2063 - A Different Look at Framing

[RonCo]

The FQOTD (fun question of the day) on my mind for this day is: does catcher framing matter in OOTP? You might find this odd, because I believe it to be a universal truth among OOTP GMs that framing does, indeed matter. I’m not going to argue with that at all here. What I’m doing here, though, is a fun exercise with OOTP game log numbers to see exactly how it matters—or more specifically, if framing actually seems to influence OOTP umpires.

I got the idea the other day as I was fiddling with pitcher balls and strikes, that using the game logs I could compare catchers similarly. In reality, this is a “well duh,” but you can say that about a lot of the silly studies I do with my game log data—there are soooo many things I can now study that I just never thought to study before. But now questions come up, and I think “hmmm…I wonder what the granular data says about that?”

So I go look.

Here is just the latest effort.

First, my supposition:

I proposed to myself that a catcher who was good at framing would have a better Called Strike to Ball ratio than one who did not, or in other words, would get more strikes called on pitches in which that batter did not swing. My view on this is that the umpire only calls and strikes on those pitches, hence these are the only places catchers can steal, or give away, strikes.

Given the above I went through the logs and made a pivot table to break this data out by individual catcher. In this case, I split them also by team. So, a guy like Rhys Atticus, who has been traded in mid-season, will appear twice. I then added in each catcher’s current Framing Rating (note, this could have changed during the season, but it is what it is right now. Once this was done, I sorted the collection, and plotted the data.

Much fun, right.

I’ll put the entire table below, but for now, let’s do a few things. First, here are”


The Top Ten Framers (by CS/(CS+B))

Catcher	Team	FRM	%CS
J. Hernandez	Charm City Jimmies	10	33.45%
P. Jimenez	Bikini Krill	10	32.03%
C. Marcillas	Twin Cities River Monsters	10	32.00%
R. Watson	Calgary Pioneers	9	32.00%
J. Smith	Cape Fear Swamp Foxes	8	31.87%
J. Behnke	Montreal Blazers	8	31.54%
A. Uecker	Portland Lumberjacks	11	31.46%
R. Fernandez	Charlotte Flyers	8	31.44%
A. Beaman	Louisville Sluggers	8	31.38%
M. Cantu	Nashville Bluebirds	9	31.30%

Well, now, that’s interesting. The three at the top are “10” framers, and Portland’s Alan Uecker at “11” is in the mix. It’s interesting that there are four “8s” in the mix, too. How much of this is simply catching guys with greater Stuff? To do this really wellwould need a controlled environment and carful matching of pitchers and catchers, but that’s not what I’m doing here because that’s no fun. What’s fun is to see Cape Fear’s Jose Smith and his “8” framing sitting at #5 on the list and asking ourselves if that’s due to sample size or CPF pitching, or what. Perhaps their opponents simply take more good pitches … who knows?

Regardless, I’m happy to see Bikini’s Pedro Jimenez on the list.

Next, I looked at …


Yellow Springs’ Augusto Annis and Vancouver’s Rhys Atticus


These are the guys who have been with two teams, hence have two different performances. I wondered how different their performances were. Bottom line, Atticus’s time in Long Beach and Vancouver are similar (ish), whereas Annis has been night and day different. Annis, while in San Fernando, was horrible this year, but has been quite solid in Yellow Springs.

Is that related to pitchers and control? Probably. Or maybe. Or maybe it’s due to ballpark effects, too. Perhaps the “Average” BP Factor is not all about BABIP. I don’t know.

Here are their lines:

Catcher	Team	Balls	Called Strikes	Total	FRM	%CS	2 Teams
A. Annis	Yellow Springs Nine	1470	660	2130	10	30.99%	<<
R. Atticus	Long Beach Surfers	2744	1174	3918	10	29.96%	<<<
R. Atticus	Vancouver Mounties	544	226	770	10	29.35%	<<<
A. Annis	San Fernando Bears	614	218	832	10	26.20%	<<

Interesting, eh?


And, Finally ...


Finally, I took all the catchers and plotted their Framing Rating against their Called Strike %, which shows that there is, indeed, a trendline up associated with the Framing rating, but the fact is that the other aspects of the game engine that affect such metrics as balls and strikes seem to add a lot of noise into the conversation.

