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

[This article was first published on Fantasy Football Analytics » R | Fantasy Football Analytics, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

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 all players from one 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 do poorly in a given week, your whole fantasy team won’t be affected.

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:

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.


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; having different asset clsses).  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.

The post How Fantasy Football is Like Stock Picking (Including a Shiny App) appeared first on Fantasy Football Analytics.

To leave a comment for the author, please follow the link and comment on their blog: Fantasy Football Analytics » R | Fantasy Football Analytics. offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)