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welcome welcome to the next episode of the analytics at Wharton AI at Wharton podcast series on artificial
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intelligence while I've enjoyed all the episodes today's really a special one
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for me not just because of my love of sports but of course cuz I'm interviewing two of my colleagues and
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co-hosts of another show here on SiriusXM Wharton Moneyball so it's my honor to introduce my colleague Kade
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Massie Kate is a practice professor in our operations information and decisions Department he teaches researchers and
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consult on how to improve decision-making in organizations and probably the part you could not have
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written it better than this thank you Kate especially by blending models and experts he's also the faculty director
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of Wharton people lab faculty co-director of the Wharton Sports analytics and business initiative and of
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course as I mentioned co-host of Wharton Moneyball so Kade Welcome to our podcast
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thanks Eric delighted to be here I'm also joined by my longtime friend and colleague in the statistics and data
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science department AI weiner I really think it's important to read ai's uh bio
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here a short bio because of all the different things he's doing around data science statistics artificial
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intelligence at the school ai's a professor of statistics and data science whose research has spanned many areas
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including applied probability modeling information Theory and machine learning he's written many articles in
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statistical methodology and also applications including other episodes like we've had just now Neuroscience
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medicine climate science and of course extensively in sports analytics we're
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along with Cade he's the co-director of our Sports Analytics business initiative
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he also runs the Penn Sports research seminar the summertime Moneyball Academy and he's also one of my co-hosts of
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Wharton Moneyball and he's the director of the undergraduate program in statistics and data science so AI
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welcome to the podcast yes thank you Eric great to be here and that completes our 28 minutes of talking today but but
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let's get serious so AI why don't we start with the following and this is a
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question I get all the time and I imagine you do too what is AI and how does it differ if any with what you and
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I might call statistics well it is actually a great great blending of the two I mean statistics have been around a
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long time um if we go back historically AI was designed to try to figure out how
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people think and build models that do that and then sometime in the 90s people realized that the smart way to do
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artificial intelligence is not try to reconstruct the thought process but just get tons of data and this was a huge
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breakthrough that I think it really happened in the '90s in machine translation and in machine speech
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recognition and then eventually vision and then eventually vision and so what happened was is in instead of trying to
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actually build machine learning and artificial intelligence people discovered that the right way to do that
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was statistics now fast forward to today we still have statistics and we have machine learning and Ai and they are
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still somewhat similar and also different so I have a rule of thumb about what kinds of problems are machine
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learning and what kinds of problems are statistics although you have to remember
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there's an enormous um feedback and and and intersection between the two and we
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all use each other's methods and the way I like to think about it is this what is
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the difficulty in solving the problem and if the difficulty is complexity so for example Vision self-driving cars
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image recognition um trying to take the entirety of the web and use that to answer a question like you think of
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large language models that's AI it's something that a human can do and now
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you trying to get the machine to do it statistics is all about what I would think of as noisy problems we have a
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colleague in in criminology who tries to predict whether or not someone commits a
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crime after they've been in jail for a while recidivism well you're never going
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to get a perfect prediction Sports is a classic place where a lot of our problems are statistics for example
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trying to figure out you know what your batting average is going to be next year
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or things like that but also um but other problems come up in both areas which are common to both where where
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there is a lot of signal um but also um and and but you can handle them with statistical methods so the way I
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generally describe it is what we call signal versus the noise and the Machine learning problems have high signal low
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noise and the statistics problem have high noise and lower signal so Kate let me ask you related to that so um whether
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it's let's maybe one could argue that uh statistics has gotten its footing in
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companies and decision-making maybe you could argue machine learning has because
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people like predict things that can help predict the future uh AI is kind of new
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how do you see companies reacting to these different methods and are we now in such I'll call it an enlightened
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explosion world like everyone's just saying of course I have to adopt it or is that like not the reality of
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today well certainly organizations are are looking at opportunities for it and they're excited about it and and
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certainly vendors who think they have a a new application for it or selling the potential of that but ultimately it
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always comes back to is somebody going to to rely on that algorithm or model for a decision they have to use the D
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thing it's it's one thing for it to spin and do pretty things it's another for a
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human to depend on it and what we've seen reliably is that that's a pretty
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steep hill to climb that people are loathed to trust decisions with to to models when models aren't perfect and in
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many many applications models are inevitably imperfect as soon as we see them be imperfect we're an to lean on
