Fantasy Football Draft Strategies Revealed With Data Visualization

Friday, August 16, 2013 - 09:45

It’s hard to believe, but summer will be over before we know it. Gone are the street fests, outdoor concerts, and beer patios that I so love to frequent. However, the silver lining to autumn’s impending arrival is the return of football! Fantasy Football Fever is in full effect here at the Inquidia office, so I thought I would use Tableau, which is one of our favorite data analytics tools, to get myself prepped for upcoming drafts.

First thing first, I need some data!  has created a point forecast by player from a consensus of eight sources, so this will be my data set. Please note that I’m using standard fantasy scoring, but also provides forecasts for Point Per Reception (PPR) leagues. Data was accessed 8/1/2013, so values could fluctuate as the pre-season progresses. Due to the small size of the data, I elected to put everything into an Excel file. Once I had that and loaded it into Tableau, it was off to the races.

My overall goal was to create a one-slide dashboard that would act as a handy guide during a live draft, but first I wanted to know how relatively important each position is. There are only so many Peyton Mannings to go around, so I need to know who else has similar stats. To achieve this I ranked each player, and plotted their rank vs. their projected fantasy points. Next, I drew an exponential trend line by each position. Tableau makes this really easy!

All that’s left is to interpret the data. Quarterbacks have a very steep slope, which indicates a large drop-off for each successive player. The next steepest slope is at running back, followed by wide receiver, tight end, and kicker. This seems to lend credence to the notion that the keys to draft day success are to get a good QB, stockpile RBs, fill other needs after that, and take a kicker last.

(Matthew Berry at has a great explanation of why RBs are so important in his Draft Day Manifesto:

The slope by position is cool, but let’s drill into each position a bit more. I wanted to know how many elite and near-elite players there are by position, so I created a modified box plot. Although the following classification isn’t perfect, it still gives me a rule of thumb: elite players have fantasy projections within 10% of the maximum projection for their position, while near-elite players are within 20%. What does this yield?

  •  There are four elite QBs (Rodgers, Brees, P. Manning, and Newton), eight near-elite QBs (Brady, Ryan, Kaepernick, Luck, Stafford, Wilson, Griffin III, and Romo, who is just outside the 20% rule), and then a big gap before you hit the also-rans. This means that in a 10-person league everyone should have a good QB.
  • RBs tell a similar story, but the implication is very different. Two elite RBs (Petersen and Foster), followed by about eight near-elite (Martin, Charles, Lynch, Spiller, Richardson, Rice, McCoy, Morris – again, extending the top 20% rule), and then a bunch of similar guys. Because most leagues play two RBs (and sometimes a flex), a 10-person league means that not all teams will have two top flight RBs. My advice is to take RBs early and, considering how frequently they get injured, often.
  • WRs are tricky. On the surface the story is that they follow a similar pattern to RBs, but our modified box-plot analysis shows that this may not lead to the same results. There are two factors that are exerting influence on our slope: 1) Calvin Johnson is such a dominant WR that he is skewing the rest of the slope upward, and 2) the prevalence of 3- and 4-receiver sets means that the WR trend line is being weighed down by a bunch of non-primary receivers. There are five or six top level WRs, and then a bunch of very similar guys. Because you need two WRs, don’t stress about drafting a few until the middle rounds.
  • The TE position continues to evolve. Gronkowski and Graham are so good that nobody else compares. However, the drop-off after them isn’t very steep, so a solid TE can be had in later rounds.
  • There are 19 Ks that are within 20% of the projected max. Once again, take a K last.

As alluded to in the WR analysis above, perhaps a modified box-plot isn’t the best way to view player buckets. Fortunately for us, Tableau makes creating histograms incredibly user-friendly! Doing a modified box-plot and histogram in tandem allows us to cross-check data. Everything from the analysis above is pretty much spot on. The additional benefit of the histogram is that it shows if and where there is a bulge of similar players (such as the 125 bucket of WRs), which is where I can likely find a decent player in late rounds.

Looking at individual players is fun, but it’s also nice to know how teams are projected to score. This concept is similar to the Juggernaut Index popularized by Yahoo Sports ( Tableau makes performing the analysis a snap. What this means is that all things held equal, if I don’t know who to draft then I’m taking someone on New Orleans, Denver, or Green Bay, and will avoid the New York (Jets), Jacksonville, and Arizona.

Finally, it’s always nice to have a cross-tab of players by position, which is easy to do in Tableau. This can act as a quick running list of who has been drafted and who is available. Nobody else thinks it’s funny when I draft someone that has already been taken, so this is especially handy for me.

Just like that, I’m all set to dominate the draft! I was able to use Tableau to investigate my data and draw some powerful conclusions. The amazing part is that all of the visualizations were done using the same simple data set. Can you think of other views that would help find hidden knowledge?

Contact us today to find out how Inquidia can show you how to collect, integrate and enrich your data. We do data. You can, too.

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