How Fantasy Football is Like Stock Picking (Including a Shiny App)

In this post, I compare fantasy football to stock picking.  There are important lessons we can learn from financial forecasting that can be applied to forecasting football players’ performance.

Fantasy Football is Like Stock Picking

When picking players for your fantasy team or when picking stocks, your goal is to pick players/stocks that others undervalue.  But what’s the best way to do that?  You could do lots of research to pick players/stocks with strong fundamentals that you think will do particularly well next year.  By picking these players/stocks, you’re predicting that they will outperform their expectations.  However, all of your information is likely already reflected in the current valuation of the player/stock, so your prediction is basically a gamble.  This is evidenced by the fact that people don’t reliably beat the crowd/market.

Not even so-called experts beat the market reliably.  There is little consistency in the performance of mutual fund managers over time.  The following charts are from Leonard Mlodinow’s book, “The Drunkard’s Walk: How Randomness Rules Our Lives“.  The chart on the left depicts the performance of the top mutual funds from 1991 to 1995.  The chart on the right depicts the performance of the same funds in the same order over the subsequent 5 years (1996 to 2000):

The best funds from 1991–1995 weren’t necessarily the best funds from 1996–2000.  This suggests that mutual fund managers differ in great part because of luck or chance rather than reliable skill.  That’s likely why a cat beat professional investors in a stock market challenge.  Although our sample size is much smaller with fantasy football projections, there also appears to be little consistency in fantasy football sites’ rank in accuracy over time, suggesting that the projection sources aren’t reliably better than each other (or the crowd) over time.

The market reflects all of the knowledge of the crowd.  One common misconception is that if you go with the market, you will receive “average” returns (by “average”, I mean that you will be in the 50th percentile among investors).  This is not true—it has been shown that most mutual funds (about 80%) underperform the average returns of the stock market.  So, by going with the market average, you will likely perform better than the “average” fund/investor.  Consistent with this, I demonstrated that crowd-averaged fantasy football projections are more accurate than any individual’s projection.

Another important lesson from investing is diversification.  If you have too much money in one asset and that asset tanks, you will lose your money.  In other words, you don’t want to put all of your eggs in one basket.  By owning different asset classes (e.g., domestic and international stocks and bonds), you can limit your downside risk without sacrificing much in terms of expected return.  This lesson can also apply to fantasy football.  If you draft your starting QB and WR from the same team (e.g., the Cowboys), you are exposing your fantasy team to considerable risk.  You can limit your downside risk by diversifying—drafting players from different teams.  That way if the Cowboys’ offense does poorly in a given week, your fantasy team won’t be as affected.  As Jonathon pointed out, however, sometimes having two players on the same team might be beneficial because some positions may be uncorrelated or even negatively correlated, which can also reduce risk.  For instance, the performance of the TE and RB on the same team tends to be slightly negatively correlated, so it might not be a bad idea to start the TE and RB from the same team.  For a correlation matrix of all positions on the team, see here.

Why Does This Matter?

Okay, fantasy football might be similar to stock picking, so what?  You are most likely to pick the best players if you go with the wisdom of the crowd (e.g., average projections) and diversify.  Most projections are public information, so you might wonder whether using crowd projections gains you anything because everybody else has access to public information.  However, this is also the case with stocks, and people still consistently perform best over time when they go with the market.  We are the only site that creates crowd-averaged projections that are customized for your league settings.  Moreover, part of drafting is picking players with the best value.  That’s why we also offer value-based drafting tools for auction and snake drafts, and for identifying sleepers.

The Efficient Frontier: A Shiny App

The ultimate goal is to draft players for your starting lineup that provide the most projected points and the smallest downside risk.  This is similar to the notion in investing of the efficient frontier, where your goal is to pick funds that have the greatest expected returns for the least risk (where risk is the variability in returns over time).  To demonstrate the efficient frontier in investing, I created a Stock Portfolio Analysis tool in Shiny that is based on Michael Kapler’s Systematic Investor Toolbox (see his blog here).  The tool downloads returns from Yahoo based on the ticker symbols you enter.  Then, it calculates a correlation matrix and the efficient frontier based on funds’ historical returns, and allows you to specify expected future returns and variability to calculate an efficient frontier for future returns.  It also determines the maximum Sharpe Ratio (ratio of return to risk), and the portfolio allocation at this ratio.  The Stock Portfolio Analysis tool is located here:

http://apps.fantasyfootballanalytics.net/stocks/

An important caveat: I am not providing investing advice, and future returns obviously do not mirror historical returns.  I just created the tool to demonstrate some of the risk and reward principles that are similar between fantasy football and investing.

How to Interpret the Graph

The graph depicts the risk vs. reward profile of different stocks/bonds and various portfolio allocations with different combinations of these funds.  The x-axis is the day-to-day variability in the returns of an asset, and represents an asset’s risk (volatility).  The y-axis is the annual return of an asset, and represents the asset’s reward.  Each stock/bond asset has some combination of risk and reward, and is plotted with an empty circle.  Often, bond index funds have lower risk and lower potential for reward (bottom left of the graph), whereas stocks tend to have a higher risk and higher potential for reward (top right of the graph).

The solid black circles indicate points along the efficient frontier.  The points along the efficient frontier indicate, for a given level of return (reward), what the lowest risk is for a portfolio with some allocation of the various assets.  The points along the efficient frontier, therefore, reflect the risk of the “optimal portfolio” for each level of return.  The solid red circle indicates the portfolio with the highest reward-to-risk ratio (i.e., the Sharpe ratio).  A table in the app indicates the portfolio allocation at the Sharpe ratio (i.e., the percentage of your portfolio that is allocated to each fund).

Conclusion

When picking stocks or fantasy players, you are best off 1) going with the wisdom of the crowd (using average projections; index funds) and 2) diversifying (picking players on different teams or, if from the same team, from positions that aren’t highly positively correlated; having different asset classes).  The goal is to pick the funds and fantasy players with the highest projected returns/points and the least risk (except when drafting bench players, see here).  Our apps are specifically designed to help you meet these goals to pick the best collection of funds and players.