3-27-24 – By Josh Booth – @bridge2buctober on X
Hi. I’m Josh.
I host the Bridge to Buctober Podcast with my brother, Jake. We like to believe that while listening to our show you feel like you’re sitting there hanging out with us. But one thing that happens every once in a while: I like to dig into data.
Let’s get real.
Baseball is a game of statistics. Ignoring how a batter hits on day games after a day off and the temperature over a certain degree is not too “baseball-fan” of us. It’s either fun and meaningless, or there’s just something to the fact that Pedro Alvarez performed well in day games. (over his career, had an OPS sixty points higher in day games.)
When I go down a rabbit hole digging into the data, I want one of two things to happen from that information:
It should either back me up or shut me up.
If you dive in with a closed mind, you will either stop looking, or filter the splits down far enough to feed a narrative. That is not the goal here. Gathering information should always help us understand the game more.
Or it is just fun to know. Learning that the Pittsburgh Pirates franchise has had 4 players hit 20+ homeruns in the same season only ONE TIME (1999) doesn’t help us understand the game. It does, however, give you something to watch in 2024, because there’s a chance this is the second time it happens. We can talk about this again around July or August.
(Giles, Martin, Young, and Sprague all hit 20+ HR in the 1999 season.)
Excuse me? Spring Training stats?
While we are looking forward to all the trends and statistics we will have fun with in 2024, this month we’re creating belief, or disbelief, in a team and players based on Grapefruit League statistics.
Looking at Spring Training stats is something we all say you shouldn’t do… then after the first two weeks, we’re using those statistics to prove a narrative about a certain player being good or bad. One of the fun parts of Spring Training position battles is putting your thoughts into the universe about who you think is winning. In all reality, after each player has played 6-8 games and less than 20 plate appearances, it’s incredibly unlikely to assume that a team has wavered from their initial outlook. They are going to let this thing play out. That doesn’t mean someone can’t impress, but they are just gathering data at that point. Those of us with podcasts and social media accounts made our picks and hot takes, but reality is that there are plenty of games and workouts left at that point.
Speaking of workouts, let’s not dismiss the difference between what fans see vs what the team sees. There are more than games during Spring Training, and a lot more than results goes into making those decisions.
“… human beings play the game, not robots and algorithms.”
I love data. But at my core, I’m still convinced of what I said earlier: they’ll back me up or shut me up. I still hold fast to the idea that baseball is still much deeper than the numbers. I believe that because human beings play the game, not robots and algorithms. People are unpredictable and have multiple layers. And data can’t show us how hard a guy is working or how his off-field life is affecting his performance. This includes Spring Training.
Let’s consider the idea that Spring Training and Regular Season stats come down to a contrast in measurement. In the regular season, results are king. In Spring Training, indicators, or projection stats, might be the focus instead.
Results aren’t king in Spring?
Think of the traditional slash line: AVG/OBP/SLG. (batting average, on base percentage, and slugging percentage)
In spring 2021, Kevin Newman (obvious choice) slashed .606/.641/.788. Those are great numbers.
In the ’21 season, however, Newman slashed .226/.265/.310. We’re not even close. It wasn’t a summer slump that brought him down, either. In Mar/Apr that season he slashed .205/.265/.495. So, right out of the gate, it was evident that the results had changed.
First and foremost, the regular season is the first time pitchers try to get hitters out, rather than working on themselves.
Now, let’s look at some of those indicator stats I mentioned. One I tend to start with is Batting Average on Balls In Play. The league average sits around .300. This removes events like strike outs and homeruns, because those are not hit “in play.” Newman had a .588 BABIP that spring. He was very fortunate to be well above that .300 mark. His BABIP in the regular season was a very low .236. This is why his career numbers fall somewhere in between those numbers. It mostly evens out.
Since 2015, we have had access to Statcast data. Giving us a plethora of fun numbers to argue about. Let’s look at one of the most popular, Exit Velocity. This is, simply, how fast, in MPH, a ball was hit.
So, let’s look at how hard he was hitting the ball. His average exit velocity (EV) during the Spring, in 34 balls hit in play, was 86.3. Not necessarily scorching the ball. In the regular season, this dropped to 83.9mph in 485 balls in play. His overall exit velocity, which includes his 5 HR, was 85.3, which was in the bottom 3% of MLB. League average was around 89.4.
