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Speaker 1: The Big Bets on Campus Podcast podcast poda.

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Speaker 2: All right, here we go.

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Speaker 3: Fifteen twenty twenty five, thirty thirty five, forty forty five fifty.

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Speaker 4: And the kicks block college Football.

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Speaker 3: Laura starts, Oh my goodness, please stop.

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Speaker 5: Old and football.

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Speaker 1: It's the lateral turn Tayter on the exode.

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Speaker 6: Do you believe.

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Speaker 4: Table off the field to night?

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Speaker 3: Oh my god, it's not the side of the dog

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Speaker 3: in the fighting, it's the size of the fight.

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Speaker 4: In a.

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Speaker 3: Welcome to the Big Bets on Campus Podcast on my

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Speaker 3: Calibery's joined by my Action Notework co hosts joshun on

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Speaker 3: Aka Duck and Christian O Jackjian. But today is a

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Speaker 3: day where we welcome in another member of this illustrious panel,

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Speaker 3: and you're gonna probably recognize his voice right away, or

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Speaker 3: at least you've seen his numbers.

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Speaker 1: It's Evan Muakawa, who has.

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Speaker 3: Done just incredible work in the college basketball space. You

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Speaker 3: can't really watch, enjoy discuss college football, college basketball, any

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Speaker 3: of these sports in modern times without getting into the

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Speaker 3: nitty gritty and when it comes to the hardwood, Evan

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Speaker 3: Miakawa is your guy.

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Speaker 1: This is a very special opportunity for us.

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Speaker 3: What we're gonna do, Evan is, We're gonna pep you

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Speaker 3: with questions. But before we get into that, how are

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Speaker 3: you feeling at this point in the season. I'm sure

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Speaker 3: you're just as excited as any college basketball fan, but

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Speaker 3: you have to be just being run ragged at this point.

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Speaker 1: In the season.

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Speaker 3: Media, you know, updating your model, everything that goes into

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Speaker 3: keeping everything fresh on your site. It seems like every

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Speaker 3: single week you're putting out something new on social media,

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Speaker 3: a new way to discuss the sport, kill shots, avalanches,

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Speaker 3: the list goes.

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Speaker 1: On and on. How are you feeling at this point

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Speaker 1: in the year.

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Speaker 5: I mean, it's a good problem to have. If you

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Speaker 5: are overloaded with stuff to do. It just means people

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Speaker 5: our care and are paying attention. And so even though

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Speaker 5: it's the busiest time of year, it's the most fun

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Speaker 5: time of year.

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Speaker 4: So I'm thankful for all of it.

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Speaker 3: As I mentioned, we have a lot to get into,

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Speaker 3: so I'm just going to kick it to my man

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Speaker 3: Duck to get things started. He has some questions about

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Speaker 3: your model, someone who also keeps his own model, that

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Speaker 3: the fine tuning, the tweaking, not you know, being moved

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Speaker 3: too much by some cistical outlayers. There's so many things

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Speaker 3: to get into, but Duck, please take it away.

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Speaker 7: Yeah, first off, Evan, thanks for joining us today. Big

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Speaker 7: fan of your work, as are most everybody here that

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Speaker 7: works at Action Networks, so we definitely appreciate the time.

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Speaker 7: One of the biggest things that I've had to pivot

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Speaker 7: in my preseason handicapping is trying to really get a

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Speaker 7: fingerprint on what these teams are going to look like

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Speaker 7: from a personnel standpoint, the players that are returning, coming

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Speaker 7: and going, coaching schematic changes, things of that nature. So

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Speaker 7: before your model interacts with a single data point from

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Speaker 7: the current season, how do you go about setting things

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Speaker 7: up in the preseason with so much of that configuration

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Speaker 7: that changes from one year to the next. How do

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Speaker 7: you formulate the baseline for where a team should be

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Speaker 7: and in game one compared to where it was last

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Speaker 7: year at the end of the year, because that could

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Speaker 7: be two very different things.

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Speaker 5: I mean, yeah, it's harder than ever to do that

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Speaker 5: well because of all the roster turnover, transfer portal and

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Speaker 5: now even with things like we're having more international recruits

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Speaker 5: coming in where we don't have any college data on them.

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Speaker 4: So it's definitely a challenge.

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Speaker 5: I think one of the pros of what I do

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Speaker 5: at evanmea dot com is everything is very much built

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Speaker 5: from the player level up. So I have a very

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Speaker 5: sophisticated algorithm for valuing how important or impactful a player

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Speaker 5: is on offense and defense in any given season over

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Speaker 5: the course of a career. And so it starts with that.

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Speaker 5: It starts with looking at every single player that's going

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Speaker 5: to be on a team's roster, what is their projected

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Speaker 5: value just kind of agnostic of what team they're playing

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Speaker 5: for in terms of their offense and defense, And it

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Speaker 5: takes into account if they're a returning player, if they're

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Speaker 5: a transfer, if they played multiple years in Division one,

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Speaker 5: how they've performed over those seasons, If they are a

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Speaker 5: younger player, what was their recruiting profile coming out of

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Speaker 5: high school and what does that tell us about the

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Speaker 5: potential that they have, or if they're a new player

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Speaker 5: too Division one. I have projections for all freshmen and

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Speaker 5: international recruits that are based on essentially how have people

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Speaker 5: in previous seasons in their first year in college performed

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Speaker 5: based on how they were ranked according to the recruiting

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Speaker 5: services coming into that year, and what's a typical baseline

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Speaker 5: for those types of players. So you get a number

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Speaker 5: for every player, and then that can go up to

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Speaker 5: essentially like there's a roster talent eval for every single

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Speaker 5: team that represents how good their roster is, and that

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Speaker 5: carries a significant part of the weight, And especially for

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Speaker 5: teams with new coaches, that is even more important because

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Speaker 5: we don't have a track record of how good that

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Speaker 5: team is with that coach and that coaching staff. But

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Speaker 5: it also does still look into account what is your

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Speaker 5: program prestige Essentially, how is your team performed in previous

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Speaker 5: year years? How has your coaching staff helped a team

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Speaker 5: perform above or below expectations in previous years based on

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Speaker 5: how good that roster is.

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Speaker 4: So a lot of those variables go.

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Speaker 5: Into the final preseason number, but the roster talent evaluation

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Speaker 5: part of it certainly is the most important piece.

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Speaker 7: So then I have a follow up question for that

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Speaker 7: real quick. And we have seen teams now that we're

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Speaker 7: getting towards the end of the season, and these teams

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Speaker 7: kind of had a mosaic for what they were going

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Speaker 7: to be a big outlier for me has been Pepperdine

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Speaker 7: and Shilling had deployed a slower paced, conservative defensive style

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Speaker 7: the first fifteen to eighteen games, and now you have

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Speaker 7: that completely scrapped and thrown out the window the last

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Speaker 7: seven or eight games of the season. So there's got

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Speaker 7: to be some sort of in season pivot or some

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Speaker 7: micro adjustments that are made by you to account for

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Speaker 7: when teams just scrap the system and start playing a

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Speaker 7: different style. I'd be interested to hear kind of when

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Speaker 7: you pivot or how you make those micro adjustments.

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Speaker 5: Yeah, Ultimately, one of the issues with doing this sort

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Speaker 5: of exercise in predicting team performance before a season starts

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Speaker 5: even within a season is different like levels or buttons

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Speaker 5: that you can press as a coaching staff to try

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Speaker 5: different things. But ultimately everyone is still trying to do

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Speaker 5: the same thing, and that is win games and score

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Speaker 5: more points than your opponent. Everyone is still hitting that

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Speaker 5: button as hard as they can, and so whatever they're

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Speaker 5: trying to do to get there, they will do that.

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Speaker 5: And so ultimately, what I have found is that there

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Speaker 5: is no real correlation just across Division one in terms

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Speaker 5: of if teams play better across the board if they

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Speaker 5: play faster, or they play better if they play slower,

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Speaker 5: or if they go deeper into their bench versus play starters. More,

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Speaker 5: these are stylistic elements that every team has, and you

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Speaker 5: can look at a singular case like Pepperdine and say, hey,

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Speaker 5: they were doing this thing up to this point and

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Speaker 5: then they changed, and we think that is good or

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Speaker 5: we think that is bad. But ultimately that's just one

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Speaker 5: data point out of three hundred and sixty five teams,

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Speaker 5: and ultimately, every single game a coaching staff is trying

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Speaker 5: to do something stylistically that will lead to them winning.

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Speaker 5: So all that to say is I don't have anything

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Speaker 5: that looks at Oh, Pepperdine made this change in the season,

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Speaker 5: and just because of this stylistic change, I'm going to

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Speaker 5: adjust my evaluation on them. It's ultimately okay. Is that

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Speaker 5: leading to better in game performance or worse in game

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Speaker 5: performance in terms of your offensive and defensive efficiency, because

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Speaker 5: it's not a given either way, and so that's really

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Speaker 5: more the most important element that it comes down to.

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Speaker 4: If that makes sense.

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Speaker 8: Hey, my first question is, as someone who's never made

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Speaker 8: a model or I don't understand all the elements that

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Speaker 8: go into creating one, how often are you tinkering with it?

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Speaker 8: And like, since you've started how what has kind of

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Speaker 8: been the progression of that.

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Speaker 2: Can you even tinker with it like at this point

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Speaker 2: in the season.

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Speaker 5: Great question. I'm always making adjustments in the off season.

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Speaker 5: So if there's something that's a new element to the

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Speaker 5: model I want to add, or something that I've noticed

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Speaker 5: during a season that I not sure if it's quite right,

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Speaker 5: I will examine it during the off season and maybe

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Speaker 5: it's actually reaffirms that it was right, or maybe there's

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Speaker 5: some sort of small like tweak I need to make. Additionally,

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Speaker 5: I often will update the model, keep the model the same,

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Speaker 5: but update it with more training data essentially, which is

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Speaker 5: just okay. Adding in the results from the previous season.

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Speaker 5: That can help make the model more accurate predicting future seasons,

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Speaker 5: because as we know, the trends in college basketball are

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Speaker 5: changing every single year, and so something that might be

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Speaker 5: right a season ago maybe a little bit off this season.

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Speaker 5: So that always is a process that I look doing

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Speaker 5: every single off season. Within a given season, I really

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Speaker 5: try and avoid tinkering with anything unless something's clearly broken,

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Speaker 5: just because number one, people notice if something changes, and

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Speaker 5: especially if you have a coach and they're like, hey,

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Speaker 5: my team was ranked ninetieth yesterday and now we're ninety six.

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Speaker 4: Then we haven't played a game. What happened That doesn't

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Speaker 4: look good?

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Speaker 5: But also generally speaking, I don't It's very easy to

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Speaker 5: often overreact to something that's happening during the season and saying, oh,

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Speaker 5: it's not capturing this one thing, right, I need to

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Speaker 5: just sort of manually adjust this thing. And typically that's

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Speaker 5: too much of a knee jerk reaction, and it will

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Speaker 5: actually come back towards what you expect because we have

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Speaker 5: years and years of data of what college basketball looks like,

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Speaker 5: and you know, a couple of weeks into a new season,

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Speaker 5: maybe there are some things that are different, but it

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Speaker 5: can often be a little bit of often kind of

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Speaker 5: regression to the mean in terms of overall trends, if

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Speaker 5: that makes sense. So I'm not typically changing anything significant

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Speaker 5: during this season.

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Speaker 3: Sure to piggyback on Christian's question there, we talk about

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Speaker 3: it all the time on this podcast, which is KYP

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Speaker 3: Know your personnel. How much does a single player, a starter,

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Speaker 3: a leading scorer, you know, a shot blocker being on

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Speaker 3: the bench due to injury or suspension impact a team.

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Speaker 3: Kind of that broader question, if you remove one plank

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Speaker 3: and replace it in a ship. It's still the same ship,

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Speaker 3: but eventually there's a tipping point where they're not the

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Speaker 3: same team. How do you before you even get into

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Speaker 3: the math, how do you just conceptualize that, both in

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Speaker 3: terms of your model and then also when you're having

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Speaker 3: conversations like this with members of the media, how do

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Speaker 3: you go ahead and you know, present that in a

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Speaker 3: way that feels even handed.

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Speaker 5: Absolutely, I think this is one of the areas where

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Speaker 5: my player evaluations really provide a lot of value. Is

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Speaker 5: essentially it gives you a math equation of Okay, I

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Speaker 5: can say exactly how good this team should be if

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Speaker 5: you have a full roster versus even take a single

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Speaker 5: player away or multiple players away. And that's actually something

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Speaker 5: that's incorporated into all of my game predictions is relatively

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Speaker 5: adjusted injury reports that are taking into an account players

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Speaker 5: that are out for the season or out for a

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Speaker 5: significant period of time, and we'll basically say, like, hey,

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Speaker 5: this is how good a team should be if they're healthy,

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Speaker 5: and if you take this player away or multiple players

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Speaker 5: away in some cases, this is how the team strength changes.

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Speaker 5: And that's all baked into the game predictions there, which

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Speaker 5: is a really nice feature, and I think it comes

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Speaker 5: down to a couple things.

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Speaker 4: Number One, what is the general.

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Speaker 5: Quality of the player that is out right? Just like

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Speaker 5: what is their how do they compare numerically compared to

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Speaker 5: all other Division one players? But equally important is who

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Speaker 5: will be taking their minutes if they're not there, Because

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Speaker 5: you could take the same player away from a same

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Speaker 5: strength player away from say a Pepperdine versus a Gonzaga,

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Speaker 5: and that's going to mean a very different thing based

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Speaker 5: on how good that team is going to perform.

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Speaker 4: So when we look at.

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Speaker 5: Recent cases of this, they're some of the most notable

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Speaker 5: examples are like, for example, Duke was missing Patrick and

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Speaker 5: Goongba for a couple of games with a wrist injury,

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Speaker 5: and I have him rated as one of the top

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Speaker 5: ten most valuable players in the country. However, Duke has

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Speaker 5: such a good team that when he was out that

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Speaker 5: was only worth about a two point adjustment for Duke's

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Speaker 5: overall prediction in terms of the line in a given game,

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Speaker 5: whereas BYU has Richie Saunders out for the season, Saunders

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Speaker 5: actually rates as a slightly worse player than go Gongla.

