One of the overlooked aspects of winning season-long fantasy football is narrowing the range of potential outcomes for your team in a given week. You always want to score as many points as possible, but fielding a consistently above-average lineup will lead to the most success in traditional head-to-head leagues. That’s also the case in daily fantasy cash games (head-to-heads and 50/50s), where you want to avoid volatility for the most part.
To demonstrate this, I charted two hypothetical fantasy teams. One of them (Team 1) is high-variance, scoring lots of points some weeks and getting killed in others. Team 2, on the other hand, shows much more consistent scoring.
The important thing here is that both Team 1 and Team 2 averaged 120 points on the season, with 115 points being the average needed to win in a given week.
In this 16-game season, Team 1 has a 7-9 record, while Team 2’s record is 12-4. That’s a massive difference based not on total points scored (since the teams were identical in that regard), but based solely on week-to-week consistency.
This example is exaggerated, but the point is that narrowing the range of potential outcomes—embracing a low-variance fantasy strategy—is a smart move in most situations. Things are different in a total-points league, for example, but normal head-to-head leagues call for consistency.
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Individual Player Consistency
My views on player consistency are probably a bit unconventional because I think that it’s really, really difficult (perhaps impossible) to use past consistency alone to predict future consistency on the individual level, especially for young players who haven’t been in the league very long.
Note that I’m not saying that week-to-week consistency doesn’t exist—I think there’s good reason to believe it does—but just that identifying it can be challenging if we’re analyzing players in isolation.
This idea is a mirror image of how I view predicting injuries. It’s probable that some players are more likely than others to get injured, but it’s really difficult to use a player’s past injuries to predict future injuries since injuries are a low-frequency event with a lot of inherent randomness.
However, we can make progress in predicting injuries by sorting players into buckets and then projecting injury probability based on certain heuristics. Tall running backs with a low body-mass index, for example, have proven to get injured at a much higher rate than shorter, stockier running backs.
By sorting players into groups, we can overcome issues with small sample sizes and hopefully make more accurate predictions.
Wide Receiver Consistency
I thought about the topic of this article when analyzing Odell Beckham’s rookie season. He’s proven to be ridiculously consistent with elite upside in his rookie year. My question is if Beckham’s early success is representative of what we can expect from him in the future.
My closest comp for Beckham is Antonio Brown—a player with similar measurables and a comparable skill set. One of the reasons that Brown is so consistent is that the Steelers throw him screen passes so frequently. He runs downfield, too, but the abundance of short targets gives Brown arguably the highest floor for any wide receiver in the NFL. Death, taxes and Antonio Brown catching five passes.
Like Brown, Beckham sees a good number of short targets; his average target length, according to Pro Football Focus, is 12.1 yards—ranking him in the bottom half of the league. I charted how the average depth of a receiver’s targets (aDOT) affects his catch rate.
This is a pretty clear relationship. Receivers who have a short average target length—under 10 yards—have caught 68.9 percent of passes this season. Compare that to just 53.4 percent for receivers whose average target comes 15-plus yards downfield. That’s a substantial difference that has a pretty clear effect on week-to-week volatility.
This is good news for Sammy Watkins, who has managed just a 53 percent catch rate despite a totally average aDOT of 13.2 yards. We’d ideally like to see a higher catch rate, but there’s good reason to believe that Watkins is an extremely talented player. Given how he’s being used in Buffalo, we should expect a boost in his future production and consistency.
Even worse is Cecil Shorts. His aDOT of just 9.3 yards suggests a high catch rate, but he checks in at only 54 percent. There are clear reasons to expect a lower-than-normal catch rate for Shorts—most notably the quarterback situation in Jacksonville—but chances are that he will improve that catch rate a bit by the end of the season.
The overarching idea is that week-to-week consistency is really important, but often challenging to predict. Using past consistency to predict future consistency on the individual level is often misleading, especially when players don’t have a lengthy game history. By sorting players into buckets—one of which is wide receivers and target depth—we should be able to get a better idea of how much volatility a player possesses on a weekly basis.
