Young Pitchers and Complete Games

A couple of weeks ago, I was helping fellow writer, Chris Cwik, look for possible reasons James McDonald fell off the cliff in the second half of the 2012 season. As Cwik pointed out, the 27-year-old McDonald started falling apart after he threw 122 pitches for his only complete game (CG) of the season. I decided to see how young pitchers performed after throwing just one complete game during a season.

I know pitchers, and their managers, would love to go out every night and throw a complete game. With the increased patience batters have in the league now, complete games are becoming rarer and rarer. When a young pitcher has a chance for a complete game, especially a shutout, managers will often give the kid every opportunity for the achievement.

Back 2001, Chad Durbin, who was with the Braves last season, was a promising starting pitching prospect with the Royals. He was 23 years old and in his first full season. Going into the July 28 game against Oakland, he was struggling a bit with a 6-9 record and a 4.76 ERA. On that night, he ended up throwing 137 pitches for a complete game (not even a shutout). After that point, he was never the same. His ERA jumped to 5.59 over the rest of the season and he spent 2002 on the disabled list. He was released by the Royals at the end of 2002.

I don’t like to base conclusions on a sample size of two with one case being 10 years old. To get an idea of a young pitcher’s results after they throw a CG, I selected the following players:

* 27 years and younger
* 1 CG in the season. I limited it to one game to have a nice before and after set of stats.
* The CG wasn’t in the first 2 or last 2 games of the season.
* The CG went 9 innings

Note: Before going on, I looked at two other ideas, did the number of pitches in the CG matter and did their disabled list chances increase. The number of pitches thrown in the CG didn’t matter at all with my sample. Second, not an abnormal amount of the pitchers went on the DL.

From 2011 and 2012, 30 pitchers met this criteria. I looked at their stats before the CG, the first five games after it, and over the rest of the season. I collected the average change and the median change. Because of the small sample size of games before and after the CG, the median value may be more useful. The CG itself was not used in any of the calculations.

Let’s start with ERA.

Time frame: Average ERA, median ERA
Before: 3.91, 4.05
After (1st 5 games): 4.53, 4.55
After (Rest of Season): 4.26, 4.23

Immediately after the CG, the pitcher saw around 1/2 point jump in ERA and 1/4 of a point over the rest of the season. Now, time to look for some causes starting with strikeouts and walks

Stat Change From Before CG: Next 5, ROS
K% (average): +.95%, +.65%
K% (median): -.24%, -.21%

BB% (average): +.17%, +.54%
BB% (median): + .22%, +.27%

By looking at the median values, there is some change for the worse, but nothing close to explain the large increase in ERA. Now, here are the changes in batted ball data:

Stat Change From Before CG: Next 5, ROS
BABIP (average): +.026, +.018
BABIP (median): +.022, -.001

HR/9 (average): +.26 +.29
HR/9 (median): +.07, +.19

Those numbers begin to explain the changes. The pitchers got hit around a little more after the CG. Their BABIP before the CG averaged .288 and it grew to .314 for the first five games and then back down to .305. Their HR/9 went from .86 to 1.14 back to 1.07. It seems like before the CG the pitchers were a little lucky and were a little unlucky after the game.

Putting all the data together, it looks like pitchers going into the CG had been a bit lucky on batted balls. They seemed to be pitching better than they actually were. The manager let them go long for the complete game. For a few games, the pitchers performed worse and then they got back into a semi-normal groove. Using this data, an fantasy owner can try to target pitchers who may seem to be struggling after a CG and pick them up on the cheap.

If a young pitcher throws a complete game during the season expect them to perform worse after the game. Most of the time, they will just regress back to their mean/expected performance. During this time, these players may end up being picked up at a discount.

Jeff, one of the authors of the fantasy baseball guide,The Process, writes for RotoGraphs, The Hardball Times, Rotowire, Baseball America, and BaseballHQ. He has been nominated for two SABR Analytics Research Award for Contemporary Analysis and won it in 2013 in tandem with Bill Petti. He has won four FSWA Awards including on for his Mining the News series. He's won Tout Wars three times, LABR once, and got his first NFBC Main Event win in 2021. Follow him on Twitter @jeffwzimmerman.

Newest Most Voted
Inline Feedbacks
View all comments
Dave Studemanmember
9 years ago

Interesting study. Is there any reason to think a pitcher has an ERA below his true talent in the games before a complete game? Might an alternative explanation be that the complete game impacted his effectiveness in both the short and long term?

Also, I think these results put a lie to the notion that BABIP is always the result of luck. If BABIP has a strong tendency to rise after a complete game, seems to me this indicates a “true” driver of BABIP, related to arm health.

9 years ago
Reply to  Dave Studeman

Perhaps advanced scouting more than fatigue? If a guy comes to town having just thrown a complete game, the opposition might put a little bit more work into reading up on him and watching tape and all that. He’s found something he’s had some success with, and if he’s getting results, he’s not going to switch it up as much, making it much more exploitable. As teams catch on and he doesn’t have this success he starts making changes that again begin to fool batters. It seems like there might be a cyclical nature to it, and how fast a pitcher adjusts probably plays into how good he is overall.

Which pitches were these? mostly middling guys or were there aces in there? Maybe it’s not ‘young’ pitchers but ‘average’ pitchers? Curious what the numbers look like for all pitchers. Also curious what happens if you take out the 1CG thing. if the sample around said CG happens to include another one, that should play may get a pitcher twice or more in the same season with overlap, but it seems like you’d be eliminating better pitchers by limiting it to one.