Effectively Wild: A FanGraphs Baseball Podcast - Effectively Wild Episode 1715: It’s Academic

Episode Date: July 2, 2021

Ben Lindbergh and Meg Rowley banter about passing the halfway point of the regular season, the early effects of the sticky-stuff crackdown, a historically high-scoring day, the white-hot Brewers (and Willy Adames) and the red-hot Nationals (and Kyle Schwarber), a Germán Márquez gem, and a study on how kids become fans that helps explain how […]

It's not that easy, baby It's not that hard to tell It's not that easy, baby Life is hell But I'm doing well It's hard to tell I'm doing well It's hard to tell

It's hard to tell Hello and welcome to episode 1715 of Effectively Wild, a Fangraphs baseball podcast brought to you by our Patreon supporters. I'm Meg Rowley of Fangraphs, and I'm joined as always by Ben Lindberg of The Ringer. Ben, how are you? I'm doing all right, and we're posting this episode on Friday, July 2nd. And if you're listening on this day, then happy 50% of the way through the regular season day. This is the day when we cross that threshold.

We'll be more than halfway through the regularly scheduled games at the end of today. Wow. Is there a word that we misapply from another sport to mark the halfway point of the season like we do with quarter pole, which means the opposite of what people think it does? Right. I don't know. Half pole. Half pole.

Is that something? I don't know. Yeah, I don't know anything about racing except that don't bring cardboard signs to the Tour de France or you'll get arrested. There are some other things in there that led to that, but here we are. Yeah. Anyway, an important milestone. Yeah.

We're past that point. So it's a time to take stock of some things, perhaps take a look at the standings. They're meaningful or more meaningful than they used to be. Although I think based on the collected works of Jeff Sullivan, we are still probably not at the point where in-season results are a better predictor of team performance, at least, than pre-season projections. Those pre-season projections, they really hold sway for some time. And, of course, you can look at the continuously updated ones, which take into account both. Even better.

Best of both worlds. But anyway, we're halfway there. Pre-season projections, the true sticky stuff. Oh! Yes, and that's a nice segue into sticky stuff, which was something I was going to bring up, because it's now been a little more than 10 days since the enforcement went into effect, and it's been longer than that since players started weaning themselves off of the sticky stuff, at least judging by the spin rates. And it's still too soon to come to any grand conclusions, I think, about the effect on offense. We know that spin is down. We know that spin

correlates to movement. And it seems like four-seam fastballs in particular, which are the most affected by spin, there's been about an inch less vertical movement or ride since some point wherever you start the cutoff for players stopping the sticky stuff or cutting back at least. It's a little harder to assess the effects on offense. So certain individual pitchers have struggled, of course, although that's the case over any span of time. The Dodgers, as Rob Arthur wrote earlier this week, had soaring spin rates prior to the enforcement going into effect and have suffered the biggest decline since, just in terms of spin rate, not necessarily effectiveness. And they still have the highest spin rate in

baseball, even after that decline. But we were wondering whether any particular teams would have bigger drops than others. And that's kind of been the case. Eno Saris of The Athletic did an article on this taking sort of a preliminary look on Wednesday, and he found that the difference is not as great as you would expect, at least when you look at whiff rates, for example. So we know that spin rates are down. He wrote 204 pitchers, 54% of the sample have seen a drop in spin rate that you would call statistically significant. 145 pitchers, that's 38%, have seen a drop of at least one standard deviation or at least 115 RPM or 1.1 RPM per mile per hour. And 63 pitchers, that's 17%, have seen a drop of two standard deviations, which would maybe equate to quitting spider tack.

And that would suggest perhaps that the ultra effective stuff wasn't super pervasive. Like we've definitely seen some sweeping declines, but not that many individual pitchers who have lost hundreds and hundreds of RPM. individual pitchers who have lost hundreds and hundreds of RPM. So accounts varied coming into this where I think most sources said, yeah, it's the vast majority of pitchers are using something, but no one was sure exactly who was using the hard stuff. And at least based on the RPM, it seems like maybe that wasn't universal at least. And when it comes to whiff rates, Eno wrote, there's no appreciable change yet on overall fastball whiff rate, despite that change in spin rate and movement. He said, if you focus only on the pitchers who are down two standard deviations, you'll see that their

whiff rates are down about 3%. That's a tiny change. And that's kind of confounding. I don't know exactly what to make of that. He noted that it seems like velocity is up a little bit over the same period. So maybe pitchers are compensating and just trying to throw even harder. Yeah, which seems like it could potentially be an injury risk. But at least in the short term could compensate for less spin, less movement. It could be something else. It could be weather effects. It could be without the sticky stuff since the ball is moving in some different way that is actually helping pitchers.

It could be pitchers changing their pitch selection to some extent. But even looking at the same pitches, it doesn't seem at least so far that there's a huge change. And I don't know if that holds up. I would be perplexed. I would be perplexed. I don't know what I would make of that because pitchers were clearly using this stuff and wanted to continue to use this stuff and objected, at least a lot of them, to not being able to continue to use this stuff. And so if it turned out that, hey, actually, this didn't matter that much, which again, too soon to say, but if that were the outcome, it would be hard to square that with pitchers being so determined to use it and teams encouraging them to use that and all the rest. I do wonder though, if it isn't consistent with some of the sort of approaches to skirting the rules that we've seen of late, where what folks are maybe in search of isn't the sort of big effect that we expect, but rather they've been trained to look for small, marginal, But rather, they've been trained to look for small, marginal, incremental gains, because over the course of an entire season, those make a really meaningful difference, right? Or they can make a meaningful difference. And even if they don't in actuality, that's sort of the assumption going into any sort of a scheme, banging or otherwise. And so maybe the way that they were thinking about this wasn't that it was going to turn everyone into a potential Cy Young winner, but that it would just give you marginal edges here and there. And I'm sure there's variation in that in much the same way that there's variation in the exact substance that was being used by a given pitcher, right? But I do wonder if it gives us some insight into how,

maybe not everyone, but some portion of that population might be thinking about advantages and how they're gained and how they accrue over time, even if they are quite small. It's like Michael Fassbender in the alien movie that had the guy turn to dust. It's like, you know, the big, what are these little things? It's about small, big things coming in little packages. And then everyone becomes an alien. It's like that.

Plot summary, yeah. Yeah. And one guy turns to dust. And there's another big white guy. Kind of looks like he's made of marble, but clearly isn't. Anyway, it was a weird movie. I don't know if I liked it, but that's not the point. Yeah.

It could just be that cheating is overrated. Like, maybe we make too much of it. I'm not someone who has a lot of hot takes in general, but I think one of my hotter takes, aside my take that lucy dacus is the best member of boy genius which uh yes see it's a it's a hot one no but that's a good hot take it is right yeah we should debate that sometime perhaps not on a baseball podcast but you know i uh i stand by that one we have to give something to the gal who's been listening to the podcast even though she's not a patreon supporter and wrote us such a great email about war yeah well she might not be a lucy dacus fan either for all i know yeah but that's probably because she hasn't been listening to home video as much as we have been yeah go go listen it'll make you sad but in a good way

yes and historian and all the rest. But my other hot take is that steroids probably are overrated, at least in the aggregate. I think a lot of people attribute all of the offensive effects that we saw during the PED era to the PEDs, whereas I maintain that there were other things going on there. And there were plenty of players who were popped for steroids who were not Barry Bonds. And so, yeah, maybe it helped Barry Bonds, but in a lot of cases, maybe it didn't help. And there were other contributing factors. And the same thing with sign stealing, really, where it certainly sounds like something that should have a huge effect, but at least based on some of the studies, it probably didn't

or seemed not to have on the whole for the Astros specifically. And there are a lot of potential reasons for that. When it came to the sticky stuff, I felt that that actually would be more meaningful. And again, I'm reserving judgment still to see if it turns out to have been. But just saying that if it turns out that it wasn't that big a deal, wouldn't be shocked if it turns out not to have been that big a deal in terms of control or hitting batters. But if it turns out that it wasn't that big a deal. Wouldn't be shocked if it turns out not to have been that big a deal in terms of control or hitting batters. But if it turns out not to be that big a deal in terms of strikeouts or overall effectiveness, that would surprise me. So I am curious and interested to see what we conclude about that. And we'll probably take a longer, deeper look at some point. And it is true that scoring has been up,

offense has been up. And again, it's tough to untangle those things and account for the fact that it's June now, it's not April and May, that there's been a historic heat wave really all across the country. And there are a lot of bad things about that. But I guess the good thing for batters is that the ball travels farther it helps with home runs etc so that could be contributing to this too so it's dangerous to just set a cut off at june 3rd or june whatever 21st and say before and after because they're all kinds of things that come into play here but we have seen some notably high scoring games and days and whether or not that is partly or wholly attributable to the lack of sticky stuff.

