44%. That’s how much better the new guys are than the old guys. Sound good? Now you can say that next time Bob from HR who loves his WAR-doopie asks "How come those M’s got rid of Smitty? Who is this Haniger feller? Isn’t Dyson a vacuum?" Okay, on with your day.

(Quick about me, I am not a writer and this is the first "big" public piece I have written. I am a computer engineer who happens to love baseball so that should explain everything you need to know about the quality of the writing here. Constructive criticism welcome in the comments.)

All right, let’s break this down. I wrote a bit in the comments a few weeks ago about how Jerry has been hoarding defensive first CF’s all offseason and maybe it correlates to some market inefficiency he’s trying to exploit. This Hardball Times article seems to agree that outfield defense is the new cool shiny thing that can give teams an edge. Personally, I think OF defense is going through the same process pitch framing was a few years ago: a stat that was always recognized as having value but its true value wasn’t really recognized until further analytics were available. GM’s have more data than ever with StatCast and I have a dream the Jerry and Co. have been able to quantify OF defense in a way that shows why they have 5 starting-caliber CF’s on the roster.

These newfangled baseball charts from Baseball Savant have been floating around the blogosphere which help to visualize an outfielder’s defensive value. The Mitch Haniger 40-in-40 gave a pretty good summary and intro to these charts. Basically they are meant to show all balls in play an outfielder had a play on based on their hang time and the player’s starting distance from the ball’s final landing spot. There’s one to show catches and one to show hits and the points in the graphs correlate to what the league average "Easy, Routine, Tough, Highlight and Uncatchable" balls are. Pretty self-explanatory once you see them.

What Baseball Savant doesn’t do is let you combine these charts with multiple players or show you them for an entire team. They’re brand new for this season but maybe that will be a feature in the future. They did make me start wondering, however, what the difference might be between the Mariner’s 2016 OF defense and its possible 2017 defense, based on these charts. So, while using some photo manipulation software, and some good old fashioned manual counting, I have combined the charts for players the Mariners fielded in the OF in 2016 and for those they hope to field in 2017 and tried to draw some conclusions from that.

Before we get to that though, there are a few disclaimers I want to address. For one thing, this analysis is RIFE with small sample size. These charts only show data from 2016 (since that's all that's available) and as such, some rookie players (like Haniger) only had 50ish balls in play while someone like Aoki had 238 chances to analyze. The charts also don’t tell you where or how the ball landed, only how long it was in the air and how far the fielder was from it, so not all points on the graphs are created equally. Finally, the charts don’t take into account a corner spot vs. center field, so there might be some bias there as well. That out of the way, I think this give a pretty good rough and cut view of the rewards the team can reap this season from its beefed-up defense.

Let’s look at what I uncovered. Basically, I combined everyone’s chart who played in the outfield for the M’s in 2016 with the charts of those who are projected to play in the OF in 2017. Below is a list of the 2016 M’s OF’s:

By taking everyone’s chart from their Baseball Savant page and using some "fancy" photoshop, I was able to combine the two charts from everyone on this list to create the below graphic.

Link

Right off the bat, there are about two dozen hits that should have been "Easy" or "Routine" catches. So the data is already visualizing why the M’s finished 23rd in URZ/150 last year (Imagine the defense without Martin last year. Woof.)

Next, let’s do the same thing with the projected 2017 OF. For this chart I used the data from Gamel, Heredia, Martin, Dyson, and Haniger. Remember that this data is also only their charts from 2016 so Gamel, Heredia, and Haniger are fairly small sample sizes.

Link

While there is less data, you can already see a shift in the points. The "Hits Allowed" graph shows fewer points in the "Easy" and "Routine" areas which is already a good sign.

So to try and visualize this even further, I combined the "Hits Allowed" and "Catches" graphs into one for each data set and put them side by side with the originals below:

Link

Now we’re getting somewhere. The combined graphs put the two original charts together onto one plot with "Hits Allowed" represented in green and "Catches" represented in black. Just by looking at the chart you can already see more green dots clustered in the "Tough", "Highlight", and "Uncatchable" sections for 2017 than for 2016. Which is good, this shows that the 2017 team would have only really missed "Tough", "Highlight", and "Uncatchable" catches and in general let fewer hits through.

Charts are great for high level but can we quantify this? I spent two hours counting the points on each player's chart for 2016 and combining them into an Excel spreadsheet for deeper analysis (I am assuming LL will be reimbursing me for the hours, so please contact me regarding where to send my free membership). I organized each player as such:

This quantifies each chart’s data points (if this is available somewhere on Baseball Savant and i just missed it please let me know so I can go cry over all the time I lost manually doing it).

So when trying to figure out what is useful from this, I first removed all "Uncatchable" data. Only 2 "Uncatchable" balls were caught all season in this set (Good job Martin and Heredia!) so I created the "Plausible" row which basically removed all "Uncatchable" balls from the equations.

Next, I grouped the "Easy" and "Routine" rows into an "Easiest" row and the "Tough" and "Highlight" rows into the "Hardest" row. I figured it was important to look at how much a player missed a catch that they were expected to make and how much a player made a catch they weren’t expected to make. In this example, Guti caught 98.89% of the balls he was expected to make and 28.13% of the catches that he wasn’t expected to make. For future reference, I am going to call these numbers Easy Percent Catch (E%C) and Hard Percent Catch (H%C) (Remember, too, that hard and easy correlate to the difficulty of the catch, not the velocity of the ball off the bat). Martin for comparison had a 97.24 E%C a 49.40 H%C total last season so he caught roughly twice as many balls he wasn’t expected to catch as Guti did.

