Team UZR (To Shift or Not to Shift)

Many people don’t realize that one of the (many) weaknesses of UZR, at least for the infield, is that it ignores any ground ball in which the infield was configured in some kind of a “shift” and it “influenced the play.” I believe that’s true of DRS as well.

What exactly constitutes “a shift” and how they determine whether or not it “influenced the play” I unfortunately don’t know. It’s up to the “stringers” (the people who watch the plays and input and categorize the data) and the powers that be at Baseball Info Solutions (BIS). When I get the data, there is merely a code, “1” or “0”, for whether there was a “relevant shift” or not.

How many GB are excluded from the UZR data? It varies by team, but in 2015 so far, about 21% of all GB are classified by BIS as “hit into a relevant shift.” The average team has had 332 shifts in which a GB was ignored by UZR (and presumably DRS) and 1268 GB that were included in the data that the UZR engine uses to calculate individual UZR’s. The number of shifts varies considerably from team to team, with the Nationals, somewhat surprisingly, employing the fewest, with only 181, and the Astros with a whopping 682 so far this season. Remember these are not the total number of PA in which the infield is in a shifted configuration. These are the number of ground balls in which the infield was shifted and the outcome was “relevant to the shift,” according to BIS. Presumably, the numbers track pretty well with the overall number of times that each team employs some kind of a shift. It appears that Washington disdains the shift, relatively speaking, and that Houston loves it.

In 2014, there were many fewer shifts than in this current season. Only 11% of ground balls involved a relevant shift, half the number than in 2015. The trailer was the Rockies, with only 92, and the leader, the Astros, with 666. The Nationals last year had the 4^th fewest in baseball.

Here is the complete data set for 2014 and 2015 (as of August 30):

2014

Team	GB	Shifted	Not shifted	% Shifted
ari	2060	155	1905	8
atl	1887	115	1772	6
chn	1958	162	1796	8
cin	1938	125	1813	6
col	2239	92	2147	4
hou	2113	666	1447	32
lan	2056	129	1927	6
mil	2046	274	1772	13
nyn	2015	102	1913	5
phi	2105	177	1928	8
pit	2239	375	1864	17
sdn	1957	133	1824	7
sln	2002	193	1809	10
sfn	2007	194	1813	10
was	1985	116	1869	6
mia	2176	125	2051	6
ala	1817	170	1647	9
bal	1969	318	1651	16
bos	1998	247	1751	12
cha	2101	288	1813	14
cle	2003	265	1738	13
det	1995	122	1873	6
kca	1948	274	1674	14
min	2011	235	1776	12
nya	1902	394	1508	21
oak	1980	244	1736	12
sea	1910	201	1709	11
tba	1724	376	1348	22
tex	1811	203	1608	11
tor	1919	328	1591	17

 

2015

Team	GB	Shifted	Not shifted	% Shifted
ari	1709	355	1354	21
atl	1543	207	1336	13
chn	1553	239	1314	15
cin	1584	271	1313	17
col	1741	533	1208	31
hou	1667	682	985	41
lan	1630	220	1410	13
mil	1603	268	1335	17
nyn	1610	203	1407	13
phi	1673	237	1436	14
pit	1797	577	1220	32
sdn	1608	320	1288	20
sln	1680	266	1414	16
sfn	1610	333	1277	21
was	1530	181	1349	12
mia	1591	229	1362	14
ala	1493	244	1249	16
bal	1554	383	1171	25
bos	1616	273	1343	17
cha	1585	230	1355	15
cle	1445	335	1110	23
det	1576	349	1227	22
kca	1491	295	1196	20
min	1655	388	1267	23
nya	1619	478	1141	30
oak	1599	361	1238	23
sea	1663	229	1434	14
tba	1422	564	858	40
tex	1603	297	1306	19
tor	1539	398	1141	26

 

The individual fielding data (UZR) for infielders that you see on Fangraphs is based on non-shifted ground balls only, or on ground balls where there was a shift but it wasn’t relevant to the outcome. The reason that shifts are ignored in UZR (and DRS, I think) is because we don’t know where the individual fielders are located. It could be a full shift, a partial shift, the third baseman could be the left-most fielder as he usually is or he could be the man in short right field between the first baseman and the second baseman, etc. The way most of the PBP defensive metrics work, it would be useless to include this data.