It's still been quite notable. Like Wednesday, we talked about the Angels' comeback after having allowed seven runs to the Yankees in an inning. That was not the only comeback by a team that allowed seven runs in an inning on Wednesday. The Brewers came back after going down seven nothing to the Cubs in that game. And Jason Stark tweeted that I think it was from 1900 up until Tuesday, the record of teams that scored seven or more runs in the first inning was six, oh eight and 25. And on Wednesday it was oh and two2 so that's one of those weird things but

it was a really high scoring day in general and it was actually the highest scoring day in years I think there were 204 runs scored and the last time an MLB day featured 200 or more runs was September 26, 2009

which was 205 runs the modern era record is 222 on June 10, 1962. And Jeremy Frank on Twitter dug into it even a little deeper than that and wrote, there were 204 runs scored and 745 outs made, coming out to 14.8 runs per 54 outs, or 14.8 total runs per full nine-inning baseball game. Of the 7,859 days of baseball since 1901 that had at least a dozen games. Yesterday's 14.8 runs per full game is the

highest. And we got up to those high runs totals, even though there was a rain shortened game and there were multiple Manfred shortened games. So we have seen a lot of offensive outbursts regardless of the reason. So I guess that's a good thing. It frees us from talking about no one hitting for a few days at least. Right. Yeah, for sure. And even after the inflation, it's not as if we're back to the halcyon days for hitters. OPS in June was 737. Batting average was 246. Those are basically the full season rates from last year, full quote-unquote late July through September, essentially. And I think that actually dipped later in June compared to earlier in June, so it hasn't been a progressive increase. So that would

suggest that more work needs to be done, more measures may be required, and you know, maybe hitters will take some time to adjust to different movement on the ball. If they've been mentally aiming for a pitch that they anticipate to have an inch more movement than it now has, maybe that could throw them off in the short term. The league batting average is all the way up to 239. Oh, wow. Yeah, better deaden the ball some more.

So it's getting too offensive heavy. When you have guys almost throwing no hitters in course, you're not out of the woods yet. Yeah. I meant to mention that. German Marquez with a Maddox in course took the no-no into the ninth, 92 pitches. And I think someone emailed us or tweeted at us about being a true win, the Sam Miller patented stat. true win, the Sam Miller patented stat. And I think technically, according to Sam's definition, it was not in fact a true win because Marquez, he did pitch a complete game. He didn't allow a run

and he did double in a run and he scored a run. So he sort of did it himself or did enough to win himself, but not quite. The true win, true definition, according to Sam, requires two things. The pitcher must complete the game. So Marquez did that, but he has to have more home runs than runs allowed. So he didn't require any other hitter to contribute to get on base or to drive him in. So it's a pretty strict definition, but going by that, it was not a true win, but it was still a near no hitter animatics in Coors Field, which is pretty impressive. And I'm sure Marquez will make some buyer at the trade deadline very happy at some point in the next few weeks, perhaps. He's good.

Yeah. Might not happen now because he's still under team control for a while. But free German Marquez. So, I mean, Marquez isn't free. You need to give up some good players to get him. But you know what I mean, Marquez isn't free. You need to give up some good players to get him, but you know what I mean. Along those lines, I mentioned the Brewers there just coming back from a deficit to overtake the Cubs, and the Brewers have been doing the day that they traded for Willie Adamas, the shortstop from the raise. And that was May 21st. So as we record here on Thursday afternoon, from May 22nd on,

the Brewers have gone 27-10. That is the best record in baseball. And part of that has been Adamas, who has been fantastic since the Brewers acquired him. And we're actually going to do an interview a little later in this episode about what happens when a team trades for an underperforming player from one league, he goes to another league and his stats sort of reset as they are often displayed. And what often happens, it seems, is that those players perhaps outperform what you would expect. And that has been the case for Adamas, who had a

625 OPS for the raise before the trade. He has an 885 OPS for the Brewers post-trade. And I don't know that you could have anticipated that exactly, but I love the early trade. Beat the rush, beat the trade deadline. If you have a need, if you're in a tight race, I know it's not always possible because some teams aren't willing to deal at that point. And the Rays had a glut of really good infielders. And so they were willing to part with Adamas at that point to make way for their many other excellent shortstops or other infielders. But good on the Brewers for seizing that opportunity to make a deal in late May instead of late July and get an extra two months of Willie Adamas, which has been big for them. You know, over that period, they've been a league average offensive team, which is not

great. It's league average, but it's a lot better than they were up to that point. You know, hitting is their weakness and they shored up one of their weakest points. Because they were my preseason pick to win that division. And I think also the favorite according to the playoff odds because of their great front of the rotation and back of the bullpen and defense. And all three have lived up to their billing, but you still have to hit sometimes. Yeah, and it's allowed Luis Reyes

to commit to second base full-time, which has seemed to have been something that he's taken to, which is good. And yeah, I love the early trade because the early trade is a commitment to winning, right? Part of why teams waffle up until the deadline is, as you said, that in order to buy, you need a seller. And sometimes those needs don't line up perfectly.

And especially if what the team that is going to be selling anticipates is a lot of competition for the players that they're going to make available, sometimes they will wait so that they can extract a greater return. But when it works out, you're saying, we're committed to the win. We're not going to let the next four weeks of baseball change our minds. We're not going to let it shift our fortunes around. We're just, we're going to play October baseball,

and here's the trade we're going to do to do it. I think it's fantastic. Yeah. And the Brewers have had to make moves like that to stay in contention because they've kind of been a perennial contender over the last several seasons. They didn't really tear down and rebuild. And so they didn't have a whole championship caliber core of homegrown position players the way the Cubs did. They've kind of had to mix and match and find undervalued players and make smart trades and also some signings. And it's not all built from within, which is a tough balance to strike year after year,

but they've done a really good job with it. And they've made a number of trades that have really worked out in their favor and some that have not at all. I'm thinking of the Trent Grisham trade, which I think they would probably like back. Now, that has worked out fantastically well for San Diego. But in a lot of cases, speaking of interleague trades with which you might be familiar, the Omar Narvaez trade, for instance, has worked out quite well. I think he's been the Brewers' best hitter this season. And maybe the offense was something that you can't anticipate, but the defense was perhaps not.

He has gone from being one of the worst framers and defensive catchers to seemingly being one of the best. And that's something that we've seen happen with catchers at times before. And we had a listener, Eric, who just did his own stat blast and sent it to us, which is nice. Inspired by Narvaez, he went back to 2002 and looked up all the players who have gone from being the worst defenders at their position to being one of the best defenders at the same position. And I will put a spreadsheet with all of those guys online, but Narvaez is one of them to this point. And sometimes you've seen that happen with other catchers who were perhaps defensively

challenged and worked at that, like JT Romudo, for instance, is another one. So that can happen. Yeah. Good for them. Yeah, for sure. And with Adamas, one thing we talked about at the time he was traded, we did a stat blast inspired by the fact that he had these extreme home road splits and he had hit so much better on the road than he had hit at the trop.

And we looked at the history of players like that and what happened after. And it seemed like for the most part, they hit better at home and worse on the road and it all kind of evened out. And it wasn't like the huge market efficiency to find someone who had been better on the road and then just have them play away from that home park where they had not produced. But in Adamas' case, it has worked out so far. And Darren, who sent the question that inspired that stop last, sent me a quote from an article in The Athletic. And Adamas said, I don't know what to tell you, man. I'm just swinging the bat good and just taking good at bats, not trying to do too much. Excellent cliches.

Like I told you in the beginning, I was good on the road. I just didn't hit at the trop. I was really good on the road. And now I feel like I'm on the road every time because I don't play at the trop anymore. So in his case, at least so far, that has worked out really well. You know, it's just kind of nice when stuff works out. And by the way, some of those victories have been predicted by Brewers players and some of the events in those games have been predicted by Brewers players.

And thanks to the many listeners who have notified us about those predictions, I will spare you all the details on the podcast because I think that horse has been beaten enough. We all know by now that players routinely predict things that happen in baseball games and they do it so often that it's probably less meaningful than we typically think, especially if we don't know how often they're predicting things that do not turn out to happen. That's been a part of this Brewers run as well. Anyway, speaking of hot teams, I just wanted to briefly shout out the Nationals and Kyle Schwarber because we have not talked about Kyle Schwarber. He's kind of been lost in the Otani hype over the past month. But what Schwarber has done without pitching, admittedly, but what he's done with the bat

has been just as impressive. And you looked at where Karl Schwerber was several weeks ago, and it was not particularly impressive. And then he went on an all-time tear, and he was moved to the leadoff spot. And sometimes you see that, you know, guys will get moved to the leadoff spot and maybe they'll be more selective or they'll take better at bats or plate appearances at least for a while. Maybe that is something that helps their motivation. That's going to be the subject of that same interview that I just teased. But I don't typically think of player gets moved to the leadoff spot and suddenly turns into Babe Ruth even more so than Otani has been lately. If anything, that power would be wasted a little in the leadoff spot, but it sure has helped the Nationals. So he hit 16 homers in his first 18 games out of the leadoff spot. I think he hit 15 in 17 days, which Jeremy Frank found was the shortest span a hitter has ever needed to hit 15

homers in MLB history. He also found that Schwarber had 16 homers in a 75 plate appearance span, which topped the 15 that Barry Bonds in 2001, Mark McGuire in 1999, and Sammy Sosa in 1998 managed. So when you're

topping peak PED era Bonds, McGuire, and Sosa, that is pretty impressive. And the Nationals over that same span that I just cited, the Brewers record, have been 22-15. So it goes Brewers, Astros, who we talked about being hot but just got swept by the Orioles, and then Nationals who have made themselves into a real rival for the other teams toward the top of the NL East suddenly. I love how the other teams in the NL East probably looked back on the year when the nationals won the World Series and they were like, okay, we're past the date where they turned things around. We're in the clear now. And then they saw this run and they probably went, oh no.

