Adam Steele is back for another guest post. You can view all of Adam’s posts here. As always, we thank him for contributing.

I love drive stats. There’s no better method, in my opinion, of measuring the performance of offensive and defensive units. However, raw points per drive has a couple of glaring flaws – it doesn’t account for field position or adjust for league offensive efficiency. In this post, I am going to correct those issues and rank every offense in the drive stat era (1997-2015).1 To accomplish this, I created a simple metric called Adjusted Points Per Drive. Here’s how it’s calculated:

Step 1: Calculate total offensive points for each team. OffPts = PassTD * 7 + RushTD * 7 + FGAtt * (LgFGM / LgFGA). I chose to use the average value of a field goal attempt rather than made field goals, as I want to minimize the effect of special teams. In 2015, for example, the average FGA was worth 2.535 points, so I plug that number into each team’s number of attempts.

Step 2: Calculate points per drive (PPD). All drives ending with a kneel down are discarded. PPD = OffPts / Drives.

Step 3: Adjust for starting field position. The expected points value of each yard line is a bit noisy, so I smoothed it out into a simple linear formula. Every yard is worth 0.05 expected points, and PPD is normalized based on an average starting field position at the 30 yard line. I call this field position adjusted points per drive, or fPPD for short. fPPD = PPD – ((AvgFP – 30) *0.05). With this step, we can accurately compare the scoring production of all teams within a given season.

Step 4: Adjust for league scoring efficiency. I normalize each season’s fPPD to a baseline of 1.75 to calculate adjusted points per drive. At the team level, AjPPD = fPPD / LgfPPD * 1.75. Now, at last, we can compare the scoring production of every team since 1997. To make AjPPD more intuitive, I also translate it into adjusted offensive points (AjPts) using a baseline of 180 drives per team season. AjPts = AjPPD * 180.

To get a sense of the scaling, the table below provides per team averages of the PPD derivatives for each season. “Mult” is the multiplier used to normalize the season to 1.75 AjPPD.

YearOffPtsDrivesAvgFPPPDfPPDMult
201534218027.31.902.040.86
2014337178.827.711.882.0150.87
2013346185.927.871.861.9820.88
2012333181.327.521.841.9720.89
2011329183.828.081.791.8990.92
2010326182.629.821.791.8080.97
2009320181.429.481.761.8040.97
2008325176.230.111.841.8490.95
2007316180.130.611.751.7451.00
2006306180.830.271.691.6921.03
2005309182.431.031.691.6491.06
200431718330.681.731.7181.02
200330718431.471.671.6021.09
2002322183.131.421.761.6991.03
2001297184.231.271.611.5531.13
2000307184.731.391.661.61.09
1999305191.231.431.601.5291.14
199831318730.611.671.6511.06
1997304183.430.911.661.6261.08

We see some interesting patterns emerge from this table. Offensive efficiency has skyrocketed in recent years, but scoring hasn’t increased quickly enough to catch up. Part of that is because teams are getting slightly fewer drive opportunities per season than they did 15 years ago. From 2011-15, teams averaged 182 drives per year, while that number was 186 from 1998-02.

However, the main culprit in the scoring lag is field position. The average starting position of drives has steadily decreased since its peak between 1999-03; there was a sharp dropoff in 2011 when kickoffs were moved to the 35 yard line, but the downward trend really started in 2004. There is a direct correlation between the league’s turnover rate and average field position, which makes sense because turnovers often result in short fields. Kickers have become progressively more adept at turning kickoffs into touchbacks, and increased field goal accuracy has resulted in fewer short fields after missed kicks.

Once we adjust for field position, the league’s offensive evolution becomes clear. Today’s offensives are scoring more often despite a significant field position disadvantage compared to 10 or 15 years ago. Since 2004, league AjPPD has risen steadily, not coincidentally in near perfect lockstep with leaguewide passing efficiency.

Now it’s time for the fun part. This table presents the drive stats of all 601 teams since 1997, sorted by AjPPD. I thought it would be illuminating to compare raw offensive points to adjusted points, hence the “Diff” column. A positive difference means the team looks better after being adjusted for drive count, field position, and league offensive efficiency. The table lists all 601 teams, and is searchable and sortable; for ease of viewing, only twenty teams are currently displayed.

