Jeff Samardzija is incredibly frustrating at times.  For the first few months of 2016, Giants fans saw a pitcher who would more than earn the five-year, $90-million contract he had signed in the offseason.  In April and May, Samardzija posted FIPs of 3.67 and 2.45, as well 11.9% and 19.1% K-BB rates.  Those numbers are pretty worthwhile considering Samardzija has forged himself into a workhorse, averaging over 200 IP over the past four seasons.  The Giants would be plenty happy with that for a full season.  All seemed well in Giants land.  The free agents were proving their worth, Madison Bumgarner’s greatest concern was with his own hitting (that may always be true), and Buster Posey was healthy.  The even-year sorcery seemed to be working.

June and July came around, though, and Samardzija saw himself regress into what looked like the 2015 version of himself.  In June and July Samardzija posted FIPs of 7.09 and 5.06.  Samardzija was giving up homers at an alarming pace and he was desperately struggling to strike people out.  Oddly enough, Samardzija was drastically altering his pitch mix in the middle of the year.

Holy cow.

That looks experimental more than anything else.  For Samardzija to maintain his level of performance even in his good months is pretty solid given such drastic changes in pitch mixes.

For reference, here is Samardzija’s FIP throughout the course of last year.

You can see the success I mentioned earlier before June and July came around, but Samardzija also set out on a strong end to the season, posting a 3.67 FIP in August and a 2.38 FIP in September/October to somehow bring his FIP below the league average.  That final stretch also saw Samardzija posting a 21.8 K-BB% as well, maintaining a similar walk rate he posted all season while striking out 28.6% of batters.

Staring through the bevy of pitches Samardzija featured through the season, you can see where he was getting to in the end.  He almost entirely ditched his cutter and found a balance between his four-seam and two-seam fastballs.  The curveball usage held steady, the slider usage went down, and the splitter continued to emerge as a favorite.  The splitter usage has appeared to come about as Samardzija’s neutralizer towards lefties, and it has worked well.  Lefties have given Samardzija trouble for his whole career and the near-60-point difference in wOBA versus lefties last year is fairly alarming (.331 vs .276), so an offspeed pitch that moves away from lefties is crucial.

That splitter itself is fairly similar in movement to Masahiro Tanaka’s.

Samardzija: -6.7 x, 3.9 z

Tanaka: -6.7 x, 3.3 z

Should Samardzija use the splitter versus lefties as much as Tanaka does (nearly 30%!) and locate it as Tanaka does (low and away from lefties), it should be effective, given his SwStr% with the pitch throughout his career (19.5%).

Here is Samardzija in his last tune-up before the season.

(Skip to 0:13 for the nasty nasty.)

In those final two months last year, Samardzija was able to continually do better against righties while limiting lefties to a somewhat manageable .410 SLG.  Should Samardzija maintain a similar pitch mix, he would look more like his four-win 2014 campaign.  Pitching isn’t that simple, but he’s making his way back to something that had worked quite well for him in the past.

The 2016 Giants season became all about the monstrous second-half collapse, but hidden in there was a bit of a Jeff Samardzija resurgence.  In 2017, Samardzija will almost assuredly be worth his salary in durability alone.  But if he can continue to utilize his splitter as he had toward the end of 2016, I would expect him to outperform his projections (Steamer 3.84 ERA 3.78 FIP 4.09 xFIP) and deliver a performance more in line with his 2014 season.  The Giants rotation already runs deep, but they could be looking at one of the most durable and effective groups of front-line starters in the game.

On April 7th, Vince Velasquez pitches the Phillies’ home opener. His electric talent makes him a linchpin for the team’s plan to return to contention, and his four-seam fastball could be the key. It’s his best pitch, and it had the 12th-best weighted value among pitchers who threw at least 130 innings last year. MLB’s 5 Statcast Storylines for the team features him and the 27.4% swinging-strike rate he got on it, tops in the league.

And oddly, even more than his dubious health, it could be his biggest obstacle to stardom.

Corinne Landrey at Crashburn Alley found that Velasquez was in the top 15 for overall fastball usage last year, and top three in two-strike counts. Immediately, we could reason he threw it too much, even when acknowledging the rankings above. But it’s worth noting how, exactly, it looked.

380 of his 428 two-strike fastballs were four-seamers. They accounted for 60% of his two-strike pitches. It’s not just that he threw a ton of heat when hitters had their backs against the wall. It’s that he didn’t use his secondary offerings to keep hitters honest.

The top 10 qualified pitchers by K/9 last year  —  and some of the best pitchers in the game  —  present various paths that can be taken with two strikes. The range between their most used and second-most used pitches in those counts goes from 1.1% to 27.3%. The range for Velasquez screams from the page: 43.7%.

It might be easiest to think of this like kids on a seesaw. His four-seamer was like a particularly stout kid (maybe Billy Butler) and his curveball was like a particularly scrawny kid (say, Jose Altuve). The way he used these pitches in two-strike counts didn’t lend itself to a fluid, balanced approach during the most advantageous situations.

