[kiryandil]

This is a comparison of Obama's record turnout in Pennsylvania in 2008 with Basement Joe's "turnout" in 2020.

[kiryandil]

A Comparison of Obama 2008 with Hillary 2016

Blue highlighted counties were won by the Democrat.
pop column is the county population rank in the state.
A negative number in the Obama minus Hillary column means that Hillary turnout was greater than Obama 2008. Note that only 2 counties fit that description.

Metropolitan areas [grouped counties] follow the main table.

													p	Obama	Hillary
													o	minus	div
		Obama 2008		McCain 2008					Clinton 2016		Trump 2016		p	Hillary	Obama
Adams	40%	17,633	59%	26,349	44,491		Adams	30%	14,219	66%	31,423	47,489		3,414	81%
Allegheny	57%	373,153	42%	272,347	651,436		Allegheny	57%	367,617	40%	259,480	650,114	2	5,536	99%
Armstrong	37%	11,138	62%	18,542	30,081		Armstrong	23%	7,178	74%	23,484	31,618		3,960	64%
Beaver	48%	40,499	51%	42,895	84,488		Beaver	39%	32,531	58%	48,167	83,571	21	7,968	80%
Bedford	27%	6,059	72%	16,124	22,443		Bedford	15%	3,645	83%	19,552	23,637		2,414	60%
Berks	54%	97,047	45%	80,513	180,000		Berks	43%	78,437	53%	96,626	183,065	9	18,610	81%
Blair	37%	19,813	62%	32,708	53,102		Blair	25%	13,958	71%	39,135	54,909		5,855	70%
Bradford	40%	10,306	58%	15,057	25,787		Bradford	25%	6,369	71%	18,141	25,708		3,937	62%
Bucks	54%	179,031	45%	150,248	332,924		Bucks	49%	167,060	48%	164,361	344,297	4	11,971	93%
Butler	36%	32,260	63%	57,074	90,425		Butler	29%	28,584	66%	64,428	97,071	19	3,676	89%
Cambria	49%	32,451	49%	31,995	65,670		Cambria	30%	18,867	67%	42,258	63,072		13,584	58%
Cameron	39%	879	59%	1,323	2,245		Cameron	24%	531	73%	1,589	2,186		348	60%
Carbon	50%	13,464	48%	12,957	26,923		Carbon	31%	8,936	65%	18,743	28,776		4,528	66%
Centre	55%	41,950	44%	32,992	75,763		Centre	49%	37,088	46%	35,274	76,148		4,862	88%
Chester	54%	137,833	45%	114,421	254,354		Chester	53%	141,682	43%	116,114	268,800	7	-3,849	103%
Clarion	38%	6,756	60%	10,737	17,766		Clarion	24%	4,273	72%	12,576	17,535		2,483	63%
Clearfield	43%	14,555	55%	18,662	33,813		Clearfield	24%	8,200	73%	24,932	34,271		6,355	56%
Clinton	48%	7,097	51%	7,504	14,791		Clinton	31%	4,744	65%	10,022	15,394		2,353	67%
Columbia	47%	13,230	52%	14,477	28,063		Columbia	32%	8,934	64%	18,004	28,228		4,296	68%
Crawford	44%	16,780	54%	20,750	38,134		Crawford	29%	10,971	67%	24,987	37,492		5,809	65%
Cumberland	43%	48,306	56%	63,739	113,304		Cumberland	39%	47,085	57%	69,076	121,617	16	1,221	97%
Dauphin	54%	69,975	45%	58,238	129,529		Dauphin	49%	64,706	47%	60,863	130,872	14	5,269	92%
Delaware	60%	178,870	39%	115,273	297,004		Delaware	60%	177,402	37%	110,667	297,634	5	1,468	99%
Elk	51%	7,290	47%	6,676	14,271		Elk	27%	3,853	69%	10,025	14,426		3,437	53%
Erie	59%	75,775	39%	50,351	127,691		Erie	47%	58,112	49%	60,069	123,679	15	17,663	77%
Fayette	49%	25,866	50%	26,081	52,560		Fayette	33%	17,946	64%	34,590	53,767		7,920	69%
Forest	42%	1,038	56%	1,366	2,444		Forest	26%	626	70%	1,683	2,401		412	60%
Franklin	33%	21,169	66%	41,906	63,641		Franklin	25%	17,465	71%	49,768	69,731		3,704	83%
Fulton	25%	1,576	74%	4,642	6,306		Fulton	13%	912	84%	5,694	6,771		664	58%
Greene	49%	7,829	49%	7,889	15,976		Greene	28%	4,482	69%	10,849	15,764		3,347	57%
