Archive for May, 2010

Perfect Games Per Year


Perfect Games

Do the Falcons Need a New Publicly-Funded Stadium?

In today’s AJC, Jeff Schultz lays a real egg in making the case for a new publicly-funded stadium for the Atlanta Falcons.

We obviously have more important needs in Atlanta than a new football stadium. The Georgia Dome is not falling apart. But if Blank wants to fund this project by himself, nobody should have a problem with that. If taxpayers are willing to pass an initiative for a special hotel-motel tax to help partially fund the project, nobody should have a problem with that, either. Yes, it would be wonderful if voters could be moved to vote for a hotel tax to help raise money for education and prevent 1,500 teachers from losing jobs. But realistically, that’s not going to happen.

We need other stuff that is more justified but, oh well, it’s going to happen anyway? Wow, how’s that for complacent apathy. Schultz is usually better than this. My guess is that a popular vote between schools and a new stadium would yield drastically different results. As of this moment, 78% of participants in an online AJC poll oppose a new stadium.

Luckily, his fellow columnist Mark Bradley picks him up with some help from the former head of the Georgia Dome Khalil Johnson.

“I love football and I love the Falcons,” Johnson said. “If they need and desire a new stadium, let the owner build it himself. In this current situation, to use tax dollars isn’t viable.”

Also this: “They’re having discussions of whether [an open-air stadium would cost] less than half a billion or more than half a billion. At the same time we’re closing schools, we’ve got transportation issues and we need to figure out Grady [Hospital] … It’s not a sports question. It’s an economic issue. There are a lot more pressing needs.”

Arthur Blank bought the Falcons in 2002, a decade after the Dome opened, and has been persistent in his desire that the building be updated. Johnson worked to placate the owner but knew the day would come when Blank would want a new building.

Said Johnson, who now works out of Douglasville as a consultant regarding events and venues: “What’s the pressing need? More money for the ownership. I don’t know how that lines up with what the public wants … I just question whether the public needs to give more when most of the benefits will go to a private owner.”

While many readers may be unfamiliar with Johnson, he is a big player in Atlanta sports. Kudos to Khalil, whom I had the pleasure to meet a few years ago, for standing up to politicians who are all too willing to dole out welfare to a billionaire.

Also, if you have been following the issue lately, have you noticed the new “open air” ruse being used to justify a new stadium? “Oh, we can’t use the Georgia Dome, we need something different.” I hope the people of Atlanta won’t fall for this.

What Is the Probability of Winning Back-to-Back Baseball Games?

At Decision Science News, Dan Goldstein asks the question, looks at the data, and finds a surprising result.

If a team wins on one day, what’s the probability they’ll win against the same opponent when they play the very next day?

We asked two colleagues knowledgeable in baseball and the mathematics of forecasting. The answers came in between 65% and 70%.

The true answer: 51.3%, a little better than a coin toss.

That’s right. When you win in baseball, there’s only a 51% chance you’ll win again in more or less identical circumstances. The careful reader might notice that the answer is visible in the already mentioned chart. The reversals of size 0, (meaning no reversal, meaning the same team won twice) occur 51,296 times per 100,000 pairs of consecutive games.

Statistician Andrew Gelman is not surprised and explains why.

I have to say, I’m surprised his colleagues gave such extreme guesses. I was guessing something like 50%, myself, based on the following very crude reasoning:

Suppose two unequal teams are playing, and the chance of team A beating team B is 55%. (This seems like a reasonable average of all matchups, which will include some more extreme disparities but also many more equal contests.) Then the chance of the same team winning both games is .55^2 + .45^2 = .505. Even .6^2 + .4^2 is only .52.

Two interesting posts, worth reading. Thanks to Jonathan for the pointer.

Team PrOPS

How are teams over- and under-performing their OPS? The Twins and Nationals appear to be over-performing, while the Astros, Angels, and Marlins have been a bit hit-unlucky to start the year.