The problems Velasquez’s fastball created were subtle because overall it was so good. Guys weren’t driving it out of the park or putting up crooked numbers against it, but they were letting him wear himself out. While he got a whiff nearly 26% of the time in a two-strike count when using his four-seamer, there was also better than a 2:1 chance the at-bat would continue because it was either fouled off or called a ball. The foul balls were a major reason he worked a ton of deep counts last year, and what made making it through even six innings a coin flip all year.

There’s a chance that could be due to where he was locating his heat, too.

A look at his heat maps shows Velasquez hammered the zone with his four-seamer when behind (left). When ahead, as he would be with two strikes, he threw it higher (right). That’s generally good when thinking about sequencing, changing the eye level of hitters, and possibly the concept of having a pitch to spare.

But Velasquez’s fastball is a riser  —  it averaged 9.75 inches of positive vertical movement last year, or about a full inch more than the league average. While the maps of different counts will show slightly different locations, the big picture suggests his four-seamer could have been easier to take when higher in the zone because hitters and umps alike perceived it was already up.

Landrey also found that Velasquez was beginning to favor his changeup toward the end of the year as a lead secondary offering. While that’s positive, it’s bizarre that it took so long to show up in the majors since it was lauded through the minor leagues. Right now, the opposition knows he’s dynamic but can be worn down and sent out before the sixth inning. As he matures, he could become a force they genuinely dread.

First game of the season for the Giants, and the bullpen’s falter in the 8th and 9th inning is terrifying. The fear comes from the reminiscence of the ghost of 2016. The addition of Mark Melancon, and departure of the core of the Giants pen, seemed to be the remedy for the expulsion of this ghost, but opening day seemed to tell a different tale.

The new setup man in the 8th inning, Derek Law, came in to relieve Madison Bumgarner, who took the Giants into the 8th with a 4-3 lead that he pretty well mustered up all alone. Law gave up back-to-back singles, before a meeting was called at the mound. Law gave up another single to Paul Goldschmidt, surrendering a run, and the lead. Ty Blach was summoned from the pen for a lefty-lefty match up against Jake Lamb, and he got him to ground out into a double play, and Bruce Bochy then went for his righty-righty matchup with Hunter Strickland against Yasmany Thomas, which ended up in a ground out to get out of the inning with a tie ball game.

My argument is that Bochy’s uncertainty on how he is going to handle his pen is, perhaps, one of the reasons for this supposed curse. Before the season started, the underlining story was that the pen would be fixed by the certainty of roles, as Melancon was the sure closer and this definitive role was going to bring stability to the pen that was not there last season. However, the setup man in the 8th gets banged up for three hits in a row, and Bochy immediately cuts the cord for his matchup ideals. These matchups end up working, and they get out of the inning relatively unscathed. However, it seems that this lack of trust for his relievers to get out of trouble may be one of the reasons the bullpen struggles when the game is tight in the late stages.

Let’s compare to the three other teams who had to pitch in tight situations in the closing stages that same day.

Their opponent, the D-backs:

J.J. Hoover comes in at the top of the 8th with his club down by one. With one out, he walks Buster Posey and allows Brandon Crawford to single. Torey Lovullo allows Hoover to get himself out of danger to end the 8th.

Fernando Rodney comes into the 9th with the game tied, and immediately gets hit for a triple. He gets a sac fly for his first out, but allows a run, to give the Giants a lead. He then allows a single, throws a wild pitch, walks Brandon Belt, throws a wild pitch, and walks Hunter Pence. Instead of pulling him after a mound visit, Lovullo allows Rodney to work out of his trouble, and Rodney gets a fly out and a ground out to end the inning.

Cubs:

Bottom of the 8th, down by a run, Joe Maddon uses Pedro Strop. First hitter he sees, he walks, then a pop-up, and then he allows a two-run HR. He then walks his next batter, but finally works his way out of the inning with back-to-back ground outs. Maddon uses Mike Montgomery in the bottom of the 9th of a tied game. He allows a one-out double, and Maddon comes out to talk him through the inning. He intentionally walks Yadier Molina to set up a possible inning-ending double play. He gets a K but then walks Kolten Wong, and is eventually led to his loss by a line drive to left field by Randal Grichuk.

Cardinals:

After doing a good job getting the final two outs of the 8th, Seung Hwan Oh was asked to close out the top of the ninth. He hits Ben Zobrist with a pitch, Ks Addison Russell, then is hit for a single and hit for a three-run HR, but then closes out the inning with a K and a pop-up.

You could argue here that the D-Backs and Cards just won because of their scoring output in the 9th, and that the Cubs had the same fate as the Giants. However, what I am trying to argue is that the short leash that Bochy demonstrated in the 8th is an outlier to the other three managers, and perhaps, may be an element that has been driving the curse of the bullpen.

Bochy’s tactics get really twisted as he allows Melancon the long leash to try and work his way out of danger in the 9th. Presumably because Melancon is the undisputed closer, and he had two outs in the inning. However, it seems like the stability of the bullpen becomes unraveled as soon as the short leash is initiated in the 8th.