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Speaker 5: Pretty similar but slightly worse but because of how bad

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Speaker 5: the depth on BYU is, that adjustment for BYU is

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Speaker 5: worth more like five or six points of him missing

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Speaker 5: because of how much of a gap between him and

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Speaker 5: the players who would take his minutes would be. And

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Speaker 5: you can actually see on my website there's a I

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Speaker 5: have an indispensability ranking, which ranks essentially who are the

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Speaker 5: players most indispensable to their teams or in another way

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Speaker 5: of saying, what's the gap between if they have the

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Speaker 5: player versus don't have the player. And that's not necessarily

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Speaker 5: the same as just ranking the best players in college basketball, Like,

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Speaker 5: for example, Cameron Booser is the most indispensible player in

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Speaker 5: the country, not surprising, but the top next players are

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Speaker 5: Jeremy Fears at Michigan State, Bruce Thornton at Ohio State,

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Speaker 5: Braden Smith that Purdue, Bennetts Starch at Iowa, and now

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Speaker 5: Christian Anderson at Texas Tech. All of those guys are

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Speaker 5: basically saying there is a massive, massive gap between if

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Speaker 5: they're on the team or not on the team. And

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Speaker 5: so I find that very interesting to look at.

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Speaker 3: One question to kind of put this in a slightly

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Speaker 3: different direction. When you think about your model evanmea dot

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Speaker 3: com as in its entirety, the website, the offerings that

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Speaker 3: it's bringing to the table. Do you take more pride

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Speaker 3: in going back and looking at previous seasons and perfectly

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Speaker 3: quantifying how good.

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Speaker 1: Teams are air to era a year to year, or do.

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Speaker 3: You care more deeply about the potential for it to

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Speaker 3: correctly predict the future?

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Speaker 1: And obviously we're all in that game.

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Speaker 3: We want to be able to you know, get as

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Speaker 3: many games correctly as possible and at times stick our

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Speaker 3: neck out and you know, show that we have some insight.

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Speaker 1: Information that someone else doesn't have.

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Speaker 3: But at the end of the day, it can be

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Speaker 3: fickle to kind of chase that that high because it's

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Speaker 3: still a.

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Speaker 1: Sport played by teenagers for the most part.

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Speaker 3: You have incredible swings from home courts to a way

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Speaker 3: to neutral floors, to the impact of you know, the

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Speaker 3: whistle from the referees. Like there's so many human elements

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Speaker 3: that can kind of slip through the you know, the

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Speaker 3: digital cracks in those spreadsheets.

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Speaker 1: So where do you come down.

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Speaker 3: Do you care more about being able to log everything

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Speaker 3: and have it be you know, apples to apples year

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Speaker 3: over year, or as time has gone on. Do you

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Speaker 3: take more pride in predicting what's going to happen in

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Speaker 3: the future.

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Speaker 5: I feel like there is something always satisfying when the

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Speaker 5: way that you portray a previous season in terms of

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Speaker 5: players or teams looks like what it should look like.

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Speaker 5: But ultimately that's a very subjective like kind of back

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Speaker 5: sort of aligning what you think should happen with your

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Speaker 5: own data points, and that doesn't really serve much of

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Speaker 5: a purpose. So to me, even though it is very

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Speaker 5: difficult to predict the future, that is where I ultimately

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Speaker 5: kind of train everything that I do is what leads

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Speaker 5: to the best future predictions, be that best predictions of

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Speaker 5: how good a team is going to be going forward,

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Speaker 5: how good a player is going to be going forward,

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Speaker 5: how good certain individual game predictions are going to be

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Speaker 5: going forward. Ultimately, you know there are so many different

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Speaker 5: data points you're never going to get them all right.

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Speaker 5: But the goal for me in kind of my mind

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Speaker 5: is I want to be right a little bit more

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Speaker 5: often than I'm wrong. And the other thing, too, is

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Speaker 5: the more mainstream Evanmia dot com becomes the less of

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Speaker 5: an edge quote unquote that you have because more people

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Speaker 5: are using it and so that's okay, But in general,

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Speaker 5: I'm always trying to optimize what I'm doing based on

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Speaker 5: predicting the future unobserved outcomes. That's that's ultimately the bread

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Speaker 5: and butter of what I'm doing, and that's kind of

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Speaker 5: how I sell it to to whether it's it's fans

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Speaker 5: who are using it or coaches and teams who are

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Speaker 5: using it, of saying like, hey, everything that I have

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Speaker 5: here when you look at it, this isn't just saying

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Speaker 5: like I'm measuring how good a team or a player

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Speaker 5: a lineup been so far. I'm actually trying to give

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Speaker 5: you intel on what's likely to be the best going forward,

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Speaker 5: and that really ultimately leads to more decision making power,

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Speaker 5: which is the end of the day, what I really

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Speaker 5: care about.

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Speaker 2: Evan.

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Speaker 7: Let's talk about a team specific question. It's kind of

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Speaker 7: been the elephant in the room. It's Miami of Ohio.

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Speaker 7: They're undefeated, they're breezing through MacPlay. It's really a team

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Speaker 7: that seems as though offensively the whole is greater than

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Speaker 7: the sum of its parts. They've got six or seven

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Speaker 7: guys that can really score the basketball. They share it well,

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Speaker 7: but it just seems as though whether it's the media

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Speaker 7: or ratings personnel, that Miami has this sort of artificial

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Speaker 7: ceiling that's pre defined based on the fact that they

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Speaker 7: play in the MAC and nobody would schedule them in

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Speaker 7: the non conference. How do you handle a team like

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Speaker 7: Miami of Ohio who has won a ton of games.

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Speaker 7: They're very talented, it's not a team reliant on one

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Speaker 7: player or two players, and they're very good. So talk

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Speaker 7: about them real quick if you would.

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Speaker 5: Yeah, I think it's such an interesting conversation because there

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Speaker 5: is a relatively speaking and massive gap between how good

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Speaker 5: their resume looks and how good they actually evaluate as

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Speaker 5: a team in terms of predicting the future. The way

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Speaker 5: I would sort of, you know, sort of explain this

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Speaker 5: is if you took a couple of their games that

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Speaker 5: they've had this season. They are five to zero games

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Speaker 5: that are one possession games. So if you flipped a

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Speaker 5: couple buckets in two to three of those, and you

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Speaker 5: gave them a couple more buckets and a few other

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Speaker 5: games that they already won, they would still evaluate as

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Speaker 5: the exact same strength team, but they would have maybe

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Speaker 5: three losses and this wouldn't be a conversation. So in

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Speaker 5: that sense they are ultimately comes down to winning. They're

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Speaker 5: very deserving to be in the at large conversation right now,

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Speaker 5: because even though their strength of schedule has been so weak,

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Speaker 5: it still takes a lot to win all of those

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Speaker 5: games against their schedule, and it's more likely than not

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Speaker 5: that an average Bubble team would not have won all

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Speaker 5: those games even against how poor competition they played. In

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Speaker 5: some cases, a lot of these games were on the road,

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Speaker 5: and road games still are tough. But equally, if they

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Speaker 5: were to play on a neutral court against Ohio State tomorrow,

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Speaker 5: my matchup prediction would have them as like five point underdogs,

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Speaker 5: which would be a pretty significant line for teams you

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Speaker 5: would typically have their evenly matched in say Dayton in

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Speaker 5: the tournament, for example. So I think about both those

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Speaker 5: things are true. They deserve to get in because even

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Speaker 5: against the lack of high quality opponents that they have,

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Speaker 5: it's still very impressive they won all those games. And equally,

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Speaker 5: there would be one of the weaker quote unquote nines,

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Speaker 5: tens or elevens that we've seen in a while in

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Speaker 5: terms of at large chances. But a lot of people

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Speaker 5: think that they've sort of cheated the system by scheduling weekly.

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Speaker 5: The reality is even based on how good of a

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Speaker 5: team we knew they had coming into the year, The

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Speaker 5: chance of them winning all of these games is one

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Speaker 5: out of like twelve hundred, as in, if they were

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Speaker 5: to do this a thousand times over, this would be

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Speaker 5: the only time that they would be undefeated right now.

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Speaker 5: This is not a cheat code that other teams can

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Speaker 5: copy to fluke their way into the tournament. They were

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Speaker 5: not expecting this to happen. They should have three or

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Speaker 5: four or five losses at this point, so it's not repeatable,

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Speaker 5: and that's why it's awesome, because it's so unexpected.

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Speaker 3: I'll put you on the spot, and I promise not

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Speaker 3: to clip this. If they go undefeated and hit the

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Speaker 3: auto bid from the MAC, where would you see them

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Speaker 3: if you had your say.

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Speaker 5: Right now, I would have them resume wise as a

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Speaker 5: nine seed, and I think if they run the table

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Speaker 5: that's probably where it I would put them. In terms

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Speaker 5: of what I predict them to have. I have heard

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Speaker 5: some people argue for them being, you know, more in

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Speaker 5: the six to eight range, which I would love. That

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Speaker 5: would be great, But I think a nine seed would

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Speaker 5: be comfortably in the field. An eight, nine game like

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Speaker 5: that would be great, and so that's that would be

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Speaker 5: my best guess right now. If they were undefeated on

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Speaker 5: selection Sunday, I think that's where they would be Christian.

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Speaker 1: Let's get you back into the fold here.

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Speaker 8: Well, the irony here is like, wouldn't they be better

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Speaker 8: off with an eleven at that point? Like if they

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Speaker 8: can't get past the eight, reminds me of like when

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Speaker 8: fau got that.

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Speaker 2: Nine or even I don't know. That just frustrates me

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Speaker 2: in a way.

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Speaker 5: Well, I think that we need to start with let's

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Speaker 5: just get them one win in the tournament. Getting the

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Speaker 5: sweet see steams not a guarantee that. So I'll take

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Speaker 5: whatever I can take to get them an easier first

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Speaker 5: round opponent.

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Speaker 4: We'll do that.

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Speaker 2: It kind of reminds me of that.

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Speaker 8: It's not Apple sapples, of course, but that Providence team

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Speaker 8: three years ago that kept winning and was like in

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Speaker 8: like the thirties in Kempom when they were like pushing

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Speaker 8: the top ten to the eight people is there. I

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Speaker 8: understand that the reason why the predictive metrics will be

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Speaker 8: lower on these teams that are winning all these close

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Speaker 8: games is because it's a predictive metric it's not about

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Speaker 8: ranking what they've done. But is there any like secret

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Speaker 8: sauce that could ever be determined where you are giving

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Speaker 8: credit to a team's ability to have things go their

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Speaker 8: way in that game because they are a good team

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Speaker 8: and because they're well coached and have good chemistry, or

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Speaker 8: is it like an outlier thing where maybe Jerome Tang

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Speaker 8: is not a good coach, but he he just happens

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Speaker 8: to keep winning these overtime games, So that would be

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Speaker 8: a mistake if you were to give them the benefit

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Speaker 8: of the doubt for that trend.

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Speaker 4: I do think that is potentially a.

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Speaker 5: Weakness in all of these efficiency based team metrics, which

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Speaker 5: is how I do. My entire side is how kem

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Speaker 5: pum works. And I think there can be said something

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Speaker 5: for are you really accounting for that winning factor? I

441
00:20:23,400 --> 00:20:25,359
Speaker 5: think by and large it does a good job of

442
00:20:25,440 --> 00:20:28,280
Speaker 5: kind of finding signal over the noise and saying like,

443
00:20:28,560 --> 00:20:30,520
Speaker 5: if you are five and oh and one possession games,

444
00:20:30,560 --> 00:20:33,600
Speaker 5: that's likely to not go in your favor as often

445
00:20:33,640 --> 00:20:35,840
Speaker 5: in the future, and maybe it's more likely you would

446
00:20:35,880 --> 00:20:37,440
Speaker 5: go three and two in those games instead of five

447
00:20:37,480 --> 00:20:41,200
Speaker 5: and oh. But the reality is winning by one point

448
00:20:41,280 --> 00:20:43,480
Speaker 5: versus losing by one point, does very little to move

449
00:20:43,520 --> 00:20:47,720
Speaker 5: your overall team strength in terms of these predictive efficiency ratings,

450
00:20:48,000 --> 00:20:50,399
Speaker 5: but in terms of what people actually care about, that

451
00:20:50,400 --> 00:20:52,920
Speaker 5: actually feels significant. People really care about in a team's

452
00:20:52,920 --> 00:20:55,720
Speaker 5: ability to win close games. So I haven't done a

453
00:20:56,160 --> 00:20:59,000
Speaker 5: extensive study on this, but if there was something that

454
00:20:59,040 --> 00:21:01,320
Speaker 5: I was able to kind of add to my model

455
00:21:01,359 --> 00:21:03,280
Speaker 5: in the future that better accounted for that, that's something

456
00:21:03,320 --> 00:21:06,640
Speaker 5: I've certainly considered and could be something that's a little

457
00:21:06,640 --> 00:21:08,840
Speaker 5: bit missing generally speaking from this type of models that

458
00:21:08,880 --> 00:21:09,480
Speaker 5: we often use.

459
00:21:10,680 --> 00:21:10,840
Speaker 4: Now.

460
00:21:10,880 --> 00:21:12,719
Speaker 3: Evan on your website says you work with over one

461
00:21:12,800 --> 00:21:15,360
Speaker 3: hundred and twenty Division one programs. How much of these

462
00:21:15,400 --> 00:21:19,000
Speaker 3: schools bring their specific needs to you and that impacts

463
00:21:19,040 --> 00:21:21,399
Speaker 3: what you're delivering? I know in recent years you've created

464
00:21:21,400 --> 00:21:24,639
Speaker 3: this robust portal dashboard for lack of a better phrase,

465
00:21:24,800 --> 00:21:26,920
Speaker 3: so that both fans and schools can come in see

466
00:21:26,920 --> 00:21:29,119
Speaker 3: who's entered the portal where they would stack up for

467
00:21:29,320 --> 00:21:32,040
Speaker 3: everyone else. You know, working with so many programs, has

468
00:21:32,080 --> 00:21:34,879
Speaker 3: that impacted the kind of products that you're bringing to market?

469
00:21:35,240 --> 00:21:38,600
Speaker 5: It certainly does impact that a lot, and ultimately I

470
00:21:38,640 --> 00:21:42,520
Speaker 5: have a lot of different types of consumers of my site, fans, media, coaches.

471
00:21:42,560 --> 00:21:45,600
Speaker 5: I think the group I care the most about targeting

472
00:21:45,720 --> 00:21:48,000
Speaker 5: or streamlining stuff or is coaches because they're the ones

473
00:21:48,000 --> 00:21:50,080
Speaker 5: who have the most decision making power in all of this.

474
00:21:50,200 --> 00:21:52,760
Speaker 5: But the reality is that the same stuff coaches can

475
00:21:52,840 --> 00:21:55,240
Speaker 5: use is also enjoyable for people who are just diehard

476
00:21:55,320 --> 00:21:56,920
Speaker 5: college basketball fans and that sort of thing.

477
00:21:57,480 --> 00:21:59,720
Speaker 4: So the way I typically structure my.

