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hi this is Kade Massie practice Professor here at the Wharton School sitting with three of my closest faculty
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colleagues and longtime Wharton Moneyball collaborators Wharton Moneyball is a show that's been on Sirus
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xm's Business Radio since its beginning 10 years ago and we decided coming up on
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the 10 year Ann we're about a week away we thought we'd pause take a moment and
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think back on those 10 years especially because the world's kind of changed in
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those 10 years what do we think we've learned from it we spent a lot of time
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with each other over the last 10 years kind of a shocking amount actually doing the show so we thought we'd gather a
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little bit and talk about what that 10 years has meant to us I'm sitting with
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aie Wier professor of Statistics Eric bradow professor of marketing and statistics and Shane Jensen professor of
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Statistics as well a bunch of stats guys and we've been doing sports analytics
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we're also data science now we've been added that since we began the show oh
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there's a change in the last 10 years statisticians have become data scientists or at least some of them have
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we can talk a little bit about that that's an interesting development in fact why don't we start there why don't
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we start with the question of how you feel the World of Sports analytics has changed in the 10 years our first show
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was March 2014 so it's been 10 years has we've seen a few changes we're gonna
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pitch questions out we're all going to take a little bit of a chance Adam and
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we'll wander through the reflections but let's start there what has changed in
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the World of Sports analytics in the last 10 years well I think I mean Audi kind of
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alluded to it a little bit here I think certainly the public discourse around analytics and data and everything I mean
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in the last 20 years of me being a faculty member in the last 10 years of the show I think just sort of the public
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consciousness of of of kind of data has really changed a lot you know I no longer have to go and explain to people
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like why why I'm a why I'm a statistician why I do why I work with data I think so I think that really has
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changed way of saying that it's much cooler now it's caught up to the coolness of Shane probably but I think
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also on the other side I think Sports has also become cooler within the sort of statistical academic community in the
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time I've you know in the last 10 years as well I was kind of looking back I
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started as a faculty in ' 04 and ' 05 was the first issue of the Journal of
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quantitative analysis of sports it was the first I think before when I was first coming up through graduate school
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I was interested in sports but I was cautioned a lot like do not you know do Sports as an academic topic you know
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maybe have it as a side hobby but don't make it your main thing and now we've
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got Flagship journals in in in in in sports anls and I think in the 10 years we've been doing this show I think you
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know Sports has become gone from kind of being this sort of Black Sheep to this something where this is where you see a
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lot of the coolest kind of spatial temporal data situations so it's almost kind of on The Cutting Edge of
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methodology now it certainly is accepted I remember when you arrived that's when
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I just got tenured that time and that was the first time I decided to work on Sports I waited until I had job security
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before I could work with sports I wouldn't I wouldn't give that advice anymore I'd say Jump Right In I think
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your comment Shane though about the field is always going to chase the coolest data and that's really I think
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the difference that's happened I think the reality is sports has data that Rivals really any other industry today
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and I think that's why Academia is moving in that direction I think that's
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why student interest is there I think also it's a great testing ground for learning statistical methods yeah it
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just is you know one particular kind of data that's blown up almost exactly in
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the window we've been doing this show is space show temporal so the kind the dawn
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of that in my mind you can pick different Don but one of the most important Don was when Kirk goldsbury
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got the data at Harvard from the NBA and he ran across Luke Bourne and I believe
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that was 2012 when Luke first got got there as a faculty member so that was less than two years but that was really
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the beginning of and the NBA was before the NFL that was just preceding our show
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so we've been there kind of from the beginning and we saw this thing blow up
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and now it's secondhand well I will say that Shane and our first paper on baseball was spatial temporal data
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evaluating Fielding but it was crappy data right right done by video and human beings and the enormous change was
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leaving this crappy imprecise sparse ER filled in incomplete data to Now tracking but it Al also changed because
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in baseball the best data was always public and now the best data is private and this is this is one of the things
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that has changed I think not for the better we sitting here in Academia could produced Cutting Edge research that was
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a value to the teams back then and I think at this point if you don't have proprietary data you're not saying
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anything the teams want yeah I think it kind of it goes hand in hand because I think as the DAT has gotten richer or
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the data has gotten very expensive to collect and is is incredibly rich but is now kind of like often sort of owned by
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a league or something like that it's less kind of accessible I think maybe Kate to answer your question for me um I
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was hoping someone would say something I didn't say which Shane did um I think
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the part that's gotten most interesting to me is the problems meaning if you
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think about the original Money Ball right it was mainly about onfield stuff like how are we going to get you know
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let's go back to Billy Bean and Moneyball how are we going to get players that you know maximize our
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chances of winning but now if you think about the role of analytics I'm even
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thinking about just the papers the people here have written it's not just the onfield part it's about the business
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of sports it's about you know trying to make decisions on sleep and training
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patterns Etc which is to me the narrow set of problems which was let's attack
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should we go for it on fourth and one you know are walks as valuable as singles I'm not saying those aren't
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important problems but the problems have grown so diverse and that's the part
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that's exciting to me and I think that's what's really changed is that the
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business side like when I started working for the Eagles the onfield side wanted something to do with me but the
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business side wanted nothing to do with me and now both sides tend to have analytics so that to me when I was
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thinking about the question I think the problems are broader and a lot more interesting and the maybe also to build
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on Shane's earlier point and and what AI just said the Divide now between Academia and practice of all the fields
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that I see you know my role as Vice dean of analytics here I think Sports has the
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closest connection between Academia and practice that I've seen if I think about
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people analytics and Neuroscience or even big tech companies there's still this divide but I think now there's this
