Archive for May, 2010
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.).
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.
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.