478
00:21:59,720 --> 00:22:01,600
Speaker 5: Side is I don't do a lot of like ad

479
00:22:01,640 --> 00:22:04,880
Speaker 5: hoc consulting work where I'm doing a specific project that's

480
00:22:05,119 --> 00:22:06,879
Speaker 5: siloed to one team and it's not being shared with

481
00:22:06,880 --> 00:22:09,960
Speaker 5: anyone else. Typically, if someone brings an idea to me,

482
00:22:10,600 --> 00:22:13,399
Speaker 5: my immediate idea is, how can I actually automate this

483
00:22:13,480 --> 00:22:16,000
Speaker 5: and then make this available to everybody. So, for example,

484
00:22:16,119 --> 00:22:18,680
Speaker 5: the big product that I launched last year which has

485
00:22:18,720 --> 00:22:23,160
Speaker 5: been a big hit is a very team specific tool

486
00:22:23,359 --> 00:22:26,640
Speaker 5: for coaches and gms called the Front Office Suite, which

487
00:22:26,680 --> 00:22:29,359
Speaker 5: is basically a roster building tool that I have that

488
00:22:29,400 --> 00:22:32,439
Speaker 5: builds on my portal stuff that actually helps identify how

489
00:22:32,520 --> 00:22:34,400
Speaker 5: much players should be worth in terms of dollar.

490
00:22:34,240 --> 00:22:35,040
Speaker 4: Amounts to teams.

491
00:22:35,600 --> 00:22:39,439
Speaker 5: And this come came from a lot of coaches being like, hey, like,

492
00:22:39,640 --> 00:22:41,480
Speaker 5: do you have numbers that I could also like attach

493
00:22:41,520 --> 00:22:44,000
Speaker 5: to this in terms of dollar amounts, and the reality

494
00:22:44,080 --> 00:22:45,639
Speaker 5: was like, oh, I could probably cook something up for

495
00:22:45,680 --> 00:22:47,560
Speaker 5: that one team, but I would actually rather build it

496
00:22:47,680 --> 00:22:50,439
Speaker 5: so that it can be reproducible in the future. And

497
00:22:50,480 --> 00:22:52,720
Speaker 5: so that's what led to building this tool. Now any

498
00:22:52,720 --> 00:22:54,719
Speaker 5: team who wants to use it can use it. So

499
00:22:55,320 --> 00:22:58,560
Speaker 5: in that sense, a lot of what I do is

500
00:22:58,920 --> 00:23:01,280
Speaker 5: kind of driven by feedback of getting from coaches, and

501
00:23:01,280 --> 00:23:02,679
Speaker 5: at the end of the day, I'm always trying to

502
00:23:02,720 --> 00:23:05,800
Speaker 5: then funnel that too. How can I automate this so

503
00:23:05,840 --> 00:23:08,360
Speaker 5: that it's widely available to everybody instead of me having

504
00:23:08,359 --> 00:23:11,679
Speaker 5: to like manually put something together for each like custom

505
00:23:11,720 --> 00:23:12,560
Speaker 5: project or client.

506
00:23:12,600 --> 00:23:14,560
Speaker 1: If that makes sense, it does.

507
00:23:14,640 --> 00:23:18,280
Speaker 3: Yeah, Christian, you were actually wondering about that automated elements

508
00:23:18,359 --> 00:23:21,080
Speaker 3: of it. What specifically did you want to know from Evan,

509
00:23:21,240 --> 00:23:24,240
Speaker 3: like behind the scenes and those interactions with with programs

510
00:23:24,240 --> 00:23:24,720
Speaker 3: and coaches.

511
00:23:27,240 --> 00:23:31,840
Speaker 8: Well, I was also just curious, like if AI has

512
00:23:31,880 --> 00:23:34,560
Speaker 8: impacted your process at all, or if you think there's

513
00:23:34,600 --> 00:23:38,359
Speaker 8: like another frontier of data to be collected with AI.

514
00:23:38,720 --> 00:23:41,520
Speaker 5: In terms of I divide the AI conversation into two

515
00:23:41,600 --> 00:23:42,919
Speaker 5: categories and we don't have to spend a lot of

516
00:23:42,920 --> 00:23:44,600
Speaker 5: time in this But one is like AI tools that

517
00:23:44,640 --> 00:23:46,919
Speaker 5: help you do what you're already doing, which I use

518
00:23:47,000 --> 00:23:49,879
Speaker 5: that a lot, like I have used AI to help

519
00:23:50,200 --> 00:23:52,520
Speaker 5: streamline my own workflows of what I'm doing. But the second,

520
00:23:52,560 --> 00:23:54,800
Speaker 5: which is the more important one in this conversation, is

521
00:23:55,920 --> 00:23:58,840
Speaker 5: like handing the keys to AI to do stuff that

522
00:23:58,920 --> 00:24:03,120
Speaker 5: you're not currently doing, or to like find new discoveries

523
00:24:03,160 --> 00:24:04,720
Speaker 5: that maybe you don't have the time to do on

524
00:24:04,760 --> 00:24:07,080
Speaker 5: your own. And in that sense, in terms of like

525
00:24:07,119 --> 00:24:11,680
Speaker 5: baking AI into stuff on my website, in terms of

526
00:24:11,720 --> 00:24:16,920
Speaker 5: it doing certain functionality, I feel like, compared to the

527
00:24:16,960 --> 00:24:21,240
Speaker 5: regular sort of tech world, sports analytics and coaches are

528
00:24:21,359 --> 00:24:23,280
Speaker 5: very very slow to adopt that, and I think for

529
00:24:23,440 --> 00:24:27,320
Speaker 5: probably good reason of a coach is not going to

530
00:24:27,359 --> 00:24:31,120
Speaker 5: trust an AI chat bot to recommend players to them

531
00:24:31,119 --> 00:24:34,080
Speaker 5: in the portal based on data. Right, even if there

532
00:24:34,160 --> 00:24:37,840
Speaker 5: is some validity to that, there is so much nuance

533
00:24:37,920 --> 00:24:40,480
Speaker 5: that goes into it that they even if they maybe

534
00:24:40,480 --> 00:24:43,040
Speaker 5: should trust AI, they just won't. They want to talk

535
00:24:43,080 --> 00:24:46,359
Speaker 5: to a human. They want to talk to interact with

536
00:24:46,400 --> 00:24:49,600
Speaker 5: someone who can bounce ideas off of them in a

537
00:24:49,840 --> 00:24:51,720
Speaker 5: I don't know, more human way, I guess, so I

538
00:24:51,720 --> 00:24:54,600
Speaker 5: feel like the coaches that I would often work with

539
00:24:54,640 --> 00:24:57,000
Speaker 5: would probably be pretty slow to adopt sort of stuff

540
00:24:57,040 --> 00:24:59,720
Speaker 5: like that, And so for that reason, I don't predict

541
00:25:00,400 --> 00:25:02,600
Speaker 5: that we're going to see a lot of AI tools

542
00:25:02,640 --> 00:25:06,639
Speaker 5: they're going to quickly kind of redefine college basketball, at

543
00:25:06,720 --> 00:25:09,680
Speaker 5: least in terms of big decisions that are being made

544
00:25:10,040 --> 00:25:11,720
Speaker 5: coaching wise, roster wise, things like that.

545
00:25:12,480 --> 00:25:14,240
Speaker 7: Evan, the last question I have for you. This is

546
00:25:14,280 --> 00:25:19,480
Speaker 7: a betting podcast, and oftentimes we go through deep dives

547
00:25:19,520 --> 00:25:22,840
Speaker 7: of matchups using analytics, using things that you can quantify

548
00:25:22,840 --> 00:25:26,439
Speaker 7: in the spreadsheets, But there are other mitigating factors that

549
00:25:26,480 --> 00:25:30,200
Speaker 7: are hard to quantify, and sometimes the eyeball test takes over.

550
00:25:30,280 --> 00:25:32,680
Speaker 7: So let's just say, maybe not a bet, but let's

551
00:25:32,680 --> 00:25:34,960
Speaker 7: say you're going to make a declaration about a team

552
00:25:35,160 --> 00:25:37,840
Speaker 7: or a matchup. Are there ever times where you may

553
00:25:37,920 --> 00:25:42,199
Speaker 7: override the model and possibly trust it less in favor

554
00:25:42,200 --> 00:25:45,280
Speaker 7: of the eyeball test or some other situational factors that

555
00:25:45,400 --> 00:25:47,800
Speaker 7: can be hard to quantify in the metric sheet.

556
00:25:48,280 --> 00:25:49,000
Speaker 4: Yeah, certainly.

557
00:25:49,119 --> 00:25:53,959
Speaker 5: I think my general stance on eyeball versus data is

558
00:25:53,960 --> 00:25:59,080
Speaker 5: that your most informed, accurate opinion or knowledge, if you will,

559
00:25:59,119 --> 00:26:01,320
Speaker 5: is when you combine those two things together. You take

560
00:26:01,359 --> 00:26:04,080
Speaker 5: the objective data tools like what I have and you

561
00:26:04,080 --> 00:26:06,240
Speaker 5: can find on other sites and things like that, and

562
00:26:06,320 --> 00:26:08,600
Speaker 5: you combine it with your own expert knowledge of the field,

563
00:26:08,680 --> 00:26:10,280
Speaker 5: your eyeball test, if you want to call it that,

564
00:26:10,680 --> 00:26:13,800
Speaker 5: and whether that's from a coach's perspective or a fan's

565
00:26:13,840 --> 00:26:17,480
Speaker 5: perspective or a better's perspective. Marrying the two gets you

566
00:26:17,520 --> 00:26:19,760
Speaker 5: to the most informed, accurate place. And I don't think

567
00:26:19,760 --> 00:26:21,639
Speaker 5: you should blindly trust the data and throw out your

568
00:26:21,800 --> 00:26:24,199
Speaker 5: eye test. I also think you shouldn't just throw out

569
00:26:24,240 --> 00:26:26,240
Speaker 5: all the data because you disagree with one data point

570
00:26:26,280 --> 00:26:28,600
Speaker 5: and therefore say it's all useless and only go with

571
00:26:28,640 --> 00:26:30,400
Speaker 5: your eye test. I think you should combine them both.

572
00:26:31,080 --> 00:26:33,040
Speaker 5: For me, because so much of what I do I

573
00:26:33,200 --> 00:26:36,240
Speaker 5: understand very intimately, all the data that I have, and

574
00:26:36,359 --> 00:26:39,120
Speaker 5: everything is ultimately built in a way that I think

575
00:26:39,200 --> 00:26:41,760
Speaker 5: I want to align with the eye test or the

576
00:26:42,119 --> 00:26:44,080
Speaker 5: expert knowledge of the field, if you will. That's why

577
00:26:44,080 --> 00:26:46,000
Speaker 5: I watch so many college basketball games. I don't just

578
00:26:46,320 --> 00:26:48,000
Speaker 5: let these numbers crunch on their own and I don't

579
00:26:48,000 --> 00:26:50,040
Speaker 5: actually pay attention. I want it to be driven by

580
00:26:50,480 --> 00:26:53,520
Speaker 5: my own kind of insights and discovery of what's going

581
00:26:53,560 --> 00:26:57,679
Speaker 5: on in college basketball. So I agree, quote unquote or

582
00:26:57,720 --> 00:27:00,520
Speaker 5: trust probably ninety to ninety five percent the stuff that

583
00:27:00,520 --> 00:27:02,000
Speaker 5: I see. I take it at face value, and I

584
00:27:02,000 --> 00:27:05,439
Speaker 5: say that's probably right. There are exceptions, though, where it's like, oh,

585
00:27:05,480 --> 00:27:08,120
Speaker 5: I'm watching this team play and though they're ranked six.

586
00:27:08,200 --> 00:27:11,560
Speaker 5: I mean, like an example would be right now, like

587
00:27:11,600 --> 00:27:15,080
Speaker 5: a Houston I have ranked as the fifth best team

588
00:27:15,119 --> 00:27:18,200
Speaker 5: in the country, but they haven't felt like the fifth

589
00:27:18,200 --> 00:27:19,919
Speaker 5: best team in the country the last several games when

590
00:27:19,920 --> 00:27:22,240
Speaker 5: I've watched them, and they seem like they have bigger

591
00:27:22,240 --> 00:27:25,119
Speaker 5: holes than that. Ultimately, does that mean I'm slotting them

592
00:27:25,119 --> 00:27:27,000
Speaker 5: down to fifteenth in my mind because that's how good

593
00:27:27,040 --> 00:27:29,399
Speaker 5: they looked. No, but it probably will give me a

594
00:27:29,440 --> 00:27:32,880
Speaker 5: little bit of slightly more hesitation about maybe picking them

595
00:27:32,880 --> 00:27:35,359
Speaker 5: to go further in my bracket than if I didn't

596
00:27:35,400 --> 00:27:38,760
Speaker 5: have hadn't had that experience of watching them. So I

597
00:27:38,800 --> 00:27:42,120
Speaker 5: typically don't like journey that far from where the data says,

598
00:27:42,119 --> 00:27:44,600
Speaker 5: but I am willing to kind of tweak it a

599
00:27:44,600 --> 00:27:46,280
Speaker 5: little bit in my mind based on what I've actually

600
00:27:46,280 --> 00:27:47,040
Speaker 5: experienced from watching.

601
00:27:47,080 --> 00:27:49,240
Speaker 4: If that makes sense, really.

602
00:27:49,000 --> 00:27:50,760
Speaker 7: Does make sense. I mean, I've said it for years

603
00:27:50,800 --> 00:27:55,159
Speaker 7: that sports betting, particularly at the collegiate level. There is

604
00:27:55,200 --> 00:27:57,600
Speaker 7: an art to it, and there is an element of

605
00:27:58,160 --> 00:28:00,359
Speaker 7: the gut or what you see with your eye. So

606
00:28:00,520 --> 00:28:03,320
Speaker 7: it's reassuring to hear you say that. At times that

607
00:28:03,520 --> 00:28:06,119
Speaker 7: can be just as important in marrying those two together.

608
00:28:06,200 --> 00:28:07,919
Speaker 7: So I really appreciate you walking through that.