Yeah. So the Mets have sort of been struggling. The Braves have been within a few games of 500 all year. And as we are recording, only two games separate the Mets and the Nationals. And then another couple of games between the Nationals and Atlanta. So, yeah, that's a real race. And Juan Soto is predictably hitting better now. So that'll be interesting. Perhaps Max Scherzer is no longer trade bait. Yeah. I mean, I have always wondered. I have always wondered as if it has been a thing

that I've had to think about for more than the last two months. But my reaction when people would talk about that was that I suspected the deferrals would make that harder than, and people acknowledge the presence of the deferrals and how that complicates his trade market. But I always thought that that might get in the way more than we were anticipating. But now they don't even have to figure out what they owe his great-grandchildren and which team is going to pay that. He's probably just going to stay put until he's a pre-agent. It's crazy. Yeah. I mean, you don't want a white flag trade if you are within striking distance and you have a realistic shot. That's what caused a lot of the outcry about the Mookie Best trade or the Francisco Endor trade or the Hugh Darvish trade. It wasn't that those teams were terrible and then traded those superstars. It was that they were still in contention. You could realistically envision them being good if they kept those players. And now you're seeing Chicago, who's

lost some ground to the Brewers lately, but contending in the NL Central and Cleveland contending in the AL Central. And if you imagine you Darvish and Francisco Lindor on those rosters, then suddenly it looks a little bit better. So in a way, it's worse to make those trades when you are still within striking distance than it is when you are already terrible and hopeless. And hopefully it's expediting your path back to respectability. Yeah. We talked over the off season about how Jock Peterson and Kyle Schwarber were like the same player, statistically speaking. They were each other's most similar player, according to baseball reference. And the Cubs swapped one for the other. They non-tendered

Schwarber and they signed Peterson for very slightly less than I guess it took to sign Schwarber. And thus far, that has worked out well for the Nationals. I'm not saying that Schwarber would have gone on this torrid tear if he had stayed with the Cubs, but sometimes moving on turns out to be a good thing for a player, a bad thing for a team, at least thus far. But it's been fun. Get to see Kyle Schwarber in the home run derby going up against Otani and Mancini and Story and all the rest. But it's good to have just a muscled up slugger looking slugger just hitting diggers at this sort of pace. A muscled up slugger looking slugger slugging slugging.

Get Tyler O'Neill in the derby, please. Why isn't Tyler O'Neill in the derby? No, that man is made for the home run derby. Yeah. Okay. So our plan for this episode, it's sort of a themed one. We are talking to two professors who produced papers that are not about baseball exactly, but made use of baseball data in really interesting ways to study psychological effects that are difficult to study in a laboratory

setting. And hey, here we have decades or centuries of great granular baseball data with thousands or millions of events that we can use to assess these things in a real life setting and sort of a natural experiment. So we're going to be talking to one professor, James Archsmith, about a study he did about decision fatigue and depleting your attention budgets, as shown by major league umpires and the fact that umpires have to ration their ability to make decisions. They are forced to make many decisions throughout the course of a game as we are throughout a course of a day. And it turns out that at times they have some interesting strategies where maybe they focus more on some pitches than others in possibly a rational, albeit frustrating way. So we're going to talk to him. And then we are going to talk to Professor Heng-Chen Dai about a study she did about motivation and performance resets and fresh starts and how it can be helpful to have one.

And she used the history of trades in baseball and comparing what happens to players who get traded within the division to players who get traded between divisions and have their stats reset in the box scores or on the scoreboards and what effects that has on their performance. So we will bring them on in just a second. Just as a lead into that, I wanted to mention another study that was done several years ago and written up in the Times by Seth Stevens Davidovitz, who is an economist. Seth Stevens-Davidowitz, who is an economist, and he looked at Facebook survey data to try to figure out what impact your age has on whether you become a baseball fan in terms of what your team of choice is doing at that particular time. So it turns out, and this is pretty intuitive, I would say, that if your team wins within a certain sweet spot age range, at least for some people, it seems like that has an outsized effect.

So if you look at boys, for instance, in New York who grew up in the beginning of the 60s or the late 70s and were in this sweet spot of eight years old or so, eight to 10, when the Mets had great exciting seasons, they were much more likely to become big lifelong Mets fans. Again, not too surprising. It seems like this effect is stronger for men than for women. It seems like according to these results that women have more of a tendency or an ability to become fans at any point in their lives. And I don't know exactly how to explain that, whether it's because of the way that we all get indoctrinated in rooting for

baseball. And maybe historically speaking, dads have been less likely to indoctrinate daughters at the ages of eight to 10 than sons. That's just one possible explanation that came to my mind. But I thought of this because we are almost the same age and we were sort of in this sweet spot ourselves when our teams hit turning points against each other, right? So you grew up as a Mariners fan, I grew up as a Yankees fan. And right when we were in this age sweet spot, those teams returned to the playoffs and faced off against each other in a very memorable series. And, you know, their fortunes diverged a bit from there. But the Mariners were good for quite a while. So, you know, during your formative years, you watched some great Mariners teams and there were other Yankees Mariners matchups.

And I guess I got the best of that, but it just goes to show that it really can make a major impact. Just the circumstances of where and when you were born and your proximity to certain teams and what those teams were doing at those times. And maybe if those were lulls, if the Mariners were terrible at that time, or if the Yankees were terrible at that time. I don't know. Maybe we love baseball anyway and we root for some other team or we just stick out until those teams were good, but maybe not. Maybe we find some other interests. In my case, I was not really indoctrinated by the rest of my family. My family, not big baseball fans for the

most part. So I kind of came to it independently. And would that have happened if I was not grown up during a dynasty a few stops away from Yankee Stadium? Who's to say? So would we be doing this podcast together right now if we were not eight to 10 years old at very pivotal times in our respective franchises' histories? I don't know, but I'm glad it worked out this way. Yeah. I mean, gosh, what would I be? Maybe not a Mariners fan, which I guess could have been better for you in some ways. Yeah. But like, what would I be in my life? It might be totally different. It's like, I could have gone to a different school, but I'm pretty pleased with my life as it is. I'm generally happy

with it. So I wouldn't have wanted to do that. And I think I probably will still take this team that I'm, you know, not really much of an active fan of anymore, just because of how your perspective on the game changes when you cover it. But, you know, all the losing did result in some good things. I mean, mostly for me, not for the Mariners. Just to be clear. for your well-being, perhaps. So yeah, pluses and minuses. But anyway, it's a nice illustration of the way that you can study baseball data to come to these conclusions. And in this piece, he broadens the conclusions beyond baseball because it's not just baseball that you form these attachments to when you are in those crucial ages. It's other things. It's other things that become lifelong habits or don't, but baseball is crucial ages, it's other things. It's other things that become

lifelong habits or don't, but baseball is a good way to illustrate those things. So sort of fascinated by the idea that baseball gives us this great data set with which to study baseball, but also with which to study all sorts of other things in really interesting ways. So we will take a quick break and then we will talk to people who have done that. All right, we are joined now by James Archsmith, an assistant professor of agricultural and resource economics at the University of Maryland, assistant professor of agricultural and resource economics at the University of Maryland, who specializes in energy and environmental economics, industrial organization, and applied econometrics, which sometimes, it turns out, intersects with baseball. He is one of four authors of a recent

study published by the National Bureau of Economic Research called The Dynamics of Inattention in the parentheses baseball field. Professor Archsmith, welcome in the Parentheses Baseball Field. Professor Archsmith, welcome to the podcast. Glad to be here. So can you talk a little bit, before we get into the specific findings here, about the utility of MLB umpire data? Because this is not the first time that you have drawn upon this data source to do a study, and it's not even the first time that it's been done in the larger academic world. You cite several previous instances within this paper. So could you give us some sense of how this information has been used in the past and what makes it so useful?

That's right. So one of the things that we do as economists is we really like to observe people in the real world doing their jobs. But in particular with labor, people frequently have a hard time observing the quality of people's output, particularly if they're making mistakes at their jobs or they're a little bit less productive than they normally would be. The nice thing about the baseball setting in particular with the PitchFX and now StatCast data is we observe umpires making ball strike decisions hundreds of times a game, we observe the decisions they make, and we observe pretty precisely whether or not they get these decisions correct. And this is a really, really nice setting for us as empirical economists, because we can actually

watch people doing their job and have really good assessments of how well they're doing at it. And it seems to be sometimes that sabermetrics and academia don't talk to each other all that well or aren't aware of each other's work. So you maybe less than you should see these sorts of studies cited in sabermetric studies. And maybe the reverse is also true. And when you work with data like this, there's a long legacy of sabermetric studies using pitch effects or stat cast data. And it's pretty complicated, as you know. It's not just a matter of, well, was this call correct or not? You also have to adjust for all kinds of factors,

the pitch type and the location and the weather, and you could go on and on. So I wonder how much you drew on that previous research when you were making these adjustments. You incorporate the concept of leverage within this paper. So I wonder how familiar you were with that existing research and just how complicated it turns out to be to take what might sound on the surface like sort of a simple data set and actually mine it for this type of information. So in the past, I have dug very deeply into the sabermetrics literature to make sure that I'm actually performing these analyses correctly. In this paper in particular, we actually don't do a lot of the adjustments that you would normally do in an analysis of an umpire strike zone, looking at, for example,

