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No Tight Race for Tight Ends- An End of Season TE Retrospective

Part 1: The Setup

Earlier this week I sent out this tweet-

Needless to say it has led to many opinions on my rankings. As was stated in the tweet, this was only my opinion. However, I genuinely was curious if the numbers would back up my feelings about how I personally ranked these Tight Ends. It was natural to factor in things such as injury, games, played, points per game, etc. But one thing remains true, it is hard to know for certainty who is a reliable Tight End in Fantasy Football.

So I am taking on this responsibility! After all I made the claim, I needed to see where the data lies. So I decided to take the top 36 Tight Ends in the League and see what the numbers showed and try to find a conclusive way to rank the Tight Ends on the season. I will be ranking these players in three areas:

1) Points Per Game

2) Consistency Metric

3) Above Average Percentage

*Disclaimer: All data was taken from and will be calculated in .5 ppr.

Part 2: Points Per Game

This was probably the easiest part of the whole article. Afterall you just go to, go to the Tight Ends, and select Half for .5 ppr and the data is given to you right there. However, this data is not just going to be used to rank players based on their season averages. This will also help me create data points for the next two areas. Take a look at the top 12 rankings as of now.

What I did next was create a series of averages. I created an average for the top 12 average points per game and I also created averages for the top 24 and top 36. From there I was able to then create these new variables:

Top 12 Average PPG: 9.5

Top 24 Average: 8

Top 36 Average: 6.9

Total Average 8.1

The Total Average will play a part later but we can then take these new data points and find a consistency ranking.

Part 3: The Consistency Rankings

Creating the Consistency Rankings I felt was the most important part of this experiment. To create this I had to factor how players weekly fantasy points correlated to Top 12, 24, and top 36 so I assigned a points system to each data point:

Top 12 finishes (9.5 points and Higher) was awarded 4 Points

Top 24 finishes (8 - 9.4 points) was awarded 3 Points

Top 36 finishes (6.9 - 7.9 points) were awarded 2 points

All finishes under the top 36 were awarded 1 point.

There was also one other important factor, games played! Players like Waller, Dulcich, and Goedert had missed multiple games and for the data to be consistent the I had to factor in the number of games where players were active and on the field to create an impartial consistency ranking. After all of this here is the new top 12:

Part 4: Boom vs Bust Percentage

One thing that is hard to determine with Tight Ends is the number that is considered to be a boom or a bust? The number I used is the total average of the top 12, 24, and 36 finishes together which is 8.1 points. That number will determine players who boomed or busted that week. Again, games played was a factor. And after going through all 36 Tight Ends and their weeks I then calculated the number of games players performed above the average and divided it by their total games, and here is the top 12 for this data point:

Part 5: Creating a Final Ranking