609
00:28:09,200 --> 00:28:11,320
Speaker 3: Just from a visual standpoint, I love when you throw

610
00:28:11,320 --> 00:28:13,600
Speaker 3: in the emojis on teams where it's like the head

611
00:28:13,640 --> 00:28:16,439
Speaker 3: bandage they're dealing with injuries, they're in the freezer. I'm

612
00:28:16,440 --> 00:28:18,760
Speaker 3: gonna throw one extra one in there. Because Houston I'm

613
00:28:18,840 --> 00:28:20,840
Speaker 3: very interested in. I followed every one of their games

614
00:28:20,840 --> 00:28:22,399
Speaker 3: this year. I have a seventeen to one ticket for

615
00:28:22,440 --> 00:28:24,520
Speaker 3: them to win it. I would throw a brick wall

616
00:28:24,560 --> 00:28:27,320
Speaker 3: emoji next to them because as Kingston Flemings hit that

617
00:28:27,400 --> 00:28:30,520
Speaker 3: freshman wall, because the last couple of games, his shot selection,

618
00:28:30,920 --> 00:28:33,640
Speaker 3: his confidence, it's just he's going from a guy who's

619
00:28:33,680 --> 00:28:35,919
Speaker 3: dropped forty plus in a game where it looks like

620
00:28:36,240 --> 00:28:38,640
Speaker 3: he was an NBA All Star to being a little

621
00:28:38,640 --> 00:28:40,760
Speaker 3: bit overwhelmed. But it shouldn't come as a shock just

622
00:28:40,760 --> 00:28:42,440
Speaker 3: because these guys have such high ceilings.

623
00:28:42,520 --> 00:28:43,840
Speaker 1: They're still eighteen year olds.

624
00:28:43,920 --> 00:28:46,600
Speaker 3: And this used to be a common conversation piece in

625
00:28:46,640 --> 00:28:49,720
Speaker 3: collegeball and college basketball that freshmen you're gonna have those

626
00:28:49,720 --> 00:28:51,880
Speaker 3: freshman nights. So is that what you think is going

627
00:28:51,920 --> 00:28:55,280
Speaker 3: on with Houston just from a personnel standpoint beyond just

628
00:28:55,320 --> 00:28:55,840
Speaker 3: the numbers.

629
00:28:56,280 --> 00:28:57,080
Speaker 4: Yeah, I think so.

630
00:28:57,960 --> 00:29:00,840
Speaker 5: You know, I think probably it's a bit exaggerated, and

631
00:29:00,880 --> 00:29:03,120
Speaker 5: I think that's the truth is probably somewhere in the middle.

632
00:29:04,080 --> 00:29:05,280
Speaker 4: But I think this is part of.

633
00:29:05,200 --> 00:29:10,040
Speaker 5: Why I have preseason projections on my site that still

634
00:29:10,080 --> 00:29:11,640
Speaker 5: carry weight all the way to the very end of

635
00:29:11,680 --> 00:29:13,960
Speaker 5: the season. So, for example, if you go to Ken

636
00:29:13,960 --> 00:29:17,120
Speaker 5: Palm right now, I don't think there's any preseason bias

637
00:29:17,160 --> 00:29:18,560
Speaker 5: if you want to call that baked into the team

638
00:29:18,640 --> 00:29:20,960
Speaker 5: ratings at this point, Nora should there be from a

639
00:29:21,000 --> 00:29:24,120
Speaker 5: sense of like fairly evaluating team's resumes and it's being

640
00:29:24,160 --> 00:29:26,720
Speaker 5: used on the team sheets by the committee on my website.

641
00:29:26,760 --> 00:29:28,000
Speaker 4: It is still a factor.

642
00:29:28,000 --> 00:29:31,280
Speaker 5: It's a small factor, but there is still an element

643
00:29:31,320 --> 00:29:33,360
Speaker 5: of what were the expectations for this player this team

644
00:29:33,360 --> 00:29:36,719
Speaker 5: in the preseason That still does matter because oftentimes teams

645
00:29:36,840 --> 00:29:39,360
Speaker 5: kind of revert to preseason expectation in terms of their

646
00:29:39,360 --> 00:29:42,280
Speaker 5: overall roster talent, in terms of expectations for certain players.

647
00:29:42,800 --> 00:29:44,760
Speaker 5: So there was a point earlier in this season when

648
00:29:44,840 --> 00:29:47,280
Speaker 5: I think I had Kingston Flemings based on again, he

649
00:29:47,400 --> 00:29:49,680
Speaker 5: was not supposed to be a top five freshman in

650
00:29:49,720 --> 00:29:51,920
Speaker 5: the sport. He was very far outside of that range.

651
00:29:52,080 --> 00:29:55,800
Speaker 5: And more extreme example would be Keaton Woggler. You know,

652
00:29:56,080 --> 00:29:59,120
Speaker 5: I had Kingston Flemings as being a much worse player

653
00:29:59,160 --> 00:30:01,080
Speaker 5: in the preseason than he's not to be. But when

654
00:30:01,080 --> 00:30:03,280
Speaker 5: we got to late December and it was like this

655
00:30:03,360 --> 00:30:06,440
Speaker 5: kid's lighting the world on fire, I probably only had

656
00:30:06,520 --> 00:30:08,760
Speaker 5: him rated as a top twenty or twenty five player

657
00:30:08,760 --> 00:30:12,280
Speaker 5: in the sport. And it's purely because it's typically very

658
00:30:12,840 --> 00:30:15,840
Speaker 5: unlikely that a player ranked where he was ranked out

659
00:30:15,840 --> 00:30:17,920
Speaker 5: of high school was able to sustain the level of

660
00:30:17,920 --> 00:30:20,239
Speaker 5: performance the entire season that we had seen from him

661
00:30:20,320 --> 00:30:22,480
Speaker 5: up to that point. Well, guess what, that's kind of

662
00:30:22,520 --> 00:30:24,760
Speaker 5: what's happening now. He's faltering a little bit. And again

663
00:30:24,800 --> 00:30:26,600
Speaker 5: that's just one data point, but that's an example of

664
00:30:26,640 --> 00:30:29,520
Speaker 5: how kind of grounding in reality and not getting over

665
00:30:30,200 --> 00:30:33,800
Speaker 5: or over exaggerating or overreacting to a certain player's performance

666
00:30:34,080 --> 00:30:36,800
Speaker 5: or team's performance, you can kind of get carried away

667
00:30:36,840 --> 00:30:38,680
Speaker 5: with it. And so and now I would argue the

668
00:30:38,720 --> 00:30:41,440
Speaker 5: opposite side of like Kingston Flemings is a better player

669
00:30:41,480 --> 00:30:43,200
Speaker 5: than what we've seen the last four to five games,

670
00:30:43,240 --> 00:30:44,880
Speaker 5: this is not the truth for him going forward.

671
00:30:45,120 --> 00:30:46,560
Speaker 4: The truth is often somewhere in the middle.

672
00:30:48,280 --> 00:30:53,520
Speaker 8: So I understand how injuries can be impacted into the model.

673
00:30:53,520 --> 00:30:55,480
Speaker 8: But something that I always think is interesting in an

674
00:30:55,600 --> 00:30:58,560
Speaker 8: edge I try to find is just how teams play

675
00:30:58,600 --> 00:31:02,120
Speaker 8: different or a different like using the rotation differently. Like

676
00:31:02,440 --> 00:31:05,200
Speaker 8: an example from last night, my guy to Barrett from

677
00:31:05,200 --> 00:31:08,600
Speaker 8: Mazoo went for twenty eight. He was playing like he

678
00:31:08,600 --> 00:31:10,480
Speaker 8: was averaging four points a game coming off the bench,

679
00:31:10,600 --> 00:31:13,680
Speaker 8: like they lost him Ole Miss he played eight minutes,

680
00:31:13,720 --> 00:31:16,040
Speaker 8: like they beat Florida and he only played eight minutes.

681
00:31:16,360 --> 00:31:17,920
Speaker 8: So that's something like if they were to meet again

682
00:31:17,960 --> 00:31:20,000
Speaker 8: the SEC tournament, that's a storyline I'm looking at many

683
00:31:20,040 --> 00:31:22,240
Speaker 8: Obiseki on A and M that was the same thing.

684
00:31:22,320 --> 00:31:24,760
Speaker 8: He like became a different player. Is there any way

685
00:31:24,840 --> 00:31:27,720
Speaker 8: that is impacted or is that something that I'm smart

686
00:31:27,800 --> 00:31:30,760
Speaker 8: for trying to like figure out on my own because

687
00:31:30,760 --> 00:31:32,440
Speaker 8: the models aren't figuring those things out.

688
00:31:32,680 --> 00:31:34,960
Speaker 5: I think that's one of those cases where there can

689
00:31:35,040 --> 00:31:38,520
Speaker 5: be a few specific examples each season where that you

690
00:31:38,560 --> 00:31:40,800
Speaker 5: can identify that and can work in your favor, but

691
00:31:40,880 --> 00:31:44,680
Speaker 5: it's very hard to system wide identify that across maybe

692
00:31:44,840 --> 00:31:47,200
Speaker 5: two hundred or five hundred cases and for it to

693
00:31:47,200 --> 00:31:49,480
Speaker 5: be very accurate each time. So I think that is

694
00:31:49,520 --> 00:31:51,120
Speaker 5: one of those cases where going back to the whole

695
00:31:51,120 --> 00:31:54,000
Speaker 5: eye test thing, it's probably sticking out to you because

696
00:31:54,000 --> 00:31:56,680
Speaker 5: you're identifying it already by the fact you're watching that

697
00:31:56,720 --> 00:32:00,000
Speaker 5: game closely or watching that team closely, and you understand

698
00:32:00,080 --> 00:32:02,320
Speaker 5: and more exactly how they work on a game to

699
00:32:02,360 --> 00:32:04,400
Speaker 5: game basis. And so that's that's an example where you

700
00:32:04,880 --> 00:32:06,760
Speaker 5: maybe have a little bit of an edge over a

701
00:32:06,800 --> 00:32:09,400
Speaker 5: computer quote unquote because you're able to see that sort

702
00:32:09,400 --> 00:32:09,640
Speaker 5: of thing.

703
00:32:10,040 --> 00:32:12,120
Speaker 8: And then I have to ask you this, how has

704
00:32:12,600 --> 00:32:19,040
Speaker 8: your quest for making the best model possible like impacted

705
00:32:20,000 --> 00:32:22,479
Speaker 8: your experience as a basketball consumer? Because like I can

706
00:32:22,480 --> 00:32:23,800
Speaker 8: tell by the way you tweet, the way you talk

707
00:32:23,840 --> 00:32:26,160
Speaker 8: that you like really understand the game and you watch,

708
00:32:26,480 --> 00:32:29,920
Speaker 8: But like, how do you prevent yourself from becoming like

709
00:32:30,040 --> 00:32:32,040
Speaker 8: a total you know what I'm saying, keeping that like

710
00:32:32,480 --> 00:32:36,560
Speaker 8: that boy nice part of yourself you know in the mix?

711
00:32:36,880 --> 00:32:41,800
Speaker 5: Sure, I really really try hard to not cheer for

712
00:32:41,920 --> 00:32:45,840
Speaker 5: results that would prove me right, like I really really

713
00:32:45,880 --> 00:32:48,080
Speaker 5: try hard, because the reality is, I'm not just a

714
00:32:49,160 --> 00:32:53,360
Speaker 5: media pontificator who puts out articles in the preseason or

715
00:32:53,400 --> 00:32:55,640
Speaker 5: as a talking head and make certain predictions, and there's

716
00:32:55,760 --> 00:32:57,320
Speaker 5: you know, ten to fifteen of them I.

717
00:32:57,280 --> 00:32:57,840
Speaker 4: Really care about.

718
00:32:57,840 --> 00:32:59,640
Speaker 5: You can literally look at every single team and every

719
00:32:59,640 --> 00:33:02,600
Speaker 5: single play and see exactly what I think about all

720
00:33:02,640 --> 00:33:04,480
Speaker 5: of them, and a lot of them are going to

721
00:33:04,520 --> 00:33:06,640
Speaker 5: be right, and a slightly less percentage of them will

722
00:33:06,640 --> 00:33:08,440
Speaker 5: be wrong, and it is impossible for me to get

723
00:33:08,480 --> 00:33:11,680
Speaker 5: them all right. So it takes the joy away from

724
00:33:11,720 --> 00:33:14,920
Speaker 5: college basketball if I'm cheering for things to be exactly

725
00:33:14,960 --> 00:33:17,840
Speaker 5: as I'm expecting or predicting them to be, and that

726
00:33:17,880 --> 00:33:20,280
Speaker 5: would certainly carry I mean, it makes sense in March madness,

727
00:33:20,320 --> 00:33:22,840
Speaker 5: right if I'm predicting that the four to one seeds

728
00:33:22,840 --> 00:33:24,920
Speaker 5: are the best teams in the tournament. It would be

729
00:33:24,960 --> 00:33:26,960
Speaker 5: a dumb if I'm cheering for those four to one

730
00:33:27,000 --> 00:33:28,840
Speaker 5: seeds to make the final four, because.

731
00:33:28,800 --> 00:33:30,240
Speaker 4: Like that's less fun.

732
00:33:30,400 --> 00:33:33,560
Speaker 5: Right, So even though that's what I'm predicting, I don't

733
00:33:33,560 --> 00:33:36,520
Speaker 5: care if that happens because I ultimately I trust the

734
00:33:36,560 --> 00:33:38,280
Speaker 5: body of work that I'm doing, and it's going to

735
00:33:38,360 --> 00:33:40,400
Speaker 5: be right more often than it's wrong, and I'll let

736
00:33:40,440 --> 00:33:42,720
Speaker 5: it do its thing and I will still enjoy the

737
00:33:42,800 --> 00:33:45,760
Speaker 5: randomness and unpredictableness of college basketball. And if I'm not

738
00:33:45,880 --> 00:33:49,240
Speaker 5: being super wrong on a team, great. If I picked

739
00:33:49,240 --> 00:33:51,280
Speaker 5: a team to be bad in the preseason and they're awesome,

740
00:33:51,320 --> 00:33:52,680
Speaker 5: I don't want to be mad about that.

741
00:33:52,680 --> 00:33:54,240
Speaker 4: I want to cheer for them. This is great. So

742
00:33:54,600 --> 00:33:56,040
Speaker 4: I really do try and fight against that.

743
00:33:56,920 --> 00:33:59,400
Speaker 3: All right, us three talking heads here on the Big

744
00:33:59,440 --> 00:34:01,760
Speaker 3: Bets on this podcast, have a lightning round for you

745
00:34:01,800 --> 00:34:04,080
Speaker 3: before you get out of here. First question, if you

746
00:34:04,120 --> 00:34:06,840
Speaker 3: had the power, would you expand the NCAA tournament or

747
00:34:06,920 --> 00:34:09,279
Speaker 3: would you have a constitutional amendment to keep it at

748
00:34:09,320 --> 00:34:10,640
Speaker 3: sixty eight teams moving forward?

749
00:34:10,800 --> 00:34:11,960
Speaker 4: No? Please, no expansion.

