Fantasy Football Expected Points & Opportunity: The 2023 Primer

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In fantasy football, volume is king. 

If you are a longtime listener or reader of The Fantasy Footballers, you are likely very familiar with this phrase. Naturally, the more targets or rush attempts that a player receives, the more opportunities they have to score fantasy points. However, it is important to remember that not every opportunity is created equal. For example, targets are usually more valuable than rush attempts for wide receivers and running backs. On the other hand, for quarterbacks, rush attempts are more valuable than pass attempts. Since each player’s usage is unique to their offense and ecosystem, how can we consistently determine their value in our lineups?

Expected Fantasy Points can help quantify these opportunities, providing a baseline value for each player based on their usage. In other words, this metric will highlight the most valuable players for fantasy football, accounting for both the quantity and quality of a player’s opportunities. 

In this new series launching in the DFS pass, I will provide you with a recap of each player’s performance using Expected Fantasy Points (xFP) and Fantasy Points Over Expected (FPOE). Each week you can expect a breakdown of every skill position – quarterback, wide receiver, running back, and tight end – highlighting the players that provide the most value for your lineups.

What is Expected Fantasy Points (xFP)?

As mentioned above, Expected Fantasy Points (xFP) is a measure of a player’s usage and assesses the value of an opportunity based on multiple factors. To arrive at this metric, I created a model for each position that uses historical data to calculate the value of every opportunity. 

First off, an opportunity is either a rush attempt, target, or pass attempt. The value of an opportunity will vary based on:

  • Location of Play and Yards to Go: The closer a player gets to the end zone, the more valuable the opportunities become. As a result, the location of the play will heavily drive the value of an opportunity.
  • Depth of Target (or Air Yards): For pass attempts and targets, downfield opportunities usually generate more fantasy points. We want to see receivers and running backs targeted on deeper routes. We also prefer quarterbacks who want to push the ball downfield.
  • Down: In conjunction with depth of target, location of play, and yards to go, my model attributes a higher value to early-down plays
  • Type of Opportunity: As I mentioned above, rush attempts, targets, and pass attempts are valued differently by position. In general, targets are 2.03x more valuable than rush attempts in half-PPR scoring, and rush attempts are 1.54x more valuable than pass attempts in standard formats.

For example, let’s assume Josh Jacobs receives a first-down opportunity at the 50-yard line. With 10 yards to go, a rush attempt would be valued at roughly 0.58 Expected Fantasy Points. If, instead, that opportunity was a target at the line of scrimmage (zero air yards), the expected fantasy value of that play would improve to 1.14 points.

In short, volume matters and will heavily drive a player’s value for fantasy football. However, the mix of opportunities (rush attempt, target, or pass attempt) is equally important. And because Expected Fantasy Points is a measure of volume, we want to target players that rank highly in this metric as their production is usually more sustainable week to week.

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What is Fantasy Points Over Expected (FPOE)?

While Expected Fantasy Points assesses usage and volume, Fantasy Points Over Expected measures efficiency. In short, FPOE is:

Fantasy Points Over Expected = Actual Fantasy Points – Expected Fantasy Points

In other words, FPOE measures a player’s efficiency relative to the average value of their opportunities. While it can be an indicator of a player’s ceiling or floor, FPOE will usually fluctuate or regress week over week.

As an example, if we take a look at Clyde Edwards-Helaire’s first four games last season, he was averaging:

  • 17.3 Half-PPR Points (RB4)
  • 10.2 Expected Fantasy Points (RB31)
  • 7.1 Fantasy Points Over Expected (RB1)

Clearly, Edwards-Helaire was heavily reliant on efficiency to produce. Notice that his usage value only ranked RB31, implying that he should be producing RB3 numbers. So what happened in the following three games? Edwards-Helaire’s efficiency regressed heavily to the mean (-1.36 FPOE per game), which means he had to rely on his volume to produce. But since his usage only equated to an xFP value of 7.2 (RB41), he was only the RB46 in his next three games.

In short, players who rely too heavily on FPOE are much more volatile and offer a wider range of outcomes. While I would not advise completely avoiding players who rely on efficiency (as opposed to volume), it is important to understand that they can simultaneously raise the ceiling and lower the floor of your lineups.

