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Welcome, welcome to Wharton Moneyball.
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Welcome to a full hour of sports analytics
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here on the Wharton Podcast Network.
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This is Cade Massey hosting this week
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with my two longtime collaborators and friends,
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Shane Jensen and Eric Bradlow,
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both of those fellows in Philadelphia.
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I am remote as I typically am these days.
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We are recording on Tuesday afternoon.
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This show will go up on Wednesday.
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We're gonna follow our usual schedule,
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the schedule for the last couple of years anyway.
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Gonna go with a guest in the first half hour,
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open it up for broader topics in the second half hour.
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We have a brand new guest this time.
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We're often welcoming people back
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and we're always happy to have people back,
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but we need to have new guests as well.
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We have Tyrell Stokes joining us this week.
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Tyrell is from the hockey world.
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I happened to be hanging out with some hockey types
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a few weeks ago and they said,
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you gotta get Tyrell Stokes on the show.
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And so, okay, here we go.
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I listened to these guys.
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Tyrell Stokes supposed to be about as sharp as it gets
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analytically in the world of hockey.
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Tyrell's coming to us from Teamworks.
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Before that, Zealous.
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You've heard us have lots of guests through here
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on Zealous before they were acquired by Teamworks.
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They work with teams, both professional and college level.
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Hockey's gonna be much more professional level.
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Tyrell's background, he's from the Canadian Prairies.
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We just learned he's from outside of Edmonton.
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So he and Shane are gonna be going at it
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about their Alberta loyalties here before the day's over.
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Tyrell's a statistician by training.
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He has a PhD in causal inference from McGill,
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another connection to Shane.
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Postdoc at NYU, he's done academic research,
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hospital network data, that kind of thing.
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But he's been doing hockey for these last few years.
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He's up there in Brooklyn, New York.
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Tyrell, we're delighted to have you.
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Thanks for making time for us.
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Yeah, really excited to be here.
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Why don't we start off by just asking your reflections
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on the Stanley Cup playoffs so far?
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We are almost two rounds through.
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One of the four series is done.
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We've seen some hockey, having some winnowing.
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Just what's top of mind?
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You as a hockey fan, you probably think analytically anyway,
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but anything specific, just like as a fan of the sport,
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what have you noticed?
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What are you paying attention to
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over the last couple weeks, last few weeks?
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Yeah, you know, good question.
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Yeah, we're definitely in this kind of,
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yeah, it's interesting the timing of the series.
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Some are still going, some already finished,
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like some really big breaks.
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That's obviously interesting.
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I think like, you know, NHL playoffs
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is really one of the cruelest
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of like the playoffs series across sports.
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Like just being, you can be really good
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and you might not win, you know, like whatever,
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like maybe 20% of the time,
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the best team is gonna win or something like that.
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Well, and so that's been really tough to see.
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You can see some series where, well, you got outplayed
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and you still won or vice versa.
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Excited to see, like I know you guys had Eric Tolsky on.
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Exciting to see that team breakthrough.
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I think they've been one of the, you know,
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better teams in the league recently.
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And obviously exciting as an analytics person.
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I think that's really cool to see a team
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that's clearly like stewarded by those analytical minds.
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And I think they've just had a couple tough,
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I think Eric was talking about it last time,
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they've had a couple of tough playoff series
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in the last few years, very good teams.
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And so cool to see them get that kind of success.
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But, you know, also knowing the playoffs,
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who knows how long, you know, that luck will go.
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You guys are playing really well.
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Tyrell, let's stop there for a second.
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Tyrell's talking about the Carolina Hurricanes
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and they are already through their second series.
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They haven't dropped a game in the tournament so far.
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They're eight, no, I don't think there's only like five teams
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or something like that have opened that strong.
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So they're looking good.
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The Avs were almost looking as strong.
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They still are really, they've dropped one game, I think.
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They're up three, one now on the wild.
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They started out behind last night.
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I just, I was getting on the bus.
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I was going up the steps to get on the Minnesota wild bus.
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And then they came roaring back and ended that
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and that series is probably over.
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But the Avs, what, Hurricanes look very strong
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out of the east.
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How are you feeling about the Avs?
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I mean, all season long,
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we kind of thought they were the top, right?
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Yeah, I think, I mean, I think they're a really solid team.
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Yeah, like five on five, it's hard to,
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hard to come up with a better team.
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I guess like the power play is one of those ones
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where you like look at the power play
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and like, it feels like it should be even better than it is.
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Just when you think of those,
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like the talent that they have,
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we obviously really like that team.
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Really like a lot of like exceptional players,
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a lot of really good puck movers,
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a lot of good puck carriers, good skaters.
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Tyrell, when you watch a game, after the game,
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do you have a go-to set of analytics
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that you review to kind of understand what you saw?
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I often want that.
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I don't know exactly where to go.
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Like I sat through the game six of the Penns Flyers,
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where the Penns seemed like
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they just beat the hell out of them.
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And then they lost every period that went along,
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including overtime.
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They were just really outplaying them more and more.
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And then they lost on a heck of a shot
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from the blue line players.
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And I wanted to go somewhere and say,
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someone tell me how bad that was
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that that team just lost that game.
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Do you have that set of analytics
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that you can lean on after a game
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to kind of better understand?
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And what goes into that set of analytics for you?
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Yeah, no, that's a great question.
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That definitely, Penns Flyers was one of those series
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that I was following closely.
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We've actually, like, we have a number of like game reports
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that I like look at,
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like, and that we generate sort of automatically
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in some cases.
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And, you know, a lot of it is pretty similar
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to what teams probably use of like,
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you're looking at kind of overall like XG,
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kind of like who's dominating in XG,
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who's dominating in shots.
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We have some like lower level fancier stuff
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that's a little bit more tracking based,
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like who won in terms of like pressure
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as measured by something like an expected shot.
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We looking at like matchups,
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something we've been playing with more recently,
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and it's like a new kind of game report
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that I've been looking at that I've found helpful
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for looking at games.
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We have like a system,
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we call it our like on-puck value system.
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If you're familiar with other flow sports,
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it's basically like an EPV model.
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We built this one out to be,
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to work on all event level games.
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So just so we can run it on multiple leagues,
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not NHL exclusive.
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And we found that kind of system is really helpful
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for getting a quick snapshot of like which players
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and which teams are dominating how.
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So like you kind of imagine
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the really important events in a game,
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like takeaways, passes, carries,
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loose puck recoveries, shots, finishing,
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about like six or eight of these like important
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like actions on the ice.
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Every one of them, we have sort of a value
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in terms of how much like XG did that add by this event.
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And you can do a number of things.
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So one, I just look at a bar chart of like which players,
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like put their positive contributions on one side,
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their negative on another side,
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order them like among forwards,
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see where they ranked,
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each player ranked among all forwards in the game,
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among all defensements in the game.
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And then you can kind of take those same categories
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and you can kind of say like which team won face-offs,
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which team won shots.
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And then these kinds of things to kind of get a quick sense
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of which team is doing better than others.
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And I found that really helpful.
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And in the case of the pens, a lot of those games,
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it looked like from the underlying sense,
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the pens were probably outplaying the players.
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Okay.
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Let's give Shane the first question.
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Yeah, I'd like a quick question of clarification.
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In these calculations,
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are you building in actual kind of positional data,
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like where into that calculation
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or is it more just based on sort of the event data
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of like this person had possession
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and then this person had possession?
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Yeah, so we have like multiple versions
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of those kinds of models.
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The one I was describing is mostly,
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yeah, it's meant to work on like as many leagues
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as possible.
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So it's a little bit cruder
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where we don't have the location of all players.
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We have the location of like the puck
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and a little bit of context around it.
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We have similar models that we're sort of building out
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in the NHL space that is like specifically tracking based
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that mirror some of the stuff that we've done
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in other sports, like say basketball, American football,
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international football, soccer.
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Eric was gonna ask a question earlier on as well.
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Well, no, I was gonna ask Terrell how much,
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so let's say Teamworks presents,
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let's say it was even a team playing the,
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who's playing the avalanche right now?
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Is it the Wild, you said?
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Let's say the Wild are playing the avalanche
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and Teamworks provides an analysis to the Wild that says,
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Colorado is killing you in the following four areas.
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How much can a team actually adapt its style
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to information given to it
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about the strengths of the other team?
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Or in some sense, after 82 games,
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after seven, eight games of the playoffs,
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you are what you are.
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Or do teams actually have,
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I'll call it multiple versions of themselves,
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like we can play the fast style,
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we can play the style that draws penalties
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because we know we can't beat them five on five.
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How much do you think about that
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when you're designing stuff for Teamworks
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because you want people to actually be able to use it
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in some efficacious way?
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Yeah, that's a good point.
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I kind of tend to think about
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in flow sports kind of generally,
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there tends to be this kind of like pyramid of like,
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you know, the lowest hanging fruit is just who's good.
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And then you try to ask like, why are they good?
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And then I think if you do a pretty good job of that,
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then you can start going to these like more
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like tactical realms.
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I would say like hockey is pretty hard,
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I think to be super useful tactically.
