Responding to Liss, Part 2April 11th, 2010 by Bill Phipps in Theoretical
A little while back, I made some remarks to the effect that:
1) Universally, the game of FBB is not played very well
2) A quantitative approach, coupled a healthy dose of game theory, could dramatically elevate the level of play
3) Anyone who is not approaching the game from a quantitative perspective is placing themselves at a competitive disadvantage
This is hardly earthshaking stuff, and I never suspected that I was saying anything controversial. But not so fast. Here comes Chris Liss’ take on the subject:
“But more importantly, I wonder whether quants can ever make serious inroads in a complex game like baseball. Backgammon, chess, poker sure – those are math games. But baseball is like the weather or the stock market – organic, unpredictable. Didn't the quants nearly take down the entire financial system a couple years ago because the housing market dropped more than they conceived it could? Is this kind of analysis really optimal when it comes to things like baseball players, stocks and weather patterns? Or is it much more useful with games where there are less unknowns like poker or chess?”
WOW. With one paragraph, Liss manages to be dismissive of the entire sabermetric community, three of my favorite games, global warming research, and the smartest guys on Wall Street. Impressive. Where to start…..
Baseball not a math game? Tom Tango, Fangraphs, Baseball Prospectus and Bill James might as well all close up shop. Sorry guys, you’re out of a job.
Baseball not a math game ?? Somebody should tell Billy Beane to forget Moneyball and rush right out for a copy of Liss’ new book “Geniusball- How to Use Your Gut in an Unpredictable World”.
Baseball not a math game ??? We all know this to be absurd. Nowhere in American life, not even in our media obsession with the political poll, are statistics more on display than in the game of baseball. It is a perfect pairing. If you google the word baseball, there are 119,000,000 hits. Pair it with statistics in your search and you get 39,000,000 choices. Math is an indelible part of baseball. With its one on one confrontations and bookkeeping friendly pace, the game is a treasure trove of statistics. It is part of what sets the sport apart, and accounts for much of our love for the game.
Liss is right when he suggests that baseball is not a game of complete information, like chess. It IS like the weather in that we cannot precisely predict if it will rain tomorrow. But that is the very nature of statistics and probabilistic thinking. No matter how much data I gather about the weather tomorrow, I will always be missing something no matter what.. Therefore, the best I can do is to give a statistical projection for the weather tomorrow. This is why weather reports are given to us as percentage chances of rain.
I would ask Liss this, if math is not the optimal approach to studying baseball, what approach would he rather use? Math and the scientific method have given us the ability to decode the human genome, understand the evolution of life on the planet, harness the power of the atom and peer 10 billion years into the past with the Hubble Telescope. Of course it is up to the task of analyzing baseball.
I work a Wall Street job, trading options for a major firm. The use of statistical pricing models is an integral part of what I do. They are a vital tool, without which I could not compete. However, equally important to my job is knowing the limits of these models. Success in my field involves a pairing of human intelligence with a concrete mathematical foundation. I am not suggesting that there is no room in FBB for human insight. Far from it. What I am saying is that the game currently lacks the type of strong mathematical framework that allows us to focus on the areas where human insight excels.
Imagine if we created a league today, using the statistics from 2009. We would be turning FBB into a game of complete information. How do you suppose the auction would go? Let’s say that Elvis Andrus was the first player brought in. We know that he will have 6 hr, score 72 runs, hit 40 rbi, steal 33 and bat .267 in 480 at bats. What is the correct bid for him? If I asked 10 different experts, how many unique answers would I get? Several, I suspect, and this is an example of why I say that the game is not well understood. I don’t think the fantasy baseball community is able to answer this question. Certainly in poker, if the game was played with the cards face up, many professionals would know how to play near perfectly.
Now, imagine we are back in 2010 and the statistics are not known. I currently own Elvis Andrus. What do I do if Eric calls and offers me Jose Lopez straight up for him. Let’s say that I project both players to have seasons very similar to their 2009. Forget for now where my projection comes from, be it CHONE, Chris Liss, or some bodily cavity. Suffice it to say that projection represents my very best guess for their 2010. Should I do the trade? Wouldn’t the decision be much easier if I knew what their 2009 seasons were worth?
This is where things get more interesting. Because, the complicated truth is, Andrus and Lopez value in 2009 varies from league to league. In some leagues, Andrus was worth more. In others, Lopez. What is important is, how did the team statistics distribute in the league? Lopez gets his intrinsic value from his power. Andrus, from steals and scoring runs. If in 2009, the power categories in your league were tightly clumped, owning Lopez was worth more. His power helped move you up further in the standings. If, on the other hand, steals were a tightly contested category, Andrus and his 33 swipes might well have been worth more.
On the surface, it might seem that we are back where we started. I can imagine Chris Liss triumphantly saying “I told you so. We can’t say for certain which player is more valuable! “ Fortunately, this is where we can turn to statistics to help us answer the question. We can measure how all of the categories in our league are currently distributing. We can explore how they are expected to disperse if nobody changes their roster. We can also do a sampling of how leagues historically distribute. If we do this, we are likely to find that the HR category is the historically tighter of the two. We can see a range of expected values for Andrus and a range for Lopez. When we put all of these results together, they help to paint a picture of what we should do.
Maybe there will be other factors to consider. There almost always are. Andrus had a cortisone shot in his hand, Lopez is the more likely of the two to get traded, Lopez is about to get third base eligibility, Chad has a burning love for Andrus and I may be able to get more by trading with him etc etc etc. In varying degrees, quantitative analysis can help us weigh each of these factors. And, to the extent that we may not be able to quantify a factor, this is where human intuition gets its chance to shine.
What the statistical modeling has done is provide us a framework. Knowing how to solve the game with complete information gives us some firm earth from which to make decisions in the face of the complexities of the unknown.
It was never my intention to suggest that all that was needed for FBB success was a mathematical model and a set of projections. The game is much too involved for just that. However, I do think that a strong mathematical model is a powerful tool that would greatly enhance even an expert's game. Brian Hastings is a very gifted poker player. His talent alone is enough to guarantee that he will be a winning player. But there is no doubt that the use of hand histories, Poker tracker and starting hand calculators make him all the tougher.