But what we can do, with impunity, is to include all ground ball data in a team UZR. After all, if a hard ground ball is hit at the 23 degree vector, and we are only interested in team fielding, we don’t care who is the closest fielder or where he is playing. All we care about is whether the ball was turned into an out, relative to the league average out rate for a similar ground ball in a similar or adjusted for context. In other words, using the same UZR methodology, we can calculate a team UZR using all ground ball data, with no regard for the configuration of the IF on any particular play. And if it is true that the type, number and timing (for example, against which batters and/or with which pitchers) of shifts is relevant to a team’s overall defensive efficiency, team UZR in the infield should reflect not only the sum of individual fielding talent and performance, but also the quality of the shift in terms of hit prevention. In addition, if we subtract the sum of the individual infielders’ UZR on non-shift plays from the total team UZR on all plays, the difference should reflect, at least somewhat, the quality of the shifts.

I want to remind everyone that UZR accounts for several contexts. One, park factors. For infield purposes, although the dimensions of all infields are the same, the hardness and quality of the infield can differ from park to park. For example, in Coors Field in Colorado and Chase Field in Arizona, the infields are hard and quick, and thus more ground balls scoot through for hits even if they leave the bat with the same speed and trajectory.

Two, the speed of the batter. Obviously faster batters require the infielders to play a little closer to home plate and they beat out infield ground balls more often than slower batters. In some cases the third baseman and/or first baseman have to play in to protect against the bunt. This affects the average “caught percentage” for any given category of ground balls. The speed of the opposing batters tends to even out for fielders and especially for teams, but still, the UZR engine tries to account for this just in case it doesn’t, especially in small samples.

The third context is the position of the base runners and number of outs. This affects the positioning of the fielders, especially the first baseman (whether first base is occupied or not). The handedness of the batters is the next context. As with batter speed, these also tend to even out in the long run, but it is better to adjust for them just in case.

Finally, the overall GB propensity of the pitchers is used to adjust the average catch rates for all ground balls. The more GB oriented a pitcher is, the softer his ground balls are. While all ground balls are classified in the data as soft, medium, or hard, even within each category, the speed and consequently the catch rates, vary according to the GB tendencies of the pitcher. For example, for GB pitchers, their medium ground balls will be caught at a higher rate than the medium ground balls allowed by fly ball pitchers.

So keep in mind that individual and team UZR adjust as best as it can for these contexts. In most cases, there is not a whole lot of difference between the context adjusted UZR numbers and the unadjusted ones. Also keep in mind that the team UZR numbers you see in this article are adjusted for park, batter hand and speed, and runners/outs, the same as the individual UZR’s you see on Fangraphs.

For this article, I am interested in team UZR including when the IF is shifted. Even though we are typically interested in individual defensive performance and talent, it is becoming more and more difficult to evaluate individual fielding for infielders, because of the prevalence of the shift, and because there is so much disparity in how often each team employs the shift (so that we might be getting a sample of only 60% of the available ground balls for one team and 85% for another).

One could speculate that teams that employ the most shifts would have the best team defense. To test that, we could look at each team’s UZR versus their relevant shift percentage. The problem, of course, is that the talent of the individual fielders is a huge component of team UZR, regardless of how often a team shifts. There may also be selective sampling going on. Maybe teams that don’t have good infield defense feel the need to shift more often such that lots of shifts get unfairly correlated with (but are not the cause of) bad defense.

One way we can separate out talent from shifting is to compare team UZR on all ground balls with the total of the individual UZR’s for all the infielders (on non-shifted ground balls). The difference may tell us something about the efficacy of the shifts and non-shifts. In other words, total team individual infield UZR, which is just the total of each infielder’s UZR as you would see on Fangraphs (range and ROE runs only), represents what we generally consider to be a sample of team talent. This is measured on non-shifted ground balls only, as explained above.

Team total UZR, which measures team runs saved or cost, with no regard for who caught each ball or not, and is based on every batted ball, shifted or not, represents how the team actually performed on defense and is a much better measure of team defense than totaling the individual UZR’s. The difference, then, to some degree, represents how efficient teams are at shifting or not shifting, regardless of how often they shift.

There are lots of issues that would have to be considered when evaluating whether shifts work or not. For example, maybe shifting too much with runners on base results in fewer DP because infielders are often out of position. Maybe stolen bases are affected for the same reason. Maybe the number and quality of hits to the outfield change as a result of the shift. For example, if a team shifts a lot, maybe they don’t appear to record more ground ball outs, but the shifted batters are forced to try and hit the ball to the opposite field more often and thus they lose some of their power.

