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Today’s guest post/contest comes from Thomas McDermott, a licensed land surveyor in the State of California, a music theory instructor at Loyola Marymount University, and an NFL history enthusiast. As always, we thank him for his hard work. You can read all of his guest posts at Football Perspective at this link.


The following is a bunch of data I’ve gathered regarding home-field advantage; hopefully some of you will find it useful for analysis, or for picking winners against the spread in your pick’em games this year!

The general consensus is that the home team in a typical NFL game has an advantage of around 2.5 to 3 points, and this is right on: since 1970, the average team wins their regular season home games1 by 2.7 points,2 with a high of 4.6 in 1985 and a low of 0.8 in 2006. If we do a linear regression, we can see that HFA appears to be in decline, but only slightly compared to points per game, which is obviously increasing:

pts hfa

But what about specific teams? If I’m trying to predict who’s going to win a Seahawks game played in Seattle, I’d probably assume the HFA at CenturyLink Field is more than just 2.7 points. To approximate a particular team’s HFA, we can use a very simple formula:

(Home point differential – Road point differential) / 2

This gives us what is called an “observed” home field advantage.3

This method has been around for a while, and although it’s not perfect (as I note below), it has the benefit of being intuitive and simple. Below is a table showing the observed HFA for each team for every season since 2002:

