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Fool’s Gold or Hidden Gems? Week 5 Regression Picks

What is Regression?

According to the Oxford Dictionary …

Regression: [Statistics] a measure of the relation between the mean value of one variable (e.g. output) and corresponding values of other variables (e.g. time and cost).

Clear as mud? 

In simpler terms, regression refers to the idea that if something performs above or below its usual level, it's likely to return to its normal or average performance over time. 

Why Regression Matters in Fantasy

In fantasy football, over-performing players (playing better than expected) will likely slow down. And underperforming players may improve to match their expected performance. 

We want to spot temporary spikes or dips to help predict the returns toward "normal."

To spot regression candidates, I like to use expected points models. Expected = average/normal.

Expected Points models (x-P) quantify the expected value of plays based on:

• down
• distance
• field position
• and game situation

They calculate how many points a player will likely score on each play and compare it to historical outcomes in similar situations. 

For example, a pass on 3rd-and-10 from the opponent’s 20-yard line has a different x-P value than a 1st-and-goal run from the 5-yard run. An x-P model assigns these values to determine how efficient players are in turning opportunities into points.

There are several great x-P models out there. For this series, I’ll use Pro Football Focus' expected point model. 

I will highlight some outlier players at RB, WR, and TE, and then break down some potential actions to take on these players. 

How to Use Expected Points

• Identify Efficiency: Use x-P to evaluate which players outperform their opportunities (scoring above their x-P) or underperform (scoring below). Players consistently outperforming x-P may have elite efficiency, while those underperforming could signal a potential bounce-back or inefficiency.

• Spot Opportunity: x-P helps highlight players getting high-value opportunities (e.g. red-zone touches, end-zone targets) even if their actual fantasy points lag behind. This can signal potential breakout candidates or buy-low trade targets.

• DFS Options: A player’s values are often tied to production and projection. The market can misprice a player’s true potential, especially in small slates. 

The quickest way to analyze data is to throw it into a graph.

The graphs below show the expected points per game (Y-axis) vs the actual points per game (X-axis), with a red line in the middle showing a perfectly balanced player. 

Players above the red line have underperformed versus expectations.

The players below the line have overperformed versus expectations.

Expecting the Expected: RB

Underperforming RBs

Javonte Williams, Denver Broncos

Denver has been an enigma to start the season, and the RB usage has been a large part of that mystery. 

Williams has underperformed through four games, averaging 8.2 PPR points per game, well below his 11.8 expected PPG (x-PPG). That’s a weekly deficit of 3.6 points.

His rushing output is the biggest issue, at 129 rushing yards vs. an expected 168.4 yards. Additionally, he has yet to score a TD despite an expected 1.32 total touchdowns. 

Given his usage, Williams is a prime candidate for positive regression, especially in rushing yards and touchdowns. It is hard to believe in the Denver offense and Williams specifically. But keep an eye on the situation. 

Over-Performing RBs

Kenneth Walker III, Seattle Seahawks

KW3 is electric, and expected points models don’t account for surviving a suplex. However, Walker outpaced his expected points significantly in his two games this year.

He has averaged 26.3 PPG, 8.5 points above his x-PPG (17.8). His overperformance stems mainly from his rushing TDs, with four scores compared to an expected 1.38. Walker’s rushing work sits slightly above expectation, and his receiving is right in line with expectation. 

The TD-heavy start raises the question: Can he sustain this high level of efficiency?

The answer: Probably not. Walker’s very good, but he’s probably not going to perform as the No. 2 fantasy RB all season.

Of course, he can regress from that and still help your fantasy team plenty. So don’t go selling Walker unless you’re getting top price and boosting your roster in other areas.

Expecting the Expected: WR

Underperforming WRs

Courtland Sutton, Denver Broncos

Sutton’s averaging 10.1 PPG. His 17.9 x-PPG leave a gap of -7.8 points per game. This indicates significant positive regression potential going forward. 

Sutton’s 192 receiving yards fall well below his expected 306 yards, and he has only 1 receiving TD vs. 3.4 expected. 

Is this a product of Bo Nix and his adjustment to the NFL? 

Denver is a situation I am keeping my eye on, but not acting yet. I want to see more from Nix before investing further. 

Over-Performing WRs

Jayden Reed, Green Bay Packers

Reed has been a strong fantasy performer through four games, while significantly outperforming his expected points. His 19.4 PPG sit a whopping 9.2 over expected.

His receiving metrics show 17 receptions and 336 yards on the season, exceeding expectations by more than 162 yards. Reed also has 2 receiving TDs, more than double his expected 0.87.

He has added 91 rushing yards and a TD on the ground, far outperforming the expected 23.5 yards and 0.02 TDs. 

Here is where expected points models break down a bit. They look at the average outcome based on a large sample of players. At this point in his career, we know Reed is elite with the ball in his hands, and his coaching staff knows how to use him. 

He is absolutely performing above expectations, but I would not be selling. He is a terrific player in a good situation. 

Expecting the Expected: TEs

Underperforming TEs

Cade Otton, Tampa Bay Buccaneers

It feels like every TE is underperforming this year, but Otton is a little interesting.

He’s averaging 6 PPG, 2.8 points lower than his x-PPG of 8.8.

Despite 14 receptions over four games, his receiving yards (104) sit below the expected 129. Additionally, Otton has yet to score a TD, while his x-TDs sit at 1.23. 

The data hints at possible positive regression if he can capitalize on these missed opportunities, making him a potential add in deeper formats. 

Over-Performing TEs

Absolutely no one 

Alex Korff, Product Manager

Alex is an engineer by trade and focuses a lot on the game theory and the “value” of players. He spends most of his time in spreadsheets and building new fantasy football tools.