RankSeaTeamYearOffPtsDrivesAvgFPPPDAjPPDAjPtsDiff
11NE2007528.615831.963.353.2658657.6
21STL2000505.618030.372.813.0554943.8
31IND2004490.016630.362.952.9953847.9
41IND2006406.414828.742.752.91523116.6
51STL2001465.417631.892.642.8751751.8
61MIN1998489.617532.412.802.8451121.2
71NO2011518.617428.242.982.83509-9.6
82GB2011510.616829.973.042.80504-6.2
91STL1999450.318231.582.472.7449343.1
101DEN2013564.419227.72.942.70486-78.9
111IND2005406.215631.242.602.7048579.3
121NE2010453.815832.662.872.6547723.4
132IND2007429.015831.512.722.6547747.5
142KC2004461.817730.732.612.624729.9
153NE2011481.117328.942.782.61470-11.0
163MIN2004382.316126.52.372.6046785.2
172SF1998484.519530.82.482.59466-18.1
182SD2006462.817931.622.592.594663.5
193DEN1998470.518432.922.562.56460-10.5
201NE2012501.118028.352.782.54458-43.2
212SF2001393.317330.492.272.5345662.8
221KC2003439.518331.762.402.5345515.4
232IND2000393.617229.582.292.5345561.1
242SEA2005438.718131.082.422.5245314.0
253MIN2000384.016730.052.302.5145268.2
261IND2009398.816427.012.432.5045151.9
274OAK2000438.317933.412.452.4944810.2
281OAK2002405.716431.372.472.4844640.2
292IND2003402.917231.522.342.4844642.8
303MIN2003388.417529.452.222.4544253.4
314SD2004431.617032.672.542.454419.4
321GB2014447.216328.62.742.44440-7.4
332NO2009453.317631.192.582.44439-14.0
343SD2005401.317330.422.322.4443937.8
353SD2009407.416130.342.532.4443931.5
362WAS1999417.619230.942.172.4443820.8
375DEN2000424.318830.852.262.4243611.7
382KC2002433.817732.12.452.424351.1
391NO2008446.417530.032.552.41434-12.1
403JAC2007381.615929.932.402.4143452.3
413IND1999375.617631.342.132.3742650.3
422NO2012426.418123.972.362.36424-1.9
432DAL2014444.117028.022.612.35424-20.2
442SD2008410.116330.582.522.3542413.6
452HOU2010382.117325.572.212.3542341.3
463IND2008343.414428.072.382.3542379.3
473SD2010417.517728.712.362.354224.7
481DEN1997406.417632.72.312.3442114.8
494IND2010391.217127.512.292.3342029.1
504DAL2007427.017731.82.412.33419-7.8
516SF2000381.617830.352.142.3341937.0
524DEN2008366.216425.852.232.3141649.6
534NYJ1998398.617731.52.252.3141516.8
544MIN1999384.918531.722.082.2841126.0
553DAL2006397.417331.882.302.2841012.8
564GB2003411.818632.62.212.28410-2.0
572CIN1997345.517227.912.012.2740963.9
585CAR1999401.318932.742.122.274097.9
592SD2013383.015827.032.422.2740925.9
604CIN2005406.717433.952.342.274092.1
614SD2011388.016627.492.342.2740920.5
625NE2004388.016931.372.302.2740820.4
633NO2013426.0173282.462.26407-18.8
643IND2001378.919229.381.972.2640727.7
654GB2001372.018530.142.012.2640634.4
666GB2004382.917928.472.142.2640623.3
674NE2009404.617430.052.332.254061.0
685KC2005381.217830.532.142.2440422.8
691ARI2015435.617527.572.492.24403-32.5
703NO2014398.417025.482.342.234023.3
715GB2009437.818531.42.372.23401-36.8
726OAK1999382.618831.832.032.2240017.8
734NO2006385.518329.222.112.2239913.9
744DEN2014458.118428.72.492.22399-58.7
755SEA2003379.317832.042.132.2239919.6
766MIN2009439.318432.222.392.21397-41.8
775CAR2011398.617527.832.282.20396-2.8
783DEN2012423.517728.392.392.19395-28.4
796NE2005368.718029.862.052.1839323.9
805PIT2014381.616825.272.272.1839210.5
813GB1997378.317931.822.112.1839213.5
825NO2010370.617028.642.182.1839221.1
833DEN2002377.717830.532.122.1638810.8
844JAC1997364.416933.062.162.1638823.7
855OAK2001360.317532.942.062.1538827.5
862CAR2015469.318530.532.542.15388-81.7
875GB2007397.918131.222.202.14386-12.1
883NO2015401.917426.332.312.14385-16.9
894SF2002356.716731.342.142.1338426.9
904WAS2012397.517627.332.262.12382-15.4
917DAL2009355.617327.372.062.1238226.3
926CIN2003325.417328.811.882.1238256.1
935NYG2008392.916832.042.342.12381-11.9
945ATL2012403.617428.712.322.12381-22.8
957DEN2005378.718331.582.072.113801.5
967DEN2003349.117930.351.952.1138030.9
976NE2008395.416633.142.382.11379-16.4
985PIT1997345.617231.162.012.1037832.4
995PHI2006364.218029.892.022.1037813.5
1006DET2011422.119228.462.202.10377-44.7
1016NE2014422.217430.272.432.10377-45.0
1025BUF2002363.718129.512.012.1037713.4
1036STL2006349.418128.141.932.0937727.3
1046PIT2001331.817231.51.932.0937644.3
1057PIT2006339.017727.941.922.0937636.7
1065ATL1998388.918433.072.112.08374-14.9
1076GB2012426.018030.532.372.08374-52.2