YearTEAMOverall RecordHome RecordPlayoffsOverall MOVPD HomePD AwayHFA
2003SFO7-9-06-2-02.916.3-10.413.3
2007DET7-9-05-3-0-6.15.5-17.811.6
2013NOR11-5-08-0-0Lost Div6.918.4-4.611.5
2009SEA5-11-04-4-0-6.94.1-17.911.0
2002MIA9-7-07-1-04.815.6-6.010.8
2014GNB12-4-08-0-0Lost Conf8.619.4-2.110.8
2011NOR13-3-08-0-0Lost Div13.023.32.810.3
2008CAR12-4-08-0-0Lost Div5.315.4-4.810.1
2013CIN11-5-08-0-0Lost WC7.817.6-2.09.8
2003STL12-4-08-0-0Lost Div7.417.1-2.39.7
2009NWE10-6-08-0-0Lost WC8.918.4-0.69.5
2005MIN9-7-06-2-0-2.47.1-11.99.5
2006JAX8-8-06-2-06.115.5-3.49.4
2003ARI4-12-04-4-0-14.2-4.8-23.69.4
2002KAN8-8-06-2-04.313.6-5.19.4
2007HOU8-8-06-2-0-0.38.9-9.59.2
2002STL7-9-06-2-0-3.35.8-12.49.1
2003KAN13-3-08-0-0Lost Div9.518.50.59.0
2011PIT12-4-07-1-0Lost WC6.115.0-2.88.9
2009BAL9-7-06-2-0Lost Div8.116.9-0.68.8
2003CHI7-9-06-2-0-3.94.8-12.68.7
2008PHI9-6-16-1-1Lost Conf7.916.5-0.68.6
2005BAL6-10-06-2-0-2.16.3-10.58.4
2005NYJ4-12-04-4-0-7.21.1-15.58.3
2004BAL9-7-06-2-03.111.4-5.38.3
2004STL8-8-06-2-0Lost Div-4.63.8-12.98.3
2010SDG9-7-06-2-07.415.6-0.88.2
2012SEA11-5-08-0-0Lost Div10.418.52.48.1
2011BUF6-10-05-3-0-3.94.1-11.98.0
2008ARI9-7-06-2-0Lost SB0.18.0-7.97.9
2010SFO6-10-05-3-0-2.65.3-10.47.8
2003MIN9-7-06-2-03.911.8-3.97.8
2009TEN8-8-05-3-0-3.04.6-10.67.6
2003SEA10-6-08-0-0Lost WC4.812.4-2.87.6
2012NYG9-7-06-2-05.312.8-2.17.4
2010ARI5-11-04-4-0-9.1-1.6-16.57.4
2009MIN12-4-08-0-0Lost Conf9.917.32.57.4
2004ARI6-10-05-3-0-2.45.0-9.87.4
2011SFO13-3-07-1-0Lost Conf9.416.82.17.3
2009CHI7-9-05-3-0-3.04.3-10.37.3
2005CHI11-5-07-1-0Lost Div3.610.9-3.67.3
2005GNB4-12-03-5-0-2.94.4-10.17.3
2008DAL9-7-06-2-0-0.27.0-7.47.2
2009JAX7-9-05-3-0-5.61.5-12.87.1
2012ARI5-11-04-4-0-6.70.4-13.87.1
2002GNB12-4-08-0-0Lost WC4.411.4-2.67.0
2006IND12-4-08-0-0Won SB4.211.1-2.86.9
2007SEA10-6-07-1-0Lost Div6.413.3-0.56.9
2003DET5-11-05-3-0-6.80.0-13.66.8
2010KAN10-6-07-1-0Lost WC2.59.3-4.36.8
2015SFO5-11-04-4-0-9.3-2.6-16.06.7
2013NYJ8-8-06-2-0-6.10.6-12.86.7
2004ATL11-5-07-1-0Lost Conf0.26.9-6.56.7
2014DEN12-4-08-0-0Lost Div8.014.61.46.6
2015WAS9-7-06-2-0Lost WC0.67.1-6.06.6
2014NWE12-4-07-1-0Won SB9.716.33.16.6
2013BUF6-10-04-4-0-3.13.5-9.66.6
2014DET11-5-07-1-0Lost WC2.48.9-4.06.4
2010SEA7-9-05-3-0Lost Div-6.10.4-12.56.4
2009CIN10-6-06-2-0Lost WC0.97.3-5.56.4
2003BUF6-10-04-4-0-2.34.1-8.66.4
2005SFO4-12-03-5-0-11.8-5.5-18.16.3
2009DET2-14-02-6-0-14.5-8.3-20.86.3
2005NYG11-5-07-1-0Lost WC6.813.00.56.3
2011GNB15-1-08-0-0Lost Div12.618.86.46.2
2011IND2-14-02-6-0-11.7-5.5-17.96.2
2015JAX5-11-04-4-0-4.51.6-10.66.1
2007SDG11-5-07-1-0Lost Conf8.014.11.96.1
2013CAR12-4-07-1-0Lost Div7.813.91.86.1
2007DEN7-9-05-3-0-5.60.5-11.66.1
2012BAL10-6-06-2-0Won SB3.49.4-2.66.0
2005WAS10-6-06-2-0Lost Div4.110.1-1.96.0
2004TAM5-11-04-4-0-0.25.8-6.15.9
2010GNB10-6-07-1-0Won SB9.315.13.45.9
2007PIT10-6-07-1-0Lost WC7.813.61.95.9
2007TAM9-7-06-2-0Lost WC4.09.9-1.95.9
2005KAN10-6-07-1-04.910.8-1.05.9
2003BAL10-6-07-1-0Lost WC6.912.81.05.9
2002DET3-13-03-5-0-9.1-3.3-14.95.8
2012IND11-5-07-1-0Lost WC-1.93.9-7.65.8
2011DAL8-8-05-3-01.47.1-4.45.8
2003NWE14-2-08-0-0Won SB6.912.61.15.8
2013DAL8-8-05-3-00.46.1-5.35.7
2007BAL5-11-04-4-0-6.8-1.1-12.55.7
2007JAX11-5-06-2-0Lost Div6.712.41.05.7
2014SDG9-7-05-3-00.05.6-5.65.6
2005TEN4-12-03-5-0-7.6-2.0-13.35.6
2012GNB11-5-07-1-0Lost Div6.111.60.55.6
2008IND12-4-06-2-0Lost WC4.910.5-0.65.6
2011BAL12-4-08-0-0Lost Conf7.012.51.55.5
2011NYJ8-8-06-2-00.96.4-4.65.5
2013ARI10-6-06-2-03.48.9-2.05.4
2011TAM4-12-03-5-0-12.9-7.5-18.45.4
2006HOU6-10-04-4-0-6.2-0.8-11.65.4
2005SEA13-3-08-0-0Lost SB11.316.85.95.4
2012BUF6-10-04-4-0-5.7-0.4-11.05.3
2012OAK4-12-03-5-0-9.6-4.3-14.95.3
2015PIT10-6-06-2-0Lost Div6.511.81.35.3
2011ATL10-6-06-2-0Lost WC3.38.5-2.05.3
2008SDG8-8-05-3-0Lost Div5.810.90.65.1
2014BAL10-6-06-2-0Lost Div6.711.81.65.1
2005DAL9-7-05-3-01.16.1-4.05.1
2005DEN13-3-08-0-0Lost Conf8.613.63.55.1
2015NOR7-9-04-4-0-4.30.8-9.35.0
2011CHI8-8-05-3-00.85.8-4.35.0
2008ATL11-5-07-1-0Lost WC4.19.1-0.95.0
2008HOU8-8-06-2-0-1.83.3-6.85.0
2008NOR8-8-06-2-04.49.4-0.65.0
