2022 TE Analytical Review part 3

Author: Bill Jones

Introduction

Welcome back to the third installment of my blog series where I perform an analytical review for tight end fantasy performance from 2022. In this blog, we will be exploring the relationships between data points that simple logic would assume are closely linked, such as rushing yards and rushing touchdowns, passing yards and passing attempts, and more. We will be using visualization tools to help us determine if these relationships actually exist and if there are any outliers or anomalies in the data. From there I will comment on what this might mean about their performance in 2023.

I'm particularly excited about this blog, as we start to get a little deeper into the realm of data science-y things. This blog touches on topics of correlation and anomalies, and how they can be used when evaluating player performance. These are easy concepts and tools which help towards a comprehensive understanding of what happened in 2022 and what could be in 2023. Let's get started!

Statistics 101

In today’s blog we will be adding two new data science terms into our study, correlation, outlier, and anomaly. 

Correlation is a statistic that describes the relationship between two data points. It is expressed as a number between -1 and 1, with 1 being a perfect positive relationship, -1 being a perfect negative relationship, and 0 indicating no relationship between the data points. A positive correlation means that as one data point increases, the other data point tends to increase. A negative correlation is the opposite whereas one data point increases, the other tends to decrease.

An outlier is an observation in a data set that lies far away from other values. Outliers can have a significant impact on the results of statistical analyses and can sometimes be indicative of measurement error or data entry errors (or football fluky results). Outliers and anomalies are often interchanged.

Ground Rules

See part 1 of the TE series for explanation.

Visualizations and Analysis

With the ground rules established and the data sources defined, let’s get into analyzing some relationships. 

In this analysis, we are examining two commonly assumed relationships in offensive production: the direct correlation between receptions, receiving yards, and receiving touchdowns. Although we acknowledge that these are simplified views of offensive production, our scatterplots provide clear indications that these assumptions hold true.

Analysis: Upon examining the data, the relationship here appears strong. There aren’t any clear outliers here in from a yards to receptions ratio but I do want to highlight we should be targeting players above the line as they are able to do more with less volume. With tight ends historically taking a longer time to become fantasy relevant (I should attempt to prove this in a later post), I like Greg Dulcich as a prime candidate for a big year 2 with Russell Wilson.

Analysis: With receptions and yards being so closely correlated, we can review the relationship with touchdowns together. Touchdowns to receptions and yards show some correlation but unlike receptions and yards it is very weak with items far on both sides of the line. I think this visual is very telling when looking for positive or negative touchdown regression candidates. Kittle’s 11 touchdown season was an outlier, duh, but I want to highlight how Pat Freiermuth’s reception and yardage production is much higher than what we are seeing out of his touchdown output. If he can hold or improve this yardage and reception production and get a bit of positive touchdown regression then we could be looking at a big year for Freiermuth. Maybe I should have renamed this post “Emerging Tight Ends for 2023”.

Conclusion

In conclusion, our analysis of the relationships between receptions, receiving yards and receiving touchdowns has yielded some interesting insights into the offensive production of various players. Stay tuned for the last blog post in this mini study where we look into yardage production consistency.

*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.*