Huntingdon	36%	6,621	63%	11,745	18,632		Huntingdon	23%	4,539	74%	14,494	19,706		2,082	69%
Indiana	46%	17,065	53%	19,727	37,302		Indiana	31%	11,528	66%	24,888	37,770		5,537	68%
Jefferson	34%	6,447	64%	12,057	18,802		Jefferson	19%	3,650	78%	15,192	19,478		2,797	57%
Juniata	32%	3,068	67%	6,484	9,711		Juniata	17%	1,821	79%	8,273	10,454		1,247	59%
Lackawanna	63%	67,520	37%	39,488	107,876		Lackawanna	50%	51,983	47%	48,384	103,456	17	15,537	77%
Lancaster	44%	99,586	55%	126,568	228,137		Lancaster	38%	91,093	57%	137,914	241,112	6	8,493	91%
Lawrence	47%	19,711	52%	21,851	42,103		Lawrence	34%	14,009	62%	25,428	40,753		5,702	71%
Lebanon	40%	23,310	59%	34,314	58,297		Lebanon	31%	18,953	66%	40,525	61,845		4,357	81%
Lehigh	57%	87,089	42%	63,382	152,473		Lehigh	51%	81,324	46%	73,690	160,993	10	5,765	93%
Luzerne	54%	72,492	45%	61,127	135,175		Luzerne	39%	52,451	58%	78,688	134,983	13	20,041	72%
Lycoming	37%	18,381	62%	30,280	49,237		Lycoming	26%	13,020	70%	35,627	50,565		5,361	71%
Mckean	41%	6,465	58%	9,224	15,947		McKean	25%	4,025	71%	11,635	16,296		2,440	62%
Mercer	49%	26,411	49%	26,565	53,821		Mercer	36%	18,733	60%	31,544	52,309		7,678	71%
Mifflin	33%	5,375	66%	10,929	16,502		Mifflin	21%	3,877	76%	14,094	18,601		1,498	72%
Monroe	58%	39,453	41%	28,293	68,443		Monroe	49%	33,918	48%	33,386	69,752	20	5,535	86%
Montgomery	60%	253,393	39%	165,552	422,419		Montgomery	59%	256,082	37%	162,731	434,687	3	-2,689	101%
Montour	42%	3,364	57%	4,574	8,023		Montour	33%	2,857	62%	5,288	8,556		507	85%
Northampton	56%	75,255	43%	58,551	135,587		Northampton	46%	66,272	50%	71,736	143,519	13	8,983	88%
Northumberland	42%	14,329	56%	19,018	33,939		Northumberland	27%	9,788	69%	25,427	36,622		4,541	68%
Perry	32%	6,396	66%	13,058	19,745		Perry	22%	4,632	74%	15,616	21,158		1,764	72%
Philadelphia	83%	595,980	16%	117,221	717,329		Philadelphia	83%	584,025	15%	108,748	707,631	1	11,955	98%
Pike	47%	11,493	52%	12,518	24,284		Pike	35%	9,256	62%	16,056	26,101		2,237	81%
Potter	31%	2,300	68%	5,109	7,507		Potter	17%	1,302	80%	6,251	7,784		998	57%
Schuylkill	45%	28,300	54%	33,767	63,057		Schuylkill	27%	16,770	70%	44,001	62,869		11,530	59%
Snyder	35%	5,382	64%	9,900	15,479		Snyder	24%	4,002	72%	11,725	16,363		1,380	74%
Somerset	37%	12,878	62%	21,686	35,168		Somerset	21%	7,376	77%	27,379	35,773		5,502	57%
Sullivan	40%	1,233	59%	1,841	3,120		Sullivan	24%	750	73%	2,291	3,136		483	61%
Susquehanna	43%	8,381	55%	10,633	19,286		Susquehanna	27%	5,123	68%	12,891	18,863		3,258	61%
Tioga	36%	6,390	63%	11,326	17,984		Tioga	21%	3,901	74%	13,614	18,325		2,489	61%
Union	42%	7,333	57%	9,859	17,400		Union	35%	6,180	61%	10,622	17,468		1,153	84%
Venango	40%	9,238	59%	13,718	23,307		Venango	27%	6,309	69%	16,021	23,348		2,929	68%
Warren	46%	8,537	52%	9,685	18,517		Warren	28%	5,145	68%	12,477	18,434		3,392	60%
Washington	47%	46,122	52%	50,752	98,047		Washington	36%	36,322	61%	61,386	101,450	18	9,800	79%
Wayne	43%	9,892	56%	12,702	22,835		Wayne	29%	7,008	68%	16,244	24,018		2,884	71%
Westmoreland	41%	72,721	58%	102,294	176,873		Westmoreland	33%	59,669	64%	116,522	182,051	11	13,052	82%
Wyoming	46%	5,985	53%	6,983	13,138		Wyoming	29%	3,811	67%	8,837	13,144		2,174	64%
York	43%	82,839	56%	109,268	194,210		York	33%	68,524	62%	128,528	205,986	8	14,315	83%
