Letter to the Editor of the Gwinnett Daily Post
Mr. Todd Cline
Editor
Gwinnett Daily Post
P.O. Box 603
Lawrenceville, GA 30046-0603
Dear Mr. Cline,
In your weekend editorial G-Braves hit a homer with Coolray Field (February 20), you praised the naming rights deal for the County’s minor-league baseball stadium.
At a time when the economy is pitching shutouts, the G-Braves came up with a big hit in the form of a sponsor to purchase naming rights for the stadium. …
Chairman Charles Bannister said “we’re in good shape” financially with the stadium, but this week’s announcement will only help ensure that. Plus, it’s a neat name, a nice double play for the team and the county.
The season doesn’t start until April 8 at Coolray Field, but both entities can already chalk up a victory.
When the financial plans were first presented to the public, the County claimed that a naming rights deal would cover $500,000 per year of the annual debt service. This deal, if correctly reported by County officials, will give the County an average of $265,000 annually for its first 17 years of operation (recall that the first year netted no naming rights payment).
This amount is about half of what County officials claimed they would be receiving from naming rights. How can you fail to report this detail and then declare this deal to be a victory? Would your sports editor declare the season a victory if the Braves won only half the games they planned to win? Would your circulation manager declare victory if only half of paid subscribers received their papers?
Sincerely,
J.C. Bradbury
Update: The Gwinnett Daily Post published the letter.
More Testing of the Verducci Effect
After doing my analysis of the Verducci Effect yesterday, I became aware of Jeremy Greenhouse’s analysis on the subject. He uses a different method, but also finds little support for the Verducci Effect. His analysis pointed me to Josh Hermsmeyer’s Free Player Injury Database, which is valuable new resource. The database contains injury information dating back to the 2002 season. Because the Verducci Effect is largely about predicting injuries I wanted to see how player workloads predicted time on the Disabled List (DL). If significantly increasing pitcher workloads raises the incidence of future injuries, then pitchers who meet Verducci’s criteria should be more likely to get injured.
The table below lists the estimates for the impact of the Verducci Effect on DL stints. I estimated several models (including continuous estimates of pitcher workload), but I report only four specifications below because the results are consistent with the unreported estimates. I looked at the number of days on the DL (continuous) and whether or not a player ended up on the DL (discrete) using random-effects estimation models, least-squares for the former and logit for the latter. I also included the number of days on the DL in the preceding seasons in two specifications to control for the natural injury propensity of players.
DL Days DL Days On DL On DL Verducci 4.27 -1.89 0.28 0.06 [0.76] [0.59] [0.66] [0.12] Mean IP -0.19 -0.16 -0.006 -0.003 [9.33]** [12.75]** [4.69]** [2.01]* DL Days (t-1) 0.64 0.10 [54.16]** [14.06]** Constant 37.67 29.48 -0.50 -1.68 [14.77]** [18.60]** [3.23]** [8.69]** Observations 1428 1428 1428 1428 Overall R2 0.04 0.63 -- -- Absolute value of z statistics in brackets * significant at 5%; ** significant at 1%
Again, the results do not support the existence of the Verducci Effect. The estimates are smal and not statistically significant. A change in workload by more than 30 innings for pitchers under 26 is not associated with more days on or trips to the DL. I would like to reiterate that there needs to be further testing of Verducci Effect, but so far there doesn’t appear to be much empirical support for the hypothesis.
Testing the Verducci Effect
For some reason, the Verducci Effect seems to be getting a lot of attention right now. I recall it being mentioned in the past, but I haven’t paid much attention to it. The effect is named for Sports Illustrated writer Tom Verducci, who came up with the concept but didn’t pick the name. Verducci uses a set of criteria to identify pitchers who are at risk for injury due to a significant increase in workload. He describes the criteria for selection and rationale in an article published this week.
