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Seattle’s HFA

by Chase Stuart on December 27, 2012

in Statistics, Theory

As usual, Aaron Schatz provided some interesting information in his weekly DVOA recap. He was looking into Seattle’s home/road splits, and found that the data support what you already know:

[W]hen you look closer at home-field advantage over a period of several years, almost every team generally has the same home-field advantage, which in DVOA works out to about 8.5% on offense and 8.5% on defense. Teams will see their home-field advantage bounce up and down if you only look at things in eight-game periods that coincide with specific seasons, but if you put together six or seven years of data you are going to end up close to 8.5% difference most of the time. The biggest exception seems to be the four NFC West teams, which over the last decade have enjoyed the four largest home-field advantages in the league. And of those four teams, the biggest exception by far is Seattle.

I don’t doubt that Seattle is a much better team at home than on the road. But here’s the question on my mind today: is Seattle much better at home because, well, they’re much better at home…. or because they simply get more favorable home games than the average team? That might sound like the same thing, but Jason Lisk has done a bunch of research on home field advantage as it relates to climate and distance between the teams.

The table below shows the distance each team has traveled this season. The “road” column represents how many miles the team has traveled when they were the road team while the “home” column shows how many miles their opponents had to travel. Note that I excluded the Patriots/Rams game in London, but instead pro-rated their half-seasons to eight games.

Team
road
home
San Francisco 49ers2202423317
Oakland Raiders2317722505
Seattle Seahawks2305921130
San Diego Chargers2075520135
Arizona Cardinals1905819569
Miami Dolphins1798118038
New England Patriots1231117764
New York Jets1084617280
Carolina Panthers906714109
Denver Broncos1493313480
St. Louis Rams1298013248
Dallas Cowboys1482613057
Houston Texans1316812705
New Orleans Saints1153912592
average1255812562
Atlanta Falcons876312016
Minnesota Vikings886511633
Tampa Bay Buccaneers1376611493
Buffalo Bills1284511336
Kansas City Chiefs1198710982
Green Bay Packers801310776
Baltimore Ravens891610642
Cincinnati Bengals780110270
Jacksonville Jaguars126079948
Detroit Lions106158659
New York Giants98988416
Chicago Bears99067167
Tennessee Titans94817141
Indianapolis Colts66087090
Pittsburgh Steelers96426792
Cleveland Browns91986784
Washington Redskins72306022
Philadelphia Eagles99925878

Seattle is the most isolated team in the NFL. Now if an expansion team was place in Vancouver or Portland, my guess is that such a team would fare no worse against Seattle than the Giants do against the Eagles or the Jets against the Patriots. But right now, no one is all that close to the Seahawks:

NFL Map

There are also climate issues at play here. Think of the coldest NFL cities — Green Bay, Chicago, Pittsburgh, Cleveland, Buffalo, New England, Denver, Kansas City. They all play in divisions with other cold-weather teams. Meanwhile, the Seahawks are playing teams from California, Arizona, or Missouri in their division. The climates are significantly different. Climate effects are very real but also very complicated, so that’s best left for another day.

Instead, let’s take a quick look at average home and road margin of victory for each team from 1990 to 2011:

Team
Home
Road
Home MOV
Road MOV
Diff
BAL1281287.2-1.78.9
ARI176176-1.3-9.27.8
SFO1761766.5-1.37.8
KAN1761765.1-2.57.6
DET1761760.4-6.97.3
JAX1361364.1-37.1
DEN1761765.2-1.76.9
STL176176-0.4-7.16.8
MIN1761764.6-2.16.7
SEA1761762.6-46.6
BUF1761763.2-3.46.5
CHI1761762.3-4.16.3
PIT1761767.20.96.3
GNB1761767.61.46.2
DAL1761765.2-0.96.2
ATL1761761.4-4.35.8
HOU8080-0.2-5.85.5
TEN1761764.3-15.3
TAM1761760.4-4.44.8
OAK1761760.6-3.94.5
SDG1761763.4-1.14.4
WAS1761761.4-2.84.2
CIN176176-2.2-6.44.1
IND1761762.4-1.74.1
CLE152152-2.7-6.63.9
MIA1761762.8-13.8
NYJ1761761.4-2.13.6
CAR1361360.8-2.73.5
NWE1761764.81.33.4
PHI1761764.20.93.3
NOR1761761.6-0.92.5
NYG1761761.5-0.72.3

What’s interesting is that if you look at the data just from 2002 to 2011 — which is when Seattle began playing outdoors — the difference between the Seahawks’ home and road margin of victory spikes to 9.6 points (which is third behind Baltimore (9.9) and San Francisco (9.9). Baltimore is an odd one — the Ravens’ struggles on the road are a constant source of material for Mike Tanier — but San Francisco also makes sense as a geographically remote NFC team.

