WRs Who Shouldn’t Have Scored So Much: Negative Touchdown Regression Candidates
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Due to the variance in wide receiver touchdowns year-to-year and other stats only weakly predicting next-season receiving TDs, regression can be used to identify wide receivers whose 2017 receiving touchdowns didn’t align with their expected touchdown total. This article will focus on negative touchdown regression candidates. You can read the positive touchdown regression candidates here.
WR Touchdown Regression EquationYou can see my methodology behind this equation here. But the jist of it is:
Expected reTDS = 0.139 + (-0.015 * Total reRTGS) + (0.008 * Total reYDS)
- For positive touchdown regression: look for WRs with a lot of yards, but few touchdowns.
- For negative touchdown regression: look for WRs with more touchdowns than their total receiving yards would predict, even if they have a lot of targets.