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Welcome, welcome to Wharton Moneyball.
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Welcome to a full hour of sports
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analytics here on the Wharton podcast
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network. This is Kade Massie hosting
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this week's show with the full crew.
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Audi Winer is here. Shane Jensen is
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here. Eric Bradlo is here. We're
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recording on Tuesday afternoon as we
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typically do these days. The show will
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go up on Wednesday. We are going to do a
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standard format. We're going to have a
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guest in the first half hour, then roll
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into open lines in the second sometime
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in the second half hour, we'll roll into
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open lines. Lots of things to talk
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about, but in particular, this Super
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Bowl week, or rather Super Bowl two
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weeks, the conference championships for
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this past weekend, we know who is on,
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and that means it's a big betting
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weekend.
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We've got a friend in the sports betting
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space, former guest, frequent guest in
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our early years in particular, Rufus
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Peabody, sports better extraordinaire,
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quant
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of some fame. Rufus, thanks for making
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time for us. Appreciate you coming on
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the show.
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>> Well, Kade, you taught me everything I
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know. So, it's
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>> I taught you everything you knew when
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you were like 20. You're like 40 now.
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So, I think I I think I can't claim that
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anymore.
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>> I'm a man. I'm 40. [laughter]
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>> You're almost You're almost a man.
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You're almost No, you are 40. You are
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40. That's right.
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>> I know. It's It's crazy. I don't know
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where all the time went.
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>> Well, you spin it well. You've done very
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well. Rufus has been He went straight
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from his undergrad into Las Vegas. Spent
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about a year working for LBSC back in
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the day and then rolled into his own
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betting group really from one year out
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of school. Been doing that full-time
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since then. Rufus is coming to us from
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Las Vegas, one of the places he spends a
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fair bit of time. In previous years,
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especially early on, this would have
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been a manic week for Rufus. He's kind
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of famous for his prop bets, Super Bowl
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prop bets, but he's um he's his
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portfolio looks a little different these
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days, and he assured me he had capacity
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to do the show this week despite that.
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Rufus, we want to catch up with you in
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general. The whole crew enjoys time with
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you. Lots of things we could ask you
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about, but the thing we're most
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interested in hearing from you on is
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what's going on in the sports betting
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world with these prediction markets.
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People have been talking about it,
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especially over the last year. Khi Poly
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Market, the two big leaders burst into
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this space, kind of wrangle their way
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around regulations, state regulations
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that don't legalize sports betting in
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like California and Texas. They've blown
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up. They're being valued at billions of
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dollars themselves. Of course, sports
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betting is massive. FanDuel and
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DraftKings, these companies are cap
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market caps are like 15 billion, $30
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billion in the case of FanDuel. So,
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massive market. We need to understand a
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little bit from you about what's going
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on here. So, you know, why is prediction
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markets advantageous from a better
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perspective in particular? Let me just
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say one last thing. Rufus has been an
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evangelist on the side of the better for
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years. his guy his cause is you know
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better markets better rules better
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access fair play in some sense for
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sports betterers and I think that's one
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of the reasons he likes prediction
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markets but let's hear from you Rufus
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first off Jade um
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they're it's predicting it's it's sports
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predicting not sports betting I think we
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should get that straight
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>> okay
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>> very very different
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>> very so different
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>> um but as you as you alluded to I I do
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think um one really good thing about
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prediction markets is that they are
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markets. There is a counterparty um
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[snorts] in the case of Kshi, it's
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anonymous that you know you nobody can
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sit the counterparty can't see that
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they're trading against you [snorts] and
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there's no cap to how much you can get
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down. Um well the cap is the market
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liquidity and so with sports books like
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FanDuel and DraftKings limiting sharper
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people uh it it becomes very difficult
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to be able to get down. So um so so
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that's a big advantage. The other thing
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is the you're going to in some cases
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have tighter margins especially if
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you're making markets rather than taking
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the fee structure the the fee structure
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at Khi right now is actually set up in a
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way that isn't super advantageous for
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taking. Um you're basically paying at a
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50 cent a line at 50 cents you're paying
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like 1.44%.
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Um, but on top of the bid ass spread. So
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if something was trading like 50 51, you
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take it at 51, that's 52.44%.
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That's a little worse than minus one or
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sorry, you take it at 51. Yeah, it's a
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little bit worse than minus 110 there.
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But um but it's it it's so so in
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general, it's not super price
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advantageous for a recreational better.
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Um and especially on call she with the a
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lot of people uh are able to bet through
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um Robin Hood. They have a distribution
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through Robin Hood and other brokers and
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I believe Robin Hood also takes a fee.
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So that makes it really
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>> coffee. Really? Wow. Okay. Interesting.
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>> But overall I would say for for somebody
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like me who like let's say there's a
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game I make it 52%. I'm never going to
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be betting that in a sports book. But if
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I can sort of make a price at 50 cents
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or 49 cents, um, you know, I I I
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suddenly have opened up a door to a lot
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more games I can bet on.
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>> Does somebody see since Ruf I don't use
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that site. Does somebody see the size of
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the, you know, let's call it ask that
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you're putting out there because one
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could imagine that if a sharp is putting
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a million dollars out there, I'll just
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use some big massive number. um then
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someone might say hm more or less less
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likely to take that bet. So h how how do
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you think about that from a how you
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structure your play on prediction sites
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like this?
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>> That that is a great point and a great
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question Eric and and it is something
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that that everybody can see. So you you
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can you can't see
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you know you can see that there's three
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million shares bid at 51 cents on some
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game. You can't see that it's one person
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to bid 3 million for example.
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But so if if like something like the
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Super Bowl money line um I don't think
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you have to worry about that at all.
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It's going to be one cent wide and with
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massive volume on either side. The
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problem there is just getting your offer
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taken because you're going to be so far
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back in the queue. Um, but it is
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definitely a concern and we ran into
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this at some point, especially with um
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with some of these other sweep stakes
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sites we were doing predictions on. Um,
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like for example, Novig um that's a a
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prediction market as well. And
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>> say more about what a sweep a sweep
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stakes site.
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>> Uh sweep stakes technically you're not
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betting money. you are you're depositing
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money and you get
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like draft or not draft kings you get
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like no vig bucks or something like that
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I don't know what it's called but but
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but you're betting those things which
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can be redeemable one to one for dollars
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at the end so the way around
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>> their scheme actually
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>> yeah yeah it's it's yeah a lot of states
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are closing this sweep stake sweep
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stakes loophole uh though now
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>> okay
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>> but
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there there was a market there might be
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a market trading at um you know maybe
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minus 110 on each side or something like
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that with $500 and if you put up $10,000
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um the first time you know it it works
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out okay but then afterwards it's it
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people or or I shouldn't say the first
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time but I think for a little while
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people were able to get good fills that
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way. Um, let's say, you know, maybe I
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want I'll take the minus 110. I'll offer
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a plus 110 um to somebody else, right?
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And they can go arb that with FanDuel or
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DraftKings maybe. Um, but if you put up
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a big enough order, someone's like, "Oh,
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this is clearly somebody sharp and and
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you're going to blow up the market."
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>> Mhm.
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>> People are right. It's, you know, I
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might get a little bit of fill, but but
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I'm not going to get enough fill to
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justify blowing up the market like that.
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Mhm.
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>> But
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>> I will say prediction markets like why
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I'm one reason I'm enjoying them so much
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is because it is a different game and
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you do need to like like the game isn't
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just clicking a bet in. It's it is there
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are market dynamics. It's understanding
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the order book. Um it's trying to put a
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position you think is going to get
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filled without um without tipping people
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off. Like iceberg orders, right? Like we
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have things that'll auto like
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automatically re-trigger a new order if
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you will have an order. So so like you
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know but then
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which which is what you need in more
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illlquid markets.
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>> Mhm.
