The Winter Warm-Up has come and gone. Pitchers and catchers will report to Jupiter, FL sometime around Valentine’s Day. We’re starting to hear how players are, again, in the best shape of their lives!
All of this triggers my internal content clock. It’s time to move away from talking about the players the Cardinals should chase this offseason, and, correspondingly, the money they should spend, and start talking about the players they have.
That means a return to some good old-fashioned player breakdowns!
I prefer the “Quick ‘N Dirty” style, where I don’t necessarily jump all the way into a player’s history or break down their entire skill set. I keep these things simple. Detailed on the critical points that will affect a player. But not a 3000-word, chart-filled monstrosity.
What went right? What went wrong? What can we expect in 2024? Quick ‘n dirty.
I have a secondary goal with these articles. In a recent comment on a podcast Q&A, one of our many (MANY) lurking members worked up the courage to register with SB Nation and present a question that I think a lot of you will share. The commenter asked if we could do an article that explained the advanced stats we used in easy-to-understand terms.
I would be happy to oblige! You see, I had to learn these stats, too. I’m not young like fellow writers Adam and Blake. I didn’t grow up with wRC+, wOBACON, barrels, whiff rates, tunneling, launch angles, and 90th-percentile exit velocities. I grew up with homers, batting average, saves, steals, and most importantly, wins and losses.
I’ve had to evolve in the way that I learn about the game, cover the game, and write and talk about the game. Why? Certainly not because our Site Managers demanded it. I evolved because I wanted to better understand the complex simplicity of the sport I love and talk about it with like-minded people.
Complex simplicity. I realize that’s oxymoronic. But it’s also true. Throw ball. Catch ball. Hit ball. It’s such a simple game. But you and I both know just how much pleasurable complexity goes into repeating those simple actions over 27 outs in 162 games!
These complicated and unfamiliar statistics that many of us VEB writers cite so often really do help us better understand where all those homers, base hits, saves, steals, and, yes, even wins and loses come from. And even though they seem like they require an advanced degree in statistical modeling, a lot of them are very simple in design.
So, as I leave behind my offseason transaction period and jump back into pre-season player evaluations, I’m going to invite you all to make that same evolution with me. I’ll use familiar stats. And contemporary sabermetrics (advanced statistical measurements in the science of baseball). I’ll intentionally build bridges between these two types of stats in a way that will help you, our wonderful and loyal readers, better understand what is happening in the field of play and why. That’s more fun than just posting an article with a bunch of definitions of stats, isn’t it? I think so!
Miles Mikolas, my first quick ‘n dirty analysis, is the perfect place to start! Because his traditional stats meet up with his advanced stats in an obvious kind of way that, no matter what statistical background you come from, you’ll be able to understand and come to terms with.
Let’s start with a question that will help us answer both what went right last year and what went wrong, using these contemporary and traditional stats in tandem.
Was Miles Mikolas Good Last Year or Bad Last Year?
The answer is somehow both.
What is the first stat you look at when you are going to evaluate a pitcher? Back in the day, I always looked at ERA first. Earned Run Average. ERA is pretty useful in telling you what has happened. It’s the average number of earned runs (not counting unearned runs scored following a fielding error) that a pitcher has given up per 9 innings pitched. Typically speaking, the lower the ERA the better the pitcher is.
Mikolas’ ERA last year was pretty terrible: 4.78. Historically, I get pretty nervous about any starter with an ERA over 4.50, especially one that’s supposed to be the team’s #2 starter. It’s not as bad as it looks, though. A 4.78 ERA in 1985 would have been horrible. Offense is higher than when I was a kid, though, and the league average was 4.45 last year.
By ERA Mikolas wasn’t good. He was bad. But not terrible. He was below average.
Better than ERA is FIP – Fielding Independent Pitching. FIP measures only the things that a pitcher is responsible for with no influence from good or bad defense. That includes home runs, strikeouts, and walks. FIP is put on a scale similar to ERA so the two stats can be used in tandem.
Historical models tell us that FIP is significantly better at predicting a pitcher’s future performance than ERA. Why? By looking at the things that a pitcher has exclusive control over (BBs, Ks, HRs), it eliminates all those wacky variables that affect runs scored. What does a squibber down the line that scores two tell you about a pitcher’s true ability? Not much. Sometimes baseball finds a way. But ERA counts it the same as a 450-foot 2-run blast.
It’s not ERA vs. FIP. We get to use them both! ERA still tells us what a pitcher did on his team in a season. FIP tells us what a pitcher did regardless of his team in a season.
By FIP, Mikolas wasn’t so bad: 4.27. That’s just above the league average (4.42).