How to Interpret and Use the Data

After breaking down each metric, let’s apply this knowledge to an example from last season. The table above highlights the top 30 wide receivers in half-PPR scoring through the first three weeks of the season.

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  • First off, context matters. While Expected Fantasy Points will tell us a lot about a player’s ability to produce for fantasy, it is only one piece of the puzzle and should be used in tandem with other metrics and data points.
  • One of the first things I do with this chart is analyze the first two columns, highlighting the leaders in half-PPR scoring and Expected Fantasy Points. Ideally, when analyzing a player’s performance, we should see very little disparity between both columns. For the most part, the top WRs in Expected Fantasy Points (or xFP) rank near the top in half-PPR scoring, emphasizing the immense value of volume for players such as Stefon Diggs, Cooper Kupp, and Amon-Ra St. Brown.

Using this table, we can easily identify negative regression candidates by finding players that rank highly in half-PPR scoring but rank further down the list in Expected Fantasy Points. In other words, these are the players that over-performed and would be far less productive if their FPOE regressed closer to zero in the following weeks.

  • The first player that stands out is Laviska Shenault, whose production was driven almost entirely by his efficiency (+14.7 FPOE). Clearly, Shenault’s production would be unsustainable and was not worth starting the following week. You likely already knew that by looking at his box score.
  • A less obvious negative regression candidate was Rashod Bateman, who was producing top-24 numbers to start the year, but was only the WR59 in Expected Points. Injuries would ultimately slow down his season. However, it was unlikely that Bateman would continue to produce WR2 numbers at only an 18.8% target share on a run-heavy Ravens offense. For him to sustain this type of production, his usage and volume needed to improve beyond his 7.8 xFP.
  • Similar to Bateman, Tyler Boyd was producing flex-level numbers despite only averaging 4.7 opportunities in his first three games. As you can see above, he was only the WR75 in Expected Fantasy Points, heavily implying that his production would regress in the coming weeks. Sure enough, in Weeks 4 and 5, Boyd would average similar usage (4.5 targets) but would finish outside of the top 45 in both weeks. In fact, Boyd is a classic example of a high-ceiling player that can easily lose you a matchup on any given week.
  • Surprisingly, there were three Saints players who ranked within the top 30 in half-PPR scoring. However, while Michael Thomas and Tre’Quan Smith were producing WR2 numbers, their xFP ranked outside of the top 28. This implies some risk in starting both players as they relied on efficiency to significantly outperform their usage. If I had to invest in one of the Saints receivers, I would have picked Chris Olave, who already ranked within the top 13 in xFP three weeks into the season. More on that in the next segment.

Using Expected Fantasy Points, we can also identify positive regression candidates or potential buy-low targets. Generally, these players rank near the top in Expected Fantasy Points, but towards the bottom of the list in half-PPR scoring. 

  • The first player that stands out is Garrett Wilson, who was the WR17 through the first three games. However, his Expected Fantasy Points ranked 2nd among wide receivers at 17.9 xFP. In other words, it was clear that Wilson’s ability to earn targets would eventually lead to WR1 production. And while his usage would fluctuate with Zach Wilson and Mike White rotating in and out of the lineup, we would eventually see glimpses of Wilson’s upside in the second half of the season.
  • Speaking of upside, Ja’Marr Chase may have been the most obvious positive regression candidate early in the season. Despite averaging an elite 28.4% target share and 34.6% air yards share, Chase finished outside of the top 40 in two of his first three games. However, his usage value of 17.9 xFP clearly indicated that his production would eventually flip in the other direction. And while it took another two games for his efficiency to regress, Chase’s production finally caught up to his volume in Weeks 6 and 7, finishing as the overall WR1 in back-to-back weeks.
  • Finally, coming off of a top-six performance, my xFP model told us to confidently buy into the Chris Olave breakout. While his Week 3 production could have been interpreted as an outlier, Olave’s Week 2 usage already alluded to his breakout potential. In fact, he was averaging borderline WR1 usage at over 14.0 Expected Fantasy Points in his first three games. While his production would fluctuate throughout the season, he would still finish in the top 20 in six of 12 games after Week 3.

If you have any additional questions on how to use this metric, feel free to reach out on X (or Twitter) @FF_MarvinE.


Josh Dillman says:

This looks like a game breaking article every week!

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