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>> But you also don't want to go so small
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that other people join you and then
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suddenly you you lose Q priority and
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can't down as much. So yeah there
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there's I mean I can talk about this for
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hours. So, but this this is the kind of
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thing that you used to never I mean you
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you were a price maker essentially. You
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were you were a model builder for years
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and this is more like the trading aspect
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of it. And there are some people like
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your uh your good buddy whose name
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escapes me at the moment who just makes
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a living like a very good living just
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doing technical trading stuff in the
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traditional sports betting markets and
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those are very different approaches but
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with the prediction markets it feels
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like you're moving a little bit more
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towards being intrigued by the technical
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aspects the trading aspects of the
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prediction market.
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>> Oh I'm I'm very intrigued by it. It's
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it's so fun like you know you can just
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explore these markets and learn
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something new every single day. like you
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know the I I've spent a lot of time
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trying to create a flowchart to
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understand the logic of some of these
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bots I'm up against and be like I can
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you know if I can predict exactly what
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they're going to do you know you can
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generally kind of figure out uh push up
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buttons and figure out like a weakness
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because every every bot has a weakness
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who's the most famous well not most
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famous but the most successful sports
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better in or not sport the most
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successful better in general I think in
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the history of the world. Um
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he says that the best trader will always
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will always be better than the best bot.
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I mean the goal is to every bot has a
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weakness
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>> and
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>> because they're too rigid because
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they're too predictable because it is
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algorithmic.
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>> There's logic. I mean they're trying to
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be human. You're trying to program in
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all the in all your intuition
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understanding markets perfectly and
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inevitably there's cases that come up
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that you haven't encountered before and
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a bot is going to behave in a very
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predictable manner. I think the best
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bots actually have some random should
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have some rand there's like a TTO bot
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right you know some of the time it's
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going to jump the order when it should
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you know some of the time it's going to
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join um
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>> you would think so right
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>> you put up 500 shares and it gets filled
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does it replenish right like maybe it
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does 95% of the time when it you know
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when you normally would right but you
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don't want to you you don't want to be
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too predictable and honestly it's the
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same thing for a sports book I think
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most sports book like sportsbook auto
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movers are too predictable.
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>> If if I bet into this line, I know
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they're going to move two points off of
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me. Um that that's really good
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information to have, but it also allows
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but knowing that allows me to manipulate
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uh uh or not manipulate, but like
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>> to to um to to move a line a little bit
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if I want to.
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>> Sure. Sure. Sure. Rufus, tell us
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something about your sense of the
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dynamics with the prediction markets
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coming in. How what percentage of your
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business do you send to prediction
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markets now versus traditional outlets?
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And what's your sense of other sharp
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better say like all the sharp money
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moved? What fraction of the sharp money?
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It's an absurd question, but what's your
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sense
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>> have? I think it's it's still a small
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percentage for most people. I would say
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relatively. I mean to I guess to give
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you some perspective we've done in the
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last 4 months or so I think something
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like 165 170 million contracts on KHI
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but the other thing with KI is it's less
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predictable so each contract has to win
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a dollar
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um it it's less predictable though so
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there's some games where we will get
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very big fills on things and others we
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won't and so uh something like the
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conference championship games, you know,
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I I end up with positions across a like
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a wide range of of outcomes like al
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alternate spreads, totals, um some some
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prop stuff, too. And I can't predict
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exactly exactly what it's going to come
00:12:58
in on. And I I have to have, you know,
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well, I should say we cuz it's me and my
00:13:04
team. We have our system set in place to
00:13:06
have some some positional limits. But it
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gets kind of difficult too when you when
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you have all these sort of correlated
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things. You know, if if I get filled a
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lot on um Seattle minus4 and a half,
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getting the Rams minus 14 and a half
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doesn't is that's not a that doesn't
00:13:22
really offset it that that much, right?
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Um but so so there's all these Yeah. So
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So there's I'm not answering your
00:13:30
question here, though. I I would say
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>> No, no. One thing you're saying is that
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it's it's less than I would I might have
00:13:36
thought. The way you talk about the
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benefits and the value, you might think
00:13:38
that you put all your business there,
00:13:40
but you're saying there's still limits
00:13:41
to what you can do.
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>> Yeah. I mean, it's if we want to fill
00:13:44
$50,000 on a college basketball side,
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like we're still not generally doing
00:13:49
that on a prediction market.
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It's, you know, we don't have the
00:13:54
certainty we're going to get down that
00:13:55
much. Like it's there's certain markets
00:13:59
for example like
00:14:01
the conference championship games NFL
00:14:03
games where you can get tremendous
00:14:05
amounts of volume down very easily but
00:14:07
others where you can't. You know most of
00:14:09
these college basketball games like
00:14:11
there's some that trade like $800
00:14:14
pregame total like across all like
00:14:17
spread total money line you know if it's
00:14:20
like Drexel against Lyola or something.
00:14:24
>> Yeah. Yeah. Okay, Rufus, what have been
00:14:26
the sports books response to this? I
00:14:28
know that DraftKings and FanDuel now are
00:14:30
starting doing prediction markets of
00:14:32
some kind, but you know that they have
00:14:34
been the subject of withering criticism
00:14:37
from people like yourselves. Are they
00:14:39
learning? Are they adapting in any way
00:14:41
or are they just going to play the same
00:14:43
game and keep on doing their typical
00:14:45
stuff on the sportsbook side?
00:14:47
>> Well, I I I think that they see they see
00:14:49
this as a risk, an existential risk, and
00:14:52
probably rightfully so. And they do have
00:14:55
their own sports prediction markets, I
00:14:59
guess, of some sort. Like I know DRA
00:15:01
DraftKings bets started and we looked
00:15:03
into that. To be honest, I'm not
00:15:05
convinced that DRA I'm I'm not convinced
00:15:08
that it's actually a legit prediction
00:15:10
market. I feel like it may just be
00:15:11
DraftKings calling it that. Like part of
00:15:13
me, the cynic in me says, right, because
00:15:16
I I don't think it's an open I don't
00:15:19
think it's like it's certainly not an
00:15:21
open API and I don't think people can
00:15:23
make on it. Um, you know, the cynic in
00:15:26
me says DraftKings is just doing this to
00:15:28
try to like bring down prediction
00:15:30
markets so that they in general so that
00:15:32
they go back to having their uh little
00:15:34
little monopoly. But
00:15:37
>> interesting.
00:15:38
Okay. What does the industry think's
00:15:40
going to happen with prediction markets?
00:15:42
I know that it's turning in there's a
00:15:43
regulatory question across states and
00:15:46
even now federal government but there's
00:15:48
also it kind of becomes a political
00:15:50
question. Do people have senses of which
00:15:52
way it's going to go? Well, I I think
00:15:54
the consensus among the the sort of
00:15:56
sharp betting community is that the next
00:15:58
three years are probably going like
00:16:01
prediction markets are a fairly safe bet
00:16:04
especially given the amount of venture
00:16:06
capital that's flowed into this and
00:16:07
given the fact that uh Trump Junior's on
00:16:10
the board of both Kali and Poly market.
00:16:12
It seems it seems more than likely that
00:16:15
unless the Supreme Court takes this up
00:16:17
that that during the Trump
00:16:19
administration prediction markets are
00:16:21
probably pretty safe. um after that kind
00:16:23
of all bets are off. So I think a lot of
00:16:25
people were kind of focused on on that
00:16:27
that sort of time frame.
00:16:29
>> All right. Well, let me let Yeah, Audi,
00:16:32
jump in, please. Oh, I'm sorry. Eric, I
00:16:33
saw that there. Let me just Audi first.
00:16:34
>> Audi, I've already asked a question.
00:16:35
Aie, go and then I'll go after you.
00:16:36
>> No, I'm just trying to get a lay of the
00:16:38
land here. So, you're moving uh heavily
00:16:39
into the prediction market. Um you never
00:16:42
really bet in the FanDuel area in like
00:16:44
because they limit you. Do you do sports
00:16:46
bettings um more still in Vegas? Is that
00:16:49
is that where things are shaking out for
00:16:51
big bettors?
