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Quantifying weather's impact on fantasy performance

By Scott Spratt
May 7, 2018

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Dec 10, 2017; Orchard Park, NY, USA; Buffalo Bills defensive back Shamarko Thomas (29) celebrates in the snow following his teams overtime win against the Indianapolis Colts at New Era Field. Mandatory Credit: Rich Barnes-USA TODAY Sports

Last fall, I ran a series of articles on weather research that used station data from the National Oceanic and Atmospheric Administration to study changes in team and player performances under various weather conditions. That research covered a variety of phenomena from cold temperatures to heavy wind speeds and precipitation, most of which had noticeable impacts on production. Those findings should be helpful for projecting player statistics — in particular for DFS — pending a couple of steps. First, the research needs to be synthesized. Second, since the research was based on historical weather data, I need to find a way to systematically pull weather forecasts for future games.

I had the former need in mind when I wrote the articles, so pretty much everything you’ll see in the subsequent tables was published with the original research. However, if you’d like to go back and read through the details of it, here are the links.

Introduction: Link
Rain: Part 1 and Part 2
Wind: Part 1 and Part 2
Snow: Link
Temperatures: Link

The takeaways from those articles I think are best split into team-level and player-level. Here are the former.

Weather’s Impact on Teams
Phenomenon Stat Change
Light Rain Pass% -0.6%
Moderate Rain Pass% -4.7%
Snow Pass% -6.0%
Snow Plays / Gm -2.2
Temp 30 – 49 Pass% -1.3%
Temp < 30 Pass% -2.2%

And here are the latter.

Weather’s Impact on Players
Phenomenon Pos Stat Change
Light Rain QB Comp% -2.3%
Light Rain QB YPA -0.32
Light Rain RB YPC +0.19
Light Rain RB Fum / Carry +0.003
Light Rain RB Avd Tackles / Carry +0.010
Light Rain RB/WR/TE Catch% -2.3%
Light Rain RB/WR/TE Drop% +1.2%
Light Rain RB/WR/TE Yards / Tgt -0.40
Light Rain RB/WR/TE YAC / Tgt -0.18
Moderate Rain QB Comp% -3.4%
Moderate Rain QB YPA -0.33
Moderate Rain RB YPC +0.04
Moderate Rain RB Fum / Carry +0.015
Moderate Rain RB Avd Tackles / Carry +0.016
Moderate Rain RB/WR/TE Catch% -2.4%
Moderate Rain RB/WR/TE Drop% +1.0%
Moderate Rain RB/WR/TE Yards / Tgt +0.03
Moderate Rain RB/WR/TE YAC / Tgt +0.02
Snow QB Comp% -4.1%
Snow QB YPA -0.07
Snow QB TD / Att -0.006
Snow QB INT / Att +0.002
Snow RB YPC +0.089
Snow RB TD / Carry +0.023
Temp 30 – 49 QB Comp% -1.8%
Temp 30 – 49 QB YPA -0.17
Temp 30 – 49 QB TD / Att +0.000
Temp 30 – 49 QB INT / Att +0.002
Temp 30 – 49 RB YPC +0.07
Temp 30 – 49 RB TD / Carry +0.000
Temp < 30 QB Comp% -3.1%
Temp < 30 QB YPA -0.19
Temp < 30 QB TD / Att +0.001
Temp < 30 QB INT / Att +0.001
Temp < 30 RB YPC +0.26
Temp < 30 RB TD / Carry +0.005
Wind 5-10 mph QB Comp% -0.7%
Wind 5-10 mph QB YPA -0.13
Wind 10+ mph QB Comp% -1.8%
Wind 10+ mph QB YPA -0.30

Across the board, those changes can be applied as multipliers for player projections if you know the weather forecasts for game locations and times. To find those, I did a bit more research and found an excellent API from an organization called Open Weather Map. Many of their services are designed for big businesses and farms and priced accordingly, but their free tier has weather forecasts that include temperatures, wind directions and speed, and precipitation estimates every three hours for the next five days. That’s about the perfect window for a weekly football projection.

After snagging an API key and writing a bit of code, I can now pull weather forecasts for the locations of upcoming games, which I already had databased via stadium latitude and longitude coordinates. As a demonstration, here are a few charts of the forecasted weather in Philadelphia this past weekend (taken in the middle of last week).

Even with a cold front coming in on Sunday, the temperatures and wind speeds would likely be too mild to make a noticeable impact on a hypothetical game there. However, the forecast did call for some light rain midday on Sunday, so if the game was then, I could apply the light rain multipliers to reduce projected team pass percentage, quarterback completion percentage, quarterback yards per attempt, etc. Of course, the real season doesn’t kick off until four months to the day from Sunday, and I won’t be able to showcase the full importance of this information until the week before then.

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