What if we look at just games between teams that are at least 2,000 miles away from each other since 2002? Our sample sizes will shrink, but the Seahawks do move down the list:

Team
Home
Road
Home MOV
Road MOV
Diff
BAL121214.1-3.918
NOR10911.4-4.916.3
DET10114.9-10.315.2
CHI1197.7-6.414.2
PIT131110.3-3.513.8
TEN980.4-11.612.1
SEA56554.4-6.511
MIN15137.7-3.210.8
JAX1182.5-8.310.8
SFO55561.6-910.5
CLE9104.8-5.610.4
STL33330-9.89.8
ARI48511.8-7.49.2
HOU1618-1-9.28.2
NYJ14177.9-0.48.2
SDG54558.10.18.1
DAL29291.7-6.17.8
TAM11144.1-3.77.8
DEN24231.9-5.37.3
NWE272910.33.17.2
NYG22247.70.87
BUF15113.1-3.56.5
GNB121016.310.45.9
PHI24228.42.95.5
OAK5655-3.5-8.14.6
CAR10116.22.33.9
KAN28280.9-1.92.8
MIA25290-21.9
WAS11105.13.71.4
ATL999.88.61.2
CIN98-0.60.9-1.4
IND772.66.3-3.7

My guess is once we control for climate — which is on the real to-do list for this off-season — the gap between Seattle and other teams will continue to shrink. Essentially, a very complex Simpson’s Paradox could explain why Seattle looks to be — and, in many is — much tougher at home.

{ 13 comments… read them below or add one }

George December 27, 2012 at 11:46 am

Really interesting post – and you could start getting some creative ideas in terms of being able to predict outcomes with teams (e.g. how to factor this in and effectively test the hypothesis). Just out of interest, do you have average differences for games between teams over 2000 miles apart, and teams that are less than 2000 miles apart (I realise I could split the figures out above – I think)?

I realise the sample sizes would be way off but on a basic level that would seem a quick way just to put a crude figure on what this is worth (obviously it wouldn’t be consistent across all teams and there is always Baltimore) and it would be a start (you could say add it into a home advantage figure, classing it effectively as fatigue due to travel being worth an extra say 2 points?).

One other random thought – familiarisation – does a change in stadium have an effect on opposing teams (e.g. did Arizona effectively lose some of their benefit that they may have gained through climate by moving effectively into a dome)? Did this affect Seattle following 2002 in games against divisional rivals (I know talent would also have an effect – just a thought)?

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Chase Stuart December 27, 2012 at 12:02 pm

George — Jason wrote a bunch of cool things on HFA, including familiarity, over at the old blog:

http://www.pro-football-reference.com/blog/?cat=52

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George December 27, 2012 at 2:20 pm

Nice – some interesting things definitely (I was aware of the blog but hadn’t had a good look). I definitely had a view on familiarity being an issue. There are various papers out there on it but none really detail the NFL due to the lack of games played per year so that it is statistically difficult to quantify. Generally from reading around for all of the other major US sports – teams seemed to have a drop-off in HFA during their first year in a new stadium, which got back to around their previous HFA in the second year (with MLB exceeding previous HFA during the second year, with the NBA and NHL about the same value – there was a study by Richard Pollard out of Cal Poly State University in 2002 that detailed this). One of the teams at Swiburne University in Australia have also thought familiarity to be an issue in their studies into their AFL league and detailed it.

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George December 27, 2012 at 5:32 pm

Chase – I went away and did a little bit of reading and think I may have got a quick/crude way of solving this on a basic level – I do the Winston method for ratings using Excel and have Excel’s Solver solve a HFA – assuming the HFA is a constant for all teams – (if I add the solved HFA into the Neil way of doing the SRS to create an adjusted MOV, I get about the same result as doing it the Solver way to about 3 or 4 dp – as good that it doesn’t make any difference in the rating but I just wouldn’t get a MOV or SOS).

