A Conversation with Joe Peta on Baseball, Stocks and Performance Analysis by Elliot Turner, Compounding My Interests
I’m very excited to share my interview with Joe Peta here. Joe is the author of Trading Bases: A Story About Wall Street, Gambling and Baseball and Managing Director at Novus. As you may realize from the title, Trading Bases combines (compounds) a bunch of my interests. In his work at Novus, Joe Peta has been doing some groundbreaking research on performance attribution by dissecting volumes of individual fund and investment manager performance records in order to gain deep insight into how to identify the true drivers of alpha. His insights for traders and investors alike from years of experience on trading desks, running his own hedge fund, and now studying some of the best alpha-makers in the world are incredibly valuable for anyone looking to make money in the stock market. Plus Joe Peta’s outlook on life, whereby he took a challenging physical injury and turned it into an intellectually stimulating and ultimately rewarding experience in baseball betting is both inspiring and a great lesson for all in critical thinking. I’m very thankful that Joe Peta took the time to discuss some of these topics with me here! Enjoy:
Elliot: Novus has been putting out some great stuff, particularly on the Julian Robertson and his Tiger Cub progeny, as well as European short interest. Can you talk a little about your role in Novus and your effort to build out the investing equivalent of baseball’s WAR–the WAGEs?
Joe Peta: Novus was founded by ex-Fund of Fund investors who were dismayed at the lack of use of analytics in evaluating fund managers. Despite the fact that study after study has shown that today’s fund winners correlate poorly with tomorrow’s winners, in 2007, our founders wondered why the entire industry of allocators (endowments, state and corporate pension plans, sovereign wealth funds, fund of funds, etc.) all based their allocation decisions on the same process: Review the 1-, 3-, and 5-year track record and have a face-to-face interview with the fund manager to see if “he gets it.”
Continued from part one... Q1 hedge fund letters, conference, scoops etc Abrams and his team want to understand the fundamental economics of every opportunity because, "It is easy to tell what has been, and it is easy to tell what is today, but the biggest deal for the investor is to . . . SORRY! Read More
With data and analytic tools available that didn’t exist in the years prior when the “track record/interview” process was the only way to evaluate managers, Novus was founded with the idea of augmenting that process with analytics. Since our founding nearly seven years ago, the firm counts as clients not just allocators but asset managers (hedge funds, for the most part) as well. Allocators use us to manage their data and provide portfolio analytics while managers, thanks to the possession of daily data, can use Novus as an invaluable self-analysis tool.
Novus found me after reading my book Trading Bases. The first time I saw their analytics platform for hedge fund managers I nearly slapped myself in the head, thinking: “these guys have commercialized the exact stuff I tried to build on Excel at my hedge fund.” The whole goal of this type of analysis is to find out what you or your firm are good at and then leverage those skills. It’s a simple concept really rooted in the division of labor – if you want your hedge fund to be successful, find out what you do that persistently creates alpha and then leverage those skills. Leveraging alpha is smart; leveraging beta is risky; and, undertaking activities that you’re not good at is stupid.
These are exactly the management concepts that have arisen out of the sabermetric revolution in baseball. Buster Posey doesn’t try to steal bases, because he’s slow-footed. Getting thrown out stealing would erode his other highly-coveted skills that make him more valuable than his peers. Yet, I see PM’s who specialize in tech consistently losing money trading bonds or oil. If confronted, they’ll spin some sort of crap about correlations, but it’s just another form of behavioral finance weakness. They either don’t know what they are good at, and therefore don’t stick to it, or they simply cannot help themselves from trying to be something they are not. (I used to tell a fabulous semiconductor PM I knew who consistently lost money trading oil that he would laugh his ass off if T. Boone Pickens dabbled in SOX trading. My friend would laugh knowingly – and then go right back to trading the USO.)
Novus, at its core, is designed to detect those skills and weaknesses. It can help an allocator invest more intelligently and it can help managers improve returns. Baseball, sadly, is way more advanced at separating skill from luck when examining results than the financial industry is.
Elliot: Are you still running the baseball hedge fund while at Novus?