There’s also the question of: How much, in real effect, is an extra strike or two worth in OOTP? Which is a question that makes a difference in real baseball, but may or may not make a difference in the sim. Unless maybe it affects pitch count?

I don’t know. I’m making stuff up there.

Regardless, the chart is interesting to look at:

One assumes that as sample size increased or as other elements were controlled for, some of the noise would go away. But as with all things baseball (and OOTP), stats are created in a very messy mishmash, and this data shows that by my ginned-up metric, in practical applications in “real life” OOTP games even a “7” in some situations can outperform a “10” in others.


Summary

Like I said, this is mostly for fun. I have no idea what any of it really means, and to be fair I’m assuming that this information is back-calculated by OOTP to some degree—meaning that the game is most likely creating data based on ratings, but also based on the fact that it almost certainly already knows the base outcome of the plate appearance before it gets around to this aspect of the game.

Full and open disclosure, though, I could 100% be wrong about that. Feel free to dig deeper and come up with hypothesis of your own. My ears are open.


For your full Silly BBA Goodness of the Day pleasure, however, here’s the full table of data.

Catcher	Team	Balls	Called Strikes	Total	FRM	%CS	2 Teams
J. Hernandez	Charm City Jimmies	736	370	1106	10	33.45%
P. Jimenez	Bikini Krill	1993	939	2932	10	32.03%
C. Marcillas	Twin Cities River Monsters	136	64	200	10	32.00%
R. Watson	Calgary Pioneers	1781	838	2619	9	32.00%
J. Smith	Cape Fear Swamp Foxes	746	349	1095	8	31.87%
J. Behnke	Montreal Blazers	547	252	799	8	31.54%
A. Uecker	Portland Lumberjacks	2479	1138	3617	11	31.46%
R. Fernández	Charlotte Flyers	809	371	1180	8	31.44%
A. Beaman	Louisville Sluggers	879	402	1281	8	31.38%
M. Cantu	Nashville Bluebirds	316	144	460	9	31.30%
H. Cerda	Nashville Bluebirds	718	326	1044	10	31.23%
T. Pitzer	Des Moines Kernels	491	221	712	7	31.04%
D. Quintana	New Orleans Crawdads	3213	1445	4658	9	31.02%
A. Diaz	Rosenblatt Bombers	1002	450	1452	7	30.99%
A. Annis	Yellow Springs Nine	1470	660	2130	10	30.99%	<<
L. Stewart	Charlotte Flyers	3134	1405	4539	8	30.95%
M. Matsunaga	Des Moines Kernels	2925	1306	4231	8	30.87%
A. Kakakhel	Atlantic City Gamblers	2280	1017	3297	8	30.85%
G. Espinoza	Long Beach Surfers	787	350	1137	8	30.78%
I. Quintana	Bikini Krill	1909	847	2756	7	30.73%
J. Appleton	Hawaii Tropics	1234	547	1781	9	30.71%
R. Castillo	Long Beach Surfers	502	222	724	9	30.66%
E. Reyes	Charm City Jimmies	3140	1377	4517	8	30.48%
M. Winchester	Madison Wolves	3261	1426	4687	9	30.42%
M. Matsunaga	San Fernando Bears	247	108	355	8	30.42%
J. Chitwood	Calgary Pioneers	1257	549	1806	6	30.40%
H. Syeda	Austin Shredders	3313	1443	4756	10	30.34%
L. Gonzales	Brook Park Brownies	3139	1358	4497	8	30.20%
T. Lopez	Phoenix Talons	3082	1333	4415	8	30.19%
C. Roman	Twin Cities River Monsters	296	128	424	10	30.19%
R. Atticus	Long Beach Surfers	2744	1174	3918	10	29.96%	<<<
J. Kinsella	Sacramento Mad Popes	3205	1370	4575	9	29.95%
B. McBearett	San Antonio Outlaws	2774	1183	3957	9	29.90%
D. Lopez	Nashville Bluebirds	3078	1312	4390	9	29.89%
League		132003	56103	188106		29.83%