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And there's also a bit of a complication,
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I think on our end of like,
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you know, I'm not embedded with a team.
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So like, typically, our approach has been
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to try to just give teams like tools
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that they can do some of that more specific stuff.
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Because a lot of them are not going to want to tell us like,
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oh, we were running this kind of thing on these games.
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And like, therefore, like you're going to change it.
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And some of those like tactical stuff are tough to,
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I think if we were to try to recognize,
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if our goal is to recognize tactics,
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like that would be the only thing we do.
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So are you guys more of a licensed platform
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than you are a team contacts you and say,
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please do this analysis for us?
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Yeah, like we, the bulk of what we have is like,
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I think of it as like we create like foundational models
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that we give the teams that allow them to do more analysis.
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We also build metrics on top of those
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and we provide the teams with those.
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But you know, our really sophisticated users
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are using like lower level outputs
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from like really big foundational models of ours.
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And they're building their own like analyses
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on top of that, metrics on that.
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Okay, hold on.
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Foundational models and lower level inputs
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from foundational models.
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So tell us, give us examples of what those are.
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Yeah, so like two of them, you know,
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I guess like the simplest like suite
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of like that listeners might be familiar of like,
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let's say like an XG model.
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So everyone is familiar with an XG model.
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So we tend to call what we have.
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Expected goals, expected goals
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in case there's a listener that doesn't have that, okay.
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Expected goals.
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We think of our, what our XG offering as a framework.
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So it's really probably 25 or more models
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that are all strung together to make multiple evaluations.
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So if you're like most of the public XGs
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will have like using all the information
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that they have in the public data set at the moment of shot,
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this is the value of it.
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Ours would look like using all of the tracking information
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at the moment of shot, this is evaluation.
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Now, as that trajectory evolves,
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there's might be deflections,
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there might be multiple deflections and so on.
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And, or just before and after the puck is released,
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those are different information states
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and they're gonna create like multiple XG values.
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And then additionally,
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in order to make something like that,
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we need, let's say the probability
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of every player on the ice redirecting the puck
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or blocking the puck or...
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So some of our clients are using,
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let's say you can use what we'd call like a post-shot XG.
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This is a concept we stole from soccer.
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So in soccer, where they have tracking data,
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you take before the shot, you take after the shot,
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you use the trajectory information.
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The difference between those says something
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about usually the shooter,
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like how talented is the shooter?
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How are they imparting something on the ball?
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We have the same concept in hockey.
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We obviously have like metrics that we've developed to say,
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like who's really good at imparting
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really good trajectories on the puck,
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which is like certainly an aspect of shooting.
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So we make those metrics,
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but we also just give you the raw values.
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And some of our teams are much more interested.
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They're like, look, like we understand hockey better.
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Our coaches are interested in like very particular cases
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of like this red redirection, that redirection,
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this shot, that shot.
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We can't possibly like anticipate all of the analyses
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or all of the things that a team is gonna want.
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So like our approach is just like, I'll give you that.
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Like, I'll give you the shot level
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redirection probabilities.
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I'll give you all of these different XG values
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as they develop across the shot.
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And sure, I also give you metrics,
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but like I want to empower like the coaches,
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the internal R&D staff to be able to do
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some of those more like tactical stuff themselves
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a little bit better.
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Tyrell, that speaks to a question
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that comes up on occasion.
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In fact, even on this show,
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I think in the recent weeks we've asked
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who's gonna benefit the most or some show,
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who's gonna benefit the most from AI and sports?
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Who benefits the most from advanced analytics?
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Forget AI, just like zealous teamworks level
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analytics in hockey.
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It sounds like perhaps that kind of,
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it's not the entry level folks
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who don't have their own staff.
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And it might not be the totally built out folks
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who have huge teams doing it all themselves.
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Is it kind of the middle
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where they've got sophistication enough
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to already be there conceptually,
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but they want your input so they can process,
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they can further refine internally?
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Or how would you say, who benefits the most?
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That might be right.
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Like, I think like, you know,
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it's probably like from,
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if I was thinking of like the,
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if I was trying to put like an economist hat on,
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it's probably is like the bottom, to be honest.
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Like if you have basically,
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if you basically have nothing built off,
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we have a bunch of like high level statistics
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and like, you know, things for like drafting
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and stuff like that,
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that you could just plug and play tomorrow.
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Like going from zero to that, I think is like huge.
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The middle, like I think does get a lot obviously,
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because yeah, like the, those middle groups,
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like some of the metrics we create,
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like just fully plug holes for them,
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but then also they have like new like data sets
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to play around with.
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And then kind of at the top level, like I said,
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like they're probably more interested
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in these like lower level,
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like features or like model outputs
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to like throw in their own stuff
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or as like auditing purposes,
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like in a sport like baseball,
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where we work with teams that have 40 people,
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like internal, like sometimes they're hiring us
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because they want to just like make sure
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their internal stuff's are good, right?
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Yeah.
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Right, right.
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How would you characterize the distribution
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of analytics sophistication in the NHL right now?
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Like compared to other sports
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or just like across like the-
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Just like these rough tiers that you just categorized,
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like roughly how many folks,
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how many teams in each tier?
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Yeah, it's tough to say, like we just had,
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there was like a Halo conference
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that we helped organize at the beginning of April.
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And-
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Tyrell, was that the one that the ABS hosted in Colorado?
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Yeah.
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That seemed really, really cool.
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They worked, yeah, they worked together with SportLogic,
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which is now Teamworks too.
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I was involved a little bit on the,
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reading some of the hackathon projects
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and things like that.
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And I think like something like 30 teams like sent somebody
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and it seemed like every team
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had at least a couple of people just there.
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So nowadays, like there's probably like five or 10 teams
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that are kind of like two to three people.
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And then maybe another like five to 10 that are in that,
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like maybe five to 15 that are in that, like three to five.
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And then you have a couple like really like strong groups
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that are in that like five to 10,
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maybe 10 plus in a couple of cases.
00:16:40
Wow, wow, wow.
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Okay, they're really pacing,
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they're lapping everybody else on that case.
00:16:48
Let me ask you a challenging question, Tyrell.
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Say we're walking, we're at some event
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and I happen to be with a couple of NHL execs, senior guys.
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They run a club, they're really deep on hockey.
00:17:02
They've accomplished a lot on the ice
00:17:04
and in the executive suite,
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but they're not analytics forward.
00:17:10
And we happen to bump into you and we share an elevator
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going up or down and you've got like a minute
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and I introduced you.
00:17:16
I said, Tyrell, why don't you pitch these guys
00:17:19
not on teamwork per se, but on analytics?
00:17:22
Because they're curious, but they're skeptical.
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And so you got a minute to pitch these senior guys
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on analytics for hockey.
00:17:32
Yeah, I think there's kind of like two routes.
00:17:35
And like, I think the first one is probably like
00:17:37
the safest is like player evaluation of like,
00:17:40
hockey is a little bit like, let's say soccer,
00:17:43
where like there really is a lot of different leagues
00:17:47
that are relevant to the NHL.
00:17:49
Like there's a lot of players,
00:17:51
like there's what, 600 players in the league.
00:17:53
And then like of guys that are kind of marginal
00:17:55
that are existing, you know, and they're in Swiss,
00:17:58
they're in the NCAA, they're like a top junior.
00:18:00
Like it's really like a large pool of players
00:18:03
that are like possible value adds,
00:18:05
like either today or tomorrow in a way that like,
00:18:08
I think sometimes we have a hard time with like,
00:18:10
let's say some basketball teams are like,
00:18:12
yeah, maybe your models are good,
00:18:13
but like, there's only 90 people that matter.
00:18:15
And like, I can watch their videotape.
00:18:17
You know, I think like hockey is one of these sports
00:18:19
where like, there really is like a pool of a couple thousand
00:18:22
and like, you probably can't watch all of them.
00:18:26
But what we can do is we can provide
00:18:28
like a digital scouting report.
00:18:29
And that's just going to be a first pass filter
00:18:32
to like, look like ultimately you guys are the hockey guys.
00:18:35
There's some guys that our model is going to say,
00:18:37
like he's a hotshot in NCAA and maybe he sucks.
00:18:41
And your scout, if he's good enough, maybe he can say that.
00:18:44
But we don't want your scout wasting time,
00:18:46
like looking at the wrong set of people.
00:18:50
And then I think with like tracking data,
00:18:52
like nowadays, like you can get metrics
00:18:54
that are a little bit more in the language
00:18:56
of a hockey player.
00:18:57
Like you can clue in on something,
00:18:59
like I can show a pass value.
00:19:01
I can say like, I value this pass like this much.
00:19:03
It's like this percentile.
00:19:05
And I'm taking in enough context
00:19:06
that like it passes the eye test in a way that like,
00:19:09
I think lower level stuff,
00:19:12
even though I think in the aggregate,
00:19:13
working off lower level data is often really powerful.
00:19:17
But we've always had a hard time with like coaches
00:19:19
or more traditional hockey people,
00:19:20
because they look at the video
00:19:21
and their eyes are seeing information that your model isn't.