So, Mikolas was both good (by FIP) and bad (by ERA). And that probably tells us something about the defense of the 2023 Cardinals, doesn’t it?
Those stats aren’t enough though. We kind of know what happened with Mikolas now. We have no idea why. ERA and FIP don’t tell us that.
So, let’s go deeper. FIP is made up of strikeouts (Ks), walks (BBs), and home runs (HRs). If we looked at those directly, we would better understand how Mikolas achieved his slightly above-average FIP.
Ks: 15.9% (7th percentile)
BBs: 4.5% (96th percentile)
HRs/9: 1.16 (1.32 is league average)
If you’ve watched baseball at all, you’re familiar with K, BB, and HR totals. They’ve been listed on baseball cards for decades. All we’re doing here is turning those totals into a by-inning rate. How often does Mikolas get a strikeout? How often does he walk someone?
Mikolas’ rate stats tell us that he is one of the worst pitchers in baseball in generating strikeouts. His 15.9% is in the 7th percentile among pitchers in the game. Among 43 qualified starters in the game, Mikolas ranked 42nd in K rate. Yuck.
Consider the ramifications of that. How do outs happen? The vast majority of the time that a pitcher faces a batter the plate appearance results in either a K, a walk, or a ball is put into play. (Include HRs in that, even though they are not technically in play.)
Since Mikolas barely Ks anyone, that’s a lot of balls in play, a lot of batted ball events that fielders or outfield fans have to deal with.
Because he can’t generate Ks, Mikolas puts a lot of pressure on his defense. The Cardinals’ defense didn’t handle that well for him in ’23 and a lot of those balls in play became runs. Those runs show up in his high ERA.
Now we have some “why” to go with our “what”. Mikolas’ FIP and his ERA would have been much lower if he had limited balls in play by generating more Ks. He didn’t and that’s why they were average or worse.
Can he change that going forward? It’s doubtful. Mikolas’ K-rate in ‘22 was 19%. That’s better but still pretty bad. His career rate is just 17.6%. The older Mikolas gets the more his velocity drops (93.5 mph avg. fastball last year compared to 94.3 in ’18), and the more likely that his K rate will hold steady or decline rather than rise.
On the flip side for Mikolas is his walk rate, which remains among the best in the league. That’s held steady his whole career. Mikolas might put a lot of balls into play, but there are not a lot of extra runners on base when he does. That allows him to limit the damage that opponents can do to him.
As does his above-average HR rate last season. We’ll get to that below.
There we have a pretty complete picture of Mikolas’ career. He can’t get Ks. He will not walk anyone. His performance – ERA and FIP – are highly influenced by the club’s defense. That certainly shows up in his better seasons, like ’18, ’19, and ’22. Those were stronger defensive clubs than last year. Mikolas thrived then. He suffers now.
And his HR rate? That’s where the variability in his performance season-by-season shows up. Since his high-quality walk rates are pretty much balanced out by his terribly low K rates, it’s his HR rate that can shift his FIP (and ERA) pretty heavily.
In ’18, Mikolas’ best season as a Cardinal, his HR/9 was just .72. WAY better than average. Correspondingly, his ERA was 2.83. WAY better than average. Batters had to string several hits together to score runs because he refused to walk anyone and didn’t allow homers.
Mikolas followed that up in ’19 with a disappointing 4.16 ERA. His K numbers and BB numbers didn’t change. What did change? His HR/9 surged to 1.32. The percentage of fly balls that left the ballpark (HR/FB) surged as well. Over 16% of his fly balls went over the wall compared to just 9.2% the season before.
Even though nothing changed about the quality of Mikolas’ stuff in those seasons – his BBs and Ks were stable – he had much worse luck on balls leaving the park. That luck makes the difference between an All-Star season and a disappointing season for Mikolas.
Bring that back around to 2023. He had a better-than-average HR rate last season. Is that sustainable? Well, his HR/FB rate was just 9.8%. That’s nearly as low as it was in 2018, which we know didn’t last. It’s much lower than most of the seasons of his career. There’s every reason to believe that Mikolas’ home run rate will increase next year.
Will Mikolas Be Good or Bad Next Year?
This is what concerns me about Mikolas going forward. His K totals are what they are. His BB totals are what they are. There’s not enough variance in those year-to-year to justify an optimistic or pessimistic outlook.
That means Mikolas’ prognosis essentially comes down to his ability to limit HRs and we should expect those to regress closer to his career rates next year.
How much will that hurt Mikolas’ performance?
