Too often, fantasy football analysis is an exercise in confirmation bias. We have all seen the Twitter threads talking up wide receivers based on 40-yard dash times, vacated targets, and esoteric efficiency statistics pulled from the depths of a PlayerProfiler page that have zero demonstrated predictive value.
The cycle is as old as time. An analyst likes a player, searches for information that confirms that belief, writes it on a page, and calls it a deep dive.
I myself am not innocent; some of my own older work—when I used to write up individual players—fell into this trap.
To guard against confirmation bias, I have made the decision to begin my analysis at a more fundamental level before moving on to how individual players do or do not fit into the process. A worthwhile piece of media ought to challenge at least some of our prior beliefs.
Nowhere in fantasy football do fantasy managers cling to their priors more than in the cases of sophomore wide receivers. I had Jaylen Waddle on zero rosters last year. It would be mentally satisfying to write him off again this year. It looks like Tyreek Hill is going to save me from being wrong on Waddle’s career arc!
The old Ryan would have run with that take. No more, I say.
Rather than fixate on individual player evaluations, I want to apply a set of objective criteria to this year’s crop of sophomore wide receivers. Hopefully, this will illustrate to what extent we should be comfortable projecting them for a jump in fantasy production and drafting them at their ADPs.
Let’s shatter some priors.
Building the Sample
My sample is made up of all wide receivers that were drafted in rounds 3 through 8 since 2010. Why did I pick this range?
- To remove players that have already reached elite status (we aren’t asking whether Ja’Marr Chase will break out this year).
- To limit the analysis to players that carry a significant opportunity cost (You aren’t missing out on much by drafting Nico Collins in the 17th round, so that decision requires a different approach than deciding whether to draft Elijah Moore in the 6th round).
These parameters gave me a sample of 331 wide receivers. Next, I broke them into two groups.
- Sophomore receivers who posted at least 0.7 yards per team pass attempt (YTMA) in their rookie year
- All other players (including rookies, players in year three and beyond, and sophomores who did not reach 0.7 YTMA)
For those unfamiliar with YTMA, it is calculated by dividing a player’s receiving yards by the number of passes their team attempted in games they played. Commonly used in dynasty analysis, I am introducing it here to separate players who were particularly effective as rookies from those who weren’t.
Receivers who perform well in this metric succeed both in earning targets when their team is throwing and in being efficient with those targets. It adjusts for overall team passing volume but not for quarterback efficiency.
Playcalling tendencies can change from year to year, but every relevant sophomore receiver in 2022 has the same quarterback as they did in 2021. This is the rationale for using a stat that is playcalling-agnostic but not quarterback-agnostic.
The 0.7 YTMA cutoff is arbitrary, but I will explore what happens when it’s adjusted later on. For brevity’s sake, from here forward I will be referring to the two groups as “good sophomores” and “other receivers” respectively.
Good Sophomores Are Good
Let’s compare how often these two groups of receivers outperformed their positional ADP based on positional points per game.
A receiver being drafted as the WR20 who finishes as the WR13 in points per game would be considered a breakout in the first row, while a receiver being drafted as the WR20 who finishes as the WR7 in points per game would be considered a breakout in the second row.
As you can see, your chance of hitting a breakout approximately doubles simply by drafting good sophomore receivers instead of other receivers. This holds whether a breakout is defined relatively conservatively or relatively stringently.
Assuming regular PPR league ADPs come to resemble the early data we have from best ball and high stakes leagues, the players who would be classified as good sophomores this year are:
- Jaylen Waddle (~4th round ADP, 0.94 rookie YTMA)
- Amon-Ra St. Brown (~5th round ADP, 0.9 rookie YTMA)
- Elijah Moore (~6th round ADP, 0.85 rookie YTMA)
- Devonta Smith (~6th round ADP, 0.83 rookie YTMA)
Important Statistical Ramblings
How confident can we be in these results? The chi-square test is a statistical technique that can tell us how likely it is that two groups are genuinely different, and do not simply appear to be different due to random chance. The test yields a p-value, determined by the sample size and the sizes of the observed differences between the groups.
A p-value of less than 0.05 can be considered strong evidence that the two groups analyzed are truly different. The small and large breakouts resulted in p-values of 0.0175 and 0.0258, respectively. In both cases, we have strong evidence to say that good sophomores outperform ADP more often than other receivers. We would describe the difference in breakout rates between good sophomores and other receivers as statistically significant.
This may seem frivolous, but statistical significance is a concept often overlooked in fantasy football since our sample sizes are often too small to achieve it. Why do I believe it’s important?
To some extent, caring about statistical significance prevents us from creating illusory trends through cherry-picking. I could pump the percentages in the above table much higher by introducing many more stats and picking the correct cutoffs, but the more I do that, the less useful the information becomes if we’re trying to predict the future. It would quickly send both the sample size and our confidence down.
To The Cherry Orchard, We Go
To illustrate, let’s cherry-pick a little. Instead of using the 0.7 rookie YTMA cutoff, let’s lower it to 0.6 so that we can include both Rashod Bateman (~5th round ADP, 0.6 rookie YTMA) and Kadarius Toney (~8th round ADP, 0.69 rookie YTMA).
At first glance, there still looks to be a pretty big difference between the two groups. From looking only at this table, you would conclude that we should be just as bullish on Bateman and Toney as the other four sophomores this year.
However, once we look at the p-values, we realize the story is slightly exaggerated. Our small and large breakouts have p-values of 0.1094 and 0.0975, respectively. Remember, the larger the p-value, the less confident we are that the two groups are truly different.
To be clear, these p-values are still pretty small. The most common thresholds used to determine whether a value is statistically significant are 0.05 and 0.1. Even in this slightly cherry-picked example, the observed trend for the large breakout would be considered statistically significant at the 0.1 level, since its p-value is lower. The observed trend for the small breakout would not be statistically significant, since its p-value is larger than 0.1. Neither of these two trends are statistically significant at the more stringent 0.05 level, unlike the trends in the first table.
If that went over your head, don’t worry too much. You can still draft Bateman and Toney, but you should be aware that we are less confident they fit into this process than we are about the other four sophomores.
As a last caveat, I want to emphasize that by definition, most sophomore receivers will not break out to these levels. The best rate in this article is 42%. That means that on average, half or more of the sophomores discussed will not outperform ADP by more than six spots. Tee Higgins was the only one to do so last year in a group that also included Brandon Aiyuk, CeeDee Lamb, Chase Claypool, and Jerry Jeudy.
While not every sophomore receiver will hit, what’s important is that we can be statistically confident that a larger percentage of them will hit compared to other receivers. Unless they are valued fundamentally differently by the ADP market in 2022 than they have been in the last decade, sophomore receivers remain among the best upside bets in fantasy football.
Photo by Ken Murray/Icon Sportswire | Adapted by Matt Fletcher (@little.gnt on Instagram)