No bloodshot eyes (except for the fact it is still in the wee hours of the morning on my birthday).
So what seems to have worked best so far is thinking of the human part as more of knowing what the models are good/bad and when to ignore them. Best & Worst Players. Fantasy Outliers’ accomplishments in 2018. So you can safely rule out RB X as a top play.” And of course, RB X would go for 100 yards, 2 touchdowns, break 5 tackles, and score 30 points. Beat ESPN for Total Season Points projections for quarterback, running back, and wide receiver (lost by a little for tight ends), and were a correct directional indicator for all positions, Were more accurate for end-of-season top 15 quarterbacks than Expert Consensus Rankings, and produced some great value finds at all positions, Predicted 27% of the variance in number of games played during the 2017 season, When I was creating the D/ST’s models, I honestly threw in actual team points as a target variable just for “sh*ts and giggles”, as they say.
We’re not sure at this point if we will want to get bigger or scale back next year, and what those paths would look like in either case. Fantasy Outliers analyze data from real, active fantasy football leagues, and weekly value based on who the starters actually were. But hey, you get the point. We’ve automated the vast majority of our processes — from data processing to making predictions to assigning a warning level to individual players to making charts for articles to assembling DFS rosters — the amount of manual effort involved on a week to week basis was greatly reduced. Another approach would focus solely on watching tape.
Daily Fantasy Football (DFS) performance results. Turns out, those models that only account for data available on Tuesday morning for the upcoming week, I’ll save the in depth article for later on in the off-season for this group. Simple as that.
Second of all, while we maintain that data+human=better, how the data and the human part work together is something we’re still trying to improve. That changed when we compared our 2017 performance to ESPN in Summer of 2018 (weekly projections comparison here, yearly projections comparison here).
Our approach has always focused on fantasy relevant players, but translating that idea into production is easier said than done and has the unsettling side-effect of over-predicting certain edge players. Both our yearly and our weekly models are not as good at predicting rookies and 2nd year players. Also, the status quo is far from perfect, so we just have to be better than what’s out there in order to provide value.” To which, this imaginary person would shrug their shoulders as if to say, “Okay, show me.”. Weeks Started is calculated for each player for the regular season (Weeks 1-13) in every year.
For reference, Adrian Peterson usually had nearly 12 Weeks Started in 2016.
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