“Econometricians are ever so pious, are they doing real science or confirming their bias?” The quote rings loud and clear from EconStories’ YouTube video. In it, John Maynard Keynes and F.A. Hayek go at each other again in a rap battle to please economics nerds like me. However, the simple lyric strikes at the heart of a matter that distinctly sets the Austrian School of Economics apart from all others, even the more free-market oriented Chicagoans. The methodology is what separates sound economics from numerical fetishism.
Where most mainstream economists place an extreme focus on logical positivism, regarding economics as an empirical science, the Austrian school places emphasis on human action, using means and ends, and deduction from true axioms. As our computers get more sophisticated, and our ability to track and store data increases, it was only a matter of time before the technocrats began to step into other areas of life. When I decided to take an economics of sports class last semester, I soon realized that the positivists had got their foot in the door. In the age of fantasy games, sports has become overly statistical. Big money is wagered in competitive and sometimes family leagues alike. Every “owner” is looking for a way to find that hidden statistic that can determine the outcome of a player’s performance, actual owners, and GM’s use stats as a reference to dole out large contracts, and analysts on TV marvel at new (and ever more obscure) statistics being broken all the time.
Since I am an American-football lover, I thought this class would be highly enlightening, and it was. One of the big takeaways was an amazing study the class did in groups on different sporting stadiums, and how it never makes sense to use taxpayer subsidies to help a billionaire owner build a new boondoggle. Because I had read Hazlitt’s “Economics in One Lesson,” I could have told them that, as the unseen factors are the deadweight loss that comes from using a government to redirect or confiscate more private sector funds. Everyone sees Jerry World, nobody sees what could have been built or saved had those funds remained in private hands. The jobs gained are just redistributions from one sector into another, yet Jerry Jones gets the distinct psychological advantage of a monstrous TV hanging from the roof (that would make a spectator wonder why they actually paid to attend).
Numerous topics were touched on in the world of sports, yet instead of getting a lesson on how Fantasy Football is not an empirical number-driven game but more a brilliant display of marginal utility at work, the final project was running regressions on past NFL statistics, and trying to find out which stat is the best predictor of future success. Needless to say, Austrian alarm bells were ringing in my head (I could have sworn I heard the echo of Mises rolling over in his grave). I knew it was a fool’s errand, but for the teacher, an accomplished econometrician, it was more a fun exercise to see which stats are the best predictors of the outcome of a game.
The reason why such a task is a wild goose chase is simple: every statistic ever compiled is not a causal trigger that enabled the team to win. A statistic, such as a rushing yard or pass completion, is simply the outcome, or the side effect, of purposeful human action. There were many statistics we could choose from to find our perfect regression correlation: rushing yards, total yards, points for/against, yards against, completion percentage… The list went on and on. This is not something that’s just delegated to the classroom, either. Professional sports teams are hiring and taking on more analytics gurus and technocrats who sometimes even butt heads with longtime coaches and scouts. It’s a battle of stats vs. the tape.
When it came time to present to the class, I couldn’t help but smile with delight as every group tried to apply their regression to that week’s edition of Monday-night Football, and every group’s model blew up in their faces. Why? Even in the sporting arena, where the rules of the game are clearly defined and there is a set amount of time, no two plays are exactly the same. Every play has a different means the team uses to accomplish the goal. Sometimes, the goal isn’t even the same on every play. If your team is losing by twenty in the fourth, the means change to conserving time and scoring as quickly as possible. Sometimes, this would mean sacrificing downs or yards to get out of bounds and stop the clock. Where does this action show up in the technocrat’s statistical regression? If an econometrician were in charge of calling the plays on a football team, and their model showed a strong correlation between running the ball for 130 yards in a game and winning, he may stick with a ground game that isn’t working, chewing up the clock even while down thirty points. Why? The model says if they break a certain yardage-barrier, their chances of winning increases (statistically) significantly. To the logical positivist, one can only know what the situation calls for by looking at statistics in the past. These statistics, however, were outcomes of past events that may or may not be comparable to the situation in the game at hand. To the econometrician, all yards are homogeneous, whether it be 2 yards for a first down in the first quarter or 2 yards for a first down in the fourth to run out the clock.
This is not to say that there is no room for economic thought or praxeology in the world of sports. While the problem with the regression models is that they that fail to predict the outcomes of sporting events, we have to look at the situation like Ludwig von Mises would, and apply theory to the situation at hand. However, in a sports world dominated by numbers, record-breaking seasons, and high-powered offenses that fall flat in the playoffs, there is a way to attempt to calculate which team is more likely to win a sporting event.
