2022 QB Analytical Review part 2

Author: Billy Jones

Introduction

Welcome to the second part of my blog series where I perform an analytical review for quarterback fantasy performance from 2022. In this blog, we will be continuing our review of the quarterback in the 2022 season through the lenses of a few classic statistical measures. In order to better understand the performance of quarterbacks, 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 quarterback 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 quarterbacks 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:

Before we jump back into the analytics, I would like to remind the readers of the ground rules we will be playing with. The most common scoring system is as follows so that’s what we will be going with:

- Passing Touchdowns: 4 points for each touchdown pass.

- Passing Yards: 1 point for every 25 passing yards.

- Rushing Touchdowns: 6 points for each rushing touchdown.

- Rushing Yards: 1 point for every 10 rushing yards.

- Fumbles Lost: -2 points for each fumble lost.

- Interceptions (INTs): -2 points for each interception thrown.

Additionally, I want to note we will be focusing on a pool of 32 quarterbacks (shown below). The data used for this analysis was obtained from pro-football-reference.com, and all games where the quarterback had less than 60 offensive snap % were removed to mitigate games where the player may have been injured or playing in garbage time. This will help ensure that the analytics aren’t skewed by anomalous game results and allow us to gain comfort in the conclusions we draw from the results (a visualization for this can be found in part 1 of the series).

Aaron Rodgers	Deshaun Watson	Josh Allen	Mac Jones	Taylor Heinicke
Andy Dalton	Geno Smith	Justin Fields	Marcus Mariota	Tom Brady
Brock Purdy	Jacoby Brissett	Justin Herbert	Matt Ryan	Trevor Lawerence
Dak Prescott	Jalen Hurts	Kenny Pickett	Matthew Stafford	Tua Tagovailoa
Daniel Jones	Jared Goff	Kirk Cousins	Patrick Mahomes
Davis Mills	Jimmy Garappolo	Kyler Murray	Russell Wilson
Derek Carr	Joe Burrow	Lamar Jackson	Ryan Tannehill

This analysis only encompasses 32 quarterbacks and therefore it's possible that some of your favorite quarterbacks were excluded. The sole exception among quarterbacks with more than 9 starts is Zach Wilson, who was not included due to widespread agreement that he is not good at football.

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 should be targeting players in the bottom right quadrant. These are the players with high output and consistent production. It is also interesting to see some clear clusters of players emerge.  They are 1) superstars (red), 2) high ceiling, low floor (blue), 3a) high consistency low-end QB1 (pink), 3b) average consistency low-end QB1 (light peach), 3c) high variance low-end QB1 (green), and 4) unplayable (black). If you consider clusters 1, 2, 3a, 3b, and 3c as reasonable starting quarterback options, there is an overabundance of startable players, so there are multiple ways to attack the position. Tier 1 = pay up for top level production and consistency, tier 2 = pay middle round price for top level production potential with known inconsistency, tier 3 = pay bargain rate for good production, your choice of varying floor/ceiling consistency profile, and can easily get multiple in this tier.

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 quarterback target a player against a team below the horizontal line (Kansas City) whereas if you are in need of a big performance target a player against a team above the horizontal line (Detroit). 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, at a later streaming centric post we will be profiling some of the best teams to steam players against based upon these results to hopefully determine a way to identify these 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, analyze 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.*