Tom Telesco's WR Preference

Author: Billy Jones

Inspiration: Arjun Menon (@arjunmenon100)

Introduction

Hey football analytics fam, welcome back to my blog! I’m excited to share another fun topic with you all today. Recently I got in a short twitter discourse with Arjun Menon, an up-and-coming superstar in the football analytics world who works for PFF, over a data point he presented. I never really questioned his overall assertion that “Tom Telesco, Chargers GM, has a love for bigger body receivers” but I didn’t think the analysis he was presenting was very good at showing that. I thought this was an opportunity for a bit of intelligent back and forth with someone I thought highly of, so I jumped in there with some comments, but I quickly got hit with the classic “I did the work for you…” line in a matter of two tweets. Worrying about things on twitter will never be productive so I tried to move on, but I couldn’t stop thinking about the interaction. This wasn’t because I was salty or mad but because I was fascinated by the concept that Arjun was presenting but still was unsatisfied with the final analysis presented. There was an assumption about a team/general manager’s preferences, and he was looking to use data to support that assumption, I could run with that. I loved the simplicity of the concept and had a very clear visualization I wanted to present in mind so carved out some time to do the work then write up this blog. Here are my results and I hope you enjoy. 

Statistics Used

In this blog I present draft capital value using the Fitzgerald-Spielberger Draft Value Chart. This isn't a huge piece of the analysis but since it is a new metric to the blog here is a brief explanation. Fitzgerald-Spielberger Draft Pick Value is a commonly used metric in the football analytics space to attribute a value to each pick in the draft. The metric scale is from 3,000 for pick 1.01 and goes incrementally down through the remaining draft picks where the rate of value loss is diminishing as the picks get later in the draft (the delta in value between the 1.01 and 1.02 is greater than the 1.02 and 1.03 and so on). This can visually be seen by the scatterplot by round below. There are many other valuation metrics, notably the Jimmy Johnson model, but the Fitzgerald-Spielberger is what I see referenced the most, so I am rolling with it.

Ground Rules

Before we jump into the analytics, I would like to remind the readers of the ground rules we will be playing with. The data used for this analysis was obtained Draft and Combine data from Pro-Football-Reference for 2013 through 2022 (the Telesco era). Not all players that are drafted attend the combine so to fill in the gaps I used the figures from the NFLFastR roster data.* I wanted the Combine data to be the default as this would be much closer to when they were drafted. The last point I want to highlight is that this study is only looking at wide receivers as classified in the draft but there may have been a few instances where these receivers became tight ends. Someone like Darren Waller was drafted at 6’6” 238lb but was classified as a wide receiver. These players were left in the population, but I wanted to note it for transparency.

* In hindsight I could have used the Combine and Draft data from NFLFastR and solely worked in that ecosystem but I only thought about that when I was decently along my way but I am noting for next time.

Visualizations and Analysis

With that now explained, let’s get into the data. Let’s start with a basic analysis to confirm our data isn’t filled with a bunch of junk. Specifically I wanted to confirm the Chargers receiver data was correct, as that is the central focus of this mini-study.

Analysis: Histograms show normal distributions for both heights and weights. Additionally, I confirmed the height and weight figures to the college and pro records on Pro-Football-Reference. The weights I am seeing in my analysis are the same as Arjun's but I have a few different height figures. I am showing differences in Dylan Cantrell (actual 75, Arjun 73) and K.J. Hill (actual 72, Arjun 71). The Hill difference could be rounding but Dylan Cantrell, where did those 2 inches go? I know I would be mad if someone was saying I am shorter than I am (IYKYK... Jordan). All jokes aside, this exercise shows the importance of a basic review of the data being presented. While it’s impossible to check every element of data, spot checks and reconciling totals to reputable websites are great ways to ensure your data is accurate. 

Next step was to begin the analysis of this group of wide receivers’ height and weight by looking at team averages during the period Telesco has been GM. 

Analysis: As we can see from the visuals, the Chargers under Tom Telesco most definitely have been drafting one of the biggest wide receiver cores on average. This holds true for both the whole team and when filtering down to the top 6 rounds (so removing the 7th round junk picks that are likely just special teams players).  Additionally, I presented the total draft capital (Fitzgerald-Spielberger) allocated to wide receivers during this period. The Chargers allocated draft capital during this period is at the bottom of the league, which Arjun pointed out on tweet comment on the twitter thread in question as well. Tom, what’re your thoughts on getting Justin Herbert some weapons?

I found this visual to be particularly insightful as it helped me understand where the Chargers receivers as a group fit but with such a small number of players per team being included there is some risk that a single player or two could skew the data. To mitigate this risk, I presented the same visuals as above but by player.

Analysis: These visuals again support the analysis of the Chargers under Tom Telesco prefer a bigger bodied receiver. During Telesco's tenure he has drafted 7 the wide receivers, of which only 2 fell in the bottom left quadrant of the scatterplot and both of those players came from the 7th round. The assertion of Tom Telesco loving himself a bigger bodied receiver only became stronger by breaking down the data to this level of granularity. 

Notes for Next Time

If I had more time I would add in a z-axis to the plot and look at it dynamically through 3-dimensions. I also believe this data would lend itself very well to a KNN clustering analysis but to produce that analysis to the quality I hold myself to would take longer than I can complete in one night. Definitely on my to-do list to mess around with in the future.

Conclusion

This concludes the Tom Telesco WR Preference blog. I hope you found this very basic analysis clear and insightful. Athletic testing is obviously so much more than just height X weight and I look forward to getting deeper into working on draft prospect data in the future. Finally, a special thank you to Arjun Menon for the topic inspiration and hopefully no hard feelings if you ever read this, I would love to connect and collaborate in the future if possible. Stay tuned for my next blog where I get back into my regular scheduled programming!

Post publish edit: check out Arjun's NFL Draft Athletic Testing Team Averages App he dropped today too, it's pretty cool. He dropped it 14 minutes before I posted this, I shouldn't have waited until lunch today as I had this ready last night.