2008SEA4-12-02-6-0-6.1-1.3-11.04.9
2005BUF5-11-04-4-0-6.0-1.1-10.94.9
2002DAL5-11-04-4-0-7.0-2.1-11.94.9
2014OAK3-13-03-5-0-12.4-7.6-17.34.8
2013MIN5-10-15-2-1-5.6-0.8-10.44.8
2008STL2-14-01-7-0-14.6-9.8-19.44.8
2014PHI10-6-06-2-04.69.4-0.14.8
2006KAN9-7-06-2-0Lost WC1.05.8-3.84.8
2002MIN6-10-04-4-0-3.31.5-8.04.8
2015NYJ10-6-06-2-04.69.3-0.14.7
2014IND11-5-06-2-0Lost Conf5.610.30.94.7
2014KAN9-7-06-2-04.59.1-0.14.6
2007WAS9-7-05-3-0Lost WC1.56.1-3.14.6
2005ARI5-11-03-5-0-4.8-0.1-9.44.6
2012NOR7-9-04-4-00.45.0-4.14.6
2010STL7-9-05-3-0-2.42.1-7.04.6
2008NYG12-4-07-1-0Lost Div8.312.93.84.6
2003TEN12-4-07-1-0Lost Div6.911.52.44.6
2015NWE12-4-07-1-0Lost Conf9.413.94.94.5
2004KAN7-9-04-4-03.07.5-1.54.5
2011DET10-6-05-3-0Lost WC5.49.91.04.4
2011SDG8-8-05-3-01.86.3-2.64.4
2004PIT15-1-08-0-0Lost Conf7.612.03.14.4
2012CLE5-11-04-4-0-4.10.3-8.54.4
2013TAM4-12-03-5-0-6.3-2.0-10.64.3
2007ARI8-8-06-2-00.34.6-4.04.3
2007GNB13-3-07-1-0Lost Conf9.013.34.84.3
2004NYJ10-6-06-2-0Lost Div4.58.80.34.3
2013NYG7-9-04-4-0-5.6-1.4-9.84.2
2012MIA7-9-05-3-0-1.82.4-6.04.2
2010DET6-10-04-4-0-0.43.8-4.64.2
2007STL3-13-01-7-0-10.9-6.8-15.14.2
2013BAL8-8-06-2-0-2.02.1-6.14.1
2012DEN13-3-07-1-0Lost Div12.016.17.94.1
2007MIN8-8-05-3-03.47.5-0.84.1
2013DEN13-3-07-1-0Lost SB12.917.08.94.1
2009DAL11-5-06-2-0Lost Div6.911.02.94.1
2004SDG12-4-07-1-0Lost WC8.312.44.34.1
2003HOU5-11-03-5-0-7.8-3.8-11.94.1
2015CAR15-1-08-0-0Lost SB12.016.08.04.0
2005PHI6-10-04-4-0-4.9-0.9-8.94.0
2014JAX3-13-03-5-0-10.2-6.3-14.13.9
2013CHI8-8-05-3-0-2.11.9-6.03.9
2009SFO8-8-06-2-03.17.0-0.93.9
2002ARI5-11-03-5-0-9.7-5.8-13.63.9
2013STL7-9-05-3-0-1.02.9-4.93.9
2006PIT8-8-05-3-02.46.3-1.53.9
2015IND8-8-04-4-0-4.7-0.9-8.53.8
2011MIN3-13-01-7-0-6.8-3.0-10.63.8
2010DEN4-12-03-5-0-7.9-4.1-11.83.8
2005NOR3-13-01-7-0-10.2-6.4-14.03.8
2013SEA13-3-07-1-0Won SB11.615.47.93.8
2012KAN2-14-01-7-0-13.4-9.6-17.13.8
2009BUF6-10-03-5-0-4.3-0.5-8.03.8
2006SFO7-9-04-4-0-7.1-3.4-10.93.8
2012MIN10-6-07-1-0Lost WC1.95.6-1.83.7
2008BAL11-5-06-2-0Lost Conf8.812.55.13.7
2005DET5-11-03-5-0-5.7-2.0-9.43.7
2011ARI8-8-06-2-0-2.31.4-5.93.6
2011JAX5-11-04-4-0-5.4-1.8-9.03.6
2013DET7-9-04-4-01.24.8-2.43.6
2008GNB6-10-04-4-02.46.0-1.13.6
2003PIT6-10-04-4-0-1.71.9-5.33.6
2015STL7-9-05-3-0-3.10.4-6.63.5
2011STL2-14-01-7-0-13.4-9.9-16.93.5
2006OAK2-14-02-6-0-10.3-6.8-13.83.5
2004DAL6-10-04-4-0-7.0-3.5-10.53.5
2013GNB8-7-14-3-1Lost WC-0.72.8-4.13.4
2010NYJ11-5-05-3-0Lost Conf3.97.40.53.4
2008NYJ9-7-05-3-03.16.5-0.43.4
2007BUF7-9-04-4-0-6.4-3.0-9.83.4
2007CLE10-6-07-1-01.34.6-2.13.4
2004CLE4-12-03-5-0-7.1-3.8-10.53.4
2015GNB10-6-05-3-0Lost Div2.86.1-0.53.3
2012HOU12-4-06-2-0Lost Div5.38.62.03.3
2012TEN6-10-04-4-0-8.8-5.5-12.13.3
2015ATL8-8-04-4-0-0.42.9-3.63.3
2004OAK5-11-03-5-0-7.6-4.4-10.93.3
2003JAX5-11-05-3-0-3.4-0.3-6.63.2
2015CLE3-13-02-6-0-9.6-6.5-12.83.1
2015DET7-9-04-4-0-2.60.5-5.83.1
2015HOU9-7-05-3-0Lost WC1.64.8-1.53.1
2014PIT11-5-06-2-0Lost WC4.37.41.13.1
2013CLE4-12-03-5-0-6.1-3.0-9.33.1
2013NWE12-4-08-0-0Lost Conf6.69.83.53.1
2012SFO11-4-16-1-1Lost SB7.810.94.63.1
2003DEN10-6-06-2-0Lost WC5.08.11.93.1
2004IND12-4-07-1-0Lost Div10.713.87.63.1
2013ATL4-12-03-5-0-5.6-2.6-8.63.0
2012PIT8-8-05-3-01.44.4-1.63.0
2009CLE5-11-03-5-0-8.1-5.1-11.13.0
2008TAM9-7-06-2-02.45.4-0.63.0
2003NOR8-8-05-3-00.93.9-2.13.0
2006WAS5-11-03-5-0-4.3-1.4-7.32.9
2014STL6-10-03-5-0-1.91.0-4.82.9
2009ATL9-7-06-2-02.45.3-0.52.9
2006SEA9-7-05-3-0Lost Div-0.42.5-3.32.9
2002ATL9-6-15-2-1Lost Div5.58.42.62.9
2002CAR7-9-04-4-0-2.80.1-5.62.9
2006SDG14-2-08-0-0Lost Div11.814.69.02.8
2005MIA9-7-05-3-00.12.8-2.62.7
2004MIA4-12-03-5-0-4.9-2.3-7.62.7
2014BUF9-7-05-3-03.46.00.82.6
2009MIA7-9-04-4-0-1.90.8-4.52.6
2004MIN8-8-05-3-0Lost Div0.63.3-2.02.6
2003NYJ6-10-04-4-0-1.01.6-3.62.6
2003OAK4-12-04-4-0-6.8-4.3-9.42.6
2002CHI4-12-03-5-0-6.1-3.6-8.62.5
2012TAM7-9-03-5-0-0.32.1-2.82.4
2010HOU6-10-04-4-0-2.30.1-4.82.4
2007CIN7-9-05-3-0-0.32.1-2.82.4
2014ATL6-10-03-5-0-2.30.1-4.62.4
2004HOU7-9-03-5-0-1.90.5-4.32.4
2010CIN4-12-03-5-0-4.6-2.3-6.92.3
2010TEN6-10-03-5-01.13.4-1.32.3
