## Is Jeurys Familia’s performance a cause for concern?September 24, 2015

Posted by tomflesher in Baseball.
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Following a blown save by Addison Reed and a perfect inning by Tyler Clippard, Jeurys Familia entered last night’s 3-3 game in the 9th. Familia struck out Jace Peterson, allowed a single to Cameron Maybin, and then walked Michael Bourn. Freddie Freeman hit a home run; Familia followed up by striking out Nick Markakis and Met-killer Adonis Garcia, but the damage was done. Pinch hitter Juan Uribe singled, but Wilmer Flores banged into a double play and Curtis Granderson grounded out to second to end the game.

Photo: slgckgc on Flickr

Familia came to prominence during the first half of the season, in which he pitched in 41 games, notching 27 saves, a 3.31 KBB ratio, and a 1.25 ERA in front of a .217 BAbip. He allowed 26 hits including 3 doubles, a triple, and 3 home runs. Since the All Star break, Familia has pitched in 31 games with 14 saves, a 6.50 KBB, a 2.87 ERA, and a .342 BAbip. He’s allowed 30 hits, including 5 doubles and 3 home runs.

Those numbers say a lot about Familia’s consistency. First, his control ratio has increased considerably – by dropping from 13 walks (one intentional) to only 6 in the second half, and striking out 39 compared to 43 in the first half, Familia has shown remarkable command in the second half of the season. Further, that change in batting average on balls in play demonstrates that Familia has gotten a bit unlucky or that the defense behind him has flagged a bit with all of the offensive moves that were made. (Although Yoenis Cespedes and Michael Conforto have been solid defensively, Daniel Murphy and Juan Uribe haven’t been fantastic solutions defensively at second base. Uribe is a fantastic third baseman playing out of position; Murphy might be more useful in the American League as a designated hitter.)

But what about those home runs? Those aren’t picked up in BAbip calculations because they’re not defense-dependent, and they might be tied to some factor that’s increasing his strikeouts.

During the first season, Familia pitched in 166 plate appearances, so those 3 home runs gave him a rate of about .018 home runs per plate appearance. Based on that, we’d expect in his 129 plate appearances since the All Star Game he’d have allowed about 2.33 home runs; 3 home runs is about .44 standard deviations away from his first-half numbers (and since fractional home runs aren’t a thing…..).

The biggest concern is that Familia has allowed more runners to reach base. All three homers in the first half were solos, two of them leading off (one with one out). Since then, Familia has allowed one solo and two 3-run home runs. The bigger issue is that these runners are reaching base – again, possibly due to that BAbip number cited above.

Familia is one of the best closers in the league. Put a solid defense behind him and he’ll continue to perform.

## Kirk’s Big SpringMarch 20, 2015

Posted by tomflesher in Baseball, Economics.
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Kirk Nieuwenhuis is having an incredible spring. All the usual caveats are in play – it’s spring training, so the stats are useless – but Kirk’s production has been exceptional. His slash line is .469/.553/.625 on 38 plate appearances. Let’s hit some sanity checks on Kirk’s production.

First of all, his BAbip is off the charts. This spring, Kirk’s batting average on balls in play is .536, which is ridiculously high. Kirk won’t be able to maintain that into the season. If he’s still got a .536 OBP by the trade deadline, I’ll eat my hat and post the video. Kirk’s BAbip has been pretty streaky, though. During his rough April, Kirk had a .300 BAbip, about the league average over the season; after coming back up in late June, he had a .377 BAbip over the remainder of the season, broken up as .625 over five June games with 11 at-bats, .267 over 28 at-bats in July, .400 over 23 August at-bats, and .348 over 32 at-bats in September.

From 2012 to 2013, Kirk’s BAbip dropped from about .379 to .246, and then shot back up to .370 in 2014. Using those numbers and taking first differences, then using the ratio of differences, that means we’d expect Kirk’s BAbip to drop to about .254 this season. Nonetheless, Kirk’s platoon splits are huge – against right-handed pitchers, from 2014, he’s got a .040/.050/.283 split (although he only made 9 at-bats and 10 plate appearances against left-handed pitchers). Though Kirk’s splits aren’t readily available, it’s possible that his big spring is residual of facing mostly right-handers.

In the spring, Kirk’s BAbip denominator (AB – HR – K – SF) is 28 and the numerator (H – HR) is 15. If we take Kirk’s previous-year .377 BAbip, over 28 trials we’d expect 15 or more successes to occur about 2.86% of the time. That’s just barely within the bounds of statistical significance (which would indicate we’d expect Kirk to hit between 6 and 15 times about 95% of the time), and well outside if we assume Kirk has a true mean of .254 (which would put our confidence interval at around 3-11 successes in 28 trials).

