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December 28th, 2020:

Precinct analysis: The judicial averages

Introduction
Congressional districts
State Rep districts
Commissioners Court/JP precincts
Comparing 2012 and 2016
Statewide judicial
Other jurisdictions
Appellate courts, Part 1
Appellate courts, Part 2

As you know, I use the average totals and percentages from local judicial races as my go-to metric for determining partisan indexes for each district. That’s because these are two-candidate races, and generally speaking people vote in them on the party label and not on detailed knowledge of the individual candidates. I’ve looked at this data in various ways over the years – in 2018, it was all about undervoting, as my contribution to the deeply annoying great straight-ticket voting debate. This year, I just want to provide as comprehensive a look as I can at what the partisan index of each district is, so without further ado here are the averages and minimum/maximum values for each district:


Dist    Avg R    Avg D  Avg R%  Avg D%
======================================
CD02  180,657  152,260  54.26%  45.74%
CD07  152,705  147,943  50.79%  49.21%
CD08   25,930   14,830  63.62%  36.38%
CD09   37,855  119,136  24.11%  75.89%
CD10  103,043   58,975  63.60%  36.40%
CD18   59,751  178,574  25.07%  74.93%
CD22   21,796   19,965  52.19%  47.81%
CD29   49,285  100,975  32.80%  67.20%
CD36   82,990   47,534  63.58%  36.42%
				
SBOE4 106,801  333,572  24.25%  75.75%
SBOE6 387,513  345,132  52.89%  47.11%
SBOE8 219,698  161,490  57.64%  42.36%
				
SD04   55,837   22,370  71.40%  28.60%
SD06   57,502  117,156  32.92%  67.08%
SD07  236,992  169,822  58.26%  41.74%
SD11   77,482   46,126  62.68%  37.32%
SD13   38,020  158,384  19.36%  80.64%
SD15  114,322  192,386  37.27%  62.73%
SD17  118,535  122,335  49.21%  50.79%
SD18   15,323   11,618  56.88%  43.12%
				
HD126  39,112   33,088  54.17%  45.83%
HD127  54,309   34,783  60.96%  39.04%
HD128  48,197   21,688  68.97%  31.03%
HD129  48,127   34,606  58.17%  41.83%
HD130  70,364   31,748  68.91%  31.09%
HD131  10,092   44,290  18.56%  81.44%
HD132  50,934   47,797  51.59%  48.41%
HD133  50,892   35,660  58.80%  41.20%
HD134  49,172   56,015  46.75%  53.25%
HD135  36,694   36,599  50.07%  49.93%
HD137  10,422   20,732  33.45%  66.55%
HD138  31,922   30,597  51.06%  48.94%
HD139  15,711   44,501  26.09%  73.91%
HD140   9,326   21,677  30.08%  69.92%
HD141   7,106   35,937  16.51%  83.49%
HD142  13,933   41,496  25.14%  74.86%
HD143  11,999   24,126  33.21%  66.79%
HD144  13,786   16,469  45.57%  54.43%
HD145  14,992   26,765  35.90%  64.10%
HD146  11,408   43,008  20.96%  79.04%
HD147  15,323   52,737  22.51%  77.49%
HD148  22,392   36,300  38.15%  61.85%
HD149  21,640   30,536  41.47%  58.53%
HD150  56,160   39,038  58.99%  41.01%
				
CC1    93,365  277,707  25.16%  74.84%
CC2   150,891  143,324  51.29%  48.71%
CC3   228,295  207,558  52.38%  47.62%
CC4   241,461  211,606  53.29%  46.71%
				
JP1    93,441  162,045  36.57%  63.43%
JP2    34,172   48,572  41.30%  58.70%
JP3    51,782   67,626  43.37%  56.63%
JP4   235,236  182,956  56.25%  43.75%
JP5   204,805  212,367  49.09%  50.91%
JP6     8,152   26,921  23.24%  76.76%
JP7    18,654   99,583  15.78%  84.22%
JP8    67,769   40,125  62.81%  37.19%


Dist    Max R    Min D  Max R%  Min D%
======================================
CD02  185,931  148,006  55.68%  44.32%
CD07  159,695  144,247  52.54%  47.46%
CD08   26,439   14,393  64.75%  35.25%
CD09   40,013  116,625  25.54%  74.46%
CD10  105,177   57,133  64.80%  35.20%
CD18   63,096  174,763  26.53%  73.47%
CD22   22,436   19,262  53.81%  46.19%
CD29   55,680   94,745  37.02%  62.98%
CD36   84,840   45,634  65.02%  34.98%
				