00:19:24
And they're like, well, this is wrong.
00:19:26
And I think with like the kind of tracking stuff
00:19:28
we have now, there's more and more metrics
00:19:30
that we can like point to and just say,
00:19:32
well, like, look at that.
00:19:33
Like, does this list not look like the 10 best passers?
00:19:37
Like, do these passes not look like, you know,
00:19:40
really good passes or really good carries?
00:19:41
Like the more we're able to zoom in
00:19:44
on these kinds of micro things,
00:19:46
like the closer it tends to get
00:19:48
to the hockey language, I think.
00:19:51
Super interesting.
00:19:52
It makes a lot of sense.
00:19:54
The way you just talked about that
00:19:55
raises a new question in my mind,
00:19:57
especially as a person,
00:19:59
I've watched hockey for a long time,
00:20:01
but I've never played hockey.
00:20:02
And I'm definitely handicapped in that way.
00:20:05
As I'm watching games,
00:20:08
how should I think about where a player adds value?
00:20:10
You just talked about through passing, for example.
00:20:13
And some of the passing,
00:20:14
good passes are so fantastic in hockey,
00:20:16
but let's just stay with like forwards.
00:20:21
So how much of the value a guy creates
00:20:23
on the ice during a game, next year, whatever,
00:20:26
comes from shots,
00:20:29
comes from carry-up versus off-puck activities?
00:20:32
Off-puck or good passes,
00:20:34
receiving passes well, recovering loose pucks.
00:20:37
Like where, if you just give me some activities
00:20:39
that maybe as an unsophisticated viewer,
00:20:41
I miss that this guy's actually,
00:20:43
he has more chances to pass well than he does to shoot.
00:20:45
So give me some sense of that.
00:20:48
Yeah, I love that question.
00:20:50
I think like,
00:20:52
especially when we've looked at this
00:20:54
in kind of our broader models
00:20:56
that are trying to encompass as many leaps as possible,
00:20:59
it often ends up getting like,
00:21:01
the things that you do like with and near the puck
00:21:04
is probably about half, like as best as we can tell.
00:21:08
And, you know, that number is probably a little bit off.
00:21:11
And like, as you sort of add, you know,
00:21:14
as soon as we get like, let's say like stick location values
00:21:16
and like these kinds of things from like Hawkeye coming in,
00:21:19
maybe those percentages will change.
00:21:21
I think like you're passing, shooting,
00:21:24
loose puck recovering, carrying, shot blocking, takeaways.
00:21:28
Like it's probably close to half.
00:21:31
It might be a little bit more for forwards than defenders.
00:21:36
I think defenders are a little bit tougher,
00:21:39
but that's typically when we try to like blend
00:21:41
like a micro and a macro stat to get something overall,
00:21:43
it's often closer to 50-50 than you probably think.
00:21:47
Okay, but that's like shockingly high percentage
00:21:49
for off-puck behavior to the uninitiated anyway.
00:21:53
And so give me one off-puck behavior
00:21:57
that I could try to monitor a little bit that's high value
00:22:00
and give me one on-puck behavior that's not obvious.
00:22:03
Like among all those things you named,
00:22:05
what's something that's especially high value
00:22:08
or maybe it's high frequency or whatever
00:22:10
that I might need to pay more attention to?
00:22:13
Yeah, so one of the like, maybe an off-puck kind of thing,
00:22:20
like there's some defenders that are really good
00:22:23
at just like reducing like pass threat
00:22:27
in whether or not they're like on the defender or not.
00:22:31
There's like pretty good evidence of like a lot of those guys
00:22:33
that have that defensive reputation,
00:22:35
like a Jacob Slavin for Carolina,
00:22:39
somebody I'm being in New York,
00:22:40
I've watched like a decent number
00:22:42
of Islanders games this year.
00:22:43
And like Adam Pelek is one of these like defenders
00:22:46
that like no matter how I slice it
00:22:48
on the defensive side of the puck,
00:22:49
he's really incredible.
00:22:50
And like one of those things,
00:22:51
like when I'm watching an Islanders game,
00:22:53
I'm always like, what are you doing, Adam Pelek?
00:22:55
Like, what exactly are you doing?
00:22:57
And it's hard to see.
00:22:59
And like, I think I'm surprised
00:23:02
that like I'm watching his positioning and I'm like,
00:23:05
whoa, like he feels out of position to me.
00:23:07
And then all of a sudden he's like there to stop a play.
00:23:10
So I think that's one of the ones like
00:23:12
of an off-puck behavior.
00:23:14
Maybe on-puck, like something that surprised me,
00:23:17
it's maybe obvious in hindsight,
00:23:20
but you being the person that is like recovering loose pucks
00:23:24
like is extremely predictive.
00:23:28
It's one of those things that like,
00:23:30
one of the things that I always try to look at
00:23:31
when I'm thinking about how predictive is a statistic,
00:23:35
I really care about how does this translate across teams
00:23:38
and how does this translate if I was to take it,
00:23:41
those players that get like swapped
00:23:43
or they're in completely new context,
00:23:44
like how predictive is it?
00:23:46
Something like loose puck recovery,
00:23:48
like if you're on a good team, if you're on a bad team,
00:23:51
if you're in NCAA, like whatever, you're in the NHL,
00:23:55
like the guys that get a lot of value out of that,
00:23:58
like it's extremely predictive.
00:23:59
Somebody that's like credible, like Celebrini,
00:24:02
I was watching him in the whole like Olympics
00:24:04
and like, I don't know how he does it.
00:24:06
Like, you know, he's not that much bigger than I am.
00:24:08
And like, he's just able to grab pucks
00:24:11
and they're just on his stick.
00:24:12
And like, it's so valuable.
00:24:15
Yeah, I was about to ask, like what behaviors lead a guy
00:24:19
to be able to get the puck like that?
00:24:21
I would think that some of that is effort,
00:24:22
but you just made it sound it's also a stick.
00:24:25
There's like some really like
00:24:26
impressive anticipation stuff.
00:24:28
Like, yeah, if you watch like, you know,
00:24:30
I feel like I didn't play the game that that long ago,
00:24:33
but like sometimes when I watch the NHL,
00:24:35
it's really in the future,
00:24:36
especially with like things like shootouts
00:24:38
and then like things with like takeaways,
00:24:39
there's these really subtle like lifts
00:24:42
and like people are faking each other out,
00:24:44
like making it look like they're going to take the puck
00:24:46
one way and they're grabbing it the other,
00:24:48
or they're out anticipating like really impressive stuff
00:24:51
these days in terms of the skill level.
00:24:55
All right, well-
00:24:57
Can I ask Tyrell just a quick question?
00:24:59
I was thinking about this when Kate asked you about,
00:25:02
you know, what you would say to, you know,
00:25:04
whether it's a CEO or someone that works for a hockey team,
00:25:07
would you hire more data engineers or data scientists?
00:25:11
Ooh, that's a good question.
00:25:14
Because it sounds like there's extraordinarily rich data
00:25:18
that's in even the Teamworks pipeline,
00:25:20
but without a set of data engineers,
00:25:22
I'm not sure what we as data scientists
00:25:24
would do with that data.
00:25:25
But on the other hand, they can do a lot of description,
00:25:28
but if you want to build predictive models
00:25:30
or think about something you studied, causal inference,
00:25:33
then you might need a statistician.
00:25:35
So I'm just interested how you think about that.
00:25:37
Yeah, like, I mean, definitely one of the theses
00:25:39
of like how we set up our product is like,
00:25:42
we take care of as much of the data engineering as possible
00:25:45
to like make that, like, make the data like accessible.
00:25:50
We do a lot of mapping, cleaning, re-representing,
00:25:53
like if you want to work with it at a smaller level,
00:25:55
a higher level, join in features.
00:25:57
We try to do a lot of that for teams
00:25:59
because I think you're right.
00:26:01
Like, if you want to be at the bleeding edge
00:26:03
of statistics at this point,
00:26:06
like we're about to get Hawkeye.
00:26:07
Hawkeye data is, you know, it's something like 36,
00:26:10
like join angles, you know, like a hundred times a second.
00:26:12
We got it in basketball about a year ago.
00:26:15
The size of like just one game is massive.
00:26:18
And like, if you really want to be able
00:26:19
to use that properly, you need biomechanists,
00:26:23
you need data engineers, but it's kind of true.
00:26:27
Like once you have like some kind of a pipeline in place,
00:26:29
like there's just so many more analyses that are possible.
00:26:33
But I think like probably the data engineering,
00:26:35
like I think a lot of us that are like, you know,
00:26:37
decent data scientists in this space
00:26:39
have had to be kind of scrappy and had to learn.
00:26:43
That's something that like Doug Farring likes to say a lot,
00:26:45
the former CEO of Zealous, now at Teamworks,
00:26:49
is like, you know, like the best way
00:26:50
to become a better data scientist
00:26:51
is become a better data engineer,
00:26:53
especially in the sports world
00:26:54
where like you have to be pretty scrappy
00:26:56
and you're going to have to do some stuff
00:26:58
like a little bit outside of your world.