If Bochy believes the curse was created from the instability of not having a definitive closer, than perhaps it is also the instability of definitive roles in the pen. If he believed that Law deserves the 8th inning setup role over Strickland, then he should stick to his guns and let Law pitch out of the 8th inning. (Still not sure how Matt Cain got the fifth spot over Blach.) If he lets up and wants to shuffle up the roles for the next game, then so be it, but the shift from short leash to long leash, concrete roles to matchup roles, all seem to be unbalancing to the pen.

Nothing is more evident of this than the series against the Cubs last October. Game 3, Bochy lets Sergio Romo finish up his work in the 9th, but not before Romo had given up two runs and allowed the Cubs to tie. The Giants would end up winning this game. Game 4, on the other hand…up 5-2 in the 9th, Bochy uses Law, who immediately allows a single and is pulled for Javier Lopez. Lopez walks Anthony Rizzo and is pulled for Romo. Romo allows a double and is pulled for Will Smith. Smith allows a single, and then gets the first out on Jason Heyward’s bunt. He is then pulled for Strickland, who allows a single, but then ends the inning with a double play. The Giants end their season with a monumental bullpen collapse in the 9th inning.

This short-leash/ r-r l-l matchup tactic that Bochy sometimes uses, and sometimes does not, seems to have a role in the haunting of this Giants pen. While last year he never had the luxury of that star closer, and definitely does not have the likings of a Clippard-Betances-Chapman bullpen, I think Bochy does fare better when he allows his bullpen to settle into roles with a margin of error. Moreover, the Giants have a great bullpen of Strickland-Law-Melancon and supporting cast. However, the bullpen probably fares better when the question mark of that order disappears and the setup men have the chance to play out their roles.

Hell, we are one game into the season and do not know if the bullpen is still cursed, but if it is, perhaps the curse is caused by the handling of the pen, and not the skill within it.

Jose Ramirez broke out for the Indians last season. Long seen as just a placeholder for Francisco Lindor, Jose hit well enough all year to keep getting starts in a utility role, and eventually moving up to become the full-time third baseman for Cleveland. Ramirez derived the majority of his value from an elite contact rate, and excellent base-running, swiping 22 bags and hitting for an average well over .300. All in all, Jose was worth nearly five wins above replacement. There has been a lot of speculation as to whether he can repeat his huge breakout last year, if we may have already seen his peak, or maybe, he’s just getting started and there are even greater things to come.

Looking at Jose’s numbers from last season, I can see three areas for improvement. First off is defense. According to defensive runs above average, Jose was only worth 0.5 throughout the 2016 season. Jose is a shortstop by training; however, he spent most of the first half of the season bouncing around positions in a utility role, before landing at third base full-time. Considering this and the fact that he has rated out as a plus defender in past seasons, I think it is safe to project an improvement here with a more consistent role.

Second is his walk rate. Jose walked in 7.1% of his plate appearances in 2016. To compare, in 2015, as well as in his Triple-A career, his walk rate hung right around 9%, so there is possibly some room for improvement there as well.

The final area for improvement is Jose’s home-run hitting. Despite recording 60 XBHs last year, Jose only left the yard 11 times. It is very difficult to put up an MVP-quality season with lower-end HR numbers. Since 2011, there have been 29 positional-player seasons worth 7+ WAR, and every single one of them included over 20 HRs.

So what will Jose look like in 2017? It’s hard to tell, unless of course you decide to overreact to this week’s opening game, in which case…

WELCOME TO THE MVP RACE JOSE RAMIREZ

Jose batted four times in the opener, and he had one walk and one HR. THE TWO THINGS HE NEEDED TO GET BETTER AT!!! Now obviously this article is a bit tongue-in-cheek, and a sample size of one game means VERY little. The walk especially tells us just about nothing. You give me 4 PA in a major-league game and I might even luck into a walk. However, there is reason to take note of the home run.

Prior to 2017, Jose Ramirez had hit 19 home runs in the majors. His previous best exit velocity was 107.8 MPH on a HR. The longest HR of his career had traveled 437 feet. Jose’s HR in game number one left his bat at 109.3 MPH and traveled 447 feet.

So MAYBE this is a hint that Jose has added some power since last season. That would be a reason to get excited. If you take Jose’s 2016 numbers, then bump him to a 9% walk rate, 22 HRs, and plus defensive value, and even account for a few points of BABIP regression, he’s a 7-8 WAR player, and looks real similar on paper to Mookie Betts.

So, if we overact to opening day, this would make Jose a legitimate star and MVP candidate. His season will be extremely exciting to follow, although in the end probably overshadowed by Madison Bumgarner’s race to 60 dingers.

Beerrios! That’s a way better title, but we’ll stick with the original. So I’m spending my Saturday brewing a batch of beer and dealing with some pitchFx data. If everything goes well, you’re going to get some baseball info and some brewing highlights. But also Happy Opening Week! It’s the greatest time of the year.

All right, let’s deal with some baseball first. Jose Berrios had a pretty brutal 2016 with the big club — all in all, he started 14 games and rattled off a 3-7 record with an 8.02 ERA, an ugly 1.87 WHIP, and a not-top-of-the-rotation strikeout rate of 7.6 K/9. His FIP and xFIP were better but still not great at 6.20 & 5.64. His BABIP certainly didn’t help his numbers, sitting at 0.344, but that alone can’t explain how truly atrocious his numbers looked in his first taste of the big leagues. I’m going to use the pitchRx package to look at pitching data from 2016 and see if we can figure out what went wrong and how we can fix it.

