3/4/2023 0 Comments Espn fantasy football![]() For example, using Safari’s “Timelines” Web Developer tool, or a proxy service like Charles or Fiddler. I suggest checking out the various projects and reddit discussions on this topic for more clues.Īnother strategy: ESPN uses this API to populate the site when you are poking around your league, so you can eavesdrop on these calls to get hints of what to explore. Some other views to explore are (non-exhaustive list):Īnd we have various params to try along with these like matchupPeriodId, forTeamId, … A difficulty I’ve noticed is requesting two views produces a different set of information than just concatenating the two views independently. assign ( Chg1 = df2 - avgs, Chg2 = df2 - avgs, Win = df2 > df2 ) ) values # add new score and win colsĭf2 = ( df2. reset_index ( drop = True ) # move the team of interest to "Team1" column For the current season, which as of this post appears to still include 2018, useĭf2 = df. We can use ESPN’s API and automate this, and also get access to a much deeper well of information. So how do we grab this data? Manual entry of scores? Boooo. you under/overperformed, and so did your opponent, but you squeaked a win out anyway - are the blue regions. Those “unlucky losses” - when you would have beat the league average team that week, but instead you got matched up against someone else who also outperformed, and lost - are the red regions. So your “points for” is a positive number if you outperformed the league average, negative if you underperformed, and same for your opponent.Ĭircles are wins, X’s are losses, blue is regular season, red is playoffs. To quantify this: take some team, plot all their games, with their score as the x-axis and their opponent’s score as the y-axis, but scaled to be relative to the league average. Since you can do nothing to affect your opponent’s score in a typical fantasy format, there is typically lots of whining that “I had the second highest score this week but got matched up to the highest scorer!” The API enables grabbing historical player projections, doing your own forecasts, automating player moves, in-season analysis of teams… But let’s start simple with simple game scores. (I’ll work in Python, but you could do everything in your environment/language of choice - the hard part is figuring out how to access the data. In this post I compare actual to optimal rosters along with ESPN projections. In a follow-up post, I show how to grab historical player projections and compare to reality. This post is a crash course in what I know about it, enough to hopefully get your feet wet before the 2019 fantasy season crashes in. Here’s a JS API Client and a Python project. Sad.)īut around the interwebs people are figuring out the new “version 3” API. (I wrote three blog posts on the old version and most of it’s now unusable. Then ESPN changed the API earlier this year (2019) and everyone’s code broke. People had figured it out though: there were various libraries, reddit discussions, and blog posts about how to use it to augment your fantasy league season with a little frivolous data science. ESPN has a weirdly undocumented API for interacting with their fantasy sports platforms.
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