Engineering Fantasy Football

Alex-2016 Alex Leave a Comment

My entire life, I have been an NFL fan and have been lucky enough to see the Baltimore Ravens (my favorite team) win two Super Bowl championships. Last year, I transformed my love of football into a money-making hobby: playing fantasy football.

In my first year playing fantasy football with friends at SC, I won a 16-team league, taking home a grand prize of $300. Highlighted by Giants’ wide-receiver Odell Beckham Jr., Packers’ running-back Eddie Lacy, and Patriots’ quarterback Tom Brady, my team was a force to be reckoned with.

As I moved into my second year playing fantasy football, I realized that I won mostly based upon luck. I was lucky that Eddie Lacy, or none of my other players, ever got injured. I was lucky that Odell Beckham Jr. turned in the best fantasy season a rookie wide receiver has ever seen. To win this year, I needed to be more than lucky. I needed to use my understanding of statistics and mathematics to engineer a fantasy football advantage.

The catch that propelled Odell Beckham Jr. to fantasy football stardom.

The catch that propelled Odell Beckham Jr. to fantasy football stardom.

I first used statistics to predict player performances. For example, Week 3 pitted Oakland running-back Latavius Murray against the Cleveland Browns. A week prior, Cleveland had given up 9 points to a paltry Tennessee Titans rush offense, and in Week 1 failed to slow down Jets’ running-back Chris Ivory, who rumbled to over 90 yards and two touchdowns (21 points). Conventional wisdom ranked Murray lower than Ravens’ running-back Justin Forsett, who I considered trading for to start in Murray’s place. Rather than make that trade, I let statistics guide my decision and started Murray against Cleveland. Murray rewarded that statistics-based decision, racking up 20 points (compared to 2 points in Week 3 for Forsett).

The Cleveland Browns had no chance of stopping Murray.

The Cleveland Browns had no chance of stopping Murray.

Similarly, I used statistics to evaluate trades. My biggest trade of the year sent Cowboys’ running back Joseph Randle and Detroit’s star wide-receiver Calvin Johnson to my friend Jacob’s team for Texan running-backs Arian Foster (injured at the time) and Alfred Blue. Before making the trade, I assigned statistical point values to each player; simply put, how many points per week would the player score on average? This value evaluated the player’s previous point outputs but also took into account the standard deviation of the player’s point outputs; how variable were the player’s outputs? Could that player be trusted to produce a certain number of points each week?

For instance, even though Randle put up 27 points versus the Atlanta Falcons in Week 3, I valued Randle at only 7 points. Randle’s 27-point output appeared as an outlier. Johnson, who had struggled on a poor Detroit passing offense, I valued at 10 points. A healthy Foster was worth 16 points a week, and Blue, as a back-up running back, was worth 3 points a week. Looking at only the value of the players being traded, I won the trade 19 points to 17 points.

However, this statistical examination is too simplistic and fails to compare the team’s value before and after the trade. When trading Randle’s 7 points and Johnson’s 10 points, I had to replace Randle with another running back and Johnson with another wide receiver. A healthy Foster would replace Randle for a gain of +9 points, and Allen Hurns (see below; I valued Hurns at 8 points at the time, severely undervaluing him) would replace Johnson for a gain of -2 points. Therefore, the trade improved my team by +7 points per week, thus demonstrating that the trade was advantageous for me to make.

Like stocks on the New York Stock Exchange, fantasy football players can be overvalued and undervalued. Quite often, fantasy football players are undervalued because of the offense they play on or their injury history; statistics can see through qualitative bias and provide owners an advantage. For example, this season, Jacksonville wide-receiver Allen Hurns started off the season with three straight weeks of 60 yards receiving. However, Hurns, the second-best wide receiver on an historically underwhelming passing offense, was undervalued. Recognizing the consistency in his statistics, I acquired Hurns from the waiver wire following Week 3. Hurns’ consistency has not faded, and Hurns is now ranked 8th in scoring among fantasy wide receivers.

Allen Hurns, the definition of statistical consistency.

Allen Hurns, the definition of statistical consistency.

Currently, my team has four wins and three loses. Check back in a month’s time to see if I make the playoffs!

Being a professional football fan does not make me unique. Using statistics to better engineer my fantasy football time: now that might be something! In true #ViterbiPlus spirit, I am proud to say I am not just an engineer; I am a fantasy football aficionado.




Astronautical Engineering, Class of 2016, Learn more on his profile here!

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