2009NYG8-8-04-4-0-1.60.8-3.92.3
2007OAK4-12-02-6-0-7.2-4.9-9.52.3
2014NYJ4-12-02-6-0-7.4-5.1-9.62.3
2012PHI4-12-02-6-0-10.3-8.0-12.52.3
2004PHI13-3-07-1-0Lost SB7.910.15.62.3
2014ARI11-5-07-1-0Lost WC0.72.9-1.52.2
2010IND10-6-06-2-0Lost WC2.95.10.82.2
2007SFO5-11-03-5-0-9.1-6.9-11.32.2
2006ARI5-11-03-5-0-4.7-2.5-6.92.2
2006DET3-13-02-6-0-5.8-3.6-8.02.2
2005OAK4-12-02-6-0-5.8-3.6-8.02.2
2008SFO7-9-04-4-0-2.6-0.5-4.82.1
2003GNB10-6-05-3-0Lost Div8.410.56.42.1
2015NYG6-10-03-5-0-1.40.6-3.42.0
2014SEA12-4-07-1-0Lost SB8.810.86.82.0
2013SDG9-7-05-3-0Lost Div3.05.01.02.0
2008CLE4-12-01-7-0-7.4-5.4-9.42.0
2006BAL13-3-07-1-0Lost Div9.511.57.52.0
2005TAM11-5-06-2-0Lost WC1.63.6-0.42.0
2008CIN4-11-13-4-1-10.0-8.1-11.91.9
2007CHI7-9-04-4-0-0.91.0-2.81.9
2005SDG9-7-04-4-06.68.54.81.9
2005STL6-10-03-5-0-4.1-2.3-6.01.9
2002WAS7-9-05-3-0-3.6-1.8-5.51.9
2013PIT8-8-05-3-00.62.4-1.31.8
2011CAR6-10-03-5-0-1.40.4-3.31.8
2007NWE16-0-08-0-0Lost SB19.721.517.91.8
2006BUF7-9-04-4-0-0.71.1-2.51.8
2002JAX6-10-03-5-00.82.6-1.01.8
2004CIN8-8-05-3-00.11.9-1.61.8
2004SEA9-7-05-3-0Lost WC-0.11.6-1.91.8
2003CLE5-11-02-6-0-4.3-2.5-6.01.8
2014MIN7-9-05-3-0-1.10.5-2.81.6
2002DEN9-7-05-3-03.04.61.41.6
2013TEN7-9-03-5-0-1.20.4-2.81.6
2010BAL12-4-07-1-0Lost Div5.47.03.91.6
2010NWE14-2-08-0-0Lost Div12.814.411.31.6
2005CLE6-10-04-4-0-4.3-2.8-5.91.6
2002TEN11-5-06-2-0Lost Conf2.74.31.11.6
2013HOU2-14-01-7-0-9.5-8.0-11.01.5
2010ATL13-3-07-1-0Lost Div7.99.46.41.5
2006CHI13-3-06-2-0Lost SB10.812.39.31.5
2010OAK8-8-05-3-02.43.91.01.4
2008CHI9-7-06-2-01.63.00.11.4
2004SFO2-14-01-7-0-12.1-10.6-13.51.4
2004WAS6-10-03-5-0-1.6-0.1-3.01.4
2002NYG10-6-05-3-0Lost WC2.64.01.11.4
2007TEN10-6-05-3-0Lost WC0.31.6-1.11.4
2004DEN10-6-06-2-0Lost WC4.86.13.51.3
2005JAX12-4-06-2-0Lost WC5.87.04.51.3
2003CIN8-8-05-3-0-2.4-1.1-3.61.3
2002SFO10-6-05-3-0Lost Div1.02.3-0.31.3
2015MIN11-5-06-2-0Lost WC3.95.12.81.2
2014CIN10-5-15-2-1Lost WC1.32.50.11.2
2012DET4-12-02-6-0-4.1-2.9-5.31.2
2004JAX9-7-04-4-0-1.20.0-2.41.2
2004NWE14-2-08-0-0Won SB11.112.39.91.2
2002NYJ9-7-05-3-0Lost Div1.42.60.31.2
2014TEN2-14-01-7-0-11.5-10.4-12.61.1
2011MIA6-10-04-4-01.02.1-0.11.1
2009PIT9-7-06-2-02.83.91.61.1
2002BUF8-8-05-3-0-1.10.0-2.31.1
2004BUF9-7-05-3-06.98.05.91.1
2015BUF8-8-05-3-01.32.30.31.0
2014CLE7-9-04-4-0-2.4-1.4-3.41.0
2008DEN8-8-04-4-0-4.9-3.9-5.91.0
2008MIN10-6-06-2-0Lost WC2.93.91.91.0
2002OAK11-5-06-2-0Lost SB9.110.18.11.0
2002PHI12-4-07-1-0Lost Conf10.911.99.91.0
2014MIA8-8-04-4-00.91.90.00.9
2011WAS5-11-02-6-0-4.9-4.0-5.90.9
2011SEA7-9-04-4-00.41.3-0.50.9
2008PIT12-4-06-2-0Won SB7.88.66.90.9
2015MIA6-10-03-5-0-4.9-4.1-5.80.8
2011NWE13-3-07-1-0Lost SB10.711.59.90.8
2003IND12-4-05-3-0Lost Conf6.97.86.10.8
2010JAX8-8-05-3-0-4.1-3.4-4.90.8
2009PHI11-5-06-2-0Lost WC5.86.55.00.8
2014CAR7-8-14-3-1Lost Div-2.2-1.5-2.90.7
2014CHI5-11-02-6-0-7.7-7.0-8.40.7
2011CLE4-12-03-5-0-5.6-4.9-6.30.7
2008KAN2-14-01-7-0-9.3-8.6-10.00.7
2006MIA6-10-04-4-0-1.4-0.8-2.10.7
2013WAS3-13-02-6-0-9.0-8.4-9.60.6
2012WAS10-6-05-3-0Lost WC3.03.62.40.6
2002CLE9-7-03-5-0Lost WC1.52.10.90.6
2013IND11-5-06-2-0Lost Div3.44.02.90.6
2009CAR8-8-05-3-00.41.0-0.10.6
2007ATL4-12-03-5-0-9.7-9.1-10.30.6
2015SDG4-12-03-5-0-4.9-4.4-5.40.5
2014SFO8-8-04-4-0-2.1-1.6-2.60.5
2011NYG9-7-04-4-0Won SB-0.40.1-0.90.5
2010BUF4-12-02-6-0-8.9-8.4-9.40.5
2007DAL13-3-06-2-0Lost Div8.18.67.60.5
2006CLE4-12-02-6-0-7.4-6.9-7.90.5
2006PHI10-6-05-3-0Lost Div4.44.93.90.5
2006TAM4-12-03-5-0-8.9-8.4-9.40.5
2005ATL8-8-04-4-00.61.10.10.5
2010PIT12-4-05-3-0Lost SB8.99.48.50.4
2002HOU4-12-02-6-0-8.9-8.5-9.40.4
2015KAN11-5-06-2-0Lost Div7.47.87.00.4
2013PHI10-6-04-4-0Lost WC3.84.13.40.4
2003SDG4-12-02-6-0-8.0-7.6-8.40.4
2014HOU9-7-05-3-04.14.43.80.3
2007NYJ4-12-03-5-0-5.4-5.1-5.80.3
2003DAL10-6-06-2-0Lost WC1.82.11.50.3
2006ATL7-9-03-5-0-2.3-2.0-2.50.3
2010MIN6-10-04-4-0-4.2-4.0-4.40.2
2010WAS6-10-02-6-0-4.7-4.5-4.90.2
2007KAN4-12-02-6-0-6.8-6.6-7.00.2
2013MIA8-8-04-4-0-1.1-1.0-1.30.1
2011PHI8-8-03-5-04.34.44.10.1
2009OAK5-11-02-6-0-11.4-11.3-11.50.1
2006TEN8-8-04-4-0-4.8-4.6-4.90.1