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real tightness between Academia and practice when it comes to sports and statistics and I think that's been
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fantastic I mean how many students are you sending to sports teams how many times are we working with people with
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sports teams whe it's the NFL Big Data bowl or some other problem I think that
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divide has really shrunk and that makes to me Sports Antics the most exciting field to be in so just for to finish up
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I would more or less say the same things statistics has always been very close to sports because we can develop
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new methods and really Advance the field of Statistics as while using Sports data
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so it really is a full-on statistics data science problem just using Sports and this has engaged so many people at
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all levels um but the I have to say just I can give you a number it wouldn't be a
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statistic show without a number when I first taught Moneyball Academy which was our high school sta statistics program
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um u high school sports analytics program over the summer the first summer we barely had more applicants than we
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had space and now we have which is how much space uh about 75 but the first time we ran we ran for 50 we had 55
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applications for 50 spots okay and this past year we have two rounds and we have
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already 350 and we I don't think we're going to do a second round right that's
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just show how it incredibly um the interest in sports analytics even at the Young level is just exploded MH MH the
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one element that you alluded to Eric that I just I think is worth underscoring is that you're so right
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that in the beginning we were talking about more or less in-game decision making and Personnel really the two the
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play evaluation player evaluation and the I think the principle third that has evolved really only in the last seven or
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eight years is performance enhancement performance changes and and the classic examples of the Astros bringing in these
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pictures and teaching them to throw a different way and that's a use of Technology analytics that just wasn't
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happening at all and it's entirely different from the other from the other two or as we've had guests on the show
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where everything in your practice now these people have sensors all over their body so when is somebody tiring when is
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someone getting their Peak Performance and of course as scientist what we care about is how do you maximize performance
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which means if you can run an experiment or manipulate things like let's try
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different you know eating patterns sleeping patterns Etc and let's see how it manifests itself on the field there's
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no reason why that shouldn't be part part of sports analytics today you know
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sensors all over the body is really good data MH and I think that kind of immediate feedback you know they be
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being able to kind of train in a way where you know if you're a p hitter and
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you're trying to H learn how to hit a fast ball you can kind of just have like
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a lot of kind of data support like and and kind of immediately learn sort of like meth you know kind of mechanically
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what you're doing right or wrong or free throw shooting or whatever there's so
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many examples where we now kind of have almost IM you know immediate feedback coaching and these kind of really helps
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Player Development mhm MH okay that question was about how the field has changed this next one's more
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or less how y'all have changed so what have you learned in the 10 years doing
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the show because of the show what do you think you have what's an example of
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something that you've learned I can go I'll go first so um I've learned a lot by listening to all
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of you and uh the thing that kept coming into my mind since I knew you'd asked
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the question was base rates which is for me to move away from a base rate so for
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example let's imagine I took two arbitrary teams just playing each other I didn't know anything about them well
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50/50 right okay so now I have these other factors I bring in there so the first thing is to move away from the
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base rate of Shane always talks about the coin flipping model to move away from that you better have a good reason
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and also I was probably one of those people that thought effect sizes like how much something really affected these
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probabilities was much bigger than I I now think it's much smaller like I used
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to think well it's a 9010 game you know Clayton kers on the Dodgers are playing
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I don't know the lowly Pirates 90% for the Dodgers no there's never you'd never
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predict you think you're different now than if we were sitting here 10 years
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ago I do I think I'm different because not you you were you were a cheered professor of marketing statistics 47
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years old 10 years ago and you were like why you keep bringing up the age what you were 47 10 years ago
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too we're all born in 1967 not I think I was only 37 I'm I'm not I'm not being critical at
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all I'm honestly querying that's phenomenal that that's been a consequence of these 10 years I do I
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think again it's both base rates in other words to move away from just or a always says if I have an empirical
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frequency let's say I've got say you know I've watched 10,000 baseball games
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over 40 years here's the empirical frequency something happens someone asked you to make a prediction that's a
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damn good starting place and to move very far away from that you better have a reason and I also think I adjusted too
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much like well it's a left-handed pitcher against this team momentum yeah well we'll momentum will get to I still
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believe in momentum that part hasn't changed but I think I believe in base rates and I don't move off them as far
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as I think I used to so let me let me just say what I've learned because it it
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kind of matches in some level what you were saying so when we started this show um I knew a lot about baseball I still
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know a lot about baseball but I was really terrified how we were going to feel two 2 hourss talking about sports
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that it can't be all about baseball because what am I going to say and uh I've certainly learned a lot about
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sports I think everyone is proud of my accomplishments in the football arena in particular um but all sports especially
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but the way you're able to do it is basically bring those base rate facts and questions to any sport so I might
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not know anything about golf but I can contribute by asking what's the mean what's the standard deviation what's the
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what's the a typical player doing these situations and sometimes what I found is
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my lack of knowledge about a sport works very well with the experts because it brings you back to Earth it it stops you
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from from overt telling the story and and forces you to ask a specific question and so one of the things that's
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amazing is that that it was I don't know how long it was but after a while we
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realize we can fill two hours no problem right AUD I just want to point out you're connecting to Something in
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Psychology called the inside view and the outside view the inside view people have very case-based detailed enriched
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models of the world the outside view is pretty much about Bas rights and often the inside view can get skewed by their
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cases and the outside view is really helpful correction in that way um Shane yeah I've been thinking about this too
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and actually it's a little related to what Eric was talking about a little bit
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too I kind of feel like I I when I with the show first started I mean you know I
