![]() ![]() Garrett Wilson, who was likely the second wide receiver off the board in rookie drafts, has also shot up the rankings. With Carr at the helm, we will see if he can build on those numbers in 2023. In his rookie season, Olave netted a slightly positive YAE and PAE. Brown doesn’t seem to keep his production down, and he can form a similar duo as Chase/Higgins or Waddle/Hill in their high-powered offense. Waddle saw one of the bigger increases, jumping from neutral to +1.47 in YAE and increasing his PAE from +1.04 to +2.63.ĭeVonta also took a big leap from -.12 to +.59 in YAE and up from -.78 to +1.22 in PAE. Jefferson saw a similar drop in his sophomore campaign and a bounce back in year 3, so it’s likely nothing to be concerned with.Īmon-Ra remained steady in YAE but fell in PAE, surprising in the high-scoring Detroit offense. However, he remained in positive territory and won’t be lacking in volume, so he remains a top WR. YAE – Fantasy points above or below expectations for yardage elements only (right side of the chart) Top Young PlayersĪfter a monster rookie season, we saw a massive drop-off from Ja’Marr Chase in both YAE and PAE. PAE – Fantasy points above or below expectation (left side of the chart) A minimum of 3 games in 2022 qualifies a player. Note: Data is sorted by 2022 fantasy points per game. With that said, let’s jump into the data by position. Data may be skewed if a player only played a few weeks of the year. It can also be used for rookies with limited data, albeit at less of a weighting. This also helps to see trends, such as when a veteran player may begin falling in efficiency, which could lead to a fall in value. A single-season value may be an outlier, so it’s best to see a few years of data. There are a couple of things to note about these variations. Their role may diminish as they move forward. Conversely, a player may have put up decent fantasy numbers but was well under expectation. This could lead to more opportunities in the future. There may be players that did not see significant playing time but outperformed their expectations. With this data, we can see which players may be breakout or regression candidates. ![]() Since touchdowns can vary greatly from year to year, it’s important to factor this into consideration. Mostly, this data should line up, but a few outliers may greatly under or overperform their expected touchdown numbers. I have also calculated the same value but for yardage only to remove scoring variance. Leveraging the data from Tan Ho and Joe Sydlowski, I have quantified the prior year’s fantasy points against expectations on a per-game basis. However, those who continue to outperform expectations will continue to see opportunity, while those who underperform may see their roles cut back. It will be much better to have a player underperforming but with a lead role. Fantasy football is about opportunity, first and foremost. A comparison of this to actual results will show who is consistently over or underperforming their expectation. When all this is quantified, one can see how many fantasy points a player should have obtained on average. For every pass, rush, or target a player receives, there is an expected fantasy value based on the game scenario at the time of the play. ![]()
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