00:26:59
Has AI helped?
00:27:01
Because it helps me be a better data engineer.
00:27:04
Yeah, I think that's true.
00:27:05
Like I think there are ways in which,
00:27:09
you know, this kind of technology,
00:27:11
if you're like strong technical in one field,
00:27:13
that you can sometimes,
00:27:14
it can help with that like domain transfer problem
00:27:17
a little bit.
00:27:18
You still have to learn stuff,
00:27:21
but I think there's definitely ways in which
00:27:24
that can be true.
00:27:26
Tyrell, you mentioned that Hockey's about to get Hawkeye data
00:27:29
and that will open up all kinds of biomechanical
00:27:32
as well as finer positioning.
00:27:33
What's an example of a question you're curious to tackle
00:27:37
with these new data?
00:27:37
Or what's something you think we're gonna be talking about
00:27:40
two years from now because of the data?
00:27:43
Like, you know, like it's probably not as exciting
00:27:45
as you might want, but like, you know,
00:27:47
we just don't know where the sticks are.
00:27:50
And that's really, it turns out,
00:27:51
it's probably a pretty big part of hockey.
00:27:53
And like, we have these spatial locations
00:27:55
and we can say two players are close,
00:27:57
but we don't have,
00:27:58
one of the things that is kind of a bummer
00:28:00
is like we only have one RFID chip in the NHL
00:28:04
on the right shoulder.
00:28:06
NFL has two, so we don't have body orientation.
00:28:09
I don't even know when a player is skating backwards.
00:28:11
So like, I really wanna know like,
00:28:13
who's good at skating backwards.
00:28:15
I just wanna know where the sticks are.
00:28:17
Like just starting at major events, like where are the sticks
00:28:19
and like, just that is going to give you like much better,
00:28:22
like expected goals models.
00:28:24
It's gonna give you a lot of stuff for free a little bit.
00:28:27
You could infer that maybe Tyrell, right?
00:28:29
But I mean, in the sense of like, you know,
00:28:32
if you had a supervised machine learning model
00:28:34
where you human coded people skating backwards,
00:28:37
I would imagine the movement of the RFID chip
00:28:39
would have to be,
00:28:40
even though it's only on the right shoulder,
00:28:42
would have to be different than forward movement.
00:28:44
And so in theory, you could infer this, right?
00:28:46
Absolutely, you must have been talking to Doug
00:28:50
cause we were very close to going down that pipeline
00:28:54
and like, kind of like realizing that Hawkeye
00:28:57
was probably closer than we originally thought.
00:28:59
Like it was one of those things that made sense to defer.
00:29:02
But we sort of thought about one of these
00:29:04
like hit and mark off like type models
00:29:05
where it's like, kind of what you're saying
00:29:08
is like, there's probably moments in time
00:29:09
where I like, I know for sure
00:29:11
a player is going forwards or backwards,
00:29:12
plus some of these like movement stuff,
00:29:14
I should be able to like chain it together some way
00:29:16
and get like a probability.
00:29:18
And that would probably help quite a lot,
00:29:20
to be honest in some of these models.
00:29:22
Tyrell, we're gonna have to let you go.
00:29:24
Thanks for making time.
00:29:25
I assume you're an Oilers fan, you grew up in Edmonton.
00:29:28
Does that make you an Oilers fan?
00:29:29
Yeah, I grew up as an Oilers fan.
00:29:32
What's the prospect for McDavid the next few years?
00:29:35
I got on the Oilers train the last few years,
00:29:37
which is just a cheap, easy thing to do as a,
00:29:39
let's watch the most exciting team.
00:29:42
What's the future for McDavid?
00:29:44
Did they miss the playoffs?
00:29:46
No, no, they took a step back this year,
00:29:49
I think it would be fair to say, but.
00:29:51
And McDavid, like it seems like he,
00:29:53
had like some kind of injury during the first round,
00:29:57
like with like the foot,
00:29:59
which I think was fairly apparent
00:30:01
in some of the reports that I was like looking at.
00:30:03
I was like, oh, these don't quite look
00:30:05
like the normal McDavid numbers.
00:30:07
I was definitely thinking that was quite likely.
00:30:12
Yeah, I don't know.
00:30:13
Like, I mean, I like the team,
00:30:14
like I think they have,
00:30:17
you know, a lot of, some of their depth players,
00:30:20
I think are a lot better than people give credit to.
00:30:22
I like Evan Bouchard, I know that's like something,
00:30:26
but I think he's like a really excellent,
00:30:28
he's like one of the best stretch passers in the league
00:30:31
and has been for probably three or four years.
00:30:35
I think like, you know, they're a team that like,
00:30:37
if McDavid and Dreisaitl are playing
00:30:39
at their absolute limits,
00:30:40
like it's really hard to count them out.
00:30:42
And yeah, like, I think like,
00:30:46
yeah, I'm interested to see where that team goes anyway.
00:30:49
Okay, okay, well, we should let you go.
00:30:51
All right, Tyrell Stokes,
00:30:52
senior manager at Teamworks Hockey Analytics.
00:30:56
I think you head the hockey operation at Teamworks.
00:30:59
I'm on the technical side anyway, yeah.
00:31:01
Great, and first time guest,
00:31:03
we're delighted to have spent some time with you.
00:31:04
Good luck with all you're doing, Tyrell.
00:31:07
Thank you, Ariel.
00:31:07
Thanks for having me.
00:31:09
That has been the first half of Wharton Moneyball.
00:31:11
We still have a half to go.
00:31:12
Come back and join us after the break.
00:31:17
Welcome back.
00:31:18
Welcome back to Wharton Moneyball.
00:31:21
Welcome back to a full hour of sports analytics
00:31:23
here on the Wharton Podcast Network.
00:31:26
Cade Massey hosting with Eric Bradlow and Shane Jensen,
00:31:29
our fourth musketeer,
00:31:31
Otty Weiner out and about doing Otty Weiner things.
00:31:33
He'll be back.
00:31:34
Some combination of us are here
00:31:36
almost every week of the year,
00:31:37
maybe 50, possibly 50 of the 52 weeks of the year.
00:31:41
I have been for more than 12 years
00:31:43
just off the line with Tyrell Stokes.
00:31:46
First time guest, Tyrell Stokes.
00:31:47
Highly recommended, referred by experts,
00:31:51
Tyrell Stokes, talking hockey analytics.
00:31:53
Great fun, great fun.
00:31:54
Yet another great hire by Zealous.
00:31:57
Continue to be impressed with those guys
00:31:58
even after being acquired by Teamworks.
00:32:02
Shane, missed you last week.
00:32:04
Didn't get to talk about hockey with you.
00:32:07
Anything?
00:32:08
Yeah, I mean, the Flyers have risen and fallen
00:32:10
in the intervening time.
00:32:12
It's very, I mean, it was probably-
00:32:15
Inevitable.
00:32:16
Kind of somewhat as inevitable as hockey gets,
00:32:19
I suppose, in the playoffs,
00:32:20
which is not very inevitable,
00:32:21
but yeah, no, the Hurricanes have been
00:32:24
an absolute wagon this postseason.
00:32:27
I think I saw that it's the first time
00:32:29
there's been two, like,
00:32:31
they've started out with two series sweeps
00:32:33
since like the 80s,
00:32:34
like since the Oilers in the 80s.
00:32:36
Jeez.
00:32:37
So anytime you're invoking a Gretzky-
00:32:40
Wait, you're saying no one has had
00:32:41
two series sweeps since the 80s?
00:32:44
In a row, to start the playoffs.
00:32:45
The first two.
00:32:45
Yeah.
00:32:46
Yeah, yeah.
00:32:47
Wow.
00:32:49
Yeah, and I think actually-
00:32:51
That's actually a remarkable statistic
00:32:53
because we have one in basketball this year,
00:32:56
obviously, as well, the OKC Thunder.
00:33:00
And I'm sure, I mean, I think,
00:33:02
I know for a fact it's happened 11 times in basketball,
00:33:05
I believe.
00:33:05
No one's ever finished this.
00:33:07
No one has ever gone, you know,
00:33:09
that was the famous Moses Malone thing.
00:33:11
We're going to go fo, fo, fo, fo.
00:33:12
They didn't.
00:33:13
They went fo, fo, fo, three, one, or four, one.
00:33:17
But no one in basketball has ever done it.
00:33:19
But I think that talks,
00:33:20
I assume you would agree with the randomness in hockey.
00:33:22
Like, you know, you could be the 0.8 team
00:33:25
and you know what?
00:33:26
You're not winning every one.
00:33:28
That's right.
00:33:28
No, and I think, you know,
00:33:30
it's carried an interesting dynamic in this playoff too,
00:33:33
because, you know, Caroline has done what they're saying.
00:33:35
I think I counted, I think, you know,
00:33:38
if the Buffalo-Montreal series goes long,
00:33:41
and it easily could,
00:33:42
those teams are really going at it with each other.
00:33:44
I think they could have like a 10 game layoff
00:33:47
or something like that.
00:33:48
Yeah.