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was trained as a statistician um I feel like I'm decent at probabilistic kind of thinking and
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thinking in the presence of probability but I have learned repeatedly over this decade of my own kind of how easy it is
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for like like biases and probabilistic thinking creep in I kind of I think of the kind of I don't know what
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inevitability kind of bias that I have you bring it up like every college football season right where we kind of
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like halfway through the season we're locked into a national championship game
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no not that when he asked at the beginning of the Season what probability do you think is assigned to these four
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teams and I'll say like 15% in case like it's like less than 1% like total
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overestimation and I think I've I've learned from myself that in situations
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especially like you know I think now of like you know the Kansas City Chiefs and
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the current Dynasty in in situations where there's kind of you know a dominant team or something like that my
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own mind I tend to like when you've got like something like a probability of0 75
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or 08 and you round it up to one or you round it to you you take a probability that's in like the 15% or 10% range you
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round you just your mind just BL rounds it down to zero and so kind of thinking about those sort of like you know like
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even intermediary probability events like a 20% you know a real mismatch in sports would be like an 80 to 20 thing
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thinking you know not thinking about that as in an inevitable Victory like you know thinking about that 20% and
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kind of processing it you know every time you see I think that's a lesson I've kind of learned and it's been
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instructive too to sort of see like me looking at the Kansas City Chiefs as an inevitable Dynasty it's because I'm not
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a fan following that team and seeing all remembering all the gut wrenches losses
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they've had over that time as well I think it's sort of like when you're
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really closely following when you're involved in a sport maybe you're less
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prone to that inevitability byus cuz you're kind of of seeing you know seeing
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those 20% turn up maybe that's interesting right yeah I'll give you a couple quick ones uh one I've I'm struck
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by how many times we figure things out collectively that we'll start kind of
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Meandering towards some subject and we'll talk about it for seven or eight minutes and after about 10 minutes we
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kind of have a better understanding collectively and it's connected to my other one which is everyone's wrong
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sometimes and I've learned that another environments one of the nice things about working in on faculty is that you
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see some really smart people be wrong it's helpful to see that we're all wrong
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sometimes you guys are some of the sharpest guys I have conversations with and you're all wrong sometimes well
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let's just take what the facts are I'm wrong more than all of you because you
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remember we've actually no no no for years remember we've picked outcomes of
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games and kept scores I think I have a I have momentum that I'm the lowest every
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single year we've done matter of fact I don't think we've done this in a few
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years matter I think it's cuz you guys want to keep me at the bottom I we need
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to go back last place every year I just tell people I'm in fourth place and like that sounds
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impressive I don't tell them there's only four of us making these predictions
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well the redeeming thing is we haven't exactly been comprehensive in our record
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keeping so for all we know you might be number one and we just hav written down way well the last thing that I feel like
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I've learned is the power of doing something a little bit of something on a regular basis I think the the cumulative
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effect of doing this show even though it's only been two hours a week and now
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just one hour a week but we've done it 50 weeks a year for 10 years the cumulative effect in terms of what I
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know my relationship with y'all the people we know in industry is amazing we
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we what's the line like we tend to overestimate what we can do in one big push and underestimate what we can do
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with a sustained regular push I think this Show's been a like a tangible demonstration of that to
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me um all right what about themes that have emerged on our show we we have come to realize we didn't sit out with any
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themes we didn't know how we were going to talk about these things at all but
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after a few years we kind of come back to some of the same bits you guys have just talked about base rates for example
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that's definitely been a theme what else would you say or some patterns or some
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themes well come on it has to be what AI mentioned I mean you guys I ridicule is
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a fine word but in a professional nice way you guys ridicule me about momentum I mean that's definitely been and you
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know I'm a strong believer that um there is heavy if you want to call it a heavy
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degree of State dependence like given a success there's a much larger put a fancier term to make momentum
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sound more sophisticated no I think moment I still believe momentum exists but I'm willing to say that there's at
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least heavy non-stationarity and whether you want to call that momentum or not matter of fact
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i' I've joked as this I think you guys know but our listeners know like I'm
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from the old school basian World which means I believe in Cross unit crossers heterogenity what I think I'm going to
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spend the next 20 years of my career on is within person heterogenity which is if you think about something we talk
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about like a hidden Markov model like people go through hot and cold States well that's about a given unit that's
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not a cross-unit statement I think we in the show have spent a lot of time talking about whether you want to call
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it momentum non-stationarity Etc and I think it's a great thing because I think
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a lot of times we focus on differences across units and not as much sometimes there's Peak periods and weak periods
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within a unit let me jump in because that that's my favorite one that's emerged over time I can name a few
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others but that one I think is the most interesting because it really has been a
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slow thing to emerge and now it seems to me terrifically important we come back to it repeatedly you always named the
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connection to marketing there but within person variation and one of the reasons
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it means so much to me is that I think it's something that we miss like people
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in the world underestimate it we tend in the world to focus on betweeners differences and we're talking about mean
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differences and we underestimate the within-person variance well you know my favorite topic on that is when we talk
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about whether it's tennis player or not is that as players age you draw more
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from a bodal distribution so we're even know the greatest tennis player of all
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time we could debate it's probably Novak jokovic I begrudgingly have to admit
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that but at the end of the day as someone ages you start to see well maybe 95% of the time we'll see a draw from
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the good distribution and a 5% of the time it'll be bad and then maybe next year because he's 37 38 it'll be 90% 10%
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and that's what happens this is definitely an Eric theme this an Eric theme he's he's learning how toing from
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me for me he's but he's learning how to talk about it in a more structured way a