SBOE4 117,378  322,667  26.67%  73.33%
SBOE6 401,507  336,009  54.44%  45.56%
SBOE8 224,690  156,133  59.00%  41.00%
				
SD04   56,905   21,704  72.39%  27.61%
SD06   64,474  110,326  36.88%  63.12%
SD07  242,602  164,480  59.60%  40.40%
SD11   79,333   44,482  64.07%  35.93%
SD13   40,293  155,638  20.56%  79.44%
SD15  118,813  187,188  38.83%  61.17%
SD17  124,541  119,169  51.10%  48.90%
SD18   15,619   11,279  58.07%  41.93%
				
HD126  40,053   31,945  55.63%  44.37%
HD127  55,452   33,703  62.20%  37.80%
HD128  49,089   20,798  70.24%  29.76%
HD129  49,387   33,547  59.55%  40.45%
HD130  71,729   30,669  70.05%  29.95%
HD131  11,027   43,306  20.30%  79.70%
HD132  52,228   46,423  52.94%  47.06%
HD133  53,008   34,318  60.70%  39.30%
HD134  53,200   53,340  49.93%  50.07%
HD135  37,600   35,481  51.45%  48.55%
HD137  10,831   20,255  34.84%  65.16%
HD138  32,956   29,493  52.77%  47.23%
HD139  16,700   43,426  27.78%  72.22%
HD140  10,796   20,276  34.75%  65.25%
HD141   7,844   35,148  18.25%  81.75%
HD142  15,015   40,325  27.13%  72.87%
HD143  13,599   22,554  37.62%  62.38%
HD144  14,965   15,326  49.40%  50.60%
HD145  16,455   25,318  39.39%  60.61%
HD146  11,924   42,368  21.96%  78.04%
HD147  16,147   51,800  23.76%  76.24%
HD148  23,754   35,054  40.39%  59.61%
HD149  22,315   29,713  42.89%  57.11%
HD150  57,274   37,933  60.16%  39.84%
				
CC1    98,310  271,971  26.55%  73.45%
CC2   158,199  135,874  53.80%  46.20%
CC3   236,301  201,920  53.92%  46.08%
CC4   248,120  205,046  54.75%  45.25%
				
JP1    99,574  157,709  38.70%  61.30%
JP2    36,841   45,917  44.52%  55.48%
JP3    54,016   65,253  45.29%  54.71%
JP4   240,145  177,376  57.52%  42.48%
JP5   211,698  206,389  50.63%  49.37%
JP6     9,694   25,425  27.60%  72.40%
JP7    19,825   98,162  16.80%  83.20%
JP8    69,422   38,580  64.28%  35.72%


Dist    Min R    Max D  Min R%  Max D%
======================================
CD02  175,786  157,942  52.67%  47.33%
CD07  145,575  154,644  48.49%  51.51%
CD08   25,520   15,264  62.57%  37.43%
CD09   36,275  121,193  23.04%  76.96%
CD10  101,112   61,042  62.36%  37.64%
CD18   56,673  182,314  23.71%  76.29%
CD22   21,218   20,673  50.65%  49.35%
CD29   45,744  105,745  30.20%  69.80%
CD36   81,336   49,507  62.16%  37.84%
				
SBOE4 100,933  342,178  22.78%  77.22%
SBOE6 373,961  359,113  51.01%  48.99%
SBOE8 215,025  167,034  56.28%  43.72%
				
SD04   55,047   23,216  70.34%  29.66%
SD06   53,562  122,474  30.43%  69.57%
SD07  231,452  175,578  56.86%  43.14%
SD11   75,844   48,065  61.21%  38.79%
SD13   36,086  160,806  18.33%  81.67%
SD15  109,597  198,247  35.60%  64.40%
SD17  112,679  127,956  46.83%  53.17%
SD18   15,000   11,985  55.59%  44.41%
				
HD126  38,215   34,107  52.84%  47.16%
HD127  53,344   35,933  59.75%  40.25%
HD128  47,390   22,477  67.83%  32.17%
HD129  46,964   36,012  56.60%  43.40%
HD130  69,298   32,900  67.81%  32.19%
HD131   9,584   44,980  17.56%  82.44%
HD132  49,625   49,260  50.18%  49.82%
HD133  48,359   37,729  56.17%  43.83%
HD134  45,698   59,519  43.43%  56.57%
HD135  35,662   37,653  48.64%  51.36%
HD137   9,997   21,240  32.00%  68.00%
HD138  30,912   31,792  49.30%  50.70%
HD139  14,891   45,442  24.68%  75.32%
HD140   8,496   22,687  27.25%  72.75%
HD141   6,751   36,444  15.63%  84.37%
HD142  13,366   42,296  24.01%  75.99%
HD143  11,100   25,218  30.56%  69.44%
HD144  13,029   17,345  42.90%  57.10%
HD145  14,011   28,167  33.22%  66.78%
HD146  10,824   43,630  19.88%  80.12%
HD147  14,469   53,867  21.17%  78.83%
HD148  21,053   38,031  35.63%  64.37%
HD149  20,955   31,398  40.03%  59.97%
HD150  55,070   40,198  57.81%  42.19%
				
