“Embracing” the Advanced Analytics Movement is a False Choice
For too long, I’ve scratched my head at how analytics are presented within the context of sports conversations. My bewilderment came to a tipping point last Sunday, as the former quarterback (and current CBS play-by-play analyst) Rich Gannon lampooned Texans interim coach Romeo Crennel for electing to go for two with his team leading by 36-29 late in the fourth quarter.
In short, Crennel’s argument was that if the Texans converted the two-point attempt, the game was effectively over (it would have been a two-possession game with less than two minutes remaining). Consider that his team was locked in a road shootout and had already missed one extra-point attempt earlier in the game. Suffice it to say, Crennel determined that putting the fate of the game in the capable hands of Deshaun Watson gave his team a better chance of winning, rather than simply trusting his defense with an eight-point lead.
Now, whether the analytics were actually on Crennel’s side are up for debate; either way, the numbers are very close. Thus, it was far from a radical decision. But the two-point attempt failed. The Titans would go on to drive the length of the field, tie the game in the closing seconds, and win on the first possession of overtime (which basically underscored Crennel’s argument — after all, his defense clearly couldn’t stop Tennessee … so why not try to effectively end the game by stretching your lead to nine points?).
But all told, this singular decision to go for two shouldn’t have been much of a story. Crennel was aggressive all game (on the aforementioned drive in which the Texans advanced the score to 36-29, they went for it on fourth down twice). What’s one more aggressive decision?
Well, Rich Gannon felt differently.
Gannon used the Texans’ failed two-point try as a platform. A platform to denounce analytics and to launch into a series of straw man arguments against allowing numbers to influence decision-making.
“Analytics, I think, sometimes get in the way of good decision making,” Gannon said (you can listen to his whole take here).
The irony of Gannon’s rant? About two minutes into it, he held up a card and said to the camera, “everybody has these things. Look, it says right here: when to go for two. And you look at it, and it says . . . if you’re up by seven [you kick the extra point]. Every coach in the National Football League has one of these.”
To put it simply: Rich Gannon attempted to denounce using analytics . . . by referencing analytics. He was given a “cheat sheet” style index-card — one that most people attribute to Dick Vermeil, who is said to have created it when he was at UCLA in the 1970s. And he followed the instructions. In short, Gannon’s argument against using mathematics to shape in-game decision making relied on using an older use of mathematics meant to shape in-game decision making. Rich Gannon effectively said, don’t let math influence your decisions, because look, I have the math to prove it.
This is important. Because to understand how people think, you must listen to how they argue. The fact that Gannon turned a singular in-game decision into a platform-opportunity to criticize all usages of advanced analytics in sports suggests that there is some status quo bias at play here. Considering his usage of the two-point card in his defense, Gannon’s argument isn’t actually that all math is bad — it’s that the new math goes too far.
And it’s times like this that I wish we could pause our hyper-partisan approach to everything and have a nuanced conversation.
Why do we allow advanced analytics to be presented — on both sides — as “all or nothing?” Why do we permit such a false choice?
Using mathematics to shape strategies and opinions in sports is actually nothing new. Athletes have been judged at NFL Combines for decades. Origins of the quarterback passer rating can be traced all the way back to 1941. Numbers have always defined our perception of individual and team performance.
Then, in 2003, Michael Lewis published his critically-acclaimed book, Moneyball, which highlighted the Oakland Athletics’ analytical approach to roster construction and in-game decisions. Due in part to that approach, the A’s won more than one hundred games that year despite a small budget. And ever since then, there has been this “scouts versus stats” narrative when it comes to determining analytics’s rightful place in sports. You’re either “with” the movement or “against” it. But why? Why does it have to be one or the other?
This type of reductive, oversimplified false choice is precisely what we’re given in so many conversations these days, whether that subject is sports, politics, or business. Take the subject of government regulation and the United States economy. Nowadays, it seems that you’re either for regulation or against it. Said differently, you’re either for “free markets” . . . or you aren’t. Hyper-partisan, right? But this is actually a false choice. As former Secretary of Labor Robert Reich notes, in any “free market,” there are plenty of rules. What can be owned? On what terms? Under what conditions? What’s private and what’s public? How should we pay for what? As Reich writes, “these rules don’t exist in nature. They are human creations.” All markets have rules – the real question is who writes the rules, and what are they?
I believe the same false choice is offered to us in conversations about advanced analytics. Because, look, nobody should be “against” using science to inform decision-making. Even in their most fundamental form, numbers have guided game strategy and player evaluation for years. The sense I get when I hear Gannon talk poorly about “these experts and these computer guys” (his words, not mine), is that this new approach to football strategy — one that Gannon wasn’t accustomed to in his playing days — is threatening. It threatens his preconceived beliefs about football. New information is often not accepted at first because of its novelty. It’s a fundamental truth of human behavior that people don’t change their minds quickly or easily.
That said, there’s fault on both sides here. To embrace analytics shouldn’t mean blindly following models and algorithms, which can be prone to faulty assumptions, too. Science is messy — it’s constantly evolving. And to believe that numbers can predict or explain player and team performance still leaves you with a question loaded with the potential for human intuition, judgment, and bias to seep through: which numbers?
In many ways, fantasy sports fueled the analytics movement. According to Lewis, some of the earliest revolutionary sport statisticians (think Bill James) were interested in creating stats with “better predictive power” because they wanted to win tabletop games that ultimately were the precursor to fantasy sports.
As a FantasyPros reader, it’s safe to assume that you, the reader of this story, believe in the predictive power of advanced analytics. Which, of course, is good. But the next time you get into a debate about their relevance, remember Rich Gannon. Remember that you’re often given a false choice in this argument. Because it isn’t really about whether we should use numbers to shape decisions. No — it’s about which numbers we’re using. And with data, age doesn’t necessarily equate quality. Sometimes old models will prove true; other times, new models will prove better. This is a conversation that demands nuance — not blanket proclamations denouncing entire consideration-sets because of one failed two-point conversion.
We can’t afford to bury our heads in the sand when new ideas are thrown our way. We can’t shun people or technology simply because it threatens what we thought we knew. Conversely, we must be aware that data can have a reductive quality to it. To take the complicated matrices of NFL player performance and in-game decision making, and reduce it to one number, or one universal decision — that has its risks. Data can still lead us to faulty conclusions.
Advanced analytics are part of improving decision making in football (and fantasy football). But if we fail to reject this false choice — if we fail to see analytics as part of the answer — we’ll fall victim to Gannon’s ideological thinking.
This means that we’ll fall victim to what the analytics movement was supposed to solve in the first place.
We’ll simply have swapped one flawed ideology for another.
Whether you’re new to fantasy football or a seasoned pro, our Fantasy Football 101: Strategy Tips & Advice page is for you. You can get started with How to Manage Early-Season Injury Problems or head to more advanced strategy – like How to Effectively Assess the Quality of Your Team – to learn more.