More than a decade ago, with the help of then-Oakland pitching coach Rick Peterson, I began tracking one element of overuse which seemed entirely avoidable: working young pitchers too much too soon. Pitchers not yet fully conditioned and physically matured were at risk if clubs asked them to pitch far more innings than they did the previous season — like asking a 10K runner to crank out a marathon. The task wasn’t impossible, but the after-effects were debilitating. I defined an at-risk pitcher as any 25-and-under pitcher who increased his innings log by more than 30 in a year in which he pitched in the big leagues. Each year the breakdown rate of such red-flagged pitchers — either by injury or drop in performance — was staggering.
I figured now would be as good a time as any to put off the other important things I should be doing in order to find out if the Verducci Effect is real. I used a sample of major-league pitchers from 1998–2007 to estimate the impact of ratcheting up pitching loads on performance on innings pitched and era, using both their recent major-league and minor-league workloads to predict performance. In some specifications I included the average between the present and past seasons’ performances (Mean IP or mean ERA) to peg a typical performance level for each pitcher. The Verducci Effect was considered to be in force if a pitcher was under 26 had increased his workload by more than 30 innings in the previous year. I also measured the Verducci Effect continuously using the actual number of innings pitched increased before the preceding season. I only looked at performance in the majors, but minor-league workload totals counted toward the Verducci Effect. I estimated the impact using a random-effects estimation technique that controlled for detected serial correlation. The regression estimates are below, but if you’re not familiar with reading such tables you can skip over them and read my write-up that follows.
IP Change IP Change IP Change IP Change Verducci 19.07 22.17 [3.18]** [3.73]** IP Change * Under 26 0.23 0.21 [3.37]** [3.15]** IP Change -0.25 -0.17 [10.41]** [7.22]** Under 26 14.89 17.04 [4.46]** [5.29]** Mean IP 0.06 0.13 [3.96]** [6.98]** Constant -12.23 -4.83 -21.97 -6.61 [5.78]** [4.90]** [8.83]** [5.98]** Observations 2383 2383 2316 2316 Overall R2 0.0122 0.0058 0.0379 0.0257 Absolute value of z statistics in brackets * significant at 5%; ** significant at 1%
ERA Change ERA Change ERA Change ERA Change Verducci -0.09600 -0.10295 [0.21] [0.22] IP Change * Under 26 -0.00391 -0.00386 [0.78] [0.77] IP Change 0.00611 0.00609 [3.71]** [3.74]** Under 26 -0.24738 -0.25085 [0.93] [0.95] Mean IP 0.47554 0.00684 [13.67]** [0.17] Constant -1.90261 0.49064 0.36013 0.39538 [8.05]** [2.98]** [1.50] [2.86]** Observations 2380 2380 2313 2313 Overall R2 0.0707 0.0000 0.0034 0.0038 Absolute value of z statistics in brackets * significant at 5%; ** significant at 1%
The first row of each table measures the straight-up Verducci effect. If you increased your workload by more than 30 innings in the preceding season and are under the age of 26, then we should expect to see a decline in innings pitched and ERA. However, it turns out that this is not the case. In terms of workload, Verducci Effect pitchers actually increased their innings pitched between 19 to 22 innings. In terms of performance quality, pitcher ERAs declined by an average of 0.1 runs; however, the effect was not statistically significant, which means it’s probably best to say there is no effect.
The last two columns of the tables represent attempts to quantify the Verducci effect as a continuous phenomenon; that is, the more your workload increases the stronger the effect ought to be. To do this I used three variables: the change in workload (measured by innings pitched), an indicator of whether or not the player was under 26, and an interaction term that multiplies the change in workload times the under 26 indicator. The interaction term (listed on the second row of each table) captures any difference in performance from workload by Verducci Effect pitchers. For innings pitched, Verducci Effect pitchers increased the number of innings pitched by about 7 innings for every 30 innings pitched. In addition, being under 26 increased expected innings by 15 innings, while the change in workload tended to lower innings pitched for all pitchers by about 8 innings. Thus, the net result for an under 26 pitcher increasing his workload by 30 innings is an increase of about 7 innings pitched. Note these results are all statistically significant, but this was not the case for ERA.