Team	OPS	PrOPS	Diff.
MIN	0.788	0.770	-0.018
WSN	0.769	0.758	-0.012
SFG	0.739	0.730	-0.009
TBR	0.733	0.725	-0.008
ARI	0.777	0.771	-0.006
MIL	0.798	0.794	-0.004
DET	0.766	0.763	-0.003
TEX	0.730	0.730	0.001
LAD	0.777	0.780	0.003
CIN	0.740	0.747	0.007
SDP	0.687	0.694	0.007
CHC	0.769	0.784	0.014
SEA	0.653	0.668	0.015
COL	0.765	0.781	0.017
BAL	0.699	0.722	0.024
NYY	0.812	0.837	0.024
KCR	0.738	0.762	0.025
TOR	0.771	0.795	0.025
STL	0.740	0.766	0.026
PHI	0.805	0.837	0.032
PIT	0.666	0.700	0.034
CLE	0.686	0.722	0.036
BOS	0.805	0.841	0.036
OAK	0.673	0.713	0.040
ATL	0.703	0.747	0.044
NYM	0.695	0.741	0.045
CHW	0.704	0.760	0.055
FLA	0.708	0.784	0.076
LAA	0.697	0.777	0.079
HOU	0.599	0.720	0.122

I would pay more attention to the ranking of the table first before focusing on the differences from OPS. The PrOPS calculation is based on the last five years of performance; and because offense is down this year, PrOPS estimates are going to be biased upwards relative to 2010 OPS (as evidenced by the asymmetry between positive and negative differences). This could be a sign that league offense has been down by chance and will rebound, but it’s hard to say when making such cross-year comparisons. But I think it is safe to say that teams at the top have had some good luck with their bats that may diminish, while teams at the bottom should improve.

For some individual PrOPS estimates, see yesterday’s post.

2010 PrOPS Over- and Under-Performers

Since The Hardball Times stopped carrying stats, I have received several requests for 2010 PrOPS. I’m happy that some people have an interest in the numbers, and PrOPS was in need of a serious update anyway, so I decided to calculate PrOPS for 2010 based on the past five seasons using data from Baseball-Reference. While I can’t list a full slate of PrOPS for all players, I will report the top over- and under-performances for this season.

If you are not familiar with PrOPS, it’s an estimate of player hitting performance (measured on the scale of OPS) according to how players hit the ball rather than using in-play outcomes (see the introductory essay). OPS is estimated from batted-ball types (line-drive percentage and groundball-flyball ratio), walk rate, strikeout rate, home run rate, hit batter rate, home ballpark, and season. The idea is to identify players who are hitting the ball well (or poorly), but the measured outcome differs from what is typical for players who hit the ball in the same way. Players who are over- or under-performing their PrOPS are more likely to decline or improve than players whose performances are in-line with their OPS. PrOPS does not remove all aspects of luck, it just highlights one common area where random bounces on the field can distort outcome-based metrics.

The tables below are based on performances through Friday, May 14 for players with more than 100 plate appearances. Here is a list of players who are playing over their heads and may be headed toward a fall.

Top-30 Over-Performers

Rank	Player			Team	OPS	PrOPS	Diff.	PA
1	Jayson  Werth		PHI	1.089	0.836	0.253	142
2	Justin  Morneau		MIN	1.142	0.948	0.194	148
3	Colby  Rasmus		STL	0.958	0.795	0.163	127
4	Carl  Crawford		TBR	0.868	0.710	0.158	154
5	Elvis  Andrus		TEX	0.791	0.646	0.145	153
6	Ichiro  Suzuki		SEA	0.853	0.708	0.145	158
7	Troy  Tulowitzki	COL	0.823	0.680	0.143	139
8	Kevin  Youkilis		BOS	0.969	0.828	0.141	157
9	Miguel  Cabrera		DET	1.088	0.948	0.140	157
10	Andrew  McCutchen	PIT	0.913	0.779	0.134	147
11	Adam  Dunn		WSN	0.917	0.788	0.129	147
12	Andre  Ethier		LAD	1.201	1.085	0.116	140
13	Stephen  Drew		ARI	0.848	0.733	0.115	140
14	Franklin  Gutierrez 	SEA	0.865	0.752	0.113	144
15	Fred  Lewis		TOR	0.828	0.716	0.112	111
16	Carlos  Ruiz		PHI	0.948	0.841	0.107	106
17	Ryan  Braun		MIL	1.007	0.906	0.101	153
18	Johnny  Damon		DET	0.814	0.716	0.098	153
19	Daric  Barton		OAK	0.791	0.694	0.097	158
20	Nick  Markakis		BAL	0.837	0.741	0.096	159
21	Josh  Willingham	WSN	0.909	0.816	0.093	137
22	Adam  LaRoche		ARI	0.802	0.709	0.093	133
23	Geovany  Soto		CHC	0.954	0.863	0.091	111
24	Blake  DeWitt		LAD	0.711	0.622	0.089	108
25	Shin-Soo  Choo		CLE	0.853	0.768	0.085	148
26	Evan  Longoria		TBR	1.007	0.923	0.084	153
27	Brett  Gardner		NYY	0.832	0.751	0.081	131
28	Casey  McGehee		MIL	0.935	0.855	0.080	147
29	Kosuke  Fukudome	CHC	0.960	0.880	0.080	118
30	Ben  Zobrist		TBR	0.725	0.646	0.079	151

