Strength of Opposition and Fantasy Points Allowed: Analysis

Garth Bryden concludes his analysis of how taking account of a team's average performance versus their opponents can add great depth to Fantasy Points Allowed statistics.

| 2 years ago

Strength of Opposition and Fantasy Points Allowed: Analysis

goreThis is the second article in the two-part series looking at how the strength of a team’s average fantasy performance in comparison to their opponents can give extra depth to the traditional Fantasy Points Allowed (FPA) statistics.

I would strongly recommend reading the first article, which explains what I have been trying to achieve and the details of how I have gone about doing so. This article will make far better sense if you do. The first article can be accessed via the link below.

Strength of Opposition and Fantasy Points Allowed

In summary, however, I wanted to know which teams, on average, outperformed or underperformed versus their average opponent from a fantasy points allowed / achieved perspective on pass offense, pass defense, rush offense and rush defense. I wanted to use that to project which future fantasy matchups might be favorable.

This second article looks at the projections in action, and how they compared to the actual fantasy scores observed.

I am specifically looking at the projections for Week 16 of the 2014 regular season because for many fantasy leagues that is the championship game. Also, Week 17 can throw up some strange results because teams may be locked in to a particular position in the playoffs, and key players may get reduced playing time. Furthermore, the more weeks of data on which the projections are based, the more likely they are to be indicative of a long-term average. Single games are less likely to cause a significant shift in a team’s average.

Click on the image below to see a summary of the Week 16 FPA projections for all teams in the four FPA categories (pass offense, rush offense, pass defense and rush defense). I should reiterate that when a team’s average performance is being compared against the team’s average opponent, it is a comparison of the opposing categories. For example, the team’s pass offense versus the average opponent’s pass defense.

The summary also provides the following information:

  • Who the next opponent is.
  • The average fantasy opponent the team has faced in each category.
  • The outperformance, or underperformance, of the team in comparison to their average opponent.
  • The average performance of the next opponent.
  • The FPA projection based on the next opponent adjusted for the outperformance or underperformance achieved by the team.

Projection summary

Click above for a larger image.


From the summary statistics I identified the top 10 teams with the highest FPA projections, in the pass offense and pass defense categories.

In the interest of brevity I have not analysed the defense FPA statistics but it is worth noting that in general, the teams expected to have a good day on offense are matched-up against those teams that are projected to have a bad day on defense. That stands to reason, especially if you get a strong offense versus a weak defense, but isn’t always the case. What it does do is serve as some confirmation of which matchups look favorable from a fantasy perspective.

The first test of the projections is whether they correlate with the real results of the teams as whole, rather than individual players. For that, I have compared the projections against the NFL box scores for Week 16, which are available from the website.

I translated the box scores to FPA using the same formula as for the projections; one point per 10 yards, six for a touchdown. The projected FPA and the box score FPA for the 10 teams with the highest offense projections are shown in the table below.

Pass Rush
Team Projected FPA Box score FPA Team Projected FPA Box score FPA
Indianapolis 45.67 29.50 Seattle 23.61 44.70
New Orleans 44.14 39.30 New Orleans 21.43 11.70
Denver 44.06 43.10 Dallas 21.15 19.70
Green Bay 43.58 37.80 Carolina 19.72 26.90
Philadelphia 42.47 49.40 Denver 19.62 14.50
New England 41.19 24.20 Green Bay 18.96 18.10
Atlanta 40.00 38.20 St. Louis 18.72 16.60
Detroit 39.14 24.30 San Francisco 18.58 53.50
San Diego 38.16 59.60 Miami 17.83 17.00
Pittsburgh 37.71 28.00 Minnesota 17.68 23.90


One immediate observation is that the results of comparing the projections versus the box scores are mixed.

Three out of the 10 pass box scores were over 10 points short of the projections, though five of them either exceeded the projections, or were within five points. Interestingly, quarterbacks Drew Brees, Aaron Rodgers, Tom Brady and Ben Roethlisberger all averaged two or more touchdowns per game over the regular season, but only scored one each in Week 16. Had they turned in an average touchdown performance for their respective teams, the projection versus box score comparison would have looked far more respectable.

In the rushing game the results were far closer to the projections, with only two falling short by more than five points.

While the team results are interesting, the main objective of the projections was to identify favorable matchups to aid in player selection.

Therefore, the next stage of my analysis was to see whether the projections translated into positive fantasy performances for players. I have split this into passing and rushing.

The Passing Game

The table below shows all pass receivers from the 10 teams with the highest projections that scored over 10 fantasy points in the passing game. They are in the same order as the team projections, highest first.

