Apr 19, 2021 · 1h 0m · capital-allocators
Cade Massey – People Analytics in Investing and the NFL (Capital Allocators, EP.190)
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gold bands on the timeline = statements, start to end. Hover to read, click to jump. CC turns on captions
Wharton professor Cade Massey joins host Ted Seides to explore how people analytics, forecasting methodologies, and behavioral science can optimize decision-making across NFL front offices and institutional investment firms. The discussion provides actionable frameworks for reducing cognitive bias, structuring independent talent evaluations, and effectively integrating algorithmic models.
How this conversation actually went
Every chapter scored 0–10 on four independent dynamics. Hover any point for the reasoning behind the score. Ted holds 22.3% of the talking time here. How this is scored →
Ted as informed peer 4.2 Guest teaching 5.2 Guest disagreement 0.9 Ted pushing back 0.9
speaking balance: gold is Ted, purple is the guest (3 minute bins)
Sharpest disagreement ▶ 49:15 Contrarian stance on draft consensus
Massey takes a deliberately provocative stance against prevailing consensus, arguing that decision science dictates betting against Trevor Lawrence being the long-term best quarterback in the draft.
Hardest push from Ted ▶ 20:12 Challenging feasibility of meeting analyticsTed pushes back skeptically on Massey's proposed data collection, asserting that real-world organizations are still far away from capturing meeting-level behavioral analytics.
Biggest teaching moment ▶ 36:30 Explaining the root cause of algorithm aversionMassey educates Ted on why people abandon algorithms, showing experimental proof that humans hold mathematical models to impossible zero-defect standards compared to human judgment.
Ted holds their own ▶ 35:11 Host articulates portfolio manager psychological dualityTed displays his own domain expertise by articulating the subtle psychological tightrope investment managers walk between non-consensus confidence and essential humility.
the scores for every segment, with the reasoning behind each
| Chapter | Topic | Ted as informed peer | Guest teaching | Guest disagreement | Ted pushing back | Why |
|---|---|---|---|---|---|---|
| Cade Massey's Academic Evolution and Behavioral Science Roots | 3 | 4 | 0 | 0 | Ted opens with broad framing questions regarding Cade's academic pedigree under Richard Thaler and migration to people analytics. Massey provides educational context on judgment under uncertainty and early tech adoption at Google. | |
| Evaluating NFL Draft Talent: Data, Combines, and Character Scoring | 4 | 6 | 1 | 1 | Ted probes into combine scouting and character evaluation methods. Massey educates on why unstructured interviews lack predictive validity and details how subjective character traits must be decomposed into quantified metrics. | |
| Measuring Performance Outside Sports and the Concept of Meeting Analytics | 3 | 6 | 1 | 2 | Ted questions the feasibility of granular performance tracking in corporate settings. Massey counters by outlining his concept of meeting analytics and explaining how pervasive video conferencing makes conversational tracking viable. | |
| Group Decision Architecture: Independence, Objectives, and Systematic Tracking | 4 | 6 | 1 | 0 | Ted asks how to turn data into decision architecture. Massey outlines key principles, explaining how even slight correlation between evaluators sharply degrades the value of independent information, using Wharton admissions and Teach for America as benchmarks. | |
| The Inside View vs. Outside View Trap in Draft Selection | 5 | 5 | 1 | 1 | Ted highlights Massey's foundational paper showing draft selections are near 52 percent coin tosses and asks why teams resist the model. Massey explains Kahneman and Lovallo's inside view versus outside view dynamic. | |
| Sponsor Break: Ridgeline AI-Native Investment Technology | 6 | 4 | 1 | 2 | Following a sponsor read, Ted brings domain expertise in asset management, questioning how to balance necessary independence with political hierarchies where analysts please portfolio managers. Massey agrees and suggests red-teaming mechanisms. | |
| Overcoming Algorithm Aversion Through User Control | 5 | 6 | 1 | 1 | Massey shares findings on algorithm aversion, noting users disproportionately reject models after failures unless granted a small degree of adjustment control. Ted quickly connects the principle to discretionary quantitative asset managers. | |
| Decision Systematization and People Analytics Advice for Allocators | 5 | 6 | 1 | 1 | Ted asks for specific advice for institutional allocators picking money managers. Massey invokes Matthew Rabin's concept of fictitious variation, cautioning allocators that performance differences may simply reflect random noise rather than skill. | |
| Analyzing NFL Draft Trends: Quarterback Premium and Position Valuation | 4 | 5 | 2 | 1 | Ted and Massey discuss draft positional value and quarterback concentration. Massey takes a contrarian position against unanimous draft consensus, noting historical base rates suggest shorting consensus top picks like Trevor Lawrence against the field. | |
| Researching 'No-Stats All-Stars' and Non-Quantified Team Impact | 3 | 4 | 0 | 0 | Ted asks about cutting-edge research topics. Massey discusses measuring Shane Battier-style 'no-stats all-stars' in corporate settings before transitioning into personal closing reflections. |
Statements from this episode (18)
Massey: Google was ground zero for evidence-based people analytics
“The people analytics world really blew up in the last, I don't know, 10 years or so. Google was ground zero and the tech world really kind of bought into this. We need to improve HR decision-making by being more evidence-based.”