The players in the table below have had some tough luck to start the year and are likely to improve as the season progresses. It’s interesting that the list includes Derek Jeter and Victor Martinez, who have both been singled out for their unexpected poor play. And David Ortiz just misses the cut (99 PAs), but his PrOPS is .906, a difference of -.140 from his actual OPS.

Top-30 Under-Performers

Rank	Player			Team	OPS	PrOPS	Diff.	PA
1	Hunter  Pence		HOU	0.705	0.927	-0.222	128
2	Skip  Schumaker		STL	0.589	0.800	-0.211	146
3	Brandon  Wood		LAA	0.414	0.622	-0.208	107
4	Chris  Coghlan		FLA	0.517	0.723	-0.206	132
5	Akinori  Iwamura	PIT	0.489	0.683	-0.194	142
6	Kendry  Morales		LAA	0.780	0.974	-0.194	150
7	Casey  Kotchman		SEA	0.627	0.811	-0.184	128
8	Derek  Jeter		NYY	0.717	0.886	-0.169	161
9	Adam  Rosales		OAK	0.677	0.837	-0.160	117
10	Victor  Martinez	BOS	0.641	0.801	-0.160	138
11	Rod  Barajas		NYM	0.860	1.016	-0.156	110
12	Melky  Cabrera		ATL	0.523	0.676	-0.153	124
13	Mark  DeRosa		SFG	0.537	0.686	-0.149	104
14	Cesar  Izturis		BAL	0.486	0.626	-0.140	104
15	Jose  Lopez		SEA	0.530	0.667	-0.137	149
16	A.J.  Pierzynski	CHW	0.547	0.680	-0.133	115
17	Russell  Martin		LAD	0.741	0.872	-0.131	140
18	Juan  Rivera		LAA	0.677	0.800	-0.123	127
19	Aramis  Ramirez		CHC	0.500	0.623	-0.123	146
20	Carlos  Lee		HOU	0.514	0.636	-0.122	139
21	Brendan  Ryan		STL	0.480	0.594	-0.114	117
22	Luis  Castillo		NYM	0.646	0.760	-0.114	122
23	Scott  Sizemore		DET	0.591	0.700	-0.109	112
24	Placido  Polanco	PHI	0.789	0.897	-0.108	149
25	Ian  Stewart		COL	0.906	1.010	-0.104	128
26	Jhonny  Peralta		CLE	0.690	0.791	-0.101	127
27	Grady  Sizemore		CLE	0.558	0.656	-0.098	137
28	Alcides  Escobar	MIL	0.622	0.719	-0.097	125
29	Pedro  Feliz		HOU	0.531	0.625	-0.094	123
30	Mark  Teixeira		NYY	0.733	0.824	-0.091	160

I want to reiterate that over- and under-performing does not guarantee a reversion, but it is one tool to identify those who are likely to deviate from their performances so far this season.

Why Is Mets Attendance Down?

Yesterday, I examined why Braves attendance is up. Today, I examine why Mets attendance is down. Michael Schmidt reports in the NY Times:

A year after moving into a sparkling new $800 million stadium, the Mets have the most home victories in the major leagues, but neither their stadium nor their record is translating into box-office success.

After 22 home games, attendance at Citi Field is down 6,852 fans a game, the largest decline by number in Major League Baseball. That translates to an average of 31,892 fans at games this season compared with 38,744 last season.

Here is a list of attendance by home games through May 13 of 2009 and 2010 (there appears to be a small disparity between the Baseball-Reference numbers and the numbers used in the story).