Since football is my area of interest, I would direct the reader to check out ProFootballFocus.com, a website whose stats are of a wholly different variety. Instead of simply recording yards gained, PFF has their researchers watch every player in every play of every game. The methodological individualism displayed warms my heart. The watcher then grades each player’s play with a range between negative and positive 2. How do they determine the grade? The film analyst takes into account the game’s situation and the goal of the play. To the extent the player accomplishes his own micro goal within the scope of the macro play, and in the game’s unique situation at large, he is graded. The technique is especially helpful for grading offensive linemen, who don’t generate many statistics in the first place. For instance, if a Quarterback throws a pass that hits a receiver right in the hands, it shows up as an incompletion in the regular statistical ledger, but it surely isn’t the QB’s fault. It looks even worse for the QB if the ball bounces into a defender’s hands. However, the PFF technique takes into account the quarterback’s accurate throw, that should have been caught, and grades the player positively for the throw (the receiver, on the other hand, would receive a minus mark). Now, you may say that there is no objective scale for weighing which players deserve a larger or smaller degree of negative or positive grade, but that’s the beauty of the method.
Being entirely subjective, this method allows coaches, scouts, and GM’s, the entrepreneurs of the sports world who seek the goal of wins, to look for which players best accomplish the ends that the coach wants to achieve during the course of a game. PFF double and triple checks their grades with scouts and former coaches, but the subjective nature shows how a coach or a scout can watch the film with a goal in mind, and try to find players who show the ability to do what they want them to do during a game. In PFF’s own words “As a result, we can show things like how two edge defenders may have the same overall grade, but one is a significantly better run defender while the other is significantly more disruptive as a pass-rusher.”
Austrian thought, means-ends grading, and using the game’s theory can be utilized in a variety of ways. Going into the last Super Bowl, much of the early money was going into the Panther’s coffers. Analysts everywhere were seemingly picking SuperCam’s team. And why shouldn’t they? All the “mainstream” measurable statistics were in the Panthers favor. Sure, it was acknowledged that the Broncos had the best defense in the NFL, but the Panther’s offense was so much more efficient than the Broncos’ than the Denver defense was superior to the Panthers’. The basic numbers all proved it, too. Peyton Manning had not been his usual self during the year, throwing nearly more interceptions than any other quarterback in the league despite missing multiple games. The game was in the bag, in the eyes of most “analysts” who conduct their analysis over the numbers in a chart.
PFF, however, saw and predicted a different story. They pointed out before the game how eventual Super Bowl MVP Von Miller was a mismatch over Panthers Tackle Mike Remmers. Sure enough football, like economics, is not a game of past aggregate statistics correlating to present success. It is about players using means to accomplish their goals throughout the course of the play. In PFF’s post game grades, Miller scored a +6.7, and Remmers scored -6.8. Cam Newton’s ability to stand in the pocket with time was severely shortened all day long, though he still graded positively (+1.6) doing what he could in the face of monumental pressure. Cam takes the blame for having two fumbles on the stat sheet while Remmers is ignored by the statisticians. One play, the Miller-Remmers matchup proved to be the first touchdown of the game (a fumble recovered by the defense). Another fumble was forced by Miller just as Newton was winding up to chuck it deep. The PFF method takes into account a player’s ability to be disruptive, and to successfully accomplish goals, not just to land on the stat sheet. How many other players were able to bring the NFL MVP down while he was running away from Miller? Miller didn’t show up on the stat sheet in those instances, but to the extent, he accomplished the end at hand was an A+.
Praxeological thinking and Austrian economics have many ways to branch out into other avenues of research, sports being one of them. Currently, the way fantasy football research is conducted is influenced by logical positivism, when in practice, fantasy owners routinely deal in marginal utility. During the fantasy draft, why do high-end running backs get drafted over high-end quarterbacks first, though the quarterbacks will generate more points on a regular basis? Marginal utility and supply and demand are the answer. The difference between the next marginal running back is significantly more expensive than the next marginal quarterback. It is the diamond-water paradox all over again. Also, looking at a player’s aggregate fantasy numbers over the course of a season is somewhat misleading. Players like Mike Wallace or DeSean Jackson may go off every few weeks with huge totals, but other weeks they flatline, making the aggregate look better than it really is. It is something that Austrian and praxeological insight can help us in explaining gaming action, instead of looking at snapshots of non-repeatable events, with non-repeatable combinations of players and non-repeatable comparative advantages all on the field at the same time.
Like economics, sports is not an empirical science. It takes basic axioms and uses a means-ends framework in order to interpret how the players should act, and then the coach (in the entrepreneur’s seat) must find and put his resources onto the field believing if they execute correctly, the goal will be achieved. Where in the positivists’ framework is there room for the toss out of bounds, the spike, or even Maurice Jones-Drew kneeling down before the endzone to run the clock out instead of score? There isn’t. To the sport’s analyst, the logical positivist, and the empiricist, the side-effect statistics of purposeful action are what counts, not the action itself. Sports seem tailor-made to adopt praxeological reasoning instead of empiricism to explain the process on the field, and even economics in general. Comparative advantage, marginal utility/cost, time preference, and opportunity cost among others more easily explain the process of competition on the field than do loose correlations of relating stats to wins. In reality, such a method really only conveys the same thing as the relation between Nicolas Cage films and swimming pool drownings. See here.