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real quickly but but this is something that you've given us an example of it's
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with pitcher this is an empirical observation you have about baseball pitchers that we tend to think oh this
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is how you rank them who's the best who's the next Who's the differences and
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you're like yeah yeah fine but a given picture looks very different on different days right they do not only do
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they look very different on any given day but any really almost any pitcher can have a fantastic game in any in any
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one moment and this is the kind of things we we've Ted aled about variability across Sports this is the
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kind of thing that happens with baseball pitching constantly doesn't happen so
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much with with quarterbacks you expect good quarterbacks to have fantastic games obviously it does happen every now
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and then but that kind of comparison across Sports amount of variability but if I were to answer like what what theme
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that we've I mean is and this is I guess so important in in basic statistics we think about
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regression and that just drives just about everything we do and I don't mean
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it just in regression as the forecasting tool because the word regression is generally used today to mean your
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prediction your predicted value what is you how did you regress this but the word actually comes from to regress
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which is to go back down and in sports they use the word constantly and you and we it's a tool for forecasting but our
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so much of our dialogue is is concentrated on how much to regress and H and how what data we use to to figure
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out and and what are we regressing to in fact our just our last show what are we
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regressing to to individuals rate to the base rate of the of the of the of the the the community the group and some of
00:21:03
our finest conversations have to do with trying to understand how much regression
00:21:06
and that of course intercepts with our basian conversations like how do we how do we integrate priors into that and so
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it's a really a great amalgam of some of the most important themes of Statistics
00:21:17
regression basian shrinkage is is the more the more complex way to think about it and we've become artists in
00:21:24
describing these hard I think so hard Concepts in our Sports and I think it's made me a much better teacher
00:21:32
because I use it in class even not necessarily with sports data has it made you a better Reasoner I I think it does
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do you think you're more you're less prone to make that mistake you're more
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prone to remember to strength your forecast because of how often we talk begrudgingly sometimes particularly when
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it comes to my Yankees I think and I think I've also kind of like I I guess you could call it
00:21:51
maybe a cynicism or something like that but I've kind of developed sort of like
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a little bit more of I think a protection against the sort of some of the recency biases like when you you
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know when when you see some unusual performance or some new thing hit a sport you know are you just looking at
00:22:06
kind of the random variation of a particular pitcher like suddenly having a stand up performance is that an
00:22:11
example of a non-stationary thing where we can see that you know we can predict that for that pitcher going forward or
00:22:16
are we just kind of biased by the recency of a really stand out performance I think back one of our um
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you know back 2015 I think when the Kansas City Royals won the World Series we spent like weeks after that talking
00:22:28
about how they've revolutionized baseball with the way they like got to the World Series with like you know shut
00:22:34
down relief pitching and fielding and all these types of things and you know I mean they did do all that but how much
00:22:40
that that actually you know how much we should have really regressed even more those kind of Trends towards you know
00:22:46
the mean because it was it it wasn't maintained you know can you can you go back 10 years can you remember the first
00:22:52
show we're in a basement here at the Wharton School we were in a different basement when we started before the
00:22:58
Studio existed what were you thinking as we Eric rounded us up let's give credit
00:23:03
to Eric I remember sitting his office the first time and then we end up in the studio over there Dion was there on day
00:23:10
one what were youall thinking on day one remember I'll tell you what I was thinking because I remember it I
00:23:15
remember two things first of all I remember being nervous speaking live remember it's been a while since we've
00:23:22
done a live show but the very first show was a live radio broadcast there's no
00:23:27
opportunity to fix what we did and I remember being concerned about that remember being concerned about saying
00:23:32
something stupid that that you couldn't take back or or have some tick or whatever so that was the first thing the
00:23:37
second thing was really bothered me I'm curious to know what your thoughts I
00:23:40
didn't know how we were going to fill two hours um and and that's of course
00:23:45
been the the most pleasant surprise I feel like we can we can fill two hours in fact there was a a crazy time where
00:23:51
none of you were there and all the the entire sound went out and there was no Communications with and it was I was
00:23:57
left alone it was and I it was live and I had to talk for a half an hour to no one about nothing for a half hour and it
00:24:04
was no problem that might be a professorial skill that you've developed over time
00:24:10
it's possible yeah I was never worried about us filling up time at all not in
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the slightest because in my case I just watch so many different sports and so much Sports and I'm just used to also
00:24:21
taking notes when I'm watching sports on things that also to me the beauty of
00:24:26
sports for me is that it's the ultimate Trojan Horse to talk about any statistical topic like should you go for
00:24:34
it on fourth and one should the coach do this at this point in time should you go
00:24:38
for three should you foul in the last 10 seconds but every one of those I could give and I'm sure you guys could do the
00:24:45
same could give an entire lecture just on that topic so I was never ever worried about it and also my my view was
00:24:54
um the one thing I've learned through teaching and you mentioned about its effect on teaching and and the radio's
00:24:59
really changed it in that way too is that I figured if I'm having fun the audience is having fun and that's always
00:25:06
been my motto for teaching as well and part of the reason I brought this group together I mean we've known each other
00:25:11
longer than I've actually known both of them and my comment was is that we were
00:25:15
going to have fun no matter what talking about sports and statistics and if we had fun I figured it would be fine no
00:25:21
whether Sirius XM would keep us on the air for a month a year or now 10 years I actually wasn't worried about that I was
00:25:27
just like if we're having a good conversation and we're having not only fun but learning something if we're
00:25:32
learning something the audience will learn something too but Eric you taught that to us I mean I learned that from
00:25:37
you that we can talk about sports at a high level and teach statistics and do this for two hours with no problem but I
00:25:43
remember not knowing that at the time no and I remember that I I can Echo the nervousness of you know I mean again
00:25:49
we've learned since then also that you're you're encyclopedic knowledge of
00:25:53
sports I can I can say stupid sh every show and you're there to kind of as you
00:25:57
know sort of a uh instant fact Checker so that that was reassuring and I also do think I I kind of worried for a while
00:26:05
that we would sort of be able to kind of generate to our you know like that amount of material every week but as we
00:26:10
started doing the show and we started seeing these kind of various themes popping up I started seeing them
00:26:15
everywhere like I you know I would be you know I'd be you start looking for them you start looking for them and you
00:26:20
and you you do sort of like kind of enabled me to sort of see connections be between sports at like kind of the base
00:26:26
you know between versus Within ation these kind of basic levels that you know basically allowed me to kind of I think
00:26:33
Branch out to sports like tennis that I didn't have much experience with before
00:26:37
the show even started Shane you're talking about between sports makes me think about um tournament design as a a
00:26:44
a late answer to an emerging theme I never knew I was so interested in tournament design until this show came
00:26:49
along what about favorite moments we talked about how you were thinking about it 10 years ago 10 years of shows any
00:26:54
favorite moments come to mind I'm sure we've got lot but like what comes to
00:26:57
mind without no one's keep keeping track or score but what might come to mind
00:27:00
well just cuz we were talking about the early days in vance H I think one of the
00:27:04
I think maybe the first Super Bowl kind of re I think it was Super Bowl 49 the the Malcolm Butler interception was one
00:27:11
of my favorite shows we came in I mean obviously you know my my team won it all but it was more coming in and just sort
00:27:17
of like it was early on in our history it just seemed like just kind of the perfect time to talk about something
00:27:23
that had like obviously a lot of consequence but also like a lot of analytical strategy discuss and just
00:27:28
seeing that kind of moment being discussed in the like 10 years to follow and how how that kind of discussion of
00:27:35
that particular like play has been informed by 10 years of kind of you know increasingly sophisticated analysis that
00:27:42