Joe Peta: My complete immersion in the world of model-based baseball betting covered the 2011 and 2012 baseball seasons. The entire 2011 experience is chronicled in my book and that led to the 2012 adventure of raising a $1million fund and moving to Vegas for the majority of the season. Other than some personal season-long futures bets that I made this year and last, I have made virtually no single game bets. It’s a time consuming venture that really ended when my book came out last year and I started to look for the next step in my career.
Two questions that arise from those that read the book are 1) if the model returned 41% and 14% in ’11 and ’12 respectively why don’t you raise a bigger fund, and 2) why would you disclose virtually your entire process in the book? The answer is that my experience in Vegas convinced me the returns aren’t scalable. I estimated my true edge to be somewhere in the low teens (based on my model’s slightly better Least Mean Squared forecasting error over more than 4,800 regular season games and my allocation schedule which produced daily volatility roughly akin to the QQQs.) I learned that my $1mm fund could only be 2 to 2.5x bigger (at the most) before the returns would have diminished due to the inability to size my bets up to reflect a larger fund. You might protest that 13% a year on say, $2mm is still $260,000 in returns a year and I would agree that’s a very material amount of money, but it’s money that would belong to my investors; managers typically get 20% of the profits. So at the most optimistic, you could say I had a business model that had an annual expected value of $50,000. The 23-year old me would have jumped at that, but as a married forty-something financial industry professional with young children, it’s just not a viable business model. I love that I had a two-year experience that resulted from a horrible accident, but I’ve essentially removed myself from that world..
Elliot: While WAGEs is a comparative stat, how can I use it to my advantage as an individual and an investor to better assess my own performance? How can I use WAGEs to think critically and smartly about myself in the quest for constant improvement?
Joe Peta: I have 100% confidence that a WAGEs (Worth of Actively Generated skills) type analysis of every single active trader/investor can yield extremely valuable insights that can lead to self-improvement and ultimately higher returns. Here’s why:
If you’ve decided to actively manage your money or your client’s money you are rejecting the idea of passive investing. The SPY’s are essentially the replacement-level player of the investing world. (Obviously, you can find any ETF to replicate a strategy that might be more refined that the S&P 500.) So by deciding to actively invest, you’re implicitly stating you can outperform the low-cost, readily available index.
It turns out that if you really think about what the SPYs are and what they represent, there are three potential areas of weakness you could exploit (skill required in parenthesis).
1) The SPYs are 100% invested at all times. (Market exposure)
2) The SPYs invest in 500 different companies (Security selection)
3) The weighting of each company is pre-defined (Sizing)
(Let’s address a potential fourth area—sector allocations are also pre-defined. You could cite sector selection as an area of SPY weakness and therefore a skill you may possess to exploit it, but in truth if that’s your skill, you’d be better off passively investing in an ETF subset of the SPYs, or what I call active-passive investing).
To justify active investing, one must possess a persistent edge in at least one of those areas of skill. A WAGEs-based analysis will determine your skill-based grades (or value) in each of those areas. The savvy investor will therefore limit his or her activities to the areas of skill and neutralize the areas where there is anti-skill. I have seen plenty of instances where poor market exposure management consistently erodes security selection skill. That can be identified with a WAGEs analysis and corrected by choosing to run a static-net exposure fund. Yet, that skill/detraction remains undetected or certainly unquantified just looking at results.
By the way, in the world of hedge fund investing, to my eye, by far the most crucial and the most persistent (or sticky) skill from one period to the next is the Sizing skill. If I ever meet a manager who tells me they run an evenly-weighted portfolio of 33 (3% equal-weighting) or 40 (2.5% equal-weighting) stocks, I would never invest with them. The ability to size positions, to outsize bets when the expected return is highest, is the hallmark of successful investors (e.g. Tiger Cubs). If you neglect to utilize that dial, I simply don’t believe you have much of a chance of ever beating a passive fund over time. In short, I suspect you are simply a style investor (say, deep value) and I’m certain I can get that factor exposure cheaper via an ETF.
Elliot: That’s a powerful point you make about position sizing. In Trading Bases you spent some time outlining how you came up with your own tiered approach, based on your model’s level of conviction in a given outcome. Was this point about position sizing something you were aware of in developing your own trading strategy? Is there anything you would do differently now with what you have learned on the performance attribution side ever since? Are there any particular approaches to position sizing that you have found effective in the fund managers you have analyzed?
