Oct 15, 2018 · 59m · capital-allocators
Michael Schwimer – Moneyball as an Investment Strategy (Capital Allocators, EP.72)
gold bands on the timeline = statements, start to end. Hover to read, click to jump. CC turns on captions
Former Major League pitcher and Big League Advance founder Michael Schwimer discusses applying quantitative modeling, biomechanical data, and venture capital principles to invest in minor league baseball players.
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 19.2% of the talking time here. How this is scored →
speaking balance: gold is Ted, purple is the guest (3 minute bins)
Michael strongly criticizes the Players Association for opposing BLA deals by focusing strictly on the rare high earner rather than the hundreds of minor leaguers who benefit from guaranteed downside protection.
Hardest push from Ted ▶ 42:28 Ted questions conflicting interests with MLB teamsTed presses Michael on whether providing proprietary pitching injury analytics to MLB front offices creates a structural conflict of interest with his primary investment fund.
Biggest teaching moment ▶ 19:37 Michael explains process-based predictive metricsMichael reframes standard Moneyball stats for Ted, demonstrating why a batter with a 0-for-100 stretch of deep line outs has vastly superior major league predictive value than one with a 100-for-100 stretch of broken-bat bloops.
Ted holds their own ▶ 50:35 Ted maps portfolio theory to player risk profilesTed demonstrates his institutional investment expertise by translating Michael's mixed-upside player selection process into clear financial concepts of beta sizing and conviction weighting.
the scores for every segment, with the reasoning behind each
| Chapter | Topic | Ted as informed peer | Guest teaching | Guest disagreement | Ted pushing back | Why |
|---|---|---|---|---|---|---|
| Athletic Beginnings, College Pitching, and Hedge Fund Roots | 4 | 5 | 0 | 0 | Ted guides Michael into his backstory and prompts him about his multi-sport athletic background and hedge fund internship. Michael explains his rational self-assessment choosing baseball over basketball and his early exposure to finance. | |
| Minor League Economic Realities and Making the Majors | 3 | 6 | 0 | 0 | Michael educates Ted on the harsh financial realities and living conditions of minor league baseball players. Ted mostly listens and prompts Michael with light questions about big league milestones and hazing. | |
| Applying Quantitative Modeling and Pitch Sequencing on the Mound | 4 | 6 | 1 | 0 | Ted asks how Michael used his intellect to gain an advantage on the mound. Michael breaks down how he built bespoke matchup models and pitch sequencing to compensate for below-average velocity. | |
| Minor League Advocacy and the Conception of Big League Advance | 4 | 6 | 1 | 0 | Michael recounts pitching a revenue-share plan to union head Michael Weiner and describes the aftermath of his career-ending labrum injury. Ted asks targeted follow-ups about Michael's transition to founding Big League Advance. | |
| Developing Process-Based Predictive Metrics for Baseball | 5 | 7 | 1 | 0 | Ted brings up basic Moneyball metrics, and Michael explains why traditional minor league stats are unhelpful compared to process-based, contextual data. Michael details how he spent 14-16 hours a day building predictive models. | |
| Initial Fund Formation and Partnering with Paul DePodesta | 4 | 6 | 0 | 0 | Michael walks Ted through his early pitch meetings, equity trade-offs with initial investors, and his serendipitous partnership with Paul DePodesta. Ted acts as an attentive sounding board. | |
| Player Outreach, Transparency Protocols, and Agent Conflicts | 4 | 7 | 1 | 0 | Michael details the player outreach model, legal safeguards, and the hidden structural agency conflict that led him to bypass agents to talk directly with players. Ted engages with clarifying prompts. | |
| Outperforming Base Rates and Initial Pitching Biomechanics | 5 | 7 | 0 | 0 | Ted inquires about fund hit rates and the underlying alpha of the strategy. Michael explains that over 75% of signees are outside the top 300 prospects and describes his early attempt to model pitching biomechanics and injury risk. | |
| Sponsor: Ridgeline AI-Native Investment Technology | 3 | 7 | 0 | 0 | Following a sponsor read, Ted asks how Michael turned 12,000 video files into actionable metrics. Michael explains how hiring top analytics talent like Jason Rosenfeld and Zach Bradshaw cracked nonlinear biomechanics modeling. | |
| Competitive Landscape, Bad Actors, and the Francisco Mejia Lawsuit | 4 | 6 | 2 | 0 | Ted asks about competitors and the Francisco Mejia lawsuit. Michael discusses predatory practices by imitators, regulatory efforts in Delaware, and the resolution of the Mejia litigation. | |
| Symbiotic Front-Office Scouting and Trade Deadline Analytics | 5 | 6 | 0 | 0 | Ted probes potential conflict of interest between advising MLB front offices and investing in players. Michael explains the symbiotic information-sharing dynamic during the trade deadline and recruitment across other sports. | |
| Venture Capital Dynamics, Misconceptions, and Management Lessons | 5 | 6 | 2 | 0 | Ted connects BLA's business model to venture capital power laws. Michael vents frustration with institutional pushback from the MLBPA and shares management mistakes regarding unstructured communication. | |
| Portfolio Construction, Bet Sizing, and Skill Versus Luck | 6 | 5 | 0 | 0 | Ted frames portfolio construction using financial terminology like conviction weighting and high/low beta. Michael affirms this framing, explaining how BLA manages long-shot upside versus high-probability major leaguers. | |
| Playoff Perspective and Data-Backed World Series Prediction | 4 | 5 | 0 | 0 | Ted asks about Michael's perspective heading into the MLB postseason. Michael breaks down why his data model favors the Astros over the Red Sox due to performance against playoff-caliber pitching. |