W. McMullin	Sacramento Mad Popes	910	386	1296	8	29.78%
R. DeVille	Mexico City Aztecs	3234	1370	4604	6	29.76%
D. Olds	Chicago Black Sox	857	362	1219	8	29.70%
C. Morgan	Las Vegas Hustlers	3223	1361	4584	5	29.69%
S. Butler	Cape Fear Swamp Foxes	3313	1396	4709	9	29.65%
A. bin Zaim	Cobble Hill Robins	1328	564	1892	7	29.62%
J. Chávez	Yellow Springs Nine	1846	772	2618	9	29.49%
M. Trujillo	San Fernando Bears	2458	1026	3484	7	29.45%
S. Wadden	San Antonio Outlaws	1230	513	1743	8	29.43%
M. Córdova	Vancouver Mounties	828	345	1173	7	29.41%
R. Atticus	Vancouver Mounties	544	226	770	10	29.35%	<<<
R. O'Donnell	Jacksonville Zombies	2552	1059	3611	7	29.33%
M. Goater	Vancouver Mounties	2642	1096	3738	6	29.32%
B. Russel	Valencia Stars	2390	991	3381	7	29.31%
A. Rumbold	Las Vegas Hustlers	838	347	1185	5	29.28%
Q. Kahil	Montreal Blazers	2366	978	3344	8	29.25%
Ã. Lüfi	Calgary Pioneers	901	371	1272	9	29.17%
F. bin Mubarak	Portland Lumberjacks	1348	559	1907	6	29.14%
B. Helwich	Valencia Stars	2020	830	2850	8	29.12%
T. Otani	Boise Spuds	3298	1355	4653	8	29.12%
M. Echevarria	Chicago Black Sox	3419	1404	4823	5	29.11%
P. Rodriguez	San Fernando Bears	911	373	1284	7	29.05%
M. Malone	Twin Cities River Monsters	1677	686	2363	5	29.03%
U. bin Saleh	Boise Spuds	907	372	1279	5	29.02%
P. Trevino	Montreal Blazers	968	395	1363	9	28.98%
E. Henderson	Rosenblatt Bombers	2998	1221	4219	7	28.94%
J. Webber	Portland Lumberjacks	199	81	280	8	28.93%
T. Trevelyan	Phoenix Talons	960	390	1350	7	28.89%
E. Medine	Cobble Hill Robins	2999	1218	4217	10	28.88%
H. Piñeiro	Jacksonville Zombies	1560	633	2193	7	28.86%
D. Sekulic	Mexico City Aztecs	789	318	1107	8	28.73%
R. Romo	Atlantic City Gamblers	1902	766	2668	5	28.71%
D. Armstrong	Louisville Sluggers	3357	1347	4704	6	28.64%
A. Medina	Hawaii Tropics	3092	1240	4332	6	28.62%
T. Tatlock	Twin Cities River Monsters	2111	843	2954	8	28.54%
A. Abiola	New Orleans Crawdads	902	359	1261	6	28.47%
E. Paz	Madison Wolves	943	374	1317	7	28.40%
R. Martìnez	Brook Park Brownies	1195	473	1668	7	28.36%
D. Davidson	Des Moines Kernels	712	280	992	5	28.23%
Ã. Gutierrez	Austin Shredders	904	358	1262	6	28.01%
C. Durdle	Yellow Springs Nine	784	294	1078	7	27.27%
A. Annis	San Fernando Bears	614	218	832	10	26.20%	<<
D. van Horn	Las Vegas Hustlers	148	47	195	7	24.10%

[Trebro]

Not really sure what to make of this. FWiW, not going into depth but a cursory glance shows that Kurt and I have similar levels of Control on our staff - no one under 5, mostly 6, a few 7/8 folks. So it might not be related to Control.

I will say having better framers by rating seems to have helped Ollie Lowery and Cory McGuire, but did nothing for Schwarz or Delgado. But our defense metrics are better, so perhaps framing is causing batters to hit worse pitches leading to easier outs?

We are on pace to walk about 60 less guys, too.

*Shrug*

Not sure what to make of this. I'm running a black hole catcher offense right now. I'd kinda hoped it would lead to more defensive improvement but again, maybe some of it just isn't measurable?

[RonCo]

For my personality, defensive metrics are so interesting because they are sometimes hard to put into easy buckets.

[Graham]

…as I was fiddling with pitcher balls…

[Graham]

Good stuff, Ron

[RonCo]

…as I was fiddling with pitcher balls…

Channeling my inner Kate?