00:16:52
>> We we sports bet out at anywhere and
00:16:54
everywhere kind of it it's like um it
00:16:58
it's this whole ecosystem Audi where
00:17:01
there are people that there's like
00:17:02
people that are movers that have
00:17:05
operations that get accounts and can you
00:17:08
know in essence get get like glorified
00:17:12
middlemen, right? But but um like
00:17:15
brokers if you will that exist. Um, we
00:17:18
have we have our own accounts at at
00:17:20
various places. There's the exchanges.
00:17:22
We have people in Europe. Um, you know,
00:17:26
we wish we had more connections in Asia
00:17:28
to be honest because that's if you like
00:17:30
China, that's where you really want to
00:17:32
be if you can if you want to bet a lot
00:17:33
of money. But, [snorts] um, it it's it's
00:17:35
honestly it's a big logistical mess.
00:17:39
Like I don't know how else to
00:17:42
>> But that's no different than it has
00:17:43
been. I mean, my sense of you, you used
00:17:45
to be in a group of four four people and
00:17:48
two of you guys were kind of the the
00:17:51
number guys and two of the guys were
00:17:53
more or less getting money down. Is that
00:17:54
was that the right way to think about
00:17:55
it? And that was kind of in traditional
00:17:58
sports betting world. So, it's a a lot
00:18:00
of the work has always been getting
00:18:02
money down and it's just getting more
00:18:03
and more complicated. It's
00:18:04
extraordinary.
00:18:06
>> Yeah. And it's it's it's very
00:18:07
decentralized. We we use a lot of
00:18:09
different people to help get us accounts
00:18:12
to get to get down basically.
00:18:13
>> Brad Loop.
00:18:14
>> Yeah. So, let me ask you. So, just
00:18:16
building off something you said earlier.
00:18:18
So, since I'm Wharton's vice of AI and
00:18:20
analytics, I might as well ask you an AI
00:18:22
question. If I build an epsilon greedy
00:18:24
reinforcement learning algorithm and
00:18:26
ingest a lot of this data, am I going to
00:18:29
start making some
00:18:31
well, I'm going to choose an algorithm
00:18:33
that um understands that if I make a bet
00:18:36
today, there's some immediate payoff,
00:18:39
but if I make a bet today, it's also
00:18:42
potentially going to change the state of
00:18:44
the world. If I, for example, could move
00:18:46
the line, it could move people's
00:18:48
perceptions of me. And so that's the way
00:18:50
reinforcement learning algorithms work.
00:18:53
Meaning there's an immediate payoff that
00:18:54
I win this bet or not. Then there's it
00:18:57
puts me it changes the future state of
00:18:59
the world. And the epsilon greedy is
00:19:01
what you said before which is I don't
00:19:04
always necessarily choose the one that
00:19:06
maximizes things. Sometimes I want to
00:19:08
play a little randomness in there
00:19:10
because randomness can have some
00:19:12
benefits. So, have you seen in your
00:19:15
world are people applying? Because now
00:19:18
it's all the rage. If I was going to
00:19:20
serve serve Rufus Peabuddy a display ad
00:19:24
or if I was going to show you a price
00:19:26
and I was a big tech firm, I would
00:19:28
probably ingest a huge amount of data
00:19:30
and optimize this using a standard
00:19:33
computer science reinforcement learning
00:19:34
algorithm. So I'm just wondering has
00:19:36
that made its way as far as you know and
00:19:38
the answer could be you don't know into
00:19:40
the betting worlds
00:19:41
>> in in in what sense
00:19:43
in terms of modeling or in terms of bet
00:19:46
like executing bets
00:19:47
>> well someone trading off of that more on
00:19:49
the quant side like it's telling you
00:19:52
it's making some AI based recommendation
00:19:56
of bets based on maximizing this
00:20:00
objective function which is some blend
00:20:02
of making money today and putting you in
00:20:03
a better place tomorrow. I'm just
00:20:05
wondering cuz this sounds to me like a
00:20:07
very standard reinforcement learning
00:20:09
problem, but I'm just wondering if like
00:20:12
there's a ton of computer scientists
00:20:14
just applying this stuff to data that
00:20:16
they can scrape off these sites.
00:20:18
>> Um, so in terms of like modeling actual
00:20:21
events, like
00:20:23
you know, I I think at some point like
00:20:26
well
00:20:27
I I'm not sure how much the future would
00:20:30
be affected by how you model the event
00:20:32
today. Um, I I think that I mean I think
00:20:36
there's a lot of really smart computer
00:20:38
science people looking at this stuff in
00:20:40
general and especially um especially the
00:20:44
the trading dynamics. I think that's
00:20:45
where this is most interesting cuz how
00:20:48
how you actually go about like trading
00:20:51
like because you you have counterparties
00:20:53
and
00:20:55
let's say some counterparty has a
00:20:57
weakness. If you exploit them like for
00:21:00
everything right now, they're gonna wise
00:21:03
up to it probably.
00:21:05
But if you if you don't, you just take
00:21:08
little bitty pieces and somebody else
00:21:10
realizes this and then figures out the
00:21:13
same weakness and then goes after them
00:21:14
big, then you're like, well, I just left
00:21:15
a bunch of money on the table. So I mean
00:21:18
I personally I am not smart enough to to
00:21:22
understand how how um how all the this
00:21:27
this AI stuff works that you're talking
00:21:28
about um the reinforcement learning
00:21:30
stuff but um it's
00:21:34
I mean it's such a
00:21:38
like everything is changing so quickly
00:21:40
and it's the kind of thing that
00:21:45
I think is inherently very difficult to
00:21:47
predict cuz you don't know I mean the
00:21:49
market participants are changing right
00:21:51
like you can build something and then
00:21:53
this new company you know Jane Street
00:21:56
launches um you know market making on on
00:22:00
Khi or something and maybe it changes
00:22:02
things right or or I mean look things
00:22:05
are I I think we think of changes as
00:22:08
happening more kind of smoothly but in
00:22:10
my sports betting career I've realized
00:22:11
that things kind of happen sometimes
00:22:13
very suddenly like I thought I had this
00:22:16
great bottle to to win betting second
00:22:19
halves in college football in the NFL
00:22:20
and I did for 5 years and then one year
00:22:25
like I suddenly wasn't winning and and
00:22:29
basically was just break even and I
00:22:31
wasn't finding much value anymore. It's
00:22:33
like the market had suddenly got more
00:22:34
efficient. And it turns out that the guy
00:22:36
setting the opening price at Bet Chris
00:22:40
um who the the previous 5 years had
00:22:43
retired um or or been told to step down
00:22:46
and somebody new had come on who
00:22:49
actually looked at the live lines before
00:22:51
he posted the second half F line and and
00:22:53
there were a lot of people out there
00:22:54
that were holding that the weak openers
00:22:58
that they had posted in place so that
00:23:00
other books copied and then like people
00:23:02
could get down and there was an
00:23:04
opportunity But suddenly sharper openers
00:23:06
had just happened just like that.
00:23:07
Suddenly the opportunity was gone and I
00:23:09
I kind of had no idea it was such a
00:23:11
precarious like it was such like my
00:23:13
edges were so fragile in that way.