00:33:48
Which, you know, I mean, you could argue, of course,
00:33:51
you could argue whether you would want such a long layoff
00:33:55
or not, either way.
00:33:56
Come on, you got it.
00:33:56
But it's just, I, it's-
00:33:58
Isn't that the perfect mid-playoff break?
00:34:00
Are you kidding me?
00:34:01
Two series in, two series to go.
00:34:02
Let's take 10 days off, get rested.
00:34:04
Because it's such a grind, these four playoff series.
00:34:06
It's got to be, maybe it costs them the first game
00:34:09
with the Canadians.
00:34:10
Maybe it costs them the first game.
00:34:11
I think that's got to be worth it
00:34:12
to take this much time off in the middle.
00:34:14
It's not necessarily going to be the Canadians, my friend.
00:34:16
Your Sabres are still in it.
00:34:18
They've not-
00:34:19
I mean, at least until they meet up with Carolina.
00:34:21
Yeah, exactly.
00:34:23
Exactly.
00:34:24
No, it is, it's an interesting playoffs
00:34:25
because I feel like we've had
00:34:26
some incredibly exciting series,
00:34:28
but there are more than I think most years,
00:34:32
like these two elephants hanging out
00:34:34
in either side of the room, basically,
00:34:36
you know, kind of waiting there.
00:34:37
It's hard not to script yourself
00:34:40
into an Aves Hurricanes final,
00:34:42
even though we've got some amazing hockey along the way.
00:34:44
I forget who does the,
00:34:46
this is the Micah McCurdy, right?
00:34:48
The database.
00:34:48
McCurdy, yeah, yeah.
00:34:49
So I'm looking at what you posted,
00:34:51
or I assume it was you that posted here.
00:34:53
And just for our listeners,
00:34:55
like, I don't know how to describe it,
00:34:57
but to say that most of the probability mass,
00:35:01
I don't want to say 90 plus percent,
00:35:02
but at least is on Colorado and Carolina.
00:35:07
Yeah, I think when you say 82 percent-
00:35:09
It's hard to squint.
00:35:10
Like I have to squint to see the other lines at the end.
00:35:14
And, you know, my middle son's girlfriend
00:35:17
is a huge Ducks fan.
00:35:18
And I'm like, are they still in it?
00:35:20
Like, I'm trying to, I know they are,
00:35:22
but I'm like, I'm trying to find them
00:35:23
at the end of the rainbow here.
00:35:26
And like-
00:35:26
No, and I mean, he produces these visualizations
00:35:29
every year, they're really cool.
00:35:29
And I do feel like this one is kind of, again,
00:35:32
it speaks to the unusual year, I think we have,
00:35:34
where, you know, two teams really do kind of soak up
00:35:38
so many, so much of the percentage.
00:35:41
And they're both on either side of the bracket.
00:35:43
So it's not like they're kind of soaking it up
00:35:45
from each other.
00:35:46
Because I think, you know, somehow if you were to kind of,
00:35:50
like if you somehow were to substitute
00:35:51
maybe Carolina and Minnesota,
00:35:53
like move the Wild over to the East
00:35:55
and get Carolina out of the East.
00:35:58
I'm not sure the Wild aren't like, you know,
00:36:00
suddenly one of the top two teams to make it.
00:36:02
I think it is, you know, I think it's kind of like,
00:36:06
it's more kind of, yeah, consolidated in a couple of teams
00:36:09
than it usually is.
00:36:11
Whether that'll actually realize or not.
00:36:13
I mean, you know, again, another test of how random
00:36:17
it can get in hockey playoffs.
00:36:18
It'd be cool to, it'd be cool if another team,
00:36:20
though I would be fine, Avalanche Hurricanes
00:36:22
would be such an amazing final.
00:36:24
I would, I'd sign up for that.
00:36:28
Okay, well, trying to enjoy the process as well
00:36:30
along the way, except for the Sabres getting blown out.
00:36:32
Other than that, it's been a delight.
00:36:34
Why don't we jump into a, what caught your eye?
00:36:37
Let's do a couple of rounds.
00:36:38
Let's see if we can do a couple of rounds
00:36:40
of what caught your eye in the world of sports.
00:36:43
Eric, probably the right place to start us off.
00:36:46
Well, I'll go back to a slightly different sport,
00:36:49
which is tennis.
00:36:53
Obviously, Alcaraz is still injured.
00:36:55
Sinner hasn't lost in forever.
00:36:57
And I don't know when the next time he's going to lose.
00:36:59
I mean, he's going through top 20 players,
00:37:01
six loves, six one, six one, six two.
00:37:04
I'm not talking about beating top 20 players.
00:37:06
I'm talking about beating top 20 players.
00:37:10
His odds right now for the French,
00:37:12
which is coming up, not now,
00:37:13
he's still playing, they're still playing the Italian Open.
00:37:15
The French is starting after that.
00:37:17
He's minus 270, you know,
00:37:19
which probably puts him at about 70%.
00:37:21
And I'm wondering if that's too low.
00:37:23
Like, I mean, I checked, the highest Nadal ever was,
00:37:28
with just, it's good to benchmark, was minus 400.
00:37:31
Now at that time, I don't know if he was
00:37:32
the five-time champion, eight-time champion at that point.
00:37:35
But like, let's put that as an upper bound.
00:37:37
Like, it's hard to imagine anyone being better
00:37:40
than Nadal in his prime at the French.
00:37:43
Like, there's no legitimate probability model
00:37:45
that would put somebody better, except, except,
00:37:49
this gets to Shane's point with hockey.
00:37:52
There ain't no Federer,
00:37:54
and there ain't no Djokovic sitting there
00:37:56
who could possibly beat him.
00:37:58
So it's not unreasonable to say,
00:38:00
Sinner could be minus 500.
00:38:02
Why can't he be?
00:38:03
Nadal had different competition.
00:38:05
I'm not saying he's better.
00:38:07
I'm saying he doesn't have the same level of competition.
00:38:11
So that's what caught my eye.
00:38:12
I think minus 270 is too low.
00:38:15
I think, unless he gets injured,
00:38:16
I think he's got a better chance.
00:38:17
I was about to ask, what's the base rate of injury?
00:38:19
Because that's going to put a ceiling on the odds.
00:38:23
I mean, getting injured within a tournament
00:38:26
is not as actually as high as you'd think.
00:38:29
Like, players typically, once they start tournaments,
00:38:32
it's rare that they lose or pull out due to injury.
00:38:36
So that's not as common.
00:38:37
Now, the fact is, Alcaraz is not playing.
00:38:39
It's not even clear Djokovic is listed as he's playing,
00:38:42
but he was just beaten in the first round
00:38:43
by like number 300 in the world.
00:38:46
So he may be playing, but he's not really playing.
00:38:49
I don't see how he's not more, anyway, that caught my eye.
00:38:52
I was like, can minus 270 actually be too low?
00:38:56
I got kind of almost a tennis design question for you, Eric.
00:38:59
If I wanted it to be less dominant,
00:39:03
like if I wanted to kind of change a rule or two
00:39:07
in tennis so that maybe the best player
00:39:10
didn't like, wasn't so kind of high,
00:39:12
to basically reduce the probability of the best player,
00:39:15
or maybe even to kind of like, you know,
00:39:17
add randomness essentially,
00:39:18
so that it's not so, you know,
00:39:20
the best player winning all the time.
00:39:22
And I'm not saying that's necessarily a goal you would want,
00:39:24
but if you wanted that as a goal, what would you do?
00:39:27
What do you think kind of-
00:39:28
I mean, you could do stupid things,
00:39:30
like say every game is one point.
00:39:33
You know, I don't mean that.
00:39:33
Let's forget stupid things like that.
00:39:35
No, of course.
00:39:36
Something that has been talked about,
00:39:38
make every round up until the finals, best of three.
00:39:42
Like you're not, I hate to say it,
00:39:44
but with the condition he's in,
00:39:46
like it's one of those things that I always remember
00:39:48
when, you know, when my middle son was playing squash.
00:39:51
You hit the ball about 10,000 times in a squash match.
00:39:55
The other guy's better than you.
00:39:57
They're better than you.
00:39:58
And you know, you can hit a bunch of winners,
00:40:00
but not after 10,000 shots, you're not.
00:40:03
Sinner right now hits the ball by far
00:40:06
more cleanly than anybody else.
00:40:09
You make it end big enough, he's going to beat you.
00:40:12
So unless you shorten the match
00:40:15
by making it best of three possibly,
00:40:18
or change, for example, the rules within a game,
00:40:21
which is unlikely,
00:40:22
tennis has been played this way for 70, 80, 100 years,
00:40:25
whatever.
00:40:26
But like get rid of tiebreakers, for example, maybe.
00:40:28
Well, they've done that.
00:40:29
They've done that now in the fifth set.
00:40:32
Like if you happen to get to the fifth set,
00:40:33
it's now the first to 10.
00:40:35
It's not like, you remember when Isner,
00:40:36
like the 48, they're not doing that anymore.
00:40:39
Yeah, they're not doing that anymore.
