Winning on the Road

Teams with good running offenses do not win any more road games than other teams, all things being equal. That's right--being good at running the ball does not help teams win on the road. Contrary to what we've been told for years, having strong passing game is far more important to visiting teams than having a good running game.

Let me make a clarification up front. I'm not suggesting that having a great running performance in an individual road game doesn't help win that game. I'm saying that being a "running team" doesn't help win on the road. Teams that are "built" to win on the road are those that pass well and don't fumble.

I stumbled on this somewhat by accident when I was studying the effect of climate on home field advantage. My usual game model is a logistic regression that estimates the probability of winning based on team efficiency stats and home field. A simplified version looks like this:

Team A season efficiency stats
Team B season efficiency stats
Team A at home [1 if true, 0 if false]

This method resulted in balanced weights for the coefficients for Team A and Team B, and the importance of home field was captured in the coefficient for 'Team A at home.' But during my research into climate effects, I decided to try an alternate model without the home field variable. It looked like this:

Visiting team season efficiency stats
Home team season efficiency stats

With this method we would expect unbalanced coefficients. Because home teams win more often, the coefficients for the home team were generally stronger than for the visiting team. This means that if two theoretically equal teams played, the home team would have a higher probability of winning, which is exactly what we observe.

By examining the imbalance of each stat we can see what kind of teams tend to win on the road. Here are the regression coefficients for each efficiency stat for both home and visiting teams. (Logisitic regression is more difficult to interpret than linear regression. The coefficients indicate the change in the log of the odds ratio of the outcome. But we are comparing the relative strength of each coefficient, so don't worry about the "log odds ratio" for now. Just pay attention to the relative size of the coefficient between home and away team stats.)












Team StatHome CoeffVisitor Coeff
O Pass0.40-0.49
O Run0.48-0.04*
D Pass-0.600.48
D Run-0.270.16
O Int Rate-16.4015.61
D Int Rate19.30-17.63
O Fum Rate-11.5730.77
Pen Rate-1.651.30


The model has very solid goodness-of-fit stats, and it is 69.9% accurate (retrodictively) in predicting game winners. The regression is based on all regular season game outcomes in the past five years (n=1280).

First, compare the coefficients for offensive passing efficiency. The coefficient for home teams is 0.40, and for visiting teams is 0.49. We can interpret these numbers by saying "having a good passing game is slightly more important in winning for the visiting team than for the home team." But the difference is slight, and may not be significant.

Next, compare the coefficients for offensive running efficiency. The coefficient for home teams is 0.48, but for visiting teams it is only 0.04--practically zero! (The difference of 0.44 is strongly significant.) The near-zero coefficient for visiting teams' offensive running efficiency is what tells us that running well simply doesn't matter on the road.

Also, for some reason, teams that tend to fumble more often than others are at a greater disadvantage as visiting teams. And conversely, teams that don't fumble tend to have a greater advantage as road teams.

There are imbalances between nearly all of the team efficiency stats, but none as stark as that for offensive running. This was such an unexpected result and I had no prior theoretical basis for the observation, so I confirmed the results with a simpler analysis using correlation coefficients.

For each team over the past 5 seasons (n=160), I added up their road and home wins. The correlations of each efficiency stat with home and road wins were calculated. If different stats affect a team's ability to win at home and on the road differently, as we saw in the regression, then we should see different correlation coefficients. For example, the correlation between offensive running efficiency and home wins should be much stronger than running and road wins.















O PassO RunO Int RateO Fum RateD PassD RunD Int RatePen Rate
Away Wins0.550.10-0.40-0.42-0.340.000.31-0.16
Home Wins0.480.22-0.36-0.36-0.45-0.070.36-0.17


The correlations confirm the results from the regression. Being a good running team is more important to winning when at home, and not nearly as important to the visiting team. (Also note that stopping the run is just not important whether on the road or at home, as we've seen in previous research.)

I also did an even simpler analysis by comparing the average season running efficiency stats of all road winners and for all home winners. Again, the data was from every regular season game over the past five years (n=1280). The average offensive running efficiency for road winners was 4.13 yds/rush and for road losers was 4.10 yds/rush--a difference of only 0.03 yds/rush.

We see the opposite with passing efficiency. Road winners average 6.18 yds/att and road losers average 5.90 yds/att--a difference of 0.28 yds/att which is about 9 times larger than the difference we found for running. Again we see indications that passing efficiency tends to be the more important stat for the road team.

The obvious question is "why?" Why doesn't being a capable running team help win road games? My only theory is that passing well helps come from behind far more than running well. If road teams tend to find themselves behind more often than home teams, then unless they can pass, they wouldn't be able to score quickly and come back from a deficit. But home teams need to come back from deficits too, so the reason why running teams don't win on the road remains puzzling.

