# Strike Throwing – Part 1 – Lots of Tables

1. Strike Throwing – Part 1 – Lots of Tables

The Tigers walked a lot of people last year. Along the way they threw a lot of pitches, and many seemed to be ill advised. The performance cost Chuck Hernandez his job, jettisoned in favor of an instructor whose students have gone on to gain some renown as strike throwing machines. Armed with a season’s worth of pitch f/x data I’m ready to start delving into this whole strike throwing thing. We’ll start today with some general league wide information.

For those unfamiliar with pitch f/x I’ll have some additional links to more information at the end of this article. The short explanation is a couple of cameras measure the direction and speed a ball is moving shortly in front of the mound. From this the pitch’s path is calculated to within an inch of where it crosses the front of home plate. And it draws the trajectory in the MLB.com Gameday application. On to the data…

Starting at the most simple level, let’s look at the respective rates at which teams threw balls and strikes. The table below shows the breakdown for all 30 teams. (These data are from pitch f/x, which doesn’t capture every single pitch, but should be sufficient).

No surprise that the Tigers rank near the worst in the league in terms or throwing balls. But what is surprising is the difference between the Tigers and the best. The best as you may notice are the Twins, but that gap is 4%. When you have 125 pitches that is a difference of 5 pitches a game being balls instead of strikes or in play. Now it only takes 4 pitches to walk a batter, but those pitches are most likely not all going to one batter. And the difference between the Tigers and the Twins in terms of walks was 238 this year. Put otherwise, the Tigers walked 58% more hitters than the Twins.

You may have also noticed from the table that the Twins lead in another category besides fewest balls thrown. They were first by a substantial margin in terms of their pitches being put in play. It speaks to the organizational philosophy about early strikes and getting the opponent to put the ball in play. For this we’ll move onto the next table which simply looks at how often the ball is thrown in the strike zone.

InZone is simply whether or not the ball crossed the front home plate in within the strike zone. The strike zone is of course 3 dimensional so the ball could clip the zone later, but this is what we have to work with. The Tigers are in the bottom third when it comes to putting the ball over the plate. The Twins are first which explains the lack of walks and the high number of balls put in play.

One other column I added was a buffer zone. I added 1.5 inches to each of the 4 sides of the strike zone. I wanted to see how how often pitches were either in the zone or “close” to the zone. The types of pitches that might entice someone to swing because it’s too close to take. The Twins have a lead here too, but the rankings don’t really change to much across the board so it doesn’t seem that teams vary too much in their ability to throw pitches near the strike zone.

The last table we’ll turn to is the product of some work Dan Fox did with pitch f/x data and is called Fish-Eye. Fox did this to look at batter plate discipline, but I turned it around to see if certain staffs could render hitters less disciplined. The column headings are defined as:

* Square: This is the new metric, defined as the percentage of pitches in the strike zone swung at and made contact with. A high value here (relative to the average of over 86.3 percent) indicates that when the batter offers at a strike he usually makes contact. On the contrary, a lower value indicates hitters who, for reasons such as a long swing, are more apt to swing through strikes.
* Fish: Defined as the percentage of pitches out of the strike zone that the hitter swung at. A higher percentage here indicates that the hitter may have trouble recognizing pitches since he is offering at pitches that would likely be called balls. Average values here are between 29 and 30 percent.
* Bad Ball: Defined as the percentage of pitches out of the strike zone that were swung at where contact was made. This includes foul balls, although there is an argument to be made that a foul ball is not the intended outcome, and so should be discounted in some way. A higher value in this category indicates that, when swinging at bad pitches, the hitter is at least able to get the bat on the ball. Average values lie around 68 percent.
* Eye: Defined as the percentage of pitches in the strike zone on non-three and zero counts that were taken for strikes. A smaller value in this metric indicates a player who recognizes strikes and aggressively offers at them. I excluded 3-0 counts, since a hitter is much more likely to let a strike go by in this situation, and we donâ€™t want to penalize them for that behavior. (I didn’t exclude these counts). Average values here are in the range of 35 percent.

I’ve also added Contact which is the percentage of time a hitter swings and makes some sort of contact.

Earlier we saw that the Twins got a high number of balls in play on their pitches but weren’t hurt too much because of it. Part of the reason is that there weren’t free passes putting guys on. But perhaps another reason is that they have a lot of hitters chasing pitches that are off the plate and not perhaps not making solid contact. It’s a recipe for success. The Twins throw more pitches in the strike zone than anyone, and they get hitters to chase more pitches off the plate than all but one other team.

But oh yeah, this is a Tigers blog. Forgive me if I look longingly at the Twins and with great hope given that the Tigers new pitching coach was heavily involved in development of the guys who did the work – Rick Knapp. As for the Tigers on these measures, they were pretty vanilla and close to the means. The exception was in Bad Ball where hitters made consistent contact with balls out of the strike zone. I don’t know if this is good or bad – yet.

Next steps are to look at the Tigers as individuals, and to look at location and results by count.