Joe Peta: My sizing decisions for the fund were informed by the experiences of any serious black jack or poker player: you need to get more chips in the pot when the odds are more in your favor. This philosophy, of course, is expressed numerically in the Kelly Criterion. I modified the Kelly formula to reflect the philosophy, but I sacrificed the chance to maximize returns (Kelly’s design by formula) by having far more respect for draw downs. (The idea of never making a bet today which can imperil your ability to take advantage of a bet with positive expected value in the future is one I spend a great deal of time on in the book, highlighted by my first-hand experience with the Lehman bankruptcy. In fact, despite zero references to baseball, it’s the longest chapter in the book). Applying that today, and merging it with my experience at Novus, I’d have to say if I were back at a hedge fund I would spend a long time with the PM and analysts exploring the idea of narrowing the number of holdings in the portfolio. We (Novus) have definitely found that ‘farm teams’ — those sets of positions, typically between one-half percent and two percent of a fund that almost all large funds carry, typically have sub-par performance. Those small positions that PMs love to say, “force me to follow the name” end up costing a lot more alpha, if not outright dollars, than many realize. So, I’d consider the merits of a more concentrated approach, and if I were an allocator investing in hedge funds, I’d be very receptive to that strategy.
Elliot: Are there differences in how we need to think about investment styles? For example, in WAR we adjust a player’s value to reflect his position. In market performance attribution, are there different ways to structure the analysis for value, growth, macro, etc. styles?
Joe Peta: I think all attribution/evaluation has to be context-dependent. For instance, if you are the healthcare PM at a multi-manager platform hedge fund forced to run 20% net long, you should not be dinged because the sandbox you are forced to invest in presented an unfavorable environment this year. There are so many things out of the control of a manger (dispersion, volatility, multiple expansion and contraction) that you want to isolate the actual factors that the manager can control. The same goes for factors that you are referring to such as value vs. growth, large cap vs. small cap, etc.
That said, if a PM by charter runs a hedge fund with zero restrictions on style, sectors, etc. they need to be graded on their ability to shift allocations whether it’s by sector, asset class, etc. I actually find this is a pretty easy trait to evaluate over time that can be revealing to the PM.
Elliot: What are some of the behavioral differences between working on a trade desk and running a baseball-based hedge fund? Do you think it’s advantageous that in baseball your wager is set upon its placement, whereas in markets you get instantaneous feedback and can change your mind (and position) on a whim?
Joe: My approach and implementation of baseball wagering, while massively informed by my time on trading desks, purposefully looked nothing like my stock-trading activity. I can explain why with a story about my mid and late-career experiences on Wall Street. While running the desk at a $200mm+ long/short equity hedge fund for five years, I became obsessed with the weaknesses I felt our head PM (and my partner) exhibited, because his shortcomings fell in the area of behavioral finance. Later, after the fall of Lehman Brothers (which was our third partner in the management company and by far, the largest investor in our fund) necessitated the closing of our fund, I moved back to the buy side. There, my assistant (who I had handpicked based on first-hand observation at a prior job) simply couldn’t “get it” when it came to executing customer orders and maintaining order in our prop book. It drove me bananas, because like my former partner, the shortcomings weren’t due to a lack of intelligence or a fear of doing the homework around our names, etc.; it was a behavioral finance weakness. Specifically, they had an inability to do nothing. Both of them felt the need to react to the latest stimuli: whether an item on CNBC, or changing prices on the screens in front of them, etc. and then take action. I was so agitated by their behavior, which consistently cost money, that I swore if I ever ran a large trading organization, in the spirit of the Notre Dame locker room in which hangs a “Play Like a Champion Today” sign that every player touches on the way to the field, I’d hang a sign over the trading desk that everyone would be forced to stare at all day. It would read: “Don’t Just do Something, Stand There”.
I ran my baseball fund after these observations, and as such, I took that philosophy to its most logical extreme; my fund was 100% model-based so I never put my fingers on the scale. Now, unless you are running a black-box, algorithm-based hedge fund, that approach isn’t entirely applicable to stock trading. After all, stocks aren’t priced with a correct answer like the outcome of a baseball game. Stock trading absolutely involves pattern recognition and the need to sometimes be a contrarian. I’ve always said: “stock trading is like surfing – you can use astronomy to read the charts, you can hire weathermen to give you tide and wind predictions and you can even put sensors in the water. But if you really want to know what surfing conditions are like, if you want to feel the tides, you’ve got to get in the fucking water.”
