Daily Report Primer, Part II

(Part I is here.)


The best prospects tend to receive aggressive assignments and are young for their levels. If all you know about a player is his age, you actually know quite a lot. Take Texas’s promotions of 20-years-olds Luisangel Acuna and Evan Carter to AA last summer. Texas is telling you they’re well-regarded without you needing confirmation from me or MLB Pipeline or Baseball America.
One shouldn’t get carried away with age, though. Of course, players drafted out of college will be older, so dismissing them for being 23 by the time they reach high-A would be ridiculous. However, the older the player, the higher the expectations. (Incidentally, that a good many college players don’t handle A-level ball reinforces just how hard the pro game is.) Catchers tend to take more time, as do many pitchers.

The Rangers don’t promote as aggressively as a decade ago. Promotions feel more player-tailored and less driven by organizational culture.

Slash Stats (Average / On-Base Percentage / Slugging)

In the Majors, batting average isn’t completely useless, but it matters far less than on-base percentage and slugging. In the minors, I still like to keep an eye on it. Putting the bat on the ball with frequency and authority is what gets players noticed and moves them up the ladder.

Here’s two fictional players with 500 plate appearances. Both have a .360 OBP and .440 slugging percentage:

A)    100 hits, 10 doubles, 25 homers, 80 walks, 160 strikeouts
B)    150 hits, 33 doubles, 8 homers, 30 walks, 60 strikeouts

Same OBP, same slugging percentage, very different hitters. Player A is kind of a cut-rate Joey Gallo, batting .238 with huge number of walks and good-but-not-elite power. Player B batted .319 but doesn’t walk much or offer much more than doubles power. There aren’t many Player B type nowadays. Luis Arraez last year. Elvis Andrus in 2016. Knowing the batting average in addition to OBP and slugging can be surprisingly informative. That said, even in the minors, OBP and slugging are much more useful.

These stats mean the least at lower levels and gain importance as players advance. They also matter more to offense-oriented positions. Except at the extreme margin and probably not even then, a first basemen cannot compensate for weak hitting with outstanding defense. He has to hit.

OPS (OBP + slugging) is an ugly mishmash of a statistic that nevertheless usually does a acceptable job of describing a hitter, but I’m more inclined to mention the entire stat line.

Walks (Hitters)

The goal of a hitter is to reach base safely, so the ability to lay off iffy pitches can define a career. Walks create hitting situations with runners on base, wear down the pitcher, and mitigate inevitable slumps. In the Aaron Zavala example from yesterday, he drew eight walks during his season-starting 0-for-16 slump, producing a .333 OBP. Would that all slumps were so productive. Zavala gave his teammates eight opportunities to hit with a runner on base, and he scored three runs in those four hitless games.

Even for Zavala, walks are a means, not an end. I do worry about players who rely too heavily on walks, which is easier to do at the lower levels where control is often absent. Selectivity is a great attribute. Passivity, not so much. Eventually, the hitter will rise to a level at which most pitchers not only have control but a semblance of command, and the hitter will have to adjust.

Strikeouts (Hitters)

To some extent, we can ignore hitters’ strikeouts. What really matters is how they perform when they don’t. Not to be flip, but strikeouts for hitters don’t matter until they do. At some point, they reach a level that forces a herculean batting average on contact just to get by. Last year, I gamed out how Adolis Garcia could achieve a .300 OBP with so many strikeouts. As I tweeted: “Let’s say he can manage a 5% BB+HBP rate (well below league average) and a 30% K rate (well above, even in 2021). That means he needs to bat .263 for a minimum .300 OBP. And with all those Ks, that requires a .376 average on contact, about 50 points above the league average and better than what he’s done in AAA.”

That season, Garcia ended up with a 6% BB+HBP rate and struck out in 31% of his plate appearances, close to my guesses. He also batted .364 when he made contact, roughly the 75% percentile among AL batters with at least 400 plate appearances. So, very good in that respect. And what did that high average on contact get him? A .286 OBP, 9th-worst among that same set of batters. He improved to an even .300 last year despite a slightly lower average on contact, courtesy of more walks and fewer strikeouts.

Some hitters are exceptionally good at avoiding strikeouts, but not particularly to their benefit. Most of the time, weak contact on a marginal pitch isn’t any better than a strikeout.


