What Objective Numbers can we use to grade MLB managers?

^{(excerpted from the forthcoming book “The Men Who Killed Baseball” ©2022, all rights reserved)}

In 1904, the New York Giants won 106 games and the National League Pennant by 13 games. They were so good, they declined the opportunity to play in what would have been the second World Series against the American League champion Boston Americans (Red Sox) as superfluous. The New York players dominated their league in all ten of the most important offensive and defensive stats.¹

In 1944, The St. Louis Cardinals repeated this feat, again leading their league in every single offensive and defensive category, winning 105 games and the World Series.

These are the only two “SuperTeams” in MLB History. As some would say, the players were so talented, the stadium hot-dog vendor could have managed them to a championship.

Conversely, eight times in MLB history managers have both won the most regular season games and taken their teams to the World Series when the team did not lead their league in any single offensive or defensive category.² Clearly, these managers must have had more significantly more influence and talent then their professional colleagues.

These “Super Managers” are:

• George Stallings 1914 Boston Braves
• Bill Carrigan 1915 Boston Red Sox
• Johnny Keane 1964 St Louis Cardinals
• Yogi Berra 1964 NY Yankees
• Red Schoendienst 1967 St. Louis Cardinals
• John McNamara 1986 Boston Red Sox
• Joe Torre 1999 NY Yankees
• Joe Torre 2003 NY Yankees
• Ned Yost 2015 KC Royals

For most teams, there is a mixture of the players’ raw talent and the manager’s abilities to make use of that talent in the most effective way. Separating the two is often difficult. It is a team sport, after all, and almost every facet of the game depends on more than one individual.

Historically, of course, we have used won/loss records as the divining rod to separate out the good managers from the bad. While there may be SOME value to this, it simply ignores the giant factors of player abilities and their contribution. Obviously, no matter how talented the manger, he isn’t going to win many games if he has a AAA level team but must compete at the Major League level. Much like a pitcher, his W/L record is dependent on the skill level of his team’s players. Conversely, a very talented team may perform poorly if saddled with a leader with poor managerial skills.

The Object of the Game.

While this may seem elementary, it is important to agree on the goal of managing a major league team in order to measure effectiveness. In short, we must agree on a few basic principles.

1. The object of any game is to win the game by scoring at least one more run than you allow the other team to score.³
2. The object of the season is to win more games (see above) than all the other teams in your league.
3. The number of wins is more important than the score of one, or even a few, individual games.⁴

What does a Manger control?

On the offensive side, the manager has control (within the confines of the defensive positions required and the DH rule) over the batting order, pinch hitting/running, calling plays such as bunts, steals, hit and runs and so forth. He has no control over what the players actually achieve, but simply over puting them in the best position to be successful. He can not make a batter hit a double, nor do it himself, but he can place the batter with a proclivity for extra base hits in a position where it will do the team the most good. With these choices, his managerial ability can best be measured in terms of what percentage of his team’s base runners actually score a run.

He does not have direct control over how many total base runners he gets, that is a function of the players’ ability. He does have control over when those baserunners are most likely to occur during the game.

Since (with the exception of a home run) runs scored by an individual player are largely a function of who hits after him and runs batted in are a function of who hits before him, the manager has overwhelming control of these stats both on an individual basis and for the team.

Home runs are solely determined by the skill of the individual player, and, thus, should not be used in calculating the effect on run production by the team’s manager, though runs driven in by those home runs should be included, as he has control over the players that precede the batter who hits it. ⁵

Managerial Offensive Performance (MOP)

Managerial offensive performance is simply the percentage of real world baserunners (potential runs) that actually score and become real scoreboard runs. It does not include home runs as part of the potential runs, as they do not exist on the bases until after the fact. ⁶

Rather obviously, the higher the MOP, the better the manager has done in supporting his player’s offensive abilities.

Managerial Defensive Performance (MDP)

Much like the MOP number, managerial defensive performance focuses on those things over which the manager has the most control in preventing the other team from scoring. Bringing in a reliever, pitcher/hitter matchups, intentional walks, pitching around a batter, defensive alignment, defensive player selection and so forth. In short, it’s a measure of what percentage of the opponent’s base runners ultimately score an actual run.⁷

Again, obviously, the lower the number, the better the manager has supported his team defensively.

Overall Managerial Performance (OMP)

What we want of course, is a measure of how well the manager performs over all. Since we are confronted with one measure that is better when it is higher, and one that is better when lower, we can not simply add them together. Rather, to get an accurate perspective we must subtract the negatives (the defensive number or MDP) from the positives (the offensive number or MOP) to arrive at a logically correct measure. Since that number will generally be a small one, with lots of zeros, we multiply it by ten to make it look more like a batting average and eliminating most of the zeros but without changing the relative numbers.⁸

The Relationship of Overall Manger Performance to Winning Percentage

Baseball is subject to significant “butterfly effects“. That is, small changes within a team, league or even weather⁹ may have pronounced effects on the ultimate outcome, far exceeding what you might expect. For this reason, we have excluded the 2012-2020 seasons from this discussion as several not-so-small factors greatly influenced the outcomes and comparisons for those seasons.¹⁰

While better managers are going to win more games in general, it’s not a one to one ratio. A really talented team may carry a mediocre manager to success, a good manager may not have the necessary player talent to win a majority of games. Like everything else in baseball, one more or less depends on the other.

