Ron Darling: Best Announcer in the Playoffs?

We know Ron Darling is a great announcer as Mets fans, having been privy to his talents on SNY since 2006. He creates great insight into the game as a former pitcher, and isn’t afraid to tell you what he thinks.  We’ve witnessed this frequently this past season with his remarks about how the Washington Nationals handled Steven Strasburg.  Unlike other former athletes turned broadcaster, Darling often goes above and beyond simply crediting players for ‘being players’ as if he’s part of some secret club that knows better than we do. He imparts knowledge like he wants us to have it, not like he wants us to know HE has it.

I was watching the third game of the Giants and Reds NLDS, and he offered a bit of analysis that blended an appreciation of advanced shifting with a thought to the game at hand and how long-term trends might not apply the same way to individual samples. The Giants were shifting against Joey Votto to pull like many teams do for left-handed hitters with power. Ron Darling noted that Votto had returned from knee surgery this season and may not be 100%. He was questioning whether or not Votto could put the same force into his front leg to generate the power and pull that the Giants were positioning for. Maybe, he mused, they should use the injury information to adjust the defensive positioning despite what the long term trends say.

Joey Votto had not hit a home run since returning from the surgery, but I have no idea if this theory has any statistical merit in Votto’s case or in knee injuries at large. That’s not really important here because Darling wasn’t suggesting that the Giants forgo the shift because it’s over-thinking or bad form or anything, he was proposing that there was another input that the Giants should take into consideration. Perhaps the player they were shifting against wasn’t quite the same player that had generated all the data they were using.

Baseball is a game with a ton of statistical data that can be used to make educated guesses about players and teams and overall results, but it’s also a collection of small sample results that can vary wildly based on any number of random inputs. A player simply waking up with a headache could throw off the projections for one afternoon. This is why the very best teams will succeed by being aware of the overall trends and still be able to make snap decisions in the moment to adjust those trends based on the fluctuating nature of many of the factors. It’s part of what makes Ron Darling a great announcer. As a pitcher he studied and learned a lot about baseball, and he’s brought that into the booth with him, and as a scholar he appreciates the studious work other people have put in as well.

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My Problems With FIP

The formula for the Sabermetric stat FIP is (13HR + 3(BB+HBP-IBB) – 2K )/ IP That’s then added to a constant to equate it to normal ERA ranges.  (xFIP  provides some extrapolation on HR rate per fly ball, but my overall concerns remain the same)


The general idea with FIP is to try to determine how well a pitcher pitches without luck and the fielders altering his results.  Once the ball leaves the bat it could be a bloop that no one reaches, or a screaming liner that happens to be right at someone.  This is not affected by the pitch the pitcher throws, and the only way a pitcher can be sure of a result is to strike out the hitter.


It’s supposed to be irrelevant to the statistic. FIP treats a screaming double off the wall the same as Jeterian soft groundout to short.  Except it doesn’t really.  The ground out is .1 IP and lowers FIP by raising the denominator in the formula.  This is part of the problem with the stat.  It does successfully remove bad defense from the equation by not penalizing a pitcher for an infield single that or a liner that finds a hole, but it also credits pitchers with good defense with extra outs.


The stat was created with the assertion that pitchers lose control of what happens after ball hits bat.  So the idea is the pitcher is ‘better’ if he can overmatch the hitter and beat him via being a better pitcher than he is a hitter, and striking him out.  Another problem is the pitcher does have some control over the batted ball, and no one knows how much or how to measure it.  You can see a pitcher one year induce a ton of soft contact, and then all of a sudden induce a lot of strong contact another year.  That’s hard to measure.  Matt Cain is one example, and Chris Young another, of pitchers that always seem to outperform their FIP.


Actually inducing soft contact, something groundball pitcher seem to do better, is something that _can_ be done.  As of yet statisticians haven’t been able to conclusively measure soft contact, and I’m not even sure pitchers can actively decide to do it with any real consistency.   It may be that the quality of contact can be reduced by keeping the hitter guessing.  Ground balls yield more outs than fly balls, but it’s also true that the times you most want to throw a pitch that gets more ground balls is also the times that the hitter knows you want to throw that pitch.


No stats are perfect, and we shouldn’t hold FIP to a higher standard, or use it in a more absolute manner, than any other stat.  I’m certainly not saying that we shouldn’t ever reference FIP, or that we should abandon the quest to expand our knowledge of baseball.


I haven’t done the math, but I could see FIP being a stat useful for picking the reliever you most want to bring into runners on situation. These pitchers should theoretically be the most likely to get an out without a runner scoring due to a lucky bloop, or a home run doing lots of damage. This is especially true of relievers because their smaller sample size of innings pitched leads to much more variance in their ERA.


The difference in ERA and FIP can also be telling, although not conclusively.  Looking to see how much a pitcher’s FIP is off from his ERA can be a way to predict if he’s getting lucky, or unlucky.  It’s not a tell-all though; it can be used as a warning flag to look further, but it’s not a direct relationship from out-performing FIP to being due for a drop off in performance.   Jonathan Niese will be a good study in this this year.  His ERA last year, as it’s been most years, was much higher than his FIP. He had a 3.36 FIP and a 4.40 ERA.  He’s rewarded for striking out a lot of guys, but the mounting data is starting to suggest that the high amount of hits he gives up aren’t just due to luck and bad fielding.


I’ve started playing around with some of the numbers, but haven’t gotten that far.  Tweaking the formula to use batters faced instead of innings pitched doesn’t make that much of a difference.  The high numbers of batters bad pitchers face tends to lower their numbers in that case.  Pitchers like Oliver Perez who face a zillion guys but then strike out the side to get out of danger would do well in that situation, but even the simplest of analysts knows that’s not a good job.


Next I tried correcting for balls in play.  Instead of simply batters faced, I removed the percentages of outs from balls in play that simple luck would turn into outs.  Then I adjusted the formula to take away extra outs the defense provided, or add back in outs they should’ve made back towards normal.   This looks better, but I’m still looking at the numbers and playing with the formulas to see if I like it.   I’m also still just trying to figure out how to smoothly create and maintain a database of baseball stats, so there is a lot of trial and error going on over here.


Sabermetrics have given us a much better understanding of baseball, but it’s only just starting.  They got there by questioning the established logic, and it’s a philosophy we shouldn’t abandon just because we’ve taken a big step in understanding.

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Omar’s Advanced Statistical Analysis

“Mets GM Omar Minaya poses ideas to him, often via email, and Baumer will run the numbers to see if they’re true. He said he’s one of 8 or 10 people who can offer their two cents when a decision is being made, such as a trade or in free agency.”

 Jenny Vrentas/The Star-Ledger is referring to Ben Baumer, the Mets statistical analyst.  We’ve known the Mets use, as do most teams, advanced statistics in evaluations of players for trades and signings and even drafting.   They hinted at it being one of the reasons they chose Jason Bay.  It provided evidence that the free agent pitchers on the market this offseason were not worth it. 

It’s worth noting that despite some criticism, the Mets do in fact use a variety of tools to evaluate players.   I have plenty of issues with some of the popular advanced stats out there, from UZR to WAR to FIP.  I’m working on a post that specifically outlines my concerns, but there is still value to looking at these numbers, especially to reinforce an opinion you might have on a player you haven’t seen that much of. Especially for a general manager of a baseball team.

For all the criticisms of Omar, and those fans that feel he just has to go before this team can win, this article suggests he might not be as clueless as you think.

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