750
00:34:12,000 --> 00:34:16,440
Speaker 5: Come on, I don't see any real argument for it

751
00:34:16,480 --> 00:34:19,319
Speaker 5: bringing more majors into the tournament. We've not seen that

752
00:34:19,400 --> 00:34:22,719
Speaker 5: be practiced by the committee in the past. And additionally,

753
00:34:22,880 --> 00:34:25,719
Speaker 5: you know, I was at the mock selection committee exercise

754
00:34:25,760 --> 00:34:27,600
Speaker 5: on Thursday, and it's like every year we look at

755
00:34:27,600 --> 00:34:29,239
Speaker 5: the bubble and these teams stink. We don't need more

756
00:34:29,280 --> 00:34:31,360
Speaker 5: stinky teams, so keep it the same size.

757
00:34:32,120 --> 00:34:32,920
Speaker 1: How did you come up.

758
00:34:32,880 --> 00:34:36,160
Speaker 3: With the kill shot slash avalanche moniker. What was the

759
00:34:36,200 --> 00:34:37,279
Speaker 3: influence that went into that?

760
00:34:38,080 --> 00:34:42,560
Speaker 5: I think it started back in twenty twenty one maybe,

761
00:34:42,600 --> 00:34:46,600
Speaker 5: and I was watching one of those those Illinois teams

762
00:34:46,880 --> 00:34:50,640
Speaker 5: with like Iodasumu and they were I think this is

763
00:34:50,680 --> 00:34:53,560
Speaker 5: the year before they were really good, and they were

764
00:34:53,680 --> 00:34:55,920
Speaker 5: a team that just felt like, man, they go on

765
00:34:55,960 --> 00:34:58,319
Speaker 5: big scoring runs, they give up momentum quickly, and I

766
00:34:58,400 --> 00:34:59,719
Speaker 5: was like, Okay, is there a way I can actual

767
00:34:59,760 --> 00:35:02,160
Speaker 5: quid this? And that's what led to saying, if I

768
00:35:02,239 --> 00:35:04,200
Speaker 5: just measure every time a team goes on a ten

769
00:35:04,320 --> 00:35:07,600
Speaker 5: er run, like, how does that actually present across the

770
00:35:07,600 --> 00:35:09,839
Speaker 5: whole season? And is there actually value in finding that?

771
00:35:10,200 --> 00:35:12,359
Speaker 5: So it this was an example of a metric that

772
00:35:12,400 --> 00:35:15,280
Speaker 5: came from me just like watching and feeling the momentum

773
00:35:15,400 --> 00:35:18,040
Speaker 5: shift in a game and being like, this is something

774
00:35:18,120 --> 00:35:19,480
Speaker 5: that we experience as fans.

775
00:35:19,520 --> 00:35:19,839
Speaker 4: Is their way.

776
00:35:19,880 --> 00:35:23,560
Speaker 5: I can actually measure this and compare this and evalidate

777
00:35:23,600 --> 00:35:26,080
Speaker 5: what we're feeling. And so that's kind of how it started,

778
00:35:26,160 --> 00:35:27,840
Speaker 5: was just sort of you know, watching games, having the

779
00:35:27,880 --> 00:35:30,799
Speaker 5: experience and then saying, you know, can I actually measure this?

780
00:35:30,960 --> 00:35:33,680
Speaker 1: So top seed right now?

781
00:35:33,760 --> 00:35:36,359
Speaker 3: Forget about what the ap pole says, you know that

782
00:35:36,440 --> 00:35:39,279
Speaker 3: you know the top eight, ten, twelve, whatever teams you

783
00:35:39,280 --> 00:35:42,920
Speaker 3: want to pick from, are headed towards the tournament with

784
00:35:43,160 --> 00:35:46,080
Speaker 3: incredible resumes, great personnel, coaching.

785
00:35:45,680 --> 00:35:47,360
Speaker 1: Acumen, YadA, YadA, YadA.

786
00:35:47,520 --> 00:35:50,080
Speaker 3: One of them has to give you pause where they

787
00:35:50,080 --> 00:35:52,400
Speaker 3: are right now in the season, that maybe they're not

788
00:35:52,480 --> 00:35:55,000
Speaker 3: such a lock for the second weekend as everyone else thinks.

789
00:35:55,160 --> 00:35:57,000
Speaker 1: Which team rises to the surface.

790
00:35:56,719 --> 00:35:58,800
Speaker 5: For you in that regard, So we're talking about teams

791
00:35:58,800 --> 00:36:01,000
Speaker 5: that would be on the like one, two, three seed lines,

792
00:36:01,040 --> 00:36:01,600
Speaker 5: that sort of thing.

793
00:36:01,800 --> 00:36:07,719
Speaker 4: Correct, Let me think about this. I feel like.

794
00:36:10,960 --> 00:36:13,880
Speaker 5: The team that I have the most concern over relative

795
00:36:13,920 --> 00:36:16,520
Speaker 5: to maybe where I feel like public perception is on them,

796
00:36:16,600 --> 00:36:19,120
Speaker 5: or at least how the fans of their own team feel,

797
00:36:19,160 --> 00:36:22,520
Speaker 5: is maybe Yukon. Yukon is right now slated to be

798
00:36:22,520 --> 00:36:24,600
Speaker 5: on the one seed line. I have them as the

799
00:36:24,680 --> 00:36:27,560
Speaker 5: just the eighth best team in the tournament or in

800
00:36:27,640 --> 00:36:30,960
Speaker 5: the nation. And additionally, a trend with them is they

801
00:36:31,000 --> 00:36:34,160
Speaker 5: play great in big games, but they don't play as

802
00:36:34,200 --> 00:36:36,600
Speaker 5: well in games against bad teams, so I think they

803
00:36:36,600 --> 00:36:38,920
Speaker 5: could be slightly more upset prone, and they've shown lapses

804
00:36:38,960 --> 00:36:42,160
Speaker 5: of concentration at times in games like that, so I

805
00:36:42,160 --> 00:36:43,879
Speaker 5: would be a little bit more worried about them maybe

806
00:36:43,960 --> 00:36:45,719
Speaker 5: than some of these other teams that they would slip

807
00:36:45,800 --> 00:36:47,040
Speaker 5: up and not make it to the Sweet sixteen.

808
00:36:47,200 --> 00:36:49,160
Speaker 3: And the more fun question on the other side of

809
00:36:49,200 --> 00:36:51,440
Speaker 3: the spectrum is what's a Cinderella.

810
00:36:50,960 --> 00:36:52,239
Speaker 1: Team that you really want them?

811
00:36:52,280 --> 00:36:53,920
Speaker 3: I know you don't root, but you would love to

812
00:36:53,960 --> 00:36:56,800
Speaker 3: see them clinch the automatic berth to get in because

813
00:36:56,800 --> 00:36:58,680
Speaker 3: if they win a low major league, you think they

814
00:36:58,719 --> 00:37:00,560
Speaker 3: could win a game in the round of sixty four.

815
00:37:01,360 --> 00:37:03,920
Speaker 5: I feel like I would go with there aren't really

816
00:37:03,960 --> 00:37:05,600
Speaker 5: a lot of teams that stick out to me right

817
00:37:05,600 --> 00:37:08,319
Speaker 5: now is from like low major conferences that I feel

818
00:37:08,400 --> 00:37:10,840
Speaker 5: very confident in their ability to win. I feel like

819
00:37:10,920 --> 00:37:13,359
Speaker 5: maybe a team that I want to get over the humps,

820
00:37:13,360 --> 00:37:18,360
Speaker 5: certainly because they've been there and they haven't had the cards,

821
00:37:18,360 --> 00:37:19,880
Speaker 5: haven't been played right by them in the past. But

822
00:37:19,920 --> 00:37:22,919
Speaker 5: it's Liberty. I love Liberty, I love Richie McKay, and

823
00:37:23,960 --> 00:37:27,000
Speaker 5: I have cheered for them in many a previous tournament

824
00:37:27,600 --> 00:37:29,960
Speaker 5: when they have been on that twelve line or something

825
00:37:30,000 --> 00:37:33,400
Speaker 5: like that, and they are on a incredible running conference

826
00:37:33,400 --> 00:37:35,520
Speaker 5: play this year, and so I think it would be

827
00:37:35,560 --> 00:37:38,240
Speaker 5: great if they could get in a maybe more often

828
00:37:38,360 --> 00:37:40,359
Speaker 5: or likely team would also be a Belmont, a very

829
00:37:40,360 --> 00:37:42,840
Speaker 5: good team, a team that also plays really well in

830
00:37:42,880 --> 00:37:45,480
Speaker 5: their best games this season. So I'm looking at Belmont

831
00:37:45,480 --> 00:37:46,680
Speaker 5: and Liberty as my two picks there.

832
00:37:47,280 --> 00:37:49,560
Speaker 3: Our producer David won't let you off the hook unless

833
00:37:49,600 --> 00:37:53,120
Speaker 3: you provide us your national title game and your champion.

834
00:37:53,239 --> 00:37:54,680
Speaker 3: Who ends up cutting down the nuts this.

835
00:37:54,719 --> 00:37:57,520
Speaker 5: Year Chalky right now, because we don't have the bracket,

836
00:37:57,560 --> 00:37:59,279
Speaker 5: but I'm taking Michigan and Duke. I think they're a

837
00:37:59,280 --> 00:38:01,239
Speaker 5: cut above everyone else, and if they were to play

838
00:38:01,280 --> 00:38:03,440
Speaker 5: again on a neutral I still think Michigan would be

839
00:38:03,440 --> 00:38:04,279
Speaker 5: the slightly better team.

840
00:38:04,320 --> 00:38:05,240
Speaker 4: So I'm picking Michigan.

841
00:38:05,960 --> 00:38:07,840
Speaker 3: Evan, thank you so much for joining the program. We

842
00:38:07,840 --> 00:38:10,080
Speaker 3: wouldn't be able to do our work without you doing

843
00:38:10,120 --> 00:38:12,520
Speaker 3: your work. We're just standing on the shoulders of giants,

844
00:38:12,760 --> 00:38:14,759
Speaker 3: just three talking heads here, just trying to pick some

845
00:38:14,800 --> 00:38:17,279
Speaker 3: winners in the college basketball space. Thank you so much

846
00:38:17,320 --> 00:38:20,280
Speaker 3: for joining us, and we'll be following all your content

847
00:38:20,400 --> 00:38:22,719
Speaker 3: very closely. As soon as those grafts come out and

848
00:38:22,760 --> 00:38:25,400
Speaker 3: there's the X and Y axis and which teams can

849
00:38:25,440 --> 00:38:27,960
Speaker 3: go to the final four, I immediately perk up.

850
00:38:28,000 --> 00:38:29,920
Speaker 1: So I love everything that you're doing. Thanks again for

851
00:38:29,960 --> 00:38:30,480
Speaker 1: shopping by.

852
00:38:30,600 --> 00:38:31,759
Speaker 4: I appreciate you, guys. Thank you.

853
00:38:32,800 --> 00:38:35,000
Speaker 3: Don't miss out on any of our best bets. By

854
00:38:35,040 --> 00:38:37,520
Speaker 3: signing up for Action Pro. Action Pro users get real

855
00:38:37,520 --> 00:38:40,160
Speaker 3: time alerts as soon as experts like myself, Stucky Duck

856
00:38:40,239 --> 00:38:42,480
Speaker 3: Christian any of us tracking anything in the app, You're

857
00:38:42,520 --> 00:38:44,359
Speaker 3: going to be first to know. You also get real

858
00:38:44,400 --> 00:38:46,840
Speaker 3: time money percentages to see where the smart money is flowing,

859
00:38:47,040 --> 00:38:50,360
Speaker 3: and access to our player projections powered by Sean Kerner

860
00:38:50,400 --> 00:38:53,480
Speaker 3: and his predictive analytics team. Doctor Nick also dropping off

861
00:38:53,520 --> 00:38:57,719
Speaker 3: his college basketball player props, which become invaluable during March Madness.

862
00:38:57,760 --> 00:39:00,239
Speaker 3: It's just been an absolute printing press. Want to be

863
00:39:00,360 --> 00:39:02,440
Speaker 3: able to get his picks as soon as they hit

864
00:39:02,480 --> 00:39:05,160
Speaker 3: the market because he is a market mover. Right now,

865
00:39:05,160 --> 00:39:07,239
Speaker 3: you can get twenty dollars off an annual subscription of

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867
00:39:10,000 --> 00:39:12,560
Speaker 3: Pro and use promo code Pod twenty to get started.

868
00:39:12,600 --> 00:39:15,600
Speaker 3: That's Promo code Pod twenty for twenty dollars off at

869
00:39:15,600 --> 00:39:17,719
Speaker 3: Actionetwork dot com slash Pro.

870
00:39:17,920 --> 00:39:18,600
Speaker 1: All right, it's been a.

871
00:39:18,560 --> 00:39:21,400
Speaker 3: Fun episode thus far cutting up with Evan Miyakawa, but

872
00:39:21,560 --> 00:39:23,719
Speaker 3: the main reason that most of you guys are here

873
00:39:23,760 --> 00:39:25,760
Speaker 3: is to hear some of our best bets from the Thursday,

874
00:39:25,800 --> 00:39:29,160
Speaker 3: Friday and Saturday card. Ring a rapid fire around the horn,

875
00:39:29,239 --> 00:39:31,600
Speaker 3: Duck get us started. I'm sure this is a nationally

876
00:39:31,640 --> 00:39:34,799
Speaker 3: ranked game. Just getting Chicago State Long Island talk to

877
00:39:34,800 --> 00:39:35,560
Speaker 3: me about the total?

878
00:39:36,680 --> 00:39:39,120
Speaker 7: Yeah, projected totals one forty three and a half. Long

879
00:39:39,160 --> 00:39:41,720
Speaker 7: Island does have the best defense in the Northeastern Conference,

880
00:39:41,719 --> 00:39:44,160
Speaker 7: and they really do well to limit success behind the

881
00:39:44,160 --> 00:39:47,200
Speaker 7: three point line. The Sharks have the best three point

882
00:39:47,239 --> 00:39:50,040
Speaker 7: defense in the conference and they're actually pretty decent at

883
00:39:50,040 --> 00:39:53,560
Speaker 7: protecting the rim. Long Island does block or alter more

884
00:39:53,560 --> 00:39:55,480
Speaker 7: shots than anybody else in this league.

885
00:39:55,640 --> 00:39:56,320
Speaker 1: Both of these.

886
00:39:56,200 --> 00:39:59,239
Speaker 7: Offenses are really bad at getting to the file a line,

887
00:39:59,280 --> 00:40:01,319
Speaker 7: so there's gonna be and chip cheap at the rim.

888
00:40:01,880 --> 00:40:04,560
Speaker 7: No freebe not as many freebies from the charity stripe.

889
00:40:04,760 --> 00:40:07,239
Speaker 7: Chicago State had a real spirited effort in their home

890
00:40:07,280 --> 00:40:09,839
Speaker 7: finale last week against Central Connecticut, but I could see

891
00:40:09,840 --> 00:40:11,680
Speaker 7: these guys throwing up a real stinker here.