Team		Win %	Manager	Manager Ranking	OMP	MDP	MOP
LAD		.670	Dave Roberts	1	1.016	.209	.311
HOU		.620	Dusty Baker	2	.672	.237	.304
NYY		.590	Aaron Boone	14	.228	.248	.271
ATL		.585	Brian Snitker	5	.512	.251	.302
SFG		.580	Gabe Kapler	8	.324	.254	.286
TBR		.574	Kevin Cash	3	.592	.248	.307
TOR		.565	Montoyo / Schneider	4	.535	.256	.310
STL		.565	Shildt / Marmol	11	.271	.263	.291
MIL		.559	Craig Counsell	7	.343	.249	.284
SEA		.556	Scott Servais	15	.165	.263	.279
NYM		.549	Rojas / Showalter	12	.248	.252	.277
CHW		.537	Tony La Russa	6	.373	.259	.297
CLE		.531	Terry Francona	9	.320	.263	.295
BOS		.525	Alex Cora	10	.281	.277	.305
PHI		.522	Girardi / Thomson	17	.106	.280	.291
SDP		.519	Tingler / Melvin	13	.236	.263	.287
MIN		.466	Rocco Baldelli	26	-.119	.287	.275
LAA		.463	Maddon / Nevin	21	-.019	.278	.276
OAK		.451	Melvin / Kotsay	22	-.020	.278	.276
CHC		.448	David Ross	18	.050	.267	.272
CIN		.448	David Bell	20	.050	.282	.287
DET		.441	A.J. Hinch	16	.165	.266	.283
COL		.440	Bud Black	27	-.138	.312	.298
KCR		.429	Mike Matheny	24	-.043	.289	.284
MIA		.420	Don Mattingly	19	.050	.257	.262
BAL		.417	Brandon Hyde	29	-.316	.305	.274
TEX		.395	Woodward / Beasley	25	-.057	.287	.281
ARI		.389	Torey Lovullo	23	-.039	.291	.287
PIT		.380	Derek Shelton	30	-.425	.297	.255
WSN		.370	Dave Martinez	28	-.245	.285	.261

Footnotes:

¹ _{Defense: Walks and hits per inning pitched; total runs allowed; Earned Run Average; Strikeouts; Fielding Average; Offense: Batting Average; Slugging Avg.; On base percentage; Home runs; Total Runs Scored}

² _{The average league champion (as measured by wins) over the last 119 years led their league in at least four of the ten major statistical categories.}

³ _{Rules of Major League Baseball Author’s note: The designated hitter rule is itself a violation of rule 1.01 and the idiot ghost runner rule is a violation of Rule 1.04. But consistency has never been a big issue for MLB.}

⁴ _{Since the institution of the “play-offs” in 1970, winning games during the regular season has become just another stat when it comes to “winning” the league championship and going to the World Series. Basically going to the playoffs, and even reaching the World Series has very little to do with how good a manager or team is.}

⁵ _{Runs produced: A variety of definitions have been put forth to define “runs produced” as a Major League Statistic, mostly as an individual player statistic. Some favor the simple (but wildly inaccurate) Runs Scored+ Runs Batted In. This has the inherent problem that a solo home run equals two runs produced, since the batter gets one run scored and one run batted in, but only one run appears on the scoreboard. A simple solution has been proposed the runs produced should be then (Runs Scored + Runs Batted In) – Homeruns, to get rid of the extra run from the home runs. This seems reasonable until you try it on a team scale, where it still results in 60% more runs produced as actually appear on the scoreboard. The only formula that come close to the real world numbers is .5 (1/2) Runs Scored + .5 (1/2) Runs Batted In. Thus the batter in the case of a solo home run gets credited with only the run that actually appears on the scoreboard. It also produces a team run production that is very close to the real word, the differences being accounted for with runs scored by errors, Wild pitches with a runner on 3rd, etc. Example: In 2021, the New York Yankees scored a total of 711 runs. They had 666 total RBI’s and 222 Home Runs. Method one, results in a Team Run Production total of 1377 runs, 666 of which do not appear on the scoreboard. Method two results in 1155 runs produced, 444 of which don’t actually exist. Finally, method three results in 688.5 runs produced, only 22.5 runs different from the actual total runs and accounted for by the runs which scored without a corresponding RBI. In any case, run production is largely influenced by managerial decisions and is a very poor measure of an individual player’s contribution.}

⁶ _{Thus the equation for determining a manager’s MOP looks like this: MOP = (Hits-Home runs)+BB+IBB+HBP / (Actual runs scored – Home Runs)}

⁷ _{The equation for determining a manager’s defensive performance (MDP): opponent’s (Hits-Home runs)+BB+IBB+HBP / (Actual earned runs scored – Home Runs)}

⁸ _{Manger’s Overall Managerial Performance (OMP): OMP= (MOP-MDP)*10}

⁹ _{For example, because of the difference in air density, 18% more homeruns are hit when the temperature is above 80^o than are hit when the temperature is below 60^o}

¹⁰ _{MLB injected the then secret “superball” after the 2015 All-Star break to influence game outcomes, and removed it after 2020; the “sticky stuff” / Spider Tack scandal of 2012-2020; the 2020 COVID season.}