1086MIN1997343.317730.211.942.0837430.4
1098NE2006378.517832.452.132.07373-5.4
1108TEN2003374.918033.732.082.07373-2.0
1118ATL2009357.716530.672.172.0737315.0
1127NYG2012406.116931.42.402.07373-33.4
1138SEA2012368.916228.92.282.073733.6
1147ARI2008386.017430.682.222.07372-13.9
1156PHI2010415.918831.562.212.07372-44.1
1164NE2015441.318230.372.422.06372-69.7
1175CIN2015414.017230.022.412.06371-42.5
1186MIN2002382.418631.182.062.06370-12.2
1197SEA1997338.618528.521.832.0536930.3
1204CHI2013390.217627.922.222.05369-21.3
1218CAR2005376.618332.622.062.04368-8.5
1225PHI2013429.619527.752.202.04368-61.6
1236NO2007348.617728.631.972.0436819.3
1247ATL2010370.617330.652.142.04368-3.0
1259PIT2005364.417433.442.092.043672.8
1267PHI2004371.618430.382.022.04367-4.8
1279PHI2003348.917632.421.982.0336617.1
1286BUF1998384.918233.962.122.03366-19.2
1298DEN2004362.418828.711.932.033652.9
1309CIN2006367.318231.122.022.03365-2.0
1317NYG2011381.719125.972.002.03365-16.8
13210STL2003393.819233.932.052.03365-29.1
1338GB2008359.217628.032.042.023645.3
1348GB2010363.217529.722.082.023640.8
1357TEN1999352.319131.621.842.0236311.0
1369HOU2009372.018029.822.072.01362-9.6
1377PIT2007363.117232.332.112.00360-3.1
1386DAL2013392.817729.12.222.00360-33.0
1399DAL2010352.117628.712.002.003607.7
1406SEA2015386.617028.942.272.00359-27.3
1418GB1999321.919029.021.692.0035937.2
1427NYJ2002331.116331.962.031.9935827.3
1437PIT2015400.518127.832.211.99358-42.1
1447IND2014435.119728.332.211.99358-76.8
1457GB2013390.018227.832.141.99358-32.2
1468ATL2002380.018432.732.071.99358-22.4
14710NYG2009372.018030.42.071.99357-14.6
1487TEN1998291.616627.71.761.9835765.5
1498DAL1998349.617532.692.001.983565.9
1509MIA2008329.416528.242.001.9735525.7
15110ATL2008351.616730.512.111.973542.7
1528ATL2011373.118228.32.051.97354-19.0
1538BAL2014386.717628.652.201.97354-32.7
15411CAR2008393.618431.262.141.96354-39.9
1558HOU2007324.016829.481.931.9635328.8
15611PHI2009377.218729.972.021.96353-24.7
1579SEA2002319.417428.691.841.9635333.1
1587NO2000321.317630.791.831.9535230.3
15910JAC2010349.217230.262.031.953512.3
16010NYG2005389.019433.312.011.95351-37.6
1618KC2000344.418731.191.841.953516.5
1629STL2004296.217226.171.721.9535154.7
1639CIN2007336.516831.212.001.9535114.1
16410MIA2002366.118431.991.991.95350-15.7
1659ATL2014353.617226.342.061.94350-3.6
16611TEN2002338.117032.021.991.9435011.9
1679CAR2012332.817125.121.951.9435017.1
16812NYJ2008367.517231.672.141.94350-17.7
16912PIT2009341.617529.031.951.943497.7
17010JAC2006358.217932.52.001.94349-8.9
17113BAL2009374.217931.842.091.94349-25.2
1729GB1998358.818432.431.951.94349-9.9
1738DET1997334.018829.561.781.9434814.4
1749JAC1999340.618233.631.871.933487.6
1758IND2013369.817827.842.081.93347-22.4
17610SF2012385.717331.152.231.93347-38.8
1779NE2013414.419129.742.171.93347-67.5
17811WAS2005331.018529.541.791.9234615.2
17911TB2010314.216328.841.931.9234631.7
18012GB2002366.119131.061.921.92345-20.6
18111SF2003374.919233.961.951.92345-29.9
1829PIT2011315.116526.611.911.9234529.8
18314TEN2009323.017926.681.801.9134421.1
18410CIN2013386.119226.982.011.91344-42.5
1859MIA1997322.417232.051.871.9134320.9
18610TEN1997306.017030.591.801.9134337.0
18710JAC1998363.119231.931.891.90342-20.7
18813HOU2008342.717129.922.001.90342-0.6
18910SEA2014373.217030.162.201.90342-31.3
1908BUF2015362.417926.252.021.90342-20.8
1919TB2000333.318331.81.821.893417.6
19210PHI2007331.518029.081.841.893419.3
19311HOU2012382.619426.851.971.89340-42.4
19411SF1997336.417832.841.891.883392.2
19510DAL2011358.017929.252.001.88338-20.0
1969TB2015339.417026.162.001.88338-1.5
19712DAL2012337.017226.91.961.883380.7
1987NE2001320.717832.741.801.8833817.0
19912NO2003323.318331.011.771.8733714.1
20012PIT2010350.417531.322.001.87337-13.0
2018DEN2001313.418630.461.691.8733723.7
20211DET2013370.318826.981.971.87337-33.3
20313NYG2010392.820130.451.951.87337-56.2
20413IND2012321.017624.351.821.8733615.5
20514DET2012363.619624.971.851.87336-27.1
20610SD2015319.117522.891.821.8733617.3
20712CAR2013336.016229.162.071.873360.3
20810SEA2004340.618929.391.801.87336-4.6
2099TEN2001309.119129.311.621.8633526.1
21015MIA2009327.317828.391.841.863357.8
21111BAL2011349.018028.381.941.86335-13.9