pitch velocity or the spin rate on a pitch, in particular because there's an econometric quibble that I don't want to dive too deep into. But the pitcher is deciding on the types of pitch they're going to throw at the exact same time the umpire is deciding on how much attention they're going to pay to a particular pitch. And if we included all of those pitcher decisions, we might actually get biased results in an econometric sense out of our estimates. So we've done it both ways where we have a ton of controls and where we don't. But most of what you see in the paper is where we don't actually control for a lot of information because the pitcher is deciding the same time as the umpire. So you're probably in Ben and I's camp where you are anti-RoboZone because it will take

an entirely useful data set away from you. I really do enjoy, A, that there have been rule changes in MLB this year that I'm thinking about ways that I might be able to exploit those in the future, in particular, the extra inning rule, because it completely changes now the amount of stress that umpires are over early on in extra innings. But I do enjoy the human element from a researcher's perspective. I can tell you as an academic, when I present this work in front of audiences of economists, half of them are diehard baseball fans and half of them have never heard of baseball. And I always get the question, if umpires are making mistakes, why don't we just have robots call balls and strikes,

which leads to exactly half of the audience laughing. I bet. So for the folks who haven't had a chance to read your paper yet, which we will link to, can you take us through sort of the high level of results that you and your colleagues found when you conducted this study? Sure. So this paper started out actually as just, we were thinking about writing a blog post after the 18 inning Red Sox playoff game a couple of years ago, just about fatigue in general. And we sat down and thought, well, you know, fatigue is actually really interesting. We talk about decision fatigue and the idea that if people

are making decisions over and over and over, they might get worse at making those decisions over time just because they get mentally tired. So we thought about this as economists. You might actually have a budget of attention that you're able to allocate over time. And if you're doing that, then if you expect that you might have to make a bunch of tough decisions in the future, or you've recently made a lot of tough decisions, you might conserve effort or allocate your effort in interesting ways over time. So at a very high level, what we find is that when calls are really important to the outcome of a game, which is what our leverage measure captures, umpires get much better at making these ball strike decisions. But also the underlying assumption there is that when

umpires are getting better, they're paying more attention. But we also find that when they have had a lot of these high stakes calls in the past, they're maybe a little bit worse right now. Like they've emptied their gas tank of attention a little bit and they just don't have as much to give. Then finally, if they have or if they're expecting a lot of these high stakes calls in the future, then they're actually a little bit worse right now as well. Like they're saving a little bit of their effort so they can use it again in the future. Right. And there's a larger body of research out there about decision budgets and decision fatigue. But this paper, because of the data set that you're using, seems to do that in sort of a new and interesting way. You write that an appealing feature of the baseball setting is that we observe our subjects making a long sequence of decisions within a contained period. is that we observe our subjects making a long sequence of decisions within a contained period. This allows us to explore both backward and forward-looking responses in a field setting for the first time to the best of our knowledge. So why is that so difficult in other settings? So again, the really nice thing here is that we have this leverage metric, basically how much an umpire's decision influences the outcome of a game. And because there's this long literature

of sabermetrics that we build on here, it's really easy for us to compute at a given point in time, how many tough decisions an empire is expecting through the rest of the inning. But also we can look backwards and say how many tough decisions they've had in the past. In real world settings, it's really tough to get a good grasp on how much effort people think they're going to have to expend in the future. But it actually just kind of falls right out of the sabermetric analysis in baseball. And I know that you do some work here to try to account for the effect of actual physical fatigue over the course of a game. But I'm curious, umpire decisions when it comes to ball and strike calls are sort of what I, well, at least to me seem like an interesting blend of things

because there's the intellectual aspect of what they're doing, but there is an actual physical component, right? Their ability to see and process individual pitches and determine where they are relative to the zone. And so I'm curious if there's any sort of follow-up that you plan to do here to try to break that down a bit more, because one of the existing theories, and maybe your paper helps to provide some evidence against this, but one of the existing theories about sort of why we have a sense that umpire decisions with the zone have gotten worse is because pitchers are throwing more pitches that are difficult for the umpire to track because of how they are breaking or moving within the zone. So how does that sort of factor into your analysis here? I think at some level, there might be just a level

of physical and mental exertion that's impossible to disentangle in the umpire's decision. One thing that we have looked at, and I don't think it actually makes it into the paper, is how these umpires are doing on hot days versus days when temperatures are more in like the 60 to 70 degree range. And the idea being that when it's really, really hot out, the umpire is going to physically tire faster, but hopefully wouldn't have the same deleterious effects on your mental concentration. But yeah, in the end, I just, it may be impossible to disentangle completely the physical and the mental aspect. And even though you did control for that physical fatigue to the best of your abilities by just accounting for the inning, I think you did say that there wasn't a huge effect. There wasn't a great trend toward umpires getting less accurate as the game goes on, except for maybe the final inning, interestingly.

Yes. So basically, we do see this slight downward trend, but not very substantial downward trend in umpire performance through the course of the game. There is kind of a big jump in the ninth inning, but part of that too is the stakes also get a lot higher in the ninth inning. And can you give us a sense of the magnitude of the effects that you're talking about here? I know that they're highly statistically significant, you found, but we're not talking about an ump makes a tough high leverage call and then the next three calls he's missing on balls right down the middle or something and calling them balls. It's not that kind of thing, but it is significant. So what sort of effects are we talking about here? So the contemporaneous effect, a really important call makes an umpire substantially better. So think what might

be the average umpire moving up into a really good umpire in terms of their historical call rates. The past and future accumulated effects are smaller, but they're still there. So think taking an average umpire and maybe moving them down to like the 40th percentile or the 35th percentile umpire. So an umpire that's maybe not so good, you know, coming from an umpire who's usually pretty average. So based on your findings, how would you characterize like the worst potential set of circumstances for an umpire making good decisions? When should fans be bracing themselves like, oh, we might be in for one here? It's after umpires have made a lot of really tough decisions or a lot of really high stakes decisions. And when they expect that they're going to have to continue to make those high stakes

decisions, maybe even add on top of that when they have a little bit of a lull. So when, you know, if you can think in a very, very close game where potentially the tying run has come to the plate, but you have gone from a full count down to a, to a zero, zero count, maybe those first couple of calls where the ball strike decision isn't incredibly pivotal because the batter still has, you know, the batter is still standing at the plate, whether you call ball or strike, uh, compared to a case when they have, when they have a full count and the umpire is going to start paying a lot more attention. And I think probably some people listening to this would say, well, you should never be taking a pitch off. It should be your maximum effort on every pitch. How dare they, you know, let certain pitches slide in a sense. But you had a footnote in the paper that I thought was enlightening.

had a footnote in the paper that I thought was enlightening. You said, if an umpire anticipates that paying high attention to one decision negatively impacts subsequent, perhaps more important decisions, he will optimally conserve attention by strategically allowing more errors in the present. An analogy would be a soccer player conserving physical effort early in a game to apply to possibly more important game situations later. Most readers likely have no objection to the idea that a sports person would seek to allocate a limited budget of physical energy across time rather than working flat out at every moment. Our results suggest umpires engage in the same sort of strategic allocation of cognitive effort. So you would expect athletes to do that in all settings. You'd probably expect all people to do that all the time. And I guess umpires are no

different. Yes, it's really nice that this is in a sports setting because I think it does fall very naturally out of just people's expectation of playing sports. You don't play flat out all the time and you exert your effort at times when you think it's the most pivotal and conserve effort at other times so you have that energy available for those pivotal moments. And this is exactly what we see umpires doing. I was struck by how important the natural inning break was to recovery of sort of our capacity for decision-making based on this. So what might you recommend? I know that asking a researcher to make hard recommendations after one study is sort of antithetical to what you're up to here. But what did it tell us about sort of the role of rest in improving decision-making in maybe non-sport settings?

I think this was a really surprising result from the paper. My expectation going in was that fatigue would be persistent across innings. And really what you see is after that two and a half minute break between innings, umpires come back and they're actually making calls at a rate consistent with their historical averages, regardless of how much stress they were under the previous inning. I think what this tells us is small breaks, even short breaks, are really good for your mental endurance. And we look at a bunch of other cases where people are making decisions, where there have been field studies of how people's performance at making decisions gets worse over time. Maybe if we were allowing people to take short mental breaks during those periods, they would sustain

higher levels of good decision-making. Yeah. So if you were an umpire and you're not allowed to just end the inning whenever you want, but you know that taking a short break could actually improve your performance, I guess it's kind of a conflict because you want to be as accurate as possible, but you're also under some pressure to keep the game moving because there's all this attention to pace of play and the length of games. So what should umpires do? Should they just take a walk, take a lap around the batter's box or something, just stare out into the middle distance for a while between calls? So that is a tough question because it's not really the umpires who get to make the decision about the pace of play in these games. They're the arbiter in the field. But if they start taking

too many breaks, I could see that being perceived negatively by baseball in general. But I do think that just taking a little bit of time and saying, I need a few seconds to recollect myself may have a really positive impact on your long-run decision-making. This goes beyond the study that you conducted here, but I'm curious if we think about decision fatigue in another umpire context, that of replay review, right, where they have something that they are able to check their decision against, obviously one initiated by one of the managers generally. But is there any research into how the presence of a mechanism like replay affects the quality of decisions that individuals are making?