So, where are we? The results do not bode well for the Verducci Effect. Pitchers who were predicted to decline actually improved. One potential problem with this study is that pitchers who pitched no innings at all in a season were not included; however, I think this bias is slight since this number is small, as even injured pitchers normally get in a few innings every season. Frankly, this is about as quick and dirty as you can get with a test; but, it’s a starting point, and I’d like to see others examine the effect further. While appreciate the intuition behind the Verducci Effect, I don’t see much evidence for it.
Naming Rights Fail
After going a year without a naming rights deal in place, which forced the county to turn over the job of finding the rights to the Braves, it was announced yesterday that the Gwinnett Braves’ stadium will be known at Coolray Field for the next 16 years. Exact financial details were not released, but even if County officials are to be believed, the deal supposedly nets the County $4.5 million over the life of the deal.
If we just do a simple breakdown of the dollars by year ($4.5 million/16 years) that comes to $281,000 per year. And if we break out out by 17 years—because we need to count the lack of revenue captured in the first year—the County has reached a deal to generate an average of $265,000 per year. How does this stack up with their initial revenue projections?
“This will represent about $4.5 million to the county over the length of the deal,” Gwinnett Convention and Visitor Bureau executive director Preston Williams said. “It falls in pretty closely to the financial model we were working on for the stadium. This is a significant deal and a good one in tough economic times like these.”
Pretty close? The County anticipated $500,000 in annual revenue from a naming rights deal. How can getting half of what you expected get be considered pretty close?
And then government officials once again roll out the canard that the stadium is somehow in the black because a car rental tax is generating revenue for the stadium (not to mention the GCVB kick-in that is funded by revenue from Gwinnett Arena which was funded by a hotel tax).
The county was able to cover the lack of naming rights revenue during the stadium’s first season because of higher than anticipated revenue from a 3 percent tax on rental vehicles that was passed to help pay for construction.
“We’re in good shape,” Bannister said. “Financially, it is working out just fine and I’m excited about the future.”
You see, if you take the revenue from a totally unrelated item and apply it to the stadium, it’s breaking even. Unbelievable. By this rationale, every government project has a balanced budget. It’s so good to see those hard-nosed fiscal conservatives on the Gwinnett Board of Commissioners demonstrating responsible financial management.
Which ism Killed Scandinavian Figure Skating?
My latest Olympinomics post is up at Olympics-Reference Blog. Today, I take a look at possible political and economics reasons why figure skating lost popularity in Scandinavia in the mid-twentieth century.
Where Are the Good Scandinavian Figure Skaters?
Today’s Olympinomics post was inspired by Keith Law, who asked “why are there no good figure skaters from Scandinavia?”
Scandinavian countries tend to be quite good at most winter sports, which is no surprise given their climate; however, no Scandinavian athlete has won a figure skating medal since 1936.
Why is this? Read more at Olympics-Reference Blog.
When Gender Matters and When It Doesn’t
My latest Olympinomics post is up at the Olympics-Reference Blog.
One of the consistent findings in the academic literature on aging and athletic performance is that women tend to reach their athletic peak earlier than men. This difference is stronger in strength and speed events which tend to peak younger than endurance events for both genders. In this post, I compare the average age of medal winners by gender to see their differences across sports.
Why Are There More Canadian Lefties?
Reader Benjamin sent me a link to this interesting article about the high incidence of left-handed hockey players among Canadians.
According to sales figures from stick manufacturers, a majority of Canadian hockey players shoot left-handed, and a majority of American players shoot right-handed. No reason is known for this disparity, which cuts across all age groups and has persisted for decades.
Most Canadians, like most Americans, are naturally right-handed, so the discrepancy has nothing to do with national brain-wiring. And how you hold a pencil, say, has little or no bearing on how you hold a stick. A left-handed shooter puts his right hand on top; a right-hander puts the left hand there.
I was curious, so I decided to check out the disparity for baseball. It turns out that Canadians are much more likely to bat left-handed than Americans.