CC1    88,636  283,723  23.80%  76.20%
CC2   146,468  149,847  49.43%  50.57%
CC3   220,181  215,729  50.51%  49.49%
CC4   234,765  219,028  51.73%  48.27%
				
JP1    87,533  168,977  34.12%  65.88%
JP2    32,564   50,632  39.14%  60.86%
JP3    50,336   69,338  42.06%  57.94%
JP4   230,567  188,394  55.03%  44.97%
JP5   197,305  219,993  47.28%  52.72%
JP6     7,269   28,198  20.50%  79.50%
JP7    17,578  100,870  14.84%  85.16%
JP8    66,324   41,925  61.27%  38.73%

There were 15 contested District or County court races, with another 12 that had only a Democrat running. All of the numbers are from the contested races. The first table is just the average vote total for each candidate in that district; I then computed the percentage from those average values. For the second and third tables, I used the Excel MAX and MIN functions to get the highest and lowest vote totals for each party in each district. It should be noted that the max Republican and min Democratic totals in a given district (and vice versa) may not belong to the candidates from the same race, as the total number of votes in each race varies. Consider these to be a bit more of a theoretical construct, to see what the absolute best and worst case scenario for each party was this year.

One could argue that Democrats did better than expected this year, given the partisan levels they faced. Both Lizzie Fletcher and Jon Rosenthal won re-election, in CD07 and HD135, despite running in districts that were tilted slightly against them. The one Republican that won in a district that tilted Democratic was Precinct 5 Constable Ted Heap, who won as his JP colleague Russ Ridgway fell; as previously noted, Dan Crenshaw clearly outperformed the baseline in CD02. The tilt in Commissioners Court Precinct 3 was too much for Michael Moore to overcome, though perhaps redistricting and four more years of demographic change will move things in the Democratic direction for 2024. As for Precinct 2, I believe Adrian Garcia would have been re-elected if he had been on the ballot despite the Republican tilt in that precinct, mostly because the Latino Democratic candidates generally carried the precinct. He will also get a hand from redistricting when that happens. I believe being the incumbent would have helped him regardless, as Jack Morman ran ahead of the pack in 2018, just not by enough to hang on.

The “Republican max” (table 2) and “Democratic max” (table 3) values give you a picture of the range of possibility in each district. At their high end for Republicans, CD02 and SBOE6 don’t look particularly competitive, while CD07 and HD135 look like they really got away, while HD144 looks like a missed opportunity, and JP5 could have maybe been held in both races. HD134 remained stubbornly Democratic, however. On the flip side, you can see that at least one Democratic judicial candidate took a majority in CD07, HD135, HD138, and CC2, while CC3 and CC4 both look enticingly close, and neither HDs 134 nor 144 look competitive at all. If nothing else, this is a reminder that even in these judicial races, there can be a lot of variance.

On the subject of undervoting, as noted in the Appellate Court posts, the dropoff rate in those races was about 4.7% – there wasn’t much change from the first race to the fourth. For the contested local judicial races, the undervote rate ranged from 5.06% in the first race to 6.54%, in the seventh (contested) race from the end. There was a downward trend as you got farther down the ballot, but it wasn’t absolute – as noted, there were six races after the most-undervoted race, all with higher vote totals. The difference between the highest turnout race to the lowest was about 24K votes, from 1.568 million to 1.544 million. It’s not nothing, but in the grand scheme of things it’s pretty minimal.

The twelve unopposed Democrats in judicial races clearly show how unopposed candidates always do better than candidates that have opponents. Every unopposed judicial candidate collected over one million votes. Kristen Hawkins, the first unopposed judicial candidate, and thus most likely the first unopposed candidate on everyone’s ballot, led the way with 1.068 million votes, about 200K more votes than Michael Gomez, who was the leading votegetter in a contested race. Every unopposed Democratic candidate got a vote from at least 61.25% of all voters, with Hawkins getting a vote from 64.44% of all. I have always assumed that some number of people feel like they need to vote in each race, even the ones with only one candidate.

I’m going to analyze the vote in the non-Houston cities next. As always, please let me know what you think.

One last (?) pointless gesture

Because true desperation never dies, I guess.

Rep. Lance Gooden of Terrell is one of the latest members of Congress to say he is going to object to the Electoral College certification on Jan. 6. Now, he says he just needs Sen. Ted Cruz or Sen. John Cornyn to join him.

The Terrell Republican said neither senator had yet responded to him on a Fox News interview Wednesday evening, but he said he was confident that another senator would step up. Sen.-elect Tommy Tuberville of Alabama has indicated that he might object.