I am not aware of any off the top of my head, although I can tell you after the introduction of PitchFX, umpires in general got a lot better at calling the MLB by rule strike zone over time. So that kind of feedback certainly seemed to be having an impact. But in terms of the other reviewable plays, I'm not sure. I wonder how players might apply this information, because there have been some studies that have shown, I think, that when pitchers take longer between pitches, it can enhance their performance just in terms of velocity, let's say, or stuff. You know, maybe they have more recovery time. But I wonder if there's also a decision fatigue effect, because as you note in the paper, umpires have to call approximately 120 pitches per game but pitchers and catchers have to make many more decisions than that those are just the

called pitches that umpires have to make a decision on but pitchers and catchers have to make a decision on every single pitch about what to throw where to throw it so i wonder if that means also that they might make better decisions when it comes to pitch selection and location, let's say, if they were to take longer. And some studies have shown that taking longer between pitches can help hitters too. And David Ortiz said as much that it helped him outthink pitchers if he had more time to do it. So again, that's another factor that suggests that if MLB wants to hurry the game along, players might not have the same incentives. But if MLB wants to hurry the game along, players might not have the same incentives. Certainly, if you're increasing the pace of the game, everyone is going to be more rushed and suffer these mental fatigue effects.

I don't know. There's the other issue that pitchers could strategically manipulate, the fact that umpires are maybe getting worse at decisions after a bunch of high stakes decisions. But we don't see them pushing their decisions in one particular direction or another. They're not making more strike or more ball missed calls as a result of the effects that we're seeing. So it's difficult for a pitcher to be strategic in that context, but certainly in terms of pace of play, I could see a lot more rushed and lower quality decisions. I know that your paper is recently published, but have you gotten any feedback from umpires on your findings? No. And given that I've written previous work looking at umpire decisions, they're very reluctant to discuss it. And I believe, in fact, the umpires union's response to my last paper was no comment. And I can understand why, because what we are doing is focusing on mistakes that MLB umpires are making, which are still relatively rare in the grand scheme of things.

And umpires have gotten better at calling balls and strikes historically over the last 10 years or so. But I can understand why they would like to avoid any attention being drawn or focused on the fact that there are missed ball and strike calls in games. I was thinking of this in terms of clutchness as well, which, as you know, is a very often studied subject in both academia and sabermetrics. Can players improve their performance at important moments? And you could frame this in terms of decision fatigue. I guess you could also put it in terms of clutchness and say that umpires raise their game at these most important moments. And you do find that there's an effect there, whereas previous studies of clutchness when it comes to players have shown that there doesn't

seem to be much to it. It isn't very repeatable or the effect is not large enough really to detect it over a small sample. So I wonder if this gives you any more confidence that clutchness could be real among players or whether the fact that in that case, it's a head-to-head matchup. And so in theory, they might both be clutcher, and thus neither of them would be clutcher when you're talking about a batter-pitcher matchup. So in terms of other research that I've done with baseball data, in particular, the effects of air pollution on umpires, I'm often asked why I don't look at the effects on batters, for example. But there you have this competitive interaction between pitchers and batters. And is it the air pollution making the batter worse or is it the air pollution making the pitcher better?

That's impossible to disentangle in that interaction, which is the nice thing about the umpires is that you're taking a step away from the game. So what other baseball data sets do you have on deck to use in your research? I don't have anything else planned at this point. You very kindly read through the list of my specializations and none of them is sports economists. So there's a lot that I do in the environment and energy space, which ties into this rational inattention a little bit. But I can't say that I have anything else on deck, if you will, in the near future. I guess we'll just have to wait and see for a couple of years. Yeah. And well, that does tie into your previous research on pollution and umpire performance. And I'm reading from the paper

here. Researchers have used the same data as a testbed for other hypotheses, including racial discrimination, the effect of status on evaluations and the so-called Matthew effect, the gambler's fallacy, how decision quality is affected by exposure to air pollution, and as a test of models of strategic interaction. And we will link to some of those other papers if people are interested in checking out what else the MLB umpire data can reveal. But could you briefly summarize your findings in your air pollution and umpire performance study? Sure. So in that paper, again, we're looking at umpire ball strike decisions. And we find that on polluted days, MLB umpires actually are worse at making ball strike decisions. This is, again, a really nice application of using MLB baseball data. So there have been previous studies looking at worker performance as a function of air pollution.

They frequently find deleterious effects. But these studies all look at a person who lives in a particular place doing a particular job. And the thing is that there's a lot of different pollutants out there that we worry about, things like ozone, fine particulates, nitrogen oxides, sulfur dioxide, and so on. They're all really strongly correlated with each other. And all of these previous studies really weren't able to tell you which pollutant was making workers less productive at work. The really nice thing about MLB umpires is I observed them working one day in Los Angeles

and potentially five days later working in New York City. So they get plucked and moved around the country in pretty much a random way. And that let me disentangle the effects of all these various pollutants. And so I can say definitively in the pollutants that are causing the negative impacts on umpires ball strike calls are fine particulates and carbon monoxide, but definitely not ozone. That's a really interesting finding, at least for economists. Well, if you didn't care about pollution and global warming before now, you know that it affects umpire performance. So there's just some extra incentive for you to recycle or live a green life. So I have one more question, which is just

about how we should apply these findings in some way in our lives outside of baseball. As you write in this paper, it's not just about baseball. You're studying something here that applies more broadly. So we might not have to make ball strike calls and we don't have innings breaks and this exact sort of structure, but what can we learn from umpire performance that we should bring to our own personal or professional lives? I think all of us have heard the term decision fatigue before. And what we've done is we've actually been able to quantify the decision fatigue, both in when you've made a lot of decisions, you start to get worse at making those decisions, but also this forward-looking effect that if you anticipate having to make a bunch of tough decisions in the future, maybe you save a little

bit of your effort right now so you're able to make better decisions down the road. But also this result that the break between innings seemed to actually be enough time for umpires to refresh their minds and come back and be able to start making decisions at the same rates consistent with their historical averages. So sometimes maybe just taking a short break is good for you. Take two and a half minutes, collect yourself, think about something other than the tough decisions that you're working on, and you'll come back refreshed and actually able to make better decisions. Isn't that some advice you've given on the podcast before, Meg? Just get up,

get some water every now and then, or get some tea, something. Yeah. This is why I listen to vinyl, because I have to get up and flip the record and then I go get a glass of water and then I come back and the fog has lifted a little bit. I go get some tea. Yeah. So we're already living this in our daily lives. But just in case you aren't now, you know, we will link to this paper and you should go check it out. We will also link to James's website, which is econgym.com. James, thank you very much for coming

on. Thanks for having me. Okay. We will take one more brief break and we will be right back with Professor Heng-Chen Dai to talk about the trade deadline and the power of performance resets or fresh starts. Okay, we are joined now by Heng-Chun Dai, an assistant professor of management and organizations and behavioral decision-making at the UCLA Anderson School of Management. She specializes in using field experiments and observational data to study self-control and motivation. And in 2018, she published a paper in the journal Organizational Behavior and Human Decision Processes called A Double-Edged Sword, How and Why Resetting Performance Metrics Affects Motivation and Performance,

which drew on baseball data in a really interesting way. Professor Dai, welcome to the show. Hi, everyone. Thanks for having me here. Thanks for coming on. So before we get into this specific study and how it relates to baseball, could you explain the concept of performance resets and maybe a little bit about any earlier attempts to quantify the effects of these fresh starts? Okay, great. So in general, I have been studying the concept of fresh starts for quite a number of years. And fresh starts generally refer to transition points in our lives that allow us to mentally separate our past performance from future performance. And the performance doesn't just mean your work performance, it can be how you have been making progress on your personal goals in your life, more broadly speaking. And I have examined a variety of types of fresh starts. For example, in my earlier work, I look at

things like your first days, at the beginning of the year, the month, the week, the beginning of a school semester. And then more recently in the paper that you just mentioned, I study a type of first start called performance reset. Performance reset refers to incidents where an individual's task performance on a metric is decoupled from their task performance on the same metric. Okay, so instead of tracking a person's statistics continuously from the past, we now give them a clean slate. We track them from a fresh start. So for example, if you think about salespeople, the managers may give salespeople a reset at the beginning of a new month, such that their performance from the previous months are not going to be incorporated into the average sales they have had in a new month. Okay, so every new month is a clean slate.

They start their sales records from zero. And this is a type of performance reset that we commonly see. So I guess one type of fresh start that people might think of is, you know, you join a gym as a New Year's resolution, right? People start resolving, oh, I'm going to work out. I'm going to go to the gym. And then it's kind of a joke, I guess, that people start out intending to do that. But then they, over the course of the year, stop doing that.

So I guess there is maybe some tendency to have a performance enhancement and have a fresh start and really do something about it. And then in some cases, maybe it doesn't continue, but there's still something to it in general, even if it's not in all cases, I suppose. That's right. That's a fair assessment. And in fact, in my earlier research, we do find that students, so we track university students' gym attendance records for about one half year, and we are able to see that students are more likely to go to the gym at the beginning of a semester, at the beginning of the year, right after their birthday, but not their 21st birthday. right after their birthday, but not their 21st birthday.

And then also, I know you will laugh, and also at the beginning of the week, right? And I do think so far my results have speak to the initiation of a goal pursuit, that is when people are more likely to tackle a goal, right? But then in terms of how long their motivation will last and whether fresh starts will make the persistence better or weaker, it's still something that to be examined. So I totally share your intuition, there is a more aggressive momentum at the beginning of each time period.