Player Team Fantasy score
Jimmy Graham New Orleans 11.3
Jordy Nelson Green Bay 17.3
Randall Cobb Green Bay 13.1
Emmanuel Sanders Denver 19.0
Demaryius Thomas Denver 11.5
Riley Cooper Philadelphia 17.3
Zach Ertz Philadelphia 11.5
Julio Jones Atlanta 10.7
Calvin Johnson Detroit 10.3
Antonio Gates San Diego 21.2
Eddie Royal San Diego 15.4
Malcolm Floyd San Diego 11.0
Antonio Brown Pittsburgh 13.2


One significant positive observation: almost all of the players you were relying on each and every week turned in respectable performances.

Another highlight is the scores for the San Diego players. TE Antonio Gates was probably started in most leagues, but WRs Eddie Royal and Malcolm Floyd are more likely to be players plugged into a line-up as a flex or wide receiver three. Both produced excellent scores for the fantasy owners that started them.

Two notable absences from the above table are Denver TE Julius Thomas, who only scored 3.3 fantasy points in the pass, 3.5 total, and New England TE Rob Gronkowski, who scored 9.1 fantasy points.

There were also some other players that were quite possibly started in fantasy that may well have hurt your team, scoring under 10 points, and examples of these are shown in the table below.

Player Team Fantasy score
Coby Fleener Indianapolis 3.6
Julius Thomas Denver 3.3
Jeremy Maclin Philadelphia 6.2
Jordan Matthews Philadelphia 5.8
Rob Gronkowski New England 9.1
Brandon LaFell New England 6.4
Roddy White Atlanta 5.5
Golden Tate Detroit 6.2
Martavis Bryant Pittsburgh 5.3


Indianapolis WR T.Y. Hilton did not play. Neither did New England WR Julian Edelman.

While Philadelphia significantly exceeded their FPA projection, those points were spread across many players. If either of the wide receivers, Jeremy Maclin or Jordan Matthews, had scored a touchdown their fantasy performance would have been transformed to something very solid. With touchdowns being very unpredictable in fantasy it can pose a real problem if faced with a quarterback that spreads the ball across many targets. If you started Riley Cooper then you were probably celebrating.

The Running Game

The running back fantasy performances were generally very encouraging, and the table below shows the running backs that scored more than 10 fantasy points. As running backs often gain significant fantasy value from the passing game, I have shown the fantasy scores for the running game alone, but also the total score.

Player Team Fantasy score (rush) Fantasy score (total)
Marshawn Lynch Seattle 23.3 23.3
Mark Ingram New Orleans 9.8 11.5
DeMarco Murray Dallas 11.8 11.8
Jonathan Stewart Carolina 12.2 18.9
C.J. Anderson Denver 14.3 19.8
Eddie Lacy Green Bay 15.9 16.4
Tre Mason St. Louis 13.6 14.3
Frank Gore San Francisco 21.8 21.8
Lamar Miller Miami 15.2 21.0
Damien Williams Miami 1.8 12.8
Matt Asiata Minnesota 17.8 19.7


All ten teams that had the highest projections had at least one running back that scored more than 10 fantasy points, but there were four performances of note by running backs who might have been week to week decisions on whether you played them, Jonathan Stewart, Tre Mason, Frank Gore and Matt Asiata.

I would also highlight that the three Quarterbacks in these games most known for rushing, Seattle’s Russell Wilson, Carolina’s Cam Newton and San Francisco’s Colin Kaepernick, all scored more than 10 fantasy points rushing – 14.8, 11.3 and 21.1 respectively. All significant contributors to their teams’ rushing games, and the fantasy owners that started them.


While this article, and my analysis, are focussed on only one week in the NFL, from my experience it provides a good example of the value that the projections can provide each week, and that is why I have been using them for several years.

I would highlight that in the early weeks of the regular season the projections are far less reliable as a single week’s performance can impact the averages significantly, but once you have four or five weeks’ worth of data, experience has shown they become more reliable.

My experience of fantasy football has shown that no matter how good a matchup looks, you can get bizarre results, with your star player putting in an awful fantasy performance, while a third-stringer gets two touchdowns. There is nothing I can do about that.

While this single week’s analysis has shown the teams’ actual performances versus projections can be mixed, I believe that as an aid to deciding on your line-up it is helpful more often than not.

That is why I will continue to produce, and use, the projections. Anything that helps, even a little, can make the difference between winning and losing your league.

  • Sifter

    Pretty good stuff Garth – simple analysis that any spreadsheeter could do. Might have to try something like this myself this season. My problem: getting it organised early so it’s all ready to go – that would help!

    • Garth Bryden

      Thanks for your comment Sifter. You are absolutely right – it does not have to be complicated to be effective. Preparation is a big part. Once I had my methodology properly laid out across the spreadsheets it saved me a stack of time.