Bats CAN USA L 46.15% 28.43% R 49.04% 65.48% S 4.81% 6.09%
My first thought was that higher participation in hockey may generate more left-handed batters, because hitting opposite your natural hand is something that can be learned. If you’re playing hockey and baseball there are higher returns to learning how to do things left-handed. Plus, being able to shoot either way makes a player more dangerous; thus, right-handed baseball players will be able to more easily learn to bat left-handed. Though, the rate of switch-hitting is about the same between the groups. I think this may be part of the explanation, but I don’t know if it explains the entire disparity.
Unlike hitting, throwing a baseball is difficult to learn. You are either born capable to throw with your left arm, or you aren’t. And when we look at throwing arms, Canadians again are more likely to be left-handed than Americans, though the disparity is much less than it is for hitting.
Throws CAN USA L 24.10% 20.41% R 75.90% 79.59%
So, for the sake of being crazy, let me throw out a crazy explanation. Several years ago I noticed that Latin American players were more right-hand dominant than the rest of the baseball population. I thought the best explanation was that cultural biases against left-handedness may have been encouraging more right-handedness. But now that we see more lefties in Canada than in the US, but less in Latin America, I wonder about a Jared Diamond-esque theory of geographic determinism. Maybe the further you go from the equator, the more likely you are to be left-handed.
Remember, I called this a crazy theory, but it’s not out of the realm of possibility. I think the place to test this would be with footedness in soccer, which is played all over the world. Are Scandinavians more likely to be left-footed than North Africans? If this data is out there, I’d love to see it examined.
Apolo Ohno Is an Old Fogey
My latest Olympinomics post is up at Olympics-Reference.
Apolo Ohno may be the most recognized American participating in this year’s games. Not only is he a Dancing with the Stars champion, but he’s competing in his third Olympics. And unless you haven’t been listening to any of the commentary, you’re probably aware that he is attempting to win more medals in the Winter Olympics than any other American. With his win Saturday night in the 1,500 meters Short Track, he tied Bonnie Blair for six total medals. Even if Ohno does not surpass Blair, his performance may be more impressive considering that he has competed across Olympic games that were all four years apart. Blair benefited from the short gap between the 1992 and 1994 games.
But why am I calling him an old fogey? At 27 Ohno isn’t even close to the oldest person to win a medal in the Winter Olympics.
Olympinomics
With the Winter Olympics starting up with the opening ceremony tonight, I wanted to announce that I will be blogging about the games over at the Olympics-Reference Blog. My first post (below) is up, and I will start posting analysis next week.
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If you’re like me, you like the Winter Olympics; not because you know much about the sports or participants, but because it’s fun to watch people play around in the snow. And while I enjoy sitting down front of the TV to watch whatever NBC decides to show me, just staring at screen and cheering for the athlete with the saddest side-story doesn’t feel right. For years I’ve been watching baseball through an analytical lens, trying to better understand the factors that matter most. I’ve blogged about it extensively at Sabernomics.com, my book The Baseball Economist was published three years ago, and my latest book Hot Stove Economics will be published this coming fall.
Well, it’s time for a little more in-depth analysis of the winter games, and thanks to Olympics-Reference this is possible. When Sean Forman rolled out the site I happened to be working on a project investigating how baseball players age, so I was familiar with how researchers had been using Olympics data to analyze aging patterns. The academic literature contained several interesting studies of Olympic sports that examined how athletes aged, but most of the analysis has concentrated on summer sports. I saw Olympics-Reference provided a fruitful data source for analysis of winter events.
Winter sports pose a new challenge, because almost every sport requires tools like skis, skates, sleds, and even guns. So, I decided to take on a project that looked at aging in winter sports. How does aging differ across sports? How has it changed over time? How does aging differ by gender? These are some of the questions that I have been examining, and I thought it would be a good idea to blog about some of my findings while the winter games were going on.
So, starting Monday, I’ll be blogging here about the Winter Olympics. If you’re familiar with the games in a way that I am not—I’ve lived most of my life in the South, so I’m not all that familiar with winter sports—feel free to chime in. I see some puzzles in the data that are ripe for examination.