892
00:40:12,120 --> 00:40:13,000
Speaker 1: In the first meeting.

893
00:40:13,080 --> 00:40:16,440
Speaker 7: Chicago State scored just fifty five points, but they had

894
00:40:16,480 --> 00:40:18,719
Speaker 7: thirty six at the under ten time stamp of the

895
00:40:18,760 --> 00:40:22,640
Speaker 7: second half. And this first matchup just never trended to

896
00:40:22,719 --> 00:40:25,480
Speaker 7: surpass the post to total of that game or this one.

897
00:40:25,760 --> 00:40:27,799
Speaker 7: Long Island is going to match the pace here, which

898
00:40:27,800 --> 00:40:31,879
Speaker 7: should be very slow, and I just feel like it's

899
00:40:31,920 --> 00:40:34,239
Speaker 7: going to be a real problem for Chicago State. They're

900
00:40:34,280 --> 00:40:36,479
Speaker 7: going to try to move the ball into the painted area,

901
00:40:36,520 --> 00:40:37,960
Speaker 7: They're not going to shoot a lot of threes. I

902
00:40:38,040 --> 00:40:40,080
Speaker 7: just don't see a lot of success here. They are

903
00:40:40,120 --> 00:40:43,279
Speaker 7: the fourth worst two point shooting team in the entire

904
00:40:43,360 --> 00:40:45,640
Speaker 7: country is Chicago State. I want the under in this.

905
00:40:45,640 --> 00:40:49,239
Speaker 3: Matchup, Christian, you're going top of the card Perdue Michigan State.

906
00:40:49,280 --> 00:40:51,239
Speaker 3: I kind of like Sparty depending on the number I'm

907
00:40:51,239 --> 00:40:53,320
Speaker 3: getting here, but you're actually targeting the total.

908
00:40:54,160 --> 00:40:56,759
Speaker 8: Yeah, I'll put my duck pants on here and taken under.

909
00:40:57,400 --> 00:41:00,879
Speaker 8: This is you know, it's great to have and I'm

910
00:41:00,920 --> 00:41:04,040
Speaker 8: all for a data back to pick. Sometimes gotta go

911
00:41:04,080 --> 00:41:06,399
Speaker 8: with the vibes. In Michigan State. Perdue games have been

912
00:41:07,040 --> 00:41:10,360
Speaker 8: gross in half court in gritty. The last season it

913
00:41:10,400 --> 00:41:12,040
Speaker 8: was seventy five to sixty six. They played a game

914
00:41:12,040 --> 00:41:14,919
Speaker 8: in the sixties in the Big Ten Tournament the year before.

915
00:41:16,000 --> 00:41:18,800
Speaker 8: I just don't trust Michigan State to just score effectively

916
00:41:18,840 --> 00:41:21,080
Speaker 8: really in any of these matchups. I understand Wisconsin just

917
00:41:21,160 --> 00:41:23,560
Speaker 8: dropped ninety on them when they had him at home,

918
00:41:23,600 --> 00:41:26,960
Speaker 8: so Purdue could obviously cook offensively and do their thing,

919
00:41:27,000 --> 00:41:29,319
Speaker 8: But just with the way Michigan State's bigs can can

920
00:41:29,560 --> 00:41:33,360
Speaker 8: guard in the post, I think it'll be difficult. Oscarclough

921
00:41:33,400 --> 00:41:36,560
Speaker 8: hasn't been much of anything lately, neither as Jacobson, so

922
00:41:37,880 --> 00:41:40,000
Speaker 8: stylistical will be interesting to see if Mission State tries

923
00:41:40,040 --> 00:41:41,719
Speaker 8: to get the ball out of TKRS and or let

924
00:41:41,800 --> 00:41:43,760
Speaker 8: him try to score one on one against Cooper and Cohler,

925
00:41:44,680 --> 00:41:48,520
Speaker 8: nonetheless very familiar opponents, I expect a half court, gritty game.

926
00:41:48,760 --> 00:41:49,359
Speaker 2: Take the under.

927
00:41:50,200 --> 00:41:52,280
Speaker 3: On Thursday, I'm heading to the Summit in North Dakota

928
00:41:52,320 --> 00:41:54,560
Speaker 3: State at Saint Thomas. The Tommy's laying two and a

929
00:41:54,600 --> 00:41:56,920
Speaker 3: half or three. The Bison and Tommy's are one hundred

930
00:41:56,920 --> 00:42:00,440
Speaker 3: and seventeenth one hundred and eighteenth respectively, and Ken Palm here.

931
00:42:00,440 --> 00:42:01,880
Speaker 3: If they go on a run and win out and

932
00:42:01,920 --> 00:42:04,960
Speaker 3: obviously cut down the nets. As the Summit Conference tournament champion,

933
00:42:05,080 --> 00:42:06,800
Speaker 3: there's a chance they could grab a thirteen seat. And

934
00:42:06,840 --> 00:42:09,200
Speaker 3: there's a big difference this year in overall quality in

935
00:42:09,239 --> 00:42:10,799
Speaker 3: terms of the opponent you're going to draw off you're

936
00:42:10,840 --> 00:42:14,520
Speaker 3: fourteen or thirteen, So bracket positioning matters. Now Round one

937
00:42:14,560 --> 00:42:16,680
Speaker 3: and Fargo, the Tommy's leaded half who is tied with

938
00:42:16,760 --> 00:42:19,279
Speaker 3: under nine minutes to go, Nick Janowitz missed a long

939
00:42:19,360 --> 00:42:21,560
Speaker 3: range three pointer at the buzzer that would have sent

940
00:42:21,560 --> 00:42:24,760
Speaker 3: it to overtime. So that first meeting demonstrated why David

941
00:42:24,840 --> 00:42:27,560
Speaker 3: Richmond's team for NDSU is so hard to kill. They

942
00:42:27,560 --> 00:42:30,600
Speaker 3: get so many second chance buckets. They're gobbling up fifteen

943
00:42:30,640 --> 00:42:32,520
Speaker 3: rebounds in that first game, and it's the reason why

944
00:42:32,560 --> 00:42:36,040
Speaker 3: they're ninth nationally. And extra scoring opportunities, which essentially is

945
00:42:36,080 --> 00:42:39,319
Speaker 3: the margin of offensive rebounds and forced turnovers visa v

946
00:42:39,440 --> 00:42:41,480
Speaker 3: what you give away to the opponent, they're plus five

947
00:42:41,520 --> 00:42:43,920
Speaker 3: point seven per game. So why step in front of

948
00:42:43,960 --> 00:42:44,959
Speaker 3: this NDSU train.

949
00:42:45,120 --> 00:42:45,960
Speaker 1: It's all about shooting.

950
00:42:46,000 --> 00:42:48,719
Speaker 3: The Tommies are first nationally in two point percentage, ninth

951
00:42:48,719 --> 00:42:51,400
Speaker 3: in shooting efficiency. They don't turn the ball over, fewer

952
00:42:51,400 --> 00:42:54,200
Speaker 3: than ten giveaways per game. And John Tower their coach.

953
00:42:54,600 --> 00:42:56,520
Speaker 3: He's a fascinating guy. If you don't know anything about

954
00:42:56,560 --> 00:42:59,520
Speaker 3: this guy, he has been there forever. He's also has

955
00:42:59,520 --> 00:43:03,160
Speaker 3: a degree in psychology in sports Performance. So he really

956
00:43:03,200 --> 00:43:05,279
Speaker 3: does a lot between the years for his players to

957
00:43:05,280 --> 00:43:07,279
Speaker 3: get them to believe that they're even better than they are.

958
00:43:07,600 --> 00:43:09,239
Speaker 3: And he's done a lot with this program. This is

959
00:43:09,239 --> 00:43:11,640
Speaker 3: the only program to jump from D three to D one.

960
00:43:11,880 --> 00:43:14,440
Speaker 3: They had that weird situation where their D three conference

961
00:43:14,520 --> 00:43:16,319
Speaker 3: was like, you guys are winning too much, get the

962
00:43:16,320 --> 00:43:18,200
Speaker 3: hell out of here. So they had nowhere to go

963
00:43:18,280 --> 00:43:19,799
Speaker 3: but all the way up to D one and they

964
00:43:19,920 --> 00:43:22,200
Speaker 3: pulled it off. This could be a potential for a

965
00:43:22,239 --> 00:43:25,279
Speaker 3: fourth straight year improving in their overall wins. This is

966
00:43:25,280 --> 00:43:28,200
Speaker 3: also a program under Tower that won the national championship

967
00:43:28,200 --> 00:43:30,160
Speaker 3: at the D three level. I think they're right on

968
00:43:30,200 --> 00:43:32,160
Speaker 3: the cusp of becoming a special team.

969
00:43:32,400 --> 00:43:33,319
Speaker 1: And when you look at their.

970
00:43:33,200 --> 00:43:37,479
Speaker 3: Two wins Nolan Minnesalle Nick Janowitz, they're microwaves. They both

971
00:43:37,520 --> 00:43:40,480
Speaker 3: had multiple thirty point games this year on their home floor.

972
00:43:40,520 --> 00:43:42,839
Speaker 3: By the way that Lee and Penny Anderson Arena, it's

973
00:43:42,880 --> 00:43:45,319
Speaker 3: one hundred and eighty three million dollar facility. The very

974
00:43:45,320 --> 00:43:47,400
Speaker 3: first year it's been opened to year, it's the best

975
00:43:47,440 --> 00:43:50,000
Speaker 3: in low major basketball and overall quality. This place is

976
00:43:50,040 --> 00:43:51,680
Speaker 3: going to be sold out. This place is going to

977
00:43:51,719 --> 00:43:54,360
Speaker 3: be a zoo. I think there's a real opportunity to

978
00:43:54,400 --> 00:43:57,160
Speaker 3: grab them anything below. Let's call it three and a half.

979
00:43:57,200 --> 00:43:59,279
Speaker 3: I think a play is on the Tommies here, All right,

980
00:43:59,360 --> 00:44:01,520
Speaker 3: duck back to you. Let's talk about another play that

981
00:44:01,560 --> 00:44:03,680
Speaker 3: you have on Friday. A team that we just you know,

982
00:44:03,719 --> 00:44:06,320
Speaker 3: wax poetic about so difficult to quantify.

983
00:44:06,400 --> 00:44:09,080
Speaker 1: Where at Miami of Ohio we talk in total? Are

984
00:44:09,080 --> 00:44:09,919
Speaker 1: we talking side here?

985
00:44:10,960 --> 00:44:13,160
Speaker 7: Well, the Spreadhawks have been covering at an alarming rate,

986
00:44:13,200 --> 00:44:16,200
Speaker 7: and their games had been going over. I didn't think

987
00:44:16,239 --> 00:44:18,560
Speaker 7: their last game against Eastern Michigan was going to go over.

988
00:44:18,640 --> 00:44:20,360
Speaker 7: I actually wrote up the under on that one, and

989
00:44:20,400 --> 00:44:23,040
Speaker 7: they did have a bad shooting night against Eastern Michigan.

990
00:44:23,400 --> 00:44:25,719
Speaker 7: Miami Ohio shot just five out of twenty eight from

991
00:44:25,719 --> 00:44:27,920
Speaker 7: deep and sixty eight percent from the foul line. They

992
00:44:27,960 --> 00:44:30,680
Speaker 7: eked out a road cover against EMU on the road,

993
00:44:31,239 --> 00:44:33,600
Speaker 7: and Ipsey is a place that they have struggled over

994
00:44:33,600 --> 00:44:35,880
Speaker 7: the past couple of years. Now they go back on

995
00:44:35,920 --> 00:44:38,000
Speaker 7: the road and they play against the worst defense in

996
00:44:38,080 --> 00:44:41,359
Speaker 7: the MAC in Western Michigan. This Western Michigan defense, they

997
00:44:41,360 --> 00:44:44,879
Speaker 7: cannot guard shooters, they can't defend the lane, and they

998
00:44:44,880 --> 00:44:47,439
Speaker 7: do not defend well without fouling what does this mean?

999
00:44:47,800 --> 00:44:51,719
Speaker 7: Antwan Woolfock and Brant Byers are going to have their

1000
00:44:51,719 --> 00:44:54,960
Speaker 7: way with Western Michigan in the lane. They should procure

1001
00:44:55,000 --> 00:44:56,959
Speaker 7: a ton of trips to the foul line in this game.

1002
00:44:57,280 --> 00:45:00,000
Speaker 7: Shooters are going to be open, and I fully expect

1003
00:45:00,120 --> 00:45:03,120
Speaker 7: Miami to be focused here. In the first matchup, they

1004
00:45:03,160 --> 00:45:05,719
Speaker 7: shot very poorly from three point range, just six out

1005
00:45:05,760 --> 00:45:08,560
Speaker 7: of twenty six, and they still scored eighty seven points.

1006
00:45:09,120 --> 00:45:12,280
Speaker 7: I look at Miami's defense. They did lock down Eastern Michigan,

1007
00:45:12,360 --> 00:45:15,600
Speaker 7: but Western has a much better offense and will match

1008
00:45:15,680 --> 00:45:18,160
Speaker 7: the blazing pace that Miami of Ohio wants to play

1009
00:45:18,200 --> 00:45:21,560
Speaker 7: with here. Miami should approach ninety ninety points or more

1010
00:45:21,600 --> 00:45:24,319
Speaker 7: than this one. I think we could see seventy five

1011
00:45:24,400 --> 00:45:27,000
Speaker 7: to eighty from Western Michigan. This one's going to echover

1012
00:45:27,040 --> 00:45:29,080
Speaker 7: with late game fouling with a projected total of one

1013
00:45:29,120 --> 00:45:30,560
Speaker 7: to sixty three. I want to go on the high

1014
00:45:30,560 --> 00:45:31,200
Speaker 7: side here.

1015
00:45:32,080 --> 00:45:34,560
Speaker 3: Christian, you're looking at another Big ten matchup on Friday night,

1016
00:45:34,600 --> 00:45:36,080
Speaker 3: Illinois against Michigan.

1017
00:45:36,440 --> 00:45:36,920
Speaker 1: The aligne.

1018
00:45:36,960 --> 00:45:40,760
Speaker 3: I scored a goddamn avalanche against UCLA twenty to nothing

1019
00:45:40,840 --> 00:45:44,040
Speaker 3: run and they couldn't win that game. The amount of shit,

1020
00:45:44,120 --> 00:45:46,600
Speaker 3: I caught online rightfully, So I was all Fad and

1021
00:45:46,680 --> 00:45:49,239
Speaker 3: Mick and just people sending me a little emojis of

1022
00:45:49,360 --> 00:45:51,600
Speaker 3: all it's a square pick. Yeah, when I'm up by

1023
00:45:51,600 --> 00:45:54,600
Speaker 3: twenty three points, I'm a loser at the ALIGNI there.

