Jon Niese's Strike Rates

by Eric Simon on Aug 3, 2009 12:00 PM EDT

Just a quick look at Jonathon Niese's strike distribution by pitch type. We're dealing with extremely small sample sizes here so it's probably not appropriate to draw any meaningful conclusions, but it's all we've got to work with so far. Niese throws a four-seam fastball (FF) a looping curve (CU), a changeup (CH), a cut-fastball (FC, which PITCHf/x mis-idenfities as a slider), and a two-seam fastball (FT). As a reminder, SwStr% is swinging strike rate, or the ratio of swinging strikes to total pitches thrown. ClStr% is called strike rate, or the ratio of called strikes to total pitches thrown. Averages for starting pitchers are included in the column labeled "League".

	FF	CU	FC	FT	CH	All	League
# Pitches	243	62	37	20	12	374	--
SwStr%	8.60%	6.50%	5.40%	0%	8.30%	8.00%	7.80%
ClStr%	19.30%	12.90%	13.50%	10%	8.30%	16.80%	17.10%

Niese is just above the league average in swinging strike rate, which is a good indicator of overall strikeout rate and general pitch dominance. The ability to miss bats is necessarily a good thing. Niese is just below the league average in called strike rate, which isn't terribly surprising; control and command are tools that arrive late for a lot of pitchers, and you'll notice that Niese's ClStr% on curveballs is well below the league; it's a feel pitch and consistent control will come with experience.

Niese's cut-fastball, which comes in a bit slower than his four-seamer and a touch faster than his two-seamer, is still a work-in-progress. He's not getting enough strikes on it in either of the swinging or called varieties, but, again, we don't have a lot of data to work with here. That his overall rates are near the league averages is encouraging given his age and limited big league resume. Good stuff so far from the guy who still needs a nickname.

Tweet Comment 11 comments | 0 recs |

Do you like this story?

Comments

Go Jona Finesse!

"I dunno. I never smoked any Astroturf"
-Tug McGraw

by squid92 on Aug 3, 2009 12:38 PM EDT reply actions

+1

"We're investigating the investigative procedure of the investigation of Tony Bernazard"---Omar Minaya (he really didn't say it but he would"

by firejerrynow on Aug 3, 2009 12:45 PM EDT up reply actions

Niese game, pretty boy!

does not his resemblance to the Simpson’s Cletus Spuckler suggest a nickname?

I.M. Forme
"When you get yourself into trouble is when you feel you have to do something, and then you get yourself in trouble." --Omar Minaya

by itsmetsforme on Aug 3, 2009 1:32 PM EDT reply actions

Incorrect Numbers

Your fastball distribution (2-seam, 4-seam, Cutter) is wrong and is probably affecting your results.

I have the full data on my computer at home but just looking at my two articles:
HERE:
http://www.metsminorleagueblog.com/2009/07/27/the-development-of-jon-niese-and-his-new-weapon-the-cutter/

and
HERE:
http://smartmetstalk.wordpress.com/2009/08/02/nieses-july-30th-start/

I can tell you the amount of each fastball he’s thrown over the last 2 starts. Niese threw 56 Cutters total this year (He didn’t throw it before these last 2 starts) and threw 18 two-seam fastballs in the last two starts.

If you’re using the BIS data or gameday data i understand the error, and i don’t think your facts are too wrong, but the numbers might be slightly skewed given that you’re off by 19 cutters.

by garik16 on Aug 3, 2009 1:38 PM EDT reply actions

The data is from PITCHf/x

Straight from the MLBAM data files (via brooksbaseball). I recategorized pitches identified as “sliders” to be “cutters” in general. I can’t say why your pitch counts vary so widely from BB’s from Niese’s most recent start.

You:

FF: 38
FT: 7
CU: 17
CH: 5
FC: 32

BB:

FF: 57
FT: 6
CU: 17
CH: 1
FC: 18

I’m just using these numbers.

by Eric Simon on Aug 3, 2009 2:15 PM EDT up reply actions

Eric, the data you’re using is using the gameday algorithm. Gameday is really really frequently incorrect, and if a pitch is fastball like, it’ll often characterize them as Four-Seamers by default. It’ll also characterize change-ups as two-seam pitches quite often.

What I’ve done in my articles is download the data myself and recatagorize them by using speed, spin direction, and spin magnitude.

You can see the spin data graphed at Brooks Baseball

http://www.brooksbaseball.net/pfx/spincorp.php?xml=http://gd2.mlb.com/components/game/mlb/year_2009/month_07/day_30/gid_2009_07_30_colmlb_nynmlb_2//pbp/pitchers/477003.xml&batterX=0&innings=yyyyyyyyy&sp_type=1&s_type=1

The pitches here cluster almost neatly (ALMOST because change ups and two-seamers look the same). The cutter is the pitch without much spin magnitude (rotation) that has a direction of roughly 180 or more degrees. The Four Seam Fastball is the pitch with more rotation starting at around 165 degrees, and the two-seamer and change has a similar amount of RPM as the Four-Seamer but clusters around the 130 degree mark.

by garik16 on Aug 3, 2009 7:01 PM EDT up reply actions

Corrected Data

Correct Info:

Change Ups:
#=22
4.545% Swinging Strikes
9.091% Called Strikes

Curve Balls
#=62
14.5% Called Strikes
6.5% Swinging Strikes

Cutter
#=56
12.5% Called Strikes
3.5% Swinging Strikes

Two-Seam Fastball
#=36
5.55% Called Strikes
8.33% Swinging Strikes

Four-Seam Fastball
#=191
21.4% Called Strikes
9.42% Swinging Strikes

by garik16 on Aug 3, 2009 7:13 PM EDT up reply actions

You're definitely right about one thing

“We’re dealing with extremely small sample sizes here so it’s probably not appropriate to draw any meaningful conclusions” – Eric Simon

Ahhh, but isn’t it fun to dream.

by djbutler73 on Aug 3, 2009 4:59 PM EDT reply actions

While inconclusive

This small sample does tell the story so far. In order to reach that potential that he’s built for, he’ll need to rock his best pitch, the curveball, as an out-pitch. Watching both his K/9 and the SwStr% as the datapool grows, we’ll begin to make actual conclusions about his progress.

by TheBigStapler on Aug 3, 2009 5:17 PM EDT up reply actions

the sample isn't as small as you might think

while he’s only pitched a few games, we’re observing each individual pitch. and niese has thrown nearly 400 pitches. still not enough, especially since we can’t make the assumption that his performance will be steady, but it’s something.

by T Pac on Aug 3, 2009 6:37 PM EDT up reply actions

If we were

only studying, say, the movement of his pitches, 400 might be a pretty good sample but we’re looking at a statistic that has a lot of dependent variables (hitters’ ability for example).

by TheBigStapler on Aug 3, 2009 6:53 PM EDT up reply actions