that play has stayed the test of time let me just say it's still discussed today I I think uh I told AI just before
00:27:48
the show taping here what I was going to talk about but part of his just I love seeing your reaction each time Kade I'm
00:27:54
going to talk about Joey Chestnut no no yes to bring that up just I mean again I
00:28:02
want to say for our listeners here I want to say it again Hot Dog Eating is a real sport you can train for it and I'll
00:28:09
tell you the thing I like about it as well is that you can measure atypicality and that's the thing is that
00:28:16
you can measure how many how much how many more hot dogs can this person eat than that one and when Joey chessnut can
00:28:22
eat 20 25 more than other people and people have now especially now he's been
00:28:27
like the 15 time mustard belt Champion why haven't people caught up to him I'm
00:28:32
just saying you have to you have to admire someone who's his exceedence is so far greater than everybody else that
00:28:41
to me so part of it is just seeing your reaction I just love it because I in my heart I'm not just saying it to piss you
00:28:47
off I really think it's a sport and I love talking and when he came on the air
00:28:50
and he talks about his training methods and everything else I'm like this a real
00:28:54
scientist working here 20 years from at our 20th anniversary Kate have totally turned around on this I think no he
00:29:00
won't you're not you're not there yet I can tell already so any other favorite
00:29:03
moments a so I have a bunch of favorite moments that so it's interesting because
00:29:07
I don't not really concentrating on the guest per se because we've had some
00:29:10
recurrent guests who are incredible um and but the moments for me are when I learned something that's shockingly new
00:29:17
to me so I remember when Rick Peterson oh told something to us remind us Rick Peterson was was a was a pitching coach
00:29:23
longtime pitching coach was the Oakland A's and he was on our show quite frequently in the beginning regular
00:29:27
guest as a regular guest and uh we used to bring him in for about 10 minutes and
00:29:32
as a baseball analyst one of the big strategic questions are why you not bringing in your relievers at the high
00:29:37
leverage moments and we analysts were always saying this is just dumb how come they have this s conventional way of
00:29:42
doing things and you should you should do some things differently and he said you know you guys that's that's fine to
00:29:48
do this but you realize that there's all this actual onfield issues that you
00:29:53
don't have any idea about like a pitcher has to warm up and once you've warmed
00:29:57
them up and you don't bring them in we might not be able to use them again and
00:30:00
we were sitting here listening to this and going oh and I remember feeling really schooled by an actual baseball
00:30:08
profession like a real Egghead moment you and like boy I think we we have a lot to say but we can't Implement
00:30:15
without actual onfield expertise okay and that's just a general lesson that we
00:30:19
should be not just knowing ourselves but preaching to other statisticians and data that's right it's a humility that
00:30:25
that Rick was saying get over yourselves on this you don't have the full story
00:30:29
and that was that moment of course there are other moments as well like when mie
00:30:32
Betts told us that the real reason why Bill Buckner let the ball go through his legs because he was running and it got
00:30:37
him so nervous I enjoyed that moment wilsonon sorry everyone I screwed that up mie Wilson
00:30:45
yes you're talking about uh Rick Peterson and and giving some background that that was I opening to you reminds
00:30:52
me out of the blue of what Chris Collinsworth told us when we talked to him about Bill bellich he said he was at
00:30:58
some practice the Patriots practice early in the season and he came away thinking that belich was actually
00:31:06
burning some wins early in the season in order to rotate defensive strategies and
00:31:12
to train his players to play multiple strategies at a way that might cost them early season in order to learn to be
00:31:18
better later in the season this was Collinsworth interpretation of what belich did and it was like oh wow that's
00:31:24
interesting and it did I do remember that now that you say it one of those really eye opening um I have another one
00:31:30
too and it's a surprising one for me it's one of our horse racing interviews no it was the one where we
00:31:37
were talking about like you know what really take makes a horse like a a true rener he's like oh it's the horse with
00:31:43
the biggest heart and we were all like all of us cynically were like oh you know we've heard that kind of wants it
00:31:48
the horse wants it the most but he was literally talking about biologically the horse with the largest heart is the one
00:31:55
most likely to win the race and I thought that was you know that was is kind of a fun little that's a top three
00:32:00
that's a consensus top three all time also in that same interview he mentioned
00:32:03
that also every horse slows down during the race just who slows down the least it's kind of like the sinking fastball
00:32:09
kind of which also was a great moment one of my favorites was I think a category that I like is um when our
00:32:17
guests have little either they share they have an epiphany or they share a moment and we had one I think the first
00:32:23
time we ever had Brian Burke on the show so Brian is an ESPN analyst and one of the guys who had been on The Cutting
00:32:29
Edge of football analytics for a while Brian had been an engineer by training he flew for the military and then he got
00:32:36
into football analytics but his background had him very black and white and when he got into football analytics
00:32:43
he started seeing Shades of Gray he literally said I had to start thinking probabilistically for the first time in
00:32:49
my life and this is a you know highly accomplished mid-30s probably guy and he was saying that's what stats and
00:32:56
thinking about sports analytics did for me it turned me from black and white Reasoner to Shades of Gray and
00:33:01
probability so what you just said maybe think about something that I'd love Audi
00:33:04
to talk about the work you're doing with Ryan Brill on the fourth down in just a
00:33:08
second but the thing that we also I surprised it didn't come up as one of our themes also is is the role of
00:33:14
uncertainty which is even if you put a probability on something that's your point estimate of the probability like
00:33:21
probability is 60% you should go for it on fourth and one okay but the uncertainty is plus or minus 10% on and
00:33:28
so one of the things I think I meaning with a coin we know with Precision what that probability is but with these real
00:33:34
world team events we're estimating something and and in general the you know the classic rule of thumb is well
00:33:40
in fact I think what I'm about to say is probably a theorem but audio correct me
00:33:44
which is by definition you're always underestimating the probability like in
00:33:48
order you could say well a fair coin's 5050 maybe I don't know that like maybe the
00:33:54
way you flip it I'm just saying you can always say there's some Ed factor that
00:33:58
you're not putting in there but I think just to me the role of uncertainty even
00:34:03
in things that are uncertain we don't take into account enough yeah and we have to evaluate that and we've learned
00:34:08
to do that over the years remarkably um and but sometimes you have to do it fast
00:34:12
but in particular you're talking about so bril Wier right so we we we came to
00:34:16
this idea saying looking at to figure out like what is the what is the uncertainty in the models and what we
00:34:21
really disc models you're talking about are the fourth down models we really
00:34:24
discovered that the data isn't really that rich which means that given data is
00:34:28
a constraint and as a result you can come up with an estimate but if you had gotten a different set of data different
00:34:34
2,000 games you might ask well how might the estimate change and that's really
00:34:39
what What's called the confidence syndrome we realized that those things were really wide almost shockingly wide
00:34:43
because there's not much data in football because there's just not that much data in football which led us to
00:34:46
the which connected with the Rick Peterson comment which says wait a minute if we don't really know we have
00:34:51
to have some humility about what we don't know just cuz I've got a point
00:34:54
estimate doesn't mean that that's the correct estimate and I can't oversell it
00:34:57
to the team because they have information that I don't have and that information should be more important
00:35:03
particularly when I don't have good information and I've got to let them let
00:35:07
them go for it okay this connects to two other of my favorite Point moments on the show one is it connects back to our
00:35:13
regress your forecast and one of my favorite standout moments me in memory was the last bit of the show the last
00:35:23
show before the 2016 presidential election so we've done a fair bit of analytics on the presidential election
00:35:28
leading up to that and if yall remember 538 famously had Hillary Clinton as I don't know 80% or something likely to
00:35:35
win that election and the last thing that happened on that show was AI saying ah you
00:35:41
know I'm gonna I'm gonna go with something I'm be like really like how
00:35:44
much less I don't 67 I play that clip to my class every election year yes I do I
00:35:50
put it on there because it's so 538s was 80% but almost every other forecast was
00:35:55
like 99% yeah they were actually the most humble Sil just said I just don't feel like it
00:36:02
and he did something completely unpraised dung for Hillary Clinton but something
00:36:09
doesn't feel right and we had a conversation about what doesn't feel right on the show and I was trying to
00:36:14
argue that it's got to be a lot lower her probability of winning is a lot lower and although we had no I had no
00:36:19
hard data to go on I was adjust looking at previous elections within that calendar cycle in other countries where
00:36:26
the populous candidate was coming out big and it had and and overturning the forecast that was a great moment so I I
00:36:32
what I like about it is that that's probably that's 2016 so we're only two
00:36:35
years into the show we're talking about we're we're all cultivating this
00:36:39
tendency to regress and pay attention to base rates and the question was always like are we like be you is that true
00:36:46
because in that moment I wasn't there's no way I was thinking regress that
00:36:50
forecast it was only when AI said I'm going to regress that forecast and now
00:36:54
because of that moment and because of the reinforcement that that came about from the