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them even if we know humans are imperfect there's an asymmetry between the penalties humans apply to models
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that are imperfect versus humans that are imperfect as soon as they see that imperfect performance they're reluctant
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they're they'd rather lean on a human for you know if I'm going to use an
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algorithm and kind of offload some of it that has to be much more accurate than in some sense we as fallible
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humans that's right that's what that's what we've observed we we along with
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some colleagues at at at pen we've we've called that algorithm aversion and again
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it's not to say that people just innately don't trust models we're happy
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to to to play with models to lean on models but if we see them performing perfectly we're much harsher in our
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treatment of them than we are humans so AI I know you wanted to jump in here and
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we'll let's get to you before we jump into the main topic of today which is of
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course our all our passion which is AI and sports but please you had a followup so I guess my followup to you Kate is
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that I think um we might want to privilege human versus a model when they both make mistakes at approximately the
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same rate but how much is that disadvantage do we really trust we really kill models even if they're
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better than humans just because they make mistakes yeah so I can't speak to the
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exact threshold where you might go back to the model but where we've run experiments we've manipulated exactly
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that and and and people can observe that the model outperforms but in let but they hold models to a higher standard
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there seems to be a model of a standard of perfection for models that they don't
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use when it comes to humans well AI let's jump in first with you with kind of the application of AI in sports
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analytics so what do you see as you mentioned kind of Statistics being the field of I let me just see if I got this
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right clearly AI is you're going to have high low noise massive data statistics
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are going to have typically smaller data sets High noise which means you have to
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rely more on you know mathematical models for decisionmaking how do you see Sports analytics today how much of it do
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you see the use of statistics how much do you see the use of AI is there some combination what do you see happening
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right now okay so certainly there's an incredible amount of statistics and the
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reason why statistics is being used so much today primarily as people see the value
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in it I think that's been a giant sea change over the years brought about by
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the Moneyball Revolution Oakland a etc etc people realized that using data to make decisions is a great thing to do
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and that data wasn't Advanced I mean Bill James really revolutionized Sports
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analytics with just counting and percentages and clever ways of adding and subtracting things not fancy so
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statistics has certainly um become essential to the operation of successful sports teams and in lots of ways player
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evaluations none of this is particularly complex um there are of course machine learning advances that have been made
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that have been useful certainly the tracking data and that's for decision makings on something like we argue about
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on our radio show all the time should we have a robotic umpire we certainly have
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the Hawkeye data in baseball we have all this backing data from Sports vision and
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sports View and that tracks where the ball is and where the players are and that leads to kind of mixture models um
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statistics mixed with with sort of machine learning which is these massively giant tracking data terabytes
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of of information that need to be processed so you can still build statistical models so there still high
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noise in the sense that you can't predict whether or not we're not able yet to predict whether or not say a
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running back is going to break through and get a large gain um that's still high noise but we have so much
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information so we have to use kind of machine likee models to to fit them so that's what we're happening I'd still
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see say that we're probably 90% in the statistics domain although a lot of the
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flashy stuff is coming out of the the machine learning and the AI the things on TV that where you everything is
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labeled and you see all these great the these great uh predictions they'll tell
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you things like what's the probability of a catch um that's kind of a mixture
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of a statistics and a and a and an ml or a machine learning model so Kade one of
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the things you even talked about in your own bio was the blend of models and experts how do you see that blend
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happening in the field let's talk about sports since I know you do work with a
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lot of sports teams how do you see that blending of machine learning Ai and human experts today what are some good
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examples well it's sadly it's largely uncomfortable that that that historically these come from very
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different communities and they don't necessarily play well together and so the organizations that actually are
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blending them most successfully are are are in some way forcing the groups to work together because you know
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traditional decision maker in NFL say isn't super interested in dialing in the
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computer science guy who just graduated from MIT that's not a that's not the way
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they usually make decisions the organizations that get it well have leadership to say we are going to do
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this they don't make it one person's model or another it's more the team's
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model the sports that are furthest along especially on the Personnel side are baseball and baseball they they do have
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models that are like the teams model they they lean on them heavily when they evaluate Personnel they've built those
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things up over years these things are not something someone writes down one time and they're off and running they
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tend to be highly iterative the modelers get lots of input from the traditional decision makers they get hypotheses from
