College pitcher development patterns

Last week, I presented some research on the aging patterns of college hitters. Of course, pitchers develop too, and it’s interesting to see how they do so at the college level.

If you want some background on the project, I encourage you to click over and read last week’s article. This week, I’ll cut straight to the chase.

To get us started, let’s look at some multipliers for a relatively select group of players. To zero in on a pool with a lot of draft-ready talent and avoid some potential pitfalls, we’ll start with those pitchers who mostly served as starters and faced at least 300 batters in two consecutive years:

Pair      Players   $HR    $K    $W   $HP  $BABIP  
Fr -> So      115  1.12  1.04  0.99  0.99    1.03  
So -> Jr      223  1.11  1.05  0.99  1.02    1.03  
Jr -> Sr      226  1.08  1.03  1.01  0.98    1.02

Each number in the table is a multiplier showing how much a rate changed from one year to the next. (The rates are calculated as outcomes per batter faced.) For example, it shows that the average sophomore gave up 12 percent more home runs per batter faced than he did the previous year.

… say what?!

In fact, it isn’t just the apparent increase in home run rate that raises eyebrows. The moderate increase in strikeouts each year is to be expected, but the changes in walks and hit batsmen are barely enough to merit the word “changes,” and while we might not anticipate a change in BABIP, a slight decrease would make more sense from pitchers who are (ideally, anyway) learning to coax more ground balls.

As I did with batters, let’s see what happens if we narrow the field down to those pitchers from the eight most competitive Division One conferences:

Pair      Players   $HR    $K    $W   $HP  $BABIP  
Fr -> So       35  1.15  1.03  1.03  0.96    1.02  
So -> Jr       78  1.12  1.04  0.97  1.00    1.02  
Jr -> Sr       82  1.09  1.05  1.00  0.95    1.01

With under 100 pitchers representing each pair of classes, there are some sample size concerns right off the bat. For the most part, though, these are the same results for all of Division One. I tinkered around with the parameters quite a bit, including and excluding relievers and varying the playing time minimums. No matter how you slice it, the results look roughly the same.

If we’re going to explain these counterintuitive findings, we’re going to have to look elsewhere.

Better pitchers, worse performance

In essence, the problem is simple. We assume that as players progress through college, gaining physical size, fitness, experience, and coaching, they improve. We certainly saw that in the results last week for batters. But here, we’re seeing numbers that suggest that performance is about the same from year to year, if not worse.

Here are a few ideas that may explain it:

  • As pitchers age, they learn to throw harder, and faster pitches, combined with aluminum bats, result in more power. This doesn’t hold true at the major league level, but when aluminum is involved, you never know. This theory is appealing because it could explain the increase in both home run rate and BABIP. Unfortunately, it leaves us saying, “Aluminum bats are weird, huh?” We’re unable to test the theory with any available data that I know of.
     
  • Some pitchers get overworked as underclassmen, resulting in lessened effectiveness the following year. This is a possibility, though I think that more and more coaches are aware of how to manage the workloads of young pitchers. The parameters of this study also exclude those pitchers who are overworked and then get hurt, thus missing the playing time cutoff of 300 batters faced.
     
  • As college pitchers post better results, they are moved into more prominent roles. Many freshmen are used as midweek starters and occasional relievers, while established pitchers start the higher-profile weekend games. This is intuitive, and best of all, it is testable.

Everybody’s working for the weekend

Especially in top conferences, the difference between weekend competition and midweek competition is substantial. After the first few weeks of the season, teams typically play three-game sets against conference opponents on the weekend and play a game or two against non-conference rivals on Tuesday and Wednesday.

If you’re playing in the SEC, one of the toughest conferences in college baseball, it stands to reason that weekend games are tougher than midweek games. As we’ll see, the difference can be striking.

With enough data on every pitcher, we can take “strength of schedule” (SOS) to a whole new level. Since hitters play every day, the specific mix of teams that a batter faces doesn’t matter much—it’s very close to the mix that the team faces. But for pitchers, it’s a different story. So instead of using the team’s overall SOS to adjust a pitcher’s stats, it’s more accurate to use a pitcher-specific SOS.