 
2009						2010			
Gm#	Date		Opp	Attendance	Gm#	Date		Opp	Attendance
7	Monday Apr 13	SDP	41,007		1	Monday Apr 5	FLA	41,245
8	Wednesday Apr 15SDP	35,581		2	Wednesday Apr 7	FLA	38,863
9	Thursday Apr 16	SDP	35,985		3	Thursday Apr 8	FLA	25,982
10	Friday Apr 17	MIL	36,436		4	Friday Apr 9	WSN	28,055
11	Saturday Apr 18	MIL	36,312		5	Saturday Apr 10	WSN	33,044
12	Sunday Apr 19	MIL	36,124		6	Sunday Apr 11	WSN	33,672
16	Friday Apr 24	WSN	40,522		13	Monday Apr 19	CHC	27,940
17	Saturday Apr 25	WSN	39,960		14	Tuesday Apr 20	CHC	27,502
18	Sunday Apr 26	WSN	40,023		15	Wednesday Apr 21CHC	25,684
19	Monday Apr 27	FLA	38,573		16	Thursday Apr 22	CHC	28,535
20	Tuesday Apr 28	FLA	38,546		17	Friday Apr 23	ATL	32,265
21	Wednesday Apr 29FLA	39,339		18	Saturday Apr 24	ATL	36,547
26	Wednesday May 6	PHI	37,600		19	Sunday Apr 25	ATL	27,623
27	Thursday May 7	PHI	37,295		20	Tuesday Apr 27	LAD	32,012
28	Friday May 8	PIT	38,496		21	Tuesday Apr 27	LAD	32,012
29	Saturday May 9	PIT	39,769		22	Wednesday Apr 28LAD	29,724
30	Sunday May 10	PIT	39,871		29	Friday May 7	SFG	34,681
31	Monday May 11	ATL	40,497		30	Saturday May 8	SFG	36,764
32	Tuesday May 12	ATL	39,408		31	Sunday May 9	SFG	35,641
33	Wednesday May 13ATL	40,555		32	Monday May 10	WSN	29,313
						33	Tuesday May 11	WSN	31,606
						34	Wednesday May 12WSN	33,024
								
				Difference	
		2009	2010	Absolute %
Overall	Mean	38,595	31,897	-6,698	-17.35%
					
Weekday	Games	11	13		
	Total	424,386	403,442		
	Mean	38,581	31,034	-7,547	-19.56%

Weekend	Games	9	9		
	Total	347,513	298,292		
	Mean	38,613	33,144	-5,469	-14.16%

Wash	Total	120,505	94,771		
	Mean	40,168	31,590	-8,578	-21.36%

There are few things to look at. First, the Mets have played two more weekday games than weekend games this year, compared to an equal number in 2009. However, when comparing the average of games weekday to weekday and weekend to weekend, attendance is down 20% and 14%, respectively. When comparing the 2009 and 2010 series with the Washington Nationals, which were both over a weekend, attendance was down 21% over last season. The bias in the 2010 sample doesn’t seem to explain much of the difference.

Some of this difference is to be expected, as the luster of a new stadium has diminished. In the 2000s, attendance declined by approximately 7% in the year following the introduction of a new stadium. Attendance seems to be down beyond what would be expected; but, I think it’s too early to read much into these numbers. The first month of last season saw very big crowds when the stadium was its youngest, and the crowds dwindled over the course of the season. The season is still early, so it will be interesting to see how the numbers stack up at the end of the year.

Why Is Braves Attendance Up?

Carroll Rogers has noticed that attendance is up for the Braves and asks readers to suggest explanations.

The Braves have only played 12 games at home this season – entering a seven-game home stand that opens on Friday at Turner Field. But so far the returns in attendance are up.

The Braves’ average home attendance has increased by 20 percent from about this time last year, according to figures in a recent Wall Street Journal article. The Braves’ increase is second only to Minnesota for the biggest increase in Major League Baseball through games of May 8….

So what do you think it’s about? Is Jason Heyward having that big an impact? Or is it because this is manager Bobby Cox’s last season? Or is it because of good weather or that three-game series against the Cubs to open the season?

Readers seem to think Heyward and Cox are the big draws, but I think there are a few other factors involved. And after looking at the numbers, I think you can make the case that the numbers so far suggest that attendance might very well go down this year.