1024
00:45:55,040 --> 00:45:57,759
Speaker 3: I can't touch this game. I'm too clearly, too emotionally

1025
00:45:57,800 --> 00:46:00,000
Speaker 3: invested in the ups and downs of the fighting line

1026
00:46:00,000 --> 00:46:01,879
Speaker 3: and I and Brad Underwood at this point. But you're

1027
00:46:01,920 --> 00:46:04,040
Speaker 3: going in with them against Michigan. Tell me why you

1028
00:46:04,040 --> 00:46:05,400
Speaker 3: think it's the right time to buy.

1029
00:46:05,600 --> 00:46:06,040
Speaker 2: Well, Miked.

1030
00:46:06,040 --> 00:46:07,399
Speaker 8: I was going to message you when it was thirty

1031
00:46:07,400 --> 00:46:09,080
Speaker 8: three to ten and say, what a call on the

1032
00:46:09,120 --> 00:46:13,520
Speaker 8: alt line. Maybe I could have undid the jinks that

1033
00:46:13,560 --> 00:46:15,319
Speaker 8: the other people were giving you if I said that.

1034
00:46:15,440 --> 00:46:20,239
Speaker 8: But I Michigan has been tough for me to read.

1035
00:46:20,320 --> 00:46:23,880
Speaker 8: I kind of stopped betting Michigan games, like in November,

1036
00:46:24,000 --> 00:46:25,840
Speaker 8: once they started just beating people by forty.

1037
00:46:25,880 --> 00:46:28,919
Speaker 2: I was just kind of a little confused after.

1038
00:46:29,000 --> 00:46:30,839
Speaker 8: I mean it was it was, honestly, no the Penn

1039
00:46:30,840 --> 00:46:33,239
Speaker 8: State game, where after you're beating everybody forty, you go

1040
00:46:33,320 --> 00:46:35,880
Speaker 8: and play Penn State, one of the bottom teams of the ten,

1041
00:46:35,960 --> 00:46:41,200
Speaker 8: and you only win by two and then there's just

1042
00:46:41,239 --> 00:46:44,360
Speaker 8: I don't know if I've been able to identify what

1043
00:46:44,480 --> 00:46:47,600
Speaker 8: the issue I have with Michigan is, but I think

1044
00:46:47,640 --> 00:46:49,319
Speaker 8: it's just that you played a good team like Duke

1045
00:46:49,360 --> 00:46:51,719
Speaker 8: and do just looked like a much more cohesive, well

1046
00:46:51,760 --> 00:46:55,680
Speaker 8: coached team in.

1047
00:46:54,320 --> 00:46:56,920
Speaker 2: That you know environment where you can't just bully everyone.

1048
00:46:56,960 --> 00:46:59,160
Speaker 8: You can't just in Northwest say okay, we're gonna start

1049
00:46:59,160 --> 00:47:00,920
Speaker 8: trying now and get it done every possession and like

1050
00:47:00,960 --> 00:47:03,400
Speaker 8: lock them up. So if you told me that Michigan

1051
00:47:03,400 --> 00:47:05,120
Speaker 8: played Minnesota at home in a game they were twenty

1052
00:47:05,120 --> 00:47:07,239
Speaker 8: three point favorites where I decided to lay twenty three

1053
00:47:07,280 --> 00:47:08,680
Speaker 8: for the first time, I laid a big sparrou of

1054
00:47:08,760 --> 00:47:10,160
Speaker 8: Michigan and the only one by ten.

1055
00:47:10,520 --> 00:47:11,520
Speaker 2: And this is in a game where.

1056
00:47:11,360 --> 00:47:13,600
Speaker 8: They hit fourteen three, shooting forty two percent from deep,

1057
00:47:13,800 --> 00:47:17,000
Speaker 8: and out readbound of Minnesota by fifteen.

1058
00:47:17,280 --> 00:47:18,520
Speaker 2: So what was the problem.

1059
00:47:18,560 --> 00:47:20,759
Speaker 8: Minnesota was just took care of the basketball and they

1060
00:47:20,760 --> 00:47:24,760
Speaker 8: only had eight turnovers, and you know, they basically played

1061
00:47:24,760 --> 00:47:26,960
Speaker 8: six They played six guys, but it was mainly the

1062
00:47:27,000 --> 00:47:28,440
Speaker 8: whole starting five. But they just took care of the ball,

1063
00:47:28,520 --> 00:47:30,880
Speaker 8: executed the offense, and created some looks from from deep.

1064
00:47:32,120 --> 00:47:34,200
Speaker 8: You know, Tyson took ten threes, Dirk and took ten threes.

1065
00:47:34,239 --> 00:47:35,880
Speaker 8: Guys with some size they can shoot it. Guess what

1066
00:47:35,920 --> 00:47:38,120
Speaker 8: Illinois has better than literally anyone in the country, better

1067
00:47:38,160 --> 00:47:42,040
Speaker 8: than the Wisconsin team that went splashed city against Michigan.

1068
00:47:42,080 --> 00:47:44,040
Speaker 8: They've got bigs who can shoot it and space it out.

1069
00:47:44,040 --> 00:47:46,759
Speaker 8: I think Illinois can execute them in the half court,

1070
00:47:47,080 --> 00:47:48,960
Speaker 8: crash the offensive glass, and battle there.

1071
00:47:49,000 --> 00:47:50,440
Speaker 2: Maybe Michigan keeps them off.

1072
00:47:50,280 --> 00:47:53,040
Speaker 8: The offensive glass, which is a problem, but I trust

1073
00:47:53,040 --> 00:47:55,319
Speaker 8: Illinois's ability to keep Michigan out of transition, and I

1074
00:47:55,320 --> 00:47:57,520
Speaker 8: don't trust this Michigan team when they're not in transition.

1075
00:47:59,040 --> 00:48:00,759
Speaker 2: So I think Illinois wins out right at home.

1076
00:48:02,080 --> 00:48:04,640
Speaker 3: All right, I am going to go to the Mainlands

1077
00:48:04,840 --> 00:48:08,160
Speaker 3: talking about my Rainbow Warriors traveling to the Continental US.

1078
00:48:08,640 --> 00:48:11,280
Speaker 3: This is a no year postseason bracket kind of game

1079
00:48:11,360 --> 00:48:13,480
Speaker 3: because the Big West, you got to be a top

1080
00:48:13,520 --> 00:48:15,400
Speaker 3: two seed. The top two get a buy all the

1081
00:48:15,440 --> 00:48:18,680
Speaker 3: way to the semifinals, so being first or second is crucial.

1082
00:48:18,680 --> 00:48:21,560
Speaker 3: And Hawaii is currently in a three way tie. You know,

1083
00:48:21,560 --> 00:48:23,120
Speaker 3: with four games to play, there's not a lot of

1084
00:48:23,160 --> 00:48:23,680
Speaker 3: room for air.

1085
00:48:23,920 --> 00:48:24,560
Speaker 1: I know I'm going to.

1086
00:48:24,480 --> 00:48:26,680
Speaker 3: Get premium effort from them here, and the Bows have

1087
00:48:26,760 --> 00:48:29,239
Speaker 3: been okay on the road. I mean generally, the story,

1088
00:48:29,239 --> 00:48:31,920
Speaker 3: whether it's football or basketball, really any sport, is that

1089
00:48:32,000 --> 00:48:34,479
Speaker 3: long plane ride throws them off. They're four and five

1090
00:48:34,520 --> 00:48:37,000
Speaker 3: both straight up and against the spread. So for as

1091
00:48:37,040 --> 00:48:38,919
Speaker 3: much as been made about the travel, I think they've

1092
00:48:38,920 --> 00:48:41,440
Speaker 3: handled fairly well. They get the best three point defense

1093
00:48:41,520 --> 00:48:43,839
Speaker 3: in the Big West thirty six overall, and Ken Palm

1094
00:48:43,840 --> 00:48:47,319
Speaker 3: adjusts the defensive efficiency. Johnson really a deaf Nemi Shah.

1095
00:48:47,360 --> 00:48:49,080
Speaker 3: They have great size. We've talked about it a lot

1096
00:48:49,120 --> 00:48:51,720
Speaker 3: throughout the season. It's reflected in all their rebounding numbers.

1097
00:48:51,920 --> 00:48:53,640
Speaker 3: But I think Johnson is the key here. That's a

1098
00:48:53,640 --> 00:48:55,720
Speaker 3: by low spot for a game that I think should

1099
00:48:55,719 --> 00:48:58,080
Speaker 3: probably close as a pick them or maybe Hawaii minus one.

1100
00:48:58,360 --> 00:49:00,520
Speaker 3: He is twenty and ten upside, but he also has

1101
00:49:00,600 --> 00:49:04,200
Speaker 3: seven single digit scoring performance this year, and he's coming

1102
00:49:04,239 --> 00:49:06,520
Speaker 3: off an over five night against UC Santa Barbara game

1103
00:49:06,520 --> 00:49:09,080
Speaker 3: that they still won. Now, he played poorly against UC

1104
00:49:09,239 --> 00:49:12,160
Speaker 3: Davis at home, scored a season low four points, but

1105
00:49:12,200 --> 00:49:14,240
Speaker 3: his guards bailed him out. They shot forty four percent

1106
00:49:14,239 --> 00:49:16,040
Speaker 3: from three in that game. They get over the hump

1107
00:49:16,080 --> 00:49:17,680
Speaker 3: against the Aggies. I think if we get the best

1108
00:49:17,760 --> 00:49:20,080
Speaker 3: version of Johnson in this game. This is a runaway,

1109
00:49:20,200 --> 00:49:23,080
Speaker 3: but I don't necessarily need and in a lot of cases,

1110
00:49:23,120 --> 00:49:25,600
Speaker 3: this is what's tied to a low major play. It's

1111
00:49:25,640 --> 00:49:26,840
Speaker 3: one player or two players.

1112
00:49:26,880 --> 00:49:27,480
Speaker 1: I don't need that.

1113
00:49:27,600 --> 00:49:30,160
Speaker 3: Why plays great team basketball. I do think it will

1114
00:49:30,200 --> 00:49:32,640
Speaker 3: be a runaway and a sweat free cover if Johnson

1115
00:49:32,680 --> 00:49:34,920
Speaker 3: plays up to his potential. But I still got Bullock

1116
00:49:34,960 --> 00:49:36,960
Speaker 3: to spur the offense from the perimeter. So I'm gonna

1117
00:49:37,000 --> 00:49:39,200
Speaker 3: go with the Bow's there, all right, Duck, close us

1118
00:49:39,239 --> 00:49:41,360
Speaker 3: out here for your card on Saturday.

1119
00:49:42,080 --> 00:49:44,080
Speaker 1: Yuck, you're going with an air Force game.

1120
00:49:44,200 --> 00:49:46,600
Speaker 3: Oh my god, we're moving our way up from low

1121
00:49:46,640 --> 00:49:47,640
Speaker 3: major to mid major.

1122
00:49:47,719 --> 00:49:49,680
Speaker 1: But what does this team have? Like three or four

1123
00:49:49,719 --> 00:49:50,680
Speaker 1: wins the whole season.

1124
00:49:51,600 --> 00:49:53,880
Speaker 7: Money spends the same whether you bet on this game.

1125
00:49:53,719 --> 00:49:56,120
Speaker 1: Or Duke against n That's true. Money's green.

1126
00:49:57,120 --> 00:50:00,560
Speaker 7: Air Force goes to Wyoming. Projected totals one. We're going

1127
00:50:00,640 --> 00:50:04,280
Speaker 7: high side. Air Force has just completely quit playing any defense.

1128
00:50:04,320 --> 00:50:06,359
Speaker 7: We just saw it again on Tuesday night against San

1129
00:50:06,440 --> 00:50:09,399
Speaker 7: Jose State eighty six to eighty. That game went over

1130
00:50:09,480 --> 00:50:11,800
Speaker 7: with seven minutes to go in the second half. Eleven

1131
00:50:11,880 --> 00:50:14,280
Speaker 7: straight games that air Force has surrendered at least seventy

1132
00:50:14,440 --> 00:50:16,600
Speaker 7: nine points to their opponent, and I think we will

1133
00:50:16,600 --> 00:50:18,879
Speaker 7: see Wyoming score a ton of points here. Wyoming scored

1134
00:50:18,960 --> 00:50:22,000
Speaker 7: ninety two in its last home game against Fresno State,

1135
00:50:22,480 --> 00:50:25,520
Speaker 7: and the Pokes were very much more aggressive on the

1136
00:50:25,560 --> 00:50:28,279
Speaker 7: offensive end. Metisia Bellock and Gavin Gorez have been out

1137
00:50:28,280 --> 00:50:30,440
Speaker 7: of the fold for a while now for Wyoming, so

1138
00:50:30,520 --> 00:50:33,680
Speaker 7: the Cowboys are going to a smaller, quicker lineup, which

1139
00:50:33,760 --> 00:50:37,719
Speaker 7: should necessitate more possessions and more open looks from deep.

1140
00:50:37,840 --> 00:50:41,759
Speaker 7: Air Force just saw a team do this in San

1141
00:50:41,840 --> 00:50:44,440
Speaker 7: Jose State and the fly Boys were able to get

1142
00:50:44,440 --> 00:50:46,959
Speaker 7: into the painted area and the foul line. Air Force

1143
00:50:46,960 --> 00:50:49,640
Speaker 7: shot twenty one out of thirty five from two and

1144
00:50:49,719 --> 00:50:52,120
Speaker 7: fourteen out of twenty two from the stripe. I think

1145
00:50:52,239 --> 00:50:54,319
Speaker 7: we will see air Force be able to do that.

1146
00:50:54,480 --> 00:50:57,279
Speaker 7: And they did procure paint touches in the first meeting

1147
00:50:57,320 --> 00:51:00,479
Speaker 7: against Wyoming where they made twenty one out of thirty

1148
00:51:00,560 --> 00:51:03,719
Speaker 7: two shots from in close and they had twenty foul attempts.

1149
00:51:03,920 --> 00:51:07,320
Speaker 7: The Flyboy defense could not keep Wyoming off the charity stripe.

1150
00:51:07,360 --> 00:51:09,839
Speaker 7: And this is going to be high scoring. You're gonna

1151
00:51:09,840 --> 00:51:10,879
Speaker 7: want the over in this one.