00:36:58
it does start maybe bringing us to that habit but it's so easy to say it intellectually but in the moment we
00:37:03
don't tend to do it you did it in that moment and that was one of my kind of
00:37:07
what I talked about inevitability bias or rounding those 80% up to one that was a mo that was a real lesson for me you
00:37:13
know that was kind of a a real learning moment I want to name one other that's
00:37:17
connected to this and that is uh and it may be another question we can pursue in
00:37:21
a seconds like we've read some books every now and then for some of the people we interview and we read a book
00:37:27
called called escape from model Land by Erica Thompson and this is written by a modeler cautioning modelers to not get
00:37:35
too caught up in your model like you got to recognize what we all love our models
00:37:39
and we begin to think the world is our model but the model is only an imperfect capture of the world and we have we have
00:37:45
to get out of that and it connects exactly what ai's saying about what's
00:37:49
your data you know how how sure you of these estimates you got to have that humility it really comes back to that
00:37:54
humility well you may also remember for years I asked Our Guest the following question I always used to end when my
00:38:00
when I got my time to speak I'd always ask the following question you could have one of three things better you
00:38:05
could have better data better model or better let's call it internal buyin I
00:38:10
never in all our years doing it I never heard someone say a better model like wow you're right my random Forest my XG
00:38:16
boost models just not working well enough no no I need a better what you always hear is either it would be
00:38:22
greater to have more impact at my company because there's better connection or I'd rather have a
00:38:27
randomized experiment or you know I have Spar data on fourth and one I'd really
00:38:30
rather have an INF never did someone say a more sophisticated model that can give
00:38:35
me another third significant digit on something ever that's you I didn't I've
00:38:39
heard you answer that asked that question in lots of environments on the show and off I've stolen it on occasion
00:38:44
myself I didn't know that you had done it enough to begin drawing inference about it and I love that no one ever
00:38:50
said the model ever matter I'm pretty sure the number could be zero but it's
00:38:54
interesting because you talked to all the the representatives of of startup companies that are selling data and
00:38:59
selling and selling pipelines like we just talked to people from data bricks and we have talked to another company uh
00:39:05
just called shot quality they aren't selling models nobody's selling models
00:39:09
they just and and I always wonder why they're not because there's a there's an
00:39:14
edge there right they're selling you know serious strategy advice and data and they just layer on top of it
00:39:20
somebody's random forest or deep net deep neural net just something we're sitting here 10 years in
00:39:27
D Wharton Moneyball all the all the co-hosts longtime collaborators here taking a moment to think about how the
00:39:33
world has changed over those 10 years how we've changed some of the things we've learned maybe some of the
00:39:38
highlights we were just talking about Erica Thompson's escape from modeland which came out she was studying partly
00:39:45
covid and talking about models in covid there was this moment in time guys where
00:39:49
we were almost actually we were pretty much full-time covid analysts so March 2020 hit and we start talking about Co
00:39:58
making sense of Co at some point we decided we're going to stay on this and we're going to dedicate the first half
00:40:03
hour I think of every week to co what are your Reflections now that's been we've we shifted back more than two
00:40:10
years ago now but it was a long stretch when you think back on it now three months was an hour we didn't have any
00:40:17
sports remember that that the beginning that's right the first three months it
00:40:20
was all pandemic all the time what Reflections do you have on that stretch of Wharton I I remember it
00:40:27
extremely well because I really dived into the data deeply you became our expert and um but just I mean the lesson
00:40:33
that modeland the book which came out much later we learned on the Fly because there were modelers coming out with
00:40:40
forecasts for the spread of covid and forecasts on diseases and on hospitalizations and everything that we
00:40:47
wanted and policy was being built on it and those forecasts were terrible and it
00:40:53
was the best Minds doing the best job with the data they had and you couldn't
00:40:57
do anything couldn't do it and it was just an incredible humbling lesson um where I was disappointed was that people
00:41:04
would put out models with their prediction intervals and their prediction intervals nobody's overlapped
00:41:11
and that said to me this is a problem if we have 10 different forecasts about where we're supposed to be going down
00:41:16
the line really time series forecasts with confidence bands or prediction bands on top of them and they were all
00:41:21
so depressingly narrow and we looked at them and this is just bad so not only were the forecasts bad but much more
00:41:27
importantly the uncertainty was horribly underestimated and that just kept appearing over and over again as we move
00:41:34
through the pandemic it was almost ridiculously tragic how little we we could say with any accuracy I was going
00:41:40
to say the one thing I remember though is that some things were relatively stable like what I do remember is
00:41:45
because I remember each week I was the one that was looking at the CDC data and just seeing like in some sense like the
00:41:51
death rate conditional in you're getting covid was actually fairly constant for a
00:41:57
long period of time now of course the question is how many people are going to get covid which people are going to get
00:42:02
covid what's the risk for a certain population but there was a period where I think the number was somewhere around
00:42:08
as I have a vague recollection maybe it was 1% or 08 of 1% where if you looked at every country and you said how many
00:42:15
people have Co reported report hug I understand reported and how many people were dying that number had a very narrow
00:42:24
Bandon interval for a fairly long period of time so one thing that was interesting because we talk about
00:42:29
moments when David Spiegel halter came in as a guest later not there that day um which he was amazing he was one of
00:42:36
the sharpest Minds on covid early earliest on he had a beautiful infection fatality curve that was by age that
00:42:44
turned out to be just about exactly right and he published that in like March of 2020 and we we publicized it on
00:42:52
our show and we talked about it and many people came up to us that you were on we
00:42:56
provided solid good data on Co that they weren't getting anywhere else I think
00:43:01
that's what I'd like to hear a little bit more from you is like setting aside
00:43:04
the vagaries of modeling the pandemic how what what do we do well what do we not do well in that show I me did we
00:43:11
stay too long do we spend too much time to little time like how did we because that was a incredible moment of
00:43:17
uncertainty and I think one of the things we did was we worked together to reduce that uncertainty we were trying
00:43:23
to make sense of it like together that's I think one of the virtues of what we
00:43:26
did what what are your thoughts on and it gave us a vantage point at least for me I mean it was obviously I think
00:43:31
probably a frustrating time for all of us but it was particularly frustrating for me kind of along kind of you know
00:43:35
Eric's way of kind of thinking about things would you rather have good data good models or or Buy in that was a time
00:43:41
in society where although we you know could talk and we were mostly focused on evaluting the models what we really
00:43:47
needed was better data and what we really needed was actual buyin you know on on public policy kind of initiatives
00:43:54
and so it was kind of I mean a little bit of therap therapy for me but also like you know actual like instructive to
00:44:02
kind of come in and hear mostly from Audi like kind of the what was really kind of going on in the kind of modeling
00:44:07
sphere and also to kind of just just be able to kind of I I don't know Express
00:44:12
both of my kind of frustration but also interest in in what was kind of going on
00:44:16
at a societal level at the time through kind of the lens of analytics and through the lens of trying to kind of
00:44:21
think about what we could actually measure you know without bias Etc I have to admit though I also I think back to
00:44:27
that period I also have to think about um how I remember thinking to myself how you know we're in we're exactly the same
00:44:34
age and so I remember there were times where you would say yeah you know what I would go out and have outside I'd have
00:44:40
some sort of party with friends and I remember I said say it on the air like oh I would never have done that our
00:44:46
internal no no no that was fascinating to me and and by the way I don't think
00:44:50
of myself as overly risk averse in any particular way and and you were you were our expert so all most of the
00:44:57
information I was getting I was getting from you just my interpretation of it was like I'm not going to go for that
00:45:03
really even it's a low probability event it's just not worth it and that to me I
00:45:08
I will remember all the decisions you guys made and like like I was proud like wow I got my like 80th shot not's like I
00:45:14
don't know if you should be getting that many shots and I'm like I've got 80 go
00:45:19
ahead oh I was just going to say I can't really it was a lesson how that distribution of like kind of risk
00:45:24
tolerance versus risk adversity how much there is across people within a person even over time in that kind of in in in
00:45:32
that it was fascinating one of the things we learned about covid during that time which has been really useful
00:45:38
is the importance of observational data is distinct from experimental data and in sports we talk about confounding it's
00:45:45
just the most important Concept in all any evaluation of a a complex game where everybody interacts with each other
00:45:51
particularly basketball soccer football much less so baseball but even that to a
00:45:55
degree and what this forced us to constantly ask when we looked at a study well these aren't these aren't
00:46:01
experiments and so much public policy and so much discussion around covid was built around observational data some of
00:46:08
it terribly done some of it better done and we were able to explain that to to our listeners to each other and make
00:46:15