00:40:41
But no, right now at best of five, I just don't see it.
00:40:46
I don't see how someone is going to beat Sinner
00:40:49
in a five game match anymore.
00:40:51
And because not only is he better than you,
00:40:53
but he's in better shape than you.
00:40:55
And he hits the ball cleaner than you.
00:40:58
And so, and his movement's better than you.
00:41:01
Do we expect Dakaraz back for Wimbledon?
00:41:04
It's unclear.
00:41:05
I mean, he's got, yes, I would think so,
00:41:07
but he's got an injured wrist.
00:41:09
And you know, he says he's not coming back
00:41:11
until he's fully 100%.
00:41:12
I just saw a video of him yesterday.
00:41:14
He is still wearing a brace on his wrist.
00:41:16
So I don't know how close he is to being ready.
00:41:20
You know, and it's always one of those things.
00:41:22
The good news about, well, it's not a good news.
00:41:24
Let's say, let's say, I don't say worst case.
00:41:25
Let's suppose Alkaraz is out for the rest of the season.
00:41:28
I don't know that he is,
00:41:29
but let's pretend he is for the moment.
00:41:31
Or he's not Alkaraz for the rest of the season.
00:41:33
And Sinner wins the next three Grand Slams.
00:41:36
So now he catches Alkaraz.
00:41:37
So now they're both at seven.
00:41:39
Is there an asterisk next to his career?
00:41:41
No.
00:41:43
No, no, I think all these careers
00:41:45
ought to be normed for their competition.
00:41:46
We've talked about that before.
00:41:47
But no, you're not-
00:41:48
I mean, maybe true aficionados like you
00:41:52
look at like, you know,
00:41:55
Nadal, Federer's kind of list of Grand Slams
00:41:58
and puts like, oh, well, these were the ones where,
00:42:00
these were the years where Nadal was injured.
00:42:02
And these were the years, like, I mean-
00:42:03
It is what I do, yeah.
00:42:04
Yeah.
00:42:07
And I mean, I'm not saying it's wrong to do that.
00:42:10
But certainly I think for, you know,
00:42:13
kind of their overall like view,
00:42:15
maybe outside of like somebody as deep in it as you,
00:42:19
call me the casual fan.
00:42:22
I make no decision.
00:42:23
It'll just be the number, the totals in the end.
00:42:25
Shane would be interested in a strength rating
00:42:28
of a career set of Grand Slams.
00:42:30
Oh yeah, yeah.
00:42:31
You should show me the ELO rating,
00:42:32
kind of all that type of stuff.
00:42:34
That would be great.
00:42:34
And by the way, except for situations like,
00:42:36
let's take the rare cases like in baseball,
00:42:38
like, you know, Ted Williams went to war twice.
00:42:41
You know, part of being great and having longevity
00:42:44
is being able to be on the field.
00:42:45
You can't contribute if you're not on the court.
00:42:47
So if Alcaraz, look, people say this about Nadal.
00:42:52
Nadal played way fewer Grand Slams
00:42:55
than Federer or Djokovic.
00:42:57
If he hadn't been injured.
00:42:59
Okay, well, yeah.
00:42:59
But if he didn't play the style he was,
00:43:01
maybe he wouldn't have won 22 of them either.
00:43:04
Let's go.
00:43:04
And we're talking about Mike Trout
00:43:05
and your favorite sport as well.
00:43:07
Okay, Shane, let's go.
00:43:07
Okay, that is, I feel like that is a good segue.
00:43:10
Cause I feel like what caught my eye,
00:43:13
obviously, you know,
00:43:14
Kyle Schwarber hitting all these home runs
00:43:16
is what actually caught my eye.
00:43:17
But, you know, it got me thinking
00:43:19
about kind of home run totals.
00:43:20
And, you know, just kind of,
00:43:21
there's a lot of,
00:43:23
there's a lot of players actually,
00:43:24
kind of this season specifically,
00:43:27
that are on track to hit,
00:43:29
go over 400 home runs.
00:43:30
I just saw this.
00:43:31
This is a great, this is great.
00:43:32
And it really kind of, I mean, you know,
00:43:34
I wouldn't necessarily use it as an absolute proxy
00:43:37
for Hall of Fame worthy,
00:43:39
but like, you know, it's certainly,
00:43:41
you get over 400 runs
00:43:42
and you're starting to get in the mix of like,
00:43:44
where it's about 50-50
00:43:45
that you'd be in the Hall of Fame.
00:43:47
How many guys have more than 400 home runs in MLB history?
00:43:51
I'm gonna guess 60, but I'll look it up.
00:43:54
Okay.
00:43:54
So anyway, the players right now,
00:43:56
so Eric Judge is only 16 home runs away.
00:43:58
And that was from a couple of days ago.
00:44:00
So he's probably only 10 home runs away now.
00:44:01
Oh, I mean, I was so far off.
00:44:03
It's 59.
00:44:04
Ha ha.
00:44:05
That's a good guess, Eric.
00:44:06
Nicely done.
00:44:08
But yeah, but it really is kind of a list
00:44:10
of sort of people, at least on the fringe of Hall of Fame
00:44:13
or definitely in the Hall of Fame.
00:44:15
So Eric Judge is only 16 home runs away from 400.
00:44:17
Manny Machado is 25 home runs away.
00:44:20
Bryce Harper's 27.
00:44:22
Freddie Freeman's 29.
00:44:24
So all of these guys, you know,
00:44:26
we could have four or five players hitting 400,
00:44:30
you know, breaking that 400 mark all in the same season.
00:44:33
There's a couple other players like Paul Goldschmidt
00:44:34
and Nolan Arenado that are also kind of in that range,
00:44:37
but they're more, you know,
00:44:38
the four players I mentioned are still enough in their prime
00:44:42
where I feel like they've got a 23rd home run season.
00:44:44
Here you go, Shane.
00:44:45
This is a fun list.
00:44:46
Here are all the players in MLB history
00:44:49
with 400 home runs not in the Hall of Fame.
00:44:53
Yeah.
00:44:54
Okay.
00:44:54
It's about 12 names.
00:44:55
Here we go.
00:44:56
Barry Bonds, we know why.
00:44:58
Yeah, yeah, yeah, yeah.
00:44:59
Alex Rodriguez, we know why.
00:45:02
Pujols, not eligible yet.
00:45:05
Sammy Sosa, we know why.
00:45:07
McGuire, we know why.
00:45:08
Palmera, we know why.
00:45:10
Manny, we know why.
00:45:12
Gary Sheffield's an interesting case
00:45:13
because he has 500 home runs, but he's off the ballot now.
00:45:16
He's had his 10 years.
00:45:19
Carlos Beltran, he's coming in.
00:45:20
So he's coming into the Hall of Fame.
00:45:22
Andrew Jones coming into the Hall of Fame.
00:45:24
Jason Giambi.
00:45:26
Paul Canerco.
00:45:28
Yeah.
00:45:29
Juan Gonzalez.
00:45:30
The name I remember from my youth, Dave Kingman.
00:45:33
Darrell Evans, Alfonso Soriano, and Mark Teixeira.
00:45:37
That's the list.
00:45:39
That is a short list.
00:45:41
That is interesting.
00:45:42
Yeah, no, and I mean, again,
00:45:43
it is a lot of obviously kind of suspected PED guys.
00:45:48
I guess maybe with Soriano, Teixeira,
00:45:50
it's also if you hit a bunch of home runs on the Yankees.
00:45:52
I mean, like, you know, whatever.
00:45:54
That's not that notable.
00:45:55
Those guys are supposed to do that.
00:45:57
So guys, one thing that stands out to me
00:45:59
is we're talking about, you know, in the next year or so,
00:46:02
we're gonna have these five active players.
00:46:05
Judge Machado, Goldschmidt, Harper, and Freeman.
00:46:08
Over 400.
00:46:09
How often have there been,
00:46:10
and there may be others who are still playing.
00:46:12
Yeah, I mean, Statton's at over 500.
00:46:14
So we've got active players that are already,
00:46:16
have already, are already over.
00:46:18
Are we at a relative high point
00:46:19
for a number of active players with 400 or more home runs?
00:46:23
Not relative to the early 2000s.
00:46:26
Well, forget those guys.
00:46:28
Forget those guys.
00:46:29
Take that out.
00:46:30
Yeah, I think we are.
00:46:31
I mean, I think, you know.
00:46:33
We're about to be.
00:46:35
Residualizing LPEDs somehow,
00:46:36
I think we're at kind of one of the highest
00:46:38
sort of home run times.
00:46:40
Well, I guess I would follow, given.
00:46:42
Yeah, it's been a high home run.
00:46:44
Although, I will say, there was an era where,
00:46:47
I'm just gonna do this from memory, guys.
00:46:49
Willie Mays, Hank Aaron, Stan Musial, Ted Williams,
00:46:55
Willie McCovey, Ernie Banks.
00:46:59
So I could keep going.
00:47:01
Those aren't all the same era, though.
00:47:03
No, no, no, no, no.
00:47:04
They were.
00:47:05
A lot of them played in the 50s.