There was enough information that Spring to, at least, say “let’s wait and see how this goes in a larger sample size.”
I do not want to make this about Kevin Newman. But it’s hard to ignore this example.
Opponent Quality
Baseball Reference has an interesting number I like to look at during Spring Training, as well. It’s not perfect, but no stat is.
It is called OppQual, or Opponent Quality. The purpose is to quantify the quality of opposing pitchers or batters faced. Baseball reference calculates this 1-10 number based on the levels their opponents played at in the previous season.
10 – MLB
8 – AAA
7 – AA
5 – High A
4 – Full-Season A
1.5 to 3, Rookie and Short Season
1 – Opposing batter is a pitcher.
Looking at the 2023 Spring Training games, it would be easy to be impressed by Travis Swaggerty (.379/.471/.690), Nick Gonzales (.391/.440/.522), or Carter Bins (.462/.563/.615). When you notice OppQual for those three, respectively, 6.8, 6.8, and 6.7, it shows they were mostly facing their peers around the AA level. Granted, that’s an average. So, it’s possible they raked against lower level and struggled against higher level, or the reverse. We would have to depend on the teams to know that sort of thing or dig into each at-bat and that could take some time.
In the same light, you can look at Ji Hwan Bae and Rodolfo Castro, who faced an OppQual of 7.8 and 7.7, and hit .244 and .226, respectively. But who made the team out of Spring Training? Ji Hwan Bae and Rodolfo Castro.
It’s important to note that the highest average OppQual for any Pirate hitter last Spring was Matt Gorski at an 8.0. Keep in mind, it’s an average. Bae was the highest OppQual of anyone with more than 7 plate appearances.
As you can see, there are a lot of numbers you could look at during Spring Training. Unfortunately, we still don’t even get to see the games as much as we like — those of us who want to break it all down, anyway. Watching them play the game goes a long way. I’ve only gone over offensive statistics, to this point. I mean, c’mon, how much time do we all have here?
2024 SPRING TRENDS
With all that said, let’s look at some trends from this Spring. We will see what can be discovered heading into the season.
Fortunately, there were a few good showings this spring. Henry Davis made sure there was no question he would be the starting catcher in Miami, Jared Triolo clearly won the 2B job, Ke’Bryan Hayes has a lot of us believing he has really figured out the offensive side of his game, Oneil Cruz let the league know he’s back to 100%, and Jack Suwinski showed early signs of lowering a strike out rate while still hitting the ball hard. They were all able to do so while facing team highs in OppQual. Davis 7.9, Triolo 8.0, Hayes 7.8, Cruz 7.7, and Suwinski 8.0.
As I noted above, an OppQual of 8.0 is an average of AAA, but it’s going to be around the highest average you’ll see in spring with any bulk of games played.
STANDOUTS
Henry Davis – 7.9 OQ, .310/.400/.667/1.067, 4 HR, 19.6 SO%, 13.7 BB%, 92.3 EV, 36.4 HH%, 6.1 Barrel%
Davis was a man on a mission. There are guys you just want to get in shape for the season, and guys you want to see do well and break camp with a job. The latter is exactly what the former #1 overall pick did. Facing mostly higher level pitching, he showed a lot of power, tied with Jack Suwinski with 7 XBH on the spring, one behind Oneil Cruz for the most on the team. The exit velocity and barrel rate are good signs heading into the season but paired with a strike out rate under 20% is even better. It’s worth saying, also, that he did so with the highest walk rate of this group.
Triolo – 8.0 OQ, .310/.383/.500/.883, but more on Triolo later. Let’s just say “he was good.”
Hayes – 7.8 OQ, .412/.434/.647/1.081, 3 HR, 11.3 SO%, 95.7 EV, 48.9 HH%
As I said in the opener of this, Hayes showed us all why we believe he’s got this figured out. He is still hitting the ball hard and has combined the right field approach with the ability to pull the ball with authority. Showing signs of a good season, if he can stay healthy, this could be that break out season.
