Statistics

Adjusted Interceptions: Career Totals

by Bryan Frye on August 26, 2022

in Defense, Statistics

Yesterday, I did a fairly simple analysis to compare interception numbers across eras. Because I covered the methodology in the previous post, I am not going to regurgitate that information here. Instead, I want to just get right into it. When I did the career adjusted sacks post, I went step-by-step in the same manner I did in the single season article. This time, however, I think we can skip past all that and look at the end results.

Career Adjusted Interception Totals

The first table is sorted by the last column, but you can re-sort by any header you like. Using Rod Woodson as an example, here’s how you read the table: Woodson intercepted 71 passes against 8401 attempts at a 0.85% rate. His passing environment modifier (Mod) is worth 142%, and the softened version of that (Soft) is worth 121%. Taking the average of his actual interceptions and interceptions per 500 attempts in order to account for volume gives us the Mid adjustment, which is 98% in Woodson’s case. Applying my homebrewed league strength multiplier (LSM) gives him a 99% adjustment.

If we multiply Woodson’s 71 interceptions by Mod, Mid, and LSM, we get a whopping 97.7 adjusted interceptions for his career. If we dampen it by multiplying those 71 picks by Soft, Mid, and LSM, Woodson’s career adjusted interceptions come to 83.2, good for the highest mark ever. ^[1]For the ModTot, that’s 71 * 142% * 98% * 99%. For the SoftTot, that’s 71 * 121% * 98% * 99%.