Here are the raw numbers from 2009 and 2010, courtesy of Baseball-Reference.

2009						2010			
Gm#	Date		Opp	Attendance	Gm#	Date		Opp	Attendance
4	Friday Apr 10	WSN	48,327		1	Monday Apr 5	CHC	53,081
5	Saturday Apr 11	WSN	34,325		2	Wednesday Apr 7	CHC	36,170
6	Sunday Apr 12	WSN	19,873		3	Thursday Apr 8	CHC	27,443
7	Tuesday Apr 14	FLA	16,293		10	Friday Apr 16	COL	27,692
8	Wednesday Apr 15FLA	19,204		11	Saturday Apr 17	COL	32,602
9	Thursday Apr 16	FLA	21,072		12	Sunday Apr 18	COL	26,546
19	Monday Apr 27	STL	16,739		13	Tuesday Apr 20	PHI	18,032
20	Tuesday Apr 28	STL	18,121		14	Wednesday Apr 21PHI	21,171
21	Wednesday Apr 29STL	19,127		15	Thursday Apr 22	PHI	22,476
22	Friday May 1	HOU	29,309		23	Friday Apr 30	HOU	30,082
23	Saturday May 2	HOU	28,203		24	Saturday May 1	HOU	27,035
24	Sunday May 3	HOU	27,921		25	Sunday May 2	HOU	25,665
25	Monday May 4	NYM	19,132					
26	Tuesday May 5	NYM	21,049					
								
	Overall Mean		24,193			Overall Mean		29,000
	Weekend	Games		6			Weekend	Games		6
		Total		177,781				Total		169,622
		Mean		29,630				Mean		28,270
	Weekday	Games		8			Weekday	Games		6
		Total		150,737				Total		178,373
		Mean		18,842				Mean		29,729
	Opening	Total		102,525			Opening	Total		116,694
		Mean		34,175				Mean		38,898
	Houston	Total		85,433			Houston	Total		82,782
		Mean		28,478				Mean		27,594

There are a few things to note here. The first thing I notice is that there were more weekday (Monday-Thursday) games in 2009 than 2010, which normally generate lower attendance than weekend games. This is going to bring the average down. This disparity is exacerbated by the fact that the Braves opened the series against the Cubs on a weekday. The opening series, especially the first game, traditionally brings a huge crowd. And when the Cubs come to Turner Field, it might as well be a home game for the Cubs. This year, the Braves actually outdrew last year’s opening series by 14%, even though the 2009 home opener was held over the weekend. That goes to show what hosting the Cubs versus the Nationals will do for you.

I think a good barometer for how attendance will change this year is the comparison between the 2009 and 2010 Houston series, because both were held over a weekend at about the same time in the season. This year the Braves drew 884 fewer fans per game to the Houston series than last year. If Cox and Heyward were a part of the 20% boost, it should show up here.

Having Heyward and Cox on board certainly don’t hurt—they may be preventing attendance from shrinking—but I don’t think that they have much to do with the rise in average attendance so far. This does not mean that attendance will not be up later in the season. If the Braves start winning as the season progresses, I expect that attendance will rise. Despite the team’s early woes, I think think the roster is built to win, which will ultimately put fans in the seats.

Edit: I initially attributed the AJC blog post to David O’Brien. My apologies to Ms. Rogers, who also does an excellent job covering the Braves.

Hot Starts: Heyward versus Francoeur

Jason Heyward has just played his 30th game in the big leagues, and oh what a start it has been. A local kid rising to meet exceptional expectations; it kind of reminds me of another young phenom.


The Natural

So, I decided to compare Jason Heyward’s first 30 games to Jeff Francoeur’s first 30 games in the majors. And I was a bit surprised by what I found.

PA AB H 2B 3B HR RBI BB SO HBP SB CS BA OBP SLG ISO OPS
Jeff Francoeur 113 110 41 10 1 10 28 0 20 3 1 1 0.373 0.389 0.755 0.382 1.144
Jason Heyward 116 93 28 5 0 8 28 20 27 2 2 0 0.301 0.431 0.613 0.312 1.044


Despite Heyward’s hot start, Francoeur’s beginning was arguably hotter. Francoeur had more homers, a higher average, a higher slugging percentage, and a higher OPS. Does this foretell a similar demise for Heyward (Francoeur currently has a .705 OPS for the Mets)? While I won’t be surprised if Heyward’s numbers fall some, there are some distinct differences between the two players.