21212NE1997327.017732.361.851.863358.0
21310NYG2000325.818032.171.811.863359.2
21413PIT2002380.019033.872.001.86335-45.1
21511DEN2006294.818226.481.621.8633439.5
21611WAS2015349.117426.852.011.86334-15.0
21711SD2007354.118331.691.931.86334-20.1
21812DET2015340.817625.491.941.85334-7.0
21914IND2002345.117732.981.951.85334-11.2
22011PHI2000316.918430.661.721.8533315.7
22111NYJ2004308.317030.051.811.8433223.8
22212PHI2011361.118728.721.931.84331-30.2
22315BAL2012356.018527.081.921.84331-25.3
22412CLE2007368.518533.311.991.83330-38.8
22513NYG2015368.118227.812.021.83329-38.9
22612ATL2005317.618430.081.731.8332911.4
22713ATL2013329.017526.231.881.83329-0.3
22813OAK1997317.618929.681.681.8332911.0
22915NO2002382.418735.482.041.82328-54.1
23014OAK2010360.319928.571.811.82328-32.4
23110KC2001311.218730.971.661.8232816.5
23212GB2000328.919131.171.721.82327-1.4
23311SD2014324.516926.631.921.813272.0
23412MIA2014366.217131.092.141.81326-39.9
23512CAR2004333.617931.731.861.81326-7.7
23613DEN2007294.017228.11.711.8132631.7
23714MIN2008345.218529.091.871.81326-19.6
23814KC1997308.317232.261.791.8132517.1
23914SEA2013377.817931.282.111.81325-52.5
24011DET1998281.817827.581.581.8132543.3
24113STL2005327.319130.241.711.81325-2.2
24212KC2006320.717731.341.811.803254.1
24313PIT2004332.017432.731.911.80325-7.2
24410DAL1999324.318733.191.731.803240.1
24514ATL2015316.616626.131.911.803247.8
24612IND1998305.118429.171.661.8032419.2
24713BUF2011329.118526.491.781.80324-4.9
24813CHI2006353.919132.241.851.80324-29.8
24911BUF1999310.318332.451.701.8032413.8
25015DAL2008342.818429.241.861.80324-18.9
25113JAC2000358.919533.931.841.80324-35.2
25215PHI1997303.719128.41.591.8032319.8
25315TEN2013341.217927.441.911.80323-17.9
25412KC1999317.320629.431.541.803235.9
25514JAC2005345.918433.761.881.80323-22.7
25616MIN2012333.617727.241.881.80323-10.5
25714ARI2007361.519231.91.881.79323-38.8
25813JAC2003288.418029.251.601.7932234.0
25913NE1998317.217931.71.771.793224.7
26014TEN2004338.419429.81.741.79322-16.7
26115OAK2005296.918029.311.651.7932224.8
26216JAC2002321.817232.741.871.79321-0.3
26314WAS2006297.317529.511.701.7832123.5
26415BAL2010323.717729.761.831.78321-3.0
26517CLE2002324.118530.491.751.78320-3.8
26616DEN2010327.819227.381.711.78320-7.5
26716JAC2009306.317428.531.761.7832013.9
26815NYJ2015391.719329.122.031.78320-71.5
26913NYG2014366.518927.841.941.78320-46.5
27015SEA2007357.519431.411.841.78320-37.6
27114OAK2011339.019027.141.781.78320-19.3
27214SD2003306.518430.81.671.7832013.2
27311MIN2001272.217729.331.541.7731946.5
27416MIN2013364.819028.361.921.77318-46.6
27515NYJ2003272.116630.551.641.7631744.8
27615HOU2011360.518730.361.931.76317-43.7
27717ARI2009347.319329.751.801.76316-30.9
27816NYG2007333.118331.371.821.76316-16.9
27916JAC2015356.118826.951.891.76316-40.1
28012BUF2001255.418326.81.401.7531660.1
28114BUF2000300.719429.021.551.7531514.1
28214KC2014327.616828.781.951.75314-13.2
28316PHI2008367.419431.031.891.74314-53.5
28418NYG2002305.418030.081.701.743148.4
28516TB1997291.817131.741.711.7431421.9
28616DAL2005320.018931.021.691.74314-6.3
28715OAK2004305.917830.151.721.743147.8
28813SEA2001283.318330.051.551.7431330.2
28915ARI2006293.417430.061.691.7431320.0
29019PHI2002373.119334.91.931.74313-60.1
29117JAC2008287.416827.481.711.7431325.5
29217BAL1997303.719029.71.601.743138.9
29317MIN2007297.618427.81.621.7331214.2
29417TB2012363.019029.181.911.73312-51.3
29514ARI1998323.819630.381.651.73312-12.2
29616TB2003285.818329.561.561.7331125.6
29713DEN1999286.919129.891.501.7331123.7
29817KC2015366.817731.252.071.72310-56.5
29915CAR2014319.217327.241.851.72310-9.2
30016MIN2011335.618528.921.811.72310-25.7
30117KC2010344.219030.691.811.72310-34.6
30216NYG2006332.018732.251.781.72310-22.4
30317SF2013366.418131.562.021.72309-57.1
30414MIA2001286.117632.031.631.7230923.0
30516NO2004317.418730.241.701.72309-8.4
30616CHI2014306.317226.151.781.713092.2
30718DET2010333.119229.311.741.71308-24.8
30817NYJ2006290.017130.821.701.7130818.1
30920NE2002328.817833.721.851.71308-20.8
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31118DET2007310.918429.71.691.71308-3.2
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60132OAK2006145.117831.120.820.79141-3.8

