It’s often asked: Does match up matter? If so, how much? Another question that comes up a lot: Does playing home/away matter? In this article, I’ll set out to answer these questions based on this year’s data. I’ll also attempt to explain the first steps I take in deriving my weekly projections,. Please understand that this is a limited data set, but I have been doing this since week 2, and the numbers are finally starting to stabilize, so I feel fairly confident that the number I present are reasonable.

Each week, I assemble team level statistics from each team and use that to try to predict how well each team will perform in the upcoming week. Based on this information and the historical distribution of targets and attempts on a given team, I also try to predict fantasy performance for individual players.

Staying on the team level, here’s a breakdown of how I go about projecting offensive performance (with defensive performance just being offense allowed). I assemble a number of team statistics including number of 1st downs, fumbles, fumbles lost, times sacked, sack yards lost, number of penalties, yards lost to penalties, interceptions thrown, completed passes, pass attempts, passing yards, passing TDs, rush attempts, rushing yards, rushing TDs, punts, attempted FGs, made FGs, and extra point attempts. I determine the average of each of these stats for all teams over all weeks played. I also determine the weekly average of all of these stats for the corresponding defenses. I then regress each statistic against the team’s average and the opponent’s average allowed for all past game level data, including whether a team is home or away.

So what is regression? In this case, regression is developing a linear equation that describes a given variable (Y = mx +b). The variables we are looking to describe are each individual statistic (1st downs, fumbles, pass completions, etc.) The variables on the other side of the equation are the team and opponent averages for each variable. Additionally a coefficient is associated with each variable. Each coefficient is solved for by minimizing the variance between the predicted variable and its actual value. So the Y = mx +b equation looks more like Y = mX + nZ …+b. But not all of these variables (X,Z, etc.) significantly contribute to the predicted value of Y, i.e. these variables are not significant. For example, regression shows that a team’s average yards lost to penalties does does not significantly contribute to the number of field goals that team will attempt. On the other hand, it shows that a team’s average rush attempts and an opponents rush attempts allowed are predictive of a team’s rushing attempts.

For most of these stats, there are only two significant variables: the team’s average for that stat and the average of that stat allowed by the opposing defense. Through 5 weeks, one exception has been punts, which is also dependent on average # of 1st downs allowed by the opposing defense and average # of passing TDs scored by the offense (more passing TDs resulting in fewer punts). So after the regression, we know which variables are significant and we also have coefficients associated with those variables. The larger the coefficient, positive or negative, the more that variable contributes to that statistic.

In addition to the team’s average and the opponent’s average, whether a team is home or away was also a contributing factor in several of the statistics. Here’s a breakdown of the coefficients for each statistic. Remember, the final equation will look something like this, where C,D,E, and F are the values listed in the table, and home or away = 1 if home, and 0 if away:

Stat = C * (team’s ave stat) + D * (opponent’s ave stat allowed) + E * (home or away) + F

With punts, I excluded the coefficients of the other variables so it will effect the F coefficient. I just want to focus on the relative effect of being home or away and the effect of the team versus their opponent.

There are several stats that are influenced by being home versus away. Being homes means roughly 2 more 1st downs, 0.3 more fumbles (though fumbles lost are not significantly influenced by game location), 0.2 interceptions thrown, 28 more passing yards, an additional 0.3 passing TDs, 3 more rush attempts, 11 more rush yds, 0.75 fewer punts, 0.4 more punts, and 0.3 more FGs. Overall, teams seem to move the ball more effectively at home (more 1st downs, fewer punts), which may be attributable to crowd noise when away. Being home or away did not effect total pass attempts, so the greater number of passing yds, interceptions, and passing TDs, suggest that there might be a slightly higher tendency for longer or riskier throws by teams when they’re home. So what does this mean for fantasy? With all else being equal, we would expect a given QB to score ~2 fantasy pts more when playing at home versus away (0.2*(-1) + 28*(0.04) + 0.3*(4) = 2). Not taking into account FG distance, we’d also expect a given kicker to score about a point more at home (0.3 * 3 = 0.9). And with an extra 10 yds, we’d expect a given team to produce 1 additional pt at RB based on rushing performance.

What about opponent strength? With every statistic, the opponent matters, but the relative importance of a team’s offensive ability versus their opponent varies with each statistic. Coefficients highlighted green above are ones which are more than 10% greater for the team average than the opposing defense average allowed. for these stats, a team’s ability matters much more than the strength of the opponent. So a team’s ability to score a first down is affected more by the team’s average ability to get a first down than the opponents average ability to prevent a 1st down. This shouldn’t be surprising with penalties. With pass completions and passing yards, the ability of the QB matters more than the defense. This makes QBs a bit more match up proof than most other positions. Defenses dominate rushing TDs allowed. This makes RBs more match up dependent. For many of these statistics though, a team’s ability and the strength of the defense they face are equally important. The quality of a team’s QB and defense that QB is facing are nearly equal contributors to the number of INTs that QB will throw.

There is a huge caveat I would like to point out about the predictive ability of these regression equations. As you can imagine, there is a high degree of variation about the linear equation. This is particularly true for statistics that are less frequent but are extremely important for determining fantasy point production (TDs and FGs). This is why fantasy football stats are incredibly difficult to predict, but it’s still fun to try. Thanks for reading.