new, called the Mayberry Method.

There’s a lot to like about the way the MM summarizes a player’s skills in a descriptive way. Yet despite it’s simplicity, I’m not convinced it is going to catch on. New stuff often doesn’t, even when it has real merit. On the other hand, the benchmarks MM describes so succinctly are becoming increasingly entrenched as leading indicators, making me wonder why–if we’re getting better at defining leading indicators–we’re not getting better predicting breakouts.

As Ron says in the piece, we may be smarter now than we were 20 years ago, but that may not be such a good thing.

Steve Moyer always gives us so-called experts a copy of the hot-off-the-press Bill James Handbook at First Pitch Arizona, for which I am very grateful. Not that I wouldn’t buy it, I have many times, but this way it ends up in my hands even sooner.

The book continues to grow, with increased focus on the defense awards and rankings, focus on baserunning skills, and the ever useful park factors. I’m a great fan of baseball-reference.com and fangraphs.com, both of which I use all day long, but I sit and read the Bill James Handbook, poring over its pages as if it were a ripping good yarn, which in many ways it is.

I’m glad for both these books and recommend them highly.

The Cluelessness of WHIP

Tout Wars AL Standings- CBSSports.com

For years, in the Fantasy Baseball Guide (which I edit), we ran the pitching stat called Ratio. Every year, people would complain and tell me that in their league they used the pitching stat called WHIP, and ask why we didn’t publish that instead.

For years, I replied that:

1) Ratio (((Hits+Walks)*9)/IP)) is much more descriptive/granular than WHIP ((Hits+Walks)/IP), and that,

2) Ratio looks better, since it’s on the same scale as ERA.

I then usually also note that I used Ratio in the leagues I played in, and if they had a problem they should do the same.

I didn’t win this argument. Many readers said they saw my point, but even if they agreed with me, the other people in their league did not, and so weren’t inclined to change. After a lengthy discussion with such readers a few years ago, I changed the magazine. We now publish WHIP instead of Ratio.

To ease the transition, the first year I included a handy WHIP to Ratio converter to cut out of the magazine, which I assume some people are still using. It featured a bodacious picture of WHIP kitten Anna Benson. Unfortunately, I’ve lost mine.

I bring this up now because I was looking at the Tout Wars AL standings just now and was struck by the WHIP category:

Team WHIP Pts Dif
Siano – MLB.com 1.32 12 0
Colton/Wolf – RotoWorld 1.34 11 0
Sam Walker – FantasyLandtheBook.com 1.34 10 0
Moyer – Baseball Info Solutions 1.37 9 2.5
Erickson – Rotowire.com 1.37 8 -1
Michaels – Creative Sports.com 1.37 7 0.5
Berry – ESPN.com 1.37 6 -2
Shandler – Baseball HQ 1.37 5 0
Peterson – STATS LLC 1.38 4 0
Collette – OwnersEdge.com 1.38 3 0
Grey – ESPN 1.41 2 0
Sheehan – Baseball Prospectus 1.42 1 0

My first reaction, assessing the three-way race between Siano, Michaels, and Shandler, is that this is unbearably close. After all, there are five teams at 1.37 and two more at 1.38. Siano is safely atop the category, but couldn’t Michaels easily gain two points? Couldn’t Shandler easily gain four?

In both cases, such gains would erase Siano’s lead. And certainly the numbers say it’s that close. It’s a virtual tie, for pete’s sake.

In fact, it’s not, but WHIP isn’t granular enough to tell you that. Here is the same rankings using Ratio.

Team Ratio Pts Dif
Siano – MLB.com 11.84 12 0
Colton/Wolf – RotoWorld 12.02 11 0
Sam Walker – FantasyLandtheBook.com 12.10 10 0
Moyer – Baseball Info Solutions 12.292 9 2.5
Erickson – Rotowire.com 12.294 8 -1
Michaels – Creative Sports.com 12.312 7 0.5
Berry – ESPN.com 12.330 6 -2
Shandler – Baseball HQ 12.367 5 0
Peterson – STATS LLC 12.387 4 0
Collette – OwnersEdge.com 12.451 3 0
Grey – ESPN 12.65 2 0
Sheehan – Baseball Prospectus 12.75 1 0

I went to the third place among the “tied” teams to show a little more information. To show how much distance there is between these tied teams, here are few facts, looking at Shandler since he’s the last of the teams with a 1.37 WHIP:

If Shandler gets 10 innings with no hits or walks his Ratio drops to 12.263, enough to pass everyone, and his WHIP drops to 1.363.