Cruz – 7.7 OQ, .261/.333/.739/1.072, 7 HR, .217 BABIP, 98.7 EV, 23.3 Barrel%, 53.3 HH%
Oneil Cruz. Not Human. I’m not sure what else to say. The man hit 7 HR this spring and they weren’t cheap. Well, there was one possibly aided by wind. A group of the hardest hit balls in the spring, he reminded a lot of baseball fans why we’ve been talking about this guy. Despite a 30% strike out rate this spring, he even suffered from a .217 BABIP. So, the .261 AVG could be a little better if luck goes more his way or he cuts down on some strike outs. See the leaders in Barrels per Batted Ball Event below, Oneil Cruz and Jack Suwinski were among the top.
Suwinski – 8.0 OQ, .269/.296/.558/.737, 4 HR, 20 SO%, .270 BABIP, 41.5 HH%, 14.6 Barrel%
Jack once again showed a bit of a streak this spring, bunching is homers together. However, the biggest signs I can see is that the hard hit balls and barrels are only second to Cruz. He also, probably more importantly, had a 20.4 strike out rate this spring. While the rate was certainly higher against LHP (36%), and MUCH lower against RHP (14%), it’s something he’s intentionally worked on. I have my suspicion that the final number would be between 25-30%, it is still an improvement and he’s still hitting the ball very hard. Which bodes well for another season over the average OPS.
Let’s get back to Triolo. He has always been the #1 candidate for regression based soley on a very high BABIP. There are some metrics that feed into that, like a high barrel rate and hard hit rate. However, even with that in mind, it’s not just high, it’s impossibly high. How long do we keep wondering if it goes down before we accept him to be an outlier? Not yet.
Triolo – 8.0 OQ, .310/.383/.500/.883, 2 HR, .423 BABIP, 90.1 EV, 39.3 HH%, 10.7 Barrel%, 29.8 SO%
The strike out rate is still high but so are the walks. His .423 BABIP is actually a tick lower than Hayes’ .429 but Hayes achieved this with a higher hard hit rate and higher average exit velocity. BABIP is based on balls in play and, well, Hayes put more balls in play. Hayes’ strike out rate was 11.3 while Triolo’s was 29.8. A small sample like .423 is obviously within reach but with everything else combined, it feels like the .310 AVG looks more like a .260-.275 AVG over time. He has some time to start the season with Peguero being sent to AAA and his superior defense. He should still be a productive player this season, with or without a .400+ BABIP.
RESULTS NOT TELLING THE WHOLE STORY
Reynolds 7.7 OQ, .190/.261/.476/.737, 4 HR, .125 BABIP, 13 SO%, 41.7 HH%, 89.9 EV, 16.7 Barrel%, 13.0 SO%
Reynolds is a prime example of a guy who’s just getting his timing and focusing on physical conditioning to get ready for the season. At first glance, he had a bad spring. Looking deeper, he hit the ball hard consistently, popped 4 HR, limited strike out rate to just 13%, and suffered from an extremely low .125 BABIP. He faced quality pitching along the way. Last season, 19 of his 24 homeruns came while hitting left-handed. This spring, 3 of his 4 homeruns were batting right-handed. If anything, I’m optimistic headed into 2024.
Olivares 8.0 OQ, .208/.224/.375/.599, 20.4 SO%, .243 BABIP, 34.2 HH%, 88.4 EV, 2.6 Barrel%
Joe 8.0 OQ, .226/.324/.355/.679, 16 SO%, .240 BABIP, 33 HH%, 0.0 Barrel%
Both Connor Joe and Edward Olivares made this team despite subpar numbers. BABIP is low for both players. Both face quality pitchers. What did they do right?
Connor Joe’s 2023 strike out rate was 23.3%, his rate for the Spring was 16%. His hard hit rate was lower this spring, though; from 41.8 to 33%. The barrel% was non-existent, but just under 7% last season. We know Spring is a small sample, so if the barrels and hard hits increase, even if it means the strike out rate goes up a little, it may normal out that BABIP and have better results.
For Olivares, he seemed to struggle for a large portion of the spring. BABIP was low, HH% was down from last year, Barrel% was low, but his average EV was good. This is likely due to the slow start. It’s hard to increase these numbers in a small sample of less than 50 at bats. If these improve, we should see numbers similar to last season, if not, Joshua Palacios and Ji-Hwan Bae are looking for opportunities, as well.
OVERHYPED
There were a few players who faced lower quality pitchers and hitters and had some success. The thing about the OppQual statistic is that it doesn’t know if those results would have still been good against better opponents.
