Applied R Wiesn Edition: Calculate Rankings with RStan & The H2O "Moneyball" App

It’s time for another Applied R Munich meetup, this time hosted & sponsored by E.ON Energy Projects GmbH (location, food & drinks).
After the last talk, Joe from H2O (2nd Speaker) will give away some special prizes.

Access description:
Georg-Brauchle-Ring 52-54, just in front on the O2 tower. For those who might drive, there is plenty of free parking space in the streets behind the building (Gärtnerstraße) and for those taking public transport is just 2 min. walk from Georg-Brauchle-Ring U-Bahn station. The best exit is the one for Hanauer Str. & Georg-Brauchle-Ring, I think it is number 5. The easiest is to take a seat at the end of the train and take the exit in the opposite direction of the U-Bahn (direction Olympia-Einkaufs-Zentrum).

Schedule:
07:00 pm - Doors open
07:30 pm - First talk (see below) + Discussion
08:15 pm - Short break
08:30 pm - Second talk (see below) + Discussion

There will be time for networking / food / drinks before, in between and after the talks.

Please, don't hesitate to contact us at appliedrmunich@gmail.com (or via twitter https://twitter.com/applied_r) if your company wants to host one of our next meetups or if you have an interesting talk about an R related topic.

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Talk 1: Calculating Counter-Strike:GO World Rankings using RStan

Bio of the speaker:
Sebastian is currently working as Data Lab Manager and Data Scientist for E.ON after having gathered experience in the telco business and automotive industry. He studied Economics and Statistics at LMU Munich. At this time he also used to be a professional player for Team Alternate aTTaX (mainly Need for Speed) and took part in (inter-)national events. He always liked competing, when studying in eSports later at Kaggle and Hackathons.

Abstract:
Today there are tournaments for professional CS:GO teams with prize money as high as $1.5m, top players earn six-digit figures a year - eSports is no longer a niche phenomenon. CS:GO is played as team vs. team (5 players each) in rounds on so-called maps. There is no single league, rather there are multiple usually single or double elimination tournaments like in chess or tennis, so there is no "natural" ranking. There is also no established authority that publishes world rankings calculated with a proper statistical methodology. This talk presents my extension to the bayesian dynamic paired comparison model underlying the Glicko-2 rating system implemented from scratch using RStan. This model not only estimates ratings and enables world rankings, but unlike other heuristics based ratings also features confidence intervals, estimation of additional effects (like T side start (dis-)advantage) and can be used for predicting win probabilities and even score differences of upcoming matches. It will be compared to popular rankings (e.g. on HLTV.org) mainly based on heuristics. Experience during implementation and details in R using RStan will be shown. Code for the scraper, analysis, model and presentation will be open-sourced afterwards.

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Talk 2: Making Multimillion-Dollar Baseball Decisions with H2O AutoML, LIME and Shiny

Bio of the speaker:
Jo-fai (Joe) Chow - Data Science Evangelist and Community Manager at H2O.ai (https://www.linkedin.com/in/jofaichow/)

Abstract:
Joe recently teamed up with IBM and Aginity to create a proof of concept "Moneyball" app for the IBM Think conference in Vegas. The original goal was to prove that different tools (e.g. H2O, Aginity AMP, IBM Data Science Experience, R and Shiny) could work together seamlessly for common business use-cases. Little did Joe know, the app would be used by Ari Kaplan (the real "Moneyball" guy) to validate the future performance of some baseball players. Ari recommended one player to a Major League Baseball team. The player was signed the next day with a multimillion-dollar contract. This talk is about Joe's journey to a real "Moneyball" application.