Continuing our analysis of 2019 NHL Draft Prospects, this article looks at Scouting Reports and tweets on 2019 NHL Draft eligible prospects. In hopes of capturing some additional information outside of the numbers as seen by the “public” scouts. If you follow our NBA Draft content, you are already familiar with our sentiment analysis on 2019 NBA Draft Prospects. For those that are unaware, sentiment is a form of NLP (Natural Language Processing) or more formally defined as “the process of computationally identifying and categorizing opinions expressed in a piece of text, especially in order to determine whether the writer’s attitude towards a particular topic, product, etc., is positive, negative, or neutral.” Using Professor Bing Liu’s graded sentiment dictionary, we can analyze text to and identify who is more positively written about. For more information, please read our My Model Monday series article.
Our NHL Draft Role Probability Model uses prospects’ statistical production, physical measurements, league prominence, and team statistics to predict the likelihood that a players assume a specific NHL Role (i.e., First Line / Top Pair, Second Line / 2nd pair defensemen, 3rd Line / 3rd Pair, Fourth Line / 7th defensemen, Two-way player, and Non-NHL player). This Model expands on our NHL Translated PPG model in order to account for variables outside of Points Per Game, such as size, scouting ranks, caliber of leagues played in, etc. Continue reading 2019 NHL Draft Role Probability Model
This Friday, June 21st, 217 NHL prospects will be drafted with the hopes of becoming the next Sidney Crosby, Jamie Benn, or Evgeny Kuznetsov. Teams are faced with the difficult task of projecting young adults to the NHL, with some of those players coming from leagues that don’t even track second assists, and others coming from the highest levels of hockey. Analysts, Scouts, GMs, etc. will scour eliteprospects and hockeydb, checking out how many points a prospect produced in each league, and then try to use their small DB of memory to remember what NHL players also played in that league, and how they performed at the NHL level. Instead of using our 0.1 GB RAM human brains, we will use our 16GB RAM MacBook Pro to reference historical data necessary to translate point production across leagues for 2019 NHL Draft Eligible prospects.
Below is results to our (preliminary) NHL Draft Model that uses prospects’ statistical production, physical measurements, and other variables to predict the likelihood that a players assume a specific NHL Role (i.e., First Line / Top Pair, Second / Third Line / 2nd pair defensemen, Fourth Line / Bottom pair defensemen, and Non-NHL player). The model is still being fine-tuned, hence the preliminary results, and an in-depth methodology article will come in the future. In addition to the aforementioned role probabilities, there is also a predicted NHL point per game that is derived from our Hockey Translation Factors.