Moneybase: Moving the A’s (Rough Draft Version, part 1)

In my previous job, I built a big database of zipcodes and geolocations, and distances between those zip codes. The server that this database lived on is getting shut down sometime in the next 24 hours. A couple days ago, I suddenly realized I could use that database to answer a few questions I’ve had about where the A’s should be moving.

So I’ve been scrambling to try to get some queries done, before the server goes away. I managed to get the work done once, but I didn’t get a chance to double-check anything, so take all this with a gigantic grain of “this is a first draft” kind of salt.

* * *

The raw data I had was from the 2000 census and included:

  • every 5-digit zip code in the United States (about 40,000 of them)
  • the latitude and longitude of each of those zip codes, and
  • population and median household income for about 30,000 of those zip codes

I’m not sure why 10,000 zip codes don’t have population and income data. Probably some of them represent entities (like governments and such) that aren’t geographic locations with residents. But not all of them. For example, the zip code that includes Safeco Field in Seattle was among the zip codes missing data. Baltimore looks like it’s missing a big chunk of data. Plus, there’s no Canadian data either, so the Blue Jays are unrepresented, as are probably some additional Tigers and Mariners fans. So I’m sure the data needs a real good scrubbing, so I’ll repeat my warning about the rough nature of this data.

From the geodata, I calculated the distance between any two zipcodes that were less than 150 miles apart.

* * *

If you’re going to build a ballpark somewhere, you’d want to put it somewhere:

  • with as many people as possible
  • who have as much money as possible
  • who live as near to the ballpark as possible

So I came up with a formula to reflect this. For this exercise, I don’t really need to know the exact amount of money a ballpark can generate, I just need a number I can use to compare with. So median household income will do just fine, even though it’s not at all an accurate representation of how much money is available to spend on baseball.

So here’s what I did: For each zip code within 75 miles of a MLB ballpark, I took the population and multiplied it with the median household income of that zipcode, to give that zipcode a total amount of money for that zipcode. (I should probably have divided by average household size, but we’re after relative comparisons here, so it doesn’t matter too much.) Then for each mile that zipcode was from the ballpark, I subtracted 1/75th of that total from the score for that zipcode.

So the closer the zipcode is to the ballpark, the more money from that zipcode is assigned to the team.

Then I repeated the exercise for five potential A’s homes: the Coliseum, Victory Court, Fremont, San Jose, and Sacramento.

Once I had done that, I did it for every minor league park that was more than 75 miles from any existing MLB park, plus Portland, Honolulu, and Anchorage.

* * *

The (rough) results, for your viewing pleasure:

zip team city state relative market size
10451 Yankees Bronx NY $ 752,743,595,112
11368 Mets Corona NY $ 748,642,916,914
90012 Dodgers Los Angeles CA $ 510,586,706,490
92806 Angels Anaheim CA $ 429,295,980,560
60616 White Sox Chicago IL $ 353,523,094,940
60613 Cubs Chicago IL $ 351,956,542,055
19148 Phillies Philadelphia PA $ 313,899,514,792
20003 Nationals Washington DC $ 313,043,794,378
94107 Giants San Francisco CA $ 276,531,798,517
02215 Red Sox Boston MA $ 258,052,953,191
48201 Tigers Detroit MI $ 194,991,345,880
76011 Rangers Arlington TX $ 184,979,524,329
77002 Astros Houston TX $ 184,030,914,939
30315 Braves Atlanta GA $ 166,992,030,231
55403 Twins Minneapolis MN $ 138,398,423,288
33125 Marlins Miami FL $ 134,237,186,849
85004 Diamondbacks Phoenix AZ $ 126,863,473,073
98134 Mariners Seattle WA $ 123,032,784,722
80205 Rockies Denver CO $ 120,203,857,817
92101 Padres San Diego CA $ 119,511,331,778
44115 Indians Cleveland OH $ 114,814,235,603
53214 Brewers Milwaukee WI $ 114,411,126,303
63102 Cardinals St. Louis MO $ 95,088,871,967
45202 Reds Cincinnati OH $ 93,961,385,672
15212 Pirates Pittsburgh PA $ 91,812,365,763
33705 Rays St. Petersburg FL $ 86,162,065,166
64129 Royals Kansas City MO $ 75,617,221,577
21201 Orioles Baltimore MD $ 51,550,959,511
 
94621 Coliseum Oakland CA $ 302,835,904,135
94538 Fremont Fremont CA $ 301,356,267,461
94607 Victory Ct Oakland CA $ 288,464,089,740
95110 San Jose San Jose CA $ 244,281,690,385
95691 Sacramento West Sacramento CA $ 122,189,968,456
 
97205 Portland Portland OR $ 86,934,977,194
43223 Columbus Columbus OH $ 82,466,829,644
48912 Lansing Lansing MI $ 80,674,718,903
46225 Indianapolis Indianapolis IN $ 76,724,547,759
29715 Charlotte Fort Mill SC $ 66,474,729,100
27401 Greensboro Greensboro NC $ 61,196,329,994
23510 Norfolk Norfolk VA $ 58,754,813,183
14020 Batavia Batavia NY $ 58,698,864,009
78664 Round Rock Round Rock TX $ 57,730,378,193
78227 San Antonio San Antonio TX $ 57,705,434,009
27597 Carolina Zebulon NC $ 56,677,487,954
49017 Southwest MI Battle Creek MI $ 54,991,532,182
27105 Winston-Salem Winston Salem NC $ 54,919,270,523
14608 Rochester Rochester NY $ 54,337,641,491
89101 Las Vegas Las Vegas NV $ 53,809,399,974
37203 Nashville Nashville TN $ 53,707,382,854
49321 West Michigan Comstock Park MI $ 53,532,262,868
84058 Orem Orem UT $ 52,659,523,005
70003 New Orleans Metairie LA $ 51,061,101,559
40202 Louisville Louisville KY $ 50,940,204,646
14203 Buffalo Buffalo NY $ 50,893,985,379
32114 Daytona Daytona Beach FL $ 48,253,823,630
18505 Scranton-Wilkes Scranton PA $ 47,728,598,665
23230 Richmond Richmond VA $ 47,551,291,211
32940 Brevard County Melbourne FL $ 47,518,011,693
84401 Ogden Ogden UT $ 46,138,307,179
38103 Memphis Memphis TN $ 44,483,924,752
13021 Auburn Auburn NY $ 43,555,185,837
29607 Greenville Greenville SC $ 43,508,445,289
91730 Rancho Cucamonga Rancho Cucamonga CA $ 39,638,600,052

* * *

These results, outside of Baltimore, smell more or less right to me.

Next, though, I tried to put some measure on what happens to a market when it is shared between teams. This is where the results surprised me, enough so, that I think I probably screwed up somewhere.

I’ll address that in an upcoming blog post.

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