Market Fantasy #10 - Beta Max Redux: Football! Edition

Hello and welcome to Market Fantasy!

This edition of Market Fantasy focuses on Beta. The tens of people who read this blog will recall that I talked about Beta in depth here.  For a quick refresher, Beta is a measure used to look at the volatility of an asset compared to an index. Or put another way, it measures how wide the swings in the returns on an asset are. Assets that move in concert with the index have a Beta of close to one, while assets that have wide swings in value have betas greater than one or negative. A negative beta stock means the large swings in value are in the opposite direction of the index, the stocks with a beta greater than one move in the same direction as the index, but more so (i.e. the index climbs 1%, while the stock gains 2%). For reference, here is a chart explaining how this works:

Value of Beta		Interpretation		Example
β < 0		Asset generally moves in the opposite direction as compared to the index		An inverse exchange-traded fund or a short position
β = 0		Movement of the asset is uncorrelated with the movement of the benchmark		Fixed-yield asset, whose growth is unrelated to the movement of the stock market
0 < β < 1		Movement of the asset is generally in the same direction as, but less than the movement of the benchmark		Stable, "staple" stock such as a company that makes soap. Moves in the same direction as the market at large, but less susceptible to day-to-day fluctuation.
β = 1		Movement of the asset is generally in the same direction as, and about the same amount as the movement of the benchmark		A representative stock, or a stock that is a strong contributor to the index itself.
β > 1		Movement of the asset is generally in the same direction as, but more than the movement of the benchmark		Volatile stock, such as a tech stock, or stocks which are very strongly influenced by day-to-day market news.

So how does this relate to fantasy? The idea is that by using a player’s Beta, you can construct a more consistent roster. In theory, you can use players with opposing betas as a hedge against each other to cancel out poor performances.

For instance, you have two running backs, we’ll say Marshawn Lynch and Matt Forte. Lynch last year had a Beta of 7.67, which means his performances moved in the same direction as the index, only a lot more pronounced. So if the index was down that week, Lynch was way down. If the index was up, Lynch absolutely went off. Forte had a Beta of -12.01 last year, meaning he performed the opposite of Lynch. If the index was down, Forte usually had his best games. Looking at a roster this way, you can pick players who tend to move in different directions to ensure consistency. Or you can pick all players who have strong movements one way or another to try to maximize upside. Or you can pick players with beta’s close to one who tend to track the index. Or you can pick players with betas close to zero whose movement is largely uncorrelated with the index.  It’s just another tool to try to predict how your roster will perform.

So before I present the positional betas, a quick note on how they were calculated. As the people who read Market Fantasy know, for baseball, I created an index of players called the FOW that is modeled after the real life DOW Jones industrial average. In the last post, I unveiled the 2013 football FOW, but for the index used in these calculations, I used the top performing players from each position (except kicker, because, come on, who cares about kicker) from last year. The players are as follows:

Brees, Drew QB NO
Brady, Tom QB NE
Rodgers, Aaron QB GB
Peterson, Adrian RB MIN
Lynch, Marshawn RB SEA
Morris, Alfred RB WAS
Foster, Arian RB HOU
Martin, Doug RB TB
Rice, Ray RB BAL
Johnson, Calvin WR DET
Marshall, Brandon WR CHI
Bryant, Dez WR DAL
Green, A.J. WR CIN
Johnson, Andre WR HOU
Thomas, Demaryius WR DEN
Jackson, Vincent WR TB
Decker, Eric WR DEN
Jones, Julio WR ATL
Graham, Jimmy TE NO
Gronkowski, Rob TE NE
Gonzalez, Tony TE ATL
Broncos DST DEN
Bears DST CHI
Patriots DST NE

I took the weekly change in the total score of the index, and then the change in weekly score for each player to calculate their beta. Player betas for each position are in the next few posts. Click through, check them out and hopefully dominate your league!

As always, comments welcome. Thanks for reading everyone!