The runaway best offense of the last two decades is the 2007 New England Patriots. Most readers will know that this team with Tom Brady and Randy Moss produced the second highest scoring total in NFL history, but very few are aware that they scored all those points on only 158 drives (second fewest in the league). That’s a remarkable feat, especially when compared to the 2013 Broncos, the only team to outscore the `07 Pats. Those Broncos needed 34 extra drives to score 17 more points, and they did it in a higher scoring environment. After adjustments, the 2007 Patriots outscore the 2013 Broncos by a startling 100 points, which drops Denver’s squad all the way down to 10th place overall.

Meanwhile, the most underrated offense is, ironically enough, the 2006 Indianapolis Colts. Peyton Manning’s SB team had a measly 148 drives and ranked 30th in average field position, yet still produced over 400 offensive points. Adjustments give them a 117 point boost and vault them to 4th place overall. The GSOT Rams check in at 2nd, 5th, and 9th, but in the opposite order most people would guess. The 2000 Rams were significantly more dominant than their 1999 champion counterparts, but hardly anyone remembers the 2000 squad because they were derailed by the league’s worst scoring defense.

At the bottom of the pile we find the 2006 Raiders, “led” by the immortal Andrew Walter (and also Randy Moss, which means he was on the best and worst offenses in modern history!). You know things are dire when JaMarcus Russell’s teams looks good in comparison. But my biggest takeaway from the cellar dwellers is the stunningly consistent ineptitude of the Chicago Bears offenses through the years. Da Bears only cleared league average AjPPD in two of the 19 seasons measured, highlighted by a ghastly 0.84 AjPPD in 2004. As bad as their offenses have been by conventional standards, their true performance has actually been far worse. Chicago has generally been near the top in drive opportunities, including four seasons with 200+ drives. Unbelievably, the Bears have also benefited from the best field position in the league, yet still haven’t been able to score with any regularity. In 2007, Chicago led the league in drives and starting field position (thanks, Devin Hester!), but only mustered the 23rd highest point total. In this era of constant roster turnover, it’s almost incomprehensible that a specific unit could remain that terrible for almost 20 years. At least Chicago has Cleveland to keep them company in the dark basement of offensive futility; the Browns have also put together only two above average offenses since 1997, but they win the tiebreaker by virtue of not existing for two of those seasons.

