2022 RB Analytical Review part 2

Author: Bill Jones

Introduction

Welcome to the second part of my blog series where I perform an analytical review for running back fantasy performance from 2022. In this blog, we will be continuing our review of the running back position in the 2022 season through the lenses of a few classic statistical measures. In order to better understand the performance of running backs, 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 the running back 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 running backs in the 2022 season, so let’s get started!

Statistics 101

As we continue down 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 quarterback 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:

Read post #1 in series for ground rules explanation.

Visualizations and Analysis

With the admin stuff now at of the way, 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: This visualization highlights how players in the same tier with nearly identical averages can exhibit vastly different consistency profiles. A case in point is Breece Hall (avg 15.98, sd 6.62) and Joe Mixon (avg 15.54, sd 11.89). Although their points per game outputs were almost the same, Breece Hall's owners could rely on him to deliver consistently solid, if somewhat limited, production every week, while Joe Mixon's owners had no idea what to expect from him week to week. This discrepancy can significantly affect a player's value to an owner, depending on their playing style, risk tolerance, team composition, and league settings.

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. For the quarterback position group we had streaming vs non-streaming populations. For this analysis we are looking at the running backs outside of the top 28 (non-RB1 or RB2s). Running backs are unlike quarterbacks in the sense that you can’t just pick up a starter off the waiver wire if you need it. So, to perform our streaming analysis we are adjusting it to those that I consider non “roster locks” as I am assuming that all RB1s & RB2s are getting played when healthy.

Analysis: We are targeting teams right of the vertical line. If you need a stable performance from your running back, target a player against a team below the horizontal line (Cleveland) whereas if you need a big performance target a player against a team above the horizontal line (Denver, New Orleans, or Houston). While identification of these teams is nice after the fact, predicting these defenses that are vulnerable is where the real value lies. Today we identified the target teams, and at a later streaming centric post we will be profiling some of the best teams to stream players against based upon these results.

Conclusion

By leveraging the scatterplot visualization with fundamental statistics, we have demonstrated the significant impact it can have on our understanding of running back performance in fantasy football. In our upcoming blog post, we will take one step further in this analysis by exploring the relationships between various metrics, verifying our commonsense assumptions, and identifying any anomalies that may have arisen during the 2022 season.

*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.*