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the decision decision makers the decision makers point out things the models are missing these things are they
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should be iterative and a dialogue between the two sides that's tough to pull off and so it's relative L rare So
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kid let me just follow up with a question on that you mentioned the idea of kind of using whether it's humans or
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Theory to come up with hypotheses that are tested just you as a scientist how important is that or can't you know
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can't I just use Ai and machine learning algorithms to just find patterns in the
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data and that's what it is like why do I I'm saying this in a factious way
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because I have an answer but this is about you guys not me why not why need Theory why not just explore the data and
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see what we find so ai's going to have a deeper answer than I am for this because
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it's very much in line with what he's been talking about but but but sadly
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sadly for some of us in many domains of sports we just don't have the data to
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support that kind of exploration so we have to bring more structure to the conversation we can't just set the
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algorithm loose and see what it tells us there are certain places where that can
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happen but by and large we just don't have enough data for an algorithm to learn reliably so
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for example I'm I'm off often worrying about personnel and we just don't see
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enough people there's a lot of variables that you could lean on we don't have
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that many observations we need to bring a little structure we need that human hypothesis in order to make traction
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that's the way I would go about it I'm curious how OD he's gonna talk about it
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well I'll start with just simply agreeing I mean we think we have a lot of data because we have lots and lots
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and lots of observations but it's not nearly as many as you think you'd want
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because you have so many variables as well and a lot of times the data is not new data it's just more and more data of
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more or less the same thing and what you need in statistics to do good forecasting and good model building is
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lots of independent data we really don't have that so you think about the incredible amounts of observations you
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can take using tracking data it's terabytes in a game that's but that's
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still one game it's still only a couple hundred plays and only one Victory right
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so all of this is is highly correlated which means we just don't have as much
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data as you think so I'll start with that the second thing is when you're
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dealing with noisy models and these are player performances are fundamentally noisy meaning people you cannot look at
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a player in the minor leagues or or watch them swing or pitch or or run and predict what they're going to do on the
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field it's just not possible they're human beings they're not they're not
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robots which means that it the and this is a technical word overfitting is easy to do and if you throw a machine
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learning AI model at a high noise not so many independent observations you are going to get it wrong this is a
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fundamental area of research and this is what we're doing and what I'm working on
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with my grad students and my students to figure out how to use Ai and statistics
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in this exact setting to build good models and I'm just it's just not trivial you just can't throw it in and
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crank it as if it's uh uh just automatically run for you so we're here on the AI at Wharton and analytics at
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Wharton podcast series we're on our episode on AI and sports this is Eric bradow professor of marketing statistics
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and data science here at the arton school vice dean of analytics and I'm here with my friends and colleagues Kade
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Massie practice professor in the oid department and AI weiner who's professor
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of statistics and data science so Kade let me ask you um I would think that a lot of the work that maybe firms are
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doing today is about collecting these novel data sets whether it's you know we
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have an episode on Neuroscience so maybe it's brain data or maybe it's motion
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tracking data that AI said or maybe it's eye tracking data how much do firms
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recognize that in some sense we don't have the data we need and that we need
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to kind of do you know whether it's putting sensors on players or measuring sleep or rest how much do firms realize
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that in some sense it's better data that's going to solve these problems not
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necessarily more correlated data I think there's pretty healthy appreciation of
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that now of course there's variation the teams teams vary in their quality of
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ownership quality of management and not everyone sees the opportunity there but increasingly teams do see the
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opportunity and of course some sports are again farther ahead on this than others I mean sport like baseball they
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are way down the path and they because they're so far down the path they are
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getting quite inventive in the in the kinds of data they're looking for in search of an edge sports like football
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and hockey there's much more variance but the sophisticated teams in those sports are in fact being creative and
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one way to think about it we I think we tend to think that there's one big model
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sitting inside the firm somewhere that's that's spitting out answers whether it's
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play calls or who to draft but in fact these organizations have a bunch of little models and they're they're mostly
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going out and solving one problem getting one Insight adding one you know measure to a player's evaluation using
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different little models whatever they can get their hands on it's much more the the the propagation of these smaller
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models and eventually they'll they'll be integrated but right now it's the
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propagation of smaller models All In Search of small edges yeah the one thing we hope at least I don't know maybe you
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guys would disagree with this I'd love your thoughts um there can be different