I’d love to hear your thoughts!

1. Drive Stats provided by Jim Armstrong of Football Outsiders, and expected points data courtesy of Tom McDermott. []
• Cool post!

As a Seahawks fan, I was surprised to see Mike Holmgren’s teams of the early 2000s (and even a random Dennis Erickson team) rate so much more highly than Pete Carroll’s recent squads. I wonder why that is, as stats like DVOA love the Seahawks’ recent offenses. Maybe they move the ball on a play-by-play basis, but kick too many field goals and punt too often on short fields now? I don’t know. But that’s my guess just from watching them regularly.

• Ben B

DVOA loves the Seahawks in part because they are exceptional at not turning the ball over, which is not taken into account here. Something like expected points added per drive would give results closer to DVOA.

• Tom

You’re exactly right about that – these stats don’t subtract points for a turnover, so you’re not going to get that negative effect as in DVOA, ANY/A or EP stats, etc.

Thanks! The disconnect between the numbers and eye test is likely a product of era adjustments. In a vacuum, Carroll’s offenses have been slightly better than Holmgren’s, but they’ve played in a much easier offensive environment, which my system penalizes them for. As far as the divergence between PPD and DVOA, it’s a combination of SoS and turnovers. From 2002-10, the Hawks played notoriously easy schedules, which DVOA docks them for while I don’t. And as Ben correctly pointed out, DVOA places a heavy penalty on turnovers (too heavy IMO), while in PPD a turnover simply counts as a drive with zero points.

• Interesting. A rough analogy might be in baseball when a team’s underlying hitting stats don’t quite match up to the actual number of runs they score.

Part of my surprise was also recency bias, I’m sure. This prompted to me to watch some old Shaun Alexander highlights on YouTube. He fizzled out so quickly, but, man, he was *so* good when he was at his best.

That’s a good analogy, and sometimes there are weird discrepancies between play-by-play and drive stats. Remember the infamous Jets-Patriots game that caused an uproar at FO a few years back? The Jets’ offense looked artificially strong by DVOA because they had a bunch of the 3-and-outs, thus minimizing their number of negative plays. But using drive stats, they looked rightly terrible.

• Jamie

So 14 Peyton Manning led offenses in the top 100, including a ridiculous 11 in the top 50. 7 Tom Brady offenses in the top 100, 4 of them in the top 20, and there would be more if we could somehow defense-adjust these.

Yeah, those guys are pretty good.

• Deacon Drake

The thing (well, one of the things) is that the only team to rank in the top 100 in starting field position was the 2011 49ers, averaging better than the 33 yard line. No coincidence that the 26th ranked offense by yards was 11th in scoring and was an eyelash from the Super Bowl.