The first difference is obvious: walks. At the same point in their major league careers, Heyward has 20 walks; Francoeur wouldn’t get his first walk until four games later, and it was intentional. Heyward has the plate discipline that Francoeur still lacks.

And more important are their minor-league performances. Francoeur had decent, but unimpressive, minor-league numbers. Heyward had a better minor-league career, absolutely destroying the Double-A. Analyzing minor-league stats is tricky, so below I use the markers I employ for predicting major-league success from minor-league performance (see my upcoming book for justification): walk rate, strikeout rate, and isolated power (age is also important). These stats are for their High-A and Double-A performances before they joined the big league team. (Stats below High-A do not predict success well. Heyward barely played in Triple-A, and I excluded Francoeur’s 2008 demotion.).

BB% K% Iso
Jason Heyward 11.75% 12.21% 0.231
Jeff Francoeur 5.31% 19.75% 0.205


So, despite their similar hot starts, Braves fans shouldn’t worry about Heyward becoming Francoeur. On the surface, the players’ hot starts may appear similar, but their skill sets are quite different. Heyward is already better than Francoeur will ever be, and his future looks very bright.

Managers: Hired To Be Fired

We miss the effects of randomness in life because when we assess the world, we tend to see what we expect to see. We in effect define degree of talent by degree of success and then reinforce our feelings of causality by noting the correlation. That’s why although there is sometimes little difference in ability between a wildly successful person and one who is not successful, there is usually a big difference in how they are viewed.
(Mlodinow, The Drunkard’s Walk, p. 212)

It’s that time in the season when fans begin to notice that teams and players aren’t living up to expectations (and to a lesser fans notice that some expectations are exceeded). When a career All-Star bats below the Mendoza line, or a pre-season playoff favorite resides at the bottom of the division, commentators go looking for answers. “He needs to make adjustments to his swing” and “This team has the wrong attitude” are common statements often heard on broadcasts. Right now, the Mariners and Braves are in last place in their divisions; the Red Sox are below .500: fans of these teams are in full panic mode. It’s fine to have some concern—losses in the early season are just as important down the stretch, and poor performance now may indicate poor talent—however, I feel that fans are too sensitive to swings that are largely the product of randomness. Sometimes good teams win and bad teams lose, All-Star sluggers strike out and bench players hit home runs.

Occasionally, these things happen in clumps (like the Braves losing nine games in a row), and fans are quick to respond with disdain and frustration. For example, the data below represent wins (w) and losses (l) in a 162-game season for a .500 team, generated randomly via a computer program (Stata code: generate x=round(uniform(),1)) . Note that this team actually finishes below .500 and has several streaks of wins and losses. In fact, there is an 18-game span where the team has two five-game losing streaks and one six-game losing streak while going 2-16. I imagine the sports pages would have a field day with this team as being one of the worst in baseball, when in fact it is an average team.

l l l l w w l l l w w w w l w w l l l w l w l l l l l w l l l l l w l l l l l l w w w l w w w w w w l w w w l w w w l w w l w w l w l l w w w l w w l l w w l w w w w l l w w w w w l l w w w l l l l w l l w l l l l l l w w w w l w l w w w w w w w l w w l l w w l w w l w w w l w l w l l w w w l w w l w w l w l w w l l l w l

Even though such runs are perfectly natural by random chance, fans often demand changes or they’ll turn away from the team. And such negative feelings can be contagious as they are spread far and wide. In old-media days, management might be able to reason with reporters and broadcasters to keep the mood light. But with the rise of the Internet, venting is impossible to control with spin jargon. In fact, managers and GMs are often mocked when they declare bad luck to be the culprit for poor play.

This has to be frustrating for management, because the belief that random fluctuations represent real and easily-correctable problems can have financial consequences. A good team that plays poorly can translate into losses at the gate. A GM may look at his roster and see a good team that he doesn’t want to change, but “hang on and be patient” doesn’t resonate well among fans who demand answers. How can a GM signal that things are going to get better when the team is already configured optimally? Fire someone who doesn’t matter.