Model-based baseball betting allowed me to completely remove emotion from the investment picture. Most traders would do well to do less trading, but still, the markets are different.
Elliot: I love the idea of “Don’t Just do Something, Stand There.” In performance analysis, is there a quantifiable connection between lower turnover and long-term returns? Alternatively, is there a way we can judge how efficient and minimalist a fund’s actions are beyond just looking at turnover?
Joe Peta: I’ve very agnostic when it comes to investing styles. I am steadfast in believing there isn’t just one way to invest or trade, or run a fund. This applies to every style, strategy, system, etc. you can think of. However, there is only one way to judge the effectiveness of any money manager and that’s value creation (think, alpha) vs. a passive benchmark (risk-adjusted, of course.)
Therefore, you will never hear me say, “you trade too much,” or “you need to let your winners run”, etc. What I will say though is this, “You should stop whipping your net exposure around because you’re no good at it.” Or, “your winners, on average, contribute only 10% more basis points than your average loser, and that’s bottom-quartile performance.” Those data-based insights suggest a change in behavior is needed (less trading and let winners run/cut losses earlier, respectively.) That’s what I love about properly designed data analysis — it doesn’t fault style, it identifies skills.
So to your second question, we can certainly measure a fund’s efficiency, and all things being equal more efficient is better than less efficient, but I’m not sure it matters. Trading/turnover isn’t necessarily inefficient, but examining the results will determine if the PM ordering the turnover makes effective decisions.
Elliot: Markets are a dynamic world, changing constantly. There are interconnections and feedback loops, and no two periods are exactly the same. Do you think this is an area where performance analysis in baseball is materially different than that of markets?
Joe Peta: Baseball, due to lack of inter-dependency is far, far, far easier to model accurately than other sports, let alone financial markets. A baseball game is basically a series of 70 or so one-on-one match-ups between pitcher and batter that can be modeled with a fair degree of certainty (thanks to modern computing power, pioneering work done on persistence and reliability of results, as well as aging curves done by the early sabermetric researchers, and of course, baseball’s wonderfully data-rich history) that just isn’t possible in other sports where teammates play such an important role in results.
I’m fond of saying that “it didn’t matter what league he played in, who his catcher was, the ballpark he pitched in or the batters he faced. Randy Johnson could be relied upon to strike out 33% of the batters he faced during a ten-year stretch of his prime.” You just can’t do that in other sports. You would have to qualify things as follows, “Tom Brady, playing behind this offensive line, throwing to these receivers, supported by this running back, and leading a Bill Bellichick-designed scheme can be expected to complete 62% of his throws.”
Trading and the markets, of course, are even harder to model (in the short-term) and the reason can be summed up, as is often the case, by a Warren Buffet quote (which he attributes to his mentor, Benjamin Graham, “In the short run, the stock market is a voting machine, but in the long run it is a weighing machine.”
Elliot: What are some of the best adaptive traits you have seen both behaviorally and empirically from traders to accommodate changing markets? Are there certain people you just knew would be able to succeed in any market environment no matter what?
Joe Peta: The best book I read on this topic is Way of the Turtle, by Curtis Faith. I absolutely believe there are people who can succeed in any market environment. They are extremely adaptive, trust their instincts and most importantly, have an ability to understand the trading environment they are currently in. By that I mean this: The greatest PMs have an ability to listen to input from their analysts and take action on it at one period of time, and then take that exact same information and do the opposite at a different period of time. That’s understanding field position and it’s an ineffable skill. However, if you run a hedge fund it’s invaluable. (Now, one can argue that if you’re a Warren Buffet-type investor or running a low-turnover, value-oriented mutual fund with massively sticky 401(k) assets, etc. that type of skill means little. I’d happen to agree with you, but it’s not the world most investors live in).