Second, take a look at Kirk’s K/BB ratio. Kirk has typically had a strikeout-to-walk ratio above 1; in 2013, he struck out about 2.67 times for every time he walked, and in 2014 it was about 2.44 strikeouts per walk. Over this small spring sample size, Kirk’s K/BB has actually dipped below 1, at 4/6 (or .667). Assuming Kirk walked 6 times anyway, using a conservative 2:1 K/BB ratio would turn 8 of Kirk’s hits into strikeouts. That would make Kirk’s BAbip tighten up to .350. Still strong, but not the obscene .536 we’ve seen. Even if we convert one walk to a strikeout and maintain a 2 K/BB, that would leave Kirk at .409, a very respectable spring.

Kirk’s numbers have been shocking, and of course he’s out of options, so he’s extremely likely to make the team. As a left-handed bat, he’d be a strong everyday player if the outfield weren’t so crowded, but with Michael Cuddyer and Juan Lagares in the mix already along with lefties Curtis Granderson and Matt den Dekker, it’s going to be tough to find Kirk a clean platoon spot.

## What is BAbip?March 16, 2015

Posted by tomflesher in Baseball.
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The first stat we all learned about as kids was the batting average, where you calculate what proportion of at-bats end with getting a hit. Then, of course, we start thinking about why there are weird exceptions – why doesn’t getting hit by a pitch count? Why don’t walks count? Why doesn’t advancing to first on catcher’s interference count? OBP, or on-base percentage, fixes that. (Well, maybe not the catcher’s interference part…)

Batting average has some interesting properties, though. It captures events that have unpredictable outcomes – when you walk, it’s basically impossible to be put out on your way to first. Ditto being hit by a pitch. Of course, BA does have some of those determined outcomes, too – home runs and strikeouts don’t have much dynamic nature to them, although you’ll occasionally see brilliant defense save a sure homer (a la Carl Crawford’s MVP performance in the or a sloppy catcher mishandle a third strike and forget to tag the batter. (I’m looking at you, Josh Paul.) Nonetheless, balls in play – balls that the batter makes contact with, forcing the defense to try to make a play – are a major source of variation in the game.

BAbip is measured as $\frac{H - HR}{AB - SO - SH + SF}$, meaning it takes the strikeouts and home runs out of the equation and (like all sane measures should!) includes sacrifice flies.

Since the ball is out of the pitcher’s control as soon as it leaves his hand, BAbip measures things that the pitcher isn’t responsible for – that is, it’s handy as a measure of pitching luck, or, teamwide, as a measure of defensive effectiveness. The NL team BAbip average was .299, and AL average BAbip was about .298.

Use Cases for BAbip:

Evaluating hitting development. If a batter has had a stable BAbip for a while and his BAbip increases significantly, be suspicious! Particularly if his walk rate hasn’t increased, his home run rate hasn’t increased, and his strikeout rate hasn’t decreased, this might be a function of lucky hitting against bad or inefficient defenses. If the biggest part of an increase in production has been on balls in play, your hitter may not have actually improved. On the other hand, if you can see physical changes, or you have an explanation (e.g., went to AAA to work on his swing), you may see a more balanced improvement in OBP.

– Evaluating pitching luck. Most of the time, all the pitchers for the same team pitch in front of the same defense. Even with a personal catcher in the mix, expect most pitchers on a team to have similar batting averages on balls in play. If you have one pitcher whose BAbip is much higher than the rest of the pitchers, he may be pitching against bad luck. With that in mind, you can expect that pitcher to improve going forward.

– Comparing defenses. In 2014, Oakland had a .274 BAbip and allowed 572 runs – the best in the American league in BAbip and 18 runs behind Seattle – while Minnesota had a .317 BAbip and allowed 777 runs, the worst in both categories in the league. Defensive efficiency (a measure of 1 – BAbip) tracks closely with runs allowed. BAbip can operate as a quick and dirty check on how well a defense is performing behind a pitcher.

## BABIP as a Defensive MetricOctober 11, 2014

Posted by tomflesher in Baseball, Economics.
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I went into commissioner mode and basically ranked everyone’s stats to go 0-550 with 550 Ks (although when I went back, OOTP changed it to give them all a few hits and a couple of walks, etc.) I did not have to edit BJ Upton, as he was already programmed to do so.

One reply asked whether 1-BABIP is a valid defensive metric, and that got the wheels turning. (Note that for statistical purposes, summary statistics for 1-BABIP will be the same magnitude and the opposite sign as statistics for BABIP, so I went ahead and just used BABIP.)

For a quick check, I checked in at Baseball Reference to get the National League’s team-level statistics for the last 5 years, then correlated BABIP to runs allowed by the team. That correlation is .741 – that’s a pretty strong correlation. Similarly, the correlation between BABIP and team wins was about -.549. It’s a weaker and negative correlation, which is expected – negative because an added point of opposing team BABIP would mean more balls in play were falling in as hits, and weaker because it ignores the team’s offensive production entirely.