Maybe it appears that more ground balls are caught, but because pitchers are pitching “to the shift” they become more predictable and batters are actually more successful overall (despite their ground balls being caught more often). Maybe shifts are spectacularly successful against some stubborn and pull-happy batters and not very successful against others who can adjust or even take advantage of a shift in order to produce more, not less, offense. Those issues are beyond the scope of UZR and this article.

Let’s now look at each team in 2014 and 2015, their shift percentage, their overall team UZR, their team UZR when shifting, when not shifting, and their total individual combined UZR when not shifting. Remember this is for the infield only.

2015

Team	% Shifts	Shift Runs	Non-Shift Runs	Team Runs	Total Individual Runs	Team Minus Individual after prorating Ind Runs to 100% of plays
KCA	20	-2.2	10.5	10	26.3	-19.6
LAN	13	-5	-7.3	-13.3	0.8	-14.2
TOR	26	-2.5	13.9	11	22.6	-15.6
CHA	15	-7.7	-12.3	-21.8	-11.9	-8.9
CLE	23	0.6	3.3	3.3	12.8	-11.4
MIN	23	3.5	-11.6	-7.6	1.8	-9.7
MIL	17	0.3	-7.1	-6.7	2.5	-9.5
SEA	14	-2.6	-8.7	-13.8	-5.1	-8.3
SFN	21	2.3	12.6	15.8	24.4	-11.8
MIA	14	0.5	2.7	2.4	8.4	-6.7
ARI	21	3.4	-1.5	2.1	8	-7.0
HOU	41	-7.6	-3.2	-11.3	-6.1	-3.1
PHI	14	-6.4	-16.4	-23.5	-19	-3.0
COL	31	-7.3	0	-5.5	-1.5	-3.7
ATL	13	3.1	6.9	9.8	12.6	-3.7
SLN	16	-1.1	-5.8	-8.8	-7	-1.1
DET	22	1.8	-16.2	-17.8	-16	0.5
ALA	16	-2.4	-0.4	-3.6	-2.8	-0.5
BOS	17	0.3	4.8	3.5	2.7	0.5
NYN	13	-3.8	3.1	0.8	-2.7	3.7
WAS	12	1.1	-9.4	-8.4	-12.6	5.1
CIN	17	5	9.8	16.2	11.2	3.9
CHN	15	0.2	18.7	17.4	10.5	6.0
BAL	25	10.6	-0.5	14.4	5.8	7.6
SDN	20	7.5	-6.8	1.5	-7.8	10.3
TEX	19	4.1	12.8	19.6	10.1	8.3
TBA	40	0.1	4.5	7	-9.2	19.3
NYA	30	0.1	11.8	12.2	-6.6	20.2
PIT	32	0.3	0.3	0.1	-21	26.0
OAK	23	3.9	-8.8	-5	-31.4	31.1

 

The last column, as I explained above, represents the difference between how we think the infield defense performed based on individual UZR’s only (on non-shifted GB), prorated to 100% of the games (the proration is actually regressed so that we don’t have the “on pace for” problem), and how the team actually performed on all ground balls. If the difference is positive, then we might assume that the shifts and non-shifts are being done in an effective fashion regardless of how often shifts are occurring. If it is negative, then somehow the combination of shifts and non-shifts are costing some runs. Or the difference might not be meaningful at all – it could just be noise. At the very least, this is the first time that you are seeing real infield team defense being measured based on the characteristics of each and every ground ball and the context in which they were hit, regardless of where the infielders are playing.

First of all, if we look at all the teams that have a negative difference in the last column, the teams that presumably have the worst shift/no-shift efficiency, and compare them to those that are plus and presumably have the best shift/no-shift efficiency, we find that there is no difference in their average shift percentages. For example, TBA and HOU have the most shifts by far, and HOU “cost” their teams 5.2 runs and TBA benefited by 16.2 runs. LAN and WAS had the fewest shifts and one of them gained 4 runs and the other lost 14 runs.  The other teams are all over the board with respect to number of shifts and the difference between the individual UZR’s and team UZR.