Name	Int	oPass	Int%	Mod	Soft	Mid	LSM	ModTot	SoftTot
Rod Woodson	71	8401	0.85%	142%	121%	98%	99%	97.7	83.2
Charles Woodson	65	9752	0.67%	162%	131%	96%	100%	101.2	81.9
Ed Reed	64	6404	1.00%	158%	129%	97%	100%	98.2	80.1
Paul Krause	81	5623	1.44%	91%	96%	117%	88%	75.7	79.3
Darren Sharper	63	6761	0.93%	152%	126%	94%	100%	90.2	74.8
Aeneas Williams	55	6313	0.87%	146%	123%	101%	100%	81.5	68.6
Ronnie Lott	63	6632	0.95%	121%	110%	99%	97%	73.0	66.7
Eugene Robinson	57	8235	0.69%	137%	118%	99%	99%	75.7	65.6
Emlen Tunnell	79	4315	1.83%	67%	83%	125%	79%	52.4	65.6
Ken Riley	65	6045	1.08%	96%	98%	112%	92%	64.0	65.4
Champ Bailey	52	7231	0.72%	153%	127%	98%	100%	78.5	64.8
Dave Brown	62	6386	0.97%	109%	105%	105%	95%	67.6	64.8
Ty Law	53	7572	0.70%	151%	125%	96%	100%	76.8	63.9
Asante Samuel	51	5953	0.86%	160%	130%	96%	100%	78.7	63.9
Deion Sanders	53	7048	0.75%	142%	121%	100%	99%	74.5	63.5
Eric Allen	54	7288	0.74%	138%	119%	98%	99%	72.7	62.6
Darrell Green	54	9839	0.55%	134%	117%	98%	98%	69.7	60.9
Everson Walls	57	5328	1.07%	118%	109%	102%	96%	65.8	60.9
Terrell Buckley	50	6907	0.72%	148%	124%	97%	100%	71.5	60.0
Ronde Barber	47	7117	0.66%	155%	127%	99%	100%	72.1	59.3
Night Train Lane	68	4466	1.52%	72%	86%	123%	82%	49.5	59.0
Lem Barney	56	3700	1.51%	90%	95%	124%	89%	55.7	58.7
Dick LeBeau	62	4273	1.45%	88%	94%	120%	83%	54.7	58.3
Ray Buchanan	47	6232	0.75%	148%	124%	98%	100%	68.1	57.0
Mel Blount	57	5810	0.98%	96%	98%	110%	92%	55.5	56.8
Troy Vincent	47	7404	0.63%	148%	124%	97%	100%	67.6	56.6
DeAngelo Hall	43	5332	0.81%	162%	131%	97%	100%	67.4	54.5
Emmitt Thomas	58	4673	1.24%	89%	94%	114%	87%	51.0	54.2
Terence Newman	42	8192	0.51%	165%	133%	96%	100%	66.5	53.4
Dre' Bly	43	5720	0.75%	152%	126%	98%	100%	64.2	53.2
Pat Fischer	56	5747	0.97%	90%	95%	115%	86%	49.9	52.7
Donnie Shell	51	6255	0.82%	106%	103%	106%	95%	54.4	52.7
James Hasty	45	6239	0.72%	140%	120%	98%	99%	61.2	52.5
Deron Cherry	50	5146	0.97%	120%	110%	99%	96%	56.8	52.1
John Harris	50	5188	0.96%	111%	105%	103%	95%	54.4	51.7
Sammy Knight	42	5650	0.74%	150%	125%	99%	100%	62.0	51.7
Reggie Nelson	38	6040	0.63%	183%	141%	96%	100%	66.3	51.3
Bobby Bryant	51	4565	1.12%	92%	96%	116%	90%	49.1	51.3
Ashley Ambrose	42	5749	0.73%	149%	124%	98%	100%	61.1	51.0
Dave Waymer	48	4590	1.05%	121%	111%	99%	97%	55.8	50.9
Cris Dishman	43	6131	0.70%	141%	120%	99%	99%	59.4	50.9
Bobby Boyd	57	3415	1.67%	87%	93%	116%	82%	47.2	50.8
Richard Sherman	37	5513	0.67%	188%	144%	95%	100%	66.2	50.7
Aaron Glenn	41	6755	0.61%	150%	125%	98%	100%	60.1	50.1
Jake Scott	49	3326	1.47%	91%	95%	118%	90%	47.1	49.6
Lemar Parrish	47	4534	1.04%	95%	97%	116%	92%	47.7	49.0
Jack Butler	52	2389	2.18%	70%	85%	134%	83%	40.0	48.8
Mel Renfro	52	5665	0.92%	91%	96%	112%	87%	46.1	48.4
Bobby Dillon	52	2524	2.06%	70%	85%	129%	83%	39.0	47.5
Mike Haynes	46	5808	0.79%	104%	102%	106%	95%	48.0	47.2
Larry Wilson	52	4994	1.04%	88%	94%	115%	84%	44.2	47.1
Tory James	39	4825	0.81%	150%	125%	97%	100%	56.5	47.1
Jimmy Patton	52	4123	1.26%	78%	89%	123%	83%	41.3	47.0
Charles Tillman	38	6312	0.60%	163%	131%	94%	100%	57.9	46.7
Willie Brown	54	6398	0.84%	87%	93%	113%	82%	43.3	46.7
Aqib Talib	35	5945	0.59%	176%	138%	96%	100%	59.3	46.5
Sam Madison	38	5706	0.67%	149%	125%	98%	100%	55.7	46.5
Donnie Abraham	38	4657	0.82%	148%	124%	98%	100%	55.2	46.3
Tim McDonald	40	6252	0.64%	138%	119%	98%	99%	53.6	46.3
Ken Houston	49	4696	1.04%	89%	94%	114%	88%	43.5	46.2
Patrick Surtain	37	5517	0.67%	150%	125%	100%	100%	55.4	46.2
Albert Lewis	42	6572	0.64%	127%	113%	99%	97%	51.5	46.0
Thom Darden	45	3624	1.24%	93%	96%	112%	93%	43.7	45.4
Brian Dawkins	37	7852	0.47%	151%	125%	98%	100%	54.6	45.4
Gill Byrd	42	4665	0.90%	126%	113%	98%	97%	50.1	44.9
Nate Clements	36	6275	0.57%	155%	127%	98%	100%	54.4	44.8
Willie Wood	48	3984	1.20%	87%	94%	119%	83%	41.6	44.6
Tramon Williams	34	6591	0.52%	174%	137%	96%	100%	56.6	44.6
Herb Adderley	48	4082	1.18%	89%	94%	117%	83%	41.8	44.4
Brent Grimes	33	4961	0.67%	182%	141%	95%	100%	57.2	44.3
LeRoy Butler	38	5985	0.63%	144%	122%	96%	99%	52.4	44.3
Todd Lyght	37	6315	0.59%	146%	123%	98%	100%	52.7	44.3
Eric Davis	38	6078	0.63%	146%	123%	95%	100%	52.6	44.3
Don Burroughs	50	3133	1.60%	76%	88%	122%	82%	38.2	44.2
Marcus Peters	31	3455	0.90%	205%	152%	93%	100%	59.2	44.1
Yale Lary	50	3821	1.31%	76%	88%	122%	82%	37.9	44.0
Richie Petitbon	48	4524	1.06%	85%	93%	116%	84%	39.8	43.3
Jimmy Johnson	47	5945	0.79%	89%	95%	113%	86%	40.7	43.2
Walt Harris	35	6295	0.56%	152%	126%	98%	100%	51.8	43.0
Donnell Woolford	36	4703	0.77%	142%	121%	98%	99%	49.7	42.3
Steve Foley	44	4987	0.88%	103%	102%	100%	94%	42.9	42.2
Darren Perry	35	4133	0.85%	146%	123%	98%	100%	50.2	42.2
Johnathan Joseph	32	7171	0.45%	176%	138%	95%	100%	53.5	42.0
Eugene Daniel	38	6694	0.57%	129%	114%	99%	98%	47.3	42.0
Tyrone Braxton	36	5744	0.63%	140%	120%	98%	99%	48.9	41.9
Deltha O'Neal	34	4184	0.81%	152%	126%	98%	100%	50.4	41.9
Terry McDaniel	35	4603	0.76%	143%	122%	99%	99%	49.3	41.8
Johnny Robinson	57	4454	1.28%	84%	92%	106%	75%	38.2	41.8
Eddie Meador	46	4508	1.02%	87%	93%	117%	83%	38.8	41.7
Devin McCourty	31	6942	0.45%	188%	144%	93%	100%	54.4	41.7
Ray Crockett	36	7020	0.51%	141%	121%	96%	99%	48.7	41.6
Earl Thomas	30	5452	0.55%	187%	144%	96%	100%	53.8	41.3
Dave Whitsell	46	3808	1.21%	87%	93%	116%	83%	38.3	41.3
Lionel Washington	37	6438	0.57%	127%	114%	100%	98%	46.2	41.2
Kevin Ross	38	6928	0.55%	129%	114%	97%	98%	46.5	41.2
Mike Adams	30	5890	0.51%	181%	141%	97%	100%	52.5	40.8
Joey Browner	37	4339	0.85%	124%	112%	102%	96%	45.1	40.7
Rashean Mathis	32	5669	0.56%	158%	129%	99%	100%	49.7	40.6
Spider Lockhart	41	3928	1.04%	90%	95%	120%	87%	38.4	40.5
Vencie Glenn	35	5015	0.70%	135%	118%	100%	99%	46.6	40.5
Charlie Waters	41	4464	0.92%	93%	97%	112%	92%	39.0	40.5
Harrison Smith	29	5181	0.56%	198%	149%	93%	100%	53.7	40.4
Clarence Scott	39	4749	0.82%	96%	98%	113%	93%	39.6	40.4
Rodney Harrison	34	7194	0.47%	150%	125%	95%	100%	48.3	40.3
Gary Barbaro	39	2953	1.32%	99%	100%	109%	95%	40.2	40.3
Troy Polamalu	32	4858	0.66%	160%	130%	96%	100%	49.3	40.1
Mike Harden	38	4945	0.77%	118%	109%	101%	96%	43.3	40.0
Joe Haden	29	4947	0.59%	189%	144%	95%	100%	52.2	39.9
Shawn Springs	33	6982	0.47%	151%	125%	97%	100%	47.9	39.9
Jack Christiansen	46	2678	1.72%	69%	84%	125%	82%	32.4	39.8
Xavien Howard	27	2734	0.99%	207%	154%	96%	100%	53.5	39.7
Tony Greene	37	2770	1.34%	91%	96%	122%	92%	37.8	39.7
Patrick Peterson	29	6266	0.46%	191%	145%	94%	100%	51.9	39.6
Antonio Cromartie	31	4391	0.71%	167%	133%	96%	100%	49.5	39.5
Mike Prior	35	5738	0.61%	132%	116%	98%	99%	44.8	39.4
Erich Barnes	45	4687	0.96%	84%	92%	114%	84%	35.8	39.4
Rolland Lawrence	39	3218	1.21%	93%	97%	112%	93%	38.0	39.3
Ray Brown	38	3718	1.02%	92%	96%	117%	92%	37.5	39.2
Dominique Rodgers-Cromartie	30	4532	0.66%	178%	139%	94%	100%	50.1	39.2
Gary Fencik	38	4501	0.84%	106%	103%	106%	95%	40.2	39.1
Dwayne Woodruff	37	5352	0.69%	117%	109%	101%	96%	42.1	39.0
Greg Jackson	32	4442	0.72%	144%	122%	101%	99%	46.0	39.0
Eric Weddle	29	5447	0.53%	180%	140%	96%	100%	50.2	39.0
Lester Hayes	39	4773	0.82%	106%	103%	102%	95%	40.0	39.0
Stephon Gilmore	27	5509	0.49%	201%	150%	95%	100%	51.7	38.7
Warren Lahr	44	3387	1.30%	67%	84%	131%	80%	31.1	38.7
Darryll Lewis	32	4746	0.67%	149%	124%	97%	100%	46.2	38.6
Anthony Henry	31	4667	0.66%	153%	126%	98%	100%	46.5	38.5
Merton Hanks	33	4524	0.73%	148%	124%	94%	100%	45.9	38.4
Bill Thompson	40	5051	0.79%	93%	97%	109%	91%	37.0	38.4
Tashaun Gipson	27	5472	0.49%	197%	149%	96%	100%	50.9	38.4
Roger Wehrli	40	5147	0.78%	93%	97%	108%	92%	37.0	38.4
Darrelle Revis	29	4811	0.60%	173%	137%	97%	100%	48.6	38.3
Raymond Clayborn	36	4972	0.72%	110%	105%	105%	96%	40.0	38.2
Glen Edwards	39	4646	0.84%	94%	97%	109%	92%	37.0	38.2
Mark Carrier	32	4575	0.70%	141%	121%	99%	99%	44.5	38.0
Kermit Alexander	43	3732	1.15%	90%	95%	110%	84%	36.0	37.9
Janoris Jenkins	27	5703	0.47%	199%	149%	94%	100%	50.3	37.8
Brock Marion	31	4665	0.66%	148%	124%	98%	100%	45.1	37.8
Samari Rolle	31	5266	0.59%	150%	125%	97%	100%	45.2	37.7
Nolan Cromwell	37	4796	0.77%	109%	105%	102%	95%	39.3	37.6
Ray Lewis	31	7601	0.41%	152%	126%	96%	100%	45.3	37.5
Dexter McCleon	31	5301	0.58%	148%	124%	97%	100%	44.6	37.4
Ryan McNeil	31	4761	0.65%	148%	124%	97%	100%	44.6	37.4
Dewayne Washington	31	5841	0.53%	148%	124%	97%	100%	44.5	37.3
Tom Myers	36	3906	0.92%	95%	98%	114%	93%	36.4	37.3
Darryl Williams	31	5256	0.59%	146%	123%	98%	100%	44.2	37.2
J.C. Jackson	25	2173	1.15%	210%	155%	96%	100%	50.3	37.2
Jack Tatum	37	3971	0.93%	93%	97%	113%	92%	35.9	37.1
Keith Lyle	31	4861	0.64%	149%	125%	96%	100%	44.5	37.1
Ed Sharockman	40	3735	1.07%	89%	94%	117%	84%	34.8	37.0
Deon Grant	30	5886	0.51%	155%	127%	97%	100%	44.9	36.9
Ron Bolton	35	3453	1.01%	95%	97%	115%	94%	35.9	36.9
Tyrann Mathieu	26	5231	0.50%	204%	152%	93%	100%	49.4	36.8
Rick Volk	38	4236	0.90%	90%	95%	115%	89%	34.7	36.7
Tim McKyer	33	5173	0.64%	129%	115%	98%	99%	41.4	36.7
Dennis Thurman	36	3785	0.95%	110%	105%	103%	95%	38.3	36.7
Stan White	34	3783	0.90%	96%	98%	116%	94%	35.6	36.3
Michael Downs	35	4454	0.79%	115%	108%	101%	96%	38.8	36.3
Ken Reaves	37	4461	0.83%	90%	95%	117%	88%	34.3	36.3
Bill Simpson	34	3097	1.10%	98%	99%	115%	94%	35.9	36.3
Tony Parrish	30	4303	0.70%	150%	125%	96%	100%	43.3	36.1
Kurt Schulz	30	3742	0.80%	148%	124%	97%	100%	42.9	35.9
Lyle Blackwood	35	4753	0.74%	100%	100%	108%	95%	35.8	35.7
Eric Turner	30	4261	0.70%	147%	123%	97%	100%	42.5	35.7
Bob Howard	37	4336	0.85%	90%	95%	113%	89%	33.7	35.5
LeRoy Irvin	35	5317	0.66%	117%	108%	97%	96%	38.0	35.3
Mike Wagner	36	3740	0.96%	93%	97%	110%	92%	34.0	35.2
Otis Smith	29	5763	0.50%	147%	123%	98%	100%	41.5	34.9
Greg Wesley	29	3808	0.76%	151%	125%	96%	100%	41.9	34.9
Willie Williams	35	2791	1.25%	90%	95%	122%	86%	32.9	34.7
Lindon Crow	38	2945	1.29%	74%	87%	126%	83%	29.4	34.6
David Fulcher	31	3494	0.89%	126%	113%	100%	98%	38.5	34.5
Carl Lee	31	5884	0.53%	126%	113%	101%	97%	38.4	34.4
Johnny Sample	41	3504	1.17%	84%	92%	114%	79%	31.3	34.2
Mark Lee	32	5252	0.61%	117%	108%	102%	96%	36.9	34.2
Brent Alexander	28	4688	0.60%	150%	125%	98%	100%	41.0	34.2
Louis Breeden	33	4229	0.78%	111%	105%	103%	95%	35.9	34.2
Chris Gamble	27	3620	0.75%	157%	128%	98%	100%	41.6	34.1
Mike McKenzie	28	5857	0.48%	151%	126%	97%	100%	41.0	34.1
Dave Grayson	48	4311	1.11%	81%	91%	108%	72%	30.5	34.0
Mark Kelso	30	2914	1.03%	127%	113%	101%	98%	37.9	33.9
Leon Hall	27	5704	0.47%	168%	134%	94%	100%	42.5	33.9
Antoine Bethea	25	5241	0.48%	178%	139%	98%	100%	43.4	33.9
Nate Wright	34	3932	0.86%	92%	96%	114%	91%	32.5	33.9
Casey Hayward	24	3849	0.62%	195%	148%	95%	100%	44.7	33.8
Brig Owens	36	3953	0.91%	90%	95%	113%	87%	32.1	33.8
Kenny Easley	32	3266	0.98%	113%	107%	104%	95%	35.9	33.7
Joe Lavender	33	3315	1.00%	97%	99%	110%	94%	33.2	33.6
Antrel Rolle	26	5476	0.47%	169%	135%	96%	100%	42.1	33.5
Herm Edwards	33	3995	0.83%	102%	101%	106%	94%	33.8	33.5
Adrian Wilson	27	5895	0.46%	156%	128%	97%	100%	40.8	33.5
Dick Lynch	37	2859	1.29%	83%	92%	120%	82%	30.4	33.4
Donnie Edwards	28	5475	0.51%	150%	125%	96%	100%	40.1	33.4
Gary Green	33	4192	0.79%	107%	103%	104%	94%	34.5	33.4
Scott Case	30	4419	0.68%	125%	113%	101%	98%	37.1	33.4
Antoine Winfield	27	7013	0.38%	156%	128%	96%	100%	40.5	33.2
Norm Thompson	33	3091	1.07%	92%	96%	115%	91%	31.7	33.2
William Thomas	27	4524	0.60%	147%	124%	100%	100%	39.5	33.2
Doug Evans	28	6063	0.46%	149%	124%	95%	100%	39.6	33.1
Phillippi Sparks	27	4067	0.66%	148%	124%	99%	100%	39.5	33.1
Jairus Byrd	25	3722	0.67%	173%	136%	97%	100%	41.9	33.1
Darius Slay	23	4465	0.52%	203%	152%	95%	100%	44.3	33.1
Dick Anderson	34	2901	1.17%	88%	94%	119%	87%	30.8	32.9
Vann McElroy	31	3840	0.81%	117%	109%	102%	96%	35.5	32.9
Tom Keane	40	2046	1.96%	66%	83%	123%	80%	26.3	32.9
Jerry Norton	35	2798	1.25%	74%	87%	130%	83%	27.9	32.8
Robert Griffith	27	5787	0.47%	150%	125%	98%	100%	39.4	32.8
Tom Carter	27	4129	0.65%	147%	124%	98%	100%	39.0	32.8
Glover Quin	24	3939	0.61%	188%	144%	94%	100%	42.6	32.6
Don Shinnick	37	4471	0.83%	81%	90%	117%	83%	29.2	32.6
W.K. Hicks	40	3568	1.12%	85%	93%	113%	78%	30.0	32.6
Dennis Smith	30	6018	0.50%	125%	112%	100%	97%	36.1	32.5
Wes Hopkins	30	3561	0.84%	125%	112%	99%	97%	36.1	32.5
Jimmy Allen	31	2442	1.27%	100%	100%	111%	94%	32.4	32.5
Sheldon Brown	26	5920	0.44%	159%	130%	96%	100%	39.9	32.5
O.J. Atogwe	25	3495	0.72%	160%	130%	100%	100%	39.9	32.5
Bill Bradley	34	2801	1.21%	89%	95%	112%	89%	30.6	32.4
Don Paul	34	2278	1.49%	68%	84%	138%	82%	26.3	32.4
Kevin Byard	23	2994	0.77%	206%	153%	92%	100%	43.5	32.3
Terry Kinard	31	4164	0.74%	121%	111%	98%	96%	35.4	32.3
Jim David	36	2592	1.39%	69%	85%	127%	83%	26.4	32.2
Maurice Hurst	27	3415	0.79%	138%	119%	101%	99%	37.3	32.2
Dwight Hicks	32	3931	0.81%	109%	105%	101%	95%	33.5	32.1
Trumaine Johnson	23	4509	0.51%	195%	147%	94%	100%	42.3	32.0
Felix Wright	29	3959	0.73%	125%	112%	101%	97%	35.4	31.9
Micah Hyde	22	4407	0.50%	204%	152%	95%	100%	42.8	31.9
Willie Clay	27	3401	0.79%	149%	125%	94%	100%	37.8	31.6
Chris McAlister	26	5339	0.49%	151%	125%	97%	100%	38.0	31.6
Jack Ham	32	4970	0.64%	92%	96%	110%	93%	30.3	31.5
John Lynch	26	5793	0.45%	149%	125%	97%	100%	37.7	31.5
Michael Griffin	25	5120	0.49%	168%	134%	94%	100%	39.4	31.5
Fred Marion	29	4369	0.66%	122%	111%	101%	96%	34.6	31.4
Brian Washington	27	4146	0.65%	139%	119%	98%	99%	36.5	31.4
Louis Oliver	27	3588	0.75%	137%	118%	99%	99%	36.1	31.3
Derrick Brooks	25	6019	0.42%	150%	125%	100%	100%	37.4	31.1
Willie Williams	26	4810	0.54%	148%	124%	97%	100%	37.2	31.1
Roland James	29	4594	0.63%	115%	107%	104%	96%	33.2	31.1
Don Doll	41	2191	1.87%	65%	82%	118%	78%	24.4	31.1
Rod Perry	30	3323	0.90%	99%	99%	110%	94%	30.9	31.1
Mark Collins	27	6270	0.43%	138%	119%	98%	99%	36.0	31.1
Justin Simmons	21	3258	0.64%	208%	154%	96%	100%	41.9	31.0
Jimmy Spencer	26	4383	0.59%	149%	125%	96%	100%	37.0	30.9
Jerry Logan	34	3970	0.86%	90%	95%	113%	85%	29.2	30.8
Jerry Gray	28	3070	0.91%	126%	113%	99%	98%	34.3	30.7
Goose Gonsoulin	46	2936	1.57%	77%	89%	110%	69%	26.8	30.7
Kwamie Lassiter	25	4096	0.61%	149%	124%	99%	100%	36.7	30.7
Marcus Coleman	25	4634	0.54%	149%	124%	99%	100%	36.6	30.6
Chris Harris	22	6151	0.36%	194%	147%	95%	100%	40.3	30.6
Chris Harris Jr.	22	6151	0.36%	194%	147%	95%	100%	40.3	30.6
Glenn Blackwood	29	3737	0.78%	113%	107%	104%	95%	32.5	30.6
Burgess Owens	30	3789	0.79%	98%	99%	109%	94%	30.2	30.5
Butch Byrd	40	2905	1.38%	84%	92%	110%	75%	27.9	30.4
Charles Romes	28	3884	0.72%	111%	105%	108%	95%	31.8	30.3
Ken Lucas	25	4940	0.51%	153%	127%	96%	100%	36.6	30.2
Victor Green	25	4193	0.60%	147%	124%	98%	100%	35.9	30.2
Cornell Green	34	4205	0.81%	89%	95%	109%	85%	28.4	30.1
Charlie Stukes	32	3121	1.03%	89%	95%	114%	87%	28.4	30.0
Cedric Brown	29	3484	0.83%	103%	102%	107%	95%	30.5	30.0
Miller Farr	35	3478	1.01%	86%	93%	115%	80%	27.7	29.9
Lawyer Milloy	25	5469	0.46%	150%	125%	96%	100%	35.7	29.8
Nate Odomes	26	3049	0.85%	134%	117%	99%	99%	34.1	29.8
Rosey Taylor	32	4098	0.78%	89%	94%	117%	84%	27.9	29.7
Kerry Rhodes	23	3568	0.64%	161%	130%	99%	100%	36.7	29.7
Duane Starks	25	3261	0.77%	147%	124%	96%	100%	35.3	29.7
Andy Nelson	33	2444	1.35%	77%	89%	122%	83%	25.8	29.6
Duron Harmon	21	5220	0.40%	202%	151%	93%	100%	39.5	29.5
Jordan Poyer	20	3232	0.62%	206%	153%	96%	100%	39.7	29.5
Vontae Davis	22	4362	0.50%	179%	139%	96%	100%	37.7	29.4
Aaron Beasley	24	4663	0.51%	149%	124%	98%	100%	35.0	29.3
Lee Roy Jordan	32	4881	0.66%	91%	95%	111%	86%	27.8	29.2
Jay Bellamy	24	4172	0.58%	148%	124%	98%	100%	34.8	29.1
Marcus Robertson	24	4125	0.58%	147%	123%	98%	100%	34.6	29.1
Bobby Hunt	42	3548	1.18%	81%	90%	106%	72%	26.0	29.1
Ray Ramsey	35	1963	1.78%	66%	83%	139%	72%	23.2	29.1
Reshad Jones	21	4978	0.42%	190%	145%	95%	100%	38.1	29.0
Jim Norton	45	4069	1.11%	79%	90%	105%	68%	25.6	29.0
Mike Bass	30	2715	1.10%	90%	95%	114%	88%	27.4	28.9
Mark Murphy	27	2159	1.25%	108%	104%	108%	95%	30.1	28.9
Bobby Butler	27	4263	0.63%	116%	108%	103%	96%	31.0	28.8
Dale Carter	24	4669	0.51%	145%	122%	98%	100%	34.0	28.7
Malcolm Jenkins	21	6330	0.33%	192%	146%	93%	100%	37.7	28.7
Kurt Coleman	21	3346	0.63%	188%	144%	95%	100%	37.3	28.6
George Atkinson	30	3416	0.88%	89%	95%	116%	87%	26.9	28.6
Mark McMillian	23	3982	0.58%	148%	124%	100%	100%	33.9	28.4
Ken Konz	30	2111	1.42%	70%	85%	133%	84%	23.4	28.4
London Fletcher	23	4927	0.47%	158%	129%	96%	100%	34.8	28.4
Darren Woodson	23	5113	0.45%	150%	125%	99%	100%	34.0	28.3
Dick Alban	30	2439	1.23%	72%	86%	132%	83%	23.6	28.3
Gene Atkins	25	4684	0.53%	133%	117%	98%	99%	32.2	28.2
Brandon Carr	21	4979	0.42%	180%	140%	95%	100%	36.0	28.0
Steve Atwater	24	5268	0.46%	139%	120%	97%	99%	32.4	27.8
Brandon Flowers	21	4226	0.50%	171%	136%	97%	100%	35.0	27.7
Dwayne Harper	24	5343	0.45%	140%	120%	97%	99%	32.3	27.7
Charles Mincy	23	4140	0.56%	145%	122%	98%	100%	32.7	27.6
Kareem Jackson	20	6121	0.33%	190%	145%	95%	100%	36.1	27.6
Seth Joyner	24	5659	0.42%	136%	118%	99%	99%	31.7	27.5
Brian Kelly	22	4027	0.55%	151%	125%	100%	100%	33.0	27.5
Sam Huff	30	4611	0.65%	82%	91%	120%	83%	24.7	27.4
Dwight Smith	22	3084	0.71%	152%	126%	99%	100%	33.0	27.4
Don Griffin	25	4931	0.51%	131%	116%	96%	99%	31.0	27.3
Frank Reagan	35	1672	2.09%	61%	80%	140%	69%	20.6	27.2
Kyle Fuller	19	3322	0.57%	201%	150%	95%	100%	36.3	27.2
Nick Collins	21	2530	0.83%	159%	130%	99%	100%	33.3	27.1
Bert Rechichar	31	2319	1.34%	68%	84%	125%	82%	21.8	26.8
Tommy James	34	2642	1.29%	66%	83%	126%	75%	21.3	26.8
Quandre Diggs	19	3003	0.63%	208%	154%	92%	100%	36.1	26.8
Andre Dyson	22	3764	0.58%	151%	126%	96%	100%	32.1	26.7
Bernie Parrish	31	2592	1.20%	81%	91%	118%	80%	23.7	26.5
Brian Urlacher	22	5743	0.38%	156%	128%	94%	100%	32.2	26.4
Jim Cason	34	2374	1.43%	67%	84%	124%	75%	21.2	26.3
Ricky Reynolds	23	4652	0.49%	134%	117%	98%	99%	30.1	26.2
Nick Harper	21	4183	0.50%	154%	127%	98%	100%	31.5	26.0
Orlando Thomas	22	3922	0.56%	149%	125%	95%	100%	31.0	25.9
Karlos Dansby	20	5551	0.36%	172%	136%	95%	100%	32.8	25.9
Deshea Townsend	21	4761	0.44%	153%	126%	97%	100%	31.2	25.8
Keith Bulluck	21	5369	0.39%	155%	127%	97%	100%	31.4	25.8
Quentin Jammer	21	4986	0.42%	159%	129%	95%	100%	31.7	25.8
Al Harris	21	4774	0.44%	152%	126%	97%	100%	31.0	25.7
Chad Scott	21	4122	0.51%	149%	124%	99%	100%	30.7	25.7
Tim Jennings	20	3377	0.59%	172%	136%	94%	100%	32.4	25.7
Corey Webster	20	3753	0.53%	165%	133%	97%	100%	31.9	25.6
Fred Smoot	21	4247	0.49%	151%	126%	97%	100%	30.8	25.6
Marcus Trufant	21	5037	0.42%	156%	128%	95%	100%	31.1	25.5
Tom Landry	32	2417	1.32%	66%	83%	122%	78%	20.2	25.3
Sam Shields	19	3919	0.48%	181%	140%	95%	100%	32.5	25.2
Drayton Florence	20	4873	0.41%	161%	131%	96%	100%	31.1	25.1
Phillip Buchanon	20	3586	0.56%	154%	127%	99%	100%	30.4	25.1
Luke Kuechly	18	4565	0.39%	194%	147%	94%	100%	32.8	24.9
Quintin Demps	18	3307	0.54%	189%	145%	95%	100%	32.5	24.8
Logan Ryan	18	3527	0.51%	197%	148%	93%	100%	32.8	24.7
Bob Smith	33	2102	1.57%	67%	83%	121%	74%	19.7	24.6
Otto Schnellbacher	34	1329	2.56%	66%	83%	125%	70%	19.5	24.6
Nick Buoniconti	32	4721	0.68%	83%	92%	108%	76%	21.8	24.1
Bill Baird	34	3091	1.10%	82%	91%	107%	73%	21.6	24.0
Jason McCourty	18	4626	0.39%	186%	143%	93%	100%	31.3	24.0
Malcolm Butler	17	3543	0.48%	205%	153%	92%	100%	32.2	23.9
Lowell Wagner	32	2058	1.55%	66%	83%	123%	73%	18.9	23.9
Corey Graham	18	5261	0.34%	185%	143%	93%	100%	30.9	23.8
Irv Comp	34	1739	1.96%	52%	76%	151%	61%	16.3	23.8
Fred Williamson	36	3057	1.18%	78%	89%	107%	69%	20.8	23.7
Tre'Davious White	16	2721	0.59%	207%	154%	96%	100%	31.8	23.6
Ha Ha Clinton-Dix	16	3303	0.48%	202%	151%	95%	100%	30.9	23.1
Bashaud Breeland	16	4497	0.36%	203%	152%	94%	100%	30.7	22.9
Rodney McLeod	16	4502	0.36%	200%	150%	94%	100%	30.3	22.7
Josh Norman	16	4551	0.35%	200%	150%	94%	100%	30.0	22.5
Willie West	30	3339	0.90%	81%	91%	110%	72%	19.2	21.4
Sammy Baugh	31	1320	2.35%	48%	74%	164%	57%	13.8	21.3
Cliff Lewis	30	1883	1.59%	66%	83%	130%	66%	17.0	21.3
Ron Hall	30	3582	0.84%	80%	90%	106%	70%	17.8	20.1
Don Hutson	30	1430	2.10%	50%	75%	155%	56%	13.0	19.6
Fred Glick	30	2692	1.11%	78%	89%	106%	68%	16.7	19.1

Using the actual historical average as a baseline appears to be a bit much, going by the numbers it produces. I think having Charles Woodson, Ed Reed, Rod Woodson, a serial rapist, and Aeneas Williams as the top five (by ModTot) is fine; giving Chuck credit for 101 interceptions is a bit much for me. Moving the all time leader in picks, Paul Krause, down to ninth also feels a tad harsh as well. Sure, I think he tends to be overrated by people who look at one number and base their entire evaluation on that single data point, but I also think having such a commanding lead over any modern player should count for something. For this reason, I think the SoftTot column produces results with greater face validity.