I do list ERA when recapping pitchers. Much of the time, it’s a handy stat, but it’s not the end-all and sometimes is lying to you. Let’s take two pitchers in low-A in 2021:

Player A: 4.28 ERA, 43% SO rate, 7% BB rate, .272 opposing OBP, 16.5 pitches per inning
Player B: 3.68 ERA, 33% SO rate, 23% BB rate, 429 opposing OBP, 23.5 pitches per inning

Player B had the better ERA, but I’d pick Player A in a critical situation without question. B had a terrific strikeout rate but a bunch of innings marred by walks (mostly stranded, luckily) and elevated pitch counts. Pitcher A combined good control with an otherworldly strikeout rate, but the batters that reached were much more likely to get home. Usually, situational performance (such as runners in scoring position) tends to even out in the long run.

Sometimes a single terrible outing can wipe out a reliever’s ERA. My favorite example is John Smoltz back in 2002. He allowed eight runs in 0.2 innings in early April and needed three months of quality outings (including 37 saves!) just to drag his ERA below 4.00.

So, you’ll occasionally read something from me like “he’s pitched better [or worse] than his ERA would suggest.” If Players A and B continue to pitch as they have, Player A is far more likely to have the lower ERA eventually.

Homers, Walks, Strikeouts (Pitchers)

These are better indicators than ERA, which is often tied to luck on balls in play and how well relievers strand runners left behind.

Homers are trickier to analyze. More fly balls equal more homers, of course, but HR rates can bounce around crazily from year to year for no other reason than variance. Walk and strikeout rates tend to stabilize more quickly.

Walk and HBP rate are up across the minors post-2020, particularly at the lower levels. A combined BB/HBP of 10%, slightly problematic a dozen years ago, is now better than average. My rule of thumb was that a BB/HBP rate of 15% was untenable for a would-be starting pitcher, because he’d run into trouble too often and force too many bullpen innings. Last year, among the 50 busiest starters in the low-A Carolina League, nearly as many had a BB/HBP rate of at least 15% (10) as below 10% (12). More pitchers seem to be able to abide the higher walk rate because they’re darn near unhittable otherwise, and they aren’t being asked to face as many batters. Still, as they climb the ladder, those walks are more likely to cause trouble.

Strikeout have risen so much that I constantly have to remind myself what constitutes an acceptable rate. In 2007, my first year on the job, the best team in the low-A Midwest League (which contained Texas-affiliated Clinton) had a strikeout rate of 21.3%. Last year, the worst team in the low-A Carolina League (including Down East) had a rate of 22.3%. Down East’s 27.2% rate (10.5 per 9 IP) was fourth in a 12-team league.

The gap between starters and relievers has shrunk. Again comparing Texas’s low-A leagues from past to present, the 50 busiest starters had a strikeout rate of 19% in 2007 and 25% in 2022. The corresponding figures for the 50 relievers finishing the most games were 22% and 26%.

HBPs are kind of an afterthought in typical stat-watching, but they’ve risen greatly in recent years, and some pitchers are plunk-prone enough to seriously degrade their performance.

I tend to refer to these stats in rates per batter faced rather than per nine innings. Per-nine accounting can be skewed by the number of runners allowed. If two pitchers strike out a batter per inning, they obviously are striking out an identical amount per nine innings, but if one is allowing one runner per inning and the other two, the stingier pitcher has a 25% strikeout rate compared to the other guy’s 20%. That 5% is meaningful.

Opposing Slash Stats

The opposing batting line relates closely to the pitcher’s core peripherals. I mention it often and think it’s interesting. I wish it would gain more traction, but I’ll be the lonely standard-bearer.

Opponents batted ..216/.288/.342 against Cole Ragans in the upper minors last year. Now, he’s a Major Leaguer. Cole Winn’s opposing line was .265/.379/.429. The slugging percentage was about 20 points below the league average, so even with his troubles, he wasn’t getting crushed. The problem is the OBP, inflated by a 16% BB/HBP rate. Did I mention that walks can cause more trouble at the upper levels?  


Fielding is trickiest to evaluate from an outsider’s perspective. Fielding percentage rarely tells the whole story.