Massey: Football is adopting analytics more slowly than baseball or basketball
“Baseball's been doing this for a long time, but then basketball caught fire, and then football is slowly getting there, and I end up working more with organizations and professional sports than any other industry.”
Massey: NFL teams will soon adopt motion tracking for personnel decisions
“Teams don't yet use a lot of motion tracking in football. Personnel decisions. Yet. I mean, they're gonna. It's just around the corner. They're beginning to build the base of it.”
Massey: Research shows job interviews predict interpersonal liking, not job performance
“I mean, we know from years of research on the academic side that job interviews don't predict a whole lot. The main thing they predict is whether I like you and whether we can get along interpersonally. That's not necessarily job-relevant information.”
Massey: Human judgments are more reliable when decomposed into component parts
“Instead of making a holistic judgment, you need to decompose it. So this is something we know from judgment and decision making that if your judgments are more reliable, if you break them down into the component parts.”
Massey: Most corporate performance evaluation relies on biased five-point supervisor ratings
“What's the modal performance measure in all industries? It's what your supervisor says on some like five point scale. That's the performance measure, which is not going to get us very far, and we're not going to be able to go out of analytics on it, and it's j…”
Massey: Meeting analytics from video calls will become a major organizational tool
“I know the data aren't yet available. It's a lot of data. So take a lot of computational power to deal with it, but that is here. And that is going to be a thing. And we're going to learn a lot from it.”
Massey: Correlating opinions drastically degrades group decision information value
“It's one of the simplest things you can do to improve group decision making is to build in more independence between opinions. The guys who do the stats on this will tell you know, it depends on how correlated these guys are, but two opinions, if they're uncor…”
Massey: Adjacent NFL draft picks outperform each other only 52% of the time
“Our 52% number is very much an outside view. It says, look, the base rate here is 52%, and that's across a lot of teams over a lot of years, and so I'm sure about that number.”
Massey: Intensive qualitative evaluation creates an illusion of predictive power
“These guys who believe in analytics and believe in the trade down philosophy and believe in uncertainty, it's a lot easier to believe in that stuff in January than it is in April, because between January and April, they're studying film. They're having debates…”
Massey: Delaying information sharing preserves independent evaluation
“So postponing information sharing as far into the process as possible is something we talk a lot about, because eventually those guys are going to sit around at a table and argue about a player, and so everyone's going to know how everyone else feels about it,…”
Massey: Investment firms should explicitly reward analysts who dissent
“Actually, the best I would hope would actually reward analysts who disagree with them. That should be part of the reward system is to disagree and poke holes.”
Massey: People abandon analytical models faster than humans when observing failure
“And what we found when we got into it was it's not that people are averse to models universally. It's that when they see models fail, They're harder on models than they are on people.”
Massey: Algorithmic buy-in requires giving users a small degree of control
“So it was the act of participating rather than the amount of influence that made the difference, and I think this is very general advice for people who are selling models or analytics. You got to let the users in a little bit. You got to let them kind of put t…”
Massey: The primary benefit of predictive models is consistency over human noise
“The main benefit of models is that they're more consistent than humans are. There's just so much noise in human judgment that it degrades the whole process and models ringing out that noise.”
Massey: Decision-makers must document predictions to counter hindsight bias
“You have to write things down. We trick ourselves. It is human nature to misremember what we thought once we know the outcome. We think we would have understood that. We think we predicted it right. There's just study after study that show this. And so unless …”
Massey: Offensive lines drive rushing production, leaving little individual running back variance
“The most important thing is that we've come to appreciate how dependent their performance is on other components, and especially the offensive line. And the simplest way to think about it is you shouldn't think about a rushing yard as being, you know, attribut…”