sense out of it and this was this was really one of the most and I think it really carried us through it it puts
00:46:19
fart in Center the scientific method which is at heart really what statistics is serving the the acquisition of
00:46:26
knowledge and truth okay so I everything you said I have to agree with entirely of course but it also feels like one of
00:46:33
the things that happened in the pandemic is that experimental evidence by itself was
00:46:39
too isolated to really explain what happens in the complicated World it goes the other direction as well we have
00:46:45
experiments well you can't experiment with you have a hard time experimenting
00:46:50
with societal uptake of policies and one and the biggest confounder especially early on was
00:46:57
people didn't act the way they were supposed to act in the models and we wouldn't have known that I don't think
00:47:02
from an experiment so in this case I think it goes the difficult is you need to go both ways we had to explain this
00:47:07
that why these weren't predictable what was the issues and we did I think that
00:47:11
was one of our our strongest points with within the field of Statistics as you know nobody would consider me an expert
00:47:17
specifically in causal inference that's not really the domain I operate in but I
00:47:21
kind of felt like you know with was happen with my friends and kind of with with the L people I was talking to like
00:47:27
having to explain just kind of very basic what you can learn from an experiment what you can not learn from
00:47:32
you know observ study is controlled observational study all right guys let's
00:47:38
shift out of the pandemic and do a lightning round before we end up with a couple of final questions lightening
00:47:43
round back to favorite moments but let's look at some specific uh some specific
00:47:47
narrow aspects of the show um any favorite guest moments or interviews any any things jump out to you over the
00:47:55
years we've talked about some so far I mean you've had some big baseball
00:48:01
guys that you just love having bunny chance we had Ron Bloomberg which I enjoyed
00:48:05
immensely we I we I don't know if Matt was the producer then but Sam Gwyn wrote
00:48:12
a book about HAL mummy the the the famous football coach who was kind of the beginning of the air raid and Sam
00:48:19
Gwen and so I had him on the show I didn't know it was going to be on the show I had just read an earlier book of
00:48:24
his that is really about the history of Texas that was profound for me and I come in some Wednesday morning and
00:48:30
Matt's lined up Sam Gwen for the interview seg like oh my God I just this guy just wrote this amazing book just
00:48:36
random you know pleasure any other guest jump out well when we uh were on radio roll for uh one of the Super Bowl I
00:48:43
think the Super Bowl in Miami um and we got to interview Justin Tuck on the show
00:48:48
and I was wearing the same Tom Brady coat and just the absolute look of disgust on his face was kind of a a Gade
00:48:55
Mo that was a great interview to because again Justin Tuck you know is not I don't think the most you know
00:49:00
necessarily analytically minded like athlete but it was a really informative kind of discussion because we talked a
00:49:06
lot about you know I mean he was you know on the defense that went up you know went up against some great teams in
00:49:11
the Super Bowl and just sort of the way he thought about you know battling Brady
00:49:15
and the way he thought about other quarterbacks I just remember it being a really cool discussion I loved also the
00:49:20
moments we've talked about we could call them the more secondary sports like when
00:49:24
we've had people on golf talk about the golf Analytics and you know like the
00:49:28
smarter players know where to forget they know they're going to be inaccurate
00:49:32
so they choose a zone that they not going to cause thems as much damage like you could go for the left side of the
00:49:38
green but you know you should shoot for the right side of the green because here's how much variance there is in
00:49:43
your shot that's something that really has always stuck with me that was one of
00:49:48
my most eye-opening interviews you i' had forgotten about that that was one of
00:49:51
those moments where I'm listening to this guy talk and it's Scott faucet
00:49:54
Scott faucet is the golf golf coach he played some golf golf coach now in the Dallas Fort Worth area and we chase him
00:50:02
down because of an article in the newspaper and he starts talked on our show about these guys have to act like
00:50:09
they're going to hit it perfectly because you got to have confidence but they have to plan knowing that they
00:50:14
can't hit it perfectly even the best golfers in the world he was talking about accepting uncertainty and
00:50:19
accommodating uncertain in your decision- making it was absolutely profound moment loved it yeah so I have
00:50:25
a few but I remember some of my favorites were David Epstein he's written two of my favorite books on
00:50:30
Sports one is a sports Gene and I interviewed him about that book I may have been alone this was a long time ago
00:50:35
and then he had a second book range which he talked to us about the importance of athletes trying out
00:50:41
different sports and how that's important for for Success that was just those were incredible interviews and
00:50:47
then of course I remember um Annie Duke's first interview with us she came live in the studio she she was writing a
00:50:53
book uh thinking in bets and she talked about poker and reading opponents and I remember one thing she said to us and
00:50:59
and we've she's been on many times she comes from our our our my Moneyball
00:51:02
Academy every summer um but she talked about how the importance of exactly the opposite of what we do which is you know
00:51:10
calmly think about data and regress but when you're in a procer tournament you
00:51:14
got to size up that opponent immediately you don't have time to wait and watch
00:51:19
their re their play and accumulate data and move yourself off the prior you've
00:51:24
got to look at everything they they do how they touch their chips how they talk to each other to quickly figure out what
00:51:31
type of type almost cluster type of player because if you get it wrong you're going to get hammered and it's
00:51:38
like very observable just the expertise that goes into being a professional poker player that's much more Beyond
00:51:45
just the the strategy of just do doing I remember that too it was super insightful I I I kind of found myself
00:51:51
coming back to that because you know it was really about what she was talking about is like very body language that as
00:51:57
a as kind of a professional in the field you learn you know you really immerse yourself in when coming out of Co one
00:52:04
thing or going through Co and coming out of it I kind of from my own teaching I got the I realized just how much I feed
00:52:12
off the body like you you are subtly measuring the body language of people like teaching in front of people in
00:52:17
person is a very different kind of endeavor because you can read the body language of people and very s you know I
00:52:25
I was I more kind of I I could perceive what I was picking up on in very sort of
00:52:29
subtle signs and I think that's probably you know for poker players that kind of
00:52:33
become that as their vocation that almost becomes probably second nature in the way I didn't even realize I was
00:52:37
doing it as a teacher a favorite offsite moment well well I can start with one you mentioned Justin Tuck before but on
00:52:49
our offsite moment I was quoting I think Rufus Peabody or Kade Massie talking about how you predict
00:52:57
future football games and how no no player can change the line when they go down except for a quarterback otherwise
00:53:04
it just doesn't affect it we're not saying those players aren't important we
00:53:07
just we can't measure it and I told this to Justus tuck and he looked at me and
00:53:12
this very imposing human being looked like he was ready to take me down like what are you saying I don't matter and
00:53:17
here I am trying to scramble wait a minute of course that's what I'm saying
00:53:21
yeah yeah one of one of mine was at the Miami Super Bowl we ended up at a bar on South
00:53:30
Beach late after dinner and Eric eager was there with somebody of his from the Kansas City area yeah but this was
00:53:37
January or early February 2020 which means pandemic was already in the air but Eric had been using some um some
00:53:48
diffusion model based on pandemics in one of his Sports papers and so he was studying pandemic models in the months
00:53:54
prior to this and he sits there late at night drinking some monster Margarita or
00:53:58
whatever it was on South Beach and he said I think y'all should be looking at
00:54:01
this pandemic thing but you know what I took out of that lesson that that conversation I think we should be
00:54:07
talking talking to Eric eager that's right he's not only been on our guest
00:54:11
many times but he's now a longtime collaborator with us at Mone Moneyball Academy so that was an incredible
00:54:16
offsite moment that's right that's right honestly I was thinking of that uh
00:54:21
actual I think that same evening talking with Eric eager because I was thinking about that kind of in the cont Tex cuz
00:54:26
that was 2020 right before the pandemic that was right before you know the chiefs were about to play the 49ers I
00:54:32
think that was the Super Bowl and he was already talking about a Chief's Dynasty
00:54:38
and I'm like this guy is this guy's getting a little ahead of himself what
00:54:41
you know what does he know this guy's a blow heart and you know it turns out
00:54:48
well I went to dinner that night with you guys and the pre- dinner bar but I went home before you guys went out with
00:54:52
Eric eager so I don't remember that you traveling with family I was traveling
00:54:55
with family but what I do I when I think about it is actually really I give a lot
00:54:59
of credit to SiriusXM I remember the it was probably the time we interviewed Justin Chuck and like we were there on
00:55:05
radio row and I have to admit we looked legitimate like there was a big Serious XM sign behind us and we were up there
00:55:12
and interviewers looking over like these are some serious broadcast I was wearing
00:55:16
this Cod so I mean you guys it actually made me feel good that like you know I make it up CBS Sports
00:55:23
was across the way and like and there's us four guys Wharton Money Ball and we're there in radio row I I felt like a
00:55:28
legitimate broadcaster for a minute do you remember when they had the NFL draft here in Philadelphia I do and we went
00:55:35
did the show on site beforehand but then we started walking around backstage and
00:55:40
we end up walking through the dadgum Green Room players sitting around with their families were like yeah we're not
00:55:46
