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Are advanced football stats really "made up"? Lindsay and Sam take on one of the most common criticisms of modern football analytics head-on. Sparked by a Twitter debate over the "Big Time Throw" metric, this episode pulls back the curtain on how stats are actually built — and why understanding the difference between objective observations, subjective assumptions, and analytical processes is the key to trusting any given number.
From EPA and pressure-to-sack ratio to SumerSports' new frame-level tracking model, they break down what makes a stat legitimate, what makes it "sticky," and why even traditional stats have always had interpretation baked in. Plus a preview of new route tracking, coverage matchup data, and real-time probabilities coming to SumerSports this fall.
What you'll learn:
What "sticky" means and why it's the standard every good stat must meet
How SumerSports' frame-level model tracks completion & sack probability every 10th of a second
Why the same stat looks different across sites and how to navigate it
What new route and coverage matchup data will unlock for evaluation and fantasy
0:13 – Intro: The "Made Up Stats" Debate on Twitter
0:37 – Big Time Throws & Why the Criticism Has Merit
1:23 – What Separates Good Analytics From Noise
1:49 – New SumerSports Data Coming This Fall
2:09 – Three Fan Reactions: Overwhelm, Misunderstanding & Mistrust
3:36 – Transparency: How Stats Are Actually Built
4:12 – Analytics Aren't New: Steve Belichick's Scouting Book
5:12 – The Real Innovation Is Storage & Processing
6:12 – Counting Stats & Their Hidden Subjectivity
7:34 – The Aaron Rodgers Jets Example: Why Passing Yards Lie
9:34 – What Is EPA? Why Yards Aren't Yards
10:59 – Objective Facts vs. Subjective Conclusions
15:06 – What Does "Sticky" Mean?
16:08 – Stickiest QB Stats: Pressure-to-Sack Ratio
16:55 – Caleb Williams: From High Sack Rate to Low
17:19 – Sack Rate vs. Pressure-to-Sack Ratio Explained
20:38 – Raw Sacks vs. O-Line Blame
23:09 – The 2.5-Second Threshold: Subjective but Consistent
24:03 – Andy Richardson vs. Jalen Milroy: SackOf in Practice
26:14 – Frame-by-Frame Completion & Sack Probability
27:32 – Play-Level (EPA) vs. Frame-Level Data
30:53 – Why Consistency Is the Goal
32:19 – Human Charting Bias vs. Automated Consistency
33:57 – Nick Saban's "The Process" Applied to Analytics
36:19 – SumerScore: Evolution of the Coach's Plus/Minus
37:19 – Outcome Bias & the Cornerback Interception Problem
40:05 – What Technology Can Do That Humans Can't
41:02 – New Stats: Full Route Tracking Back to 2016
42:13 – Coverage Metrics: Target Rate + Yards Allowed Per Snap
43:37 – Riley Moss: When High Target Rate Is a Good Sign
44:25 – Coverage Matchup Data: Gonzalez on JSN in the Super Bowl
45:21 – Fantasy Implications of Matchup Data
46:30 – Play Action: Where Does the Boost Actually Come From?
47:43 – If You Can Tell the Story Behind a Stat, It's Not Made Up
48:44 – Why Stats Differ Site to Site: The Target Share Example
51:15 – Stick to One Site for Consistent Comparisons
52:45 – Outro & Subscribe
#NFL #NFLAnalytics #FootballAnalytics #AdvancedStats #NFLStats #SumerSports #MadeUpStats #EPA #NFLData #DataScience #SportsAnalytics #NFLPodcast #FootballPodcast #NFLMetrics #CalebWilliams #AaronRodgers #FootballIQ #DataDriven #FantasyFootball #SportsTech
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ABOUT SŪMERSPORTS:
SūmerSports combines 500+ years of NFL front office experience with innovative sports AI to democratize elite football intelligence. Our team includes former NFL GMs, coaches, scouts, and engineers from Google, Meta, and Microsoft.
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