00:47:07
I mean, Ted Williams and Hank Aaron,
00:47:08
did those guys really overrun?
00:47:09
They did.
00:47:10
Ted Williams, of course they did.
00:47:12
No, no, no.
00:47:12
Five seasons.
00:47:13
Ted Williams' last season was 1960.
00:47:18
Willie Mays' first season was 1951.
00:47:21
Hank Aaron's, I think, was 55.
00:47:23
Stan Musial played forever.
00:47:25
He played in the 40s and 50s.
00:47:27
Ernie Banks started in the 50s.
00:47:28
Willie McCovey.
00:47:29
So no, these guys all played in the late 50s.
00:47:32
Mickey Mantle, sorry, I should have named the Mick.
00:47:35
Duke Snyder.
00:47:36
So I could keep going if you want,
00:47:38
but I'm just saying, there were a lot of guys
00:47:41
in the late 50s that had 500, 400 plus home runs.
00:47:45
Interesting.
00:47:45
Okay, very good.
00:47:47
And they never were in the Hall of Fame at the time,
00:47:49
because it just started to start to run.
00:47:52
I'm gonna jump to another,
00:47:53
I'm gonna jump to the female version of this sport,
00:47:55
the Women's College World Series,
00:47:59
has just been seeded on Sunday.
00:48:01
They seeded this thing.
00:48:02
The regionals are this weekend.
00:48:04
It's a 64-team tournament.
00:48:06
There are 16 regionals, each with four teams.
00:48:09
The winners of those will roll up to eight super regionals,
00:48:12
and then the winners of the supers
00:48:14
will go to the College World Series.
00:48:15
So that's the basic structure.
00:48:18
So there are 16 seeded teams.
00:48:21
Those are the teams that host the regionals.
00:48:24
They're ordered, and so you know which eight
00:48:26
will host the supers if they win their regional.
00:48:29
And what is it, it's not single elimination.
00:48:31
Is it best of three?
00:48:32
No, no, this is this wonderful mixed format
00:48:35
where they play round robin,
00:48:37
a double elimination for the regional,
00:48:40
and then at supers, they play two out of three.
00:48:43
And then for the first round
00:48:45
in the College World Series,
00:48:46
they play double elimination brackets,
00:48:48
and then the champions play two out of three.
00:48:50
And so it's this-
00:48:50
Oh, yeah, yeah, they rotate.
00:48:51
Yeah, they rotate back and forth.
00:48:54
It's an efficiency thing, I think.
00:48:58
But anyway, it's just starting.
00:49:00
It's a fun sport.
00:49:02
It's a fast-paced sport.
00:49:04
It's probably my favorite women's sport to watch,
00:49:07
and it helps that the University of Texas is good.
00:49:11
But notably, guys, six of the top seven teams
00:49:15
are from the SEC, and then three more
00:49:18
from the second eight are from the SEC.
00:49:21
So nine out of, I mean, I think it's only 16 teams
00:49:25
in the SEC.
00:49:26
Nine out of 16 teams are hosting regionals.
00:49:29
They're one of the top 16 teams,
00:49:30
but six of the top seven.
00:49:33
Nebraska is number four.
00:49:34
Nebraska is the only one of the top seven
00:49:35
who's not an SEC team.
00:49:38
So good fun.
00:49:39
UCLA is also-
00:49:40
Well, probably a different SEC representation
00:49:42
in the college hockey version of this tournament.
00:49:46
Yeah, that's right.
00:49:47
It's, we get, some of these sports are pretty regionalized,
00:49:50
college hockey being one of them.
00:49:52
Baseball actually always surprised, or softball, baseball.
00:49:54
That one always surprises me how regional,
00:49:58
because the history of it,
00:50:00
so much of the history of it is Northeast,
00:50:02
but-
00:50:02
But it's weather.
00:50:03
The powerhouse is obviously Texas, the southern-
00:50:06
It's weather.
00:50:07
It's a weather thing.
00:50:08
The guys can't play in the early spring in the Midwest,
00:50:11
in the Northeast.
00:50:12
It's just a, it's totally a weather thing.
00:50:14
All right, let's do another quick round.
00:50:15
I've got, I'm gonna end with a question,
00:50:17
a little application of stats for you guys,
00:50:20
but let's go one more round of what's caught your eye.
00:50:23
All right, so I'll just go quickly.
00:50:25
My son, Zach, told me a stat yesterday.
00:50:28
Relates to what Cade was talking about,
00:50:29
Shane was talking about with the hurricanes being 8-0.
00:50:32
So I mentioned OKC is 8-0.
00:50:36
So on Kalshi, which we all know is a betting site,
00:50:39
here are the odds.
00:50:42
It's not a betting site, Eric.
00:50:43
It's a prediction market.
00:50:45
Sorry, it's a-
00:50:46
Economic rationale for needing to place
00:50:49
these investment opportunities, predictions.
00:50:53
All right, there's an investment opportunity on Kalshi
00:50:56
with the OKC going undefeated.
00:51:00
And anyone wanna guess what the odds are on Kalshi?
00:51:03
I put it in the sheets.
00:51:04
You probably saw it.
00:51:06
So we're halfway through.
00:51:07
We're halfway through, right?
00:51:08
We're halfway through.
00:51:08
So they'd have to win eight straight games
00:51:10
against whoever the finalist is in the Western Conference
00:51:15
and whoever, of course, goes into the NBA Finals.
00:51:17
So eight straight games.
00:51:19
So what do you think the odds are according to,
00:51:23
if you didn't look, according to Kalshi,
00:51:25
that I can make a prediction-style bet
00:51:28
that they will go undefeated?
00:51:31
And I'll give you a benchmark.
00:51:34
0.8, let's pretend they were 0.8 in every game.
00:51:38
0.8 to the eighth is 16%.
00:51:42
And remember, they're gonna play
00:51:43
half these games on the road.
00:51:46
So it should be less than that
00:51:47
because we don't think they're 0.8 to win a given game.
00:51:50
All right, well, let me also tell you,
00:51:52
hold on a second here.
00:51:54
0.6 raised to the eighth is 1.6%.
00:52:01
Okay.
00:52:03
So somewhere between 16 and 1.6.
00:52:08
10 seems something reasonable.
00:52:11
Okay, so you're not far off.
00:52:13
I would have guessed it's totally unreasonable.
00:52:14
The number's 7%.
00:52:16
I thought that was just way too high.
00:52:20
Well, back out the implied game-level probability for that
00:52:25
and ask yourself, you know,
00:52:28
and then get some spreads of games.
00:52:31
And what have been the spreads?
00:52:33
Of course, they're gonna be tighter in the next two rounds
00:52:34
than they have been the last two.
00:52:36
Are they drumming?
00:52:37
72%.
00:52:38
72% game-level probability would justify a 7%,
00:52:42
8-0 run.
00:52:44
Including, by the way, there are road games.
00:52:47
Again, half those games are roads.
00:52:48
You have a mixture distribution between half home,
00:52:51
half away.
00:52:52
And the problem is, I'm not saying they would be,
00:52:55
but the minute you go to 50-50, let's say,
00:52:58
for any of the road games,
00:53:00
you slice that probability way, way down.
00:53:03
People have bad intuition, as you know.
00:53:06
Like you say, what's the difference?
00:53:08
70%, 50%?
00:53:09
Oh, wow, that's 30% difference in each of the games.
00:53:13
That's a ratio scale.
00:53:15
That's absolutely crushes it.
00:53:17
Well, does it say something about the expected strength
00:53:20
of the Western Conference?
00:53:21
Is that, are they saying the second
00:53:24
and third best teams out there just aren't that,
00:53:26
second, third, fourth best streams?
00:53:28
It must be saying something about that.
00:53:30
Well, San Antonio had a,
00:53:31
I think it's four and one or five and one record
00:53:33
against OKC during the regular season,
00:53:34
for whatever that's worth.
00:53:35
Now, OKC may not get out of its series,
00:53:37
but it's 2-2 with Minnesota.
00:53:38
You have to believe that whoever comes out of the West
00:53:41
is a dominant, dominant,
00:53:44
against whoever comes out of the East.
00:53:46
And when I say dominant, at least 80%.
00:53:49
Like, I don't see how you can get,
00:53:50
well, I don't see how an East game,
00:53:53
and that includes the road ones,
00:53:55
because I don't see how you can put OKC more than 60
00:53:59
or even two-thirds against any road game,
00:54:03
against whether it's San, let's say it's San Antonio.
00:54:06
You can't put them at more than a two-thirds chance
00:54:09
to beat San Antonio on the road,
00:54:11
which then I have to take all that other probability
00:54:13
is only over six games,
00:54:15
which means I'm gonna have to tremendously
00:54:17
cut down that probability.
00:54:19
I just don't see it.
00:54:20
Either way, it's that, you asked me what caught my eye.
00:54:23
And remember, I don't know that this is worth anything.
00:54:26
Let's just remember, this has never happened
00:54:30
in the history of basketball.
00:54:32
Never, despite whatever gap between one and two
00:54:36
you ever want to say was the maximum.