All right, now on to the beer portion. Today I’m making my Deep Lake Dark Lager. Behind every great beer there is a great story. This story begins when I worked out at a remote research camp and fridge space was not reserved for amateur beer-making. A key process in lagering a beer is fermenting the beer at low temperatures, which is why I mentioned the fridge space. We got around lagering our beer in the fridge by putting the fermenting beer into a keg and dropping the keg into a lake to about 15 meters deep (~49 feet). At this depth the temperature was steady at about 5°C (41°F). A little tip for any newcomers to the brewing community: Your beer needs to maintain command, something Berrios couldn’t do. Zing!

If anyone wants to take a quick look at some gifs of Berrios’ sick curveball (and other pitches), check them out here: http://www.pitcherlist.com/Jose-Berrios/

I tried to find a comp for Berrios related to pitch velocity, and if we ignore his slider, Jacob deGrom comes out looking like a pretty good match. Here are how their pitch velocities line up using 2016 pitchFx data. I know it’s not a good idea to exclude one of deGrom’s best pitches, but I’m more interested in consistency between starts.

Just eye-balling, they look pretty good. Let’s take a look at pitch velocity by start.

Just looking by eye, it’s hard to tell if you could consider one guy more consistent than the other. But obviously we might be able to give deGrom the benefit of the doubt here, since he was pitching with scar tissue or bone spurs in his elbow. Either way, he was pitching in discomfort. There is one thing that catches my attention, though — it’s those last 11 starts by Berrios, and you can see his change-up velocities start to sneak up from ~83.5 to 86 MPH. The unfortunate thing is that there is no concurrent increase in four-seam (FF) or two-seam (FT) velocity. Near the end of the season Berrios was trying to complement his fastballs with a change-up that had a really poor velocity difference. Let’s check that out in a bit more detail.

Okay, you give that plot a bit more thought. My timer just went off and I’ve got to go sparge the grains. I converted a five-gallon water cooler into a mash tun for steeping my brews, which works awesome, because it’s insulated so it holds the heat really well. The aspect I really love about this beer is that is has a really light lager taste, but it has a nice dark colour, which makes it a great spring beer. And to get this effect in your beer is really simple. For the 45 minutes where you are steeping your grains, only add the light grains, then just before you sparge, throw on your dark grains (in my case carafa III). That way, as you sparge you get the colour from the dark grains and none of the taste. Yaaaay beer. Everything is all sparged and now I’ve got to bring the wort to a nice rolling boil.

All righty, let’s discuss that velocity difference. I’d say there is a similar trend from his early-season call-up and his late-season starts as well. He starts out with a pretty decent velocity difference, but with each start that difference gets smaller and smaller. Especially in the second half of the season — he started out with a fantastic velocity difference. That combination should have led to some really effective pitches, but as we move into September and October those pitches are starting to look more and more similar and their effectiveness all but disappears.

Back to brewing — boiling achieved! I’ve got to let the liquid boil down for a couple hours, so through the magic of the internet, let’s fast-foward to the next step. And what a fantastic surprise, the battery in my scale is dead and of course it’s some weird specialized kind that I don’t have on hand. Well luckily I wrote the weight on the bags when I packed the hops last fall so I’m going to eyeball it and hop(e) for the best. At least now I can honestly say I can never reproduce this batch, but that’s part of the fun. So I figure I added about an ounce of hops; we’ll really never know. I’ve got to let that boil for another 40 minutes then add the flavour hops and some irish moss.

I think we can agree that this was not a season marked by consistency for Jose Berrios. But I was curious as to how his release point affected the velocity of each pitch. For all of the data presented here, I used the pitchRx package to download and store the 2016 pitchFx data. In the pitchFx data, you can pull out the release point for each pitch recorded throughout the season. Using this data, I created a general additive model using the bam() function for the R peeps out there, and within the bam function I modeled pitch velocity and pitch break separately using a Gamma link. I like to use the Gamma link because, in a not very sciencey description, it’s very flexible and fits a wide range on models. So first, a couple of notes; 1) There are two plots coming up; the first predicts pitch velocity and the second predicts pitch break length (movement). 2) Pay attention to the prediction window, the coloured box, for each pitch. And 3) These models were only run on Berrios.

And pitch break (break_length):

You can tell that velocity and break length change with different release points. I mean, there is a pretty complicated relationship with how his release point affected both the pitch break and the velocity, and I’m not really sure what the sweet spot actually is. His change-up velocity plot has a really nice faded red area sort of right in the middle of the prediction grid. This area represents roughly an 84.5 MPH change-up which would complement his 93 MPH fastball quite nicely, but unfortunately his arm slot seems to be drifting along an axis which we will get to in a second. Did you happen to notice how the coloured boxes moved slightly among pitches?