00:23:17
>> Right. Right. AI uh I got I guess I ask
00:23:20
a question of a comment. I'll start with
00:23:22
a comment because I like what you just
00:23:24
said. I do with my sports and gaming
00:23:26
analytics class an example. I look at
00:23:28
old NFL lines going back like 65 years
00:23:31
and I show that in every decade betting
00:23:33
heavily on a big home [laughter] dog is
00:23:36
a very profitable bet and then it and
00:23:38
then all of a sudden it's not just
00:23:41
and I get them all to bet on whether
00:23:44
they should continue that everyone's
00:23:45
like oh my god yeah it's great how we're
00:23:46
not doing this and it just disappears
00:23:48
one day and and I have no idea why. You
00:23:50
might actually be able to tell me why
00:23:51
but it just is gone. Um so that's my
00:23:53
comment. My question is guess sort of
00:23:55
related to what Eric had to say. um when
00:23:59
you're looking for edges, I can imagine
00:24:00
there's two directions. One is to just
00:24:02
build better models and have better
00:24:05
prices and know the probabilities better
00:24:07
and find mismatches and buy them. Um,
00:24:10
the other would be to kind of look at
00:24:12
just in general at the behaviors of the
00:24:14
markets and find out there's anything in
00:24:16
the betting the the the the demand
00:24:18
structure the way and this is kind of
00:24:20
what Eric was talking about where you
00:24:22
can find bets not by knowing not by
00:24:24
studying say football or baseball but by
00:24:26
studying the market and figuring out if
00:24:28
there's advantages because I think that
00:24:31
the question is Eric has is about that
00:24:33
and you're like how is reinforcement
00:24:35
going to learn about football you know
00:24:37
and which it's not by the there's no end
00:24:40
effort to do that something else and I
00:24:42
imagine I don't know these markets well
00:24:44
there's probably opportunities in both
00:24:46
areas but but I but how about in the in
00:24:48
the way this the bets are structured I I
00:24:50
mean I think that's the whole I mean
00:24:52
that like like so we're building
00:24:54
automated market making stuff and this
00:24:56
is what I've spent the last like
00:24:59
four or five months like primarily doing
00:25:03
um aside from my normal you know I still
00:25:05
have to run my golf model every week run
00:25:07
the Massie Pbody ratings etc. But but
00:25:11
the whole idea of understanding the
00:25:13
market dynamics, understanding like um
00:25:17
how the market moves and I mean I I
00:25:22
trying to trade and get two-way action.
00:25:24
And what I've learned is that a lot of
00:25:26
there's a lot of bots out there and by
00:25:28
bots I mean automated market makers that
00:25:31
other people are, you know, have created
00:25:34
um that don't know anything about the
00:25:38
mark like the the the sports betting
00:25:40
market as a whole and are just trading
00:25:42
based on on the trades of other or the
00:25:46
orders put up by other market
00:25:48
participants.
00:25:49
And I've found some that are very weak
00:25:51
at this because they have nothing to
00:25:52
anchor to. um you know in some wider
00:25:56
markets they can be uh pushed to a to
00:26:01
give you a really good price. Um you
00:26:04
know there there's bots that literally
00:26:06
in illlquid markets you know let's say
00:26:09
something is trading um maybe it's like
00:26:11
a no touchdown market. It's it's trading
00:26:13
like 82 cent bid 97 ask. Um you know if
00:26:18
I put up 85 they'll put up 86. So I put
00:26:21
up 87 they'll pull up 88. if I put up,
00:26:25
you know, 93, they'll put up 94, right?
00:26:28
I mean, there's there's there's some
00:26:30
bots that are just like infuriating in
00:26:33
in certain ways. And so the ones that
00:26:35
are infuriating, you kind of want to
00:26:36
teach them a lesson so they stop so so
00:26:38
they, you know, but I I I guess just to
00:26:43
kind of comment on this, I feel like I
00:26:44
maybe brought up that like there's this
00:26:46
like there's really two dimensions here.
00:26:47
There's the predictability of an event
00:26:49
and then there's the irrationality of
00:26:52
behavior around that event. You know
00:26:54
what I'm saying? And and and I kind of
00:26:56
feel like I guess yeah, Audi was talking
00:26:59
about like you can build better and
00:27:00
better models for finding out which
00:27:02
events are truly like unpredictable
00:27:04
versus not or getting an edge, but it's
00:27:05
like again without without even a
00:27:08
comment on how predictable the actual
00:27:10
event is, are there certain behaviors?
00:27:12
Yeah. like either bots or people exhibit
00:27:16
that kind of like are are you know
00:27:18
arbitrageable or whatever like it's kind
00:27:19
of an irrationality of betting behavior
00:27:21
whether it even automated or
00:27:25
>> okay no no like no it was more of a
00:27:32
>> but what I think one of the things
00:27:34
>> for me at least it increased my instinct
00:27:35
to kind of have it on these two
00:27:36
different dimensions
00:27:38
>> I will say you're right like so as Kade
00:27:39
said like I've spent most of my career
00:27:41
building predictive models and and being
00:27:43
less focused on the actual trading and
00:27:45
and I've had partners doing that, but
00:27:47
that's more logistics than anything
00:27:48
else. It's it's not like, you know, we
00:27:51
understand market dynamics, but the
00:27:52
market like in general is going to move
00:27:54
towards our price and we want to get
00:27:56
down as early as we can where we can
00:27:59
still get enough money down, right? And
00:28:01
and prediction markets are very
00:28:03
different. Um there is this whole it's a
00:28:06
whole another game you're playing. Um
00:28:08
not trying to tip your hand. um you know
00:28:11
like let's say I want to bet
00:28:14
um and and actually putting up orders at
00:28:18
prices that you don't even think are
00:28:20
good because if you know you can get
00:28:23
two-way fill um you can effectively get
00:28:26
a better price that way. So, let's say
00:28:28
the most I want on a particular event is
00:28:31
200,000 contracts. Um, and let's say I
00:28:35
price this event at 60%. 60 cents. You
00:28:39
know, I could get let, you know, I could
00:28:42
get 200,000 contracts at 55 cents and
00:28:45
that's a great bet. But what if I could
00:28:48
get a million at 55 cents and then sell
00:28:52
800,000 at 57? That's way better, right?
00:28:55
And so a lot of it is like predicting
00:28:57
where the order flow is going to come
00:28:59
from. And like I made up this word fill
00:29:02
velocity where I you're kind of like
00:29:05
it's my idea of like trying to figure
00:29:07
out um and I haven't actually built a
00:29:09
model to do this but but it's
00:29:10
fascinating to me because I don't it's
00:29:13
very I I don't think one can do this
00:29:15
without AI by the way but basically
00:29:18
given how how quickly something's been
00:29:20
filling what the top uh you know how how
00:29:24
deep the queue is how many contracts are
00:29:27
at the sort of top ask price. So, um,
00:29:30
and like trying to figure out what the
00:29:32
likelihood I get filled in, you know, a
00:29:35
certain amount of time is. Um, but then,
00:29:38
you know, you can have this and then
00:29:40
something changes. the market move as a
00:29:42
whole moves and and so it's you know the
00:29:46
way Kali is set up with 1 cent tick
00:29:49
sizes um is m makes it a very different
00:29:53
game because it is so Q position uh or Q
00:29:58
priority is such is so important. So you
00:30:01
really have to sort of like think ahead
00:30:05
um and and try to like like if I want to
00:30:10
get filled on both sides of something
00:30:12
and you know where one side might not be
00:30:16
posit isn't positive EV for me but
00:30:18
overall I think I can get a better price
00:30:20
that way like I have to put that order
00:30:22
up before I even get filled for the
00:30:24
first you know the side I actually want
00:30:26
right and so I mean there's some games
00:30:28
but but you know there's nothing more
00:30:30
satis satisfying to me than some game
00:30:32
where I was able to like,
00:30:34
>> you know, take one side at 85 cents and
00:30:37
the other side at like 12 cents. It's
00:30:40
just even even though it's not the most
00:30:43
it's not those aren't the biggest EV
00:30:45
trades I'll make, um there's something
00:30:47
satisfying about getting filled both
00:30:50
ways and especially if I have something
00:30:52
up and I literally see the action coming
00:30:54
in on both sides, I'm kind of like, you
00:30:56
know, it's it's basically what a sports
00:30:57
book is, you know, trying to do. Are are
00:31:00
you saying they're happy to get
00:31:01
arbitragees, but they're not that
00:31:03
they're not they're not really that much
00:31:05
profitable because the the gap is tiny,
00:31:07
>> right? I mean, well, I'm I mean, they
00:31:09
can be quite profitable and the gaps,
00:31:11
you know, aren't the gaps are, you know,
00:31:15
you won't see a market sitting there
00:31:16
with like a, you know, 90% over round or
00:31:20
something like that most of the time.
00:31:22
But, um, normally it's going to add up
00:31:24
to like 98 or 99 cents. Um,
00:31:28
>> Rufus, one of the things I think I'm
00:31:29
hearing from you is that you you and I
00:31:32
think maybe I've heard from you this
00:31:33
other places is that the combination of
00:31:36
understanding
00:31:38
fundamental value and understanding
00:31:41
market mechanisms and market dynamics.