The last column gives us a top ten of seven Hall of Famers, one senior candidate who will likely get the necessary votes soon, possibly the best safety of the 1990s who would be in Canton already if he played for Dallas or San Francisco, and a a vile monster who was good at picking off passes and not really much else.

Let’s Be Reasonable

The wacky looking career totals form the table above convinced me to try using a new baseline. I decided to use the last 40 years of football, which incorporates nearly the entire period of open offense football. ^[2]I refer to football in the wake of the Mel Blount Rule and rules enabling offensive linemen to extend their hands to block in 1978, as well as the subsequent offensive revolution heralded by the … Continue reading When I looked at that timeframe, the historical baseline moved from 4.80% to 3.16%. because of that, I have dubbed the new baseline the Austin Percentage. Having a lower baseline means that fewer players will see their totals go up, and only the most recent players will their totals go up significantly.

The table below is sorted by the last column, but you can sort by any of the headings. Using Krause as our example, read the table thus: Krause picked off 81 passes against 5623 attempts at a 1.44% rate. His volume adjustment is worth 117%, and his league strength multiplier is worth 88%. His Austin figure is 60%, which comes to 80% when the effect is halved. ^[3]Recall from the first table that his Mod and Soft were 91% and 96% because of the highest baseline. If we apply the Mid, LSM, and Austin modifiers to Krause’s 81 actual interceptions, his total plummets to 49.9, which ranks tenth on the career list. If we replace the Austin modifier with the softened version, Krause’s number falls to just 66.4, which allows him to maintain his place atop the interception mountain. ^[4]To arrive at the numbers in the Austin column, we use: 81 * 117% * 88% * 60%. To find the results for the HalfTot column, we use: 81 * 117% * 88% * 80%. These figures are rounded and will produce … Continue reading

Name	Int	oPass	Int%	Mid	LSM	Austin%	Half%	Austin	HalfTot
Paul Krause	81	5623	1.44%	117%	88%	60%	80%	49.9	66.4
Rod Woodson	71	8401	0.85%	98%	99%	93%	96%	63.8	66.3
Charles Woodson	65	9752	0.67%	96%	100%	107%	103%	66.6	64.5
Ed Reed	64	6404	1.00%	97%	100%	106%	103%	65.5	63.7
Darren Sharper	63	6761	0.93%	94%	100%	100%	100%	59.6	59.5
Emlen Tunnell	79	4315	1.83%	125%	79%	45%	73%	35.4	57.0
Ken Riley	65	6045	1.08%	112%	92%	64%	82%	42.5	54.6
Aeneas Williams	55	6313	0.87%	101%	100%	96%	98%	53.6	54.6
Ronnie Lott	63	6632	0.95%	99%	97%	81%	90%	48.7	54.6
Dave Brown	62	6386	0.97%	105%	95%	72%	86%	44.9	53.4
Eugene Robinson	57	8235	0.69%	99%	99%	90%	95%	50.1	52.8
Champ Bailey	52	7231	0.72%	98%	100%	103%	101%	52.5	51.8
Night Train Lane	68	4466	1.52%	123%	82%	50%	75%	34.1	51.3
Deion Sanders	53	7048	0.75%	100%	99%	95%	97%	49.8	51.2
Ty Law	53	7572	0.70%	96%	100%	99%	100%	50.7	50.8
Eric Allen	54	7288	0.74%	98%	99%	93%	96%	48.8	50.7
Asante Samuel	51	5953	0.86%	96%	100%	106%	103%	52.1	50.6
Everson Walls	57	5328	1.07%	102%	96%	79%	90%	44.3	50.1
Lem Barney	56	3700	1.51%	124%	89%	60%	80%	36.7	49.2
Darrell Green	54	9839	0.55%	98%	98%	89%	94%	46.1	49.1
Dick LeBeau	62	4273	1.45%	120%	83%	57%	79%	35.6	48.7
Terrell Buckley	50	6907	0.72%	97%	100%	97%	99%	47.1	47.8
Mel Blount	57	5810	0.98%	110%	92%	64%	82%	37.2	47.6
Ronde Barber	47	7117	0.66%	99%	100%	102%	101%	47.5	47.1
Ray Buchanan	47	6232	0.75%	98%	100%	98%	99%	45.1	45.6
Emmitt Thomas	58	4673	1.24%	114%	87%	58%	79%	33.5	45.4
Troy Vincent	47	7404	0.63%	97%	100%	97%	99%	44.4	45.0
Pat Fischer	56	5747	0.97%	115%	86%	59%	80%	32.9	44.2
Donnie Shell	51	6255	0.82%	106%	95%	70%	85%	35.6	43.3
DeAngelo Hall	43	5332	0.81%	97%	100%	107%	104%	44.6	43.1
Bobby Bryant	51	4565	1.12%	116%	90%	61%	81%	32.8	43.1
Terence Newman	42	8192	0.51%	96%	100%	113%	107%	45.6	42.9
Bobby Boyd	57	3415	1.67%	116%	82%	57%	79%	31.0	42.7
John Harris	50	5188	0.96%	103%	95%	73%	87%	36.0	42.5
Deron Cherry	50	5146	0.97%	99%	96%	79%	89%	37.5	42.5
Dre' Bly	43	5720	0.75%	98%	100%	100%	100%	42.3	42.2
James Hasty	45	6239	0.72%	98%	99%	92%	96%	40.4	42.1
Jack Butler	52	2389	2.18%	134%	83%	46%	73%	26.4	41.9
Jake Scott	49	3326	1.47%	118%	90%	60%	80%	31.1	41.6
Dave Waymer	48	4590	1.05%	99%	97%	80%	90%	36.8	41.4
Sammy Knight	42	5650	0.74%	99%	100%	99%	99%	41.0	41.2
Lemar Parrish	47	4534	1.04%	116%	92%	63%	82%	31.8	41.1
Bobby Dillon	52	2524	2.06%	129%	83%	46%	73%	26.0	41.0
Cris Dishman	43	6131	0.70%	99%	99%	94%	97%	39.7	41.0
Ashley Ambrose	42	5749	0.73%	98%	100%	98%	99%	40.3	40.7
Mel Renfro	52	5665	0.92%	112%	87%	60%	80%	30.3	40.4
Jimmy Patton	52	4123	1.26%	123%	83%	53%	77%	28.0	40.4
Richard Sherman	37	5513	0.67%	95%	100%	129%	114%	45.4	40.3
Aaron Glenn	41	6755	0.61%	98%	100%	99%	99%	39.7	40.0
Reggie Nelson	38	6040	0.63%	96%	100%	120%	110%	43.6	40.0
Mike Haynes	46	5808	0.79%	106%	95%	71%	86%	33.1	39.7
Larry Wilson	52	4994	1.04%	115%	84%	58%	79%	28.9	39.5
Willie Brown	54	6398	0.84%	113%	82%	57%	79%	28.6	39.3
Ken Houston	49	4696	1.04%	114%	88%	59%	80%	29.1	39.0
Thom Darden	45	3624	1.24%	112%	93%	64%	82%	29.9	38.4
Don Burroughs	50	3133	1.60%	122%	82%	51%	76%	25.8	38.0
Yale Lary	50	3821	1.31%	122%	82%	50%	75%	25.3	37.7
Willie Wood	48	3984	1.20%	119%	83%	58%	79%	27.7	37.7
Tim McDonald	40	6252	0.64%	98%	99%	93%	96%	36.1	37.5
Albert Lewis	42	6572	0.64%	99%	97%	85%	92%	34.3	37.5
Tory James	39	4825	0.81%	97%	100%	99%	99%	37.2	37.4
Charles Tillman	38	6312	0.60%	94%	100%	110%	105%	39.0	37.3
Sam Madison	38	5706	0.67%	98%	100%	99%	100%	37.1	37.2
Herb Adderley	48	4082	1.18%	117%	83%	58%	79%	27.3	37.2
Aqib Talib	35	5945	0.59%	96%	100%	120%	110%	40.6	37.1
Donnie Abraham	38	4657	0.82%	98%	100%	98%	99%	36.5	36.9
Patrick Surtain	37	5517	0.67%	100%	100%	100%	100%	36.8	36.8
Richie Petitbon	48	4524	1.06%	116%	84%	57%	78%	26.6	36.6
Jimmy Johnson	47	5945	0.79%	113%	86%	59%	80%	27.0	36.3
Brian Dawkins	37	7852	0.47%	98%	100%	100%	100%	36.3	36.2
Gill Byrd	42	4665	0.90%	98%	97%	81%	90%	32.2	36.0
Nate Clements	36	6275	0.57%	98%	100%	104%	102%	36.6	35.9
Tramon Williams	34	6591	0.52%	96%	100%	119%	110%	38.7	35.6
LeRoy Butler	38	5985	0.63%	96%	99%	95%	97%	34.4	35.4
Steve Foley	44	4987	0.88%	100%	94%	70%	85%	29.1	35.4
Eric Davis	38	6078	0.63%	95%	100%	96%	98%	34.5	35.3
Todd Lyght	37	6315	0.59%	98%	100%	96%	98%	34.6	35.3
Eddie Meador	46	4508	1.02%	117%	83%	57%	79%	25.5	35.1
Johnny Robinson	57	4454	1.28%	106%	75%	55%	77%	24.7	35.0
Dave Whitsell	46	3808	1.21%	116%	83%	57%	79%	25.4	34.8
Brent Grimes	33	4961	0.67%	95%	100%	119%	110%	37.5	34.5
Eugene Daniel	38	6694	0.57%	99%	98%	88%	94%	32.3	34.5
Jack Christiansen	46	2678	1.72%	125%	82%	45%	73%	21.3	34.2
Spider Lockhart	41	3928	1.04%	120%	87%	60%	80%	25.5	34.1
Walt Harris	35	6295	0.56%	98%	100%	99%	100%	34.0	34.1
Marcus Peters	31	3455	0.90%	93%	100%	135%	117%	39.1	34.0
Charlie Waters	41	4464	0.92%	112%	92%	62%	81%	26.1	34.0
Clarence Scott	39	4749	0.82%	113%	93%	65%	82%	26.7	33.9
Lionel Washington	37	6438	0.57%	100%	98%	87%	93%	31.5	33.9
Darren Perry	35	4133	0.85%	98%	100%	97%	98%	33.2	33.7
Kevin Ross	38	6928	0.55%	97%	98%	87%	94%	31.4	33.7
Johnathan Joseph	32	7171	0.45%	95%	100%	121%	111%	36.9	33.7
Donnell Woolford	36	4703	0.77%	98%	99%	93%	97%	32.5	33.7
Warren Lahr	44	3387	1.30%	131%	80%	45%	73%	21.0	33.7
Erich Barnes	45	4687	0.96%	114%	84%	57%	78%	24.4	33.7
Tyrone Braxton	36	5744	0.63%	98%	99%	93%	96%	32.4	33.7
Gary Barbaro	39	2953	1.32%	109%	95%	66%	83%	26.8	33.6
Ray Crockett	36	7020	0.51%	96%	99%	94%	97%	32.4	33.5
Terry McDaniel	35	4603	0.76%	99%	99%	94%	97%	32.5	33.4
Deltha O'Neal	34	4184	0.81%	98%	100%	101%	100%	33.5	33.4
Tony Greene	37	2770	1.34%	122%	92%	61%	80%	25.2	33.4
Ray Brown	38	3718	1.02%	117%	92%	62%	81%	25.3	33.1
Rolland Lawrence	39	3218	1.21%	112%	93%	63%	81%	25.6	33.1
Rashean Mathis	32	5669	0.56%	99%	100%	109%	104%	34.2	32.9
Devin McCourty	31	6942	0.45%	93%	100%	127%	113%	36.6	32.7
Joey Browner	37	4339	0.85%	102%	96%	80%	90%	29.1	32.7
Mike Harden	38	4945	0.77%	101%	96%	78%	89%	28.5	32.6
Gary Fencik	38	4501	0.84%	106%	95%	71%	85%	26.9	32.5
Vencie Glenn	35	5015	0.70%	100%	99%	88%	94%	30.3	32.4
Lester Hayes	39	4773	0.82%	102%	95%	71%	85%	26.8	32.4
Roger Wehrli	40	5147	0.78%	108%	92%	63%	81%	24.9	32.3
Mike Prior	35	5738	0.61%	98%	99%	91%	95%	30.7	32.3
Earl Thomas	30	5452	0.55%	96%	100%	125%	112%	35.8	32.3
Bill Thompson	40	5051	0.79%	109%	91%	62%	81%	24.6	32.2
Rodney Harrison	34	7194	0.47%	95%	100%	99%	100%	32.1	32.2
Glen Edwards	39	4646	0.84%	109%	92%	63%	81%	24.8	32.1
Troy Polamalu	32	4858	0.66%	96%	100%	107%	104%	33.1	32.0
Shawn Springs	33	6982	0.47%	97%	100%	100%	100%	31.8	31.8
Kermit Alexander	43	3732	1.15%	110%	84%	60%	80%	23.8	31.8
Mike Adams	30	5890	0.51%	97%	100%	119%	109%	34.5	31.7
Dwayne Woodruff	37	5352	0.69%	101%	96%	76%	88%	27.4	31.7
Raymond Clayborn	36	4972	0.72%	105%	96%	74%	87%	27.0	31.6
Joe Haden	29	4947	0.59%	95%	100%	127%	113%	35.0	31.4
Antonio Cromartie	31	4391	0.71%	96%	100%	112%	106%	33.0	31.3
Ron Bolton	35	3453	1.01%	115%	94%	65%	82%	24.6	31.2
Patrick Peterson	29	6266	0.46%	94%	100%	129%	115%	35.1	31.2
Harrison Smith	29	5181	0.56%	93%	100%	130%	115%	35.3	31.2
Greg Jackson	32	4442	0.72%	101%	99%	95%	97%	30.3	31.2
Ed Sharockman	40	3735	1.07%	117%	84%	59%	79%	23.1	31.1
Tom Myers	36	3906	0.92%	114%	93%	63%	82%	24.1	31.1
Nolan Cromwell	37	4796	0.77%	102%	95%	73%	86%	26.2	31.1
Jack Tatum	37	3971	0.93%	113%	92%	62%	81%	23.7	31.0
Rick Volk	38	4236	0.90%	115%	89%	59%	80%	23.0	30.9
Dominique Rodgers-Cromartie	30	4532	0.66%	94%	100%	118%	109%	33.4	30.8
Mark Carrier	32	4575	0.70%	99%	99%	95%	97%	29.9	30.8
Anthony Henry	31	4667	0.66%	98%	100%	102%	101%	30.9	30.7
Xavien Howard	27	2734	0.99%	96%	100%	137%	118%	35.3	30.6
Darrelle Revis	29	4811	0.60%	97%	100%	117%	109%	32.9	30.5
Ken Reaves	37	4461	0.83%	117%	88%	59%	80%	22.7	30.5
Merton Hanks	33	4524	0.73%	94%	100%	97%	98%	29.9	30.5
Darryll Lewis	32	4746	0.67%	97%	100%	96%	98%	29.8	30.4
Stan White	34	3783	0.90%	116%	94%	64%	82%	23.8	30.4
Lyle Blackwood	35	4753	0.74%	108%	95%	70%	85%	25.1	30.4
Samari Rolle	31	5266	0.59%	97%	100%	100%	100%	30.1	30.2
Bill Simpson	34	3097	1.10%	115%	94%	64%	82%	23.6	30.1
Eric Weddle	29	5447	0.53%	96%	100%	116%	108%	32.3	30.1
Brock Marion	31	4665	0.66%	98%	100%	98%	99%	29.7	30.0
Lindon Crow	38	2945	1.29%	126%	83%	51%	75%	20.2	30.0
Dennis Thurman	36	3785	0.95%	103%	95%	71%	86%	24.9	30.0
Tim McKyer	33	5173	0.64%	98%	99%	87%	94%	27.9	30.0
Michael Downs	35	4454	0.79%	101%	96%	77%	89%	26.0	29.9
Ray Lewis	31	7601	0.41%	96%	100%	101%	100%	30.0	29.9
Dexter McCleon	31	5301	0.58%	97%	100%	98%	99%	29.6	29.8
Bob Howard	37	4336	0.85%	113%	89%	60%	80%	22.2	29.8
Dewayne Washington	31	5841	0.53%	97%	100%	98%	99%	29.5	29.8
Tashaun Gipson	27	5472	0.49%	96%	100%	130%	115%	33.7	29.7
Ryan McNeil	31	4761	0.65%	97%	100%	98%	99%	29.4	29.7
Stephon Gilmore	27	5509	0.49%	95%	100%	131%	115%	33.6	29.7
Darryl Williams	31	5256	0.59%	98%	100%	97%	98%	29.2	29.7
Keith Lyle	31	4861	0.64%	96%	100%	98%	99%	29.2	29.5
Deon Grant	30	5886	0.51%	97%	100%	103%	102%	29.9	29.5
Mike Wagner	36	3740	0.96%	110%	92%	62%	81%	22.5	29.5
Janoris Jenkins	27	5703	0.47%	94%	100%	131%	115%	33.2	29.3
Willie Williams	35	2791	1.25%	122%	86%	60%	80%	21.8	29.2
LeRoy Irvin	35	5317	0.66%	97%	96%	78%	89%	25.3	29.0
Johnny Sample	41	3504	1.17%	114%	79%	56%	78%	20.6	28.9
Dave Grayson	48	4311	1.11%	108%	72%	54%	77%	20.1	28.8
Tony Parrish	30	4303	0.70%	96%	100%	98%	99%	28.4	28.7
Kurt Schulz	30	3742	0.80%	97%	100%	98%	99%	28.3	28.6
Nate Wright	34	3932	0.86%	114%	91%	62%	81%	21.7	28.5
J.C. Jackson	25	2173	1.15%	96%	100%	137%	119%	32.9	28.4
Tom Keane	40	2046	1.96%	123%	80%	43%	72%	17.2	28.4
Brig Owens	36	3953	0.91%	113%	87%	59%	80%	21.1	28.3
Eric Turner	30	4261	0.70%	97%	100%	95%	98%	27.6	28.3
Dick Lynch	37	2859	1.29%	120%	82%	55%	77%	20.0	28.2
Mark Lee	32	5252	0.61%	102%	96%	79%	89%	24.9	28.2
Herm Edwards	33	3995	0.83%	106%	94%	70%	85%	23.2	28.2
Louis Breeden	33	4229	0.78%	103%	95%	74%	87%	23.8	28.1
Jerry Norton	35	2798	1.25%	130%	83%	49%	74%	18.4	28.1
Tyrann Mathieu	26	5231	0.50%	93%	100%	132%	116%	32.0	28.1
Don Shinnick	37	4471	0.83%	117%	83%	55%	78%	19.9	28.0
Carl Lee	31	5884	0.53%	101%	97%	84%	92%	25.5	28.0
David Fulcher	31	3494	0.89%	100%	98%	84%	92%	25.4	27.9
Joe Lavender	33	3315	1.00%	110%	94%	64%	82%	21.8	27.9
Don Paul	34	2278	1.49%	138%	82%	45%	73%	17.3	27.9
Jim David	36	2592	1.39%	127%	83%	46%	73%	17.6	27.8
Norm Thompson	33	3091	1.07%	115%	91%	60%	80%	20.9	27.8
Kenny Easley	32	3266	0.98%	104%	95%	76%	88%	23.9	27.7
Dick Anderson	34	2901	1.17%	119%	87%	58%	79%	20.4	27.7
Mark Kelso	30	2914	1.03%	101%	98%	85%	92%	25.3	27.6
Gary Green	33	4192	0.79%	104%	94%	70%	85%	22.7	27.5
W.K. Hicks	40	3568	1.12%	113%	78%	56%	78%	19.7	27.5
Scott Case	30	4419	0.68%	101%	98%	85%	92%	25.1	27.4
Bill Bradley	34	2801	1.21%	112%	89%	60%	80%	20.5	27.3
Don Doll	41	2191	1.87%	118%	78%	43%	71%	16.2	27.0
Jimmy Allen	31	2442	1.27%	111%	94%	65%	83%	21.3	27.0
Vann McElroy	31	3840	0.81%	102%	96%	78%	89%	23.5	26.9
Jack Ham	32	4970	0.64%	110%	93%	64%	82%	20.8	26.8
Wes Hopkins	30	3561	0.84%	99%	97%	84%	92%	24.3	26.6
Dwight Hicks	32	3931	0.81%	101%	95%	73%	87%	22.5	26.6
Casey Hayward	24	3849	0.62%	95%	100%	132%	116%	30.1	26.5
Rod Perry	30	3323	0.90%	110%	94%	68%	84%	21.3	26.3
Goose Gonsoulin	46	2936	1.57%	110%	69%	52%	76%	17.9	26.2
Dennis Smith	30	6018	0.50%	100%	97%	81%	90%	23.4	26.2
Terry Kinard	31	4164	0.74%	98%	96%	79%	90%	23.1	26.1
Jerry Logan	34	3970	0.86%	113%	85%	59%	80%	19.3	25.9
Butch Byrd	40	2905	1.38%	110%	75%	55%	78%	18.3	25.7
Burgess Owens	30	3789	0.79%	109%	94%	65%	83%	20.2	25.5
Andy Nelson	33	2444	1.35%	122%	83%	53%	76%	17.6	25.5
Cornell Green	34	4205	0.81%	109%	85%	59%	80%	18.8	25.3
Charlie Stukes	32	3121	1.03%	114%	87%	59%	80%	18.8	25.3
Miller Farr	35	3478	1.01%	115%	80%	57%	78%	18.3	25.2
Ray Ramsey	35	1963	1.78%	139%	72%	43%	72%	15.2	25.1
Rosey Taylor	32	4098	0.78%	117%	84%	59%	79%	18.6	25.0
Bobby Hunt	42	3548	1.18%	106%	72%	54%	77%	17.3	24.7
Jim Norton	45	4069	1.11%	105%	68%	52%	76%	16.9	24.6
Ken Konz	30	2111	1.42%	133%	84%	47%	73%	15.6	24.5
Lee Roy Jordan	32	4881	0.66%	111%	86%	59%	80%	18.2	24.5
Mike Bass	30	2715	1.10%	114%	88%	60%	80%	18.1	24.3
Dick Alban	30	2439	1.23%	132%	83%	46%	73%	15.3	24.1
George Atkinson	30	3416	0.88%	116%	87%	58%	79%	17.7	24.0
Frank Reagan	35	1672	2.09%	140%	69%	40%	70%	13.4	23.7
Sam Huff	30	4611	0.65%	120%	83%	55%	77%	16.5	23.3
Bert Rechichar	31	2319	1.34%	125%	82%	46%	73%	14.6	23.2
Tommy James	34	2642	1.29%	126%	75%	44%	72%	14.2	23.2
Bernie Parrish	31	2592	1.20%	118%	80%	55%	78%	16.2	22.7
Jim Cason	34	2374	1.43%	124%	75%	44%	72%	13.8	22.6
Tom Landry	32	2417	1.32%	122%	78%	44%	72%	13.4	21.9
Otto Schnellbacher	34	1329	2.56%	125%	70%	43%	72%	12.9	21.3
Irv Comp	34	1739	1.96%	151%	61%	36%	68%	11.2	21.2
Bob Smith	33	2102	1.57%	121%	74%	43%	72%	12.8	21.2
Lowell Wagner	32	2058	1.55%	123%	73%	44%	72%	12.5	20.7
Bill Baird	34	3091	1.10%	107%	73%	54%	77%	14.4	20.4
Fred Williamson	36	3057	1.18%	107%	69%	53%	76%	14.0	20.3
Nick Buoniconti	32	4721	0.68%	108%	76%	55%	77%	14.4	20.3
Sammy Baugh	31	1320	2.35%	164%	57%	33%	67%	9.6	19.1
Cliff Lewis	30	1883	1.59%	130%	66%	43%	72%	11.0	18.3
Willie West	30	3339	0.90%	110%	72%	53%	76%	12.5	18.1
Don Hutson	30	1430	2.10%	155%	56%	33%	66%	8.6	17.4
Ron Hall	30	3582	0.84%	106%	70%	52%	76%	11.7	17.0
Fred Glick	30	2692	1.11%	106%	68%	52%	76%	11.1	16.3

