When it comes to fantasy football -- and daily fantasy football or best-ball fantasy football -- touchdowns matter.

A lot.

Outlier games (which are crucial for every type of fantasy football) are tied quite heavily to touchdown variance, and that means we need to target touchdown scorers.

But we also don't want to chase touchdown scorers -- because that can be a bad long-term strategy.

Though that sounds like a bold claim, touchdown regression is a very real thing, and history indicates that we should take advantage.

I'll be looking at wide receivers who overperformed (and underperformed) in the touchdown department in 2022 -- and lay out why it makes sense to put stock into what we see.

Defining Touchdown Regression

The idea of regression can be both complex and oversimplified, and I'm going to err on the side of oversimplifying it initially.

Simply put, regression just suggests that players will return to a baseline level of performance over a larger sample.

When a player scores a ton of touchdowns, he's probably good, yes, but he's also likely to score fewer touchdowns moving forward and trend back toward his long-term baseline.

Likewise, when a player -- say -- racks up elite yardage but scores just a handful of times, he's likely to score at a higher rate moving forward.

To make it a little more complex now -- and don't worry if math isn't your thing; we're just talking touchdowns here -- we can leverage a regression model to compare various stats to touchdown totals, determine a mathematical relationship between the two stats, and then use that to find expected touchdown output.

To varying degrees, passing stats such as passing yards, passing air yards, and numberFire's Passing Net Expected Points (NEP) tell us a lot about touchdown totals. (NEP is just an Expected Points Added [EPA] model by another name.)

History tells us that when yardage goes up touchdown totals go up. And we can put a tangible number on exactly how much yardage and NEP tell us about touchdowns. That's our key here.

Testing the Process

I've already done this process for quarterbacks, running backs, and wide receivers, but tight ends are always a bit tricky when it comes to touchdowns.

Yardage doesn't always correlate with touchdowns (I mean, it does correlate -- but not as strongly as we see with other positions), and tight ends can post highly volatile touchdown rates on a per-target basis given their red-zone roles.

Does that mean we can't properly identify regression candidates? Nope. We still can.

The strongest relationships with touchdowns and receiving stats I examined were

1. Receiving Yards
2. Reception NEP
3. Target NEP

The discrepancy between Reception NEP and Target NEP is that Reception NEP is the EPA added on all catches but Target NEP accounts for all targets, including incomplete passes and interceptions.

When using the same process with receivers to identify year-over-year regression (i.e. players with a touchdown rate [touchdowns per target] over or under expected by 1.0%), the numbers check out.

Among 101 tight ends since 2012 with at least 35 targets in back-to-back seasons after an overachieving touchdown campaign, 82 of them (81.2%) saw a decrease to their touchdown-per-target rate the following season.

Among the 62 underachievers, 77.4% increased their touchdown rate the next year -- by an average of 2.6%, too.

So, even with weaker predictive metrics from the tight end position, we should expect mathematical corrections for more than three-fourths of players, so using expected touchdown totals for tight ends still works out.

Tight End Regression Candidates for 2023

Here's a snapshot of the 2022 tight end class, their touchdown numbers, their expected touchdown numbers, and the difference between their touchdown total and their expected touchdown total.

The table includes receiving touchdowns only and is sorted by total expected touchdowns from 2022's performance.

Travis Kelce, Kansas City Chiefs

Both of these things are true: Travis Kelce led all tight ends in 2022 in receiving touchdowns (12) and in expected receiving touchdowns (9.4).

That means Kelce -- again the TE1 by average draft position (ADP) in FanDuel's best-ball fantasy football format -- had a 2.8-touchdown gap over expected and a touchdown rate over expectation of 1.7%, which puts him in the expected regression range.

His 7.9% touchdown-per-target rate in 2022 was a career-high output. After a 7.6% season in 2020, his TD rate fell to 6.7% in 2021, for context.

None of this is to say that Kelce is a player to avoid, but he might come back to earth in the scoring column.

George Kittle, San Francisco 49ers

George Kittle's 11-touchdown season in 2022 came on just 86 targets, giving him a 12.8% touchdown rate. Not only is that a career high for him, but his career rate entering last season was only 4.3%.

He actually entered the year with -10.5 touchdowns over expected in his career, which would've suggested he had some real touchdown regression coming in his favor. He made good on that by overperforming by 4.9 touchdowns in 2022.

That 4.9 touchdowns-over-expected total is 11th-most of any tight end since 2012.

The odds are still that his touchdown rate comes down from 12.8%, but perhaps that 4.3% touchdown rate entering last year was always too low.

Kittle is the TE4 this year.

Dallas Goedert, Philadelphia Eagles

Dallas Goedert's potential breakout season was stifled in 2022 when he was limited to 12 games. He set a career-high in receptions per game (4.6) and yards per game (58.5), however.

And he underperformed his touchdown total by 2.3.

Goedert (TE6) held a strong 26.9% red zone target share but saw only 11.1% of the team's end-zone targets.

With Jalen Hurts' goal-line prowess, touchdown numbers can again be flukey for his pass-catchers, but Goedert is a regression candidate based on the math.

Pat Freiermuth, Pittsburgh Steelers

Pat Freiermuth isn't the only Pittsburgh Steelers player due for some sort of regression as both Kenny Pickett and Diontae Johnson were flagged as regression candidates in the positive sense when looking at quarterbacks and wide receivers, respectively.

But Freiermuth (TE10) should've had 4.4 touchdowns instead of 2, making him the largest underperformer at the position (-2.4) by pure volume.

Cole Kmet, Chicago Bears

Cole Kmet is a name floating around the bottom of the TE1 draft tier this season.

He's the TE15 in FanDuel's best-ball format despite playing in a low-volume passing offense.

Kmet scored 7 times on 69 targets (10.1%), one of 17 double-digit touchdown-rate seasons since 2012 among tight ends with at least 65 targets.

Of the 13 of those names that had a qualified follow-up season, 12 of them had a touchdown rate decrease, and that sample fell off by an average of 5.4%.

A lot will need to change for Kmet in terms of volume for him to put up another high touchdown total in 2023.

While you wait for Week 1's daily fantasy football slates to lock, you can get in on some best-ball fantasy football drafts on FanDuel, as well. Just draft your team and watch the points pile up throughout the season.

The above author is a FanDuel employee and is not eligible to compete in public daily fantasy contests or place sports betting wagers on FanDuel. The advice provided by the author does not necessarily represent the views of FanDuel. Taking the author's advice will not guarantee a successful outcome. You should use your own judgment when participating in daily fantasy contests or placing sports wagers.