00:54:40
The Golden State Warriors at its peak,
00:54:42
the Lakers in 71 at its peak,
00:54:44
the Boston Celtics at its peak,
00:54:46
the Chicago Bulls and Michael Jordan at its peak.
00:54:49
No one has ever done this.
00:54:52
Yeah, and there really haven't been many that are close,
00:54:54
right?
00:54:54
I mean, it's not like there've been many 15,
00:54:57
I mean, 16 and ones either.
00:54:59
Correct, I think the Sixers are the only team
00:55:02
to have even gone 16 and one.
00:55:04
Maybe Golden State did it one year,
00:55:06
but you're right, there's not even been a lot of,
00:55:07
that's a great point.
00:55:08
There's not even been a lot of 16 and ones.
00:55:11
Right, that gives you some sense of how unlikely it is
00:55:13
that there aren't even those that are not.
00:55:15
And let's even remember, to make it even worse,
00:55:17
I think people, when some would say nobody went on 15,
00:55:19
remember Kate, the early rounds used to be best of five.
00:55:21
She didn't even have to win 16.
00:55:23
It was less and you still didn't do it.
00:55:25
Right, okay.
00:55:27
Well, that adds some spice to the playoffs
00:55:30
and it would be a heck of a way for the Thunder to repeat.
00:55:33
I mean, to do it that way.
00:55:34
I think, and I think you would agree with this,
00:55:36
if they happen to do it, and then I'd love to hear it.
00:55:38
I guess you said you had a question at the end.
00:55:40
If they do this, that does put them into a special category.
00:55:46
In addition to the fact that they'll have won back-to-back,
00:55:49
in addition, we've talked about this,
00:55:51
they've had the greatest point differential per game
00:55:53
the last two seasons.
00:55:54
From that simple metric, Dave,
00:55:57
this is the top two seasons in the history of the NBA.
00:56:00
If they go ahead and also go undefeated in the playoffs,
00:56:06
I'm not trying to present them as the 90 Bulls
00:56:09
or the Celtics or Showtime,
00:56:11
but this starts to put them in a special category.
00:56:14
Yeah, Eric, if we're gonna start throwing around that margin
00:56:18
given how much tanking
00:56:19
and how weak the bottom of the league has been,
00:56:21
we probably need to norm that margin for era, right?
00:56:26
In some sense. That's fair.
00:56:27
Okay, my last, what caught my eye,
00:56:30
comes from a listener, listener question
00:56:32
down here in Central Texas about,
00:56:35
you'll like this, Eric, this is completely up your alley.
00:56:38
You're the perfect person on this podcast to ask.
00:56:42
You run a golf tournament,
00:56:45
and you don't trust the handicaps that the golfers put in.
00:56:49
It's not a big, serious tournament,
00:56:51
so you have discretion to assess handicaps
00:56:53
in any way you want.
00:56:55
And you believe that it's important to observe
00:56:58
the players playing the course
00:57:00
that you are hosting the tournament on.
00:57:02
And so you're gonna use data from the tournament,
00:57:06
within the tournament,
00:57:07
the tournament they're actually playing to win,
00:57:09
you're gonna use some subset of that data
00:57:12
to establish the handicap.
00:57:15
So I don't get to use other tournaments,
00:57:16
just this, what happened here?
00:57:18
You don't believe any cards
00:57:20
that they reported from other tournaments.
00:57:22
You're just skeptical.
00:57:23
You're golfer skeptical about handicaps.
00:57:25
And so, plus maybe you have a unique course,
00:57:28
so you're just gonna use something about there,
00:57:31
you're gonna play one round, 18 holes.
00:57:34
So basically it's a simple,
00:57:36
it comes down to a simple question.
00:57:37
What subset, how many holes do you wanna use
00:57:41
to establish the handicap,
00:57:43
and then apply it to the remaining holes?
00:57:44
Yeah, so the answer is, I'm gonna use,
00:57:47
as a matter of fact, this is,
00:57:48
I'll give an answer,
00:57:49
and maybe this is not what you were thinking
00:57:50
I was gonna say.
00:57:51
I'm gonna relate it to your comment on tanking.
00:57:54
I'm gonna look at every hole
00:57:56
that they had every incentive to play at their best ability,
00:58:01
which means there was a legitimate probability
00:58:03
for them to win the tournament,
00:58:05
or they had to play best,
00:58:08
but let's say it's match play,
00:58:09
whatever it happens to be,
00:58:10
I'm gonna use all of those holes
00:58:13
for which they should try to play their peak performance.
00:58:20
That's what I'm going to use.
00:58:22
Again, assuming, wait, wait,
00:58:23
I'm assuming, but maybe I've read your question wrong,
00:58:26
they're trying to hide their true strength
00:58:29
on the other holes.
00:58:30
Well, they're trying to win the tournament,
00:58:33
and so you have the challenge of,
00:58:35
you're gonna conduct the tournament,
00:58:37
you're gonna award a winner,
00:58:38
but you wanna adjust for their skills.
00:58:41
You wanna try to put everyone in the same playing field.
00:58:44
So you have to declare a winner at the end of the day
00:58:47
based on the 18 holes they played,
00:58:49
but you're gonna use some subset of them.
00:58:51
But here's the thing,
00:58:52
if someone knew this,
00:58:53
so let's imagine I said 18 holes,
00:58:56
let's say it's a 72 hole tournament for a second.
00:58:58
Let's suppose I said 18 holes,
00:59:00
and I'm Scottie Scheffler.
00:59:02
I might have some incentive
00:59:04
to dump in those first 18 holes,
00:59:08
depends the percentage hand you have.
00:59:09
Eric, we're only gonna play 18 holes.
00:59:11
So you're conducting a one-day tournament.
00:59:13
Oh, all right, you didn't say that.
00:59:15
Well, that's what you got.
00:59:17
And also, we're not gonna announce ahead of time which holes,
00:59:20
because of course people would game it, okay?
00:59:23
So we get to, after the fact,
00:59:26
by some method, choose unannounced holes.
00:59:28
All right, so I'll just, here's what I'm gonna do.
00:59:30
I'm gonna give you an answer, I don't, all right.
00:59:33
This is what I'm gonna say,
00:59:33
and I'll tell you the principle by which I'm saying it.
00:59:36
50-50, and let me say why.
00:59:38
I'm gonna maximize power on both split samples.
00:59:42
I'm gonna get the best estimate possible of your handicap,
00:59:45
and I'm gonna get the best estimate possible
00:59:47
of your performance, and so I'm gonna do nine and nine.
00:59:51
That's what my gut tells me to do if I only have 18.
00:59:54
Well, so that was my instinct as well,
00:59:55
and so I'm glad to hear you say that.
00:59:58
I wonder how the golfers would feel
01:00:01
about having the tournament
01:00:03
essentially reduced to a nine-hole.
01:00:05
You're basically turning the tournament
01:00:06
down to a nine-hole tournament.
01:00:09
I know, but I also wanna get fair handicap estimates too.
01:00:12
Exactly, exactly.
01:00:12
So this person, this comes from Susan Skaggs,
01:00:17
a listener here in Austin, and she runs tournaments,
01:00:20
and she does a six-hole handicap, randomly selected.
01:00:23
That would have been my other number.
01:00:24
It would have been six,
01:00:25
because I want more for the performance.
01:00:28
I'm willing to take a little more error,
01:00:29
and by the way, you know what we're gonna do?
01:00:32
I'm gonna tweet at him, this is a Mark Brody question,
01:00:35
if there ever was a Mark Brody question.
01:00:37
I'm not saying he has an answer to it,
01:00:39
but he seems really smart to me,
01:00:41
and he was just on last week.
01:00:42
If we tweet at him,
01:00:44
I think he's gonna give us some sort of answer.
01:00:46
I'd love to hear his answer.
01:00:47
How about that?
01:00:48
But nine and nine, and six and 12
01:00:50
were the two numbers that I would have said.
01:00:51
Well, those also are the most mathematically simple
01:00:55
to do as well, which is not nothing.
01:00:58
Well, it's not nothing.
01:00:59
We want practical and implementable.
01:01:03
All right, why don't we wrap it there then?
01:01:05
On behalf of the whole team,
01:01:06
Audie, Weiner, and Absentia,
01:01:09
Shane Jensen, who slipped away here at the very end,
01:01:11
Eric Bradlow, who's been here for the whole show,
01:01:12
this has been Kate Massey.
01:01:14
Big thanks to Dion Simpkins making this thing happen.
01:01:18
This might possibly be Dion's last live show
01:01:21
after God knows 600, 500 plus episodes,
01:01:25
which I'm gonna jump out of a window
01:01:27
after this show is over and protest,
01:01:29
but Dion Simpkins can't say enough for the man
01:01:32
and what he's done for the show over the years.
01:01:34
For Marissa Reyna, our producer,
01:01:36
and Deep Patel, the boss lady, thank you guys.
01:01:38
And for you guys, the listeners,
01:01:39
thanks for being with us.
01:01:40
Come back and join us next time.
01:01:41
Between now and then, enjoy your sports.