This is where some of our “X” stats come into play. “X” stats mean “expected” or “extrapolated”. X stats calculate what a player’s actual stats might have been under a specific set of conditions. Fangraphs has both xERA – expected ERA – and xFIP – expected FIP.
xFIP calculates a player’s FIP but it replaces their HR rate with the league average HR rate per fly ball (HR/FB). HR/FB rates aren’t stable. They depend on where a ball was hit in a stadium, what stadium it was hit in, weather conditions for specific games, etc. Over a significant amount of innings (like multiple full seasons), HR/FB drifts toward the same average number for pretty much all pitchers. Some pitchers are better at limiting HRs than others but that mostly comes from their ability to limit fly balls and not so much what happens to those fly balls.
Last year, Mikolas’ xFIP – expected FIP – was a 4.76. That’s quite a bit higher than his actual FIP and about the same as his actual ERA. What does that mean? It’s statistical evidence for what we said above. He was overly lucky with his HR per fly ball rate.
Why would we think that luck will continue? We shouldn’t. Mikolas will give up more homers next season. It’s almost a certainty.
Worse for Mikolas is xERA. xERA doesn’t just factor in a pitcher’s earned runs, like ERA. It looks at the quality of the contact a pitcher gives up overall. That squibber down the line might score two runs, but more often than not, it’s an out. A very hard hit ball to a gap can get run down by a well-positioned fielder, but more often it gets down. ERA accounts for the squibber, not the hard-hit out. xERA accounts for the hard-hit out but not the squibber.
Why? Because hard-hit balls of any kind tell us more about a pitcher’s expected results than any kind of ball that happens to result in a run.
Mikolas’ xERA in ’23? It was 5.41. Ouch! That’s worse than his already bad ERA, and much worse than his actual FIP. That indicates that defense was only partially to blame for Mikolas’ high ERA. He also gave up a ton of hard contact that no fielders would have handled cleanly.
Would you believe there’s a stat for that, too? Baseball stadiums are equipped with Statcast systems that measure pretty much everything that happens during a baseball game – pitch velocity, pitch break, sprint speed, jump, routes, a catcher’s pop time, how hard a baseball is hit (exit velocity), the angle they are hit at (launch angle), and many more things!
Statcast can take every ball put into play on a pitcher and calculate their “Hard Hit %” – the percentage of balls hit at 95 miles per hour or higher. 95 mph is about the speed where the velocity of a struck baseball begins to make a significant difference in whether it is an out or not. Balls hit at 95 mph or over become hits much more frequently than balls hit at 80 mph.
That sounds complicated but it’s just a measurement of what baseball has known since its inception. Hard-hit balls are more likely to find holes in the defense than softly-hit balls. This is the old-fashioned frozen ropes versus duck snorts.
Now we can tell you exactly how hard a frozen rope is and how softly a duck snorts.
I bet you can already guess, based on his xERA and K rate, where Mikolas ranked last year in Hard Hit%. That’s right! He had a hard hit rate of 43%. That’s 22nd percentile or 8th worst among qualified starters. Pretty bad. While the defense didn’t help Mikolas last year, he was the one giving up 95+ mph screamers all game long.
That gives us a (not so) quick ‘n dirty look at Mikolas last year and what we should expect in ’24:
1) Mikolas can’t generate Ks and allows too many balls in play. This puts a lot of pressure on the Cardinals’ degraded defense.
2) He can limit walks at an elite level. That’s not likely to change.
3) His overall performance is very dependent on his HR luck. That’s likely to regress.
4) He gives up a very high rate of hard hit balls. That could bounce back. Or he’s reaching the point of his career where his stuff has regressed.
That provides us with the pivot points for Mikolas’ 2024 season: Defense, which he can’t control, but might be a little bit better this season. Home runs and hard-hit balls, which he can control, but are likely to be just as bad or worse next season.
My prediction for Mikolas? I’m not particularly optimistic.
ZiPS – a system that projects player stats based on the kinds of things we looked at above – thinks Mikolas will decline to a 4.61 FIP next year. It thinks Mikolas’ HR rates will increase. It credits him with a lower ERA (4.31), but it does that for all the Cardinals pitchers. That’s a system-wide output rather than something specific to Mikolas’ performance.
That seems like a very fair projection from ZiPS. I would probably drop Mikolas with an FIP/ERA of 4.50. And based on his disturbing hard-hit rates, I think it’s more likely that he’s worse than that than better.
Sorry to start you out with a downer! But I hope that helps you understand Mikolas better and the variety of stats that go into his performance. There’s a lot more we could look at – including pitch varieties, spin rates, and other types of contact measurements. Those things matter, too. We’ll hit some of those as the series continues.
Next time? We’ll flip the script and go optimistic: Jordan Walker.