Advising on home keeper leagues can be challenging since there are various ways to run these leagues. Some allow keepers to be retained without any added cost, some permit players to be kept forever in the round in which they were initially drafted, and others have a depreciation tax year over year. And still, other leagues operate like mini-dynasties with non-snaking drafts in reverse order of finish. Along with these factors, the number of keepers, positional requirements, and scoring format also warrant consideration. With so many variables involved, it is easy to see why there isn’t much hard-hitting advice on the subject.
Regardless, the general rule of thumb remains the same across all formats. The Keeper Round Value (KRV) is the round in which your keeper is attached, and the round in which the player is currently being drafted across similar leagues is his Average Draft Position (ADP). The calculation KRV-ADP=X gives us the difference between the two values and the player with the highest positive delta between the two values is generally recommended regardless of other considerations.
To say it another way: You can keep Player A for the cost of a 10th-round pick, and he is going in the third round of drafts. You can keep Player B for the price of a third-round pick, and he is going in the second. Since Player A represents nearly a seven-round savings on ADP, and Player B represents approximately a one-round savings on ADP, Player A should be selected, even though he is typically chosen after Player B in most drafts.
But are we doing it right? Could it be possible that we are overlooking a chance to profit off of our league mates who stick to this way of thinking? Let’s put it to the test.
COULD YOUR DEFAULT DECISION BE CAPPING YOUR UPSIDE?
I’m in a 12-team PPR home league that allows for two keepers, and we drafted this past weekend. I have been holding Deshaun Watson through the past two seasons explicitly to gain an edge at this particular moment in time. Unfortunately, my faith in that had grown shakier due to recently studying Watson’s likelihood of reverting to his legendary first four seasons in the league, so I began to have wandering eyes.
I knew I would keep Garrett Wilson for the price of my 13th-round pick, but I wondered if Watson at a 14th-round value was better than Amon-Ra St. Brown in the sixth. I contemplated this fundamental question: Is St. Brown more likely to be a top-ranked WR than Watson is to be a top-ranked QB? There’s a big reason that matters.
THE HEAD VS. THE TAIL
In fantasy football, it is easy to fall into a pattern of thinking about things linearly. Analysts make lists, and we tend to consider the first player on a list at a value of X and a subsequent player as a value of X+1, on and on in perpetuity. The problem is that intervals between data points are logarithmic, not linear, so some might be X+1 while others are really X+3. There are slight variations, but the wholesale pattern we see from every position group annually is that the closer we get to the top-end fantasy scorers, the advantages grow exponentially.
Consider the WRs from 2022 in the following graph. These WRs are from a home league with three WRs, no flex, and default Yahoo half-PPR scoring.
The players at the top, or head of the logarithm, are the ones who help us win our fantasy league the most — Apex Predators, if you don’t mind a little wordplay. The first data point we see on the far left of the graph is the point total of Justin Jefferson, who led all drafted wide receivers with a remarkable 265 fantasy points. The median, or middle point, for all drafted wide receivers in this league last year was 67.5 fantasy points. This indicates that Jefferson offered a significant advantage of 197.5 points compared to the typical wide receiver in the league. When spread across 17 games, this advantage translates to an extra 11.62 points per game (PPG) over what an average owned WR would provide.
JuJu Smith-Schuster ranked 30th in his position with 117 points, slightly above the middle of the pack in terms of performance. Jefferson outperformed Smith-Schuster by a considerable 8.33 points per game. Smith-Schuster’s stats are obviously a little further down the rankings, and they lie along the start of the flatter part of the scale known as the tail.
If we scroll down 30 spots on the list from Smith-Schuster, we come to Rondale Moore, who scored 52 points. This places Moore below the league median, but his performance is about 3.82 points per game behind Smith-Schuster and 12.53 PPG behind Jefferson. As we progress down the list, the differences between subsequent players become smaller, unlike how they would on a linear scale.
Every year, every position group follows a similar pattern. A small group of elite players is at the top, followed by a larger pool of less valuable players. When drafting, it’s essential to aim for the top players and then work to fill in the rest of your team with those still on the higher end of the scale. This strategy may seem obvious, but it’s critical to success.