The author of Diamond Dollars: The Economics of Winning in Baseball, Vince Gennaro is a consultant to Major League Baseball teams and a regular on MLB Network’s Clubhouse Confidential, which is the first show of its kind on baseball analytics. In addition, he is the President of the Society for American Baseball Research (SABR).
With a resume that includes a successful business career outside of baseball, Gennaro is well-versed in methods that produce results. The subject of our latest “Hot Corner,” Gennaro explains how his journey has helped get him to where he’s at today, and offers some explanations to what statistics in baseball really represent:
“I was a huge baseball fan growing up, and played at the semi-professional level. Jim Bouton [1962 World Series champion, 1963 MLB All-Star] actually played in our league when he was between major league stints with the Braves, it was kinda cool. I ended up not playing any further than that- I had too many injuries and couldn’t hit the breaking pitch anyway- so I went to college and ended up working as an Economic Analyst at a Federal Reserve Bank in Philadelphia. But, I always had a passion for baseball.”
“I got my MBA at the University of Chicago and started analyzing the game from a standpoint of the economics and the business of it and understanding the value of players. Back in the 1970s there was no computing power to speak of and virtually no data to manipulate, but I pieced some things together, made some assumptions and started creating some statistical models of a player’s value to a team. In fact when I was in my late 20s, I went down to the winter meetings one year and tried to sell my services as a guy who could help a team assess value to its players. It was interesting, I talked to a lot of people, but nobody was every in a position to move forward.”
It was then that Gennaro got his first break, when The Sporting News wrote an article about his player evaluation system, in March of 1979.
“Nolan Ryan saw it and called his agent, who had me come to New York and explain what I was working on and what I could do for him. After having that discussion, the conclusion was that it was fascinating stuff but[the agent] had to sit across the table from the Mets’ General Manger and didn’t think it was going to play. The agent told me I was ahead of my time.”
“I didn’t really get the message. I raised some capital and bought a franchise in the Women’s Professional Basketball league, vintage 1979. Of course the league lasted a couple of years and folded. At that point I was four years out of business school with this crazy resume and I decided to do something more mainstream, so I joined PepsiCo. I spent seven years at the Frito-Lay division and thirteen at the Pepsi division, working my way up to President. And I was the guy who launched Cool Ranch Doritos. It’s funny, when I go somewhere to speak about baseball and they mention that, I get more questions about Cool Ranch Doritos than baseball!”
“When I left PepsiCo I decided I would dive back into baseball and statistical analysis. I found that it was much more accepted and disciplined and while I still followed the game, I wasn’t an avid reader of Bill James, even though I was certainly aware of him. I started analyzing the game and realizing the amount of data available, the amount of computing capability. I built some models and wrote Diamond Dollars: The Economics of Winning in Baseball. I was able to get some MLB teams as clients for my work.”
IP: With the information that’s now available across all levels of baseball, what are the best ways to consider baseball statistics?
“I think it can be overwhelming to try to consider all of them, so what you have to do is pick the handful of numbers that you believe captures the quality of the players’ performance- what he can do offensively, defensively, on the bases, and so forth. Evaluating injury risk is not something that’s easy to quantify, but it’s important. Then I think there is a whole other set of numbers that are not quantitative, they’re soft numbers if they’re numbers at all. Things like the player’s makeup, like the scouts’ ratings of a player independent of their performance stats. There’s a wide range.”
IP: Do you think analytics should be weighed more heavily than traditional metrics like pitcher’s ERA or batting average? In other words, is there a way to consider the process rather than the results?
“It’s all about the process. I think that in many ways, the statistical movement went as far as it could in its detailed evaluation of outcome stats. I just don’t believe that outcome stats are all that reliable. The context is too different and the opposition varies dramatically, even at the major league level. If you’re David Price pitching in the American League East, you’re pitching against must tougher competition than the National League Central. The other thing is the ballpark factor, which is huge. I don’t believe we adequately adjust for the ballpark factor with outcome stats. I could go on chapter and verse of why outcome stats are interesting and tell you a little, but not nearly as much as you would like. I’d much rather know the speed of the ball coming off the bat- you’re going to bat .700 on line drives, .150 on fly balls and .240 on ground balls. It’s important to know how a guy launches the ball.”
IP: What are some of the best ways to introduce analytics to “old-school” baseball people?
“I would say there are a lot of things that those of us who have been around the game a long time have an intuitive feel for. Perhaps 70-90% are probably accurate perceptions of the way the game works, accurate judgments of what you’re seeing, so there’s two things I would say to that:
There’s still a 10, 20, 30% percent error rate of misjudging something, because you’re not deep enough into the objective information that’s available, and even with the things we know we know, wouldn’t it be wonderful to deconstruct those and find out what makes players the kinds of players that they are, and how effective or ineffective in-game strategies are?
That’s what objective analysis is- the study of the game at a quantified level that helps you with your understanding and appreciation of how the game works. One interesting example would be the use of the sacrifice bunt. The thought is boy, I would rather have the guy at second, and so instead of no outs and someone at first, you go with one out and a guy at second.”
“But when you really look at that- if you look at the percentage of times over the last 10, 50, 70 years- and you look at how many runs were scored in each of those two scenarios [sacrifice bunting versus letting hitter swing away], when you bunted you scored 0.4 runs in that inning and when you swung away, it was 0.7 runs. It fights your intuition, but it does get back to the theory that there’s nothing more valuable than conserving the 27 outs that you get in a game. We don’t have a clock, we have our 27 outs as our timepiece, and giving one of those up consciously and by your own volition is a good strategy only under certain circumstances. In the eighth inning of a tie game, the sacrifice bunt may be the exact right thing to do; you’re playing for the highest probability of getting one run, not for the most runs.”
“That’s a nuance that can get lost by those who aren’t really thinking the right way about statistical analysis- you have to understand the situation. Sometimes people get a little too overzealous about the conclusions of some of these studies, they look at them on a top-line basis and don’t go deep enough and they sort of miss the point. That’s an example of how analytics has changed the way we think about and operationalize the game today.”