Welcome back to the Data Viz DFS NFL Preview utilizing data science & data visualizations – today our focus is wide receivers. We will be utilizing % of team air yards, targets per game, yards per game, & total TDs to understand what WRs have the best value for week 8.
The reason I chose these 4 statistics is because they all have an r squared score > .5 when compared to fantasy points – which means they have a high positive correlation. You may be saying to yourself “Okay math nerd – what the hell does r squared score & high positive correlation mean & why should I care?” Well luckily for you – I am going to break down correlation, scatter plots, & basic data science principles to give you a more informed choice when picking week 8 WRs. Basically I’m going to use nerdy math shit to win you money. Keep reading for a full breakdown & analysis.
Today’s first viz is 4 different scatter plots – with each being compared to total fantasy points (y axis). The four statistics on the x-axis are: receiving yards per game, receiving TDs, % of team air yards, & receiving targets per game. Each circle represents a different WR, the color is based on total fantasy points (blue is good & orange is bad), & all are positively correlated based on their r squared score (>.5).
Before we jump head first into the analysis, let’s define a few key terms.
Key Terms:
Scatter Plot: A graph where the values of two variables are plotted along two axes (x & y), the pattern of the resulting points reveal if any correlation is present. The purpose of a scatter plot is to show possible associations or relationships between two variables.
Correlation: The process of establishing a relationship or connection between two or more variables.
Positive Correlation: The values of one variable increase as the values of the other increase. Basically if you improve one variable – you will see improvement in the other.
R Squared Score / Correlation Score: The main purpose is to predict future outcomes on the basis of other related information. In general the higher the R-squared score, the more confident you can be when predicting future outcomes.
% Of Team Air Yards: The sum of the receivers total intended air yards (all attempts) divided by the sum of his team’s total intended air yards. Represented as a percentage, this statistic represents how much of a team’s deep yards does the player account for.
Intended Air Yards: The vertical yards on a pass attempt at the moment the ball is caught in relation to the line of scrimmage. Air Yards is recorded as a negative value when the pass is behind the Line of Scrimmage. Additionally Air Yards is calculated into the back of the end zone to better evaluate the true depth of the pass.
Key Takeaways:
Takeaway #1: Receiving yards per game & TDs have a high positive correlation to total fantasy points because the equation that makes up fantasy points combines yards & TDs – this is not a surprise. So the fact that rec yards per game & TDs have high positive correlation makes sense. We can utilize these 2 statistics to predict future performance because they are directly involved in the equation that produces fantasy points.
Takeaway #2: Percent of team air yards & receiving targets per game have high positive correlation (r squared score = .53), but are not directly involved in the equation to produce fantasy points. This is where it starts to get interesting – we can utilize these 2 stats with relatively high confidence to predict future outcome due to the r squared score being > .5.
Takeaway #3: We are going to take a deep dive with these two statistics being the main course & receiving yards per game & TDs being the side dishes – that way we will have all 4 statistics that have a positive correlation to fantasy points contained in 1 easy to read visual. This way we can utilize all factors to predict week 8 WR performance. I will present players I like & don’t like for week 8 below, but remember this is just my opinion based on the data. There is no correct answer when it comes to Fantasy Football, so do your own research & always make your own picks based on how comfortable you feel with the player. Hopefully this gives you a great starting point & a general direction to follow when researching for the week.
This viz is a vertical bar graph where the length of the bar shows % share of team air yards, the width of the bar represents targets per game, the color of the bar represents yards per game (blue is good & orange is bad), & the grey circle indicates total TDs. The WRs are ordered by DK fantasy points per game. All four statistics that have a positive correlation are included in this viz – continue reading for the WRs I like & don’t like for week 8.
Top 15 WRs
WRs I Like For Week 8
Allen Robinson vs Chargers | Michael Thomas vs Cardinals | D.J. Chark vs Jets |
DeAndre Hopkins vs Raiders
WRs I Do Not Like For Week 8
T.Y. Hilton vs Broncos | Keenan Allen vs Bears | Marvin Jones vs Giants
16 – 30 WRs
WRs I Like For Week 8
Julian Edelman vs Browns | Courtland Sutton vs Colts | Golden Tate vs Cardinals |
John Brown vs Eagles | Kenny Golladay vs Giants
WRs I Do Not Like For Week 8
Sammy Watkins vs Packers | Tyler Boyd vs Rams | D.J. Moore vs 49ers
31 – 45 WRs
WRs I Like For Week 8
Larry Fitzgerald vs Saints | JuJu Smith-Schuster vs Dolphins | Phillip Dorsett vs Browns
WRs I Do Not Like For Week 8
Curtis Samuel vs 49ers | Cole Beasley vs Eagles| Dede Westbrook vs Jets
46 – 60 WRs
WRs I Like For Week 8
Marquez Valdes-Scantling vs Chiefs | Auden Tate vs Rams | Auden Tate vs Jaguars | Corey Davis vs Bucs
WRs I Do Not Like For Week 8
Nelson Agholor vs Bills | Mecole Hardman vs Packers
Ask me any questions about Data, Visuals, DFS, etc. on Twitter (@StephenDataViz) or Instagram (@StephenDataViz)
