About this video

• Video Title: Bet The Process With Mark Pattinson aka sprotzbettor
• Channel: Bet The Process
• Speakers: Mark Pattinson, Jeff (host)
• Duration: 00:58:16

Overview

This video features a discussion with Mark Pattinson, also known as "sprtzbetter," about his approach to NBA betting and prop betting. The conversation delves into quantifying subjective opinions, the impact of AI on sports betting, and touches upon recent scandals in baseball betting. The latter half of the video includes sports betting picks and insights into the evolving landscape of sports analytics and betting strategies.

Key takeaways

• NBA and Prop Betting Process: Mark Pattinson shares his strategies for NBA and prop betting, emphasizing the integration of qualitative information with quantitative data.
• AI and Sports Betting: The discussion explores the potential future impact of AI on the sports betting industry, including the possibility of AI "destroying" betting markets.
• Baseball Betting Scandal: The recent scandal involving pitchers being indicted for betting on micro-betting markets is analyzed, with concerns raised about market manipulation and its impact on game integrity.
• Micro-betting Concerns: Micro-betting markets are criticized for their resemblance to slot machines, offering instant gratification that can be detrimental to problem gamblers.
• Sports Betting Industry Evolution: The conversation highlights the rapid changes in the sports betting landscape, including the rise of prediction markets, legalized betting, and the increasing sophistication of data science in handicapping.
• Betting Picks: The hosts provide betting picks for college football games, including Penn State, Florida, and Iowa.
• Novig App Promotion: The Novig app is mentioned as a sponsor and highlighted for its potential to help recreational bettors lose more slowly and for providing entertainment value.
• Data Science and Alpha: The importance of asking better questions of data, integrating various data sources (like ETR projections and market adjustments), and the challenges of scaling sophisticated betting models are discussed.
• Qualitative vs. Quantitative: The value of human insight and subjective analysis ("vibes") in handicapping, even in an increasingly data-driven world, is acknowledged.

Yes, Rufus does mention NCAA football in relation to betting. He states:

"I I mean, I think if you look at like the structure of Star Wizard and like all of these companies that are doing things in an industrial scale for for soccer for example, like that's what all of their analysts do. Like they have these insane front ends on their models where you can make all of these manual changes and adjustments to whatever the forecast might be for a particular game. And I think like you um you well no no I'm the one interrupting here. I have to to stay on brand for for about the process. But like quantifying qualitative stuff almost in a model you have some maybe you could put in some sort of like I don't know."

He also discusses how in college basketball, like in NCAA football, the math can be skewed by things like coaching changes and player movement, making it difficult for models solely reliant on numbers to be accurate. He mentions that this is a challenge they face with international basketball and that it's comparable to the college basketball problem.

Yes, college football games are discussed. The following picks are mentioned:

• Penn State: The hosts like Penn State laying seven points on the road against Michigan State. The reasoning provided is that Penn State is a much more talented team and that betting lines can sometimes overreact to recent performance.
• Florida: A bet is mentioned on Florida as an underdog (plus 15 points, though this line had moved). The reasoning is to fade the narrative that the team has quit after firing their coach and losing to Kentucky, suggesting that there might be value in betting on them despite recent struggles.
• Iowa: The Iowa Hawkeyes are picked to cover +7 points on the road against USC.

The discussion also touches on the difficulty of modeling college football (and college basketball) due to factors like coaching changes, player transfers, and other subjective elements that are hard to quantify.