If Shandler gets 10 innings with 10 hits+walks, a pretty good performance, his ratio drops to 12.338, and he gains no points.

What if Shandler pitches 25 innings the rest of the way, with an excellent Ratio of 9.00 (a WHIP of 1.00) which would be way good, his Ratio would end up at 1.366, which would gain him two points but would still look like 1.37 on the CBSSports reports. His Ratio would drop to 12.297.

The point is that using WHIP, especially displayed to the second place, it looks like there’s a virtual tie, when the reality is that the standings are close, but it would take an extraordinarily good effort for one team to break ahead of the others. Ratio better illustrates this and it provides better and more information, which is why I still think it is a vastly superior stat.

Which is why I think you should change. Let me know when you do.

I Love New Metrics!

Except when I don’t.

This story is about O-Swing %, which measures the number of times a batter swings at pitches out of the strike zone. The writer says that O-Swing % is really interesting, and then goes on to prove (unless his numbers are wrong) that it is pretty much meaningless.

What is actually interesting is that the writer does a decent job of demonstrating why the apparently broad swing in O-Swing % numbers is meaningless. It boils down to the fact that some batters swing more, and so they hit the ball more. While some batters swing less, and hit the ball less. Consider 0-Swing % exhausted, at least for now.

When there is reliable pitch location information there will doubtless be information derived from these numbers that will be of interest, but it certainly won’t be simple or absolute. The game isn’t simply a matter of cause and effect, but a complex system of adjustments and readjustments that change how everything happens. It seems to me the miracle is that the game is played on the same sized field now as it was 100+ years ago. In that context, the variation in results should lead us to explore what changes have been made.

But that has nothing to do with O-Swing %.

Some Post-Oscar Thoughts on Forecasting

FiveThirtyEight.com: Politics Done Right

Nate Silver is taking some guff for his foray into Oscar predictions. What is revelatory in this 538 post is how his venture into understanding why he missed two of three contested Oscars tracks his approach to baseball projections.

The model may be wrong, but that’s fixable. Which is why PECOTA gets better every year. What isn’t, as Nate so politicly admits, are the vagaries of unprojectable circumstances. Nate found out that projecting six Oscars with a dubious data set focuses much of the attention on the vagaries and the unprojectable. Um, he got them wrong.

Which is why his protracted explanations in this post are both admirable, he’s trying to figure it out, and a little sad–didn’t we trust him because he knew that already?

Regular readers know that I admire Nate’s work, but that I also think his great insight into projections is one of marketing. Not statistics. Nate figured out how to get everyone to ascribe the failure of his subjects to follow his model to his subjects, rather than to him. That isn’t a bad thing, it is a perfectly fine (perhaps brilliant) way to convey the confidence interval, but it doesn’t do much to help us explain the large swath of the numbers (in my case Baseball, in Nate’s, all of them) that are unpredictable.

If the season were a horse race. Isn’t it?


When I was a kid I had a toy race track and I spent an inglorious number of hours turning the dice to see which horse prevailed in that race.

As we all know now, but I didn’t as a magical thinking second grader, the winners came completely at random (though I may have given blue an advantage, since it was my color).

Baseballrace.com animates each season’s pennant race, so you can see in a picturesque display how far ahead the front runners were and how far behind were the laggards.

I’m not sure there’s much actual utility here, but the imaginative display of information may well help you or me or someone else to come up with an idea that changes the way we think. And even if it does not, coming up with something no one else is doing is reason enough to be proud. And wouldn’t it be a great idea for him to license the software to fantasy league stats providers, so that we can live and relive the year of our grief in a horse racey animation?

Okay, maybe not. But maybe.