So remember how Berrios was apparently tipping his pitches this past year? Well, if not, check this out. So the way he was delivering the ball basically gave the batters a full view of what was coming. I mean, I know I don’t possess the ability to spot small things in deliveries and assess pitches. I watched gifs of Berrios throwing all of his pitches over and over many times and I can’t pick anything up. But I am sure that there are players out there who can pick up those minor details. So I’m thinking there may be more to this than just how he started his wind-up, and where he was releasing the ball was also giving batters a clue as to what was coming. Check out this plot showing how Berrios and deGrom released their pitches.

So you’re probably wondering what’s going on there. Each ellipsoid represents a different pitch, curveballs in blue, 2-seamers in orange or orange-red etc. Each ellipsoid contains 95% of the pitches thrown for each pitch type. Generally deGrom releases the ball about a foot over in comparison to Berrios, but that’s not what it important. What’s important is how each pitcher’s change-up overlaps with their respective fastballs. deGrom has remarkable consistency to throw both types of fastballs and his change-up, and the ellipsoids are basically completely overlapping. Right away we can see that something is going on with how Berrios is releasing his change-up. It only overlaps with about half of his fastball release points, but his arm angle also seems to be drifting, and you can see the ellipsoid is stretched one direction. So I’m guessing he’s not only tipping his pitches in his wind-up, but there is also some release-point trouble happening here that no doubt some hitters are able to pick up on.

Final update on the beer: I added in the flavour hops and irish with about five minutes left and took everything off the heat. Luckily, it’s still a bit cold here so I left the beer outside to cool for a couple hours to get it down to room temperature so I could pitch the yeast. And fast-forward a couple of days…I let the yeast start the fermentation process at room temperature for a couple of days, then moved it into a fridge. I’ll leave it there for about three weeks, transfer the beer to a keg, and then it’s basically ready to drink!

Thanks for sticking it out to the end; I hope you enjoyed “Beerrios.” This ended up having a lot more deGrom in it than initially planned, but I think it was a good comparison to include. I think we were able to successfully identify a couple serious flaws from Jose Berrios’ debut season, and hopefully he’ll be able to shake that off, work on fixing his mechanics, and take another shot at the majors in 2017. I have a feeling we are going to see him mid-April or early May, and I really hope we get to see what he can do over an entire season. If he can transfer just a fraction of his minor-league success to the majors, we will get to see a pretty dynamic young pitcher, and the Twins have been waiting a long time to get a pitcher of this caliber back into their rotation.

The analytics revolution in sports has led to profound changes in the way in which sports organizations think about their teams, players play the game, and fans consume the on-field product. Perhaps the best-known heuristic in sports analytics is sample size — the number of observations necessary to make a reliable conclusion about some phenomenon. Everyone has a buddy who loves to make sweeping generalizations about stud prospects, always hedging his bets when the debate heats up: “Well, we don’t have enough sample size, so we just don’t know yet.”

Unfortunately for your buddy, sample size doesn’t tell the whole story. A large sample is a nice thing to have when we’re conducting research in a sterile lab, but in real-life settings like sports teams, willing research participants certainly aren’t always in abundant supply. Regardless of the number of available data points, teams need to make decisions. Shrugging about a prospect’s performance, or a newly cobbled together pitching staff, is certainly not going to help the bottom line, either in terms of wins or dollar signs.

So the question becomes: How do organizations answer pressing questions when they either a) don’t have an adequate sample size, or b) haven’t collected any data? Fortunately, we can use research methods from social science to get a pretty damn good idea about something — even in the absence of the all-powerful sample size.

Qualitative Data
Let’s say you’re a baseball scout for the Yankees watching a young college prospect from the stands. You take copious notes about the player’s poise, physical stature, his hitting, fielding ability, and running abilities, as well as his throwing arm power. For instance, you might write things like, “good approach to hitting” and “lacks pure run/throw tool.”

All of these rich descriptions of this player are qualitative data. This observational data from one game of this college player is a sample size of 1, but you’ve got a helluva lot of data. You could look for themes that consistently emerge in your notes, creating an in-depth profile of the prospect; you could even standardize your observations on a scale from 20-80. Your notes help build a full story about the player’s profile, and the Yanks like the level of depth you bring to scouting.

Mixed-Methodology
You’ve worked as a scout for a few years, and the Yankees decide to bring you into their analytics department. It’s the end of the 2011 season, and one of your top prospects, Jesus Montero, just raked (.328/.406/.590, in 69 PAs) in the final month of the season. The GM of the Yankees, Brian Cashman, knocks on your door and says that they’re considering trading him. What do you say?

You compile all of Montero’s quantitative stats from the last month of the season and the minors, as well as any qualitative scouting reports on him. Good job. You’ve mixed quantitative and qualitative data to provide a richer story given a small sample of only 69 PAs. You’ve also reached the holy grail of social science research, triangulation, by which you examined the phenomenon from a different angle and, bingo, arrived at the same conclusion that your preliminary performance metrics gave you. Montero is a bum. Trade him, Brian.

Resampling Techniques
It’s four years later and Cashman knocks on your door again (he’s polite, so he waits for you to say, “come in”). It’s early October and you’ve just lost to the Houston Astros in a one-game playoff. Cashman asks you about one of the September call-ups, Rob Refsnyder, who Cashman thinks is “pretty impressive.” You combine Refsnyder’s September stats (.302/.348/.512, in 46 PAs), minor league stats, and scouting reports, but the data don’t point to a consistent conclusion. You’re not satisfied.