If you earnestly believe older players relied too much on archaic passing to glean their big interception totals, the Austin column might be for you. Before we find Krause at number ten, only Rod Woodson and Eugene Robinson had any action prior to 1990. Recent ball hawk Richard Sherman is in a fourteen-way tie for 104th place in career interceptions, with 37. However, when Austin 3.16 comes around, Sherman jumps to 18th, which does feel more appropriate for one of the premier turnover artists of recent vintage. In fact, his 8.4 interception boost is the highest number of any player, just beating out the bonuses of 8.3 and 8.1 for fellow playmakers Xavien Howard and Marcus Peters. Wandering mercenary Aqib Talib finds himself pretty high on the career list when looking at the Austin total.

While some recent players saw modest gains, older players saw their totals fall off a cliff with the lower baseline. Emlen Tunnell, a real life hero who picked off 79 passes—but did most of his damage in the 1950s—suffers a reduction of 43.6 from his total. He goes from ranking second on the official list to 54th on the Austin list. That seems a little steep, even to a noted old school player hater like I am. Night Train Lane and Johnny Robinson join Krause and Tunnell as the only other players to lose at least 30 from their totals. Turn-of-the-century players like Sam Madison and Patrick Surtain see almost no change in their career numbers.

I think the last column makes the most sense at first glace. Tunnell, Robinson, and Jim Norton all lose more than 20 from their real numbers, and no one gains more than 3.6. Krause loses 14.6, but because Tunnell lost 22 and his lead over anyone else was huge, he remains in first place. Rod Woodson loses 4.7 from his total, while Charles Woodson and Ed Reed each lose about half a pick, resulting in the three ending pretty clustered, and all close to Krause at the top. While Tunnell has a large reduction, his actual number of interceptions was so high to begin with that he still ranks sixth here.

I am often interested to see where Ken Riley and Dave Brown will fall, relative to one another. Riley has 65 interceptions to Brown’s 62. The Austin adjustment puts Brown ahead, while my preferred adjustment leaves the Bengals legend with a 54.6 to 53.4 lead. Riley never made a Pro Bowl, but he earned first team all pro honors once and second team honors twice. Brown made one Pro Bowl and one all pro second team. Given how close together these two are in terms of actual production, the gap in their public perception is pretty interesting to me. When you consider the fact that Brown was his team’s top corner, while Lemar Parrish was the top corner in Cincinnati until 1977, the issue is further muddled.

I will leave further commentary to the FP faithful, if any remain.

References[+]

References
↑1	For the ModTot, that’s 71 * 142% * 98% * 99%. For the SoftTot, that’s 71 * 121% * 98% * 99%.
↑2	I refer to football in the wake of the Mel Blount Rule and rules enabling offensive linemen to extend their hands to block in 1978, as well as the subsequent offensive revolution heralded by the likes of Bill Walsh, Don Coryell, and Joe Gibbs.
↑3	Recall from the first table that his Mod and Soft were 91% and 96% because of the highest baseline.
↑4	To arrive at the numbers in the Austin column, we use: 81 * 117% * 88% * 60%. To find the results for the HalfTot column, we use: 81 * 117% * 88% * 80%. These figures are rounded and will produce slightly different results if you copy and paste to work with them yourself.

Tagged as: adjusted interceptions, Bryan Frye, Guest Posts

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Boxscore vs. PFF: Era in Review

by Bryan Frye on July 26, 2022

in Passing, Quarterbacks, Statistics

Adam Steele is back with more analysis of traditional box score stats versus Pro Football Focus’s big time throw and turnover-worthy play metrics. And we thank him for it.

A couple of weeks ago, I compared TD/INT and BTT/TWP numbers for the 2021 season. Today we’ll be looking at the entire Pro Football Focus era going back to 2006.

Before compiling the data, I hypothesized that TD/INT and BTT/TWP would track in relative lockstep, though perhaps the upward slope of the PFF metrics would be less severe. That turns out to be true for 2006-07 and 2014-21, but oh boy was there some wackiness taking place in between. In the graph below, you’ll see league TD-INT difference in blue and league BTT-TWP difference in red: [continue reading…]

Tagged as: Adam Steele, Guest Posts, luck

{ 1 comment }

Jonathan Taylor Runs Away With The Rushing Crown

by Chase Stuart on January 10, 2022

in History, Rushing, Statistics

Jonathan Taylor had a season for the ages. Here are the top 10 yards per carry seasons by a running back with at least 300 carries:


							Games		Rushing
Rk	Player	Age	Draft	Tm	Lg	Year ▼	G	GS	Att	Yds	Y/A	TD	Y/G
1	Jonathan Taylor	22	2-41	IND	NFL	2021	17	17	332	1811	5.45	18	106.5
2	Adrian Peterson	27	1-7	MIN	NFL	2012	16	16	348	2097	6.03	12	131.1
3	Chris Johnson	24	1-24	TEN	NFL	2009	16	16	358	2006	5.60	14	125.4
4	Frank Gore	23	3-65	SFO	NFL	2006	16	16	312	1695	5.43	8	105.9
5	Barry Sanders*	29	1-3	DET	NFL	1997	16	16	335	2053	6.13	11	128.3
6	Barry Sanders*	26	1-3	DET	NFL	1994	16	16	331	1883	5.69	7	117.7
7	Eric Dickerson*	24	1-2	RAM	NFL	1984	16	16	379	2105	5.55	14	131.6
8	Walter Payton*	23	1-4	CHI	NFL	1977	14	14	339	1852	5.46	14	132.3
9	O.J. Simpson*	28	1-1	BUF	NFL	1975	14	14	329	1817	5.52	16	129.8
10	O.J. Simpson*	26	1-1	BUF	NFL	1973	14	14	332	2003	6.03	12	143.1

Provided by Stathead.com: View Stathead Tool Used

He joined Jim Brown, Jim Taylor, O.J. Simpson, Walter Payton, and Clinton Portis as the only players to average 100 rushing yards and 1 rushing TD per game while having a YPC average of at least 5.4. But perhaps most remarkably, he won the rushing crown by over 500 yards. If that sounds like a lot to you, it’s because it is. The last time a player run the rushing crown by such a large margin was Simpson back in his record-breaking 2,000 yard 1973 season. [continue reading…]

Tagged as: Jonathan Taylor

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Leaders in Rushing Yards Over Every 10-Year Period

by Chase Stuart on October 17, 2021

in History, Rushing, Statistics

InIn one of the first posts at Football Perspective, I looked at the leaders in rushing yards over every 10-year period. The question asked in that article was who would ultimately lead the NFL in rushing yards from 2012 to 2021. We can now answer that question.

The answer may surprise you. I suggested that Trent Richardson was the obvious favorite. Among the names I offered as potential candidates were Mark Ingram, Dion Lewis, Jacquizz Rodgers, LeSean McCoy, Beanie Wells, DeMarco Murray, Doug Martin, David Wilson, Ronnie Hillman, Lamar Miller, Isaiah Pead, Kendall Hunter, and LaMichael James. I said that we could not rule out college stars like Marcus Lattimore or Michael Dyer or Montee Ball or Malcolm Brown or De’Anthony Thomas.

