A look at strikeouts

By: Jared Ellis

Follow on Twitter @jarlyjarhead

Baseball is a simple game. You throw, catch, hit and run. The team that scores the most runs wins the game. The winner of the last game of the season is the champion. When put simply, yes, baseball is a simple game. When put less simply, it is anything but. The statistics that were on the back of my childhood baseball cards of Batting Average (AVG), Earned Run Average (ERA) and home runs (HR) have evolved into statistics that aren’t as intuitive. Now we analyze the game using Weighted On-Base Average (wOBA), Weighted Runs Created (wRC) and perhaps most importantly Wins Above Replacement (WAR). These and many other advanced statistics are the new normal for the ever changing sabermetric baseball world.

Thing is, I don’t know how to analyze most of these statistics; at least not as quickly as I would the now arcane statistics. They require reading and sometimes rereading the description before fully grasping the stat. And then after a few months, I’ll probably need a refresher of reading and rereading the description again.

The question I ask myself with most of these stats is:

  1. What new stats are the most important?
  2. When can we trust the stats?
  3. What is league average for these stats?

The answer to the first question is that there probably isn’t only one way to analyze a player. Sure WAR encompasses all that a player does, but there are inconsistencies with it. The first and most obvious inconsistency is that there are multiple versions as both Fangraphs and Baseball Reference have their own formulas. In addition WAR uses defensive metrics that vary, as both Ultimate Zone Rating (UZR) and Defensive Runs Saved (DRS) are considered. Not only do they have different ways to measure performance but are often times far apart in the actual analysis on an individual player. In addition, WAR doesn’t tell specific stories about the player’s statistics, it is simply the long range view. The reason why a hitter struggles against right handed pitchers will require some additional digging.

Questions two and three are what I hope to cover over a period of time on this blog. The sabermetric community will often times cite small sample size (SSS) when analyzing a player. What is so neat about SSS is that someone incredibly smart has found out when a specific stat will stabilize (you can find the tool here) and becomes a large enough sample to have proper context. And to find league average of each these stats, we have decades of data to apply to what is now a very large sample size.

To begin in this young season of only April, a time when a more casual observer will say that the Milwaukee Brewers are destined for a championship and that Charlie Blackmon will be our first .400 hitter since Ted Williams, we have two stats that have stabilized for most regular players around the league. The first we’ll cover is strikeout rate, a stat that stabilizes at 60 plate appearances. Since this is a Seattle Mariners blog, we’ll stick to them.

Of the Mariners 25 man roster, only eight have qualified for stabilization of strikeout rate. This shows not only how young our season is but also shows consistency with Lloyd McClendon’s daily lineups. And out of those eight, a whopping five are higher than league average in 2014…that’s a bad thing.

K% 2013 2014
League Average 19.90% 20.90%
Corey Hart *24.3% 18.80%
Mike Zunino 25.40% 29.70%
Justin Smoak 22.80% 23.80%
Robinson Cano 12.50% 13.30%
Kyle Seager 17.60% 21.50%
Dustin Ackley 16.90% 18.40%
Abraham Almonte 25.60% 35.10%
Brad Miller 15.50% 30.20%
*2012

My first takeaway from this data is, oh geez this is bad. The youngest of the Mariners’ players are the ones that seem the worst off. Brad Miller’s strikeout rate has nearly doubled since his short stint with the team last year. Mike Zunino has regressed as well and sweet Christ look at Abraham Almonte! This guy is our lead off hitter and he strikes out more than a third of his at bats.

Let’s take an even closer look at these three folks. For a guide on these fancy pants stats, go here.

2014 O-Swing% Z-Swing% Swing% Contact%
League Average 30% 65% 46% 81%
Mike Zunino 47.20% 76.90% 62.70% 66.70%
Abraham Almonte 19.50% 59.10% 39.70% 72.00%
Brad Miller 41.60% 62.10% 51.90% 74.50%

Zunino can be explained as a swinger with limited pitch recognition, the dude swings at everything. His contact rate will obviously be low when he’s swinging at nearly half of the pitches he sees that are out of the strike zone. I also mentioned in an earlier post that he has a high swing and miss rate on fastballs, which explains his high swing rate for pitches in the zone coupled with having a low contact rate.

Miller has pitch recognition issues as well, as his O-Swing% is nearly as bad as Zunino’s. That also explains his contact problems and I think that this is representative of both players being very young still.

Almonte is an interesting case. I think he simply has trouble making contact. His O-Swing% explains why he is at the top of the order as he has a strong understanding of what his strike zone is and while walk rates haven’t stabilized yet for this season he has a history of promising walk rates in the minors. He simply has trouble making contact, once again I believe this is mostly a sign of him being a young player that is still getting used to big league pitching.

While those three young players are concerning and are certainly the outliers when looking at their strikeout rate, it is encouraging to see Dustin Ackley maintaining average strikeout rates considering his struggles up to this point. In addition, Corey Hart has seen a significant dip since his last full season in 2012 and the dude is starting to rake, also an encouraging sign as he is sorely needed in the middle of that lineup.

The past week of Mariners baseball has been tough and it has been the same old story, they can’t score runs. I get the feeling though that as these young guys start to settle into the season and feel more comfortable, that the strikeout rates will drop, the contact rates will increase and we’ll start to see some hits drop in.

Next up on my sabermetric tour will be the counterpart of strikeout rates for batters, but with pitchers!

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