Using Regression Tree Analysis To Better Understand WR Prospects, Predict Where They Will Be Drafted, and Project the 2023 WR Class
Image Credit: Rich von Biberstein/Icon Sportswire. Pictured: Josh Downs.

Heading into the 2021 draft, Ja’Marr Chase was lauded by many as one of the best wide receiver prospects to enter the NFL in a decade. He was the highest-ranked prospect in our Rookie Guide that season and sat inside our top 20 dynasty rankings prior to the NFL Draft. Why were we, and many others, so optimistic about his future? It was pretty simple. He projected as an early Round 1 pick, made an impact at an early age in college, and had one of the most productive WR seasons in the history of college football.

When considering prospects, we often question whether they “check all of the boxes.” It’s great when players like Chase accomplish this, but we know that there are plenty of successful players that only checked a handful. Some boxes are more important than others and in some cases, the combination of checks is more important than the total.

Regression trees are a great way to help us reframe this idea. Throughout the history of the site, writers such as Kevin Cole and Anthony Amico used regression trees to consider the importance of production and age in predicting NFL outcomes. Regression trees help us to understand the mixture of attributes that tend to drive NFL performance and provide a visual way to understand how these attributes interact. Heading into the 2021 Draft, I built a simple regression tree model with the intent of outlining a simple “rubric” that readers could use to better understand a WRs profile and if it lent itself to NFL success. With two seasons now behind us, this model has proved to do a solid job of predicting the results of that class. That said, the tree produced in this model was a little too specific about the touchdown rates in certain branches. As a result, I decided to revisit the use of regression trees to better understand the incoming class of rookies.

Since you’re probably interested, here are the model results I published in late February of 2021, sorted by projected draft positions that were sourced from NFLMockDraftDatabase.

Player School Projected Draft Position PPG
Ja’Marr Chase LSU 5 15
DeVonta Smith Alabama 6 11
Jaylen Waddle Alabama 12 11
Rashod Bateman Minnesota 23 11
Kadarius Toney Florida 27 11
Rondale Moore Purdue 37 11
Terrace Marshall Jr. LSU 39 6.3
Elijah Moore Ole Miss 56 11
Amon-Ra St. Brown USC 72 8.3
Tylan Wallace Oklahoma State 77 7.6
Chatarius Atwell Louisville 85 6.3
Sage Surratt Wake Forest 91 7.6
Seth Williams Auburn 93 6.3
Marquez Stevenson Houston 101 6.3
Nico Collins Michigan 106 8.1
D’Wayne Eskridge Western Michigan 106 4.5
Tamorrion Terry Florida State 107 6.2
Anthony Schwartz Auburn 113 8.1
Jaelon Darden North Texas 116 8.1
Amari Rodgers Clemson 126 4.5
Dyami Brown North Carolina 129 6.2
Shi Smith South Carolina 131 4.5
Marlon Williams UCF 140 6.2
Dazz Newsome North Carolina 170 4.5

What Is A Regression Tree?

So what in the world is a regression tree? For the purposes of this article, think of a regression tree as a series of questions. The response to each question leads to another question, which leads to another question. This process is repeated until all questions are answered and an estimate of the points per game a prospect is expected to score in the first three seasons of his NFL career is reached.

To create the regression trees used in this article, I gathered collegiate production, age, athletic measurables, and draft data for every WR included in the Prospect Box Score Scout. I filtered this listing to only include players that had logged two or more NFL seasons. With some help from an algorithm, I then worked through different combinations of measures until I arrived at a mixture that was well tied to NFL fantasy points, easy to visually follow, and included intuitive inputs.

The specifics of how the tree was built fall outside the scope of this article. But for a little more background, I split my listing of players into training sets and test sets. I then fed the training sets into an algorithm that ran through the process of building the tree by continually separating players based on thresholds. Once the tree was built, I fed the test sets into the model and compared each player’s predicted result (as calculated by the regression tree) to his actual result. I repeated this process, workshopping different mixtures of statistics until the differences between predicted and actual results were limited while also producing a tree that was small enough to easily interpret.

Keep in mind, this exercise isn’t as much about trying to build as accurate of a model as possible as it is about giving us another tool to help us understand how WR metrics interact. Further, it gives us another input into building an expected range of outcomes for incoming rookies. One challenge with regression trees is that as you get into the lower branches, you’ll often find a couple that seem counterintuitive. This can happen for a variety of reasons but more often than not it is because the model is overfitting. (This happens when the model aligns too closely to the specific training data, and it prevents the model from being usefully applied to new data.) Also, it’s possible that there are counter-intuitive relationships, or specific profiles that tend to violate some of the general lessons we have learned.

The Results

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Welcome to Going Deep! This weekly article will make use of charting data, advanced metrics, and other RotoViz tools — specifically the Passing Game Matchup Rater — to help readers better understand the challenges facing wide receivers and tight ends in the coming week. As my process makes heavy use of the Matchup Rater, I’d highly recommend that you set aside half an hour to watch the tutorial/walkthrough and…...

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Welcome to Going Deep! This weekly article will make use of charting data, advanced metrics, and other RotoViz tools — specifically the Passing Game Matchup Rater — to help readers better understand the challenges facing wide receivers and tight ends in the coming week. As my process makes heavy use of the Matchup Rater, I’d highly recommend that you set aside half an hour to watch the tutorial/walkthrough and…...

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