2002SEA7-9-03-5-0-0.9-0.8-1.00.1
2013OAK4-12-03-5-0-8.2-8.1-8.30.1
2010NYG10-6-05-3-02.93.02.90.1
2014NYG6-10-03-5-0-1.3-1.3-1.30.0
2012ATL13-3-07-1-0Lost Conf7.57.57.50.0
2011KAN7-9-03-5-0-7.9-7.9-7.90.0
2006DEN9-7-04-4-00.90.90.90.0
2005CAR11-5-05-3-0Lost Conf8.38.38.30.0
2002TAM12-4-06-2-0Won SB9.49.49.40.0
2012CIN10-6-04-4-0Lost WC4.44.44.5-0.1
2009STL1-15-00-8-0-16.3-16.4-16.3-0.1
2003CAR11-5-06-2-0Lost SB1.31.31.4-0.1
2004NYG6-10-03-5-0-2.8-2.9-2.6-0.1
2003NYG4-12-01-7-0-9.0-9.1-8.9-0.1
2008WAS8-8-04-4-0-1.9-2.1-1.8-0.2
2004TEN5-11-02-6-0-5.9-6.1-5.8-0.2
2003TAM7-9-03-5-02.32.12.5-0.2
2012DAL8-8-04-4-0-1.5-1.8-1.3-0.3
2009GNB11-5-06-2-0Lost WC10.310.010.5-0.3
2002BAL7-9-04-4-0-2.4-2.6-2.1-0.3
2002IND10-6-05-3-0Lost WC2.32.02.5-0.3
2002SDG8-8-05-3-0-2.1-2.4-1.9-0.3
2009HOU9-7-04-4-03.43.13.8-0.3
2009NOR13-3-06-2-0Won SB10.610.310.9-0.3
2005NWE10-6-05-3-0Lost Div2.62.32.9-0.3
2010CAR2-14-02-6-0-13.3-13.6-12.9-0.4
2011HOU10-6-05-3-0Lost Div6.46.06.9-0.4
2008NWE11-5-05-3-06.35.96.8-0.4
2008TEN13-3-07-1-0Lost Div8.88.49.3-0.4
2006NYJ10-6-04-4-0Lost WC1.30.91.8-0.4
2003ATL5-11-02-6-0-7.7-8.1-7.3-0.4
2010DAL6-10-02-6-0-2.6-3.1-2.1-0.5
2007PHI8-8-03-5-02.31.82.8-0.5
2015DEN12-4-06-2-0Won SB3.73.14.3-0.6
2015PHI7-9-03-5-0-3.3-3.9-2.8-0.6
2005HOU2-14-02-6-0-10.7-11.3-10.1-0.6
2004NOR8-8-03-5-0-3.6-4.1-3.0-0.6
2011TEN9-7-05-3-00.5-0.11.1-0.6
2005IND14-2-07-1-0Lost Div12.011.412.6-0.6
2010CLE5-11-03-5-0-3.8-4.5-3.1-0.7
2002PIT10-5-15-2-1Lost Div2.82.13.5-0.7
2012NYJ6-10-03-5-0-5.9-6.8-5.0-0.9
2009NYJ9-7-04-4-0Lost Conf7.06.17.9-0.9
2008BUF7-9-03-5-0-0.4-1.30.5-0.9
2007IND13-3-06-2-0Lost Div11.810.912.6-0.9
2004CAR7-9-03-5-01.00.11.9-0.9
2014WAS4-12-03-5-0-8.6-9.5-7.6-0.9
2012CHI10-6-05-3-06.15.17.1-1.0
2011OAK8-8-03-5-0-4.6-5.6-3.6-1.0
2011CIN9-7-04-4-0Lost WC1.30.32.4-1.1
2004GNB10-6-04-4-0Lost WC2.81.63.9-1.1
2002NOR9-7-04-4-02.81.63.9-1.1
2014TAM2-14-00-8-0-8.3-9.5-7.1-1.2
2005CIN11-5-05-3-0Lost WC4.43.35.6-1.2
2008JAX5-11-02-6-0-4.1-5.4-2.8-1.3
2015CIN12-4-06-2-0Lost WC8.87.410.1-1.4
2007MIA1-15-01-7-0-10.6-12.0-9.3-1.4
2012NWE12-4-06-2-0Lost Conf14.112.615.6-1.5
2015BAL5-11-03-5-0-4.6-6.1-3.0-1.6
2007NOR7-9-03-5-0-0.6-2.11.0-1.6
2006STL8-8-04-4-0-0.9-2.60.9-1.8
2004CHI5-11-02-6-0-6.3-8.0-4.5-1.8
2010NOR11-5-05-3-0Lost WC4.83.06.6-1.8
2005PIT11-5-05-3-0Won SB8.26.410.0-1.8
2015TAM6-10-03-5-0-4.7-6.6-2.8-1.9
2011DEN8-8-03-5-0Lost Div-5.1-7.0-3.1-1.9
2015TEN3-13-01-7-0-7.8-9.9-5.6-2.1
2013SFO12-4-06-2-0Lost Conf8.46.310.5-2.1
2002NWE9-7-05-3-02.20.04.4-2.2
2015CHI6-10-01-7-0-3.9-6.1-1.6-2.3
2010PHI10-6-04-4-0Lost WC3.91.66.1-2.3
2009DEN8-8-04-4-00.1-2.12.4-2.3
2003PHI12-4-05-3-0Lost Conf5.43.17.8-2.3
2002CIN2-14-01-7-0-11.1-13.4-8.8-2.3
2015SEA10-6-05-3-0Lost Div9.16.811.5-2.4
2006CIN8-8-04-4-02.60.35.0-2.4
2009IND14-2-07-1-0Lost SB6.84.49.3-2.4
2012SDG7-9-03-5-00.0-2.52.5-2.5
2006DAL9-7-04-4-0Lost WC4.72.17.3-2.6
2009WAS4-12-03-5-0-4.4-7.0-1.8-2.6
2009SDG13-3-06-2-0Lost Div8.45.611.1-2.8
2010MIA7-9-01-7-0-3.8-6.6-0.9-2.9
2012JAX2-14-01-7-0-11.8-14.8-8.9-2.9
2015ARI13-3-06-2-0Lost Conf11.08.014.0-3.0
2015DAL4-12-01-7-0-6.2-9.3-3.1-3.1
2014NOR7-9-03-5-0-1.4-4.51.6-3.1
2003WAS5-11-03-5-0-5.3-8.4-2.3-3.1
2015OAK7-9-03-5-0-2.5-5.60.6-3.1
2012STL7-8-14-3-1-3.1-6.30.1-3.2
2008OAK5-11-02-6-0-7.8-11.0-4.6-3.2
2006CAR8-8-04-4-0-2.2-5.41.0-3.2
2006NYG8-8-03-5-0Lost WC-0.4-3.62.8-3.2
2013JAX4-12-01-7-0-12.6-15.9-9.4-3.3
2009KAN4-12-01-7-0-8.1-11.4-4.9-3.3
2004DET6-10-03-5-0-3.4-6.6-0.1-3.3
2010TAM10-6-04-4-01.4-1.94.8-3.3
2006MIN6-10-03-5-0-2.8-6.10.5-3.3
2010CHI11-5-05-3-0Lost Conf3.0-0.56.5-3.5
2008MIA11-5-05-3-0Lost WC1.8-1.95.4-3.6
2007CAR7-9-02-6-0-5.0-8.6-1.4-3.6
2003MIA10-6-04-4-03.1-0.56.8-3.6
2012CAR7-9-03-5-0-0.4-4.63.9-4.3
2014DAL12-4-04-4-0Lost Div7.22.611.8-4.6
2009ARI10-6-04-4-0Lost Div3.1-1.57.8-4.6
2006NWE12-4-05-3-0Lost Conf9.34.514.0-4.8
2013KAN11-5-05-3-0Lost WC7.82.912.8-4.9
2006GNB8-8-03-5-0-4.1-9.00.9-4.9
2006NOR10-6-04-4-0Lost Conf5.70.810.6-4.9
2007NYG10-6-03-5-0Won SB1.4-3.86.5-5.1
2009TAM3-13-01-7-0-9.8-15.8-3.8-6.0
2008DET0-16-00-8-0-15.6-22.0-9.1-6.4