Let’s look at that last column for 2014 and compare it to 2015 to see if there is any consistency from year to year within teams. Do some teams consistently do better or worse with their shifting and non-shifting, at least for 2014 and 2015? Let’s also see if adding more data gives us any relationship between the last column (delta team and individual UZR) and shift percentage.

Team	2015 % Shift	2014 % Shift	2015 Team Minus Individual	2014 Team Minus Individual	Combined 2014 and 2015 Team Minus Individual
HOU	41	32	-5.2	45.6	40.4
TBA	40	22	16.2	12.7	28.9
PIT	32	17	21.1	5.5	26.6
TEX	19	11	9.5	9.9	19.4
WAS	12	6	4.2	13.0	17.2
OAK	23	12	26.4	-9.3	17.1
BAL	25	16	8.6	7.6	16.2
NYN	13	5	3.5	9.0	12.5
NYA	30	21	18.8	-8.4	10.4
CHA	15	14	-9.9	12.5	2.6
CHN	15	8	6.9	-5.8	1.1
TOR	26	17	-11.6	12.6	1.0
DET	22	6	-1.8	2.4	0.6
SFN	21	10	-8.6	6.0	-2.6
CIN	17	6	5	-8.2	-3.2
CLE	23	13	-9.5	5.2	-4.3
MIL	17	13	-9.2	3.1	-6.1
ARI	21	8	-5.9	-0.2	-6.1
SDN	20	7	9.3	-15.7	-6.4
MIA	14	6	-6	-0.9	-6.9
BOS	17	12	0.8	-10.6	-9.8
KCA	20	14	-16.3	6.3	-10.0
ATL	13	6	-2.8	-7.5	-10.3
PHI	14	8	-4.5	-6.2	-10.7
ALA	16	9	-0.8	-11.6	-12.4
SLN	16	10	-1.8	-12.2	-14.0
LAN	13	6	-14.1	-2.5	-16.6
MIN	23	12	-9.4	-9.3	-18.7
SEA	14	11	-8.7	-11.3	-20.0
COL	31	4	-4	-23.0	-27.0

 

Although there appears to be little correlation from one year to the next for each of the teams, we do find that of the teams that had the least efficient shifts/non-shifts (negative values in the last column), they averaged 14% shifts per season in 2014 and 2015. Those that had the most effective (plus values in the last column) shifted an average of 19% in 2014 and 2015. As well, the two teams with the biggest gains, HOU and TB, had the most shifts, at 37% and 31% per season, respectively. The two worst teams, Colorado and Seattle, shifted 17% and 13% per season. On the other hand, the team with the least shifts in baseball in 2014 and 2015 combined, the Nationals, gained over 17 runs in team UZR on all ground balls compared to a total of the individual UZR’s on non-shifted balls only, suggesting that the few shifts they employed were very effective, which seems reasonable.

It is also interesting to note that the team that had the worst difference in team and individual UZR in 2014, the Rockies, only shifted 4% of the time, easily the worst in baseball. In 2015, they have been one of the most shifted teams and still their team UZR is 4 runs worse than their total individual UZR’s. Still, that’s a lot better than in 2014.

It also appears that many of the smarter teams are figuring out how to optimize their defense beyond the talent of the individual players. TB, PIT, HOU, WAS, and OAK are at the top of the list in plus value deltas (the last column). These teams are generally considered to have progressive front offices. Some of the teams with the most negative numbers in the last column, those teams which appear to be sub-optimal in their defensive alignment, are LAN, MIN, SEA, PHI, COL, ATL, SLN, and ALA, all with reputations for having less than progressive front offices and philosophies, to one degree or another. In fact, other than a few outliers, like Boston, Texas, and the White Sox, the order of the teams in the chart above looks like a reasonable order of teams from most to least progressive teams. Certainly the teams in the top half appear to be the most saber-savvy teams and those in bottom half, the least.

In conclusion, it is hard to look at this data and figure out whether and which teams are using their shifts and non-shifts effectively. There doesn’t appear to be a strong correlation between shift percentage and the difference between team and individual defense although there are a few anecdotes that suggest otherwise. As well, in the aggregate for 2014 and 2015 combined, teams that have been able to outperform on team defense the total of their individual UZR’s have shifted more often, 19% to 13%.

There also appears to the naked eye to be a strong correlation between the perceived sabermetric orientation of a team’s front office and the efficiency of their shift/non-shift strategy, at least as measured by the numbers in the last column, explained above.