Therefore the 2.11 it is giving me at the moment (through 16 weeks) is it’s average (?) HFA based on the ratings it is solving. Now here’s where I am not 100% on the maths/idea (and am happy for any pointers – also the sample size would be only 7 or 8 games) – surely just splitting out the Home games of a team, leaving the ratings as a fixed number (based on the rating through week 16) and solving the square of the errors with the HFA as the only variable should (?) result in the individual HFA for the Home Team (as in the context of the above we are assuming HFA isn’t constant – which I think is a fair assumption and that it varies year on year e.g. the new stadium thing/familiarity etc.)?

Basically I’ve done this for 4 – 12/13 teams so far;

12/13 Average (assuming constant effectively) HFA = 2.11 (which comes closer to the 2 value for HFA in “Stefani, R. T. (1980). Improved least squares football, basketball and soccer predictions” IEEE Transactions on Systems, Man and Cybernetics, SMC -10 (2), 116-123 – haven’t got a copy of this, but just read it referenced elsewhere)

Teams;
San Francisco: 1.97
Cleveland: 2.23
Carolina: -1.78 (I am assuming a negative is possible?)
Seattle: 11.12 (substantially above average)

To make sure this wasn’t just freakishly high because of the Arizona game a couple of weeks ago, I did the same thing for Seattle with my 11/12 sheet and got the following:

11/12 Average HFA = 3.22
Seattle HFA = 1.25

I only got into this kind of thing about a year ago (so I can’t go back any further – yet) and sample sizes are small (only 7 or 8 games – so we can’t really quantify this) and I’m not convinced in my method yet but is it just a possibility that everything is just combining and coming together for the Seahawks this year?

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Richie December 28, 2012 at 4:01 pm

By 12/13 do you mean the 2012/2013 season? (I almost never see NFL seasons named like that.)

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George December 28, 2012 at 4:53 pm

Yes I did mean the 2012/2013 Season (I think you refer to that typically as the 2012 Season in the USA don’t you?). Sorry, it’s more of a common arrangement in the UK and where I’ve started to do this kind of thing for a variety of sports, I always add a start and finish year (where the season runs into a new year) so I have complete clarity and can remember which files are which (if that makes sense).

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George December 28, 2012 at 1:51 pm

I went away and did the numbers on this for previous seasons using the above approach and now doubt my 11/12 figures as over the last 6 years from 07/08 through week 16 of 12/13, Seattle’s HFA is 3.85 points above the league average HFA. To make sure I was comparing like with like – I didn’t include playoff games over the stretch, and by including 6 years of data this definitely plausible.

12/13 – through week 16
League Average HFA: 2.11
Seattle HFA: 11.12 (+9.01)

11/12
League Average HFA: 3.22
Seattle HFA: 1.25 (-1.97) – can anyone think of a reason why this may be low?

10/11
League Average HFA: 1.89
Seattle HFA: 6.56 (+4.67)

09/10
League Average HFA: 2.21
Seattle HFA: 7.07 (+4.86)

08/09
League Average HFA: 2.56
Seattle HFA: 5.23 (+2.67)

07/08
League Average HFA: 2.87
Seattle HFA: 6.73 (+3.86)

Total difference = 23.1, divided by 6 seasons = 3.85 average above the league average. I hope this helps somebody and now think there may be some merit in assuming some grounds provide more of a HFA than others to the home team.

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Richie December 28, 2012 at 4:21 pm

11/12
League Average HFA: 3.22
Seattle HFA: 1.25 (-1.97) – can anyone think of a reason why this may be low?

Here’s a theory. I took a look at their “Expected points” on PFR: http://www.pro-football-reference.com/teams/sea/2011.htm and compared it to 2010. Their expected points at home was actually BETTER in 2011 than 2010.

BUT, due to big road wins in 2011 (@CHI, @NYG), their “Expected Points” on the road was WAYYY better than it was in 2010.

So it might not be that Seattle’s HFA was particularly worse in 2011, but it’s just that they were much better on the road.

And this year, their average expected points on the road is +2.6, while at home they are at +20.0. A HUGE difference.

Admittedly, I don’t know what that “Expected Points” metric is all about.

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George December 28, 2012 at 5:09 pm

Interesting thought and I don’t know enough about the expected points metric myself – don’t think it’s anything to do with travel though (didn’t look into the schedule and the games that the teams that went to Seattle had before), but in 2011 they played 4 teams from states that are as East as Seattle is West (Atlanta, Baltimore, Washington, Philadelphia who were on a short week and going cross country from New England – and Seattle then had a Monday night home game against St.Louis) but in 2010 they only played I have it as three (Atlanta, Carolina and NY Giants). Maybe it was just a regression towards the mean kind of thing?