The most important thing to note about team-specific HFA is that it is wildly unpredictable, as we should expect considering the ups and downs of teams’ fortunes from season to season. Probably the best example of this is the 2003 49ers. Here’s a plot of San Francisco’s HFA numbers since 2002:

sfo pg

Their point differential at home in 2003 was 16.3, on the road it was -10.4, making for an observed HFA of 13.3, the league highest since 2002. But what about the year before and year after? In 2002, it was 1.3, the following year, 1.4. The graph above, with its drastic spikes, is fairly typical of every NFL team.

So, beyond general interest and trivia, the single season numbers don’t tell us much. But taking each team’s average over a longer period of time does give us numbers that go along with our common consensus, e.g., “Seattle has great home field advantage, the Panthers, not so much”. Below is a table of each team’s HFA using data since 2002:4

RkTeamStadium *for the 2015 season* (year first used)Tot W/L%Home W/L%Tot MoVHome MoVAway MoVObs HFA
1BALM&T Bank Stadium (1998)0.5670.7322.97.4-1.64.5
2SEACenturyLink Field (2002)0.5670.6963.07.3-1.24.2
3ARIUniversity of Phoenix Stadium (2006)0.4640.598-2.51.6-6.64.1
4SFOLevi's Stadium (2014)0.4690.571-1.72.4-5.84.1
5GNBLambeau Field (1957)0.6250.7054.98.71.13.8
6STLEdward Jones Dome (1995)0.3710.464-5.2-1.5-8.83.7
7DETFord Field (2002)0.3390.438-4.6-1.1-8.23.5
8MINMall of America Field (1982)0.4910.6340.03.5-3.53.5
9BUFRalph Wilson Stadium (1973)0.4200.509-2.21.0-5.53.3
10KANArrowhead Stadium (1972)0.4780.5630.02.9-3.03.0
11NYJMetLife Stadium (2010)0.4820.563-0.42.4-3.32.9
12PITHeinz Field (2001)0.6340.7144.77.51.82.8
13JAXEverBank Field (1995)0.4020.491-3.1-0.5-5.72.6
14HOUNRG Stadium (2002)0.4330.500-2.00.5-4.62.6
15SDGQualcomm Stadium (1967)0.5580.6343.35.80.82.5
16INDLucas Oil Stadium (2008)0.6790.7323.86.31.32.5
17ATLGeorgia Dome (1992)0.5270.5980.32.7-2.22.4
18DENSports Authority Field at Mile High (2001)0.6070.6792.54.80.32.3
19CHISoldier Field (1971)0.4960.554-0.61.7-2.82.3
20NORLouisiana Superdome (1975)0.5450.5712.14.20.02.1
21CLEFirstEnergy Stadium (1999)0.3350.393-4.8-2.8-6.82.0
22DALAT&T Stadium (2009)0.5270.5540.52.4-1.31.9
23TENNissan Stadium (1999)0.4640.500-2.2-0.3-4.01.8
24NWEGillette Stadium (2002)0.7630.8489.311.17.51.8
25NYGMetLife Stadium (2010)0.5180.5180.41.8-1.11.4
26OAKO.co Coliseum (1995)0.3300.384-5.9-4.5-7.31.4
27WASFedExField (1997)0.4060.464-2.8-1.4-4.21.4
28PHILincoln Financial Field (2003)0.5800.5803.04.41.71.4
29CINPaul Brown Stadium (2000)0.5090.5710.21.6-1.11.3
30TAMRaymond James Stadium (1998)0.4150.455-2.2-1.0-3.41.2
31MIASun Life Stadium (1987)0.4420.482-1.3-0.3-2.41.1
32CARBank of America Stadium (1996)0.5360.5710.61.6-0.31.0

Some thoughts:

1. Having a high HFA number doesn’t necessarily mean that a team was consistently outscoring their opponents at home, or even had a winning record there; it simply implies that they’re better at home than on the road. The St. Louis Rams had a losing record (0.464) and a -1.5 margin of victory at the Edward Jones Dome since 2002, but were even worse on the road, losing by an abysmal 8.8 points per game during that same period. This gives them a healthy HFA of 3.7, but they’re certainly not “dominant at home” the way Green Bay is, who have a similar HFA.

2. If you’re looking for another reason to hate – or love – the Patriots, look no further: since 2002, they have a road point differential of +7.5, easily the best in the league (maybe the best in history) and far ahead of second place Pittsburgh at +1.8. Granted, a good chunk of this comes from two epic seasons – 2007 and 2012, in which they clobbered teams on the road by an average of 17.9 and 15.6 points, respectively – but it’s still remarkable. Since they also dominated at home during this period, winning by an average of 11.1 points, their observed HFA is a lowly 1.8, the 24th “worst” in the league.

3. As mentioned above, and as can be seen by the standard deviation numbers in the tables, using these HFA numbers for predictive purposes might be a fool’s errand. But, on the flip side, if we do agree that, say, the Packers have a better home field advantage at Lambeau Field than the Dolphins at Sun Life Stadium, than these numbers are a good place to start.