I think the most important thing to take away from this discussion is that there can be a fairly large difference between team infield UZR which uses every GB, and the total of the individual UZR’s which uses only those plays in which no shift was relevant to the outcome of the play. As well, the more shifts employed by a team, the less we should trust that the total of the individual performances are representative of the entire team’s defense on the infield. I am also going to see if Fangraphs can start publishing team UZR for infielders and for outfielders, although in the outfield, the numbers should be similar if not the same.

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Which teams have over or under-performed their expectations over the last 5 years?

Ok, enough of the bad Posnanski and Woody Allen rants and back to some interesting baseball analysis – sort of. I’m not exactly sure what to make of this, but I think you might find it interesting, especially if you are a fan of a particular team, which I’m pretty sure most of you are.

I went back five years and compared every team’s performance in each and every game to what would be expected based on their lineup that day, their starting pitcher, an estimate of their reliever and pinch hitter usage for that game, as well as the same for their opponent. Basically, I created a win/loss model for every game over the last five years. I didn’t simulate the game as I have done in the past. Instead, I used a theoretical model to estimate mean runs scored for each team, given a real-time projection for all of the relevant players, as well as the run-scoring environment, based on the year, league, and ambient conditions, like the weather and park (among other things).

When I say “real-time” projections, they are actually not up-to-the game projections. They are running projections for the year, updated once per month. So, for the first month of every season, I am using pre-season projections, then for the second month, I am using pre-season projections updated to include the first month’s performance, etc.

For a “sanity check” I am also keeping track of a consensus expectation for each game, as reflected by the Las Vegas line, the closing line at Pinnacle Sports Book, one of the largest and most respected online sports books in the internet betosphere.

The results I will present are the combined numbers for all five years, 2009 to 2013. Basically, you will see something like, “The Royals had an expected 5-year winning% of .487 and this is how they actually performed – .457.” I will present two expected WP actually – one from my models and one from the Vegas line. They should be very similar. What is interesting of course is the amount that the actual WP varies from the expected WP for each team. You can make of those variations what you want. They could be due to random chance, bad expectations for whatever reasons, or poor execution by the teams for whatever reasons.

Keep in mind that the composite expectations for the entire 5-year period are based on the expectation of each and every game. And because those expectation are updated every 6 months by my model and presumably every day by the Vegas model, they reflect the changing expected talent of the team as the season progresses. By that, I mean this: Rather than using a pre-season projection for every player and then applying that to the personnel used or presumed used (in the case of the relievers and pinch hitters) in every game that season, after the first 30 games, for example, those projections are updated and thus reflect to some extent, actual performance that season. For example, last year, pre-season, Roy Halladay might have been expected to have a 3.20 ERA or something like that. After he pitched horribly for a few weeks or months, and it was well-known that he was injured, his expected performance presumably changed in my model as well as in the Vegas model. Again, the Vegas model likely changes every day, whereas my model can only change after each month, or 5 times per season.

Here are the combined results for all five years (NL 2009-2013):

Team	My Model	Vegas	Actual	My Exp. Starting Pitching (RA9-)	Actual Starting Pitching (FIP-)	My Exp. Batting (marginal rpg)	Actual Batting (marginal rpg)
ARI	.496	.495	.486	103	103	0	-.08
ATL	.530	.545	.564	100	97	.25	.21
CHC	.488	.478	.446	103	102	-.09	-17
CIN	.522	.517	.536	104	108	.01	.12
COL	.494	.500	.486	102	96	-.04	-.09
MIA	.493	.472	.453	102	102	.01	-.05
LAD	.524	.526	.542	96	99	.02	-.03
MLW	.519	.509	.504	105	108	.13	.30
NYM	.474	.470	.464	106	108	-.02	.01
PHI	.516	.546	.554	96	98	-.01	.07
PIT	.461	.454	.450	109	111	-.19	-.28
SDP	.469	.463	.483	110	115	-.12	-.26
STL	.532	.554	.558	100	98	.23	.40
SFG	.506	.518	.515	98	102	-.19	-.30
WAS	.497	.484	.486	103	103	.01	.07

…

If you are an American league fan, you’ll have to wait until Part II. This is a lot of work, guys!