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11 Responses to “Winning on the Road”

  1. coldbikemessenger says:

    A few comments,
    first, great stuff as always.
    Why, in the first chart, are the first 4 numbers so small(below 1),
    and the next 4 numbers all so big(over 1)? I thought all correlation coeffecients were between 1 and -1?
    I am ok with math, but no way am I smart enough to fly some advanced fighter jet so if you could simplify it a bit that would be great.

  2. coldbikemessenger says:

    "The obvious question is "why?" Why doesn't being a capable running team help win road games? My only theory is that passing well helps come from behind far more than running well."

    Yeah I can see that.
    Perhaps another thing to consider is that good passing teams get ahead and stay ahead.
    I have always felt that many if not most games are won in the first half.
    If you good at the really teams in your database I am guessing most of them have a pretty big winning margin in the first half.
    If this is the case, then at some point, even with a not very efficient running game, the clock is going to be your friend.
    I suppose this years Pats are a good example. Their offensive passing game is so good, that even if their running game truly sucked, say 2.0 yards per carry, who cares?
    They are still gonna score a lot of points.

    Another thing I just thought of.
    Isn't the standard deviation of running efficiency rates lower than that of passing?
    would that matter at all?
    Just throwing some idea's out there.

  3. coldbikemessenger says:

    Sorry, in my second post I meant to say really good teams in your database.

  4. Brian Burke says:

    The first table doesn't contain correlation coefficients but regression coefficients. The geeky explanation is that a correlation coefficient is the standard deviation change in the dependent variable for every standard deviation change in the independent variable. A regression coefficient is the unit change in the dep variable for every unit change of the ind variable.

    The reason some are small and some are big is because of the units used for each ind variable. For example, the coefficient for pass efficiency is small because pass eff ranges between 4 and 8 yds/att. But the coefficient for something like int rate is very large because int rates are tiny--0.02-0.04 or so. That's ok, because we're comparing the run coefficient for the home team against against the run coefficient for the visiting team. Apples to apples, etc.

    By the way, you don't have to be a math geek like me to fly fighters. Just really cool and good looking :) Actually, I bet flying a low-level through the mountains can't be much different than dodging cars downtown on a road bike.

    And you're correct about the running efficiencies having smaller std deviations than passing efficiencies. But that's accounted for in the regressions and correlations.

    Yeah, the Pats' running game (for both off and def) is actually very avg this year. But as you say, who cares?

  5. Derek says:

    Per the request, I took a look at my own data to confirm the findings.

    I used correlation coefficients with final score margin (home points - away points).


    With season-to-date efficiencies (yards per play)
    HRO 0.0998
    HRD -0.0734
    ARO 0.0357
    ARD -0.0457
    HPO 0.1875
    HPD -0.1485
    APO 0.1219
    APD -0.0872

    With in-game efficiencies
    HRO/ARD 0.1213
    ARO/HRD -0.0959
    HPO/APD 0.4459
    APO/HPD -0.4843

    In other words, running the ball is valuable (though certainly not to the extent that passing is), but running efficiency is harder to predict/less consistent.

    Running averages are probably heavily context-dependent. Many successful running plays gain only a few yards or burn clock. Passing plays are expected to gain yardage in much larger chunks.

    Running backs have also been shown to be fungible goods. Look at what the Colts and Patriots and Broncos have been able to do. The talent pool for RBs is deep.

    A good running game relies on the offensive line and the back. A good passing game relies on the offensive line, the receivers, and the quarterback. There are fewer moving parts in the running game.

  6. Brian Burke says:

    Thanks Derek. It initially looks like your data supports my findings. Home Run Off correlates 3 times as strong as Away Run Offense.

    I'm still not sure why. I agree with everything you say about passing/running and RB fungibility. But those things should apply both to home and road games. I'm still baffled why a good running game doesn't help win on the road.

  7. Brian Burke says:

    One theory on why fumbles hurt visiting teams far more than home teams is that home teams might be awarded possession more often.

    In a pile-of-humanity scrum for a lose ball, I have no idea how the refs award possession. I'm not sure that they even have a system.

    One established theory regarding home field advantage in all sports is that officials are subconsciously influenced by the desires of the crowd.

    If home teams recover significantly more than half of fumbles, that would explain a lot of the findings here, and a lot of home field advantage in football.

  8. Anonymous says:

    Good site! Have you ever calculated the odds for in season division re-matches? That is, what are the odds that the same team wins if
    1) The original loser is now at home for rematch
    2) The original loser is now on road for rematch

    Thanks,
    Kevin from Sacramento

  9. Matt says:

    I think this conclusion (about running being more important at home than on the road) makes sense based on another article you posted.

    In that article you said that the favored team should generally play a more conservative game, while the underdog should play more aggressive. Since the visiting team is more often the underdog, this puts more emphasis on the passing game, since while running may help them stay in it, to win the game they need to be effective throwing the ball.

  10. Brian Burke says:

    Matt-Excellent point.

  11. Jack says:

    Was the 69.9% game winners using season-to-date stats for each week or did you just plug in the season ending efficiencies.

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