Elliot: How are we to think about something like situational awareness in both baseball and investing? Some traders I have seen are great at knowing when to press (swing for the fences) and when to be defensive, how can we quantify such traits? Further, how can we determine when this management of net exposure is a sustainable, persistent edge versus something that has been executed well in the past, though will not necessarily be repeatable in the future?
Joe Peta: Great questions that to some degree combines a few of my answers above. I used the term “ineffable” to describe the allocation process of a PM, so clearly I agree with the premise of your question. Additionally, when fellow golfers desperate for improvement confronted the notoriously prickly Ben Hogan, he’d growl “the answer is in the dirt.” Well, I firmly believe that when it comes to a trader/PM identifying a persistent edge, the answer is in the data.
Not necessarily in the results, but the data. Successful investors, whether they excel at exposure management, security selection, or sizing do so in very identifiable ways, and it can be detected in roughly 140-150 trading days (or about 7 months). From an analytics standpoint, I can’t identify how a PM or trader might be good at exposure management, but I’m telling you I think, by examining the data, I believe I have an excellent chance of identifying it going forward – at least a better chance than someone who is just examining returns.
A lot of that confidence comes from my study of baseball analytics. For example, looking at the strikeout, walk, and groundball rates of a pitcher after he’s faced just 70 batters offers a better chance of predicting his ERA going forward than anyone else can relying on just his current ERA or the “eye test.” That’s because I’m looking at past skill demonstration, not results. It should be very easy to see how that type of analysis is applicable to asset manager evaluation.
Elliot: I must say, in reading Trading Bases, I was extremely impressed with your optimistic worldview and how you used that perspective in order to turn a difficult injury into an exciting and rewarding challenge. Although you’re not working at a trading desk right now, do you think there are any skills you acquired, or something you learned about yourself in your time running the baseball fund which would benefit you in the markets?
Joe Peta: I think every good trader realizes they benefit from every new day they sit in front of the screens. It’s another observation for the internal, pattern recognition database. In that way I think it’s a lot like being a quarterback. The more reps you get, the slower the endeavor becomes, leading to better decision making. Fortunately, unlike athletes, as we get more experience, we don’t lose our physical (actually, cognitive) skills — at least not at anywhere close to the rate athletic skill ages. That said, while my 15+ years as a trader were enormously helpful in everything about conceiving, modeling, and running a baseball fund, I can’t think of a way it would make me a better trader of stocks today.
Elliot: Do you think the presence of uneconomic/irrational gamblers, whose primary interest in placing a wager is supporting a partisan rooting interest in baseball betting, creates a persistent inefficiency?
Joe Peta: That is an interesting question because my knee-jerk reaction is yes, of course. In my book, I describe the Yankees, prior to the 2011 playoffs, possessing a retail mark-up similar to an Hermes scarf. (Analogies like this helped my wife, the initial editor of my manuscript, wade through the baseball stuff).
You only need to look at the history of point spread results to know this. For years, and years, if you simply bet against the Super Bowl Champion in every game the next year, you made money. Blindly betting against the Dallas Cowboys and Notre Dame appear to have a small positive expected value, because they attract irrational, fan-based wagers. (Vegas-based wisdom has it that if you’re going to bet against USC when they play a night game at home, never place the bet before the last minute. That’s because an 8:00 PST start means it’s the last game on the board on Saturday night and deep-pocketed Los Angeles-based gamblers, desperate to make up for earlier losses, can be counted on to bet on USC in droves pushing up their line in the last hour before kick-off.)
However, there is some strong evidence that very sharp gambling syndicates have neutralized that play to the point that currently there may not be much meat left on that bone. The biggest reason the house wins isn’t dumb money; it’s the $11 to win $10 spread on a football and basketball game (however this house edge is considerably smaller in baseball).
Elliot: Did your model produce a higher IRR on long-term plays (ie your “futures basket”) or on your single-game wagers?
Joe Peta: I don’t have any confidence in projecting the true expected value of my futures bets because the sample size is too small. I do know that from 2011 to 2013 I had a smaller forecasting error each year than any other publication I could find, in print or online, including the Vegas line. (That was independently verified in 2013 by the website “Stats in the Wild” and all my futures picks from 2011 forward are in print.) About 40% of the way through the 2014 season, I’m struggling to maintain a 50% pace on my preseason ten-pack of picks. At about 10 picks a year, and since futures plays are so sensitive to changes in personnel due to trades, injuries, etc. I don’t have as much confidence as I do in battling through 2,430 games on a daily basis – and my capital allocation in 2011/12 reflected that.