And maybe this is obvious, just based on the sheer volume of data that is produced in even an individual baseball season. But analyzed. So in the baseball context, there is one unique element that really makes the study appeal for studying performance reset, of course, beyond the bottom and the grand analogy of the data, the performance data itself. So specifically, in baseball, as I assume people listening to this episode know a lot about baseball, right? So I don't need to say much. Yes, probably. They probably all know more about baseball than I do. But anyway, so in the baseball context, performance reset can happen when players are traded across leagues in the middle of the season,

in the sense that their contribution to a new league will be tracked from a clean slate. So their performance for the previous league will stay stale. That has already happened. Now they're moving to a new league. Now there is opportunity for them to make new contributions to the new league from a fresh start. And if players are traded during the season but within the same league, players are traded during the season but within the same league, their contribution to this league will continuously be tracked from how they have been performing prior to the trade. So as a result, there is a natural experiment for me to compare players who are traded across leagues within a

season with players who are traded within the same season, but traded within the same league. And that will allow me to say something about the effect of performance reset in the baseball context. Of course, as the listeners will realize, there are a lot of differences between across-league trades and within-league trades. So in my research, I do as much as I can to as cleanly as possible to say this may have something to do with the fact that performance statistics following an across-league trade will be reset. But I'm always open to other suggestions the baseball fans may have about alternative explanation and have a heated discussion with them. Yeah, I was going to ask you about that because I know from doing my own baseball research

that it tends to be more complicated than you think on the surface. And so you start out doing something and then you realize, oh, maybe there's a confounding factor here. Maybe there's regression going on. Maybe I need to adjust for this or that. And you can get misleading results. And it seems like from reading your paper that you went to a lot of effort

to try to take all of those things into account. So I wonder if you could sort of explain the various adjustments you made and the alternate explanations you considered. Sure. And maybe I should take a step back to first say, what is my main finding? I realize I'm getting excited about context. Yes. So in this, over in the paper, the argument I want to make is how people respond to a performance reset

depends on their prior performance, depends on how they think about prior performance. prior performance. It depends on how they think about prior performance. And if they have been performing poorly in the past, having a clean slate is great. Now they feel more confident, they think they're different from their past self, they can do it. So they are more motivated and their performance would be better if they have a reset than if they do not have a reset. So however, when people's past performance is strong or they view their past performance as strong relative to a benchmark, they may be discouraged by having a reset

relative to not having a reset. They may feel less confident. They're not sure if they will be able to build up the strong record as before. And they may also feel a little bit resentment that, ah, now I have to start all over again. I just lost all the awesome track record. Well, of course, by lost, I use the term very loosely, right?

So in the baseball context, the players is not really losing all the performance aesthetics that they already have prior to the treat. But it just feels to me that they have to build up their record from a plain slate when they come to a new league. So I do want to clarify, I use the term lost the record quite loosely here. Okay, so then in the baseball context, what I find is, if a baseball player performed relatively poorly before they were traded, then their performance after the trade would be better if they are traded across the league than if they are traded within the same league. However,

if a player performed quite well before they were traded, and then the player's performance after the trade would be worse if they are traded across leagues than if they are traded within the league. Again, remember across leagues are associated with a reset by my definition, and then within league trades are not associated with a reset. Right. It's important, I guess, to do that comparison, to have that control group, because otherwise you would expect the player who has not been playing well to do better. And you would expect the player who has been playing well to do worse just because of regression. So by comparing the players in both of those groups, some of whom were traded across leagues and some of whom were traded within leagues,

you can control with that and still see that some effect persists there. Exactly. And you read my mind. That's exactly what I was about to say in terms of the importance of having this within league trades as the control group. And usually regression on the mean is the number one alternative explanation that comes to people's mind, which is also why I want to highlight it first. But then in terms of other alternative explanations, people might be thinking, well, if a player is treated to a different league, maybe they're less familiar with the pitcher there. So in my regression analysis, I control for how many times a batter has met or has faced a given pitcher. After each incidence, they come at a bet. That allows me to some extent control for the influence of familiarity with

pitchers in another league. I want to press on one of the explanations that I think in aggregate is sort of easy to dismiss, but I wonder if we might think about it a bit more, which is just the effect, the disruptive effect that being traded at all and thus having to relocate has on an individual. And I know that in your analysis, you looked at the sort of average distance that that would sort of account for for a given player is the same regardless of whether or not they're being traded within the same league or across leagues. But I wonder if you looked at any of the individual performances rather than an aggregate to see like, is this just that a guy went from a place he knows to being 1500 miles away more often than not? I know you were looking also at sort of off-season trades and the effect that that might have. And

it struck me that when players are moved in the off-season, they have a good deal more time to sort of adjust to a new place. And I imagine that that kind of physical relocation makes these sorts of performance resets a little unusual within the field, right? Most people, when they get a new manager, they don't have to move to another city to start working for that person, right? They get to stay in their desk. So I'm curious if you could talk a bit more about the potential effect that that would have. Yeah, this is a great question. So first of all, exactly because a general transition would cause a disruption in people's life, I do not simply compare people who are traded

with people who are not traded. Because those, well, the apple to orange comparison, of course, across and within league trades, it's not perfect. As I said, it's probably a bigger orange or a small orange. But at least I can say that I'm not comparing people who are traded with those who are not traded, which would totally be an apple to orange comparison, exactly for the reason

we pointed out. But then in terms of the within and across-league comparison, if all my results are simply driven by people having to go through a larger disruption in their lives following across leak trades than within leak trades i don't have a specific reason why this would differ between people who had great performance versus who had poor performance prior to the trade right so i would imagine disruption in general could hurt people's performance. For example, like if you believe that disruption can harm performance, I would imagine this influence strong and weak performer. Similarly, so I'm curious whether you have a explanation for

why this influence might differ based on people's prior performance. That's a good question. I mean, I think that part of it is that the degree of disruption is probably highly variable, right? I think that there are players for whom having to move in the face of a trade is not just something that affects them, but also affects their family and potentially their children if they have any. And there are other guys who are maybe younger or are just starting out in their careers and are more accustomed to moving as opposed to being more settled. So I think it would be tricky to sort of isolate,

but I do wonder if it's something that is operating in the background for guys at times when they're having to relocate and deal with the transition of that. So that's sort of my thought on it. But maybe other people are less intimidated by moving than I am. And I am overreacting to it. No, no, no.

I think it's totally legitimate question. And that is also why, as you said, I did two things. Like one is I at least confirm at aggregates, right? There is no difference in average moving distance between winning league trades and across league trades. But then also I look at off-season trades to make sure, okay, not any type of trades would show the effect as what I show for within-season trades. But I realized in my analysis, I did not go more granular than that to control for individual players' specific or idiosyncratic situation in terms of how much they moved uh how how much time they have in advance to prepare for the move but

in general again i would say if moving is disruptive uh i would expect not necessarily find that uh across league trades is beneficial for poor players. Poor players means people whose performance was weak before the trade. But then cross-league trade is more harmful for those who actually did better. I would expect to be similar effect across the board. And we should also note there are various other factors you controlled for, which people can see the details of that and the regression in your paper, the park factors and various team factors and the month in which the trade was made, which could have some effect for offense or temperature, that sort of thing. And so you focus largely on batting average, which is sort of a simple indicator. I wonder whether you looked at any

other metrics. I don't know why you would observe some effect for batting average that was not mirrored in on-base percentage or slugging percentage or whatever metric you choose. But I wonder whether you looked into that at all, just to see if it extended across other performance metrics. Yes. So I did look at on-base percentage as well, and I find basically the same results. The reason I pick betting average is I totally recognize now there are a lot of fancier statistics and betting average really is a very simple metric. But I think it's a powerful metric for a couple of reasons.

One, I believe it still remains to be a very salient metric to players, even though it's very simple, but it's still very salient. And in prior research, it suggests some other scholars in my field have found that players actually track their own betting averages closely, and they may even modify their behavior to reach a desirable betting average threshold, or they may even modify their behavior to reach a desirable betting average threshold. They may do something strategically, players really want to hit 300, so they will do something different before and after they reach 300.

But basically, the point is, prior research also suggests that players actually modify their behavior in order to reach a desirable betting average threshold, suggesting that they pay attention, they care about this metric. And also for a very long time, batting average historically has been one of the key components by which players were judged. And also game box scores at a minimum include the batting averages. And when players come at a bat, you will see like his betting average displayed on the scoreboard on television so this still remains a very salient metric to players so as a result it feels to me that when there is a reset on this core statistics it may influence people's motivation and then subsequently i also track their performance on this metric. And that's my

rationale for focusing on batting average. But I did replicate it with on base percentage as well. That does make sense to me. I wonder, because you were looking at a period of what, 40 years or so of trades, I wonder whether that effect would change at all over that period as batting average became de-emphasized a bit. Batting average is still certainly, as you say, a very visible metric and some players continue to care about it, but I think it's less correlated with, say, how players get paid or valued by teams these days than it used to be. So I wonder whether you'd see any change in the effect or more emphasis on base percentage or other metrics if you sort of sliced up the

sample. But then you'd be dealing with smaller samples and maybe that would come with some dropbacks too. But I think it makes sense to focus on that for the reasons you stated and also to focus on batters. And you didn't incorporate pitchers into this study and you explained why. Could you lay out why you went with batters? Cool. Yeah. So in general, the one reason I focus on baseball is because people usually say like baseball is an individual sports masquerading as a team sports, right? So like each member, more or less still play on his own. And in particular, that is the case for batting performance. The batter's performance are more reasonably

