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How long can a run of bad luck last before we start to suspect a football...
I give two simple rules for deciding if a big name team might actually be crap this season.See it on Scoop.it, via Fantasy Sports Statistics
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Recurrent Models and Examples with MXNetR
As a new lightweight and flexible deep learning platform, MXNet provides a portable backend, which can be called from R side. MXNetR is an R package thaSee it on Scoop.it, via Fantasy Sports Statistics
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The subtle brilliance of Portland | Basketball Analytics
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Sportsbook Betting (Part 1): Odds | Exegetic Analytics
Understand the basic off odds in the context of sportsbook betting.See it on Scoop.it, via Fantasy Sports Statistics
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Estimating NCAA Football Coaches’ Abilities An Application of Item Response...
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A Bayesian Hierarchical Model of Pitch Framing in MLB / Deshpande & Wyner
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Euro 2016 analytics: Who’s playing the toughest game?
I am really enjoying Uefa Euro 2016 Footbal Competition, even because our national team has done pretty well so far. That’s why after browsing for a while statistics section of officialSee it on...
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Update: Predictive Bookmaker Consensus Model for the UEFA Euro 2016
(By Achim Zeileis) Three weeks we had published a Predictive Bookmaker Consensus Model for the UEFA Euro 2016 that provided probabilistic forecasts for allSee it on Scoop.it, via Fantasy Sports Statistics
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The ffanalytics R Package for Fantasy Football Data Analysis | Fantasy...
Introduction We are continuously looking to provide users ways to replicate our analyses and improve their performance in fantasy football. To that aim, we are introducing the ffanalytics R package...
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jalapic/engsoccerdata
engsoccerdata - English and European soccer results 1871-2016See it on Scoop.it, via Fantasy Sports Statistics
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znmeb/mastering-dfs-analytics-bookdown
mastering-dfs-analytics-bookdown - Bookdown source repo for https://masteringdfsanalytics.comSee it on Scoop.it, via Fantasy Sports Statistics
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Mastering DFS Analytics
Mastering DFS AnalyticsSee it on Scoop.it, via Fantasy Sports Statistics
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Understanding beta binomial regression (using baseball statistics)
Previously in this series:See it on Scoop.it, via Fantasy Sports Statistics
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Understanding Bayesian A/B testing (using baseball statistics)
Previously in this seriesSee it on Scoop.it, via Fantasy Sports Statistics
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The not-so-small-world network of MMA fighters
Matchups between 140,000 MMA fighter are shaped by geography, gender, weight and experienceSee it on Scoop.it, via Fantasy Sports Statistics
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Building a large database of MMA fight results II: quantitatively summarizing...
summarizing the results and finishes of over 240,000 MMA fightsSee it on Scoop.it, via Fantasy Sports Statistics
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Building a large database of MMA fight results I: scraping with rvest
Scraping a large database of MMA fights using rvestSee it on Scoop.it, via Fantasy Sports Statistics
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Building a large database of MMA fight results III: Summarizing the...
Network-based inference of MMA fighter demographicsSee it on Scoop.it, via Fantasy Sports Statistics
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znmeb/dfs_toolbox: Jupyter Notebook Server for DFS Analytics
dfs_toolbox - Jupyter Notebook Server for DFS AnalyticsSee it on Scoop.it, via Fantasy Sports Statistics
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Winning Daily Fantasy Sports Hockey Contests Using Integer Programming
I've held off posting this because IMHO the math isn't right ... what do you all think? In particular, in their simulations they've varied the mean and variance of simulated lineups as if they're...
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