A fancy statistical method that might help in this instance is called bootstrapping; it works by resampling Refsnyder’s small 46 PA sample size over and over again, replacing the numbers back into the pool every time you draw another sample. The technique allows you to artificially inflate your sample size with the numbers that you already have. You can redo his sample of 46 PAs 1,000, 10,000, even 100,000 times, seeing each time how he would perform. Based on your bootstrapped estimates, you feel like Refsnyder’s numbers from last year are a bit inflated, but that he’d fit nicely as a future utility guy.

Non-Parametrics
Cashman, who’s still in your office, now wants to know about two pitching prospects who were also called up in the 2015 class: James Pazos (5 IP, 0 ER, 3 H, 3BB, 5.4 K/9, 1.20 WHIP) and Caleb Cotham (9.2 IP, 7 ER, 14 H, 1BB, 10.2 K/9, 1.56 WHIP). If the team can only keep on of these pitchers, who should we keep? Who is better?

Normally you’d use a t-test to make comparisons between the two pitchers, but with such a small sample of innings for each guy, the conclusions wouldn’t be reliable. Instead, you decide to use a Mann-Whitney U test, which is basically the same thing as a t-test, adjusted for small samples. In fact, there’s a whole litany of statistical tests that are adept at handling small sample sizes: Wilcox’s t, Fisher’s exact, Chi-square, Kendal’s tau, and McNemar. You conclude that Pazos is slightly better, and that Cotham might be better suited for the bullpen. Cashman holds on to Pazos and deals Cotham to the Reds in the trade that brings over Aroldis Chapman to the Yankees. You pat yourself on the back.

Questions Need Answering
Having an adequate sample size brings confidence to many statistical conclusions, but it is certainly not a binary prerequisite for analyses. It’s easy for your buddy to watch his hindsight bias autocorrect for his previous wait-and-see approach, but organizations need to answer questions accurately. As amateur analysts and spectators, let’s change the lexicon by changing our methods.

Why are we so weird?

We don’t have 13th floors in hotels, walk under ladders, or pick up coins facing tails-up because all of these things are bad luck. People knock on wood when they talk about the future. They say “God bless you” if you sneeze, for fear of your soul escaping.

And as if those habits weren’t odd enough, ballplayers and baseball go and take superstition to a whole new level of silly and agitating.

The worst is the concept of the jinx during a no-hitter. Under what circumstances does uttering some passing phrase about a pitcher’s no-hitter suddenly doom it? Even if it’s deliberate, how does that change a guy’s ability to paint the black or shoot a blooper? Maybe it’s some cosmic understanding that goes over the head of simpler folks. But baseball is a game that is constantly relying more and more heavily on numbers, odds, and percentages. A no-hitter is one thing we can accurately acknowledge in the moment and without in-depth analysis. Doing so is no foible.

A pitcher’s team not talking to him during a no-hitter is just fine, though. It makes out a single game as something special, and how often do we get to do that during the regular season? That pitcher is on a mission that has been accomplished only 252 times since 1901. Currently, there are nearly 2,500 games in a single season. If a guy’s doing something that’s only been done a fraction of a single percentage in all the games in modern history, there’s no reason to goof with him like it’s just another day at the park. To that point, it hasn’t been.

Other superstitions are ones that have become prominent because of the volume at which they occur. Guys skip over the chalk at the start and finish of every inning on the way out of and to the dugout. It’s okay to think, “But what would happen if they did hit the line just once? No one is going to get hurt. It isn’t going to break a teammate’s mother’s back like stepping on a crack.” Let’s remember, though: the inning is over. Commercials are about to start. That silly moment is an easy one to tune out, so we’d be best off doing just that when we find ourselves fixated on it.

But when the game is back, and a player’s getting ready to pitch or step into the box, we’re paying attention. And we notice those ridiculous, idiosyncratic tics that turn into superstition which so many guys maintain. They work them into their mechanics and if they don’t perform them they’re thrown off. I’m looking at you, Matt Garza. Your little glove twitch has been the visual equivalent to a throw-up burp. It’s unpleasant and people might take a drink of the nearest beverage just to forget it.

Though he’s retired, Nomar Garciaparra remains the king of batting-glove love. Each time he stepped to the plate he might as well have played pat-a-cake with himself. It’s nothing compared to Moises Alou, though, who refused to wear batting gloves and would pee on his hands to toughen them up. Gross.

In all this strangeness, through all this exercised peculiarity, there might be some logic, even though the very definition of superstition tells us there isn’t.

In an episode of Fresh Air titled “Habits: How They Form And How To Break Them,” we learn about something called the habit loop from Charles Duhigg, author of The Power of Habit: Why We Do What We Do in Life and Business. There are three steps to it: a cue, a routine, and a reward. The cue enables the brain to let a behavior happen, while the routine is the actual action, and the reward is the brain enjoying it all and making it easier to remember.