Elliot: Betting on teams, games and seasons seems to me the macro equivalent in baseball, while fantasy baseball strikes me as a kind of micro. In macro, mean reversion tends to be a powerful force, while in micro there are far more feedback loops. Does this analogy translate to baseball? What are some implications of differences in performance analysis and attribution in macro vs micro?
Joe Peta: I’d be lying if I tried to force my answer to fit that analogy. I would simply make this distinction: there is a much larger variance in outcomes in the short-term even if the model is just as accurate. Therefore, the real skill is proper capital allocation. You can make the same distinction in stock market investing when comparing a “trade” versus an “investment.”
Elliot: I’m not sure if you have read some of the recent analyses of John Maynard Keynes’ investment track-record (here’s a good overview from Jason Zweig). There were some interesting observations about how the man who had perhaps the best access to global economic data, and the deepest understanding of the macro system could not make money on his macro wagers, and his results only became stellar once he shifted to a focus on micro. Given the analogy between the micro and macro in the baseball world with the market world, and given your results and conviction were highest in the micro realm in contrast to the macro, do you think there is a broader implication of this? Have you seen anything in your performance analysis at funds that suggests there’s a better, more consistent opportunity set in micro than macro?
Joe Peta: Let me try and give you an answer that touches on both your questions — I think I speak for everyone at Novus who has taken a look at performance data and reached this conclusion: Value-adding micro skills (stock selection and sizing, to name two big ones) are far more prevalent and persistent than value-adding macro skills (say, exposure management, sector/strategy allocation, etc.). That said, that does not mean the latter does not exist. My personal view is this: Those that add value via macro-informed implementation are infrequent, but the managers that have “it” display it consistently. For instance, as I state in my conference presentations (backed up with some fun data) I believe Jim Cramer has that macro skill. I would trust him to set my net exposures on a daily basis, but there are a whole lot of fund managers (and probably RIAs, etc.) who would be better off not touching that dial.
Elliot: The question about hot and cold hitters has bugged me. On the one hand, I completely acknowledge the connection between luck/skill and ultimate outcomes. On the other, I think Yogi Berra nailed it in saying that “baseball is ninety percent mental and the other half is physical.” I have seen players change their approach when in slumps, and hot hitters bring a more patient plate approach, to the point where at least some degree of the outcome can’t merely be the random dispersion of non-related, uncorrelated events and simple cluster luck. To connect this to trading, I have seen great traders lose track of their process when their mental fortitude was in a vulnerable position. Is there something to what I’m saying or am I overthinking this?
Joe Peta: I think the biggest danger in your conclusion is that as humans, “our eyes lie.” We do a very poor job of recalling the events that don’t fit our narrative. I’m not saying you aren’t right; I’m simply noting that proving a connection between those events is monumentally difficult. Look, I get the fact that as a Knicks fan you were petrified when Reggie Miller had the ball late in a game. It’s true that he had the ability to “heat up” and make five straight long-range buckets quickly, but the truth is, he has that ability at any time of the game, because like Steph Curry today, he’s one of the most skilled shooters. Time and time again, studies show there is a miniscule, if any, amount of evidence to point to athletes performing at a higher level “in the clutch.”
I believe that’s true in our profession as well. If you truly are a trained, skilled professional, you will have periods of under-or over-performance throughout your career. It’s just that our need to explain, or attach a narrative to everything which leads people to conclude, “ahh, I was going through a divorce during that bad period” while completely ignoring the fact that a parent died four years earlier during a period in which they outperformed.
Elliot: Let me rephrase this question to ask “apart for the clutch hitter/shooter/scorer” point for I do think that’s fairly convincing. I think my reluctance to fully embrace (as opposed to largely believe in) the concept is most evident in cold streaks. What starts as a random distribution can lead to self-doubt, which leads to an adjusted approach, making ruts a self-perpetuating feedback loop. In investing and trading, I’ve seen this manifest as the idea where mistakes beget more mistakes. Is there a way to attribute this to some other factor rather than mere random distribution?