00:31:43
That combination is especially powerful.
00:31:46
I think that combination is what sets us
00:31:48
apart because I think a lot of just
00:31:50
market makers don't have something to
00:31:52
anchor to or they're anchoring to the
00:31:54
market the sports betting market as a
00:31:56
whole but
00:31:59
the sports betting market as a whole is
00:32:01
also often times like well there are
00:32:05
large moves sometimes and and it there's
00:32:07
times when it's more fragile than others
00:32:10
and so um you know somebody can be like
00:32:13
oh the market's minus 2 and a half like
00:32:15
I'm very happy to just take collect, you
00:32:18
know, to to give people big bets, you
00:32:20
know, if I'm if if if I'm getting 47
00:32:24
cents on the dollar, basically if I'm
00:32:26
getting the plus 110 or plus 112. Um,
00:32:29
but that market might, you know, not
00:32:32
actually be the most efficient thing at
00:32:35
the moment, especially if there's more
00:32:37
volume on Koshi. I think that's the
00:32:38
bigger thing. There's there's we I've
00:32:41
seen this multiple occasions where um
00:32:44
I'll get hit and then after that
00:32:48
somebody will hit the sports books that
00:32:50
uh the on-screen sports books and
00:32:52
basically the the sports betting market,
00:32:56
DraftKings, FanDuel, Pinnacle, all those
00:32:58
books will move afterwards because
00:33:01
normally you think like the thing is
00:33:03
most market makers are sort of using
00:33:05
this the market the sports betting
00:33:08
market as a whole as this sort a sense
00:33:09
of truth but or
00:33:12
yeah that is that's their sort of golden
00:33:15
or their northern north star I guess
00:33:19
but I mean it's I I it's it's
00:33:23
interesting to see if how it'll keep
00:33:25
changing because I think a lot of you
00:33:27
know what makes something efficient
00:33:28
right price discovery
00:33:30
where is there more price discovery
00:33:31
being done you know in some cases it's
00:33:35
on koshi
00:33:36
>> right for sure okay Eric has a question
00:33:38
But Eric, we need to wind this down
00:33:40
pretty quickly. So, what do you got?
00:33:41
>> I was just going to ask one last
00:33:43
question. Like, how complicated can bets
00:33:45
be in the sense of is it ever like the
00:33:48
way we're going to make money on this,
00:33:50
let's call it event is to make 17
00:33:54
different bets, like three of them on
00:33:56
FanDuel, four of them contracts on Khi,
00:34:00
six of them through this or that's just
00:34:03
over complicating things. It's never I
00:34:05
mean I'm not saying you couldn't do that
00:34:07
but is that just not ever done?
00:34:09
>> I mean I it sounds over like it's over
00:34:12
complicating a little bit but I think it
00:34:13
all depends on price. If you can get the
00:34:14
best price on FanDuel you know then take
00:34:17
the FanDuel. If you can get the best
00:34:18
price on KI take the
00:34:19
>> KHI. This is a a lesson you hear from
00:34:22
Sharps again and again and again. It's
00:34:24
like price shop. Find you you need
00:34:26
information go to the places where the
00:34:28
prices are best. That's going to be the
00:34:29
quickest way to get yourself in an
00:34:31
advantageous position. Mhm.
00:34:33
>> Um, Rufus, thanks for all of that
00:34:36
phenomenal, very dynamic moment in that
00:34:39
whole world. Before you go though, this
00:34:41
thing I've said about you for years is
00:34:42
fun to say. I say you're the world's
00:34:44
best golf better. And I and it's fun
00:34:46
because it's a ridiculous thing to say,
00:34:48
but it also might be true. And um, so
00:34:50
before you go, give us something on
00:34:52
golf. Scotty Sheffller wins his 20th PGA
00:34:55
title this weekend. First, I think the
00:34:57
second event of the year or whatever.
00:34:58
Scotty Sheffer. Can
00:34:59
>> it was his first event of the year, but
00:35:01
not Yeah.
00:35:02
>> Not the overall. Okay. Give us something
00:35:04
on golf. Eric probably has some question
00:35:06
about golf. Can you I know sometimes
00:35:07
you're like, I'm not even paying
00:35:08
attention to Scotty Sheffer. I'm just
00:35:10
running my model and betting, you know,
00:35:11
the 79th most likely guy because he the
00:35:14
numbers are right. Do you have anything
00:35:15
to say about Scotty Sheffler? I mean I
00:35:18
think if you look at his last what two
00:35:21
three years I mean it's right up there
00:35:24
with it is close to peak Tiger. It
00:35:29
really is. I mean Tiger Tiger sustained
00:35:32
it for a decade. Um but but he really is
00:35:36
on another level. I think it's kind of
00:35:38
surprising because like Tiger looked so
00:35:40
athletic and and his swing looks so
00:35:42
perfect and and Sheffller looks like
00:35:45
he's about to fall down half the time
00:35:46
and and he he he doesn't really look
00:35:50
like the athlete Tiger does but and it's
00:35:53
not classically beautiful but it's you
00:35:56
know he is
00:35:59
you know what is it what do they say
00:36:00
face to path impact or path path to face
00:36:03
impact I don't know I mean he he's able
00:36:05
to consistently
00:36:07
do it and and his distance control is
00:36:10
just on another level. Um, and I think
00:36:12
that's something that is I think has
00:36:14
separated him uh more than anything else
00:36:17
is his distance control with his irons
00:36:20
is so much better than any other player
00:36:21
on tour.
00:36:23
>> Give us a sense in your golf modeling
00:36:25
like what's the cutting edge for you
00:36:26
right now? Like what are you trying to
00:36:27
do in your golf model right now to make
00:36:29
it better? What give us a sense of what
00:36:30
the frontier is? Well, well, the
00:36:32
frontier is doing things that other
00:36:34
people aren't doing, Kade, and other
00:36:36
people haven't even thought about doing.
00:36:37
And and so if I actually talk about it,
00:36:40
then it it becomes not as valuable.
00:36:42
[laughter]
00:36:45
>> Give us something you did a year ago
00:36:46
that was that was on the frontier and
00:36:48
now is no longer on the frontier. What's
00:36:50
an example of something you did you
00:36:52
thought maybe [snorts] nobody else was
00:36:54
doing even five years ago?
00:36:57
Well, I mean, there's so many
00:36:59
interesting things I want to talk about
00:37:00
here and I would if this was private and
00:37:05
but
00:37:06
I would say look, I think it's just it's
00:37:09
just asking interesting questions and
00:37:11
trying to quantify more nuanced elements
00:37:14
of skill.
00:37:15
>> Okay. Okay. Well, that's that's actually
00:37:17
informative that you're you're talking
00:37:18
about going pretty obscure places to
00:37:21
find edges essentially because all the
00:37:22
basics are baked in. Does it have to be
00:37:24
baked on top of an already very sound
00:37:27
structural that's got the structure
00:37:28
that's going to capture most of the
00:37:29
fundamentals or can you come in kind of
00:37:31
new if you if you've got a new thing and
00:37:34
it's the only thing you got is there
00:37:35
edge there or does it have to be on top
00:37:37
of a foundation? For me, it's on top of
00:37:39
a foundation. But I guess it depends on
00:37:41
if you're like if you could come in and
00:37:43
say, "Okay, the market is broadly
00:37:45
efficient, but I think I found that this
00:37:47
certain thing is undervalued or
00:37:49
overvalued or I I don't think the market
00:37:50
is fully capturing this." Um, you know,
00:37:53
that that's certainly one one way you
00:37:56
could could do it.
00:37:58
>> But I'm not using the I mean, I'm
00:38:01
basically making the market.
00:38:02
>> You use the market as your foundation
00:38:04
basically. All right.
00:38:07
You got your model. Yeah, your model's
00:38:09
just getting sounder and sounder. Okay.