1152
00:51:11,680 --> 00:51:13,839
Speaker 3: All right, let's close things, uh, Christian with a play

1153
00:51:13,840 --> 00:51:15,440
Speaker 3: out of the A ten for you, bust out the

1154
00:51:15,440 --> 00:51:19,399
Speaker 3: Cuban cigars castro is back for George Washington. You think

1155
00:51:19,400 --> 00:51:21,480
Speaker 3: that's going to be enough for them to hand Dayton

1156
00:51:21,520 --> 00:51:23,879
Speaker 3: a loss after the Flyers just secured the best win

1157
00:51:23,960 --> 00:51:26,680
Speaker 3: in the entire eight ten season handling Saint Louis. Saint

1158
00:51:26,719 --> 00:51:28,920
Speaker 3: Louis did not get off the bus. They got absolutely

1159
00:51:28,920 --> 00:51:32,200
Speaker 3: whooped in that game. The Billicans at this point in

1160
00:51:32,239 --> 00:51:34,520
Speaker 3: a bit of free fall, losing to Rhode Island and Dayton.

1161
00:51:34,600 --> 00:51:37,440
Speaker 3: But where are you in terms of Dayton? You know,

1162
00:51:37,560 --> 00:51:40,040
Speaker 3: maybe being in an emotional spot or is this.

1163
00:51:40,040 --> 00:51:41,280
Speaker 1: All castor related?

1164
00:51:41,360 --> 00:51:44,480
Speaker 3: Because you know, George Washington one of the revolutionaries I

1165
00:51:44,520 --> 00:51:47,600
Speaker 3: can't remember their name change. They played so much better

1166
00:51:47,600 --> 00:51:50,000
Speaker 3: with him on the floor. He's someone he's what almost

1167
00:51:50,040 --> 00:51:53,080
Speaker 3: seven feet tall, He can be a double double machine,

1168
00:51:53,120 --> 00:51:55,360
Speaker 3: and from a defensive perspective, he has three and a

1169
00:51:55,360 --> 00:51:57,440
Speaker 3: half stocks per game. So he's someone that you have

1170
00:51:57,480 --> 00:51:59,120
Speaker 3: to keep an eye on and really, you know, move

1171
00:51:59,160 --> 00:52:02,200
Speaker 3: your offense around and factor him in in every half

1172
00:52:02,239 --> 00:52:02,920
Speaker 3: court possession.

1173
00:52:03,320 --> 00:52:05,200
Speaker 8: Yeah, they were two and four in those games that

1174
00:52:05,239 --> 00:52:08,320
Speaker 8: he missed, So huge to have him back. Totally aligned

1175
00:52:08,320 --> 00:52:10,840
Speaker 8: with you on the emotional spot for Dayton, but really

1176
00:52:10,840 --> 00:52:13,520
Speaker 8: the key to me is not only Castor but Luke Hunger.

1177
00:52:13,600 --> 00:52:16,480
Speaker 2: You have two Power Conference bigs that came back down

1178
00:52:16,520 --> 00:52:17,280
Speaker 2: to George Washington.

1179
00:52:17,440 --> 00:52:20,160
Speaker 8: And the problem for Saint Louis is as amazing as

1180
00:52:20,239 --> 00:52:23,080
Speaker 8: Robbie is as an offensive player and positionally sounding can

1181
00:52:23,120 --> 00:52:25,880
Speaker 8: be defensively. Lea Tang, I'm sorry if I'm saying his

1182
00:52:25,920 --> 00:52:28,719
Speaker 8: nameahar on Dayton is really freaking good and big and versus.

1183
00:52:28,760 --> 00:52:31,759
Speaker 8: And he had twenty six and ten, took fifteen free

1184
00:52:31,760 --> 00:52:35,879
Speaker 8: throws and had three assists, and I chased and went

1185
00:52:35,880 --> 00:52:37,880
Speaker 8: for Saint Louis plus ten and a half alive last night,

1186
00:52:37,920 --> 00:52:39,400
Speaker 8: and then they cut it to eleven. I'm feeling good

1187
00:52:39,440 --> 00:52:41,439
Speaker 8: until La Tang gets doubled in the post and rips

1188
00:52:41,480 --> 00:52:44,480
Speaker 8: it behind the back pass to Durkhac for an and one.

1189
00:52:44,480 --> 00:52:47,040
Speaker 8: He's tough to deal with, and I think George Washington

1190
00:52:47,040 --> 00:52:50,200
Speaker 8: has the personnel to deal with him better, which is

1191
00:52:50,200 --> 00:52:52,200
Speaker 8: where everythings start. As good as Bennett and some of

1192
00:52:52,280 --> 00:52:54,880
Speaker 8: these other guys on Dayton are so like them at home,

1193
00:52:55,320 --> 00:52:58,080
Speaker 8: and like you mentioned to me before, big spot for

1194
00:52:58,080 --> 00:52:59,319
Speaker 8: George Washington to push out of.

1195
00:52:59,239 --> 00:53:01,040
Speaker 2: That eight nine game in the A ten bracket.

1196
00:53:01,760 --> 00:53:03,839
Speaker 3: Yeah, when I look at you W, if I can lay,

1197
00:53:03,960 --> 00:53:05,680
Speaker 3: let's call it three and a half or less, I

1198
00:53:05,680 --> 00:53:07,520
Speaker 3: don't mind with the spread. Otherwise, I'm going to use

1199
00:53:07,560 --> 00:53:09,359
Speaker 3: it kind of as a money line sweetener put into

1200
00:53:09,400 --> 00:53:12,040
Speaker 3: a couple of small parlays. We mentioned, you know, two

1201
00:53:12,040 --> 00:53:14,799
Speaker 3: and four with alt Castro in the lineup. Also, when

1202
00:53:14,800 --> 00:53:16,879
Speaker 3: he was in the lineup, they gave Florida game. They

1203
00:53:16,880 --> 00:53:19,160
Speaker 3: were within three of McNeice in the final two minutes.

1204
00:53:19,320 --> 00:53:21,920
Speaker 3: They you know, their overall ceiling goes up considerably when

1205
00:53:21,920 --> 00:53:25,440
Speaker 3: he's out there playing. And like, I don't have all

1206
00:53:25,440 --> 00:53:27,040
Speaker 3: that much to add other than the fact that I

1207
00:53:27,080 --> 00:53:29,440
Speaker 3: think there's a great situational spot. All the reason in

1208
00:53:29,480 --> 00:53:31,839
Speaker 3: the world for GW to sprint through the tape here

1209
00:53:32,000 --> 00:53:33,720
Speaker 3: to get the best seeding possible.

1210
00:53:33,360 --> 00:53:36,600
Speaker 1: In the A ten tournament. All right, let's close things out.

1211
00:53:36,360 --> 00:53:40,040
Speaker 3: With our best bets parlay. We're going to bring our

1212
00:53:40,080 --> 00:53:43,840
Speaker 3: three best ones to the table. Duck as always being sneaky,

1213
00:53:44,000 --> 00:53:46,359
Speaker 3: keeping this one till the final second here and it's

1214
00:53:46,360 --> 00:53:47,799
Speaker 3: Pepperdine with the buzzer beater.

1215
00:53:47,920 --> 00:53:49,799
Speaker 1: Talk me through the waves and what they're bringing to

1216
00:53:49,840 --> 00:53:50,200
Speaker 1: the table.

1217
00:53:51,000 --> 00:53:52,840
Speaker 7: Yeah, you want to listen to this podcast all the

1218
00:53:52,840 --> 00:53:55,160
Speaker 7: way through to get the best action from me. We

1219
00:53:55,239 --> 00:53:57,959
Speaker 7: talked last week and with Evan a little bit about

1220
00:53:57,960 --> 00:54:01,360
Speaker 7: Pepperdine and how they have completely changed to the mosaic

1221
00:54:01,400 --> 00:54:06,160
Speaker 7: of their team. Pepperdine closes out on Saturday with Washington State.

1222
00:54:06,560 --> 00:54:09,719
Speaker 7: The projected total is one fifty four and Wazoo's team

1223
00:54:09,760 --> 00:54:12,080
Speaker 7: total is eighty and we're gonna go over on both

1224
00:54:12,160 --> 00:54:15,600
Speaker 7: not a parlay, but two bets that are correlated. I

1225
00:54:16,040 --> 00:54:19,480
Speaker 7: want to not shy away from what we've seen from

1226
00:54:19,800 --> 00:54:23,400
Speaker 7: Ed Shielding, just completely changing the defensive makeup and just

1227
00:54:23,440 --> 00:54:26,680
Speaker 7: the spirit of how this team is playing absolutely zero defense.

1228
00:54:26,800 --> 00:54:29,439
Speaker 7: Nine straight games that Pepperdine has surrendered at least eighty

1229
00:54:29,440 --> 00:54:32,440
Speaker 7: points or more and nine straight overs from the Waves.

1230
00:54:32,760 --> 00:54:35,480
Speaker 7: Now we have the two worst defenses in the WCC

1231
00:54:35,600 --> 00:54:39,760
Speaker 7: squaring off. Wazoo has run some more uptempo in recent games,

1232
00:54:39,760 --> 00:54:42,320
Speaker 7: and now they get Tomas Thrasterson back in the mix

1233
00:54:42,560 --> 00:54:46,040
Speaker 7: to complement nd okafor these two are going to shred

1234
00:54:46,080 --> 00:54:50,239
Speaker 7: the interior of Pepperdine's defense. For Pepperdine styles Phipps and

1235
00:54:50,280 --> 00:54:52,680
Speaker 7: Aaron Clark, They're going to have opportunities to get open

1236
00:54:52,680 --> 00:54:55,400
Speaker 7: looks in this one. Both of these defenses are horrific

1237
00:54:55,440 --> 00:54:58,479
Speaker 7: at defending the arc. Pepperdine is going to play really

1238
00:54:58,480 --> 00:55:01,600
Speaker 7: hard on Senior Day in the season finale, and travel

1239
00:55:01,640 --> 00:55:04,440
Speaker 7: concerns for Wazuo are mitigated here as they're already in

1240
00:55:04,600 --> 00:55:07,080
Speaker 7: La playing against Loyola Maramount. They just have to make

1241
00:55:07,080 --> 00:55:09,359
Speaker 7: the short trip up to Malibu, maybe get an extra

1242
00:55:09,400 --> 00:55:11,600
Speaker 7: day of shoot around in that gym. I want the

1243
00:55:11,680 --> 00:55:14,600
Speaker 7: over here at one fifty four or so, and I

1244
00:55:14,640 --> 00:55:17,200
Speaker 7: want the Wazoo team total over for anyone listening in

1245
00:55:17,520 --> 00:55:20,080
Speaker 7: the first number you see, because this is going to steam.

1246
00:55:20,120 --> 00:55:23,120
Speaker 7: We saw the Pepperdine game against Seattle which is being

1247
00:55:23,120 --> 00:55:26,359
Speaker 7: played on Wednesday, steam eight points. You want to get

1248
00:55:26,400 --> 00:55:27,360
Speaker 7: the first number you see?

1249
00:55:28,280 --> 00:55:30,320
Speaker 3: All Right, I'm gonna stick with my boys, the Tommy's

1250
00:55:30,320 --> 00:55:32,400
Speaker 3: Saint Thomas covering on their home Florid.

1251
00:55:32,160 --> 00:55:34,640
Speaker 1: Against NDSU Game of the year.

1252
00:55:34,680 --> 00:55:37,680
Speaker 3: There, Janowski or Minnsali, I think is going to go

1253
00:55:37,719 --> 00:55:41,240
Speaker 3: for twenty five plus empowered that offense past the Bison.

1254
00:55:41,840 --> 00:55:43,200
Speaker 1: All Right, Christian closes out.

1255
00:55:43,080 --> 00:55:46,600
Speaker 8: Here Illinois, They're gonna get done over Michigan. I'm going

1256
00:55:46,640 --> 00:55:47,680
Speaker 8: to be there. I'm excited.

1257
00:55:48,600 --> 00:55:48,960
Speaker 2: All right.

1258
00:55:49,000 --> 00:55:51,279
Speaker 3: That's it for the Big Bets on Campus podcast A

1259
00:55:51,320 --> 00:55:54,640
Speaker 3: special thanks to Christian to Duck and to Evan Miyakawa

1260
00:55:54,920 --> 00:55:57,480
Speaker 3: for joining us. As always, if you want to throw

1261
00:55:57,520 --> 00:55:59,440
Speaker 3: us a five star rating interview, we would love to

1262
00:55:59,480 --> 00:56:01,640
Speaker 3: hear from you. You doesn't matter what you say, just

1263
00:56:01,640 --> 00:56:03,759
Speaker 3: give us that five star rating interview. If you're over

1264
00:56:03,800 --> 00:56:06,040
Speaker 3: on the YouTube channel, make sure to subscribe and like

1265
00:56:06,080 --> 00:56:08,200
Speaker 3: because we have a lot of content that's going to

1266
00:56:08,239 --> 00:56:09,920
Speaker 3: be coming in the next few weeks. You don't want

1267
00:56:09,920 --> 00:56:12,000
Speaker 3: to miss any of it. For YouTube the Saturday live

1268
00:56:12,000 --> 00:56:14,839
Speaker 3: show at ten thirty am Eastern, and then in terms

1269
00:56:14,880 --> 00:56:18,640
Speaker 3: of our conference tournament previews, don't worry, We're doing every

1270
00:56:18,680 --> 00:56:20,680
Speaker 3: single one of them. Sucky is doing the Lord's work

1271
00:56:20,800 --> 00:56:23,920
Speaker 3: coordinating with everyone. You're gonna hear from every single person

1272
00:56:23,960 --> 00:56:25,200
Speaker 3: in the BBOC universe.

1273
00:56:25,280 --> 00:56:26,640
Speaker 1: We're going to share the basketball.

1274
00:56:26,719 --> 00:56:28,160
Speaker 3: At the end of the day, we're going to give

1275
00:56:28,200 --> 00:56:30,200
Speaker 3: you picks who's going to win out right in every

1276
00:56:30,239 --> 00:56:32,120
Speaker 3: single one of those tournaments. So you don't want to

1277
00:56:32,120 --> 00:56:34,000
Speaker 3: miss any of that. Thanks again for listening in to

1278
00:56:34,040 --> 00:56:36,480
Speaker 3: the Big Bets on Campus podcasts. Best of Block with

1279
00:56:36,560 --> 00:56:37,120
Speaker 3: all your bets.

1280
00:56:37,360 --> 00:56:49,440
Speaker 6: We'll see you at the window.

1281
00:56:55,800 --> 00:57:00,320
Speaker 7: Action Network reminds you please gamble responsibly. If you or

1282
00:57:00,360 --> 00:57:03,120
Speaker 7: someone you care about has a gambling problem, help is

1283
00:57:03,160 --> 00:57:06,280
Speaker 7: available twenty four to seven at one eight hundred Gambler