supposed to be in here well it's funny because some of my favorite off-site moments was going down to spring
00:55:51
training bringing our equipment talk about being somewhere you're not supposed to be right and and I get
00:55:55
myself press access and I wander into the Yankee Dugout and the Yankee Dugout and
00:56:01
the Yankee Clubhouse and I'm walking around with all the real reporters I'm
00:56:04
going what do I do with myself so I go find CC Sabathia and I'm like no one's
00:56:08
talking to him he's which they were I talked to him and I'm trying to explain
00:56:12
the opener to CC Sabathia did you tell us remind us what the opener so the opener is an idea that I've talked about
00:56:17
on the show for years is that the baseball team can get an advantage by beginning the game with one of their
00:56:22
good very good relief pitchers for influence perhaps when he was at Toronto not Toronto Tampa Bay Rays I talked to him
00:56:32
just they were the first team to really do it but I was trying to evangelize on it and I said to Cece you know you
00:56:37
should be in favor of the opener and he's like no we're starters we we're
00:56:41
we're trained to start the game and he's an imposing human being and and it's
00:56:46
just a ridiculous idea that I would start the game um in anything other than the first inning and I said well you
00:56:51
know has some advantage and I start to explain them and I one of the things I said was you'll have an opport
00:56:56
opportunity to get the win without having to pitch five innings and he just without even missing a beat he says I
00:57:02
thought you stat heads don't value wins that is awesome there's so much in this story
00:57:11
absolutely incredible um okay last lightning round question Eric you mentioned the less popular sports like
00:57:17
golf but what about non sports are there the pandemic we do sometimes wander off
00:57:23
the playing fields into other areas any favorite non sport moment and to give yall a moment to think about I'll tell
00:57:29
you I'll tell you my favorite it's the Moneyball for fire guys the forest fire
00:57:33
guys we we just them well we hypothesized that they must this was the summer of I think it might have been
00:57:42
20120 and bad forest fires in the American Northwest and we thought someone is running analytics on that and
00:57:47
we asked Matt to run it down and he's like shed I found these guys who were publishing papers one of which is called
00:57:53
Moneyball for fire and now we have a multi-year relationship with these guys we've had on the the show multiple times
00:57:59
and they fight the same battles the exact same culture battles that we see fought in football organizations
00:58:07
basketball organizations baseball organizations it's incredible yeah I mean talk about a cool spatial temporal
00:58:12
data situation that basically pops up every year and links to so much of the kind of climatic things that are going
00:58:18
on right now as well it's one of my favorite non non-sports topics was the traffic engineer we had on years years
00:58:26
ago right before Thanksgiving you may not remember this I don't think I was on
00:58:29
this show you may not have been on it I wasn't alone I know there was a few of
00:58:32
us there remember um and he was talking about money essentially Moneyball for Designing highways and one of his
00:58:40
comments was that we in America don't do things properly we just build wider
00:58:45
roads when we want to accommodate more traffic but that can be accomplished by learning how to
00:58:51
drive really we just don't drive properly huh and he explained the various ways in principle he was saying
00:58:58
stay out of the left lane unless you're passing and how much trouble that causes
00:59:02
and and he explained how the models show how much how your roads become inefficient when people drive improperly
00:59:08
and he was of course saying but in Germany where he was a German engineer we don't build wide roads we teach
00:59:14
people to drive properly speaking of Germans we have the DI the diet researcher out of Duke whose name I'm
00:59:21
going to forget but he's given us some great stuff over the years on calories
00:59:26
really good stuff all right we need to wrap up and so I want to ask one last question we've been talking about the
00:59:31
last 10 years of course and appropriately hopefully we'll do another 10 years and More in front of us what is
00:59:38
one hope you have or ambition you have for the show over the next 10 years I think it's going to be really cool five
00:59:46
six seven years from now when we all have Hall of Fame votes and we can on the air we argue about the Hall of Fame
00:59:53
for weeks every year won't it be great as voting members to do that I'd like us
00:59:59
to do an offsite at the hall of fame I mean I'm saying that self Baseball Hall
01:00:02
of Fame but a matter of fact we could do all of them I mean actually I have you can actually do the baseball basketball
01:00:09
Football Hall of Fame in one swing I mean there's not that far from each other the major sports I don't even know
01:00:14
where's the Hockey Hall of Fame but Toronto all right well it's not it's not
01:00:18
totally out of the question sounds either way um I have such a passion for the history of baseball especially and
01:00:24
the Baseball Hall of Fame that I think it would be great to do a show from there and I'm confident that we could
01:00:29
get a number of Hall of Famers to come on the air and I'm sure they would talk
01:00:33
to us about the role that analytics played in their careers and it would be great to get a spectrum you know getting
01:00:38
someone whose major career was in the 70s and 80s in the pre-analytics ER then get someone in the 90s and 2000s when it
01:00:44
was emerging and then get even the most recent Hall of Famers that would be a ton of fun yeah I mean I sorry to just
01:00:50
piggyback on that like I I I as I was sort of thinking about the the the last 10 years I feel like we we do kind of
01:00:57
you know because we've been doing this a while I think we have a historical perspective things on things I would
01:01:02
like this to do even more of a kind of like you know this year like you know 10 years ago what was going on both in like
01:01:08
sports itself and also in analytics you know I think so many you know it the the
01:01:13
field has moved so quickly in say the last 20 years that you know kind of like you know we're the last keepers of the
01:01:20
generation without internet or whatever we have we we have an obligation to remind people what it was like and where
01:01:25
we Shane what we may find out in the next 10 years is you know Audi was talking about you know running more
01:01:31
sophisticated models maybe artificial intelligence and these large language models are going to change the way we do
01:01:37
even Sports analytics like maybe we're going to be able to just upload a massive amount of video and other stuff
01:01:43
and just say you know run you know compress this using a Vari variation encoder and just run a large just run
01:01:52
some analysis on this and it'll get automatically done I have a feeling that
01:01:55
10 years from now the the use of video data and other types of data will be much more prevalent in sports because of
01:02:02
all these technology and AI that's going on I think that's a fascinating uh
01:02:07
prediction um I of course I have to second Eric's Hall of Fame show I mean we're kind of a force in this department
01:02:14
it's my obsession and and just because loving baseball and baseball history in
01:02:18
particular so I'm just seconding that one um but if I just say I'd like to see
01:02:22
something in the next 10 years um I'd like us to be to reflect a little bit about what's happened on on the sports
01:02:28
analytics side kind of uh on the research end and kind of reflect on our particular area which is the academic
01:02:36
contribution of of sports analytics and methods and just reflect on some of the great papers the great contributors
01:02:43
interview some of the scientists involved we haven't done too much of that um we do the new stuff we're very
01:02:48
good with new but we haven't had the opportunity maybe it's my bias of reflecting historically about what's
01:02:53
happened and and kind of not a Time based moment but uh you know talk to some of the people who are really
01:02:59
involved in in pushing our our subject forward that's imminently doable right
01:03:04
that's great yeah the road trip I'll pitch for is the is a combine so the
01:03:08
combine week right now in Indianapolis um the base save me any time CU you know I watch the whole thing
01:03:14
anyway we you have to bring your second and third and fourth screens for other sports that's true you got to pick up on
01:03:20
that subtle body language of being in person that we've discussed already though you don't get that across the TV
01:03:26
there's a baseball combine now that could be interesting yeah um but I do think it'd be you know we were we saw
01:03:32
this technology evolve over the last 10 years and we were having conversations with people when it first came out I'm
01:03:37
curious can we remain on that Cutting Edge and what does it take for us to remain on that Cutting Edge so you just
01:03:42
mentioned Ai and you talked about you know dumping a bunch of video data but it may be even earlier that what AI does
01:03:50
it levels the coding playing field Y and other people can get into analytics without having to go through years
01:03:56
basically of coding training could be that could be one of the big things are we going to be doing the interviews and
01:04:02
the research and the reading to really know what happens next we we we did it the last 10 years we need to keep on
01:04:08
whatever that was that kept us at the front edge we need to keep on doing or all the companies like Pro footb Focus
01:04:12
that do a lot of human coding well once we have a lot of human coded data we have supervised learning we can do so
01:04:18
now we we're going to use human coders but we're going to use them to train our
01:04:21
models and then we're just going to let our models rip on all the dat we can
01:04:26
collect in some large automated way as a matter of fact over the years we've had
01:04:29
a lot of people talk about the different you know value of having cameras and locations and other stuff in stadiums I
01:04:34
think the volume of especially video data we're going to have to figure out a
01:04:39
way to deal with that in a large automated way all right guys thanks to our audience and thanks uh big thanks
01:04:46
shout out before we give uh before we leave to the crew here helping us bring this to life our boss man matd dats
01:04:52
overseeing even the live show Michelle young standing in as co-director and Deion Simkins always vital to the show
01:05:00
on behalf of my colleagues and Friends longtime collaborator Shane Jensen Eric bradow AI wer thank you guys it's been a
01:05:06
great 10 years hope we stick around for the next 10