I said Ray Rice and Ryan Mathews, at 25-year-old in 2012, were probably too old to consider. That logic applied to 26-year-old stars Arian Foster and Marshawn Lynch. And while they may have been stud running backs, a quartet of 27-year-olds in Maurice Jones-Drew, Matt Forte, Adrian Peterson and Chris Johnson were clearly too old to consider.

I did not include Ezekiel Elliott or Derrick Henry, as both players were still in high school. As it turns out, barring injury in 2021, they will both finish in the top 5 of rushing yards from 2012 to 2021 despite both entering the league in 2016. Ingram, who was a 2nd-year player in 2012, will fall to 6th when Henry and Elliott pass him. The top 3? In a big surprise, the 27-year-old Peterson — then still recovering from a torn ACL — will wind up third on the list, and just over 300 yards away from the decade-lead. I named the 24-year-old McCoy one of the top candidates, and he will wind up 2nd on the list. But the leader in rushing yards from 2012 to 2021? [continue reading…]

Tagged as: Frank Gore

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Total Adjusted Yards Per Play Value: Full Careers

by Bryan Frye on September 4, 2021

in G.O.A.T., Quarterbacks, Statistics

We’ve come to the end of the line. After several posts ranking and reranking, thinking and rethinking, quarterbacks with Total Adjusted Yards per Play and its descendants, this is the one I imagine most readers really want to see. Today, we are looking at measured performance in the regular season and playoffs combined. This is where guys like Y.A. Tittle, who feasted in the regular season but nearly always faltered in the postseason, see their positions fall down the list. Where passers like Jim Plunkett, whose regular season performances left much to be desired but went full tilt bozo in the playoffs, rise up the ranks. As far as the NFL record book is concerned, the playoffs don’t count toward career stats or win-loss totals. While I understand not rewarding players for getting to participate in more games, I can see the argument that it is equally unfair not to reward them for playing well enough to continue the march toward a championship. In order to balance those ideas, I have only counted playoff performances that measured above average by TAY/P.

A quick word on the numbers I’m using. You can find more detail in previous articles in the series, but this should be sufficient to introduce the rookies and refresh the veterans. [continue reading…]

Tagged as: Bryan Frye, Guest Posts, total adjusted yards

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Total Adjusted Yards Per Play Value: Full Seasons

by Bryan Frye on September 3, 2021

in Quarterbacks, Statistics

We’re back at it with quarterback rankings based on Total Adjusted Yards per Play and its abundant offspring. This time, we’re getting into combined regular and postseason stats for single seasons. For the purposes of this article, I will refer to this as a full season. None of the stats are new and have been explained in what I hope is sufficient detail in previous posts. ^[1]Here are links for the base methodology, the introduction of Z Value and positive value, the methodology and refinement of championship leverage, and a brief explanation of retroactive leverage. Fun … Continue reading As fun as it would be to call this “the greatest quarterback seasons in history!” or something like that, I seem to have a deeply held grudge against page views and web traffic, because I can’t get behind calling it anything of the sort. This is one measure of how much quarterbacks produced in a given full season. I believe it is the best measure when trying to compare across eras in which superior metrics don’t exist, but that’s about as far as I can go on the hubris tip. Anyway, these are my numbers. I hope you like them. [continue reading…]

References[+]

References
↑1	Here are links for the base methodology, the introduction of Z Value and positive value, the methodology and refinement of championship leverage, and a brief explanation of retroactive leverage. Fun fact: with the addition of a game to the schedule, championship leverage will increase for the 2021 season!

Tagged as: Bryan Frye, Guest Posts, total adjusted yards

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Total Adjusted Yards Per Play Value: Postseason Careers

by Bryan Frye on September 2, 2021

in Quarterbacks, Statistics

We have already looked at postseason performance in single games and single seasons. Today, we’re finally having a go at full playoff careers. All of the metrics I am using today have been explained, in great detail, in the previous four posts, so I am not going to belabor the point here. I will, however, remind the reader that Total Adjusted Yards per Play, and its many variants, is just one approach to measuring quarterback performance. It doesn’t account for weather, and it is not adjusted for the strength of opposing defenses (not yet, at least). Moreover, these numbers are based on box score stats and do not include more granular information, like time on the clock, field position, and yards to go on a set of downs. A four yard pass on 1st and 10 is much less valuable than a four yard pass on 3rd and 3, but TAY/P treats them equally. This is by design, because the goal of this metric is to do the best possible job of comparing quarterbacks across eras. I can’t do that with DVOA or EPA/P, because the play by play data just don’t go back far enough. However, when I looked into the correlations of TAY/P with more granular metrics, the r value tended to land between .93 and .94 (even for ESPN’s QBR, with its often wacky use of win probability). This suggests, to me, that most of these issues smooth themselves over in the long run. [continue reading…]

Tagged as: Bryan Frye, Guest Posts, total adjusted yards

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Total Adjusted Yards Per Play Value: Single Postseasons

by Bryan Frye on September 1, 2021

in Quarterbacks, Statistics

Yesterday, we looked at playoff performance in individual playoff games, as measured by Total Adjusted Yards per Play (and its copious derivatives). Today, I’m taking the next logical step and looking at performance over a full postseason in any given year. In other words, instead of examining who produced the most in the Super Bowl last year, I am determining who produced the most over the entire 2020 playoffs. By that, I mean who produced the most as measured by this particular set of numbers. They happen to be my preferred numbers for comparing across eras, but your mileage may vary.

For the uninitiated, here is a brief rundown of the metrics used:

Total Adjusted Yards per Play is like ANY/A with rushing included. It is (pass yards -sack yards + rush yards + 20*pass TDs + 20*rush TDs – 45*interceptions – 25*fumbles) / (passes + sacks + rushes). This version of TAY/P doesn’t include first downs, since I only have reliable first down data back to 1991 and want to make the playing field as level as possible when comparing back to 1936. [continue reading…]

Tagged as: Bryan Frye, Guest Posts, total adjusted yards

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Total Adjusted Yards Per Play Postseason Game Value

by Bryan Frye on August 31, 2021

in Quarterbacks, Statistics

Before the 2021 season starts, I figured I would try to add a little more to the Total Adjusted Yardage information I have been posting. Today, I want to discuss the same stats as before, as well as a few additions, for every postseason game in which a quarterback had at least five action plays. Not all information is complete, as sacks are unavailable prior to 1948. However, I’m working with what I have and not looking back. ^[1]If you want to look at all the raw data, including quarterbacks with just one plays, you can check out this Google sheet. Because this is the postseason, it is inherently worth more with regard to both earning a championship and establishing one’s legacy. Therefore, I am going to include championship leverage in the discussion. I don’t have much to say, so let’s get to the numbers. [continue reading…]

References[+]

References
↑1	If you want to look at all the raw data, including quarterbacks with just one plays, you can check out this Google sheet.

Tagged as: Bryan Frye, Guest Posts, total adjusted yards

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Total Adjusted Yards Per Play Career Value

by Bryan Frye on August 24, 2021

in Quarterbacks, Statistics

Yesterday, we looked at the best (and worst) regular season performances, as measured by Total Adjusted Yards per Play and its many derivatives. Today, it’s time to look into total career values. Keep in mind, these figures don’t include the postseason, where many legends cemented or defined their legacies. We will get to that later, I promise.

Because I apparently hate driving traffic to the site, I will not title this anything to do with the greatest or best quarterback. Instead, I want to be honest about the fact that the results below are simply one measurement of career performance and are not meant to be definitive. I do believe it is the best approach I have seen when it comes to using numbers to compare quarterbacks across eras, but it isn’t perfect. When you see “Johnny Unitas,” what you are really seeing is Unitas, throwing to Raymond Berry, John Mackey, Lenny Moore, Jim Mutscheller, and Jimmy Orr, handing off to Alan Ameche, and standing behind Jim Parker and Bob Vogel, while glancing over at Weeb Ewbank and Don Shula standing on the sidelines. When you see “John Elway,” what you are really seeing is Elway throwing to a ragtag group of receivers, playing behind a ho hum offensive line, and under the tutelage of an unimaginative head coach during his prime, before getting basically the opposite of that late in his career. The average reader at Football Perspective has a good grip on both history and stat and should have little trouble contextualizing the numbers presented today. [continue reading…]

Tagged as: Bryan Frye, Guest Posts, total adjusted yards

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Total Adjusted Yards Per Play Value: Single Seasons

by Bryan Frye on August 23, 2021

in Quarterbacks, Statistics

It has been a while since I published anything specifically relating to my niche homebrewed metric, Total Adjusted Yards per Play (TAY/P). It has been longer, still, since Chase has posted his latest entry into the corpus of his legendary QBGOAT series. I figured I would kill one bird and dizzy another with one stone. This post is specifically about TAY/P and its derivatives. It is not a treatise on the greatest quarterbacks of all time. Instead, it is a look at how several quarterbacks have fared in a few different variations of a single measurement since 1932. If you want to view it as a GOAT list, you are free to do so, though I would not encourage it. I believe statistics should be used to support your argument rather than serve as the entirety of your argument.

I am not under the delusion that this is the premier metric to use when evaluating quarterbacks. I prefer a holistic approach that includes everything from simple box score stats to convoluted algorithms and subjective grades. I consider EPA/P, DVOA, and Total QBR to be superior measurements. ^[1]CPOE is a useful stat when trying to get a better idea, from the numbers, who was more or less accurate than their actual completion rate may have indicated. ANY/A is good but only looks at … Continue reading The only problem is that they don’t cover a large enough portion of NFL history to make comparisons. Thus, I continue to use TAY/P because it uses simple box score numbers to create a metric that can compare quarterbacks dating back as far as we have box scores. ^[2]With some caveats. We have full stats dating back to 1967. Prior to that, we don’t have full sack and sack yardage information for the AFL. We have data for sack yardage lost in the NFL dating … Continue reading [continue reading…]

References[+]

References
↑1	CPOE is a useful stat when trying to get a better idea, from the numbers, who was more or less accurate than their actual completion rate may have indicated. ANY/A is good but only looks at dropbacks. Success rate is usually instructive when looking at how a quarterback leads an offense, but it is defined differently by different entities and, thus, can be difficult to discuss without first defining the term. I prefer to count plays with positive EPA successful, rather than the 40/70/100 division or some variation thereof.
↑2	With some caveats. We have full stats dating back to 1967. Prior to that, we don’t have full sack and sack yardage information for the AFL. We have data for sack yardage lost in the NFL dating back to 1947, but we don’t know the number of sacks themselves prior to 1963. We have precious little sack information for the AAFC. In the NFL, we don’t have fumble data earlier than 1945, and we don’t have fumbles or for the AAFC at all. Prior to 1936, NFL teams didn’t even play the same number of games, which makes serious analysis tricky. And before 1932, we only have touchdowns. This all ignores the fact that yards are awarded a whole numbers, even when only half yards are gained. A touchdown from the one inch line still counts as a one yard run, by rule. On one play, that is a small deal, but over the course of a long career, it can add up (or take away). Though it mostly evens out.

Tagged as: Bryan Frye, Guest Posts, total adjusted yards

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Week 16 (2020) Passing Stats: Brady Hits Peak Performance

by Chase Stuart on December 31, 2020

in Statistics

Previously:

Was this the best game of Tom Brady‘s career? Blaspehmy, you say. Given the year and the opponent, that’s probably true. Against Miami in 2007, Brady had a game where he went 21 for 25 for 354 yards with 6 touchdowns and no interceptions and one sack, putting up a perfect 158.3 passer rating and averaging 17.85 ANY/A. That went down as the 17th highest single-game ANY/A performance since 1970. New England opened that game with touchdowns on each of their first four drives, and five out of their first six.

But this may have been, statistically speaking, Brady’s second most efficient performance ever, at least without any opponent or era adjustments. Against the Lions, Brady completed 22 of 27 passes for 348 yards and 4 touchdowns, with no interceptions and just one sack. One of about 100 games since 1970 where a quarterback averaged at least 15 ANY/A on 20+ passes. The Bucs scored a touchdown on 5 of their first 6 drives, picking up a whopping 405 yards. That was all in the first half, which was more than enough: Brady did not return to the game after halftime, with the Bucs ahead 34-0.

The full week 16 passing stats below.

Rk	Passer	Team	Opp	Result	Att	Yd	TD	INT	1D	Sk	Yd	FL	AdjY/DB	DB	VALUE
1	Tom Brady	TAM	DET	W 47-7	27	348	4	0	17	1	7	0	19.21	28	289
2	Brandon Allen	CIN	HOU	W 37-31	37	371	2	0	18	0	0	0	15.00	37	226
3	Josh Allen	BUF	NWE	W 38-9	36	320	4	0	17	0	0	0	14.36	36	197
4	Andy Dalton	DAL	PHI	W 37-17	30	377	3	1	13	2	15	0	14.59	32	182
5	Ryan Fitzpatrick	MIA	LVR	W 26-25	13	182	1	0	6	0	0	0	19.00	13	131
6	Derek Carr	LVR	MIA	L 25-26	34	336	1	0	15	3	22	0	12.43	37	131
7	Deshaun Watson	HOU	CIN	L 31-37	33	324	3	0	14	1	2	1	12.68	34	129
8	Aaron Rodgers	GNB	TEN	W 40-14	25	231	4	1	14	1	17	0	13.04	26	108
9	Drew Brees	NOR	MIN	W 52-33	26	311	0	2	13	0	0	0	13.00	26	107
10	Matt Ryan	ATL	KAN	L 14-17	35	300	2	0	17	4	23	0	11.59	39	105
11	Ben Roethlisberger	PIT	IND	W 28-24	49	341	3	0	18	1	9	0	10.54	50	82
12	Mitchell Trubisky	CHI	JAX	W 41-17	35	265	2	1	18	1	2	0	11.17	36	82
13	Kirk Cousins	MIN	NOR	L 33-52	41	291	3	0	17	2	17	0	10.70	43	77
14	Blaine Gabbert	TAM	DET	W 47-7	15	143	2	0	5	1	7	0	12.69	16	61
15	Justin Herbert	LAC	DEN	W 19-16	33	253	1	0	12	2	26	0	9.89	35	35
16	C.J. Beathard	SFO	ARI	W 20-12	22	182	3	0	11	3	11	1	10.12	25	31
17	Taylor Heinicke	WAS	CAR	L 13-20	19	137	1	0	6	1	7	0	9.75	20	17
18	Lamar Jackson	BAL	NYG	W 27-13	26	183	2	0	10	0	0	1	9.42	26	14
19	Patrick Mahomes	KAN	ATL	W 17-14	44	278	2	1	14	0	0	0	8.66	44	-10
20	Russell Wilson	SEA	LAR	W 20-9	32	225	1	0	10	5	28	0	8.05	37	-31
21	Sam Darnold	NYJ	CLE	W 23-16	32	175	2	0	10	2	16	0	7.97	34	-31
22	Jarrett Stidham	NWE	BUF	L 9-38	11	44	0	0	2	1	2	0	5.00	12	-47
23	Daniel Jones	NYG	BAL	L 13-27	41	252	1	0	16	6	37	0	7.87	47	-48
24	Jalen Hurts	PHI	DAL	L 17-37	39	342	1	2	12	3	16	1	7.26	42	-69
25	Cam Newton	NWE	BUF	L 9-38	10	34	0	0	1	2	20	0	1.92	12	-84
26	David Blough	DET	TAM	L 7-47	10	49	0	1	1	1	10	0	0.27	11	-95
27	Teddy Bridgewater	CAR	WAS	W 20-13	28	197	1	1	11	4	30	1	5.69	32	-103
28	Chase Daniel	DET	TAM	L 7-47	18	86	0	0	3	3	29	0	4.00	21	-103
29	Philip Rivers	IND	PIT	L 24-28	34	270	1	1	10	5	32	1	6.26	39	-103
30	Jared Goff	LAR	SEA	L 9-20	43	234	0	1	15	3	18	0	6.65	46	-103
31	Tua Tagovailoa	MIA	LVR	W 26-25	22	94	1	0	4	3	23	0	4.72	25	-104
32	Mike Glennon	JAX	CHI	L 17-41	37	211	2	2	10	1	8	0	5.92	38	-113
33	Ryan Tannehill	TEN	GNB	L 14-40	24	121	1	2	9	2	17	0	4.08	26	-125
34	Drew Lock	DEN	LAC	L 16-19	47	264	0	2	15	2	1	0	6.29	49	-128
35	Kyler Murray	ARI	SFO	L 12-20	50	247	0	1	9	3	17	0	5.02	53	-206
36	Dwayne Haskins	WAS	CAR	L 13-20	28	154	0	2	4	2	6	1	1.47	30	-223
37	Baker Mayfield	CLE	NYJ	L 16-23	53	285	0	0	12	4	31	2	4.60	57	-245

Baker Mayfield was the worst passer of the week, but there’s a decently large asterisk there: the Browns were down two starters on the offensive line (including the team’s first round pick, left tackle Jedrick Wills), and the team’s top four wide receivers Jarvis Landry, Rashard Higgins, Donovan Peoples-Jones, and KhaDarel Hodge — in addition to Odell Beckham. So for Mayfield, his top two wide receivers were Marvin Hall and Ja’Marcus Bradley, who both played nearly every snap in their first game ever in a Browns uniform. The results were not very good.