For example, over the course of a season, let’s pretend two infielders share shortstop duties equally. On their first 400 grounders, they’re identical statistically. But then on their next 20 grounders apiece, Shortstop 1 never touches a single one, but Shortstop 2 reaches all of them and turns 15 into outs and throws 5 into the stands, allowing those hitters to reach second. Shortstop 2 will have a worse fielding percentage, but he also turned 15 more balls into outs. Would you rather an opposing batter reach first safely 20 times, or reach second 5 times but get put out the other 15 times? Shortstop 2 is far more effective despite making more errors.

Even with no stats, you can learn plenty simply from where someone plays. For example, in 2021, Frisco had a quartet of Bubba Thompson, JP Martinez, Josh Stowers and Steele Walker for last season’s first 80 games. Who played CF the most? Thompson with 40 starts, followed by Martinez with 28. Walker made about three-quarters of his corner outfield starts in right, while Stowers worked each corner equally. The guy getting the most starts in center might not necessarily be the best on his team in that role, but at the least he’s who the front office wants to see there the most. (In this case, Thompson actually was the superior defender.)


Statcast data became available for Texas’s AAA league last year.

In the past few years, my means of tracking pitch speeds in Round Rock has upgraded from asking or eavesdropping behind scouts to checking the stadium gun on the scoreboard (which was inconsistent pre-Hawkeye/Trackman) to checking my computer or phone. Hallelujah. I also get horizontal and vertical movement and pitch categorizations (which aren’t infallible, especially with new and unusual pitchers). Did the starter emphasize a changeup and ditch his curve last night? Did he hold his velocity into the 6th? Is a hitter seeing a particular type of pitch more often? Now I have that info even if I couldn’t attend or follow the game.

Exit velocity and launch angles are also available. For my use, I created nine categories of velo/angle combinations. The first four are hitter-favorable and encompass solid contact: very likely homer, possible homer, likely double, and likely single. The fifth I call “lucky,” contact of below-average velocity hit at the right angle to fall between the infielders and outfielders, so gentle liners, flares, bloops.

Then, four pitcher-friendly groups:
U = “unlucky,” airborne contact at above-average velocity that usually ends up in a glove
G = “grounder,” not a great chance at a hit unless absolutely demolished (and I fit those in the “S” category)
SO = “soft out,” airborne but below-average velocity contact that doesn’t produce much
P = “pop,” not strictly infield pop-ups but any ball hit too high to leave the park; these are almost always outs, rarely more productive than a strikeout.  

I categorize every ball in play based on what it typically produced in the Majors, not in AAA. Plenty of balls that become extra-base hits in the hitter-friendly Pacific Coast League may just be long outs in a typical MLB park.

Is a player hitting the ball hard but too low or high? Are his homers more frequent in the “nearly certain” or “borderline” group? Did a pitcher who allowed five runs get nickled-and-dimed? All are questions I can analyze with this data.

Some quick thoughts about last year and the future based on this data:

Josh Jung’s walk/strikeout rate cratered in AAA, but his contact quality was fine.

Josh Smith didn’t hit especially hard but had a knack for a launch angle that produced plenty of hits.

Sam Huff’s rates of exceptionally hard contact and almost-sure-thing homer categorizations were an order of magnitude above everyone else.

As I’d mentioned, Winn caught my eye in Surprise and on Opening Day with a new pitch. I guessed cutter on my own, but it’s still nice to check the data and confirm its speed/movement and my belief that he didn’t throw it in 2022. Per the data (and a knowledgeable source), Winn is also generating more sweep on his slider.

Zak Kent’s AAA debut was terrific (1.67 ERA, oppo line .181/.278/.277) but his batted ball data told a different story. 12 contacted balls (one of every six in play) were in the possible or probable homer group, but only two actually left the yard. Opponents batted only .433 and slugged .667 on balls in a favorable group, compared to .679 with a 1.249 slugging percentage for Round Rock as a whole. In other words, given that type of contact, he was lucky.

Conversely, reliever Daniel Robert suppressed hard contact (23% of balls in play categorized in a hitter-friendly group versus 36% for the team) but defeated himself with walks. In his six multi-walk appearances, he allowed 16 runs. In eight appearances with one walk: eight runs. In 23 appearances with no walks: 5 runs.

Runs Scored, RBI, Pitcher Wins/Losses

No, no, no.