So now that we have our metrics for comparison, let’s combine everyone’s data into two data sets: The 2016 team and the 2017 projected team:

So boiling it down, the 2016 team had a 97.25% E%C rate vs the 2017 teams 97.74% E%C. Hmmm, this doesn’t seem like a huge difference, about a half a percent. Given that the 2016 had 874 chances, the 2017 would have "hypothetically" missed ~19 vs the 2016’s team 24. So five-ish "oopsie daisies" avoided. That probably translates to a few runs but nothing substantial. So let’s look at H%C rate.

The 2016 team only had a 40.69% E%C rate vs the 2017 team’s 61.04% rate. That’s a much better number! However, from these graphs, it seems balls fall into the "Easy"/"Routine" category 3 times as much as the "Tough"/"Highlight" category, so the ~20% difference won’t as impactful as if it had been 20% in the E%C category. That being said, the 2017 team would have caught 177 of the "Tough"/"Highlight" balls to the 2016 team's 118 catches. 59 extra hits against the Mariners last year is nothing to sneeze at. I am not exactly sure how to quantify it to runs/wins but I think it’s fair to roughly quantify this at 3-5 wins. Nice! So by catching about 20% more of the balls a league average OF’er might not catch, the M’s are having a quantifiable improvement.

So from this analysis we have determined roughly that most players catch what they are supposed to catch, but it’s the tough catches that separates out the cream of the crop. Makes sense; even if a bad player misses more easy plays, they don’t miss enough to make a huge difference (although it can feel like it).

Important to note is that both of these teams had Martin, who really helped buoy the 2016 defense, along with a bunch of the rookies. But how much of an improvement are Dyson and Haniger over the old guys? I ran this same process comparing the old guys getting shipped out of the outfield (Aoki, Smith, Guti, Cruz, O’Malley) to the new guys who will replace them (Dyson, Haniger). The results are even more dramatic:

Link

There is almost NO green in that new guys chart (except the "Uncatchable" section) so right off the bat we can see a pretty dramatic shift.

Of important note, Haniger and Dyson BOTH had the highest E%C and H%C out of anyone in this group.



E%C H%C Haniger 100.0% 77.8% Dyson 97.83% 77.50%

Haniger only had 50 chances this season so again "SMALL SAMPLE SIZE WARNING" but come on man...

...believe in something:

The two worst (a surprise and not at all a surprise) from last year were Guti and Cruz:



E%C H%C Cruz 97.52% 28.57% Guti 98.89% 28.13%

Side note: Guti will always have a special place in my heart (in fact I actually have his helmet on my shelf from this transcendent 2015 walk off) but this is further proof that he has seriously lost a step. I don’t know how he would fit on the roster this year beyond being stashed in Tacoma in case the team needs a DH/1B bench player later in the season, but I still have a dream of him coming off that bench and hitting a clutch double in the wildcard before riding off into the sunset.

Anyways, using our calculations from before, the 2016 Old Farts had a E%C of 33.51% to the New Kewl Kidz™ 77.55%(!). That’s a 44.04% upgrade and would translate to 82 hits prevented if Haniger or Dyson had been playing all the chances those guys had last year. Hot damn, that’s a pretty big upgrade from who was pumped out there last year.

So it does look like Jerry knows what he’s doing. By my rough calculation, the guys he brought in would have prevented 82 hits the other guys let in. From an offensive standpoint, we aren’t losing Cruz’s production (yay DH!) and I’d wager Haniger’s production is at least equal to Smith and Guti without having to be platooned. Aoki and Dyson are a wash.

One last note would be that most of Haniger and Dyson’s numbers come from CF. There is a possibility putting them in the corners only increases their defensive production.

Ok so 44% isn’t a straight answer but hey that’s why you’re supposed to always finish articles, right? But the outfield is really shaping up this year and I think the defense will carry the team quite a bit. If Martin can continue his power production and Haniger lives up to his potential (while Dyson, Gamel, and Heredia keep doing their things), look out. It is also comforting to know they are all under control for several years to come and at dirt cheap prices. Here’s hoping the OF is a strength for years to come instead of the black hole we don’t talk about from 2013.

As far as talking to Bob in HR about how this analysis applies to outfield? I’d say there probably won’t be a noticeable less amount of this overall:





But there will probably be a lot more of this:





End note:

I attached a link to my Google Sheet where I have the rest of the tables on the players on this analysis. Feel free to play with it as you will. Again, I had to manually count these points from the charts since they aren’t on Baseball Savant (hopefully they will be in the future) so there is a tiny bit of error if I missed some points. Additionally, I had to trick the site to give me some players (namely Nelson Cruz’s) defensive data since they aren’t treated as OF’ers. Basically go to the player's page and add "&tab=defense_tab#" to the end of the URL. If you have trouble, let me know and I’ll help in the comments.





(Edit: Jeff Sullivan wrote at Fangraphs today about the most improved defenses for next year. Looks like it'll be the M's and this post supports that!)