“On January the 6th, I suspect that more senators will come out and join me in this objection,” Gooden said. “But we’re starting here at home with Sen. Cornyn and Sen. Cruz.”

Gooden is not the only Texas Republican who has pledged to object to the Electoral College count. He signed a letter with Reps. Brian Babin of Woodville, Louie Gohmert of Tyler and Randy Weber of Friendswood saying they all would object to the results of the presidential election if Congress does not investigate claims of alleged voter fraud by Jan. 6.

In a letter to the senators, Gooden called for a full audit of ballots in Georgia, Arizona, Michigan and Pennsylvania, where the Trump campaign alleges fraud has occurred, despite little evidence.

[…]

The Electoral College voted for President-elect Joe Biden with a 306-232 majority on Dec. 14, but the last opportunity to challenge the results of the election will take place when Congress meets to certify the Electoral College results on Jan. 6. If at least one member of the Senate and one member of the House object to the results of the election, Congress must debate the matter.

Unless a majority in each chamber votes to reject the electors, the tally will stand. Since Democrats control the House, it is unlikely to be successful.

That’s “zero evidence”, and “not going to be successful”, but do go on. Louie Gohmert is stupid enough to believe his own bullshit, but I suspect the others have to know this is all a farce, and they’re doing it anyway because Donald Trump means more to them than any of the American values they have so piously intoned at us over the years. In the spirit of Christmas, I’m just going to leave it at that.

Too much virus, not enough treatment

Still a bad combination.

Three weeks after Gov. Greg Abbott visited Lubbock to celebrate new antibody treatments amid a surge of infections, the city remains in crisis. Its two main hospitals had nearly two dozen patients waiting for beds Friday, and the city has administered only about 200 doses of the new medications, with about 4,500 active cases countywide.

Hospitals are also filling in Dallas, Houston, San Antonio and other parts of the state that were slower to be hit by the fall surge. The state is hovering around 9,000 hospitalized COVID-19 patients and reported fewer than 700 available intensive care beds for the first time this week, less than half the supply in September.

While most hospital officials in Texas welcomed the new treatments and remain hopeful that they prevent some hospitalizations, the limitations are also becoming apparent. Without enough doses or a way to distribute them quickly, hospitals will continue to be strained unless infections slow or until vaccines become widely available, not likely until at least early summer.

In Lubbock, hundreds of nurses and other hospital employees are out sick or quarantining from the coronavirus, and administrators worry that the hundreds more who have come to help from across the state and country will be called back as outbreaks in their home communities worsen. More than 231,000 new cases were reported Friday nationwide, nearly 4,000 above the previous record set on Dec. 4.

“We always have more contingency plans, and we’re deep into the middle of some of those where we truly are turning away patients from outlying communities because we can’t take them,” said Dr. Ron Cook, Lubbock’s health authority and the chief health officer at the Texas Tech University Health Science Center.

The treatments, made by the companies Eli Lilly and Regeneron, were granted emergency use authorizations last month to help prevent hospitalizations for the most vulnerable patients, including those over 65 and with chronic illnesses such as diabetes, obesity or kidney disease. They are the same treatments President Donald Trump and his lawyer Rudy Guiliani have received.

Texas got about 20,000 doses in the past five weeks, while it reported 330,000 new infections. Early clinical research suggests the drugs prevent about 1 in 20 people who receive them from being hospitalized.

Doctors at University Medical Center in Lubbock are encouraged by the early outcomes, but have often struggled to contact and persuade enough eligible patients to receive the treatments. The drugs need to be administered early on, before a person is hospitalized, and patients may not yet have developed symptoms. Some have never heard of the treatments or spoken with the hospital’s doctors before.

[…]

Combating the virus has been especially tough in Lubbock, a college town in a fiercely independent swath of the state where pandemic science has been regularly questioned and the governor’s tepid mask mandate largely unenforced. In recent weeks, the mayor and others have resorted to pleading with residents to physically distance and wear face coverings.

“Our independence is also hurting us,” Cook said.

Abbott’s mask order includes several exceptions and calls for fines only on the second offense, which county officials have said is nearly impossible to track.

It’s like I was saying. Prevention will have an exponentially better effect on the pandemic than treatment will, and that’s true even if the treatment we’d been given was much more effective than preventing five percent of its recipients from being hospitalized if they take it in time. Donald Trump and Greg Abbott have failed us at so many levels.

(This story is from two weeks ago, it’s been in my drafts because there’s been so much news as well as the holidays. It’s possible things are a little better in Lubbock now – I sure hope they are – but the point still stands. We are reacting instead of trying to take control of the situation. We’ve been doing that for months. The fact that we have better tools now to react with doesn’t change that.)