Finally, I thought it might be interesting to see which teams have the toughest schedule by combining their average opponent’s HFA and their average opponent’s 2015 SRS ratings. Note that this was written before last night’s game.

RnkTeamAvg Road Opp HFAAvg Road Opp SRS (2015)2016 Road SOS
1MIA3.03.26.3
2BUF2.62.75.3
3SFO2.82.55.2
4NYJ2.82.35.1
5STL3.01.54.5
6NWE2.81.24.1
7ARI2.91.14.0
8SEA3.00.83.7
9PHI2.60.83.3
10KAN2.21.13.3
11NOR2.50.83.3
12TAM2.60.53.1
13WAS2.60.63.1
14ATL2.20.72.9
15SDG2.10.72.8
16BAL2.10.52.6
17NYG2.50.02.5
18IND2.6-0.12.5
19HOU2.5-0.12.3
20DAL2.5-0.22.3
21CIN2.5-0.32.2
22CAR2.3-0.41.9
23CLE2.2-0.81.4
24JAX2.7-1.61.1
25CHI2.6-1.80.7
26DET2.5-1.80.7
27MIN2.2-1.60.6
28TEN2.5-2.10.4
29DEN2.0-2.00.1
30PIT2.2-2.20.0
31OAK2.5-2.8-0.3
32GNB2.4-3.0-0.6

After the usual caveats – 2015 SRS numbers might be meaningless (Vegas projections would be better), and the already noted volatility of the HFA numbers – we can see that it’s going to be a tough year for Miami. Like the other brethren of AFC East, the Dolphins have to take on two of the meanest divisions in football, the NFC West and the AFC North; on top of that, their opener is in Seattle, and they have winter games in Baltimore, New York (the Jets) and Buffalo. Their two-week stay on the West Coast against the Chargers and Rams in November might seem like a vacation.

As always, would love to hear any comments,5 thanks for reading.

  1. The HFA number during the playoffs over that same period is 6.5, but that’s probably due to playoff seeding than fan/stadium involvement; it might be interesting to look into this further. []
  2. As far as what causes home teams to have an advantage at home, Brian Burke suggests in this article that it has more to do with environmental familiarity, and other factors, than the effect of screaming fans. []
  3. Bill Barnwell used this method in a nice article back in 2012. At the time, the Seahawks had the best home field advantage at 5.2 points; since then, they’ve regressed down to a still strong 4.2 points – and have since been surpassed by the Ravens who currently have a 4.5-point HFA. []
  4. Notes on the table:
    1. Games played in other countries are treated as they are billed; e.g., the Arizona Cardinals were the “home team” in their game against the 49ers when they played at Azteca Stadium in Mexico City in 2005.
    2. For teams with stadiums built after 2002 – the Eagles, Cardinals, Colts, Cowboys, Giants, Jets and 49ers – the HFA data obviously includes the team’s previous stadium (for the 49ers, that means 12 years of Candlestick Park). However, I have included the stadium information in each cell in case you want to run your own research.
    3. For predictive purposes for the Rams, now that they’re in LA, I’ll probably use the standard 2.7 points for HFA. []
  5. Chase comment: What do we make of the Jets at MetLife vs. the Giants? Perhaps fodder for another post. []
  • Did you consider looking at performance versus expected performance (i.e., instead of point differential at home versus away, point differential relative to SRS-expectation or something like that)?

    • Tom

      Bryan – no I didnt, but that’s a great idea. There’s a lot that can be done with this, and looking at expected performance seems worthwhile, especially since we are supposedly using these numbers to predict outcomes. Were you thinking of looking at a teams PD, then comparing that to their expected performance (SRS or Pyth, etc.) for that year, and then seeing how HFA figures in?

      • Basically, yea. For example, if a home team wins by 5, then it counts as a +5. However, if the team was expected to win by 3, then they really only outperformed by +2 (so HFA is worth just 2). On the other hand, if they were expected to win by 13, they actually underperformed by 8 (so no HFA at all).

        • Tom

          This sounds good…now when you say “was expected to win by 3” (by whatever method we use) you’re NOT (want to italicize but can’t) taking into account home field, because that’s the whole point, right? So, using 2015 SRS numbers, Carolina was expected to win last night by 2.3 points on a neutral field…Denver won the game by 1 point, so we would say Denver’s HFA (for this exercise we’re attributing the over performance to HFA) is +3.3. Yes?

  • Ryan

    Thomas, great post. It’s interesting that all 4 NFC West teams are in the top 6 (though AFC West are average), whereas the bottom 3 teams are all in the Southeast.

    • Tom

      Ryan – thanks, glad you enjoyed. Yeah, I did a sheet in Excel that showed the values overplayed on top of a map of the country, and that stuck out to me as well – generally (not strictly) the below-average HFA’s are in the southeast, and the above-average in the north. I’m assuming the weather has something to do with it, maybe other stuff. Going to broaden by going all the way back to 1970; of course I think we’ll just see more regression to 2.7, etc.

    • Dave Archibald

      Isn’t there a demonstrated negative effect for West Coast teams playing 1 PM games on the East Coast? The Observed HFA method not only captures Home Field Advantage but also Road Field Disadvantages. It would be interesting to tease that out and see how HFA looks.

      https://www.sportsinsights.com/blog/nfl-west-coast-teams-traveling-east/

      • Tom

        Dave – I haven’t come across anything that addresses that idea, at least in its entirety. I know that for ESPN’s FPI (Football Power Index), Brian Burke mentioned that teams that have a long distance travel generally have about a half-point disadvantage. So, on top of the Seattle’s already strong 4.2 HFA, you could have add another 0.5 points to that in a prediction for the Seattle-Miami game this past weekend.

  • AgronomyBrad

    What about teams’ performances at certain stadiums vs their performance at home? i.e. Seattle played 8 home games last year against CHI, DET, CAR, ARI, SF, PIT, CLE, & STL and outscored them by an average of 7.625 points per game. Those same teams each played 8 home games at their respective stadiums, and outscored their opponents @ home by an average of 2.922 points per game (which as you mentioned is right in line with the general consensus). So the Seahawks HFA by this method was 10.547 points per game.

    This was just a few quick calculations that I did for one team.