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models for different purpos as a matter of fact there should be probably um but there should be a common data set so
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could you talk about I know you work with a lot of students how do you guys let's say you want to solve a problem
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like for example I know we're going to be doing a high school sports competition and you're going to doing
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something around soccer how like how do you collect a data set like suppose you have like one data set which is motion
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tracking and you have another data set which might be training and another data set like how do how do you even think
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about integrating these disparate data sets together when you're trying to solve a problem so interesting uh I
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actually would call that data science so the idea that a now wait we've got statistics but and now we've got machine
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learning we've got AI now you want to add data science yeah so we our department has been the statistics
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department for many years that's that was my appointment and all of a sudden I
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got an extra an extra title on statistics and data science and uh people still wonder what what is it and
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what exactly does how is it different from statistics and obviously these things overlap and I would say that the
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new um the the new direction is that because we have so much data it's so large and it needs to be integrated and
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managed and um curated if you will uh wrangled sometimes people is the word they use that's that task is data
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science and that pushes Us in the direction of Cs computer science engineering and that's a hard task um I
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personally don't work in it our my colleagues in engineering and Cs and some of my colleagues and statistics do
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more of that it's a challenge and I would say that teams invest heavily in the kinds of personnel who can do
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those things and it's a big it's a big Direction and it's expensive because
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they're highly in demand in lots of areas so having people who can who can collect data integrate them make
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dashboards software this is not modeling this is not statistics it's not even
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machine learning or AI it's just the support structure expensive and and needs to be done we don't even have a
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lot of the the basic data set so one of the things that we in the in the University land we have to deal with
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only what's public and sometimes that's hard and there's often very good data
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with the individual teams and some consulting firms have it and you can of course buy it but a lot of that data set
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is data cost is outside of our our budgets um and so this is always a challenge and we're we're working on it
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so Kate I know you have some thoughts on this as well I just wanted to emphasize AI got
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there eventually but I wanted to emphasize that there's the backend side of that and there is the front end side
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as well and as analy list we tend to lose sight of both of those things so the computer science part of integrating
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these data sets building the the plumbing and the infrastructure is vital you can't do anything and often the
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first hire in this space for an organization going down this road is on the Cs side and then Audi eventually
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talked about like the dashboard the front end these organizations all have these they do have these database
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systems that people drop reports into and people pull reports out of and it is the single primary way that that the non
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analyst interact with the data so it's that front end that dashboard and related issues on the front end is vital
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and that's development I mean this is again this is not this is not the traditional data science that we think
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about and yet it's vital to the way data are used in the organizations so AI let
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me start with you um when you think about the most sophisticated use or interesting use for you of
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AI now I guess I have to say AI machine learning statistics or data science today
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what are what is it for you what's the one that you find the most interesting
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and if you want you could rely on what's interesting to you is what you and your
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students are doing research on what's most interesting to you that's going on
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today and then Kate I'd like to ask you if you know after that you know if we're
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sitting here in 10 years what do you see us talking about well we'll still be on
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our Wharton Moneyball show in 10 years and we'll just talk about it there but
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AI what what's the most sophisticated application you see today well there's a
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lot of really sophisticated ones that I'm not sure have really borne fruit yet
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so so there have been a bunch of competitions in football that have released tracking data and some of that
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information has been used and Incorporated by teams by by um ESPN or or the NFL in what they call Next
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Generation stats to provide you the kind of information you couldn't have dreamed
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of you know years ago so one example would be um you see a running back has handed a ball and you want to know well
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on average what would an average running back do in that situation and then you can compare what the actual running back
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in that situation did and you and you post that that Delta that differential and that's an example of either poor
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performance or great performance but you wonder whether or not that model was really high quality and and and some of
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our students have had real have looked at this and and and and it's been interesting but I don't think we're
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there yet to see that these things have really provided value because the data is is hard to make good sense out of it
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so that's one kind of problem um the problem that I've been working with with
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our our students is actually on uncertainty so a model produces a a forecast all right well let me get right
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to it then I was going we we'll probably talk about this on our radio show tomorrow but let's talk about
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uncertainty for a second the New York Giants are fourth in one from the 17 yard line in the game yesterday and the
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question is should they kick the field goal forget that they missed it there has to be a lot of people like you