With the kickoff moved up and default starting field position the 25, look for the big picture minds (BB, Tomlin, Kubiak, Arians) to take advantage of that and use that as an additional means of depressing their opponents point per drive. It looks like a 3 yard difference over the course of a season could be worth as much as 1 win, and if somebody can truly hone their special teams talent and gain a 5-7 yard advantage, it would be huge.

Yeah those forgotten yards of field position really add up over the course of a season. Even a mediocre offense can score frequently if they consistently start closer to the end zone.

• Tom

Agreed, which makes those 4th and goal calls far more interesting…Jeff Fisher kicks a field goal on 4th-and-short/goal (can’t remember) against the Bills so that he can be down by 4 and the Bills have the ball – most likely – on the 25-yard line? Heck, if the Bills can move the ball 45 yards, then they’re already in field goal range and their goes your 3 points.

• Richie

” Since 2004, league AjPPD has risen steadily, not coincidentally in near perfect lockstep with leaguewide passing efficiency.”

Since you are using an expected field goal rate, how does do you think your conclusion ties into Chase’s conclusion a couple years ago that the mean reason scoring is up is due to field goals? http://www.footballperspective.com/scoring-distribution-since-1940/

Since touchdowns don’t seem to have increased much, is the main factor that teams are willing to attempt more field goals? It doesn’t seem like better offenses would only lead to more FG attempts.

The rise in scoring is driven by the combination of improved FG accuracy and increased offensive efficiency. However, stronger offense has been mostly cancelled out by poorer starting field position, which is the reason TD rates have barely moved. This creates an illusion that FG kicking is the sole driver of the scoring increase, when it’s really two variables increasing and only one decreasing.

• Tom

Adam, this is really cool…there’s a lot to digest here! What stands out immediately to me is that, although it’s less clean and a little more complicated, it’s a great idea to use the average value of a field goal, as opposed to just straight up 3 points. Over the course of the season, it does have the effect of giving a noticeable bump to the weight of a TD, as it should be.

Thanks for giving me credit for the EP stats, but for full disclosure: the numbers I have were downloaded from Brian Burke’s site about 3 or 4 years ago; I filled in some holes and smoothed out a few values that didn’t seem to fit.

Thanks! This whole thing seemed simple in my head, but when I actually type out all the steps it does get a little complicated. The reason I went with average FG value instead of 3 pts/FGM was the large discrepancy between kicking accuracy for teams within a season, and I didn’t want to penalize offenses for being saddled with a bad kicker (or being helped by a great one). When Mason Crosby had his major slump in 2012, for example, that in no way reflected the performance of the Packers offense. I toyed with the idea of only counting touchdowns and ignoring field goals altogether, but a FG attempt is a positive outcome from a PPD standpoint, so I decided to include them.

Since Brian shut down his site and moved to the Dark Side, I thought it would be more appropriate to give you credit 🙂

• WR

It amazes me that just 3 of the top 20 teams on this list won the Super Bowl. Adam, are you planning to do a similar study of the top defenses?

• sacramento gold miners

Agreed, I would have expected a greater percentage. But this goes back to how a strong defense can neutralize a potent offense, especially in the playoffs. Also, the defenses seemed to have the advantage the second or third time they faced these offenses.

• mrh

Top 5 offenses and 12 of top 15 can be summed up in three words: Peyton, Brady, Vermeil. Honorable mention to Moss, who would make it the Top 6, 13 of 15 and Martz – maybe I’m biased in crediting StL to Vermeil.

Considering how dominant Vermeil’s offenses were in Kansas City, I think you can credit him for the GSOT Rams more than Martz. Pretty impressive for one coach to lead two dominant units with completely different rosters.

Randy Moss is, in my opinion, the biggest game changer WR in history. Rice had a better career obviously, but Moss seemed to have more impact on the offenses he played with.

• Most appearances in the top 100 by franchise:
1. Indianapolis Colts (11)
2. Denver Broncos (10)
3. New Orleans Saints (9)
4. New England Patriots (8)

An interesting tidbit: The Saints and Colts each show up in the top 100 with only one QB (Brees and Manning, respectively). The Patriots show up with two (Brady and Cassel)The Broncos show up with five (Manning, Cutler, Plummer, Griese, and Elway).