Joe Peta: Personally, you will get no disagreement from me. I don’t think people can perform better in the clutch, or in high leverage situations in any endeavor. The implied premise in asserting as much is that they don’t try as hard at other times. However, I’m equally sure that ignoring or dismissing the opposite side of that coin is a fallacy. Pressure can absolutely destroy someone’s decision making and performance.
I’m reminded of the very first mentor I had on Wall Street, a relationship which is fondly recalled in Trading Bases. The head of Lehman’s Nasdaq trading was my age, almost to the day, but it was always a mentor/mentee relationship. He was a former defensive lineman on Penn State’s 1986 NCAA championship team, a huge physical presence. And, during my first months on the desk, he said to me in an effort to test my fortitude and confidence: “Peta, do you know what the vomit zone is? It’s what I call putts from 5 to 7 feet. Everyone can make them on the practice green, but when you need to make one to win a hole, it makes some people want to vomit.”
I think that marries both of our points. You can’t get better in a match than the skill level you possess on the practice green, but you damn well can get worse, or as that noted investor M. Mathers has said, “Knees weak, arms are heavy, there’s vomit on his sweater already, mom’s spaghetti”
Elliot: So far this season, which baseball teams are most over and underperforming your model? Do you think any of these teams are legitimate in their over / underperformance? Do you see any quantifiable lessons or adjustments that could have captured some of these differences?
Joe Peta: I haven’t run my team models on a daily basis like I used to, but the degree of the Giants success has surprised me. They’ve got the best record in baseball even after dropping six of their last seven games. While I was wrong on the early season success of their pitching staff because they’ve been better – through a skill-based lens – than I projected, the same wasn’t true of the offense. The hitters also had better results than I projected but it wasn’t due to skill-based performance, it was luckier. Through the first quarter of the season, they benefitted from the sequencing of their hits, which is uncontrollable, or as I dub it in my book, “cluster luck.” That has reverted in the last 30 or so games, as they’ve cooled off. There are some other examples as well, but the ability to make money off of observations like this is dependent on pricing, of course. Since I’m not betting daily, I don’t know what sort of adjustments the oddsmakers have made to each team’s expectations. It’s always possible that I may have been right to be pessimistic on the Giants over the last week but if the oddsmakers were more pessimistic, I may not have made any money.
The main thing to do from a model-building standpoint is attempt to identify the results which are repeatable. Michael Mauboussin, who I know we are both big fans of, captures it perfectly when he describes the need to detangle skill from luck when looking at results. I love that description because it makes me think of a thoughtful and curious scientist in a lab using tweezers to examine a strand of DNA.
Elliot: Which team out there do you think has been hit by particularly bad luck, and should have a better run from here-on-out?
Joe Peta: The Dodgers, with their stellar starting rotation and a quite potent lineup had no business being 9 1/2 games out of first place as they were when we started conversation (as recently as June 8). It’s down to 4 games now and they are not only going to win the NL West, but they are also going to the World Series. Now, I bet on the Nationals to win the NL during Spring Training, but that was based on price. The Dodgers are the best team in the NL by a comfortable margin. We’ll see if it plays out.
Two other teams, that are far better than their records are the two teams with the least amount of wins in each league: the Tampa Bay Rays and the Chicago Cubs. In the case of the Cubs though, it might not translate into any more success during the 2nd half of the season because they very well may sell off any of their assets with value.
On the other side of the coin, if there’s a team I think could lose 100 games despite being comfortably ahead of that pace now, it’s the New York Mets. (Sorry.)
Elliot: I want to dispute one of your assured suggestions in Trading Bases. Wayne Gretzky is the second best hockey player ever, to Mario Lemieux. Discuss…
Joe Peta: I took a shot at hockey fans in a footnote in the book (suggesting certain sabermetric formulas could be used in hockey, but until we find a hockey fan that can operate a calculator it remains unproven) and incurred the wrath of many readers for it. I simply won’t take the bait here and admit I know nothing about hockey.
Elliot: It is torture being a Mets fan…
Joe Peta: I so wanted to be on the Daily Show during my book tour just so I could use the ‘50 Shades of Shea’ line on Mets’ fan Jon Stewart.