Best-Selling Album by Year, 1992-2016

Top-Selling Albums

This chart shows the best-selling album by year from 1992 to 2016. Only albums sold within each year are counted.  Since it’s the best-selling album within a year, it often helps for an album to be released earlier in the year, since it then gets the more months of sales. Some albums which were the best-selling in a certain year were actually released the previous year, such as Jagged Little Pill by Alanis Morissette, which was released in 1995 but was the best-selling album of 1996. Another example is 21 by Adele, which was released in 2011 but was the best-selling album in both 2011 and 2012.



Projected Healthcare Coverage Loss under the Senate Republican Healthcare Bill, by US State

Healthcare Losses

This map shows the projected net drop in the number of people with healthcare coverage by 2026 as a percentage of each state’s projected 2026 population. The non-partisan Congressional Budget Office estimated that the Better Care Reconciliation Act (BCRA), the Senate Republican’s version of the healthcare bill, would increase the number of people without insurance by 22 million people in 2026, relative to current law.  The Center for American Progress, a progressive think-tank, then created their own estimates of how this 22 million drop in coverage would be distributed across the US.

I took these state by state projected coverage losses and divided them by the population of each state in 2026 to get the net % of the state’s population that would lose coverage. I was unable to find a projection for the 2026 population, but the Cooper Center had population projections for each state for both 2020 and 2030, so I used those to create an estimate for the 2026 population. This projection will obviously not be exact, but it should only affect the estimates on the margins.

North Carolina would see the largest share of their population lose coverage under the BCRA, with 1,348,300 people losing coverage, equaling 12.1% of the state’s 2026 population. The majority of southern states would be hit hard with coverage losses, as would north-western states like Nebraska, Wyoming, Idaho, Montana, and Alaska. The Southwest, Midwest, and New England would see smaller losses as a share of their population, although Vermont and Maine would lose a lot of coverage. North Dakota would see the smallest reduction, a loss in coverage for 25,100 people, 2.8% of the state’s 2026 population.

State Net Coverage Loss by 2026 2026 Population % of Population losing Coverage
Alabama 480,500 5,022,882 9.57%
Alaska 64,500 799,748 8.07%
Arizona 461,000 7,825,693 5.89%
Arkansas 172,400 3,108,621 5.55%
California 2,483,000 43,340,158 5.73%
Colorado 240,100 6,462,721 3.72%
Connecticut 206,800 3,623,350 5.71%
Delaware 59,500 1,052,949 5.65%
District of Columbia 41,200 847,324 4.86%
Florida 2,086,500 23,692,297 8.81%
Georgia 963,200 11,392,280 8.45%
Hawaii 58,200 1,590,275 3.66%
Idaho 144,700 1,850,456 7.82%
Illinois 654,800 12,912,550 5.07%
Indiana 270,400 6,903,913 3.92%
Iowa 127,900 3,287,772 3.89%
Kansas 198,200 3,033,930 6.53%
Kentucky 231,400 4,603,890 5.03%
Louisiana 343,000 4,963,945 6.91%
Maine 117,900 1,330,507 8.86%
Maryland 227,400 6,513,263 3.49%
Massaschusetts 285,300 7,328,967 3.89%
Michigan 489,400 9,996,796 4.90%
Minnesota 217,600 5,889,676 3.69%
Mississippi 278,000 3,042,504 9.14%
Missouri 479,000 6,279,214 7.63%
Montana 81,100 1,128,361 7.19%
Nebraska 173,100 2,047,640 8.45%
Nevada 122,500 3,327,906 3.68%
New Hampshire 45,500 1,359,273 3.35%
New Jersey 418,300 9,303,740 4.50%
New Mexico 133,400 2,138,070 6.24%
New York 1,139,000 20,670,766 5.51%
North Carolina 1,348,300 11,173,353 12.07%
North Dakota 25,100 899,823 2.79%
Ohio 469,600 11,765,616 3.99%
Oklahoma 395,100 4,261,102 9.27%
Oregon 283,300 4,468,784 6.34%
Pennsylvania 731,000 13,002,441 5.62%
Rhode Island 45,800 1,063,341 4.31%
South Carolina 458,000 5,505,311 8.32%
South Dakota 63,700 957,376 6.65%
Tennessee 634,600 7,153,339 8.87%
Texas 2,430,600 33,007,950 7.36%
Utah 186,000 3,541,234 5.25%
Vermont 51,200 626,280 8.18%
Virginia 521,800 9,225,884 5.66%
Washington 298,700 8,186,933 3.65%
West Virginia 118,100 1,825,027 6.47%
Wisconsin 394,100 5,944,911 6.63%
Wyoming 49,000 635,003 7.72%

NBA Win-Share Charts,


I last looked at each NBA team’s win-share charts in Mid-December, see this link to look at those older versions of the charts.