That process becomes automated rather quickly. Scientists attribute it to the basal ganglia, which “plays a key role in the development of emotions, memories and pattern recognition.” You might realize how none of this speaks to the actual decision of players to do quirky things like skip over foul lines or fiddle with their equipment a certain way. That’s because the part of our brain that makes decisions — the prefrontal cortex — checks out once a behavior becomes automatic. It appears that once someone starts a habit, in many cases they’re not actually choosing to continue it.

Habits do provide comfort, though. And habits held in the belief of good fortune are why we get silly baseball superstitions that we can laugh at or hate. Whether they’re rare or regular occurrences, they’re one more way the game gives back to us.

Over the past few seasons, there seems to have been an uptick in power breakouts for hitters. J.D. Martinez, Jose Altuve, and Daniel Murphy are examples of guys who dramatically increased their power output seemingly out of nowhere. One of the most notable cases is Justin Turner, who transformed himself from a mediocre utility player with the Mets into an elite third baseman with the Dodgers. The Dodgers were rewarded for identifying a player with untapped potential and extracting that potential. Today I’m here to tell you that they’ve done it again, with Rob Segedin.

All right, a little background first. Rob Segedin was drafted in the third round by the Yankees back in 2010, and was traded to the Dodgers last year for pitching prospect Tyler Olson and KATOH star Ronald Torreyes. Despite having a pretty decent track record in the minors, he debuted in the majors just this past season and has never received much fanfare, even from the statistical community (the last article he was mention in on FanGraphs.com was a prospect report written back in 2012). Part of this is probably due to the fact that he’s usually been old for his level and never really hit for much power. The slew of injuries didn’t help, either.

Last year, however, all that changed. Well, the power and the health changed; he was still relatively old. Over 424 plate appearances in Triple-A last year, Segedin slashed .319/.392/.598 with 21 homers and a .279 ISO. That’s really good! The year before, his ISO was .136. Now, Segedin did move from Scranton to the PCL, which is significantly more hitter-friendly. But still, it’s hard not to be impressed with those numbers. And looking at his spray charts, the difference is stark (via MLB Farm).

Basically every home run Segedin hit in 2015 was pulled far left. In 2016 there’s a lot more action to center-left, and even some to the opposite field. And while, having looked over some footage, there doesn’t appear to be any obvious change to his swing, there’s another possible explanation for the sudden improvement. Segedin largely credits it to more consistent playing time after moving to the Dodgers organization – “It was a little frustrating for me last year to not be an everyday player and not get those everyday at-bats,” Segedin said. “I think playing for another organization was better for my career.” (Idec, Keith. “Baseball: Old Tappan’s Rob Segedin at Home in Dodger’s Organization.”NorthJersey.com. The Record, 14 July 2016. Web. 27 Mar. 2017.)

As mentioned earlier, Segedin had his big-league debut last year, so we have some MLB data to work with. And I’m gonna be honest. It doesn’t look great. Not on the surface, at least. In 83 PA, he slashed .233/.301/.370, good for an 83 wRC+. The great power numbers he had in Triple-A didn’t seem to translate, as he posted a mediocre .137 ISO. So yeah, that’s not very encouraging.

But there’s reason for optimism! For one, despite the low ISO, his exit velocity was pretty good. Rob Segedin’s average EV last year was 91.6 MPH, which is the same as Carlos Santana and Evan Longoria, and puts him higher on the list than Edwin Encarnacion. Segedin’s problem was less about hitting the ball hard, and more about putting the ball in the air: his average launch angle was 8.6 and his ground-ball rate was 52.8%. Which is a major problem (it’s hard to hit for power when you hit everything on the ground), but it may not be as bad as it seems. For one, it was only 83 plate appearances, and though GB% stabilizes pretty quickly, there’s still a good bit of noise in that sample. Also, remember what Segedin said about inconsistent playing time hurting his performance? Well in 18 of his 83 plate appearances, he came to the plate as a pinch-hitter. In those 18 PA he hit a whopping 24 wRC+ , as opposed to a 99 wRC+ when playing as a regular. I mean, I know that’s a ridiculously small sample, but it fits the narrative, so here we are. For what it’s worth, he’s batting .444/.500/.944 with 2 home runs in 20 PA this spring.

It’s kinda hard for me to look at Segedin’s current situation and not be reminded of Justin Turner. That said, he’s probably gonna strike out a bit more than Turner did. And he might struggle to find playing time in a crowded Dodgers infield. So there probably isn’t quite as much upside. But all the signs of a Rob Segedin breakout are there. All he needs is the opportunity.

Pitch counts have become an integral part of the game of baseball, so much so that it’s impossible to find a TV telecast that doesn’t display the pitch count side-by-side with the score and the inning. Yet pitch counts continue to be maybe the most annoyingly simple and arbitrary metric used to craft crucial in-game strategy. 99 mph fastball down the middle: +1 pitch. 76 mph curveball in the dirt: +1 pitch. Intentional ball: +1 pitch. Dirty ball tossed to the umpire: +0 pitches. Pitchout +1 pitch. Warmup pitches: +0 pitches. My goal here is not to fix this problem — just explore some interesting data that I believe should eventually be used to bring pitch count into the modern era.