00:38:11
Um, Rufus, we'll let you go. Thank you
00:38:13
for the time. I do want to say that
00:38:15
Rufus is the co-host of a longunning
00:38:18
sports betting podcast. It must be the
00:38:19
best established sports betting podcast
00:38:21
out there, Bet the Process. Bet the
00:38:23
Process with Jeff Ma. Rufus and Jeff
00:38:25
have been doing that for a long time.
00:38:27
They, you know, they knocked around for
00:38:29
a couple years and they got real serious
00:38:30
about it. It's a pretty serious show.
00:38:32
It's a fantastic show. Strong recommend.
00:38:34
Bet the process. You can catch Rufus and
00:38:36
Jeff there most weeks. Rufus Peabody,
00:38:40
track him down. Catch him anytime you
00:38:42
can. Always entertaining. Rufus, thanks
00:38:43
for the time. Love seeing you.
00:38:44
>> Hey, thanks so so much for for putting
00:38:46
up with me and and all my digressions on
00:38:49
predition markets.
00:38:50
>> We barely got any digressions by Rufus
00:38:53
standards. That was a linear
00:38:54
conversation. That was real tight. Thank
00:38:56
you, Rufus. That is the first half of
00:38:58
Wharton Moneyball. We will have a second
00:39:00
half. Come back and join us after the
00:39:02
break.
00:39:04
Welcome back. Welcome back to Wharton
00:39:06
Moneyball. Welcome to the second half,
00:39:08
second part of the show. We ran a little
00:39:10
along with our guest, Rufus Peabody.
00:39:13
Longtime friend of the show, my
00:39:15
collaborator on Massie Peabody for many
00:39:17
years now. Rufus graduated almost 20
00:39:19
years ago. We're coming up on I think
00:39:21
this is 18 would be 18. Been in the
00:39:24
industry for a long time. Informative
00:39:26
discussion with him about prediction
00:39:27
markets in particular. Fellas, Super
00:39:31
Bowl 60 got set on Sunday.
00:39:34
Unsurprisingly, the Patriots made it
00:39:36
past a quarterback list Denver Broncos
00:39:39
in the Blizzard
00:39:41
and they're going to be in their 47th
00:39:43
Super Bowl. Did you all know the
00:39:44
Patriots have been in 47 out of the 60
00:39:46
Super Bowls? It's really [laughter] high
00:39:47
number.
00:39:50
>> They are playing, who are they playing
00:39:52
against? The Seahawks. The Seahawks beat
00:39:53
the Rams. the a the NFC champion NFC
00:39:57
West division championship that produced
00:39:59
the Super Bowl champion here. The
00:40:00
Seahawks are favored early lines holding
00:40:03
tied at four and a half I think. Um this
00:40:06
game is in San Jose, sometimes referred
00:40:08
to as San Francisco. Um Shane, God
00:40:12
almighty, I mean what's it like to be
00:40:14
from New England these [laughter] days?
00:40:17
No, I mean to I I mean I'm not I'm not
00:40:19
actually from New England, but to have
00:40:21
arrived there on the scene in like, you
00:40:23
know, 1999, it was very
00:40:25
>> timing timing was good, Shane.
00:40:27
>> Timing was good.
00:40:29
>> Yeah. No. And uh this this is probably
00:40:31
the most unexpected of all of them. I I
00:40:33
I think thus far this run, um I think
00:40:36
it's been a I think they're an
00:40:38
intriguing team because I think we could
00:40:40
still I don't know what you guys feel,
00:40:42
but you can convince yourself they're
00:40:43
not actually even that good. [laughter]
00:40:45
wanted to uh but they're in the Super
00:40:47
Bowl so I don't I don't know. Um and I
00:40:51
think that you know if if anything I
00:40:52
think if I was setting the line I mean
00:40:54
sharper minds than I do set those lines
00:40:56
but I if I was setting the line it would
00:40:58
be even even more substantial. I mean
00:41:00
just based on how good Seattle looked um
00:41:04
against the 49ers. I think they're
00:41:05
really kind I mean the only hope I guess
00:41:07
the Patriots have is that time Donald
00:41:09
sees Ghost again or whatever.
00:41:12
>> Yeah.
00:41:12
>> Well, what other reactions from the
00:41:14
weekend? Um the
00:41:17
the the Ram Seahawks game was tight as
00:41:19
as as would be expected. Fun could have
00:41:21
gone either way. The Denver New England
00:41:24
top game was a lot tighter than a lot of
00:41:25
people would have thought and that snow
00:41:27
added wonderful flavor to it. Um Payton
00:41:30
forewent that field goal opportunity
00:41:32
early. That sure did feel painful pretty
00:41:34
quickly when nobody else could score. Um
00:41:37
any other observations coming out of the
00:41:38
championship round?
00:41:41
>> How do you guys feel actually about that
00:41:43
decision? Like let's let's assume full
00:41:46
information like Sean you're Shawn
00:41:48
Payeyton and you have the weather
00:41:49
report. Maybe you don't know it's going
00:41:52
to go quite that dramatically like like
00:41:54
like go to unplayable levels but you
00:41:56
know the it's going to get worse. I feel
00:42:00
like that maybe kind of I feel like the
00:42:03
analytics probably suggest that you
00:42:04
shouldn't you should go for you know on
00:42:06
fourth and one you should go for it
00:42:08
there. Does you know if you can
00:42:11
incorporate that kind of you know future
00:42:13
weather does that move the needle
00:42:15
enough?
00:42:16
>> Weather was part of it but also just you
00:42:18
know what are you forecasting about your
00:42:20
offense's ability and stood them and I
00:42:22
what I I think the most interesting bit
00:42:24
is that they had they drove down the
00:42:26
first
00:42:27
um drive and possession and then the
00:42:30
second they go down again. So they've
00:42:31
got they've had the ball twice. They've
00:42:32
moved the ball really well twice and he
00:42:34
probably wasn't thinking I'm going to
00:42:36
get 35 more yards for the rest of the
00:42:38
game. you know, that's that's the part
00:42:39
that really fell out of sync with the
00:42:41
first part of the experience. So, I'm
00:42:43
sympathetic to him in that way.
00:42:44
>> Yeah. I mean, the reaction I had at that
00:42:46
play wasn't so much I like the way Shane
00:42:48
framed it. If I knew the weather report
00:42:50
was going to be bad, I might have
00:42:52
changed. But also,
00:42:54
I I'll go look, I'll go to my deathbed
00:42:56
by saying I don't understand that play
00:42:59
call. And let me say why I stayed out.
00:43:01
>> Oh, really? Oh, wow. Well, maybe you're
00:43:03
going to catch them off guard, but
00:43:05
again, you're trusting a crucial point
00:43:08
in the game to a quarterback that has
00:43:10
not played in over two years. Why are
00:43:14
you throwing the football?
00:43:16
>> Oh, the play call. You mean the play
00:43:18
call? I don't going for it. Okay, that
00:43:20
makes more sense.
00:43:21
>> Not that call. That's what I don't And
00:43:24
that's how Shawn Payton and that's why
00:43:26
what I put in the rundown. Did Shawn
00:43:29
Payeyton maybe, and I love the way you
00:43:30
said it, Cade, because of the first two
00:43:33
series, actually it was the third series
00:43:35
because the first series they were three
00:43:36
and out, but the first the second and
00:43:38
third series, maybe he saw Stidum doing
00:43:41
so well, he said, you know what, I'm
00:43:43
going to trust this guy on this crucial
00:43:45
play to throw the football. And to me,
00:43:48
it was the right call to go for it, but
00:43:51
not that play. No. And also to me, I've
00:43:54
got, look, I've said this about the
00:43:56
previous Seattle Patriots Super Bowl,
00:43:59
too. You say something to your team when
00:44:03
you don't think you can run the ball one
00:44:06
yard
00:44:08
on fourth down. You don't think you can
00:44:10
do cuz if you thought you could, you
00:44:12
would do it. And this idea that I'm
00:44:14
going to out trick them. It reminded me,
00:44:16
it did. reminded me of Pete Carol having
00:44:19
Russell Wilson throw the football when
00:44:21
you got beast mode running the goddamn
00:44:24
ball and I think even on the Patriot
00:44:26
game, maybe you can correct me, that
00:44:27
wasn't even fourth down. That might have
00:44:28
been first or second down.