		3,276,363		2,655,885					2,926,441		2,970,733
													p	Obama	Hillary
													o	minus	div
Pittsburgh		Obama 2008		McCain 2008					Hillary		Trump		p	Hillary	Obama
Allegheny	57%	373,153	42%	272,347	651,436		Allegheny	57%	367,617	40%	259,480	650,114	2	5,536	99%
Butler	36%	32,260	63%	57,074	90,425		Butler	29%	28,584	66%	64,428	97,071	19	3,676	89%
Beaver	48%	40,499	51%	42,895	84,488		Beaver	39%	32,531	58%	48,167	83,571	21	7,968	80%
Washington	47%	46,122	52%	50,752	98,047		Washington	36%	36,322	61%	61,386	101,450	18	9,800	79%
Westmoreland	41%	72,721	58%	102,294	176,873		Westmoreland	33%	59,669	64%	116,522	182,051	11	13,052	82%
													p	Obama	Hillary
													o	minus	div
Harrisburg		Obama 2008		McCain 2008					Hillary		Trump		p	Hillary	Obama
Dauphin	54%	69,975	45%	58,238	129,529		Dauphin	49%	64,706	47%	60,863	130,872	14	5,269	92%
Cumberland	43%	48,306	56%	63,739	113,304		Cumberland	39%	47,085	57%	69,076	121,617	16	1,221	97%
Lebanon	40%	23,310	59%	34,314	58,297		Lebanon	31%	18,953	66%	40,525	61,845		4,357	81%
Franklin	33%	21,169	66%	41,906	63,641		Franklin	25%	17,465	71%	49,768	69,731		3,704	83%
Adams	40%	17,633	59%	26,349	44,491		Adams	30%	14,219	66%	31,423	47,489		3,414	81%
York	43%	82,839	56%	109,268	194,210		York	33%	68,524	62%	128,528	205,986	8	14,315	83%
Perry	32%	6,396	66%	13,058	19,745		Perry	22%	4,632	74%	15,616	21,158		1,764	72%
													p	Obama	Hillary
													o	minus	div
Williamsport		Obama 2008		McCain 2008					Hillary		Trump		p	Hillary	Obama
Montour	42%	3,364	57%	4,574	8,023		Montour	33%	2,857	62%	5,288	8,556		507	85%
Lycoming	37%	18,381	62%	30,280	49,237		Lycoming	26%	13,020	70%	35,627	50,565		5,361	71%
Northumberland	42%	14,329	56%	19,018	33,939		Northumberland	27%	9,788	69%	25,427	36,622		4,541	68%
Snyder	35%	5,382	64%	9,900	15,479		Snyder	24%	4,002	72%	11,725	16,363		1,380	74%
Union	42%	7,333	57%	9,859	17,400		Union	35%	6,180	61%	10,622	17,468		1,153	84%
													p	Obama	Hillary
													o	minus	div
Philadelphia		Obama 2008		McCain 2008					Hillary		Trump		p	Hillary	Obama
Philadelphia	83%	595,980	16%	117,221	717,329		Philadelphia	83%	584,025	15%	108,748	707,631	1	11,955	98%
Bucks	54%	179,031	45%	150,248	332,924		Bucks	49%	167,060	48%	164,361	344,297	4	11,971	93%
Delaware	60%	178,870	39%	115,273	297,004		Delaware	60%	177,402	37%	110,667	297,634	5	1,468	99%
Chester	54%	137,833	45%	114,421	254,354		Chester	53%	141,682	43%	116,114	268,800	7	-3,849	103%
Montgomery	60%	253,393	39%	165,552	422,419		Montgomery	59%	256,082	37%	162,731	434,687	3	-2,689	101%
Lancaster	44%	99,586	55%	126,568	228,137		Lancaster	38%	91,093	57%	137,914	241,112	6	8,493	91%
													p	Obama	Hillary
													o	minus	div
Allentown		Obama 2008		McCain 2008					Hillary		Trump		p	Hillary	Obama
Lehigh	57%	87,089	42%	63,382	152,473		Lehigh	51%	81,324	46%	73,690	160,993	10	5,765	93%
Berks	54%	97,047	45%	80,513	180,000		Berks	43%	78,437	53%	96,626	183,065	9	18,610	81%
Northampton	56%	75,255	43%	58,551	135,587		Northampton	46%	66,272	50%	71,736	143,519	13	8,983	88%
Monroe	58%	39,453	41%	28,293	68,443		Monroe	49%	33,918	48%	33,386	69,752	20	5,535	86%
Pike	47%	11,493	52%	12,518	24,284		Pike	35%	9,256	62%	16,056	26,101		2,237	81%