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always go fourth in one and you've shown that's not true so could you talk about
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briefly the role of uncertainty in these models and that most people think it's a
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yes no decision when it's really not okay so there is a couple of issues here
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so when you build a model the model will give you what we call a point estimate and the point estimate will tell you for
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example the probability of winning the game if you go for it and the probability of winning the game if you
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don't go for it and then you can easily just pick the one that's higher now the
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problem with that is we don't really know the probability we've had to estimate it using a model and there is a
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whole bunch of actual probabilities that are all kind of equally supported by the
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data and what we want to do is take all that information and say wait a minute if I had gotten a different set of games
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different Universe of of of information and tried to build the same model with that would I've come up with the same
00:22:06
decision and if every single time I do that it's always go for it then you can
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be clear that that's a a firm decision if on the other hand only 55% of the historical somewhat what we call
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simulations the word actually is called bootstraps of the of the his historical data produce one result then you have to
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throw up your hands and say I don't really know and you have to communicate to that to the coaches on on the field
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to say you know we don't really know you decide so Kade let me ask you since I
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believe in some of your research you've covered the topic and then we'll get to
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the the future in 10 years we you've covered the topic or you've done some
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research on you talked about algorithm aversion but even how about risk aversion so how does uncertainty play
00:22:46
into you know you're asking a human decision maker to trust a model when there's massive potential uncertainty he
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she they see the situation in the game and then decide hey you know what my read of this is the uncertainty does not
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swamp out what I've historically done how do decision makers think about risk
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aversion in using models well I think the primary role of risk aversion there is is to there's an asymmetry in the way
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you you get it wrong if you get it wrong doing the conventional thing the punishment is less harsh than if you get
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it wrong doing the unconventional thing that's that's the strongest Dynamic
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there that affects especially coaches using these models or not in decision- making
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on the field but what I love about what um Audie and Ryan are doing in this research is they're introducing this
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idea to model output that we've been talking about for humans for a long time
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people need to know what they know and what they don't know they need to know
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when they're sure and when they're less sure and they need to be able to say and
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own and explain as part of my recommendation this is one with high confidence this is one moderate
00:23:57
confidence or this this is one with light confidence we've talked about that
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with humans for decades and now Audi and Ryan are saying hey we should do the same thing with our models and it's
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vital because those guys on the field are going to be incorporating whatever comes out of the model with other
00:24:12
factors and we as analysts have to recognize there's always other factors the models rarely if ever have every
00:24:18
possible consideration so there's always other factors and so it does depend on
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how sure the model is if the model is absolutely certain then it's going to swamp other factors but it's not always
00:24:27
is going to be absolutely certain and we haven't talked about models in these
00:24:30
terms in the in the past it's fantastic fantastic new development from these
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guys so in the last minute or so so AI tell me uh if we were sitting here 10 years from now what have you been
00:24:42
working on and what do you think the field of AI and machine learning in sports has done over the next 10 years
00:24:48
or if we're sitting here 10 years from now over the past 10 years what are the
00:24:51
big new advances well I'm fairly certain that we're going to have unbelievable
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Graphics we're going to have great statistics that are going to be computed on the fly I think when our broadcast
00:25:00
experience will be really different um I'm hoping that we've made progress on
00:25:04
some of the really big questions which are still amazingly unanswered which is injuries we have really no clue how to
00:25:11
predict an injury although there are lots of startups trying to offer that that that product if you will even in
00:25:16
it's infant stage to to teams because we just really have a hard time with that
00:25:20
um I think player evaluation and the complex sports will become much better really good at it in baseball there's
00:25:27
still some things to work on I'm working on them can't can't keep keep my hands
00:25:30
off of it but I think in football and in basketball and in soccer um you'll see
00:25:35
much better ways of evaluating a players building rosters and this is going to become necessary every team's going to
00:25:42
have to have it it'll be Baseline right now in some sports you don't have to
00:25:45
have anyone and you're not automatically behind ex except in baseball I think
00:25:49
it's going to be everywhere every team will have a substantial uh staff doing
00:25:53
the routine things that just need to get done um that's the extent of my imagination I'm I'm curious to know what
00:25:59
Kate has to say me too well I on the same page with you think the newest Frontier is biomechanics and the
00:26:06
greatest opportunity there is in injury reductions that's the biggest development we're gonna see the steepest
00:26:11
development will be in biome mechanics over the next five years um the one that I hope I I agree with AI that player
00:26:18
evaluation is going to get better I think in particular the reason baseball's always been ahead is that we
00:26:23
can kind of just add up the players in a linear model and get a reasonal output for the for the team we know that we
00:26:30
suspect there are interactions on basketball courts football fields soccer pitches and we don't have a great way of
00:26:36
modeling those interactions right now that means there are players that we think are doing more than they actually
00:26:42
are to to advance the team success and importantly there are players who are making very important contributions that
00:26:47
are being underappreciated right now and I'm hopeful I believe that the models
00:26:51
will get better at identifying those in the next five maybe 10 years well I'd like to thank my
00:26:58
colleagues friends Wharton Moneyball co-hosts uh Kade Massie practice Professor here at oid also the faculty
00:27:05
director of Wharton people lab faculty co-director of Wasabi and co-host of Wharton Moneyball and my colleague AI
00:27:11
Wier professor of statistics and data science and while he's running everything we're doing here in sports
00:27:16
and statistics I'd like to thank both Ai and Cade for joining me for this episode
00:27:19
on AI and sports