The Blue Jays announced that lefty reliever Ryan Borucki has been outrighted to Triple-A Buffalo. He went unclaimed on waivers after being designated for assignment on Monday.
Borucki had sufficient service time to decline a minor league assignment. He probably would’ve remained unsigned for the rest of the season had he chosen free agency. Manager John Schneider said Monday that Borucki was hoping to stick with the organization (relayed by Arden Zwelling of Sportsnet). That’ll come to fruition after he accepted the outright assignment.
Toronto signed Borucki to a minor league contract late last month after he was released by the Pirates. The Jays selected his contract a little over a week later. Borucki managed 4 1/3 scoreless frames across four appearances, though he walked four of the 19 hitters he faced. The southpaw tossed 30 2/3 innings for the Bucs earlier in the season, working to a 5.28 earned run average. He had middling strikeout and walk numbers but got ground-balls at a 55% clip.
The Jays are familiar with Borucki, whom they drafted out of high school more than a decade ago. That came under a previous front office, but he spent his first four and a half MLB seasons with Toronto under the current regime. Brendon Little and Eric Lauer are the two southpaws in John Schneider’s bullpen. Mason Fluharty, Justin Bruihl and Easton Lucas are on the 40-man roster and on optional assignment. Borucki no longer carries a 40-man spot but that’s largely because he could not be optioned. The Jays might still view him as their third-best lefty reliever and could bring him back if Little or Lauer suffer an injury.
Borucki’s results (ERA ~4.6) lined up with FRA, but SIERA suggests he could be better than the surface numbers if his strikeout and walk profile sustains. The gap between SIERA (~4.0) and ERA/FRA (~4.6) points to him being undervalued if you only look at runs allowed.. Borucki may have some hidden upside that his raw ERA doesn’t fully show.
And all pretty useless stats because he pitched about 35 innings. Just way too small of a sample size to draw conclusions. Although FIP, xFIP, SIERA, are better metrics than ERA (especially for relievers) because they stabilize better in smaller samples, it’s to too small of a sample. At best they are indicators of performance.
@NoSaint
Calling FRA, SIERA, or xERA ‘useless’ over 35 innings misses the entire point. Nobody’s pretending relievers give us 200-inning samples — the models are designed to squeeze signal out of limited data by focusing on K/BB/HR rates instead of noisy run totals. ERA takes hundreds of innings to stabilize, but strikeout and walk rates don’t. That’s why front offices actually use these metrics. If you throw them out because the sample is small, you may as well admit you’ve got no way to evaluate relievers at all.
If you want to throw out a single season because it’s 35 innings, fine — but we’re talking about a guy with 8 seasons and 256.1 career innings. That’s plenty of sample to see the pattern. His career ERA (4.28) lines up almost exactly with his FIP (4.34) and xFIP (4.40), with xERA (4.63) and FRA showing the same mid-4 pitcher profile. The whole point of FRA, SIERA, etc. is to strip out noise in small samples, and when you zoom out over his career the story is consistent: he’s a middle reliever with stretches of over/underperformance. Pretending the numbers are ‘useless’ just ignores the fact they keep converging on the same conclusion year after year.
Now, back to your man cave and don’t return until you understand a bit of baseball.
@Old York
Look at leverage stats when looking to evaluate relievers. That lesson in baseball was free. No need to thank me for helping you understand baseball.
@NoSaint
That’s all you have to contribute? Wow!
If you look at his career leverage stats they sit around league average or slightly below average.
fangraphs.com/players/ryan-borucki/16350/stats?pos…
What are you looking at?
@Old York
I’m looking at a post that is using ineffective stats for relievers over a small size. Don’t get me started using career stats. That’s another kettle of fish.
@NoSaint
I posted his season stats AND his career stats. I’m looking at someone who don’t understand anything he’s posting about. Try again… Which leverage stats are you crying about? Clearly, you don’t understand what you’re talking about…
Old York, do not cite statistics you can’t explain or comprehend. Not to mention you overlooked the bigger picture as people who attempt to deep dive into analytics tend to do.
Your last line is laughable, as a breakdown of why Borucki reached the stats he’s attained could be broken down for a variety of reasons. Rather than take a hard look you decided to take these numbers at some form of face value. Showing us you are the one that might not understand baseball. I would suggest going beyond the numbers.
@Old York
I’ve already told you the numbers that you posted are pretty much irrelevant because of the sample size. Specifically the ones that you posted are not useful for relievers especially in a small sample and directed you to use leverage stats as better statistical descriptors for a relievers performance. Yet again you are under performance based on a small sample size.
Now you want to talk career stats? OK. As the body ages it falls apart. Things just don’t work as well. Performance scales positive before reaching a peak then scales negative. Another point is players are exposed to different coaches. Different coaches means new approaches which means outcomes changes (look what happened to Gausman when they told him to stick with primarily the splitter and fastball). There are other factors that influence a players which effect performance over a career.
Here’s what I’m going pass onto you. The best conclusions and inferences are made with recent data in sufficient sample sizes.