These charts look at each team’s distribution of “win shares” across players. Win Shares are a measure of a players total contribution to a teams success, as explained here:

A few notes:

-If you don’t see a player listed in the win-share pie chart, it’s because they either have 0 win shares or negative win shares.

-The change in win-shares is stated as being from December 15th to February 15th. That is slightly incorrect, as the change is actually measured from December 8th to February 16th.

-Obviously a lot of the changes in win-shares for players come from players being injured or traded. However, there are still some large changes in the win-share % of certain players who did not suffer any major injuries. See Anthony Davis as an example in New Orleans, who’s percentage of the team’s win-shares has dropped sharply as other contributors have picked up some of the slack for him.

-The change in a players win-share percentage is sometimes greater than their total win-share percentage. This is for one of two possible reasons:

1. In Mid-December those players had a negative win-share total.

2. It is an artifact of the fact that I could not include players with negative win-shares in the pie charts. I made the mistake of including those players when calculating the change in each players win-share %, which meant that players who were on a team with lots of negative win-share players saw their percentages inflated, since the total number of win-shares on the team was lower. I probably shouldn’t have calculated them this way for consistency’s sake, but by the time I realized it was too late and I was too lazy to go back and change everything.



NBA Championship Run Win-Share Charts, 1990-2016

These charts look at each NBA champion’s distribution of “win shares” across players during their playoff runs. Win Shares are a measure of a players total contribution to a teams success, as explained here:

Some players on a championship team are not listed, either because they had negative win shares, a net of zero win shares, or did not play in the playoffs.

Some random observations on a few team’s championship team’s win-share distributions:

2014: The Spurs player with the most win-shares during their 2014 run was Tim Duncan, at 17% of their win-share total. This is by far the lowest percentage for any championship team’s top player. The 2014 Spurs really live up to their reputation as a team that shared the ball and had everyone contribute, as 6 different players had over 10% of the team’s playoff win shares apiece.

2013: This is the most recent year where the top player on a championship team had over 30% of the team’s playoff win shares (Lebron obviously). Interesting to note that the “Big Three” according to win-shares for this playoff run was not Lebron, Bosh, and Wade, but actually Lebron, Bosh, and Andersen. Wade had a bit of a down playoffs, and the Birdman was able to sneak into the top three in win shares.

2012: Like in 2013, one of the “Big Three” didn’t make the Heat’s top three in win-shares. In this case, the Big Three was Lebron, Wade, and Mario Chalmers, with Bosh falling to fourth in win-shares during this playoff run.

2004 Pistons: This is one of the most unique win-share distributions of any championship team. Chauncey Billups led the team with 20.4% of their playoff win shares (only Duncan on the 2014 Spurs had a lower % of win shares as the top player on a championship team), but Ben Wallace also had 20% and Richard Hamilton 19.4%. That means there was only a 1% difference between the top player and the third player in win-shares, by far the lowest of any team. This is one of the few championship teams without a clear-cut top player, or even top two players.

2003 Spurs: This was a very un-Spursy Spurs team, with Duncan carrying a massive load with little help at 34.9% of all the team’s playoff win-shares.

2000 Lakers: Shaquille O’Neal had the highest win-share % on any championship run, at 35.3%. During this playoff run Kobe wasn’t quite on his level yet, at only 15.8% of the team’s playoff win-share total.


NBA Win-Share Charts, as of 12/8/2016

These charts look at each team’s distribution of “win shares” across players. Win Shares are a measure of a players total contribution to a teams success, as explained here:

Some of the teams with interesting win share distributions:

New Orleans Pelicans: Anthony Davis has 42% of all New Orleans’ win shares, the highest % for any player. The next highest player on the Pelicans is Tim Frazier, all the way down at 9%.

