Projecting Favorable IDP Matchups by Positional Performance – Part One: Introduction


Owners in offense-only league, or non-IDP if you prefer, have long utilized prior performances against current player opponents as a tool to help identify which players to start. Given the choice between two players who have traditionally performed in a similar nature, owners will often turn their analysis towards each player’s matchup for that week.

If Player A is facing a defense who performs well against wide receivers, and Player B is facing a team that performs poorly against the position, most owners will choose Player B for the start given the likelihood of a better performance over Player A. Many websites will post weekly pieces or collections of statistics detailing which players have the best positional matchups for the week.

Those of you who do not play in IDP leagues may be surprised to learn that such lineup setting logic rarely is used when selecting which defensive players to start. The logic is usually much more simplistic than that, instead relying on how an offensive unit, as a whole, performs. Even top IDP pundits usually rely heavily on past offensive performance, both in terms of passing and rushing, as well as point spreads to determine who well individual defensive players will perform on a given week.

Why then, would anyone be surprised when IDP projections are generally more inaccurate than their offensively-based counterparts?

Furthermore, why haven’t past positional performance metrics been utilized on the defensive side of the ball? That’s not a rhetorical question, I legitimately do not know and would love to hear why.

Nonetheless, I decided to approach the task and sought to share with DLF’s readership so as to provide everyone with yet another tool to help make those tough lineup decisions each week. In order to do so I employed the following approach:

  1. I focused on isolating plays where a full tackle is recorded. This required identifying aspects of the play: The defensive and offensive team on any given play, if the play registered a defensive tackle (e.g. no penalty on the play, no turnover and not a special teams play)
  2. Each play where a tackle was registered was then divided into the position of the player who recorded that tackle (i.e. DT, DE, LB, CB, S)
  3. Tackles were summed together by position and opponent by game (e.g. Arizona DTs recorded two tackles in their game against Atlanta)
  4. Tackles were summed together by position and opponent by season (e.g. 14 DTs were recorded against Atlanta thus far through the season)
  5. The percentage of tackles each position accounted for per team was determined (e.g. Arizona DTs recorded 11.8 percent of the Cardinals tackles in their game against Atlanta)
  6. Tackle percentages were averaged by position and opponent (DTs recorded an average of 10.3 percent of their team’s tackles versus Atlanta)
  7. Each team’s offensive positional average against each position was compared to the league average versus that position (e.g. the 10.3 percent of tackles generated by opposing DTs against Atlanta was a full eight percent lower than the average percentage of tackles generated by opposing DTs leaguewide which was 18.3 percent)
  8. The reverse was calculated as well, each team’s defensive positional average against all opponents was compared to the league average of that position

Collectively, this data paints a very comprehensive and illuminating picture of how each position on each team performs against the league as a whole. It also highlights favorable weekly matchups for defensive positions based upon prior game performances against weekly opponents. As I continue to build layers of analysis upon this data, I hope to also identify which positions on each team outperform their expected output on a consistent basis and, likewise, which offenses outperform the expected tackles scored against them by each position.

Additionally, this analysis is more basic than it, perhaps, should be due to the constraints of IDP fantasy position classification. Rather than classifying a broader and more full range of defensive positions (e.g. NT, 3-4 DT, 4-3 DT, 3-4 DE, 4-3 DE, 3-4 OLB, 4-3 OLB (SLB & WLB), ILB, MLB, NB, CB, SS, FS), we are restricted to what fantasy hosting websites permit (DT, DE, LB, CB, S). This certainly removes much of the nuance and context of the data which now groups players as diverse as 3-4 OLBs with MLBs despite playing vastly different roles. However, since fantasy groups them this way and they play the same position in our lineups, the grouping must occur as such, so is life.

I intend to provide rankings in this article so that readers can get a taste of what is occurring. Going forward, I will produce a second article outlining the most exploitable for IDP fantasy points, individual defensive performances and how well above or below average those team positions performed in another, with a final article highlighting matchups to exploit deep into the fantasy playoffs and focusing on some team position deficiencies where possible free-agent signings or rookie draft selections may help improve those weaknesses. Depending on how easy I can make the future weekly extraction of these metrics and statistics to perform, as well as the community reception to this data, I will consider publishing a weekly column exposing favorable defensive position matchups for our IDP owners and readers.

Now, without any further ado, here are the position rankings by team in regard to both how defensive positions rank and how offenses defend against tackles to opposing defenses. The higher the rank, the better and in terms of heat map coloration, green is the most favorable to the position on the defensive rankings and green is the toughest for the defensive position (most favorable for the offense) on the offensive rankings.

defense tracking image

Thank you for reading, I hope the matchup charts are useful to our IDP owners out there. Be on the look out for the additional articles I outlined earlier in this piece. Referencing the dual tables above, as an example, a favorable matchup for a defensive position would be Philadelphia’s linebackers (first) versus Indianapolis (32nd). While a bad matchup for a defensive unit would be Jacksonville’s defensive ends (32nd) versus Atlanta (first). These rankings were compiled over the first 12 weeks of the 2019 season.