[Antoninus]

Thanks for posting this. I’ve been posting it in dribs and drabs since 2020, but this shows what happened quite clearly. Montgomery County was ground zero for vote fraud in PA in 2020. I’m guessing that those 66K found votes for Biden were mostly manufactured.

[Eccl 10:2]

Ping

[Ronaldus Magnus III]

I am listening to Rush Limbaugh’s podcasts from 2008. There was MASSIVE Acorn voter fraud in many States. . One Secretary of State in a battleground state went to court after it was revealed she had 200,000 ballots with possible ‘issues.’ (Secretary of State in each State are the ones in charge of each State’s election)

[lee martell]

Just in case you didn’t realize it, much of your post is unreadable as it now exists on the main FR page.

The first few sentences are fine, but after “turnout” was greater than Obama 2008, ALL OF THE COPY IS JAMMED TOGETHER into a big high wall of type, with no spacing between words or spacing between the lines.

You are putting out some instructive information about
The Great Voter Fraud of 2020, which is appreciated.
Thought you should know how it appears from my end.

[kiryandil]

ping to you:

Rigging Pennsylvania: Obama 2008 by county compared with Biden 2020 by county
https://freerepublic.com/focus/f-bloggers/4063855/posts

[kabar]

Bookmark

[Robert A Cook PE]

Pittsburgh and Harrisburg (regions) more corruptible than Philadelphia itself, eh? But the Philadelphia regional counties more than made up the differences.

[PubliusMM]

Ping

[Erik Latranyi]

The fraud was easy to explain.

Where there was no fraud, Democrats did not turn out for Joe in big numbers.

But in tightly controlled blue cities, Democrats turned out in record numbers.

How?

By taking every registered Democrat and filling out a ballot on their behalf.

You see, even if you did a fraud investigation, you would have three problems:

1) Asking people how they voted
2) People (registered Democrats) being honest, which would show the election was stolen
3) If you matched ballots to registered voters, you would find no problems, just that turnout was 100%.

It is a very clever means of fraud and could only happen with drop boxes where multiple votes could be delivered.

It also explains why Democrats won seats in the Senate (statewide races were tilted blue) while Republicans gained seats in the House (congressional districts cannot be defrauded this way unless they were already blue)

In those swing states that Biden won, Democrats won almost every statewide race. The fraud only tilts statewide races, not district races.

If the Democrats use the same fraud this fall in the mid-terms, we should see a shocking outcome....Republicans take 40+ seats in the House while the Democrats hold or increase their 50-50 split of the Senate.

Or they could decide to turn off the fraud machine for the mid-terms, take their hits and say “see, no fraud” and turn it back on for 2024 when everyone has forgotten.

[Pollard]

Blue highlighted counties were won by the Democrat.

They're all blue because they're hyperlinks.

[Michael.SF.]

Bump

[Michael.SF.]

First off: Hat's off to you for all the hard work and effort put into this analysis.

I will add that I have long believed the election was stolen.

Second. As I was looking at the numbers, I was focusing on the highlighted counties and was thinking: Wait, Biden's numbers are up over Obama, but Trump's numbers are way up as well. Thus, I doubted if the data 'proved' fraud was a major factor vs. greater overall voter turnout.

But then I looked at the counties not highlighted. If high voter turnout was attributable to a strong desire to defeat Trump, then those counties would show a much higher level of votes for Biden. But......

I did not do a full analysis, but the turnout level in those counties for Biden compared to Obama were either up slightly or in many cases were down. But the Trump turnout in those counties were way up vs. prior election.

Their was excitement to vote for Trump across the board. But not to vote for Biden.

Yup you are right and my initial reaction was wrong.

[Vision]

Vote mules gone wild.

[Hiddigeigei]

Thank you for your informative post, and I appreciate what must have been considerable effort in putting it all together.

[Albion Wilde]

Please! Use Post #1 next time?

[Gay State Conservative]

Lots to digest here!

[InterceptPoint]

I didn’t check in to good old FR until about an hour ago. So I missed all the fun.

But the post itself is brilliant and, I would say, invaluable to we Freepers. The fraud just jumps off the page.

Donations are fine but that’s nobody’s business but yours. It is posts like this one that are the real value that FR provides. It is what brings us all back everyday.

[Nifty]

This is good. Thanks for doing this.