If BABIP accurately describes a team’s defensive power, then a statistical model that models team runs allowed as a function of fielding-independent pitching and pitching-independent fielding should explain a large proportion, but not all, of the runs allowed by a team, and thereby explain a significant but smaller proportion of the team’s wins.

I crunched two models to test this, each with the same functional form: Dependent Variable = a + b*FIP + c*BABIP. With Runs as the dependent variable, the R2 of the model was .8625; with Wins as the dependent variable, the R2 was .5246. Since R2 roughly describes the percent of variation explained by the model, this makes a lot of sense. In the Runs model, about 14% of runs come due to something other than home runs, walks, or hits, such as baserunning and errors; in the Wins model, about 47% of team wins are explained by something other than defense and pitching. (Say…. offense? That’s crazy.) In both models, the coefficients are statistically significant at the 99% level.

BABIP’s coefficient in the Runs model is 3444.44, which means that a batting average on balls in play of 1.000 would lead to about 3444 runs scored over a season; more realistically, if BABIP increases by .01, that would translate to about 34 runs per season. Its coefficient in the Wins model is -328.757, meaning that an increase of .01 in BABIP corresponds to about 3.29 extra losses. That’s surprisingly close to the 10 runs-1 win ratio that Bill James uses as a rule of thumb.

Since the correlations were strong, this bears a closer look at game-level rather than simply team-level data.

Posted by tomflesher in Baseball.
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Moneyball was an influential book for two reasons. First, it described the process by which a GM can attempt to min-max a winning team every year. That’s interesting. Second, it showed a lot of the fans – not the front offices, who had already corrected the inefficiency by the time the book was published, but the fans – about the importance of walking and generally getting on base.

I never thought last year that I’d be typing this sentence, but Ruben Tejada is leading the Mets in OBP for qualified players (3.1 plate appearances per team game). Two players outstrip Tejada’s .400 mark – professional pinch hitter Ike Davis and starter Jonathan Niese, each at .500 – but neither has enough plate appearances to be on pace to qualify for rate stats. In retrospect, it shouldn’t be surprising that Tejada’s eye is developing. As a 21-year-old in 2011, Tejada had an OBP of .360 in 376 plate appearances over 96 games, walking 35 times – that’s one walk every 10 3/4 plate appearances – and striking out 50, for a K/BB ratio of 1.42. Last year, Tejada went pear-shaped, walking only one out of every 15 plate appearances, but he still only struck out 1.6 times for every walk he took – which is hardly the mark of an inconsistent hitter. Last year, it looks like Tejada just got really unlucky, batting .228 on balls in play versus a team average of .291. This year, he’s swung all the way to the other side of the pendulum – so far, his BAbip is .400 versus a team average of .241.

Tejada may never be a brilliant shortstop like Jose Reyes was, but his batting is gaining in consistency.

## Good news, everyone!April 5, 2014

Posted by tomflesher in Baseball.
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Perhaps I’m just giddy with the excitement of the Mets notching their first win of the season last night. Everything seemed to fit together – Jenrry Mejia was solid early on, and despite two brushes with injury, he pitched six excellent innings (6 IP, 4 H, 1 R (earned), 4 BB, 8 K) before turning it over to the bullpen. John Lannan is struggling as a reliever, credited with a hold despite allowing two runs on as many hits (one home run) and striking out one in his 2/3 of an inning; Kyle Farnsworth pitched a baffling perfect inning and a third before Jose Valverde came in and struck out one, walking one, to get his first save of the inning.

Professor Farnsworth was similarly perfect in nineteen games last year. Those include three appearances with one batter faced, four with two batters faced, thirteen complete innings, and one five-out situation. Three of the complete innings were finished games for Pittsburgh, where he finished seven games, most of them losses. Shockingly, Farnsworth blew only one save, earning two saves in Pittsburgh and two one-out holds in Tampa Bay. That means with last night’s hold, Farnsworth is halfway to last year’s mark. Hopefully, Farnsworth won’t be pressed into service as an emergency closer this year: His time in Tampa Bay had a 5.70 ERA and a .337 batting average on balls in play against a .298 league average BAbip. Since Tampa Bay’s team BAbip was .286, that means they got a little lucky, and Farnsworth got unlucky sometimes. When he headed to Pittsburgh, though, it was like Farns was a totally different player – and he was. Against an NL with a league average .296 BAbip, and playing for Pittsburgh with a team .289 BAbip against, Farnsworth’s BAbip was a surprisingly low .250. That’s a .087 drop from his Tampa Bay average, or about 2 hits every 23 balls in play. Hopefully, Farnsworth can keep up the luck in 2014, but frankly the better news would be if we had a more reliable setup man.

## Quick thoughts on the MetsAugust 11, 2012

Posted by tomflesher in Baseball.
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