The players are the main input to success on a baseball team, and are the last thing a manager or GM wants to adjust. Replacing Chipper Jones (2010 OPS: .770 with Brooks Conrad (2010 OPS: .822) will only hurt the the Braves’ chances of winning. The next step up the line are coaches. The Mariners have already fired hitting coach Alan Cockrell. Now, there may be good reasons to fire Cockrell, but I don’t think one month of poor performance can be attributed to Cockrell, or that any damage he did will remedy the Mariners’ hitting woes. I have no doubt that Mariner hitting will improve as players regress toward their true talent level with or without a new coach. I’m also certain that someone will attribute the expected turnaround to the installation of Alonzo Powell as hitting coach. But firing Cockrell serves the purpose of appeasing the masses. It’s management’s way saying “hey, we’re mad too, and we’ve fixed the problem.” Many fans who declared their anger may head back to the ballpark that they would have otherwise abandoned.

The next level up is the manager, and managers are often fired in mid-season on under-performing teams. Though we often give lots of credit to managers for the successes of their teams, I don’t think managers contribute too much to the game. The nature of the game requires sending individuals up to the plate on their own to perform. There aren’t plays to draw up, junk defenses to employ, or reacting to another coaching strategy. After picking the lineup, all managers can really do is pull pitchers, shift the defense, and call hit-and-run type strategies. And most of these choices could be dictated by a computer algorithm. Managers may do some coaching and stroke player egos, but I don’t believe that managers have much effect on teams. Rather I think they serve as public figureheads, who handle the media and put a public face on the franchise. In this sense, one of a manager’s main responsibilities is to serves as a scapegoat, sacrificed to the fan gods to preserve good will with fans and keep them coming through the turnstiles.

Now, I don’t believe that managers are totally benign, just overrated. But, I wanted to examine who managerial changes affected fans. If new managers affect attendance positively, then the manager-as-scapegoat theory has some support. So, I looked at managerial changes within seasons, where the talent of teams remains someone consistent, and observed the attendance after such changes. This is a complicated exercise because managers are typically replaced on bad teams; attendance is expected to be falling, so this analysis requires controlling for several factors. Using Retrosheet game logs (and double-checking with Baseball-Databank‘s managerial records), I identified how attendance changed when a new manager (managing a minimum of 10 games) was brought in.

As controls I used the performance in the last 10 home games (to proxy recent local excitement regarding the team), the winning percentage for the entire season (to proxy the quality of the club), and the career winning percentage of the manager (to proxy managerial quality). Because of the panel aspect of the data, I corrected for detected serial correlation over time and used fixed effects to control for unique properties in each market. I also used year dummies to capture the impact of individual seasons, month dummies to proxy seasonal shifts in attendance, and day-of-week dummies to capture daily fluctuations. The results below are from a sample from 2000–2009.

			Impact	T-statistic
New Manager		1041	2.60
Wins in Last 10 Games	187	3.62
Team W%			32928	16.50
Manager Career W%	26821	11.66
		
Obs			18614	
R2			0.41	

The estimate indicates that a new manager nets a club about 1,000 additional fans per game. So, even while a team may be losing, and winning more may bring in more fans, the addition of a new manager seems to boost attendance. Thus, it appears that the manager-as-scapegoat theory has some legs.

But, another interesting finding is that this relationship does not appear in the 1990s or 1980s. In the 1990s, the effect is positive, about 140 fans per game, but the estimate is not statistically significant. In the 1980s, the estimate is negative and significant. Are fans more responsive now than they were in the past? Maybe the new social media puts more pressure on teams to act swiftly and fans respond in kind. Or, maybe this is just a spurious correlation. Still, I think the preliminary findings show that it is worth further investigation. And if a team has a run of bad luck, I wouldn’t blame a GM for firing a coach or manager as a PR move. It’s not like ex-managers have a problem getting second and third chances. In fact, firing appears to be part of the job description of managers.

It may seem unfair to put blame on blameless parties, but coaches and managers also receive praise for performances that they have little control over. It’s not like they are not paid well. They continue to receive their contract salary, and they will likely find work again within baseball. So, for the sake of the fanbase, I say fire away.

The Baseball Police

Here is a video of my segment on Stossel.

Addendum: The embed code seems to be having some problems, so here is a link to the show’s site.