Rickie Weeks going to the Mariners moved a lot of eyebrows, raising some, furrowing others. Weeks’s deal will be worth $2 million for one year, according to Jim Bowden. To the casual fan, this move might seem a little unnecessary: Seattle already has a pretty good second baseman in Robinson Cano. If you take a closer look, however, there are some hidden metrics that would point to Weeks having a resurgence.

Let’s first look at this acquisition from the position of the casual fan. Weeks is coming off a 2014 season wherein he only had 286 plate appearances, and saw a substantial reduction in power. Long story short, Weeks was a singles hitter last year. In those 286 trips to the plate, Weeks had 41 singles. In 2013, Weeks had 42 singles in 113 more plate appearances. While this helped his overall batting average get back on track, from .209 in 2013 to .274 in 2014, it did nothing to increase his power numbers.

Weeks is also a below-average fielder. Scratch that, Weeks is the worst fielder at the second base position in all of baseball, and he has been for some time now. If we are going by FanGraphs’s UZR, Weeks has a career total UZR of -56.5 for his career. That puts Weeks right at the bottom as far as second baseman who have played more than 5,000 innings since 2005 (Week’s first full season). Below are the bottom five second baseman according to UZR in that same time frame. Recognize anyone?

Notice that current Seattle second baseman Robinson Cano is four from the bottom. This really doesn’t tell us anymore than that Seattle does not put a premium on defense, and we might have suspected this all along if we had first taken a look at team UZR from the last two seasons.

There we have it. A match made in heaven. It is no coincidence that two of the bottom five defensive teams over the last two years contained two of the bottom five defensive second basemen, in Cano and Weeks. So what does this all have to do with Seattle and their recent free-agent acquisition of Mr. Weeks?

Ceteris paribus. All other things being equal, meaning if we take defense out of the evaluation (because Seattle is not focusing on defense at the time), we can better understand what Seattle saw in this now 32-year-old utility man.

Our answers lie within the batted ball statistics. Over his career, Weeks has had a fly ball percentage of 35-36% consistently. Even in 2013 it was 32.7%. Last season that percentage sunk dramatically to a career low of 25%. This may or may not be a bad sign. We will come back to the fly ball percentage shortly. Now let us look at the HR/FB ratio statistic.

Last season Weeks saw a spike in his HR/FB ratio. It reached an all time high of 17.8%.  His career average for that metric is 14%. Knowing that his fly-ball percentage was at an all time low, with his HR/FB ratio at an all time high we can reasonably expect those two metrics to meet somewhere in the middle this upcoming season.

There is one last measurement we should look at in order to fully understand Weeks’s value possibility. Jeff Zimmerman and Bill Petti, of FanGraphs fame, run their own website, baseballheatmaps.com, where one can look at batted-ball distance for any player going back to 2007. When we look at Rickie Weeks, we see that he has a career average fly-ball distance of 292 feet. Last year, his average fly-ball distance was 285 feet. This slight decline is understandable due to the age factor. Weight this how you wish, but it doesn’t seem like Weeks is going through any more of a power decline than other professionals have gone through at his age.

Putting it all together, if Weeks starts to hit more fly balls, and (if nothing else) maintains his career average HR/FB ratio, the Mariners will reap the full value of his services. His defense is subpar at best, but Seattle does not seem too concerned about that. Right-handed power seems to be scarce at the moment, especially at the second base position. Rickie will add depth to Seattle, but the real value might come during the season when teams start looking for power to boost their playoff lineups—that is, if Weeks can deliver.

Now that the baseball season is over I thought I would throw together a little data breakdown of the 2014 playoffs according to the public StatCast records available. I created a rough relational database that will allow me to run a few simple queries to give us an idea of what information the new system will be able to spit out on a daily basis (fingers crossed, next season). I built the database with the anticipation of adding to the records next year as more data is released. I hope, eventually, there will be complete statistics available for each play because in the current format  there are many null values which drives me nuts, but it is what it is.

Seven tables make up the database that is designed to catch each play in it’s entirety. The four main tables are BATTING, FIELDING, PITCHING, and RUNNING. This is where all of the new fancy data is stored. Now as to not get further into the weeds lets take a look at what we got.

BATTING

First, lets look at  the batting statistics for each play in the playoffs monitored by StatCast (and revealed to the public) sorted by batted ball type. Please note each row is an individual play that was tracked and recorded during a given playoff game.

I purposely left the null values in the tables to demonstrate the inefficiencies that exist due to the lack of data for each play.

FIELDING

This is were the data starts to get a little more thorough. Once again the tables are sorted by batted ball type and each row represents a particular fielders input on a given play.

Rather than bore you to death with more tables I will just summarize the other two entities, PITCHING and RUNNING. To date, the RUNNING (base running) entity contains more records than any other aspect of the game. MLBAM has been extremely fond of recording players peak running speeds, which I find to be the least informative of the current metrics recorded. What intrigues me about the RUNNING aspect of StatCast are statistics such as a player’s average lead length on a steal and how that might correlate with SB% or which player has the quickest “first step” when stealing a base. I’m sure all of you have thought of countless other ways to utilize StatCast for base running so I wont go into a brainstorming session. Here are just a few quick facts about the base runners of the 2014 playoffs:

The average lead length by all runners was 10.89 feet.

The average secondary lead was 16 feet.

The player who reached the highest max speed rounding the bases was Jarrod Dyson at 22.3 mph.

Jarrod Dyson also had the fastest first to third speed at 21.1 mph.

The quickest first step came on a sac fly tag up by Hunter Pence. It registered at -.17 sec. I wonder if this means he left early?

For all of the talk about KC’s running game, the Giants actually had an average team lead length higher than KC during the playoffs and there was a decent number of records for each to substantiate it. (50 records for KC, 49 records for SFN)

SFN Average lead length in playoffs 11.1 feet

SFN Average secondary lead length in playoffs 16.4 feet.

KC Average lead length in playoffs 10.9 feet.

SFN Average secondary lead length in playoffs 15.8 feet.

The PITCHING entity is by far the most complete, but contains little data. As of today, MLBAM has used StatCast to track four pitching measurements, Extension, Actual Velocity, Perceived Velocity, and Spin Rate. To be honest I have never thought about two of these metrics and how they could affect a pitchers performance; those two being extension and spin rate. Extension might simply need to be recorded for each pitcher so that we could analyze trends. Say a pitcher’s average extension starts to decrease. What steps need to be taken to correct it? Could this be a sign of an injury? and so on. Fun fact, Yusmeiro Petit has had the longest extension recorded by StatCast at 92 inches. There is only one pitcher who has multiple records. Yordano Ventura has an extension of 60 inches and 68 inches. I wonder what the average extension range is for pitchers?  It would be interesting to find what affect the spin rate of the pitch had on batters. With more data, I might first start to analyze the correlation between spin rate and batted ball type. Currently, there is not enough public data available to be able to do this accurately.

I hope this was not too boring and at the least will spark your enumerative imaginations for this off-season.