2022 TE Analytical Review part 2

Author: Bill Jones

Introduction

Welcome to the second part of my blog series where I perform an analytical review for tight end fantasy performance from 2022. In this blog, we will be continuing our review of the tight end in the 2022 season through the lenses of a few classic statistical measures. To better understand the performance of tight ends, we will be utilizing the simple statistics of average and standard deviation to further understand 2022 player production. By doing so, we will hope to uncover trends and patterns in the data and gain insights into tight end fantasy production. So, whether you're a seasoned veteran or a newcomer to the world of fantasy football, this blog will provide valuable insights into the performance of tight ends in the 2022 season, so let’s get started!

Statistics 101

As we continue our analytical review, we are going to use specific data science concepts as tools to help us gain insights. As referenced in the intro, this blog will be using average and standard deviation calculations as the base of our analysis. We all should know what an average is but some of us may need a refresher on standard deviation. Standard deviation is a way to quantify the variability or dispersion of values in a set of data. A low standard deviation indicates that the values are clustered closely around the average, while a high standard deviation indicates that the values are more spread out. In our analysis of tight end fantasy performance, the standard deviation will help us understand how consistent a player is on a week-to-week basis, where a player with a high standard deviation indicates the player is very inconsistent (low floor, high ceiling) vs. a low standard deviation means a player is putting up consistent performances on a weekly basis (e.g., 2018 Keenan Allen).

Ground Rules

See part 1 of the TE series for explanation.

Visualizations and Analysis

With the ground rules established and the data sources defined, we are now ready for the next step in our analysis series. As any fantasy football player knows, consistency is key. When it comes to drafting players for your team, you want to choose players who will reliably score you points week after week, rather than players who might have one great game but then disappear for the rest of the season. That's where this visualization comes into play. By plotting players' average points scored on the X-axis and their standard deviation on the Y-axis, we can get a sense of how consistent they are as performers. 

Analysis: we want players in the bottom right quadrant. These are the players with high output and consistent production. Side analysis, players above the line have increased value in best ball formats due to their potentially high ceiling. It is also interesting to see some clear clusters of players emerge.  There is 1) Travis Kelce (red), 2) High ceiling, low floor (blue), 3) consistent solid production (pink), 4) high ceiling streaming options (green), 5) high floor streaming options (light peach), non-playable (black). This highlights how bad Kyle Pitts was in 2022 as he fell in the non-playable cluster.

Checkout our points per game visual from part 1 with our updated tight end groupings. It is interesting to see how Evan Engram is grouped with players like George Kittle or T.J. Hockenson but is way down the rankings.

Another way to use this visual is to identify opponents that are good streaming targets. Instead of plotting players' average points and standard deviation, we plot opponents to identify favorable matchups for streaming players off the waiver wire. This allows us to identify teams who consistently allow more fantasy points, as well as teams that have high variance in their defensive performance. 

Analysis: We are targeting teams right of the vertical line. If you are in need of a stable performance from your tight end target a player against a team below the horizontal line (Jacksonville) whereas if you are in need of a big performance target a player against a team above the horizontal line (Tennessee). I look forward to investigating some of the underlying statistics about these defenses to hopefully identify these matchups in 2023.

Conclusion

As we've seen, a scatterplot visualization equipped with some basic statistics can be a powerful tool when appropriately applied. In our next blog post, we'll further our use of scatterplots to explore the relationships between different statistics that we assume exist, if these assumptions are in fact true, and if any anomalies appeared in the 2022 player performance.

*This blog post was enabled by ChatGPT. The text was generated by me, and the content is my own, but some sentences and wording were provided by the model. I take full responsibility for all information produced in this blog. More information about OpenAI and their technology can be found at https://openai.com.*