Warning: If you don’t care about the reasoning and only want to know what players provide value for your upcoming fantasy draft – scroll through and only look at the pictures / picks below. For everyone else, enjoy the ride.
With Fantasy Football season right around the corner – it’s a perfect time to preview each position. Today we will begin with the most exciting group in Fantasy Football, the wide receiver.
A little housekeeping for the new readers, my goal is to utilize stats and analysis to help you win money playing fantasy football. I will explain the analysis in the simplest terms possible & provide an easy to read graphic to back up the analysis.
Fantasy football is simple when you boil it down to it’s core – score more points than the other person or people you are facing. First we need to understand scoring for WRs- most leagues take yards, touchdowns, & sometimes catches to spit out an ultimate number for fantasy purposes. No shit Stevie Stats – this is day one shit.
Just hang with me, I promise this will pay off.
I’m going to provide a few key terms/ definitions that will make this a little easier, don’t worry this will be brief & not too complicated. If you just muttered to yourself “we don’t care nerd boy” then keep scrolling to the pictures.
Regression Analysis: A set of statistical processes for estimating the relationships between a dependent variable and one or more independent variables. Basically we will be using fantasy points as the dependent variable & all other WR stats as the independent variable to see what stat DIRECTLY IMPACTS fantasy points.
Scatter Plot: A graph of plotted points that show the relationship between two sets of data. One set of data (fantasy points) will be on the y-axis & the other set of data we are comparing will be on the x-axis. This is the graph we will use as a basis of all our analysis.
R-Squared Score / Correlation Score: Measures the strength of the relationship – the higher the number (0-100) the stronger the relationship (AKA directly impacts fantasy points).
Okay breath – I know its a lot. But remember the ultimate goal is to make you $$$ & no one said it would be easy.
For our 1st analysis & visual I wanted to see what other stats directly impact fantasy points, after all the ultimate goal is to score as many points as possible. For this we utilize 4 scatter plots to show how these 4 stats HEAVILY IMPACT fantasy points.
Yards The Pass Traveled In Air Before Being Caught: This was the biggest surprise for me when I ran this analysis – I did not expect to see that fantasy points & yards traveled in air would have a .93 correlation score. This is a ridiculously high score – it’s is so high that I thought I was making a mistake, so I double checked. In fact it is correct & a huge piece of information that we can use going forward.
Targets per game: This one is pretty straight forward – more targets = more catches which = more fantasy points. With an r-squared score of .81, we feel VERY CONFIDENT using targets per game as a predictor of fantasy points.
Total TDs: Just needed to include this since we will be using TDs as a key indicator going forward. All fantasy point systems utilize total TDs, so its not a shock that the correlation score is also .81 (note that TDs which is part of the fantasy points equation has a lower correlation score than yards traveled).
% of Team Air Yards: This was the lowest of the correlation scores bu still a very respectable .62. % of Team Air Yards is a key indicator because it shows the WR has plenty of opportunities to put up big plays which is key for fantasy football.
Our second viz utilizes the 4 key statistics outlined above.
For this viz: Bar Length is Receiving Targets Per Game | Bar Color is Yards Pass Traveled Before Catch (Blue=Good/Orange=Bad) | Bar Width is Team Air Yard% | Grey Circle is Scoring TDs
One note I must make is this does not take into consideration players that switched teams or have a new Qb coming in & also the team listed is last seasons team – keep that in mind as you read.
If you are trying to read this graph – it is very simple. Look for long, fat, blue bars. The grey circle (TDs) don’t matter as much because TDs is a little tough to predict year in and year out, but it is still a key metric we have to consider. If players are similar I lean toward the player that scored more TDs or has a more prolific QB throwing him the rock.
Good Value: Michael Thomas | Julio Jones | Julian Edelman | Allen Robinson | Amari Cooper | DeVante Parker
Average Value: Chris Godwin | Mike Evans | DeAndre Hopkins | Davante Adams | Keenan Allen | Kenny Golladay
Bad Value: Cooper Kupp | Tyreek Hill | Robert Woods
If you are trying to read this graph – it is very simple. Look for long, fat, blue bars.
Good Value: DJ Moore | Michael Gallup | DJ Chark | | Jarvis Landry | John Brown | Courtland Sutton
Average Value: Calvin Ridley | Marvin Jones | Tyler Lockett | Tyler Boyd | Terry Mclaurin
Bad Value: Sterling Shepard | Stefon Diggs | Golden Tate | AJ Brown
If you are trying to read this graph – it is very simple. Look for long, fat, grey or light orange bars.
Good Value: Christian Kirk | Odell Beckham | Jamison Crowder | DK Metcalf | Mike Williams
Average Value: TY Hilton | Cole Beasley | Alshon Jeffery | John Ross | Preston Williams | Larry Fitzgerald
Bad Value: Deebo Samuel | Will Fuller | Darius Slayton| Adam Thielen
Look out for the RB & QB Preview dropping soon