By the way, if you think that the Vegas line is remarkably good, and much better than mine, it is at least partly an illusion. They get to “cheat,” and to some extent they do. I can do the same thing, but I don’t. I am not looking at the expected WP and result of each game and then doing some kind of RMS error to test the accuracy of my model and the Vegas “model” on a game-by-game basis. I am comparing the composite results of each model to the composite W/L results of each team, for the entire 5 years. That probably makes little sense, so here is an example which should explain what I mean by the oddsmakers being able to “cheat,” thus making their composite odds close to the actual odds for the entire 5-year period.

Let’s say that before the season starts Vegas thinks that the Nationals are a .430 team. And let’s say that after 3 months, they were a .550 team. Now, Vegas by all rights should have them as something like a .470 team for the rest of the season – numbers for illustration purposes only – and my model should too, assuming that I started off with .430 as well. And let’s say that the updated expected WP of .470 were perfect and that they went .470 for the second half. Vegas and I would have a composite expected WP of .450 for the season, .430 for the first half and .470 for the second half. The Nationals record would be .510 for the season, and both of our models would look pretty bad.

However, Vegas, to some extent uses a team’s W/L record to-date to set the lines, since that’s what the public does and since Vegas assumes that a team’s W/L record, even over a relatively short period of time, is somewhat indicative of their true talent, which it is of course. After the Nats go .550 for the first half, Vegas can set the second-half odds as .500 rather than .470, even if they think that .470 is truly the best estimate of their performance going forward.

One they do that, their composite expected WP for the season will be (.430 + .500) / 2, or .465, rather than my .450. And even if the .470 were correct, and the Nationals go .470 for the second half, whose composite model is going to look better at the end of the season? Theirs will of course.

If Vegas wanted to look even better for the season, they can set the second half lines to .550, on the average. Even if that is completely wrong, and the team goes .470 over the second half, Vegas will look even better at the end of the season! They will be .490 for the season, I will be .450, and the Nats will have a final W/L percentage of .490! Vegas will look perfect and I will look bad, even though we had the same “wrong” expectation for the first half of the season, and I was right on the money for the second half and they were completely and deliberately wrong. Quite the paradox, huh? So take those Vegas lines with a grain of salt as you compare them to my model and to the final composite records of the teams. I’m not saying that my model is necessarily better than the Vegas model, only that in order to fairly compare them, you would have to take them one game at a time, or always look at each team’s prospective results compared to the Vegas line or my model.

Here is the same table as above, ordered by the difference between my expected w/l percentage and each team’s actual w/l percentage. The firth column is that difference. Call those differences whatever you want – luck, team “efficiency,” good or bad managing, player development, team chemistry, etc. I hope you find these numbers as interesting as I do!

Combined results for all five years (NL 2009-2013), in order of the “best” teams to the “worst:”

Team	My Model	Vegas	Actual	Difference	My Exp. Starting Pitching (RA9-)	Actual Starting Pitching (FIP-)	My Exp. Batting (marginal rpg)	Actual Batting (marginal rpg)
PHI	.516	.546	.554	.038	96	98	-.01	.07
ATL	.530	.545	.564	.034	100	97	.25	.21
STL	.532	.554	.558	.026	100	98	.23	.40
LAD	.524	.526	.542	.018	96	99	.02	-.03
SDP	.469	.463	.483	.014	110	115	-.12	-.26
CIN	.522	.517	.536	.014	104	108	.01	.12
SFG	.506	.518	.515	.009	98	102	-.19	-.30
COL	.494	.500	.486	-.008	102	96	-.04	-.09
NYM	.474	.470	.464	-.010	106	108	-.02	.01
PIT	.461	.454	.450	-.010	109	111	-.19	-.28
ARI	.496	.495	.486	-.010	103	103	0	-.08
WAS	.497	.484	.486	-.011	103	103	.01	.07
MLW	.519	.509	.504	-.015	105	108	.13	.30
MIA	.493	.472	.453	-.040	102	102	.01	-.05
CHC	.488	.478	.446	-.042	103	102	-.09	-.17

…

As you can see from either chart, it appears as if my model over-regresses both batting and starting pitching, especially the former.

Also, a quick and random observation from the above chart – it may mean absolutely nothing. It seems as though those top teams, most of them at least, have had notable, long-term, “players’ managers,” like Manuel, LaRussa, Mattingly, Torre, Black, Bochy, and Baker, while you might not be able to even recall or name most of the managers of the teams at the bottom. It will be interesting to see if the American League teams evince a similar pattern.