fantasy outliers

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. A vlog series about creating Outliers Fantasy Clash. 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. What better way to spend 5:00 a.m. on my birthday than to share with you all that we’ve accomplished in this past year at Fantasy Outliers. NOTE: This tool is currently in BETA testing. No one else looks at data this way. Fantasy Outliers’ accomplishments in 2018. Complete your details below to get the latest news and content from the global sports & technology industry. First of all, we are resting easy in the new year. Then the next week — again, for non-obvious reasons — it would project 20 points and they’d score 22. 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.”. Both our yearly and our weekly models are not as good at predicting rookies and 2nd year players. No one else looks at data this way. Don't forget to toggle between Total Season Value and Average Weekly Value. Fantasy Outliers analyze data from real, active fantasy football leagues, and weekly value based on who the starters actually were. 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. 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. 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. 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. Daily Fantasy Football (DFS) performance results. Last year, at this time, Fantasy Outliers’ future seemed to be some combination of the responses on a Magic 8 Ball. Simple as that. This kind of accuracy can be very helpful in DFS…, We didn’t start out with daily fantasy sports in mind, but. 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. It simply calculates the proportion of leagues in which the player started, and then, adds them up. Andrew Ericksen identifies NFL players with impressive Week 1 performances that are outliers and could be mirages. 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. From a data scientist’s perspective, I thought predicting NFL performance was an interesting problem to work on. I woke up early today, because I wanted to give myself this special treat. But, as with most things, we have some ideas for how to fix this going forward. 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. Finally, at this point last year, we didn’t know if our predictions were worth the bytes they were stored on. 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). Fantasy Outliers analyze data from real, active fantasy football leagues, and weekly value based on who the starters actually were. Our significant others wondered what the hell we were doing— spending so much time on a pipe dream. Our eyes were bloodshot. Were a useful directional indicator relative to ESPN for likely starters across all positions with 99.9% statistical confidence. The good thing about learning iteratively every week of the season is that you come up with a lot of ideas for improvement. So 3.7 Weeks Started in 2016. Below is a list of Sports Analytics and Sport Technology companies. You should keep playing him as a WR1.” But due to the personnel around him, he would continue to produce lackluster results. No bloodshot eyes (except for the fact it is still in the wee hours of the morning on my birthday). While we can’t ‘fix’ this problem, we do have some ideas for how to improve it. Another approach would focus solely on watching tape. 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. 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. 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. First of all, we are resting easy in the new year. 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. 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. So here goes…. It is a challenging problem, no doubt. That changed when we compared our 2017 performance to ESPN in Summer of 2018 (weekly projections comparison here, yearly projections comparison here). In this episode, we crash a bit, with a misalignment between the vision and available skills. However, this reduces performance on average. But hey, you get the point. 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. And sure enough, they’d end up with 35 points. But regardless of what happens, it will definitely be interesting! And I’m excited for what those ideas will bring this offseason. But that takes time, so rather than wait months to give you anything, here are some notable results from the season…. 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. Statistically tied or beat ESPN for top 20 running backs, wide receivers, and tight ends, doing better in PPR (points per reception) scoring than standard scoring. But we did it, in part, because we believed in an idea that robust data analysis supplemented with human insight would produce better results than either by itself — and also because, let’s face it, we’re total nerds. We initially thought the human part was replacing the data with the punditry sometimes when we felt compelled to do so. 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. 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. To stay in touch, please, join us by following us on Twitter (@fantasyoutliers) or subscribing to our weekly newsletter. COPYRIGHT © 2020 VUMERO, ALL RIGHTS RESERVED. We’d spent five months cranking out manually produced fantasy football podcasts, articles, and social media posts on a weekly basis for no money. But we don’t know yet what that world will be even if we do get to it. Of course, everyone is going to be wrong often in this field, but you get the point. Click below to learn about the metrics in greater detail. We’re a small team, so let’s grow together! 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. A Synopsis of Fantasy Outliers Methodology and The League Settings Used In Our Data Analysis of 12-Team Half PPR Leagues Nobody’s burnt out. Convert Outliers into Depth This is where it gets tough to offer specific fantasy advice to a mass audience. Last year at this time, we were all burnt out. We all feel pretty good, much of which has to do with increased levels of automation. No bloodshot eyes (except for the fact it is still in the wee hours of the morning on my birthday). Compare individual players' performance to see who was truely great and who ruined your season. We will do a thorough analysis of our 2018 performance, as we did above for 2017, at some point this offseason. We unleashed three new models this year: defense/special teams, actual team game winner, and kickers. 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. 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.

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