The other notable event of the week was Patrick Mahomes and the Chiefs losing the passing efficiency battle and still winning, something that is very rare in Kansas City these days. But sometimes it does come down to the kicking game, and for Kansas City, Harrison Butker hit a 53-yarder in the 4th quarter while Younghoe Koo missed a 39-yard field goal in the final seconds that would have forced overtime. The Chiefs also had a decided advantage in the rushing game, picking up 9 first downs on 20 carries to Atlanta’s four on 21 attempts.

Tagged as: Weekly ANY/A

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Week 1 (2020) Game Scripts: Cam Sends New England Back To The ’90s

by Chase Stuart on September 18, 2020

in Statistics

The Jets and Eagles both lost division games in week one by the same 27-17 score. But don’t let the final score deceive you: these two games were as different as can be. With 5 minutes left in the 2nd quarter of both games, the Jets trailed Buffalo 21-0 while the Eagles led 17-0 over the Washington Football Team. The final scores were not at all indicative of how the game unfolded: the Bills dominated the game start to finish, and only a late Jets touchdown kept the final score respectable. Buffalo finished with a +12.4 Game Script, meaning the Bills led — on average — by 12.4 points over every second of the game. Meanwhile, Washington had a -5.3 Game Script — trailing big early, trailing entering the 4th quarter, and only taking a lead with less than 7 minutes remaining — in the comeback win.

Every year, I calculate the game scripts each week for each NFL game. The Game Script is simply the name I assign to the points differential over every second of the game. Last year, Baltimore had the highest Game Script of week 1, and they repeated that feat on the opening weekend of 2020. There were several notable comebacks in week 1 of the 2020 season, but Washington and Chicago stood out with huge comebacks. The full Game Scripts data below. [continue reading…]

Tagged as: Game Scripts

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The Chargers Had The Worst Fumble Luck In 2019

by Chase Stuart on March 8, 2020

in Statistics

Every season, I like to look at each team’s fumble recovery data. The evidence suggests that when the ball is on the ground, teams aren’t better or worse at recovering those fumbles; in other words, the recovery is driven more by randomness than skill.

Let’s begin with looking at fumbles from the perspective of the offensive team. The New Orleans Saints had the fewest fumbles in the league last season, at just 9. Even more impressive, the Saints lost just two fumbles all year! Of course, that luck didn’t hold up in the postseason: New Orleans lost a critical fumble in the team’s opening playoff game, contributing to the surprise loss to the Vikings.

On average, the fumbling team recovered (or the ball went out of bounds, so the fumbling team retained possession) 53.5% of all fumbles in 2019. No team was “luckier” at recovering their own fumbles than the Saints, but the Broncos actually gained the biggest advantage due to having fumbled much more often. Denver fumbled 21 times last season; that means we would “expect” the Broncos to lose 9.75 of those fumbles. In reality, the team lost just 6 fumbles, meaning Denver recovered 3.75 more fumbles than we would have been expected. Non-QBs for the Broncos fumbled 10 times, but they lost just 2 of those fumbles.

The least fortunate team was the Colts. In 2019, Indianapolis fumbled 18 times, and lost 11 of them! Jacoby Brissett himself lost 5 of 7 fumbles. The table below shows the full fumble data for each offense in 2019: [continue reading…]

Tagged as: Fumbles

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2019 Concentration Index: Which Offenses Spread It Around The Most?

by Chase Stuart on February 25, 2020

in Statistics

Saints WR Michael Thomas had another dominant season in 2019. He easily led the NFL in receiving yards with 1,725, and he also was responsible for 38.9% of all Saints receiving yards. That was the largest percentage of a team’s receiving pie for any one player in 2019, followed by Bronco Courtland Sutton in a distant second place (32.7%), and Bears WR Allen Robinson (32.1%); only three other players (Buffalo’s John Brown, Cleveland’s Jarvis Landry, and Minnesota’s Stefon Diggs) topped 30%.

Regular readers know that I like to calculate something called the Concentration Index for passing offenses: it’s relatively simple to calculate, and it measures how concentrated a team’s passing offense is among a small or large number of players. To calculate, you simple take each player’s receiving yards, divide that by his team’s total receiving yards, square that result, and then add that number for each player on the offense. For the Saints, Thomas is at 38.9%; the square of that is 15.2%, so that’s the number we use. Jared Cook was second on the team with 705 yards, or 15.9% of the team’s receiving yards; the square of that number is 2.5%. Do this for every player, and the Saints have a total Concentration Index of 21.1%… which is highly concentrated. [continue reading…]

Tagged as: Concentration Index

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The 2019 NFC East Looks Historically Bad

by Chase Stuart on November 29, 2019

in History, Statistics

We knew the Washington Redskins would be awful this year, and the New York Giants are playing down to poor expectations, too. Both teams are 2-9 through 11 weeks, with one of those wins being a head-to-head Giants victory. The other three wins?

The Giants beat Tampa Bay, 32-31, when the Bucs missed a 34-yard field goal at the final gun.
The Redskins beat Miami, 17-16, when the Dolphins failed on a 2-point conversion attempt with 6 seconds remaining.
Washington beat Detroit (playing backup Jeff Driskel), 19-16, with a game-winning field goal in the final 20 seconds.

The strength of the division was supposed to be Dallas and Philadelphia, but that hasn’t quite worked out, either. The Eagles have been big disappointments, particularly on offense (the team ranks in the bottom 10 in yards, yards per pass attempt, and turnovers): Philadelphia is just 5-6, and 4-5 outside of NFC East play. The Cowboys have played really well against bad teams and rank in 8th in both points per game and points per game allowed; and yet Dallas is just 6-6, with all 6 wins coming against teams with losing records. The Cowboys are 2-6 outside of the division, and have lost as touchdown favorites to both the Jets and Bills.

Altogether, the NFC East is just 9-24 this season in non-division games, easily the worst mark in the NFL. And this is despite the division drawing the AFC East, projected to be the worst division in football (again) this year. In fact, the AFC East has the second-best record among the 8 divisions, but there’s a chicken-or-egg situation going on here: does the AFC East have a good record because it’s good, or because it’s playing the NFC East? The AFC East is 10-4 against the NFC East so far this season. [continue reading…]

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Super Bowl Teams And Passing Efficiency

by Chase Stuart on September 5, 2019

in History, Passing, Statistics

Over the last two days, I have argued that the value of a top passing offense is lower than it used to be. One natural counter to that might be, hey Chase, haven’t you noticed that Tom Brady and the Patriots tend to always win the Super Bowl?

But that’s not exactly as convincing an argument as you might think. The Patriots won the Super Bowl in 2014, 2016, and 2018. And let’s be super clear about what I’m saying: I am not saying that quarterbacks are not critical, just that they are less critical than they used to be! From 1958 to 1979, the team that won the NFL championship or Super Bowl had a Hall of Fame quarterback in all but two of those seasons.

So yes, Tom Brady may be winning Super Bowls, but that’s hardly evidence that quarterbacks matter more than ever. Especially when you consider that Brady being at his best has borne little relation to whether or not the Patriots win the Super Bowl.

The graph below shows the Patriots passing offense in each season from 2001 to 2018, measured by New England’s Adjusted Net Yards per Attempt average minus league average ANY/A. Yes, New England has had an above-average passing offense each year. The team has won 6 Super Bowls, and those dots are in gold and black. [continue reading…]

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Doug Baldwin Leaves As A Seahawks Great — And With An Unusual Career

by Chase Stuart on May 10, 2019

in Receiving, Statistics

A sight Seahawks fans loved to see.

Doug Baldwin was released by the Seahawks yesterday due to multiple injuries that leaves his career in doubt. It is not expected that he will ever play in the NFL again, and if this truly is the end, it was a special run for a unique player.

Baldwin will be remembered as a Seahawks great, one of the engines of the best era in Seattle’s history. He was an undrafted free agent out of Stanford, and while he wasn’t quite Rod Smith, Wes Welker, or Drew Pearson, he can make a reasonable case as being one of the top 10 or so wide receivers to get overlooked in the draft.

But when we look back on his career, his statistics won’t tell much of a story. With just 6,563 career receiving yards, he will get lost with the many of talented wide receivers in pro football history. Even in the postseason, Baldwin’s 734 yards and 6 touchdowns in 13 games won’t quite stand out. [continue reading…]

Tagged as: Doug Baldwin

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The Vikings Had The Worst Fumble Luck In 2018

by Chase Stuart on April 13, 2019

in Statistics

The Vikings and Falcons were two of the most disappointing teams in the NFL in 2018. They happened to be the two teams with the worst fumble recovery rates last year, which is largely driven by luck. On the other side, the Seahawks and Rams had the best two fumble recovery rates, and were two of the most overachieving teams in football last year.

The table below shows the fumble numbers for each team last year when they were the ones with the football. Seattle and Washington had the best fumble recovery luck in this state of the world (i.e., teams on offense). The Seahawks and Redskins each had 18 fumbles, and only lost 4 fumbles. That’s a 78% offensive fumble recovery rate, the best in the league. The average team recovered 57% of their own fumbles. The final column, therefore, shows the number of own fumbles a team recovered over expectation. For Seattle and Washington, they each recovered 3.8 more fumbles than we would expect, since they had 18 fumbles (we would have expected them to recover 10.2 own fumbles, but actually recovered 14). The full list below: [continue reading…]

Tagged as: Fumbles

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2018 Is The Most Offensive-Powered Final Four Ever

by Chase Stuart on January 15, 2019

in History, Playoffs, Statistics

The 1981 San Diego Chargers led the NFL in points and yards, but ranked 26th in points allowed and 27th in yards allowed in a 28-team NFL. The Chargers made it to the AFC Championship Game, but had the poor fortune of playing in Cincinnati in a game with -32 degree wind chill. The game wound up being known as the Freezer Bowl, and the Chargers lost 27-7. San Diego might have been better off playing in 2018.

The table below shows where each of the final four teams rank in points, yards, points allowed, and yards allowed, along with each team’s average rank of the two offensive categories and average rank of the two defensive categories. The final column shows the difference between the team’s offensive and defensive ranks, as a way of describing whether a team is offensive-powered or defensive-powered.

Year	Team	PF Rk	Yd Rk	PA Rk	YA Rk	Avg Off Rk	Avg Def Rk	Off Heavy
2018	NOR	3	8	14	14	5.5	14	8.5
2018	NWE	4	5	7	21	4.5	14	9.5
2018	LAR	2	2	20	19	2	19.5	17.5
2018	KAN	1	1	24	31	1	27.5	26.5

Let’s use the Saints as an example. New Orleans ranks 3rd in points for and 8th in yards, while ranking 14th in points allowed and 14th in yards allowed. Therefore, New Orleans has an average offensive rank (3, 8) of 5.5, and an average defensive rank of 14 (14,14). The Saints are considered offensive heavy to the tune of 8.5 slots (14 minus 5.5). And that makes the Saints the least offensive-heavy team remaining in the final four.

The graph below shows the average offensive rank (taking the average of each team’s rank in points and yards) and defensive rank for each of the final four teams in each season since the merger. As you can see, this is a heavily-slanted year for offense: [continue reading…]

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Week 2 (2018) Game Scripts: Los Angeles Domination

by Chase Stuart on September 20, 2018

in Statistics

Previously:

Week 1 (2018) Game Scripts

In week 2, the two best Game Scripts belonged to the two Los Angeles. The Rams blanked the Cardinals 34-0 in the most lopsided game of the week. Arizona was never competitive, and Los Angeles outgained Arizona 432-137, and won the first down battle 24-5. Buffalo didn’t fare much better against the Chargers, falling behind 21-3 after 20 minutes, and trailing by 15+ points for all of the second half until the final minute. This is a good example of how Game Script can tell a more accurate story of a game than the final score: both of these games were blowouts, but the Chargers won by “only” 11 points.

The Titans were the league’s most run-heavy team of the week. Blaine Gabbert threw just 20 passes (he had a sack, and two non-QBs threw a pass for Tennessee), an extremely low number given that Tennessee actually trailed in the 4th quarter of this game. But the Titans were happy to ride Derrick Henry (18 carries) and Dion Lewis (14 carries). Did it work? Not really — neither Gabbert no the running backs were particularly effective. Tennessee won the game on special teams, with a touchdown, winning the time of possession battle, and Houston missing a field goal.

The table below shows the full week 2 Game Scripts data: [continue reading…]

Tagged as: Game Scripts

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Which Receivers Played On The Most Efficient Passing Offenses?

by Chase Stuart on June 9, 2018

in Receiving, Statgeekery, Statistics

On Thursday, I posted a methodology to determine which wide receivers played on the most pass-happy teams, and yesterday, I posted another method of examining the same concept. Today, we will use the same approach to measure which receivers played in the most efficient passing offenses.

Efficiency is defined using Relative ANY/A, which is Team ANY/A minus League Average ANY/A. Let’s use Jerry Rice as an example. You will not be surprised to see that he generally played on very efficient passing offenses. In 1995, Rice had 1,848 receiving yards, which was 8.1% of his career receiving yardage. The 1995 49ers had a Relative ANY/A of +1.19 which means 8.1% of Rice’s career RANY/A grade is going to have a weight of +1.19. Do this calculation for every season of his career, and you see that Rice had a career RANY/A of +1.57.

The table below shows the career RANY/A grades for all receivers with at least 5,000 receiving yards:

Rk	Receiver	Rec Yd	First Yr	Last Yr	RANY/A	HOF
13	Jerry Rice	22895	1985	2004	1.57	Yes
62	Terrell Owens	15934	1996	2010	0.82	Yes
234	Larry Fitzgerald	15545	2004	2017	-0.21	Likely
34	Randy Moss	15292	1998	2012	1.18	Yes
118	Isaac Bruce	15208	1994	2009	0.38	No
102	Tony Gonzalez	15127	1997	2013	0.52	Likely
112	Tim Brown	14934	1988	2004	0.41	Yes
220	Steve Smith	14731	2001	2016	-0.12	Not El.
16	Marvin Harrison	14580	1996	2008	1.51	Yes
28	Reggie Wayne	14345	2001	2014	1.34	Not El.
169	Andre Johnson	14185	2003	2016	0.14	Not El.
168	James Lofton	14004	1978	1993	0.14	Yes
101	Cris Carter	13899	1987	2002	0.54	Yes
197	Anquan Boldin	13779	2003	2016	0.03	Not El.
145	Henry Ellard	13777	1983	1998	0.24	No
95	Torry Holt	13382	1999	2009	0.56	No
128	Andre Reed	13198	1985	2000	0.34	Yes
151	Steve Largent	13089	1976	1989	0.21	Yes
207	Irving Fryar	12785	1984	2000	-0.05	No
85	Art Monk	12721	1980	1995	0.63	Yes
83	Jason Witten	12448	2003	2017	0.64	Not El.
103	Jimmy Smith	12287	1995	2005	0.51	No
209	Brandon Marshall	12215	2006	2017	-0.06	Not El.
50	Charlie Joiner	12146	1969	1986	0.95	Yes
111	Hines Ward	12083	1998	2011	0.42	No
136	Derrick Mason	12061	1997	2011	0.29	No
61	Michael Irvin	11904	1988	1999	0.83	Yes
164	Don Maynard	11834	1958	1973	0.16	Yes
232	Calvin Johnson	11619	2007	2015	-0.18	Likely
39	Antonio Gates	11508	2003	2017	1.11	Not El.
228	Muhsin Muhammad	11438	1996	2009	-0.15	No
68	Rod Smith	11389	1995	2006	0.77	No
91	Keenan McCardell	11373	1992	2007	0.61	No
154	Chad Johnson	11059	2001	2011	0.19	No
256	Joey Galloway	10950	1995	2010	-0.45	No
119	Roddy White	10863	2005	2015	0.37	Not El.
66	Gary Clark	10856	1985	1995	0.78	No
131	Stanley Morgan	10716	1977	1990	0.32	No
203	Keyshawn Johnson	10571	1996	2006	-0.02	No
93	Harold Jackson	10372	1969	1983	0.57	No
137	Santana Moss	10283	2001	2014	0.29	Not El.
38	Lance Alworth	10266	1962	1972	1.11	Yes
141	Andre Rison	10205	1989	2000	0.27	No
63	Donald Driver	10137	1999	2012	0.81	No
120	Shannon Sharpe	10060	1990	2003	0.37	Yes
217	Eric Moulds	9995	1996	2007	-0.08	No
12	Wes Welker	9924	2005	2015	1.63	Not El.
70	Antonio Brown	9910	2010	2017	0.77	Likely
139	Drew Hill	9831	1979	1993	0.28	No
21	Marques Colston	9759	2006	2015	1.39	Not El.
213	Amani Toomer	9497	1996	2008	-0.07	No
80	DeSean Jackson	9487	2008	2017	0.66	Not El.
264	Rob Moore	9368	1990	1999	-0.57	No
19	Raymond Berry	9275	1955	1967	1.48	Yes
176	Herman Moore	9174	1991	2001	0.1	No
233	Anthony Miller	9148	1988	1997	-0.19	No
30	Charley Taylor	9110	1964	1977	1.28	Yes
89	Vincent Jackson	9080	2005	2016	0.62	Not El.
155	Tony Martin	9065	1990	2001	0.19	No
54	Julio Jones	9054	2011	2017	0.91	Likely
138	Harold Carmichael	8985	1971	1984	0.29	No
11	Mark Clayton	8974	1983	1993	1.63	No
35	Fred Biletnikoff	8974	1965	1978	1.18	Yes
49	Wes Chandler	8966	1978	1988	0.95	No
206	Roy Green	8965	1979	1992	-0.03	No
214	Ricky Proehl	8878	1990	2006	-0.07	No
9	Mark Duper	8869	1983	1992	1.74	No
156	Terry Glenn	8823	1996	2006	0.19	No
153	Terance Mathis	8809	1990	2002	0.19	No
278	Mark Carrier	8763	1987	1998	-0.87	No
130	Joe Horn	8744	1996	2007	0.32	No
105	John Stallworth	8723	1974	1987	0.46	Yes
167	Johnnie Morton	8719	1994	2005	0.14	No
92	Cliff Branch	8685	1972	1984	0.61	No
79	Demaryius Thomas	8653	2010	2017	0.66	Not El.
252	Laveranues Coles	8609	2000	2009	-0.39	No
23	Paul Warfield	8565	1964	1977	1.37	Yes
187	Plaxico Burress	8499	2000	2012	0.06	No
170	Billy Howton	8459	1952	1963	0.14	No
100	Tommy McDonald	8410	1957	1968	0.54	Yes
116	Eddie Kennison	8345	1996	2007	0.38	No
173	Wesley Walker	8306	1977	1989	0.12	No
46	Greg Jennings	8291	2006	2015	1	Not El.
160	Carroll Dale	8277	1960	1973	0.18	No
225	Curtis Conway	8230	1993	2004	-0.15	No
133	Ernest Givins	8215	1986	1995	0.31	No
152	A.J. Green	8213	2011	2017	0.2	Not El.
245	Jeff Graham	8172	1991	2001	-0.3	No
165	Eric Martin	8161	1985	1994	0.16	No
235	Sterling Sharpe	8134	1988	1994	-0.22	No
195	Webster Slaughter	8111	1986	1998	0.04	No
263	Haven Moses	8091	1968	1981	-0.56	No
198	Mike Wallace	8072	2009	2017	0.02	Not El.
243	Art Powell	8046	1960	1968	-0.29	No
261	Bill Brooks	8001	1986	1996	-0.51	No
4	Don Hutson	7991	1935	1945	2.24	Yes
71	Tony Hill	7988	1977	1986	0.77	No
158	Ozzie Newsome	7980	1978	1990	0.19	Yes
45	Bobby Mitchell	7954	1958	1968	1.01	Yes
182	Jackie Smith	7918	1963	1977	0.07	Yes
22	Jimmy Orr	7914	1958	1970	1.38	No
15	Jordy Nelson	7848	2008	2017	1.54	Not El.
17	Drew Pearson	7822	1973	1983	1.49	No
159	Bobby Engram	7751	1996	2009	0.19	No
175	Anthony Carter	7733	1985	1994	0.11	No
250	Chris Chambers	7648	2001	2010	-0.34	No
276	Brian Blades	7620	1988	1998	-0.86	No
140	Pierre Garcon	7568	2008	2017	0.27	Not El.
230	Greg Olsen	7556	2007	2017	-0.16	Not El.
36	Nat Moore	7546	1974	1986	1.16	No
190	Roy Jefferson	7539	1965	1976	0.06	No
106	Gary Garrison	7538	1966	1977	0.45	No
72	Dez Bryant	7459	2010	2017	0.76	Not El.
57	Ed McCaffrey	7422	1991	2003	0.86	No
20	Bob Hayes	7414	1965	1975	1.42	Yes
87	Pete Retzlaff	7412	1956	1966	0.63	No
204	Wayne Chrebet	7365	1995	2005	-0.02	No
56	Otis Taylor	7306	1965	1974	0.88	No
40	Boyd Dowler	7270	1959	1971	1.1	No
53	Antonio Freeman	7251	1995	2003	0.91	No
212	T.J. Houshmandzadeh	7237	2001	2011	-0.07	No
273	Dwayne Bowe	7208	2007	2015	-0.75	Not El.
283	Lionel Taylor	7195	1960	1968	-1.63	No
14	Rob Gronkowski	7179	2010	2017	1.55	Likely
199	Darrell Jackson	7132	2000	2008	0	No
222	Carl Pickens	7129	1992	2000	-0.12	No
269	Ken Burrough	7102	1970	1981	-0.68	No
52	Isaac Curtis	7101	1973	1984	0.92	No
201	Vernon Davis	7072	2006	2017	-0.01	Not El.
178	Reggie Rucker	7065	1970	1981	0.09	No
81	John Gilliam	7056	1967	1977	0.65	No
7	Elroy Hirsch	7029	1946	1957	1.86	Yes
184	Shawn Jefferson	7023	1991	2003	0.07	No
229	Pat Tilley	7005	1976	1986	-0.16	No
97	Jake Reed	6999	1992	2002	0.55	No
255	J.T. Smith	6974	1979	1990	-0.44	No
110	Derrick Alexander	6971	1994	2002	0.43	No
224	Nate Washington	6954	2006	2015	-0.15	Not El.
51	Marshall Faulk	6875	1994	2005	0.93	Yes
144	Michael Crabtree	6870	2009	2017	0.25	Not El.
104	Gene Washington	6856	1969	1979	0.46	No
177	Jeremy Maclin	6835	2009	2017	0.09	Not El.
180	Ahmad Rashad	6831	1972	1982	0.08	No
200	T.Y. Hilton	6827	2012	2017	0	Not El.
77	Charley Hennigan	6823	1960	1966	0.7	No
32	Jimmy Graham	6800	2010	2017	1.23	Not El.
248	Larry Centers	6797	1991	2003	-0.32	No
27	Dwight Clark	6750	1979	1987	1.34	No
281	Frank Sanders	6749	1995	2003	-0.91	No
37	Kellen Winslow	6741	1979	1987	1.16	Yes
142	Frank Lewis	6724	1971	1983	0.27	No
282	Marty Booker	6703	1999	2009	-0.92	No
33	Cris Collinsworth	6698	1981	1988	1.21	No
67	Deion Branch	6644	2002	2012	0.78	No
74	Mel Gray	6644	1971	1982	0.74	No
171	Willie Gault	6635	1983	1993	0.13	No
259	Jerricho Cotchery	6623	2004	2015	-0.48	Not El.
188	Al Toon	6605	1985	1992	0.06	No
147	Brett Perriman	6589	1988	1997	0.24	No
143	Michael Haynes	6588	1988	1997	0.25	No
73	Heath Miller	6569	2005	2015	0.74	Not El.
236	Henry Marshall	6545	1976	1987	-0.24	No
2	Dante Lavelli	6488	1946	1956	3.13	Yes
75	Ricky Sanders	6477	1986	1995	0.72	No
44	Del Shofner	6470	1958	1967	1.04	No
254	Mike Quick	6464	1982	1990	-0.41	No
216	Quinn Early	6448	1988	1999	-0.08	No
109	Golden Tate	6419	2010	2017	0.43	Not El.
183	Sammy White	6400	1976	1985	0.07	No
231	Ike Hilliard	6397	1997	2008	-0.17	No
210	Carlos Carson	6372	1980	1989	-0.06	No
134	Troy Brown	6366	1993	2006	0.31	No
96	Max McGee	6346	1954	1967	0.56	No
161	Stephone Paige	6341	1983	1991	0.17	No
114	Haywood Jeffires	6334	1987	1996	0.4	No
185	Steve Jordan	6307	1982	1994	0.07	No
227	Sean Dawkins	6291	1993	2001	-0.15	No
246	Alfred Jenkins	6267	1975	1983	-0.32	No
240	Darnay Scott	6193	1994	2002	-0.27	No
174	Emmanuel Sanders	6156	2010	2017	0.12	Not El.
135	Jeremy Shockey	6143	2002	2011	0.3	No
31	Eddie Brown	6134	1985	1991	1.25	No
215	Steve Watson	6112	1979	1987	-0.08	No
202	Ronnie Harmon	6076	1986	1997	-0.02	No
266	Red Phillips	6044	1958	1967	-0.62	No
10	Lenny Moore	6039	1956	1967	1.66	Yes
192	Santonio Holmes	6030	2006	2014	0.06	Not El.
258	Louis Lipps	6019	1984	1992	-0.46	No
60	Jack Snow	6012	1965	1975	0.84	No
272	Lee Evans	6008	2004	2011	-0.72	No
223	Sonny Randle	5996	1959	1968	-0.12	No
247	Brandon Lloyd	5989	2003	2014	-0.32	Not El.
88	Doug Baldwin	5945	2011	2017	0.62	Not El.
211	Bobby Joe Conrad	5902	1959	1969	-0.06	No
179	Billy Wilson	5902	1951	1960	0.09	No
208	Todd Christensen	5872	1981	1988	-0.05	No
257	Todd Heap	5869	2001	2012	-0.46	No
268	DeAndre Hopkins	5865	2013	2017	-0.64	Not El.
47	James Jones	5861	2007	2015	0.98	Not El.
267	Jessie Hester	5850	1985	1995	-0.62	No
55	Freddie Solomon	5846	1975	1985	0.88	No
98	Eric Decker	5816	2010	2017	0.54	Not El.
76	Mike Ditka	5812	1961	1972	0.71	Yes
150	Riley Odoms	5755	1972	1983	0.21	No
244	Roy Williams	5715	2004	2011	-0.29	No
58	John Jefferson	5714	1978	1985	0.86	No
262	Gail Cogdill	5696	1960	1970	-0.52	No
172	Vance Johnson	5695	1985	1995	0.13	No
218	Jabar Gaffney	5690	2002	2012	-0.1	No
260	Danny Abramowicz	5686	1967	1974	-0.49	No
271	Antonio Bryant	5685	2002	2009	-0.71	No
29	Dallas Clark	5665	2003	2013	1.33	Not El.
108	Owen Daniels	5661	2006	2015	0.44	Not El.
121	Keith Byars	5661	1986	1998	0.36	No
84	Nate Burleson	5630	2003	2013	0.64	Not El.
251	Delanie Walker	5621	2006	2017	-0.37	Not El.
125	Dave Parks	5619	1964	1973	0.35	No
82	Pete Pihos	5619	1947	1955	0.65	Yes
1	Mac Speedie	5602	1946	1952	3.48	No
3	John Taylor	5598	1987	1995	2.25	No
194	Eric Metcalf	5572	1989	2001	0.04	No
226	Ben Coates	5555	1991	2000	-0.15	No
181	Mark Jackson	5551	1986	1994	0.07	No
43	Malcom Floyd	5550	2004	2015	1.05	Not El.
279	Braylon Edwards	5522	2005	2012	-0.87	No
249	Rich Caster	5515	1970	1981	-0.34	No
5	Ray Renfro	5508	1952	1963	2.05	No
59	Chris Burford	5505	1960	1967	0.85	No
270	Chris Calloway	5497	1990	2000	-0.7	No
26	Jerry Smith	5496	1965	1977	1.35	No
123	Ben Watson	5485	2004	2017	0.35	Not El.
41	Lynn Swann	5462	1974	1982	1.07	Yes
193	Reggie Langhorne	5446	1985	1993	0.06	No
117	Bernie Casey	5444	1961	1968	0.38	No
122	Buddy Dial	5436	1959	1966	0.36	No
25	Frank Gifford	5434	1952	1964	1.35	Yes
157	Frank Clarke	5426	1957	1967	0.19	No
205	Bob Tucker	5421	1970	1980	-0.03	No
196	Marcus Allen	5411	1982	1997	0.03	Yes
8	Tom Fears	5397	1948	1956	1.81	Yes
115	Michael Jackson	5393	1991	1998	0.4	No
274	Gary Ballman	5366	1963	1973	-0.84	No
126	Bobby Walston	5363	1951	1962	0.34	No
238	Fred Barnett	5362	1990	1997	-0.25	No
86	Flipper Anderson	5357	1988	1995	0.63	No
24	Brandon Stokley	5339	1999	2013	1.35	Not El.
148	Alshon Jeffery	5338	2012	2017	0.24	Not El.
48	Gary Collins	5299	1962	1971	0.97	No
241	Rocket Ismail	5295	1993	2001	-0.27	No
265	Elbert Dubenion	5294	1960	1967	-0.58	No
191	Wesley Walls	5291	1989	2003	0.06	No
94	Keith Jackson	5283	1988	1996	0.57	No
242	Peerless Price	5281	1999	2007	-0.28	No
69	Miles Austin	5273	2007	2015	0.77	Not El.
166	Brandon LaFell	5263	2010	2017	0.15	Not El.
65	Russ Francis	5262	1975	1988	0.79	No
253	Bob Chandler	5243	1971	1981	-0.4	No
275	Kellen Winslow	5236	2004	2013	-0.85	Not El.
18	John Mackey	5236	1963	1972	1.49	Yes
237	Hugh Taylor	5233	1947	1954	-0.24	No
162	Dave Casper	5216	1974	1984	0.17	Yes
127	Tim McGee	5203	1986	1994	0.34	No
6	Brent Jones	5195	1987	1997	2	No
124	Mike Pritchard	5187	1991	1999	0.35	No
221	Tiki Barber	5183	1997	2006	-0.12	No
186	Kevin House	5169	1980	1987	0.07	No
42	Randall Cobb	5141	2011	2017	1.06	Not El.
239	Qadry Ismail	5137	1993	2002	-0.26	No
280	Kenny Britt	5137	2009	2017	-0.88	Not El.
277	Elbie Nickel	5131	1947	1957	-0.87	No
113	Frank Wycheck	5126	1993	2003	0.41	No
149	Charle Young	5106	1973	1985	0.21	No
163	Mickey Shuler	5100	1978	1991	0.16	No
219	Jimmie Giles	5084	1977	1989	-0.11	No
99	Hakeem Nicks	5081	2009	2015	0.54	Not El.
129	Yancey Thigpen	5081	1992	2000	0.33	No
78	O.J. McDuffie	5074	1993	2000	0.67	No
132	Ted Ginn	5072	2007	2017	0.32	Not El.
90	Roger Carr	5071	1974	1983	0.61	No
107	Johnny Morris	5059	1958	1967	0.45	No
146	Duriel Harris	5055	1976	1985	0.24	No
64	Raymond Chester	5013	1970	1981	0.8	No
189	Jim Colclough	5001	1960	1968	0.06	No

None of the Hall of Fame receivers have negative RANY/A grades, although Larry Fitzgerald and Calvin Johnson will probably change that.