When it comes to Termarr Johnson and Tsung Che Cheng, fans were impressed. While both had good results here and there, both also were mostly facing their peers. (Johnson 6.4, Cheng 6.5.) Termarr also benefitted from a .500 BABIP.
Chase Anderson
Some were impressed with what Chase Anderson was doing before he opted out and was released. Anderson could have been mostly successful because of who he was up against.
His OppQual was only 6.6. It makes sense that a lot of fans thought adding him was something the Pirates would do, but maybe it was the right call all along to let him sign elsewhere. He threw a heavy dose of changeups, cutters, and four seams. The pitch he relied on the most might have been the one that got hit the hardest.
According to Statcast (which is never complete in Spring Training), 25% of his offerings were hard hit balls and opponents hit .273. The results on his fastball seem odd. Only 8 were hit into play and none were hits. So, 25% of his four seam fastballs were hit hard, but none of them were a hit. Wait… so, 2? Well, yes. Keston Hiura lined out to RF Matt Gorski on a 101.8 mph line drive and Ryan Vilade hit a 95.9 mph fly ball to Gorski in the same inning. He also threw 11 curveballs tracked by Statcast this Spring. 6 balls, 3 called strikes on first pitch curves, an 0-1 foul ball hit 95.7 mph, and a 108.5 mph ground out on a first pitch curve.
I’ll be honest, look up the guys he faced, I dare you to remember more than 25% of them. I did just say that Ryan Vilade was one of them and since he was in the Pirates org last year, he doesn’t count. (Although it’s possible some of you don’t even remember him.) The point is: there is no way to REALLY tell if that success would have translated or not, but according to who he faced and the odd success of certain pitches, it didn’t feel sustainable. The only remaining question is whether you thought he would be a better choice than Bailey Falter, who did make the team.
Every spring, there’s a guy who doesn’t have it. The results are poor against low level opponents.
The first two guys that come to mind here are relief pitchers. The Pirates started the spring with a deep bullpen that looked to be a strength. After Dauri Moreta went down for the season and Holderman and Mlodzinski open the season on the IL, the Bucs will look to see some young guys step up and fill that hole. Two guys, already on the 40-man roster, come to mind: Colin Selby and Kyle Nicolas. While the latter still has some serious upside, the former looks to be one of the casualties of the non-roster invites who made the team.
Selby’s OppQual was only 6.3. In his 4 innings (small sample, I know), he walked 4, gave up 2 HR, and 5 ER – with a 2.750 WHIP. He did strike out 4 guys. Cam Eden and Nathan Lukes from Toronto, Ryan McKenna of Baltimore, and Keshawn Ogans of Atlanta. Ogans hasn’t played above High A, McKenna has a .221 AVG in 447 MLB at bats over the last 3 years, Lukes made his debut last year and hit .192 in 29 G, and Eden made his debut last year and hit .167 in 6 PA. There are not a lot of Statcast numbers for a guy who threw 4 innings, but the reason for 4 innings may have been his 4 innings. There’s a lot to work on there.
Nicolas faced an OppQual of 6.5 and pitched to a 2.250 WHIP (8.44 ERA) in 5.1 innings. His first outing was his worst, giving up 3 runs in 1 frame, striking out 2 and giving up a HR. He managed to use the strike out to get out of some jams he put himself in. His next four innings: H+BB, 2 H, 2 H+BB, 2 BB. He did strike out 6, but too many baserunners. All while considering the batters he was facing weren’t MLB players. That first outing saw 2 singles by Yoyner Fajardo and Alex Isola and a homerun by Dalton Shuffield. 3 players with a combined 48 AB in AAA. (All 48 by Shuffield) While he struck some MLB hitters out, as well, the overall mix was mostly younger players. You would like to see better from a guy who has a fastball-slider combo that should eventually be good enough for a big league reliever.
IN CLOSING
Spring Training data is always difficult to parse through. It’s all small sample, there’s not Statcast at every stadium, and the goal of the individual can be different.
The season starts this week and the stats count. Players will be going 100% with the intent to win games. I am looking forward to watching this team grow and see how far they can take the 2024 season. I am also looking forward to digging into the data and analyze the trends that help us tell the story of our summer.
Thanks for nerding out with me for 10-15 minutes. Now let’s play ball… and LET’S GO BUCS!
Steel City Pirates 