Are “Good Keepers” the Key to Success? A Data-Driven Retrospective on the 2025-26 Regular Season

With FFNUTHANG’s transition to a keeper league now complete, there’s a growing league-wide appetite to answer one question: does keeper quality determine a team’s destiny?

The end of this year’s regular season felt like as good a time as any to dig into a moderately-detailed quantitative analysis. TLDR: No, “good keepers” don’t make a team; a lack of good keepers doesn’t break a team either.

But onto the analysis.

1. Defining “Good Keepers”

To keep the analysis grounded, we need to define what makes a good keeper. Intuitively, the main way a keeper provides value (i.e., is “good”) is by being drafted at a discounted auction cost and enabling a team to spend money on other players. We can define this advantage as “Keeper Savings”:

• “Keeper Savings” = Projected Auction Cost – Keeper Draft Cost

Note: A keeper could also provide value by outperforming expectations implied by projected auction cost. But first, this is harder to objectively quantify and wouldn’t be known until after the season ended. And second, keepers this year largely UNDERperformed projections based on the difference in pre-season projected player rank v. actual regular season player rank (“Keeper Season Rank Delta”). See the second chart in Section 3 below.

2. Analytical Framework

Based on this assumption, we can start with an initial hypothesis that teams with high Keeper Savings materially outperform those without. Some teams entered the season with significant Keeper Savings advantages, so we would expect to see positive correlations among the following:

• Team Keeper Savings vs. Regular Season Rank
• Team Keeper Savings vs. Points For (Average/Total and Median)

3. Analysis 1: Keeper Savings  vs. Regular Season Rank

The first analysis is straightforward: did teams with higher total Keeper Savings finish higher in the regular season standings? As reflected in the chart below, the short answer is “no.”

There doesn’t appear to be any clear relationship between Keeper Savings and final regular season rankings. The closest we get is by focusing only on the bookends—Gabs had the highest Keeper Savings and ended the season in 1st; Aaron had the lowest Keeper Savings and ended the season as Sacko.

But even with that narrow focus, we should do some deeper inspection. Specifically, did Gabs’s Keeper Savings actually lead to real keeper value? The next chart below suggests “no.” 

While Gabs’s keepers held much pre-season promise, they ended up being the league’s second biggest disappointments in actual regular season production—Bucky Irving got injured and Brian Thomas Jr. couldn’t find QB chemistry.

In other words, good keepers did not consistently lead to more wins.

4. Analysis 2: Keeper Savings vs. Points For

But wins can be noisy based on matchup luck, so we should also look at scoring: did teams with higher total Keeper Savings finish with higher “Points For”? Again, the short answer is “no.”

The first scatterplot compares each team’s average Points For against their respective Keeper Savings—there was no material positive correlation. To control for week-to-week variance and outlier performances, the second scatterplot examines each team’s median Points For. Again, there was no material positive correlation. (The R2 Values for average and median are 0.07 and 0.08, respectively; removing Gabs for the above stated reasons produce negative correlations with R2 values of 0.01 and 0.00).

In other words, teams with good keepers did not reliably outscore others. Offensive output was distributed broadly across the league, independent of Keeper Savings.

5. Conclusion

The data paints a clear picture. While it may feel good to enter a season with good keepers, it didn’t translate into regular season success in our league this year. Winning is more likely driven by the usual factors: draft execution beyond keepers, ongoing roster management, lineup decisions, and of course luck.

**Bonus Content**

Heatmap Table

For those who prefer tabular data formats (and heat mapping), here is another view of the data covered in this post:

A couple of other takeaways based on this view:

• Eric and Aaron had bad matchup luck this season (higher relative Points For than season rank)
• Jeff Lin got relatively wrecked on his keepers compared to projected value

Keeper Data by Player

The above analysis focuses on the aggregate Keeper Savings per team, but I thought people might be interested in how each individual player panned out:

A few takeaways on this:

• It looks like Abraham, Andrew, Jeff Lin, and Eric made gambles that certain keepers would overperform their preseason projected ranks/values--unfortunately none of those players did
• At least for Andrew, he was able to balance that out with the only keeper that outperformed preseason expectations (Jaxon Smith-Njigba)
• All of this is caveated by the fact that Yahoo! is hard to navigate for tracing preseason ranks and actual ranks; I used these preseason ranks and the Yahoo! player rankings as of Week 15 (so a little late)