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George December 29, 2012 at 8:50 am

I went away and looked at the concept of using Solver to solve individual HFA’s (based on one teams home games at a time) and in principal it works (if you work out all of the individual HFA’s and average them you get the league average for the year, by keeping the end of season ratings constant – I’m not 100% on this mathematically but in principle it sounds reasonable).

So I broke this down for the 2009/2010 season (I picked this season as the current one still has a week to go, last years I didn’t like my ratings, the year before there were a couple of neutral site games etc.);

One thing off of the top – there were two (I think, Tampa and Buffalo) neutral site games, as my end of season rating accounted for them I am treating them as home games for the teams concerned (just to make a start);

I would post the whole table but couldn’t get the formatting to work to well when pasted on to the comment page;

League Average HFA: 2.21

Top 5 HFA’s
1) New England 9.11
2) Minnesota 8.59
3) Seattle 7.07 (matching the comment from the other day)
4) Tennessee 6.16
5) Baltimore 6.09

Bottom 5 HFA’s
28) San Diego -1.91
29) Denver -2.48 (was surprised at this – with one year of data it could just be a blip though)
30) Washington -3.34
31) Tampa Bay -3.35 (includes the game in London against New England)
32) Kansas City -3.52

I hope this helps someone (as a start) and am now more convinced into their definitely being a case for individual HFA’s. I will do this for the current season as well once the weekend games are done (just to see how the figures match up).

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George December 29, 2012 at 6:54 pm

I have now done this for 3 seasons 2007/2008 to 2009/2010, and got conformation from elsewhere that there is validity to this approach (provided that there is a strong enough pattern in the results e.g. is 3.85 points above the league average over 6 years statistically significant?).

Highlights from 2007-2009/2010 (I will do this for the last 6 seasons and post back);

Seattle – average rank of 5th in HFA, 3.80 points above league average HFA
Baltimore – average rank of 8th in HFA, 2.94 points above league average HFA
Minnesota – average rank of 10th in HFA, 2.52 points above league average HFA

and at the bottom;

Oakland – avg.23rd, 2.52 points below league average HFA
New York Jets – avg.24th, 2.22 points below league average HFA (the last 3 seasons they were in Giants Stadium)
Miami – avg.25th, 2.94 points below league average HFA
Washington – avg.25th, 2.88 points below league average HFA
Kansas City – avg.27th, 3.08 points below league average HFA

As per usual, I hope this helps somebody.

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George December 31, 2012 at 4:58 pm

I have now done the individual HFA’s derived over 6 seasons from 2007/2008 through the current season 2012/2013. I have a slight query about my 2011/2012 figures and think they maybe slightly (but not significantly off e.g. better than 95%) but think this is a start (will check the 2011/12 figures at some point).

In short I think anything on average over 2 points different to the league average is a touch significant (I can’t prove this yet but think it is possible to prove mathematically – on the positive side Seattle at 3.47 and Baltimore at 2.64, on the negative side Carolina -2.11, and Miami -2.61 which would be the only team to have a HFA which on average was negative).

League average HFA = 2.58

Top 5 (in terms of difference to league average HFA e.g. points above the average);
1) Seattle (average HFA rank over 6 years – 7th) +3.47
2) Baltimore (average HFA rank – 9th) +2.64
3) Arizona (average HFA rank – 12th) +1.67 (would almost be interested to do the previous 6 years to get a before and after difference in terms of moving to the University of Phoenix Stadium, possible issues this would cover/affect climate and familiarity)
4) Green Bay Packers (average HFA rank – 11th) +1.38 (rounding issues with the difference to Arizona resulting in the 11th/12th thing – also Arizona came 3rd in 2008, 2nd in 2010 and 6th this year which skewed their average HFA rank high)
5) Detroit Lions (average HFA rank – 11th) +1.30 (same issues as above)

And at the bottom;

Bottom 5 (in terms of difference to league average HFA e.g. points below the average);

28) New York Giants (average HFA rank – 19th) -1.08 (the time period covers a change in stadium to the Metlife Stadium so I don’t feel that this is a clean figure)
29) Washington Redskins (average HFA rank – 22nd) -1.85
30) Oakland Raiders (average HFA rank – 21st) -1.87
31) Carolina Panthers (average HFA rank – 23rd) -2.11 (they had one really good year coming 2nd to Philadelphia in 2008/2009 but apart from that was mostly negative)
32) Miami Dolphins (average HFA rank – 24th) -2.61 (the only on average negative HFA).

Hope this helps someone, I just found it interesting to try and put a number on something.

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