Right now, I’m just going to look at 4-seam fastballs and how hard they’re thrown. All data comes from the 2016 regular season. Thank you Baseball Savant. The question I set out to answer is simple: When a pitcher needs to make a pitch, does he try harder? Common sense says yes, of course this is what happens. Relievers throw harder than starters in general because they don’t have to worry about throwing more quality pitches in later innings. But the data shows that pitchers change their effort levels within innings as well, especially when they have two strikes and/or runners in scoring position. Eventually, we should be able to use this knowledge to craft a better pitch count that takes this extra effort into account. Read the rest of this entry »

A long, slow dance in free agency for Mark Trumbo culminated with a three-year pact worth $37.5M to return to his 2016 team, the Baltimore Orioles. Trumbo, a classic slugger, reportedly hoped for an extra year and a total value of $75-80M on the heels of a season in which he led Major League Baseball with 47 home runs. Those who favor traditional statistics would point to Trumbo’s home-run totals and argue that he is one of the premier sluggers in the game, but in a baseball landscape run by the sabermetric crowd, Trumbo is seen as a one-dimensional player. In this chart, we will look at statistics that paint the picture that Trumbo is a one-dimensional player.

Mark Trumbo and His Contemporaries (2016)

It is argued that Trumbo’s year was inflated by an unsustainable .288 batting average in the first half, comparing him to Mark Reynolds, a cautionary tale of a player who peaked with a rather one-dimensional 44-homer season of his own. This is only accentuated by the fact that Trumbo’s batting average collapsed to .214 in the second half; this is nearly identical to fellow 40-homer masher, Chris Carter, who was non-tendered for being one-dimensional himself. Incidentally, Carter has been mentioned as a cheaper and nearly as valuable alternative for teams unwilling to make the splurge this offseason on Trumbo. On the field, Trumbo has been worth just about -10 runs per 150 games, which is more negative value than Jose Bautista, who was ravaged by injuries this season. On the bases, he provided enough negative value to compare to Joe Mauer, a former catcher.

There are several issues with this argument, though. The first is that Trumbo’s 2.2 fWAR is significantly higher than the one-dimensional sluggers (and others) he is being labeled alongside. Another is that he was stuck in the outfield by Baltimore in 2016 despite having no business being there. In fact, in his career, Trumbo grades out as an above-average first baseman. On the basepaths, Trumbo’s value is 105/146 of all qualified players, which isn’t as much of a tanker as one would think. As for his fluctuating halves, there is a tale behind that, too.

Mark Trumbo, Above Average First Baseman

Batting Average on Balls in Play (BABIP) assesses whether a player is going through a lucky (or unlucky) streak based on deviation from their normalized rate. The average BABIP is .290, and Trumbo is no different, checking in at .288 for his career. His first half was above the average rate, while his second half was at an extreme (and unsustainable) low. As you can see in the chart, his wRC+ is in line with the offensive-minded first basemen of the league, and there is room for some uptick. His defense at first base, even if 6.3 is too optimistic, can make him a $75M man. A lot of Trumbo’s depressed value comes from spending too much time in right field; this chart will break down the calculation behind Trumbo’s 2016 fWAR and estimate what he can provide if played at his true position (and some time at DH).

Mark Trumbo as Full Time 1B (2016, 2017 Projection)

fWAR calculation: (BR+BsR+FR+Positional Adjustment+League Adjustment+Replacement Runs)/(R/W)

*Assumes a 6.3 UZR/150, 135 games played as 1B, 15 games played as DH

This is an aggressive projection, but Trumbo proves that he is not a one-dimensional player. A 3.4-win player is extremely valuable, and if he produces to that level over the next three years, he will provide a significant amount of surplus value.

Mark Trumbo Projected Surplus Value, 2017-2019

*Assumed aging curve via FanGraphs: +0.25 WAR/yr (18-27), 0 WAR/yr (28-30),-0.5 WAR/yr (31-37),-0.75 WAR/yr (> 37, assumes a 5% inflation/year in $/WAR

*$4.5M of Mark Trumbo’s contract is deferred and to be paid in $1.5M increments from 2020-2022; that amount was subtracted from the overall surplus.

This chart shows the full potential of Mark Trumbo, quality first baseman. As calculated in the “Value Produced” column, he is rather close to the $75M man he marketed himself as. Because of the stigma surrounding his 2016 season, his market did not develop, and clearly overcorrected. Contending teams with needs at first base went elsewhere – the Red Sox signed Mitch Moreland, the Indians signed Edwin Encarnacion, and the Blue Jays signed Kendrys Morales. Even the Colorado Rockies signed SS/CF Ian Desmond for $70M (plus the 11th overall pick in the draft) to learn yet another new position. Unfortunately for Mark Trumbo, the team he signed with, the Baltimore Orioles, already employs a first baseman in Chris Davis. This redundancy will force Trumbo to again be a square peg in a round hole; part-time DH, part-time right fielder. This has been an unfortunate circumstance for him throughout his career, playing for teams that already had Albert Pujols and Paul Goldschmidt. What might have been to see Trumbo realize his full value, on a contract he deserves, and hitting moonshots out of Coors Field or Fenway Park.