Class dismissed.
@NoSaint
You really didn’t say a lot, but if I look over the thread above, you brought up all sorts of silly metrics, to make you think you’re smart and then I debunked them all. All you have left are shrugs. Maybe when you’re ready to learn a bit about baseball, come talk to me. Otherwise, go enjoy your favorite twitch streamer…
@Old York
Leverage index stats for relievers are the best metrics to assess reliever performance because they generally pitch in higher leveraged situations. FIP, xFIP, SIERA, and the like don’t take into account leveraged situations.
More recent data is better data. Accompanied with sufficient sample sizes promotes more accurate predictions.
How are you not understanding this?
…and, do we account for certain appearances with reasons beyond the numbers where he was blown up? There are considerations.
It’s like a guy hitting 5 bombs in a season off position players. The stats do not say he did, but we know he did. There are layers to all this.
@NoSaint
I’m understanding it. You’re the one who’s struggliing with the fact that I pointed out you’re incorrect with your own stats that you’re presenting. Wow!
I’m all for the analytics a lot of times, but I’m sure I’m not the only Jays fan who’s seen enough of Borucki over the years.. They brought him back this year to get lefties out and he walked them all lol. Talk to me once he finds that “hidden upside” irl..
I’m with you 100%.
What I saw was not an effective reliever at all. His stuff just looked pedestrian at best.
Consider how many position players have pitched a clean inning with 35 to 50 mph stuff. So the fact he was scoreless over 4-1/3 innings is irrelevant.
His stuff seemed to lack movement and the crispness we saw when he was younger.
Old York, one issue I see here is taking his overall numbers. Borucki has been good against left handed batters, but uncompetitive against right handers. It makes it difficult to find the spots to use him. There are rarely three left handed batters in a row in a lineup. And if there are, as a reliever, he’s coming into the game at a time when the other team can counter with a pinch hitter. So he kind of needs a very specific part of the order early enough in the game that the opposing manager doesn’t want to go to the bench yet. I think it’s difficult to be that specific a specialist and warrant a full time job in a bullpen.
Also, while SIERA that wants to level out “luck” stats shows he’s been slightly unlucky, xERA that actually measures the events that SIERA is making assumptions about shows that his actual results are lucky (5.05 xERA compared to actual 4.63).
All of that isn’t to say there isn’t upside with Borucki. I just don’t think it’s found in these numbers that scale to ERA. I think it would be found in the pitch metrics and seeing if there are pitch shapes he has that could be effective against right handed batters. If he could change a pitch mix and improve the characteristics on some of the pitches, then sure there’s upside. But what he’s working with now isn’t effective enough. Command seems to be a bigger part of it than the pure stuff. But I still think that’s a pitch mix issue. You need something you can command in zone if you’re going to get ahead.
@KamKid
Fair points — platoon splits absolutely matter for Borucki, and you’re right that his usage window is narrow if he can’t handle righties. But that’s exactly why I brought in the run estimators: ERA alone makes it look like he’s just volatile or unlucky, while FRA, SIERA, and xERA help frame the difference between ‘results’ and ‘process.’
And to your point on xERA — yes, it does suggest he’s been a little lucky in 2025. But zooming out, his career track record has consistently hovered in the mid-4s across ERA, xERA, FRA, FIP, and xFIP. That’s not useless, it’s a baseline. Whether he can move off that baseline depends on exactly what you said: pitch shapes, mix, and whether he finds something that works against righties.
So sure, pitch metrics will drive the ceiling. But ignoring run estimators altogether is like throwing away the thermometer because you also want the Doppler radar.
Absolutely all information is worth considering. If not on the surface, as a starting point to dig a bit deeper. But to that point, “throwing away the thermometer because you also want the Doppler radar” feels like a statement that can also apply to the run estimators like SIERA and FIP that ignore or make assumptions about balls in play which account for about 2/3 of events. Why ignore those events when they are indeed measurable? It is why I pointed to xERA which includes ball in play data. xERA probably has its blind spots too. It’s derived from xwOBA that certainly has a spray direction blind spot for hitters. So in as much as pitchers can control spray direction, it would have the same blind spots. But I do like it a lot better than SIERA that wants to assume average BABIP when pitchers do control (or are prone to) their GB/LD/FB profile and expected BABIPs on each of those types of batted balls are wildly different (~.250/.700/.100 respectively). Furthermore, if you give up a lot of hard contact, you’d expect higher BABIPs than those averages and higher slugging on balls in play than average which can really change the run expectancy. Scaling to average HR/FB might suggest someone is lucky/unlucky even if their HR/Barrel is right where you’d expect it to be.
I guess for me, the question is is it worth averaging all the stats that try to estimate the same thing or is it better to weigh the one that has the best methodology most heavily? I lean towards the latter. Though certainly would take notice if any of the others were way out of line to look at why.
Trump will be crying when we win it all
@Darkomilicic
They’ll get to visit the WH when they win.
I doubt Trump has ever cried one tear in his life. He is not going to start now.
He didn’t get the job done in Pgh either