independent of team's effort compared with a fielder or and the pitcher's performance more depends on batter. So in the end of the day, I focus on batting performance so I can more cleanly capture the batter's own motivation effort. And I guess there's some other factors there that you mentioned in the paper, like the DH rule being in one league and another. And so that could kind of complicate things if pitchers are hitting in one league and they're not in the other, then maybe they're different offensive baselines. So you would have to figure out a way to adjust for that too, if you're talking about inter-league trades, which would be another complications. Totally. And this case study is the most relevant

to us as baseball analysts, but it was not the only one that you did in the course of this paper. And for the folks who haven't had a chance to read it yet, could you talk a bit about how what you found in the field setting, how it interacted with and perhaps differed from some of the other results that you got in your other case studies? Yeah, thanks for asking about the other studies that I've run to corroborate the evidence from the baseball study. And partially, as much as I'm excited about baseball studies, so far, we have our discussion also pointed out to certain limitations or open questions that the baseball data cannot address. For example, what is going on in those baseball players' mind? that I cannot address. For example, what is going on

in those baseball players' mind? What might be driving those effect? So to further understand the phenomenon, I actually conducted several laboratory experiments where I put people in a more controlled setting and I measure their performance on specific tasks. I give them and I incentivize their performance to make sure they're motivated

to perform well. So more specifically, for example, in one of the experiments, I give people an anagram task and I ask them to come up with as many words as possible using certain letters I provide them. And then people are paid based on the number of words they could create using the list of letters. And I manipulated the feedback I presented to those participants. So I either give them a sense of progress, make them feel like they have been consistently meeting certain performance benchmark. They are doing great. And for another group of people, I make them feel that they have not really been performing well. So in both cases, people get the same objective performance feedback, that is, they know how many words they have correctly generated. But on top of that, I manipulated how they subjectively or

psychologically feel about their performance relative to certain benchmark. And then in addition to my manipulation of how people think about their past performance, I also manipulated whether or not those participants received a performance reset after 12 trials of this task. So for some people, after 12 trials, I tell them, look, now going forward for the next 12 trials, I will give you a clean slate. Your performance will be tracked only for those 12 trials for you to receive the subjective feedback. So in other words, what they have already accomplished in the previous 12 trials still count towards their final payments. They're not losing their objective records. But in terms of my feedback on whether they meet a certain performance standard and how they should subjectively think about their

performance, that one is refreshed for people in the performance reset condition going forward for the next 12 trials. And in the no reset control condition, people are told that they will continuously see their performance being tracked from the previous 12 trials they're going to get feedback based on overall how they have been doing and while they are working on the next 12 trials they will still be able to see a graph that depicts their performance in the previous 12 trials okay so overall either we try to make people feel like huh now you have a slate, you are looking at a clean graph that only is going to show you your performance in the next 12 trials.

Or people are going to work and then see a graph that continuously track their performance throughout this task. Okay, and then at this point, I offer people an opportunity to switch to a different task. I ask them, well, do you want to continue this anagram task or do something else? That is my main outcome variable to capture people's motivation to continue working on this task. And what I find is actually quite consistent with the general pattern observed in a baseball setting.

For those people who were led by me to view their past performance as weak, as below a certain performance standard, those people really embrace the opportunity of fresh start or reset. They are more willing to stick their head with the anagram task if they get a reset than if they do not get a reset. if they get a reset than if they do not get a reset. However, for those people who are led to view their prior performance on the first 12 trials as awesome as outperforming a certain performance standard, they're actually less likely to continue with the anagram task if they get a reset than if they do not get reset. So again the idea is people become more motivated to continue working on something as a result of reset if they didn't do well on that task,

but then they become less excited about working on that task after a reset if they have performed them very well in the past. And so I know that you're more interested, I think, in the broader societal implications of this than the baseball trading season implications. But we do have the baseball trade deadline coming up at the end of this month. And I wonder whether you think this is a significant enough effect that general managers should be taking this into account as they are trying to construct trade. Should they be trying to go after underperforming players who are in other leagues instead of the same league? Is the magnitude of this enough that all else being equal that they should do that? I guess it's harder sometimes to arrange trades with teams in your

own league because sometimes they're in your own division and you can be direct competitors. But aside from that, is it something that you would recommend that MLB teams take into account? Yeah, that's a great question. I did try to quantify the effect size at some point while I'm working on this research just to see, okay, what does the effect size I observed mean? How would that influence the rank of a given player? But what I'm actually going to say is not necessarily how manager, like my research is less about telling managers how they should pick players. But rather, I think having them become aware of this result, they will be able to communicate with the new, the players who are just treated to their team better. So they are not going to, for example example the strong players are not just going to lose confidence or feel discouraged or feel demotivated like i've been doing well in my prison league

why am i trading to a different league and then we are hoping that the managers will actually be able to use certain interventions and communication strategies to help the strong performers coming from a different league to remain motivated and continue to feel the sense of urgency and efficacy and be able to keep the momentum. And then for those people who actually did not perform well in the past and also did not have a chance for a reset, think about there is any way to make them feel psychologically they could have a fresh start and then motivate them to pick up their performance. So I think my research speaks more about what communications the managers can do now knowing

that reset has a different influence for strong versus weak players. Yeah, that's interesting because I'm sure a lot of people listening might think, well, how could major league players not be motivated to the max all the time, right? Because people are watching them. They're on the national stage. They're in the spotlight. There's a lot of money riding on every hit, every plate appearance, a lot of pressure, a lot of scrutiny. So you would think that that might keep them motivated to the max at all times. But I guess no matter what profession you're in, you can lose motivation or gain motivation. These things ebb and flow over time, even if you're a major league baseball player. And so, yeah unquote. But if you're following on the scoreboard when you come up to the plate and they show just your stats in that league

and it wipes away what you did before you came to that team, for instance, you could see how that might just get in your head a little bit. Even if people aren't looking at newspaper box scores the way they once used to, there are still ways that this comes to the fore. So yeah, I wonder in that case, maybe it would even affect how you display those stats on the scoreboard scores the way they once used to there are still ways that this comes to the fore so yeah i wonder in that case maybe it would even affect how you display those stats on the scoreboard when someone comes up maybe you show them their full season stats or you show them their reset stats depending on which would be more beneficial or yeah you could just talk to them and say hey we uh we value

what you did before and we know you're a good hitter so it's not as if you're starting from scratch here what you did still still matters so you're a good hitter. So it's not as if you're starting from scratch here. What you did still matters. So maybe it's just a matter of having that conversation. Yeah, totally. I think you had a great suggestion. And I like the idea of thinking about how to strategically display information and communicate the feedback to the player. So I had an opportunity to talk to some baseball players and coaches.

And not all of them are actually for major league baseball but I basically also asked them well besides people's motivation when they come to bat are there other cases where motivation matter so they actually mentioned like how good whether players take good care of themselves after they arrive to a new city, before they actually are going to the game, if they are motivated, they are going to take really good care of themselves. And they are also going to be still, like continue to go through the training and exercise.

But if they are not motivated, they could also choose to indulge. And even before they go on bat, they can choose how hard they practice. So it sounds to me that besides the momentary motivation people have at bat when they're under close scrutiny, there are also situations where players can choose their effort level when they practice and prepare. So the motivation may also change for those incidents as well. I think one of the most interesting findings you had, one of the things may also change for those incidents as well. because intuitively it would make sense to me that a player who is performing well on a team

that is doing badly, who is then perhaps traded to a team that is doing well and has the potential to reach the postseason and maybe win the World Series, that their motivation might have sort of a longer time horizon, right? That suddenly they are able to look to the potential for postseason play as a new motivator rather than their individual batting average. And so I wonder if you could talk a bit more about that interaction of variables, because I was glad to see that you control for that. I think that's important, but I was a little surprised by that finding. So maybe you could talk a bit more about that. Okay, can you clarify why you find that surprising, that controlling for team quality and team performance? Just because I would expect that if you're a good player, right, so one of these players who we

would expect to perform less well post trade after the performance reset, because they had been above league average from a batting average perspective prior to the trade, I might expect a player who falls into that bucket and was on a losing team prior to the trade, so a player who would be likely to be dealt, right, and then is acquired by a team that is good, I might expect that they would feel renewed motivation to perform well, even though their average resets, because now they're on a winning team, right, and they might get to play in the postseason. And so I was just a little surprised by that. And maybe it just says something interesting about sort of how we understand individual motivation that something like batting average, which you see in the ballpark every day,

rather than something that's a little further out from a time horizon perspective, like postseason play. The thing that we see every day is more sort of resonant in terms of our motivation than something that's further out. I see. Okay, so I think it might be helpful to clarify that what you are speculating is, well, when those strong players are moving from losing team to a team that potentially could be qualified for the postseason, they should be more motivated. That could be true. But then my results could also be true, because my results does not necessarily study how people change before and after their trade, but rather how they perform after a reset relative to another trade without reset, right?