Here’s a look at the 23 receivers with 8,000 career receiving yards and played on below-average passing teams:

Rk	Receiver	Rec Yd	First Yr	Last Yr	RANY/A	HOF
234	Larry Fitzgerald	15545	2004	2017	-0.21	Likely
220	Steve Smith	14731	2001	2016	-0.12	Not El.
207	Irving Fryar	12785	1984	2000	-0.05	No
209	Brandon Marshall	12215	2006	2017	-0.06	Not El.
232	Calvin Johnson	11619	2007	2015	-0.18	Likely
228	Muhsin Muhammad	11438	1996	2009	-0.15	No
256	Joey Galloway	10950	1995	2010	-0.45	No
203	Keyshawn Johnson	10571	1996	2006	-0.02	No
217	Eric Moulds	9995	1996	2007	-0.08	No
213	Amani Toomer	9497	1996	2008	-0.07	No
264	Rob Moore	9368	1990	1999	-0.57	No
233	Anthony Miller	9148	1988	1997	-0.19	No
206	Roy Green	8965	1979	1992	-0.03	No
214	Ricky Proehl	8878	1990	2006	-0.07	No
278	Mark Carrier	8763	1987	1998	-0.87	No
252	Laveranues Coles	8609	2000	2009	-0.39	No
225	Curtis Conway	8230	1993	2004	-0.15	No
245	Jeff Graham	8172	1991	2001	-0.3	No
235	Sterling Sharpe	8134	1988	1994	-0.22	No
263	Haven Moses	8091	1968	1981	-0.56	No
243	Art Powell	8046	1960	1968	-0.29	No
261	Bill Brooks	8001	1986	1996	-0.51	No

You will not be surprised to see Joey Galloway on there at -0.45, at least not if you have been paying attention.

Tagged as: WR Project

{ 1 comment }

13 Points > 14 Points, Part III

by Chase Stuart on February 17, 2018

in History, Statistics

Over the last two days, I’ve looked at the football oddity that teams that score 13 points had a better winning percentage than teams that scored 14 points. Today, let’s look at the winning percentage for all points scored.

That’s what the graph below shows: the winning percentage, based on points scored, for all points scored totals from zero to sixty. To make it a little easier to follow, I’ve colored in red the multiples of 7; as you can see, those numbers (7, 14, 21, and 28) also represent dips in the graph. What’s interesting is that three field goals is better than two touchdowns across a number of multiples. For example, scoring 9 points is better than scoring 14 points, scoring 16 points is better than 21 points, scoring 23 points is even better than scoring 28 points, and scoring 30 points is better than 35 points. Take a look:

[continue reading…]

{ 8 comments }

Is This The Best Eagles Team Ever Through 11 Weeks?

by Chase Stuart on November 27, 2017

in History, Statistics

The Philadelphia Eagles are 10-1 for the fourth time in franchise history. The Eagles have never started a season 11-0, so this season makes the short list for best start in franchise history.

In 1948, behind head coach Greasy Neale, QB Tommy Thompson, and future HOFers RB Steve Van Buren and WR Pete Pihos, and RB Bosh Pritchard, the Eagles went 9-2-1 and won the NFL title. In 1949, the Eagles brought back Neale, Thompson, Van Buren, Pihos, and Pritchard, and had similar success. The team lost to the Bears in week 4 but finished the regular season with a sparkling 11-1 record. Philadelphia repeated as champions, defeating the Rams 14-0 in the NFL title game.

In 1980, the Eagles lost to the Cardinals in week 4, but started the season 11-1 before finishing 12-4 and winning the NFC. The head coach was Dick Vermeil, the QB was Ron Jaworski, and while RB Wilbert Montgomery and WR Harold Carmichael were the stars on offense, Philadelphia sported a dominant defense that ranked 1st in points allowed, and 2nd in rushing yards allowed, net yards per pass attempt allowed, and rushing yards allowed. Alas, despite being 3-point favorites, the Eagles lost in the Super Bowl to the Raiders.

The 2004 Eagles was the best Philadelphia team of the modern era. The team began the season 13-1, with the only loss coming to the 15-1 Steelers in Pittsburgh. Philadelphia clinched the NFC East after week twelve. The Packers were the 2nd best team in the NFC, and the Eagles bludgeoned them in December 47-3 before a pair of garbage time touchdowns. Philadelphia had a great defense, but the offense centered around Donovan McNabb, Brian Westbrook, and Terrell Owens was unstoppable. In the 14th game, however, Owens broke his fibula and injured his ankle; expected to miss the rest of the year, Owens returned for the Super Bowl, but it was not enough: Philadelphia fell to the Patriots.

If you are an Eagles fan, that’s some pretty good company: all three teams made it to the championship game.

This year’s team seems worthy of being in that discussion. Philadelphia leads the NFL with a 31.9 points per game average, thanks in part to an otherwordly (and unsustainable) red zone success rate of 73.3%. The Eagles rank 8th in points per game allowed (17.4), and rank in the top 10 in just about every major defensive category. The Eagles rank 1st in the NFL in points differential, at 14.5 per game. That’s also the 3rd best in Eagles history through 11 games, behind the ’49 team (+19.6), ’48 team (17.8), and ahead of the 1980 team (+14.3). [continue reading…]

Tagged as: Eagles, Pythagenpat Wins

{ 6 comments }

Week 9 Game Scripts: Rams, Eagles, and Saints Blowouts

by Chase Stuart on November 15, 2017

in Checkdowns, Statgeekery, Statistics

Let’s get to the week 9 Game scripts! Yes, these are a week late: my apologies, as well, other topics wound up being covered last week.

The biggest stories of week 9 were the blowout wins by Los Angeles, Philadelphia, and New Orleans. The Rams and Saints followed that up with another pair of blowout wins in week 10, while the Eagles were on bye. But before turning to week 10, let’s review some of the biggest outliers from week nine.

In week 9, the Jets and Panthers were very run-heavy. Lest you forget, the Jets beat the Bills on Thursday night in week 9, and while quarterback Josh McCown did have 5 carries, the running backs combined for 36 carries, while McCown had just 21 attempts. The Jets blew out Buffalo, but consider that the Lions had a similar Game Script and passes on 50% of plays.

Carolina beat Atlanta in a close game where the Panthers trailed for most of the first half. Still, behind Cam Newton and his 9 carries, Carolina wound up passing just 25 times while running 38 times! That’s really run-heavy.

The full Game Scripts data below: [continue reading…]

Tagged as: Game Scripts

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The Jaguars Are Maybe Really Good?

by Chase Stuart on October 24, 2017

in Statistics

In games when they allow 10 or more points, the Jaguars are 0-3 so far this year.

In games when they score fewer than 27 points, the Jaguars are 0-3 so far this year.

If those stats sounds like those of a really bad team one month into an NFL season, well, you’re right. The thing is, Jacksonville has played 7 games this year. Which means maybe they’re a really good team? Because in Jacksonville’s 4 non-losses — things commonly referred to in most parts of the country as wins — the average score has been Jacksonville 32.5, Opponent 5.75. The Jaguars four wins have come by 21+ points, the first team to record four such wins through seven games since 2007.

Entering the 2017 season, the Jaguars had allowed fewer than 10 points in four out of their last 100 games. In 2017, the Jaguars have allowed fewer than 10 points in four out of seven games. The Jaguars had scored 27 or more points in just 13 of their last 100 games entering 2017; so far this year, they’ve scored 27 points in four out of seven games. So yeah, Jacksonville is suddenly a lot better than they used to be.

Jacksonville ranks 2nd in the NFL in points differential at +73. So… are the Jaguars actually good? Well, through seven weeks (but before Monday Night Football), Jacksonville also leads the NFL in Adjusted Net Yards per Attempt differential, which would have sounded impossible two months ago (especially if you knew Allen Robinson would tear his ACL one catch into the season): [continue reading…]

Tagged as: Jaguars

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Le’Veon Bell, Antonio Brown, And Two Teammates Dominating The Pie

by Chase Stuart on October 23, 2017

in History, Statistics

In 2014, Le’Veon Bell and Antonio Brown combined to account for 57.9% of the Steelers 6,777 total yards. In 2015, Bell missed most of the year with a knee injury, but in 2016, the duo combined to account for 51.8% of Pittsburgh’s offensive yards, despite the pair combining to miss five games! Through six games in 2017, Brown had 700 yards and Bell had 706 yards, placing both of them in the top five in yards from scrimmage. In fact, since the Steelers had 2,165 yards through six weeks, it means Bell and Brown were responsible for 64.9% of the team’s offensive production. In week seven, Bell and Brown combined for 257 yards; only a fake punt that netted 44 yards prevented the pair from again picking up two-thirds of the offense (Pittsburgh had 420 yards of offense, so Bell and Brown had 61.1% of the Steelers yards from scrimmage; that number would have been 68.4% without the fake punt).

That made me wonder: which pair of teammates have accounted for the largest share of their offense’s production? The 1978 Bears had a really good player in the backfield who rushed for 992 yards and caught 43 passes for 340 yards. They also had Walter Payton, who led the NFL for the second straight year with 1,875 yards from scrimmage. His backfield teammate was fullback Roland Harper, who actually finished second on the team to Payton in receptions (WR James Scott did lead the team be a healthy margin in receiving yards).

The ’78 Bears had a mediocre offense, finishing with 4,747 yards from scrimmage (Chicago ranked 27th out of 28 teams in ANY/A, tho the Bears of course were a very good rushing team). But since Payton had 1,875 yards (39.5%) and Harper had 1,332 yards (28.1%), the two combined for over two-thirds of all Chicago yards from scrimmage that season.

The table below shows the top 200 seasons: [continue reading…]

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Completion Percentage and Passing First Down Percentage, Part II

by Chase Stuart on October 10, 2017

in Passing, Statistics

Back when passes were completed for first downs.

The New York Jets have the second best completion percentage in the NFL through five weeks. That’s a shocking thing to say for many reasons, including the key fact that 38-year-old Josh McCown has taken every snap at quarterback for the team this season. The Jets are completing 71.6% of their passes, which is truly remarkable for this franchise.

Today I want to compare the 2017 Jets to their predecessors from 45 years earlier. The 1972 Jets were an interesting team. That year produced a low key entry for the best Joe Namath season: he went 7-6 (missing one game due to injury) but led the NFL in passing yards, touchdowns, yards per attempt, Net Yards per Attempt, and Adjusted Net Yards per Attempt. Namath was the best QB in the NFL that year, and was named a first-team All-Pro by the Pro Football Writers, the NEA, and Pro Football Weekly. ^[1]Namath was a 2nd-team choice by the AP, which went with Earl Morrall, 9-0 QB of the undefeated Dolphins, as their first-team choice. But it’s not controversial to say that Namath was the best … Continue reading But Namath completed just 50% of his passes that year, and as a team, the Jets completed just 49.6% of their passes.

It’s easy to look at the 2017 Jets with their 71.6% completion rate — a whopping 22 points higher than the ’72 squad — and conclude that, era adjustments aside, the 2017 Jets passing offense is more efficient. To be clear, era adjustments are enormously important when comparing passers across eras. You almost never want to compare players from different eras without making those adjustments. But today is the rare day where that’s not where I want us to focus. Because as discussed yesterday, completion percentage ignores two key elements of a passing game. [continue reading…]

References[+]

References
↑1	Namath was a 2nd-team choice by the AP, which went with Earl Morrall, 9-0 QB of the undefeated Dolphins, as their first-team choice. But it’s not controversial to say that Namath was the best QB in the NFL that year, given that he led in ANY/A and won the majority vote for best QB, and also beat out Morrall in the organizations that made All-Conference (Sporting News and UPI) votes rather than All-Pro votes. Of the five organizations that chose between Namath and Morrall, only one went with Morrall.

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Philip Rivers and The Chargers Are 5-18 In Their Last 23 Games Decided By One Score

by Chase Stuart on September 12, 2017

in Checkdowns, Statgeekery, Statistics

Sometimes, the headlines speak for themselves. After last night — the Chargers lost when the potential game-tying field goal was blocked in the final second — Los Angeles nee San Diego has now lost 18 of its last 23 games decided by 8 or fewer points.

Query Results Table
												Poin	Poin	Poin
Rk	Tm	Year	Date ▼	Time		Opp	Week	G#	Day	Result	OT	PF	PA	PD
1	SDG	2017	2017-09-11	10:20	@	DEN	1	1	Mon	L 21-24		21	24	-3
2	SDG	2016	2016-12-24	1:00	@	CLE	16	15	Sat	L 17-20		17	20	-3
3	SDG	2016	2016-12-18	4:25		OAK	15	14	Sun	L 16-19		16	19	-3
4	SDG	2016	2016-12-04	4:25		TAM	13	12	Sun	L 21-28		21	28	-7
5	SDG	2016	2016-11-27	1:00	@	HOU	12	11	Sun	W 21-13		21	13	8
6	SDG	2016	2016-11-13	4:05		MIA	10	10	Sun	L 24-31		24	31	-7
7	SDG	2016	2016-11-06	4:25		TEN	9	9	Sun	W 43-35		43	35	8
8	SDG	2016	2016-10-30	4:05	@	DEN	8	8	Sun	L 19-27		19	27	-8
9	SDG	2016	2016-10-23	4:05	@	ATL	7	7	Sun	W 33-30	OT	33	30	3
10	SDG	2016	2016-10-13	8:25		DEN	6	6	Thu	W 21-13		21	13	8
11	SDG	2016	2016-10-09	4:25	@	OAK	5	5	Sun	L 31-34		31	34	-3
12	SDG	2016	2016-10-02	4:25		NOR	4	4	Sun	L 34-35		34	35	-1
13	SDG	2016	2016-09-25	4:25	@	IND	3	3	Sun	L 22-26		22	26	-4
14	SDG	2016	2016-09-11	1:05	@	KAN	1	1	Sun	L 27-33	OT	27	33	-6
15	SDG	2015	2016-01-03	4:25	@	DEN	17	16	Sun	L 20-27		20	27	-7
16	SDG	2015	2015-12-24	8:26	@	OAK	16	15	Thu	L 20-23	OT	20	23	-3
17	SDG	2015	2015-12-13	1:03	@	KAN	14	13	Sun	L 3-10		3	10	-7
18	SDG	2015	2015-11-29	1:03	@	JAX	12	11	Sun	W 31-25		31	25	6
19	SDG	2015	2015-11-09	8:30		CHI	9	9	Mon	L 19-22		19	22	-3
20	SDG	2015	2015-11-01	1:02	@	BAL	8	8	Sun	L 26-29		26	29	-3
21	SDG	2015	2015-10-25	4:05		OAK	7	7	Sun	L 29-37		29	37	-8
22	SDG	2015	2015-10-18	4:25	@	GNB	6	6	Sun	L 20-27		20	27	-7
23	SDG	2015	2015-10-12	8:30		PIT	5	5	Mon	L 20-24		20	24	-4

Provided by Pro-Football-Reference.com: View Original Table

For his career, Philip Rivers has a 54-26 record in games decided by more than 8 points, and a 43-54 record in games decided by 8 or fewer points. Read differently, Rivers has lost 28 *more* times in close games than in non-close games. That is (for now) tied with Rich Gannon for the largest spread ever. [continue reading…]

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Look Who Is Alone In First Place In The AFC East

by Chase Stuart on September 10, 2017

in History, Statistics

The New England Patriots are 0-1. The Dolphins, due to Hurricane Irma, have had their week 1 game postponed to week 11, giving Miami a week 1 bye. And the Jets and Bills square off in Buffalo today. The winner of that game will therefore be alone in first place in the division. Which is pretty unusual in the Tom Brady era.

The last time that Buffalo was alone in first place in the AFC East was after week 2 of the 2014 season, when the Bills were 2-0 and the rest of the division was 1-1. Before that, the last time was week 3 of 2011, and other than a few weeks during 2008 (the year Miami won the division and Matt Cassel started 15 games for New England), the only other times since 2001 were after the first two weeks of the 2003 season.

For the Jets, it’s been even longer. New York was last alone in first place in the division after week 6 of the 2010 season, when the Jets were 5-1 and the Patriots were 4-1 (a week later, both teams were 5-1). Since 2002, the only times the Jets have been alone in first place were weeks 11-13 of the 2008 season, weeks 2 and 3 of the 2009 season, and weeks 5 and 6 of the 2010 season.

Looking ahead to week 2, the Bills travel to Carolina while the Jets head to Oakland. So there’s a very good chance the winner of the Jets/Bills game will be 1-1 next week, and New England (playing in New Orleans) will either be 1-1 or 0-2. That would allow the Dolphins, with a win over the Chargers in the first NFL regular season game at the StubHub Center, to be alone in first place in the division. The last time that happened? Week 2 of the 2010 season, and before that, week 4 of the 2005 season! Yes, there has been exactly one week in the last 11 years where Miami was alone in first place (in 2008, the Dolphins never achieved that status, despite winning the division on a tiebreaker).

The graph below shows how many games above .500 each team in the AFC East after each week of the NFL season for the 2001 through 2016 seasons. The Bills and the Patriots share blue and red as their primary colors, but that’s not a huge issue in this chart. [continue reading…]

Tagged as: AFC East

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