Andrew Ericksen identifies NFL players with impressive Week 1 performances that are outliers and could be mirages. NOTE: This tool is currently in BETA testing.

Of course, everyone is going to be wrong often in this field, but you get the point.

So for Deangelo Williams in 2016, it would look something like, (Week 1) 0.7 (or 70% of leagues started him) + (Week 2) 0.8 + (Week 3) 0.0 + ... = 3.7. It is a good avenue to try out new ideas, since the cost of getting things wrong is minimal (I’d rather use NFL predictions as a learning ground, than say, predicting someone’s suicide risk, which happens to be another project I’m working on). Click below to learn about the metrics in greater detail. It simply calculates the proportion of leagues in which the player started, and then, adds them up.

Our eyes were bloodshot. At our early stages a couple years ago, people would often say to me, “Can you even do that?” Meaning, the game of football is too complicated to predict with numbers alone. That said, there were multiple cases this year when MathBox would, for non-obvious reasons, project a running back to score, say, 36 points in a game. Below is a list of Sports Analytics and Sport Technology companies. The good thing about learning iteratively every week of the season is that you come up with a lot of ideas for improvement. We’d spent five months cranking out manually produced fantasy football podcasts, articles, and social media posts on a weekly basis for no money. One type of analysis would weave a few cherry picked stats into what sounds like a convincing story: “Running Back X has only broken 5 tackles the last five games and he’s facing a team who has given up the 10th fewest yards to running backs so far this year, and we have inside word the star linebacker’s mom told him to get into gear this week. 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.

To which, I would say (and still agree with), “Yes, that’s why we think humans need to be supplemented with well better data in order to make their own decisions. tied Vegas’s up-to-kickoff adjusted win rate through Week 13 of 2018, How the Best NBA Teams Use the G League to Hack the System, U.S. Open in Coronavision: I Got Nothing Right Now, Gronk, The Meathead: White Privilege NFL/ESPN-Style, For Blue Jays’ Roberto Osuna, a slap on the wrist for domestic violence, The Shameful History Behind Women’s Football Must Not Be Forgotten, How Kobe’s Death Brought New Life to His Work. In this episode, we crash a bit, with a misalignment between the vision and available skills. Fantasy Outliers analyze data from real, active fantasy football leagues, and weekly value based on who the starters actually were. A Synopsis of Fantasy Outliers Methodology and The League Settings Used In Our Data Analysis of 12-Team Half PPR Leagues Finally, at this point last year, we didn’t know if our predictions were worth the bytes they were stored on. In other words, we’d had this idea for a couple of years, and all we had to go on was a feeling. Our interactive charts allow you to explore your own hypotheses, and learn more about fantasy football than the so-called 'experts' know themselves. Weeks Started is calculated for each player for the regular season (Weeks 1-13) in every year. These QB, RB, WR, and TE shouldn't be added or started based on one big game. FO Fantasy Podcast: October 13, 2020 13 Oct 2020, 09:12am by Scott Spratt Week 5 Quick Reads 12 Oct 2020, 08:00pm by Vincent Verhei OFI: SEC Separation 12 Oct 2020, 04:18pm by Parker Fleming Our interactive charts allow you to explore your own hypotheses, and learn more about fantasy football than the so-called 'experts' know themselves. Daily Fantasy Football (DFS) performance results. Nobody’s burnt out.

So 3.7 Weeks Started in 2016. 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.

Card Number We do not keep any of your sensitive credit card information on file with us unless you ask us to after this purchase is complete. But hey, you get the point. But regardless of what happens, it will definitely be interesting! No one else looks at data this way.

Fantasy Outliers’ accomplishments in 2018. From a data scientist’s perspective, I thought predicting NFL performance was an interesting problem to work on. Find The Players That Were Overvalued and Undervalued in 10-Team PPR leagues vs. their Average Draft Position (ADP)
No one else looks at data this way. For reference, Adrian Peterson usually had nearly 12 Weeks Started in 2016.


A vlog series about creating Outliers Fantasy Clash. 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.

COPYRIGHT © 2020 VUMERO, ALL RIGHTS RESERVED. Another reason we were motivated by a data-driven approach was because we had listened to the pundits consistently give shotty advice to fans on their popular podcasts with millions of listeners. This kind of accuracy can be very helpful in DFS…, We didn’t start out with daily fantasy sports in mind, but. 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.”. We all feel pretty good, much of which has to do with increased levels of automation.

First of all, we are resting easy in the new year. Compare individual players' performance to see who was truely great and who ruined your season. 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. 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. Best & Worst Players.

To stay in touch, please, join us by following us on Twitter (@fantasyoutliers) or subscribing to our weekly newsletter. First of all, we are resting easy in the new year. They’d say, “Wide Receiver Y is really good, possibly the most talented in the league. While we can’t ‘fix’ this problem, we do have some ideas for how to improve it.

But, as with most things, we have some ideas for how to fix this going forward. Our significant others wondered what the hell we were doing— spending so much time on a pipe dream. Statistically outperformed ESPN’s weekly projections for quarterbacks as both a raw projection and a directional indicator relative to ESPN for all QB’s but especially QB’s ranked in the top 10 going into the week. Last year at this time, we were all burnt out. Last year, at this time, Fantasy Outliers’ future seemed to be some combination of the responses on a Magic 8 Ball. We’re a small team, so let’s grow together! Our weekly models (without any manual adjustments for situational changes): Our yearly models (again, without any manual adjustments for off-season situation changes not accounted for in our dataset): Keep in mind that a lot of the models and processes that generated these results have since been improved (and we have even more ideas to improve them this offseason) so just like fine wine or a pair of underwear (Wayne’s World reference), our models get better with age. However, this reduces performance on average. Then the next week — again, for non-obvious reasons — it would project 20 points and they’d score 22. That changed when we compared our 2017 performance to ESPN in Summer of 2018 (weekly projections comparison here, yearly projections comparison here). We unleashed three new models this year: defense/special teams, actual team game winner, and kickers. I woke up early today, because I wanted to give myself this special treat. 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. Now, our future seems more like a drone that is almost out of batteries, but if it keeps flying just a bit longer, it could reach a whole new, never-seen-before world. It is a challenging problem, no doubt. Fantasy Outliers analyze data from real, active fantasy football leagues, and weekly value based on who the starters actually were. Each year, we calculate three statistics: The entire analysis, from The Basics to Drafts to Best & Worst Players to Draft Patterns are based on these metrics. 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. And sure enough, they’d end up with 35 points. And I’m excited for what those ideas will bring this offseason. Simple as that.

No bloodshot eyes (except for the fact it is still in the wee hours of the morning on my birthday).

There are so many variables with countless interactions between them, it seemed to me that a computer might be good at making sense of all the data.


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