Because if a player has been strong and in a losing team, but then it's traded to another team within the same league that has the potential to be qualified for the postseason, they could also be more motivated. So my question is, who is more motivated? Those people who are traded within league or across league? Fair enough. Does that make sense? Yes. Yeah, fair enough. Yeah, that's interesting. I was thinking of Albert Pujols as an example, who this year was released by the Angels and then traveled essentially across town,

more or less, and also between leagues and signed with the Dodgers. And he has been much better with the Dodgers thus far. There are many reasons why that could be true, but it's about the same sample and he's going from a not very good team to a good team. And you might think that that might motivate him, but he was released not traded and you could probably do a whole separate study about what happens after a player is released because then you might have even more motivation to redeem yourself or prove yourself or maybe you're more demoralized because you were released and not valued by your original team so i guess that's sort of a different situation but an interesting individual case study for this season. So I wanted to ask, we talked a little bit about

the baseball implications, but maybe the broader implications for people like us, people who fortunately are not in professions for the most part, where we can just get traded between different cities without warning or in some cases without approval. But we all struggle at times to maintain motivation in whatever work we do or not even work in personal lives. So I wonder if you've learned anything about what we can do, barring having a birthday come along or New Year's or something, a milestone like that. Is there anything we can do to maintain our motivation?

Yeah. So I think from, yeah, we talked about from a manager's perspective, not just baseball, right? What we discussed about manager, the communication and expectation that performance reset work differently for different people. That applies to managers in all contexts. But then for individuals who pursue their goals, I think broadly speaking, recognize the influence, both the possible pros and the cons of having fresh start matter.

So in general, my research has shown that when you didn't have a great performance in the past, fresh start can make you more motivated. So take that opportunity and look for fresh start in your lives and seize those opportunity to jump on a goal that you mean to pursue in the past so instead of thinking instead of being tarnished by your failure right so maybe after new year you try to go to the gym you only did it for one month instead of thinking huh this year i'm going to totally give up on it give yourself a reset maybe like on july the first which is like halfway through the year think about this as a new beginning as a new beginning of a second half of the year and take this uh embrace this opportunity to give yourself a reset so i think

that's one lesson is to recognize moments in your life that may make you feel personally that you are having a fresh start and then use this opportunity to restart a goal pursuit. And also, I think, interestingly, like for teachers or for parents who are trying to motivate others in their lives, right, that also means you can look for fresh start opportunities for the clients or for the kids or for the students you are trying to influence. And that could also be a good strategy for you to motivate them. So for example, after the birthday, maybe we used to send kids gifts that they like,

whatever that might be. But now we can think about, well, maybe we can take this as an opportunity and send them some tools that could help them get started on their goals. So just be more mindful of the fresh start moments in the lives of those you are trying to influence and see whether you can instill the motivation to them when they're most likely to be motivated to pursue goals. So last question, and maybe sort of a two-parter that might have been better at the

beginning of this conversation than the end, but I wonder first how you got the idea to use baseball to study this, because it's a very clever way to look into it. And also just what got you interested in this subject in the first place, and why have you made this a focus of your research? Yeah, so let me answer the question about baseball first. While I was working on this project, and after I find some initial evidence from the lab experiments suggesting past performance influenced people's response to a reset, and start looking for field contacts that are consequential or high status and have granular data for a long period of time. And then very naturally, I thought about sports and I thought about baseball because I know RetroSheet provides

very detailed baseball data. And I've seen prior researchers leverage this, the amazing data set that I've been put together there. So initially, actually, I was thinking, maybe I just look at reset at the beginning of a new season right because at the beginning of a new season everyone's season to date statistics will be reset but then soon I realized there is a problem related to regression to the mean it's not gonna be as clean as I want. Luckily I had a friend who is both a researcher and an avid baseball fan, Anton Green who is a professor at Wharton and I had a friend who is both a researcher and an avid baseball fan, Anton Green, who is a professor at Wharton.

And I had an opportunity to pick his brain, and he immediately thought about winning league trades versus across league trades as a better comparison. And that's where this idea comes from. So I definitely owe a lot to his inspiration. And in terms of why, broadly speaking, I'm interested in the concept of first starts, there are multiple reasons. But then the one that is relevant to my personal reason is even since I was a kid, I have been making resolutions at those moments that may

seem silly, like at the beginning of like right after Chinese New Year, or after I finished my summer vacation at my grandma's place. So I already intuitively started leveraging those transition points, big or small, in my lives to make resolutions, even since I was a kid. even since I was a kid. And then there was one time my former PhD advisor visited Google and then she was asked an interesting question about when people, when should we try to implement

behavioral change interventions on other people? What are the moments people are most receptive to changes or influence or interventions designed to shape people's behavior in a good direction and then my advisor come back to me and said hey hen chen do you know any prior research that has looked at this i was like no but then once we started talking both of us actually shared an intuition that those transition points could shape people's motivation and i told her about all the stories I have

about how when I set goals for stop biting nails, that is after the personal transition points. Of course, I'm still working on that goal. That again suggests that fresh start has its limitation. It only make you motivated, but it may not necessarily help you accomplish the goal. But anyway, that's a long story about me stopping biting my nails. But then more generally, after we talk and share anecdotal evidence from our own personal experience, we find that actually those temporal landmarks, those transition points, those points all share certain characteristics.

That is, there are moments when we're going to start a new calendar period or we are going to start a new chapter in our lives. They always symbolize certain closure of a previous period. And that's how we come to the idea that we should study fresh starts as a source of motivation. Well, thank you very much for doing the research and for sharing it with everyone and for joining us to talk about it today. And we will link to the full paper for anyone who wants to check that out, as well as Professor Dai's website, where you can find much of her other research. Thank you so much for coming on.

Of course. Thank you for having me. It has been really fun. And hearing your interpretation, Megan's interpretation of my research and how I try to address alternative explanation is quite helpful because I tend to describe it in a more academic way. And hearing how you describe to the baseball fans is very helpful. I think we just learned that I really hate moving, mostly. Well, having the audience know a little bit about a host is fun.

So thank you again. Thank you. Nice meeting you all. Bye. All right. That will do it for today and this week. Thanks as always for listening. After we recorded, the Brewers won again.

The Nationals did not. But Atlanta beat the Mets despite another strong effort from Jacob deGrom, who went seven innings, struck out 14 with no walks, only five hits, but he allowed three earned runs, which sent his ERA skyrocketing from 0.69 to a less nice 0.95. Just a really strong start and his ERA went up by almost 40%. Turns out it's pretty tough to beat Bob Gibson. There's just not much margin for error. One thing I meant to mention when we talked about sticky stuff earlier, as noted, it's tough to isolate the offensive effects of the enforcement, and we haven't seen any additional pitchers rebel or drop their pants

upon being inspected. But we still have seen a war of words and some pettiness, and frankly, I am all for it. So Josh Donaldson of the Twins, who was one of the more outspoken players about the need to police sticky stuff, he hit a home run off of Chicago's Lucas Giolito. And as he crossed home plate, he seemed to yell, not sticky anymore, and gestured with his hands toward his own team's bench. So Lucas Giolito, who lost that battle but won the war, he won the game. The White Sox beat the Twins.

He took exception to that, unsurprisingly. I'm not going to make Dylan bleep out a swear here, but Giolito said, he's an effing pest. That's kind of a classless move. If you're going to talk, talk to my face. Then in the next act of this saga, Donaldson showed up for a Zoom interview wearing a t-shirt that said, you got something to say? And he clarified that his comment did refer to Giolito, but that he intended to rally his teammates more than as a shot against the pitcher. He said, I didn't think I was showing him up during the game. I was talking to my bench. I wasn't pimping my home run. I wasn't talking to him saying anything. I'm trying to get our boys fired up and ready to go, which is interesting, right? So Giolito was mad at Donaldson for not

saying it to his face. Donaldson was mad at Giolito for being mad at him for not saying it to his face. His defense was, hey, I wasn't saying it to his face. I was saying it to my teammates. Anyway, then Donaldson comes to the plate the next day, gets booed, hits another home run, dropped his bat. So I don't know who's ahead here. Donaldson has always been sort of a fiery player who is willing to snipe at opponents. He's kind of in that A.J.

Pruszynski mold, I suppose, of you like him if he plays for you. You hate him if he doesn't. But I'm enjoying this war of words as long as it remains nothing more than a war of words. As long as we don't get into a beanball war here and no one gets hurt. I am all for this kind of petty drama. Bring it on. I don't think it's bad for baseball at all. I guess the galaxy brain take would be this is what MLB wants. They're turning union members against each other so that they won't be able to stand up against us. But I don't know that that was a major motivation, and I don't know that that will stop them from bonding together against a common adversary. It's just the stuff that comes out

in the heat of the moment. And I like it. Gioito's spin rates are down a couple hundred RPM, by the way, though he too has pitched pretty effectively nonetheless. You can support Effectively Wild on Patreon by going to patreon.com slash effectively wild. The following five listeners have already signed up and pledged some small monthly amount to help keep the podcast going and get themselves access to some perks. Mary Barnes, Paul Vogelsberger, Maximiliano Burgess, Patrick Heal, and Jacqueline. Thanks to all of you. You can rate, review, and subscribe to Effectively Wild on iTunes and Spotify and other podcast platforms. Keep your questions and comments for me and Meg coming via email at podcastatfangraphs.com

or via the Patreon messaging system if you are a supporter. You can join our Facebook group at facebook.com slash group slash effectively wild. Thanks as always to Dylan Higgins for his editing assistance in sweltering Portland this week. We hope you have a wonderful long weekend if you are in the U.S. and celebrating, and we will be back to talk to you early next week. We hope you have a wonderful long weekend if you are in the U.S. and celebrating, and we will be back to talk to you early next week. Going gone. Going gone. Going gone. Going, going gone.