00:44:30
>> I mean, it was it was it was in fact
00:44:32
second down though. I will note the
00:44:34
first down was them handing it to beast
00:44:36
mode and him not getting into the end
00:44:37
zone.
00:44:38
>> I understand that. That's all I'm
00:44:39
commenting on where they
00:44:41
>> tried to do exactly. It reminded me of
00:44:44
>> You don't have to rehash that history.
00:44:45
It just reminded me of the coach trying
00:44:47
to almost be too smart there. I liked
00:44:49
going for it. I didn't like the play
00:44:51
call.
00:44:51
>> And I and I you you're you're kind of
00:44:53
echoing. My general frustration with
00:44:55
fourth down decisions is after the fact
00:44:58
we analyze them. And so far so so often
00:45:01
we're we're convolving the decision to
00:45:04
go for it or not with like a really
00:45:06
suboptimal play call. Like I mean my my
00:45:08
bugaboo is always like you know some
00:45:10
kind of fourth and three and they're in
00:45:11
shotgun or something like that. fourth
00:45:13
and one and they're in shotgun. You
00:45:15
know, the these types of kind of, you
00:45:17
know, I wish we could also observe the
00:45:19
be what what happens in the alternate
00:45:21
universe where they kind of did the
00:45:22
optimal thing at least conditional on
00:45:24
that.
00:45:25
>> But let's also let's talk about
00:45:26
something Shane said earlier did a
00:45:28
better thing.
00:45:28
>> Suppose I told you that let's imagine
00:45:31
they had kicked a field goal there,
00:45:33
okay? And let's say they have 10 points.
00:45:35
As a matter of fact, let's even imagine
00:45:36
that was all they scored in the first
00:45:38
half cuz people are talking about the
00:45:39
Patriots defense and it is a good
00:45:41
defense. But let's not make it seem
00:45:43
great. Let's just remember
00:45:45
>> Jared Stum who hadn't thrown a pass in
00:45:47
over two years. Let's imagine he put up
00:45:51
10 points in the first half against the
00:45:52
Patriots. That's not the 85 Bears
00:45:55
performance. That's my point is that
00:45:58
like nothing. So right now, if I had to
00:46:02
update any of the
00:46:03
>> Patriots didn't score 10 points in that
00:46:05
Super Bowl, [laughter]
00:46:06
>> right? Well, no, no, but but also if you
00:46:09
had me update any part of like after
00:46:12
watching the two games, if you had me
00:46:14
update anything, here would be my
00:46:16
update. If I think about the four sides
00:46:18
of the ball, I was disappointed in the
00:46:21
Patriot offense. I think the Patriot
00:46:23
offense is worse than I thought going
00:46:26
into the game. And I think Seattle's
00:46:29
offense is better than I thought going
00:46:31
into
00:46:32
>> in I can't disagree generally, but in
00:46:35
evaluating your in your evaluation of
00:46:38
the Patriots offense, are you building
00:46:39
in that they have faced three of the top
00:46:42
five defenses now in their playoff run
00:46:46
in the NFL?
00:46:49
because they have the Denver. Denver,
00:46:51
Houston, and San Diego all have I mean
00:46:54
San Diego's five, but or sorry, LA
00:46:56
Chargers are number five, but like yeah,
00:46:58
those are three of the top five defenses
00:47:01
and as well as snow conditions. And
00:47:03
again, I'm not I'm I'm playing devil's
00:47:04
advocate because I'm actually on your
00:47:06
side on this one. It also I I I also
00:47:09
came away underwhelmed with the uh
00:47:11
Patriots offensive performance. But this
00:47:12
is kind of, you know, the
00:47:13
counterargument that people are making
00:47:15
right now is they have faced some very
00:47:18
legitimate, you know, kind of, you know,
00:47:20
maybe not 85 Bears level, but strong
00:47:23
defenses in this run and have done
00:47:25
barely enough to win and been lucky
00:47:27
while they've been doing that. But,
00:47:30
>> you know, this is this is why it's fun.
00:47:31
This is why we're going to play the
00:47:32
game.
00:47:33
>> I will say Seattle is another great
00:47:35
defense. So, it's not like if you get to
00:47:37
be better or anything like that.
00:47:39
>> My our friend and colleague Joe Simmons
00:47:41
uh wrote uh over the weekend about
00:47:44
comparing the media's reaction to Drake
00:47:47
May versus Sam Darnold. And
00:47:50
>> the media versus who
00:47:52
>> on on May versus Darnold. And you know,
00:47:55
Darnold like anytime something goes
00:47:57
well, you know, it's probably the
00:47:59
receiver or the offense or the offense.
00:48:01
But with anything goes wrong, it's like
00:48:03
Darnold because he's a flawed guy. And
00:48:04
then May is like this darling. And is
00:48:08
their performance really that different?
00:48:09
I mean, can we really make these kinds
00:48:11
of And do people Here's the thing.
00:48:12
Here's the premise, I guess, is that
00:48:14
guys get these reputations early and
00:48:17
that they color for years. They color
00:48:19
people's impressions. And I feel like I
00:48:21
mean, look, I'm not I don't pay enough
00:48:22
attention to know in detail, and some
00:48:24
people pay a lot of attention.
00:48:26
>> But I love that Darnell is doing as well
00:48:27
as he is, and I feel like he's just born
00:48:29
through bad early experiences in a way
00:48:32
that most don't. But I don't doubt that
00:48:34
that negative early experience is still
00:48:36
coloring people's perceptions of him.
00:48:38
>> No. And I I mean I think it's amazing.
00:48:40
Such a wonderful career arc for him. Uh
00:48:43
I mean if they weren't playing the
00:48:44
Patriots, I'd be probably cheering
00:48:45
pretty hard for him honestly in the
00:48:46
Super Bowl. But uh but yeah, question
00:48:48
Shane, who would you rather have as your
00:48:50
quarterback for the Super Bowl right
00:48:52
now, Drake May or Sam Darnold?
00:48:56
>> Great question.
00:48:57
>> Great question.
00:48:58
>> Drake May. Drake May has never seen
00:49:01
>> Sam Darnold can be had. We've seen Sam
00:49:03
Darnold be had. He has not been had in
00:49:06
this run. He has not been had this
00:49:08
season, but as recently as last season's
00:49:11
playoffs, he can be had. I'm not saying
00:49:13
they necessarily will get him, right?
00:49:15
But
00:49:16
>> I'm going to take the guy that's won,
00:49:17
including the playoffs, I'm going to
00:49:18
take the guy that's won 31 games the
00:49:20
last two seasons or whatever the number
00:49:22
is. But the only guy besides Tom Brady,
00:49:24
>> you would have taken Kurt Warner going
00:49:26
into that super first Super Bowl, too,
00:49:28
right? Come on.
00:49:30
>> Yeah, I would have.
00:49:31
>> Oh, for sure. But it's an interesting
00:49:33
question. I like this with Eric. I'm
00:49:35
going I'm going Darnold as well, but I
00:49:37
realize it's an interesting question and
00:49:39
I am I am worried about, you know, next
00:49:41
next level stakes,
00:49:43
>> how things how things go for him. Um,
00:49:46
all right. What else do we have around
00:49:48
the world of sports? Why don't we give
00:49:50
Eric a little time on the Australian
00:49:51
Open? the first major of the eight
00:49:54
majors we'll see in golf and tennis. The
00:49:55
first major is being played right now
00:49:57
down in Australia.
00:49:59
>> Yeah. So, I mean, let's ignore last
00:50:02
night for just one second or the the
00:50:04
matches that would happen. Something I
00:50:06
put in the rundown. So, in the final
00:50:08
eight here were the men's seeds left. 1
00:50:12
2 3 4 5 6 8 and 25. in the women's 1 2 3
00:50:22
4 5 6 12 and 29.
00:50:26
>> Now that's remarkable.
00:50:29
>> Yeah.