For example, here are SOS ratings for the mix of opponents faced by six different Louisiana State pitchers last year:

How an Ace Performance Impacts Reliever Workloads
Bullpenning has its advantages, but it's great when an elite starter eats up a bunch of innings, too.
Pitcher            SOS  
Louis Coleman     1.40  
Anthony Ranaudo   1.38  
Austin Ross       1.31  
Chris Matulis     1.17  
Ben Alsup         1.12  
Jordan Nicholson  1.07

Any guesses who started weekend games for LSU last year? These are huge differences. No team had an SOS as high as 1.31, while 1.07 is fairly common. Coleman and Nicholson may have pitched for the same school, but they were presented with very, very different challenges.

While many top prospects (like UCLA’s Gerrit Cole) spend their whole college career in the weekend rotation and thus don’t see the difficulty of their competition change much, that isn’t true for the “average” college starter. For instance, Blue Jays prospect Chad Jenkins had a 1.06 SOS in 2008 and a 1.15 SOS a year later. Matt Bashore went from 1.01 to 1.07. Diamondbacks farmhand Jordan Meaker went from 1.00 to 1.19 between ’07 and ’08.

The proof, or at least some pudding

For the pitchers in elite conferences, I re-ran the study using pitcher-specific opponent ratings, rather than team-wide numbers. Thus, if a pitcher’s numbers got worse simply because he faced tougher competition, that effect should be neutralized.

Does adjusting each for each pitcher’s competition make a difference? Well, it doesn’t solve the paradox of the rising home run rate, that’s for sure:

Pair      Players   $HR    $K    $W   $HP  $BABIP  $SOS  
Fr -> So       35  1.13  1.04  1.01  0.95    1.01  1.01  
So -> Jr       78  1.12  1.05  0.97  1.00    1.02  1.01  
Jr -> Sr       82  1.09  1.04  1.00  0.95    1.01  0.99 

The results are about the same. They are a tiny bit more appealing than the last set of numbers, but only a tiny bit.

As noted in the “$SOS” column, the average starter doesn’t see his competition change that much from year to year. I assumed that a large number of hurlers moved from midweek gigs to weekend assignments. That might be true, but perhaps many of the midweek starters, in their roles as swingmen, don’t get enough innings to show up in these results.

For whatever reason, the counterintuitive conclusion remains. As college starters develop, they give up more home runs, even adjusting for the specific opponents they face. As if college baseball didn’t hold enough mysteries already, we can add one more to the list.

References & Resources
Thanks to John Barten and Josh Kalk for their help.


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Gilbert
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Gilbert

If there was a way to get stats of players in general who faced each other over 20 times in a 2 year period, whether the rates are different for the first 5 PA, 2nd 5, next 10.
Possibly the first few matchups the batter wants to take a few pitches unless they are fastballs in his zone.  When he has faced him a couple of times he might guess and try to power one, resulting in a few more HR and K.
If that is a general strategy, that would cut down somewhat on HR against new pitchers.

Duke
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Duke

The question about the effect of aluminum bats on these statistics might be answered by looking at stats for the summer wood bat leagues, if you can find them.

Jeff Sackmann
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Jeff Sackmann
Thanks guys. What @Gilbert and @Jonas Fester are suggesting is somewhat similar—that familiarity makes it easier to hit a pitcher.  That’s intuitive; I’m guessing it’s been tested with pro data. It’s tougher to test with college data, since the season is shorter and teams face each other much less frequently.  I’m not sure if there are many college matchups, especially in competitive D1 conferences, that happen 20 times.  Even 10 might be a stretch—figure three PAs against per game, we need 3 or 4 games, and unless teams face each other during the regular season *and* the postseason, that’s just… Read more »
Jonas Fester
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Jonas Fester

Just a quick thought for another reason to explain it.  The longer they are in the conference, and facing the same teams and batters, the more in-depth the scouting report gets, so the hitters are more prepared.

I think you’re idea about the players getting overworked early is a good one though.  Probably one of the most likely.  As a college coach, this is great info!

Jonas Fester
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Jonas Fester

Thanks for the nice words.  The crowd at appleton was great when I was playing there in ‘08.  Some really quality people in that area.  It was quite an experience.

Chez Angeloni
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Chez Angeloni
NCAA coaches micro manage pitchers. On the surface, the argument holds for simply calling pitches and dominating the pitchers’ decision making process. On a broader scale, and more to the point, NCAA coaches inadvertently sap athleticism from their athletes’ deliveries over time with the altruistic goal of increasing command and making them more polished. The team metrics and internal competition overvalue command to such a degree that it’s only natural for the athlete to slow down, be more careful and deliberate and try place the ball in the zone. This leads to pushy throwers. College pitchers slowly lose momentum in… Read more »
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