Detroit Pistons: Andre Drummond has the most win shares on the Pistons, but this is only 18% of the teams total, which is tied for the lowest % for the top player on a team. The Pistons have a very egalitarian distribution of win shares, with 5 players having above 10% of the total win shares.

Denver Nuggets: The Denver Nuggets has two players tied for the most win shares on the team, both also at 18%: Danilo Gallinari and Kenneth Faried. The player with the third-most win shares, at 17% of the total, is Wilson Chandler, while Nikola Jokic has 16%. Thus the gap between the player with the fourth-most win shares, Jokic, and the player with the most, Gallinari, is only 2 percentage points, by far the lowest in the league.


Largest Non-White Racial Group, by County, 1980-2010

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The map above shows the largest Non-White group, by county, from 1980 to 2010. Through the map you can see the growth of the hispanic population, as it became the largest non-white group by 2010 in many counties that formerly had black, native american, or asian populations as the largest group. One thing to note, it was not until 2000 that “multiracial” was added as an option on the census.

Data Source:

Minnesota Population Center. National Historical Geographic Information System: Version 2.0. Minneapolis, MN: University of Minnesota 2011.

Third-Place Candidate, by county, 2016 Presidential Election


The map above uses preliminary results from the 2016 US presidential election to show the third-place finisher in the presidential election in each county. In almost every county the top two candidates were Donald Trump and Hillary Clinton (with the exception of several extremely conservative and heavily mormon counties in Utah and Idaho where Hillary fell to third behind Independent Evan McMullin).

The Libertarian ticket of former New Mexico Governor Gary Johnson and former Massachusetts Governor William Weld received 3.3% of the nationwide popular vote, over 4 million votes total, which was by far the strongest performance of a third-party this year. It was also the best result for a third-party since Ross Perot’s Reform Party run in 1996. This success is evident on the map, as the Johnson ticket reached third-place in the vast majority of counties in the US.

Jill Stein of the Green Party came in fourth in the national popular vote with 1.3 million votes (1%). She was only able to reach third-place in a handful of counties. Among these were several major cities, including Portland, San Francisco, Oakland, and New York City (she had more votes than Johnson in every borough except Staten Island). She was also the strongest third-party candidate on most of the islands of Hawaii (and the San Juan islands of Washington state), part of the northern coast of California (Humboldt and Mendocino counties), and two Native American reservations in North Dakota and one reservation in Wisconsin, along with a couple of other counties.

Evan McMullin is an interesting case. He ran as an independent conservative, hoping to gain the votes of Republicans who were unhappy with Donald Trump. However, he entered the race late and was unable to get on the ballot in most states. Additionally, Republican voters ended up coming home to the party, voting in strong numbers for Trump. McMullin, who is LDS, ended up as the “mormon candidate”. Trumps was unpopular among the conservative Mormon population, which allowed McMullin to pull 21% of the vote in Utah and 7% in Idaho. Nationally, he came in fifth, receiving only 0.4% of the vote, but he came closest of any third-party candidate to capturing a state. McMullin actually placed second in several counties in Utah and Idaho, beating Hillary Clinton in those areas, though he placed third overall in both states. Donald Trump did not fall to third in any counties, though he came closest in the District of Columbia, where he received 4% of the vote and write-in’s received 2.5%.

This year also saw an uptick in the number of “write-in” votes. For example, write-ins dominated the third-party vote in Vermont, and these votes were almost certainly  for the state’s own Senator Bernie Sanders. Almost 8% of Vermont voters wrote-in a presidential candidate on their ballot. Write-ins also placed third in one county in western Wyoming, and in the District of Columbia.

Nevada is the only state with a “None of these Candidates” option on the ballot, and it got 2.6% of the vote in that state and won third-place in two rural Nevada counties.

Finally, the Prohibition Party’s candidate, James Hedges, received 5,565 votes nationwide (apparently the best showing for the Prohibition Party since 1988), which comes to 0.00004% of the national vote. Nonetheless, despite this tiny showing, James Hedges somehow came in third in Arkansas County, Arkansas, with 133 votes, beating Gary Johnson’s total by 7 votes.

Several other third-parties ran candidates, such as the Constitution Party (0.14% of the vote) and the Party of Socialism and Liberation (0.04% of the vote), but none of these parties finished third in any counties.