00:50:30
>> The amount of top seeds that are still
00:50:32
in. Now I will say last night um 29 got
00:50:37
eliminated badly by one. Sabalanka uh
00:50:41
beat um I forget the woman's name. The
00:50:44
American woman that uh I forget her
00:50:46
name, but she got dusted off pretty
00:50:48
quickly.
00:50:48
>> 29th,
00:50:50
>> what' it say?
00:50:50
>> She was 29th ranked.
00:50:52
>> 29th ranked. Um 12vidilina
00:50:56
destroyed Koko Goff yesterday actually.
00:50:59
So 12 is in the semi-finals. Matter of
00:51:00
fact, 12 is playing one. Sidilena is
00:51:03
playing Sabalanka in one semi-final on
00:51:05
the women's side. on the men's side.
00:51:09
Wow. I mean, Alcarez played brilliantly.
00:51:13
I mean, he played the number six in the
00:51:15
world, beat him 75, should have been 63
00:51:18
in the first set. 62 61. And the guy's
00:51:21
Australian, so playing on his home
00:51:23
court. um Alex Demenor who had I think
00:51:27
he was 15-1 in his last 16 matches and
00:51:32
>> got pedestrianed if you'd like by um by
00:51:37
Alcarz. The interesting part will be you
00:51:40
know Jookovic. So here's the here's the
00:51:42
question. He got defaulted into the
00:51:44
quarterfinals. So he has only played
00:51:46
three matches not four. What does it
00:51:49
mean got defaulted into
00:51:50
>> the guy he was going to play in the
00:51:51
quarter in the sorry in the round of 16
00:51:53
defaulted before the match.
00:51:55
>> He was going to play the number 16 seat
00:51:57
who had beaten him the last time they
00:51:58
played by the way Jacob
00:52:00
>> Mensik.
00:52:00
>> Okay.
00:52:01
>> He didn't have to play him. So he's had
00:52:02
four days rest now.
00:52:04
>> His first three matches the top ranked
00:52:06
player he played was number 85 in the
00:52:08
world. He's played two wild cards in an
00:52:11
85. He beat them all in straight sets.
00:52:13
So, he's in the quarterfinals and he's
00:52:16
played nine sets of tennis so far.
00:52:19
>> He's got to be fully rested.
00:52:21
>> Now, he's playing the number six or five
00:52:23
in the world, Musetti, who I think he's
00:52:26
not. I know he's nine and one against.
00:52:28
Now, here comes the major question.
00:52:31
What's going to happen in the next round
00:52:34
potentially when he plays S? That's the
00:52:36
magic question. Can he on S side of the
00:52:39
draw, so he'd have to get past center to
00:52:42
get to the finals. But this I my
00:52:43
question coming into this conversation
00:52:45
was to explain to me center right now.
00:52:47
So there was this match sometime this
00:52:49
past week where if not for the heat
00:52:51
regulations
00:52:52
>> I can tell you a personal story.
00:52:54
>> So but so the question though that I
00:52:56
want you to land on is what's your
00:52:58
assessment of center's condition right
00:53:00
now and why is it any different than the
00:53:02
top level it has been?
00:53:03
>> Yeah. So just so you know it was like
00:53:05
105 degrees out. The reason I know that
00:53:08
match well is because the person he was
00:53:10
playing against is the twin brother of a
00:53:12
guy who was on Zack who's on the pen
00:53:14
squash team.
00:53:16
>> So Nick Spazeri was number one and two
00:53:18
for about the last five years on pen
00:53:20
squash team. His twin brother is Elliot
00:53:22
Spazeri who's the one that S plays. So I
00:53:25
was watching the match.
00:53:26
>> Um yeah, S would have lost that match if
00:53:30
they didn't close the roof and they
00:53:33
didn't have a 10-minute break after the
00:53:35
third set. He was cramping severely.
00:53:37
This is something that has happened. The
00:53:39
thing now is is that they're now down to
00:53:41
so few players. They're going to be
00:53:43
playing all the matches in Rod Labor
00:53:45
Arena with a shut roof cuz it's 110°
00:53:48
out. So, it's going to be 70° inside.
00:53:51
So, um the no there's nobody on this
00:53:53
planet more upset that they shut that
00:53:55
roof in the middle of that match than
00:53:56
NovakJokovic because he saw center going
00:53:59
out. Now, his path to the finals is
00:54:02
clear. And just to be clear, last year,
00:54:05
last year, not five years ago,
00:54:07
>> he beat Alcarez in the Australian Open
00:54:10
in the quarterfinals. So he was thinking
00:54:13
he's one inch away from potentially
00:54:16
steamrolling into the finals against a
00:54:18
guy he beat.
00:54:19
>> Okay.
00:54:19
>> On the on on this surface.
00:54:22
>> Yeah. Yeah. Yeah.
00:54:23
>> But no, I don't think there's any
00:54:24
lasting uh Center looked great in his
00:54:28
last match.
00:54:29
>> Um I believe, by the way, Center has a
00:54:31
very intriguing match. It'll be at like
00:54:33
3:00 or 4 in the morning. I'm pretty
00:54:35
sure he's playing Ben Shelton. Now,
00:54:39
that's no gimme a match. I mean, that's
00:54:41
a very interesting match.
00:54:43
>> Tell us more about Ben Shelton.
00:54:44
>> Well, Ben Shelton's been rising up uh
00:54:47
quickly. He was a collegiate squash play
00:54:50
uh tennis player. Um he's got about 140
00:54:53
mph serve and so when he wants to, if he
00:54:56
gets it in, he'll just blow you off the
00:54:58
court. extremely athletic, great serving
00:55:01
volier, allcourt tennis guy. Um, a lot
00:55:04
of people believe if there is a third
00:55:06
player that will be joining the big two
00:55:08
of SR and Alcarz, it will be uh Ben
00:55:11
Shelton.
00:55:12
>> And so, um, do I think he's going to
00:55:14
beat S, the two-time defending champion
00:55:17
at the Australian Open? No. Do I think
00:55:19
he's going to beat the guy who I don't
00:55:21
think he's lost in maybe 30 consecutive
00:55:23
matches on hard courts? No, I do not
00:55:25
think he's going to. But it's not a
00:55:28
trivial match. And so look, it's all
00:55:31
it's going to come down to it's it's a
00:55:33
fascinating f And by the way, do not
00:55:36
assume that Alcarez is making the
00:55:38
finals. Alcarz is playing Zarev in the
00:55:41
other semiinal. And by the way, their
00:55:43
career record against each other, six
00:55:45
and six.
00:55:47
>> So this this idea that it's a feta
00:55:49
comple like bet the house that it's
00:55:51
center versus Algarz. I ain't betting
00:55:53
the house on that. that ain't I mean
00:55:56
there's no I mean look
00:55:58
Al Perez is six and six against Verv so
00:56:01
there's no guarantee he'll be the
00:56:02
favorite but not that heavy a favorite
00:56:04
and center still has to get past Ben
00:56:06
Shelton and probably Novak Djokovic I'm
00:56:08
not saying he doesn't have a 60 or 70 or
00:56:11
80% chance to beat them both but it's
00:56:14
not for sure it's a fascinating both the
00:56:16
men's and the women's side is
00:56:19
extraordinarily exciting this year
00:56:21
>> well strong draw everyone's still in a
00:56:24
lot of
00:56:25
LA fun last few days here as we roll
00:56:27
into the finals on on Sunday. Um why
00:56:30
don't we wrap it there? We just lost
00:56:32
Audi and we've run about our full time
00:56:35
now. So we'll wrap it there. We will
00:56:38
come back next time of course. Until
00:56:40
then, thanks for Audi Winer and Absentia
00:56:43
on behalf of Eric Bradlo and Shane
00:56:44
Jensen, Kate Massie. Many thanks to Dion
00:56:48
Simpkins who makes the whole thing
00:56:49
happen. Marissa Raina, our producer and
00:56:52
booker, very helpful. the big boss lady
00:56:56
D Patel. Many thanks to D and to you
00:56:58
guys for listening. Come back and join
00:56:59
us next time. Between now and then,
00:57:01
enjoy your sports.