    • Tom

      (Check your numbers, the Seahawks outscored their opponents at home by 6.8 points). Gotta be honest, I’m not sure how this works. Basically you’re comparing how the Seahawks did at home vs. how the Seahawks’ opponents they faced did at home did in their stadiums? I’m thinking all this tells us is that the Seahawks played better at home than those teams played at home by 3.9 points (6.8-2.9). I think I’m missing something here, could you explain further (probably some way of thinking that I’m not getting)? Thanks!

  • Great post! I particularly like the point about the Rams, as it illustrates something I’ve noticed about how we use the term “home field advantage.” It doesn’t quite mean a team who is much better at home than on the road; it means a team who is much better at home than on the road AND who is decent at home overall.

    It’s similar to how we use the word “consistency.” Consistency is generally thought of as a good thing, but obviously it’s much better to fluctuate wildly between mediocre and excellent than to be consistently below average. When a coach says “we need more consistency at the quarterback position.” It’s implied that he means consistency *at a high level*.

    This is a nice intersection of football and the vagaries of language, which happen to be two of my favorite topics.

    • Tom

      Right, exactly…it’s kind of a funky stat. No one would say that New England doesn’t have great home field advantage, but it appears that way here because they’re dominant on the road as well…so we can’t really tell, unless, of course, we just use home field point differential (and of course, they’re at the top).

      As far as consistency, I had some standard deviation numbers in my original table, Chase and I thought they weren’t needed, so we took them out (but I forgot to edit out my mention of them), and Cleveland has the most consistent HFA with a standard deviation of 1.4 (using data from season to season since 2002). But unfortunately, it’s consistently below average – their average HFA is 2.0. The Lions have great overall average HFA of 3.5, but they’re all over the place with a standard deviation of 4.4. In any case, I agree that when we hear the word “consistent” in sports, it’s usually meant to be positive, and means “consistently good”.

    • Tom

      Here are the charts for Cleveland (consistent) and New Orleans (inconsistent)

  • Adam

    Great post Tom! I’ve been curious about this topic for a while so it’s nice to see some numbers. Regarding the weak HFA for southeastern teams, I think there are two main factors at play – the lack of weather advantage and relatively apathetic fanbases. On the latter point, it’s fascinating that the SEC has the strongest HFA in college football, while the pro teams in the same region don’t seem to benefit. This doesn’t surprise me since college football is king in the south, and the fans are clearly more engaged than they are for NFL games.

    • Tom

      Right, I’m thinking the same thing…the SEC just dominates life down there (from what I read and see, etc.), and yet for pro football, it’s like “Yeah, great the Dolphins. Let’s go to Disney World instead.”

      • Adam

        I don’t know why the NFL has three teams in Florida. Why saturate a market that was shallow to begin with? If I were in charge, I’d keep the Dolphins in Miami (for Richie’s sake) but move the Bucs and Jags to parts of the country that don’t have pro football. Cities like Oklahoma City, San Antonio, and Las Vegas would probably support an NFL team more fervently than Tampa or Jacksonville.

        • Richie

          I live in Los Angeles and have never even BEEN to Florida. You can move the Dolphins wherever you want. LOL

          • Adam

            Haha I’ll keep that in mind when I unseat ol’ Roger. So how did you become a Dolphins fan?

            • Richie

              I was 12 years old in 1984 as Dan Marino was becoming DAN MARINO, so I latched on. And this was a time when the Dolphins were regularly on Monday night football (and before games on satellite) so it was easier to become a fan from afar.

              I’ve spent the past 15+ years thinking it’s a little weird that I am still emotionally attached to a somewhat arbitrary decision I made as a 12-year-old.

  • Adam

    Interesting that New Orleans and Denver have middling HFA, despite the constant media harping of these being exeptionally tough places for visitors to play. Also I wonder if Philly’s weak HFA is a result of having a fanbase that boos its own team at every opportunity. That can’t be a very uplifting environment for Eagles players.

    • I do know that historically, the Giants HFA was better than the Jets. I don’t recall exactly whether it was because the Giants were really good at home or the Jets just really bad, or a combination of the two, but that was definitely my recollection of Giants Stadium.

      MetLife Stadium does seem to have brought about a change, but it may just be randomness. MetLife Stadium opened in 2010. Over that time period, the Jets are 28-20 at home, while the Giants are 25-23. That’s a small difference, but it’s even more significant when you consider the reverse: over the last six years, the Jets are 19-29 on the road, while the Giants are 22-26 in road games. (Note that this includes the two games where the Jets and Giants played at MetLife Stadium; the “road” team won in both games.)

      So the Jets are +9 in games at MetLife, while the Giants are +3. The Giants have been outscored by 116 points in road games over the last six seasons, while the Jets have been outscored by 253 points. But, and here’s the key: the Giants have actually been even better than the Jets, outscoring opponents by 111 points, while the Jets have outscored opponents by only 94 points. A big reason then for why the Jets have the better home record? The Giants are 8-13 in home games decided by 7 or fewer points since 2010, while the Jets are 13-8.

      • Adam

        Interesting…sounds like the splits at MetLife are random noise. Why do you think the Giants have had a historically stronger HFA than the Jets? I can’t come up with a plausible explanation.

    • Tom

      This makes some sense, wouldn’t have thought of that (the vertical vs. rushing idea).

    • Josh Sanford

      Adam, I think the answer to this line of questioning is that the Broncos appear to have a lower HFA because they are good wherever they play. Because HFA is a qualitative variation against road play. I think.

  • Richie

    For your league HFA, did you use the same method as you used for individual teams? (Home Diff – Road Diff)/2

    Is opponent strength considered at all? I’m guessing when looking at a whole league season or one team over a decade, these things will even out. But a quick look at those 2003 49ers tells me that every one of their non-division home games was against a team that finished under .500 and every road game was against a team that was .500 or better. I imagine that has a lot to do with their extreme HFA in 2003.

    • Tom

      Richie – yes, I used the same method for league average.

      No, strength of opponent isn’t taken into account at all…this is very vanilla, just straight up points. But I agree that’s worth looking at and it would make an impact on this, although I’m not sure how I’d bring it in…maybe something like what Bryan and I talked about below.

      • Richie

        I am imagining using a recursive system like SRS, but haven’t fully wrapped my head around it.

  • Mickey Hill

    So to do a game specific comparison (in theory), would you take the average of the home team’s “home mov” and the visiting team’s “away mov”? For instance, would the Cardinals (-6.6 away mov) at Panthers (+1.6 home mov) produce a +4.1 HFA for the Panthers?