FOOTBALL IS FINALLY BACK! And since there is such a shortage of Fantasy Football content out there, we thought we needed to give the people some material to prepare for their drafts. The following rankings are loosely based on model predictions for each position, which use historical player/team data to predict fantasy points for the coming season (i.e., separate models for QB, RB, WR, TE, K, DST). That said, there are plenty of factors that any model will have trouble capturing perfectly (injury status, team depth charts, suspensions, QB/Coach changes, etc.), and thus we have made some subjective adjustments to the rankings where necessary. For example, if a model weighs last year’s cumulative statistics too heavily, Odell Beckham Jr. is not going to come out very high since he only played 4 games. Our rankings can be found below, along with a short description of the model we used for each position. All rankings reflect PPR scoring.
All posts by Marc Richards
My Model Monday: Hockey Aging Curves
In this week’s My Model Monday, I explored aging curves in not just the NHL, but in other professional leagues and junior hockey leagues. First and foremost, what are aging curves? Aging curves are just what they sound like: curves that associate player performance and health over time. For a point of reference, despite his mighty accomplishments at an old age, Jaromir Jagr saw his point production dip from over a point per game at age 25 to about 0.3 PTS/G at age 45. Jaromir Jagr is an incredibly interesting case and likely outlier, as few players have played into their mid-forties. At any rate, one can imagine that many players experience similar increases and decreases in production with age; therefore, using many samples of players, one can construct a curve that resembles a mean of all players aging, or, as we like to say in the reinsurance industry, an industry exposure curve. Continue reading My Model Monday: Hockey Aging Curves
NHL Draft Model Results 2018 (Preliminary)
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.
NBA 2018 Draft Prospect Sentiment Analysis
A couple months ago, we put together a My Model Monday series article that did a text and sentimental analysis of NBA Draft Prospects based on scouting reports. For those that are unaware, sentiment is “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 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.
Prospect Profile: Lonnie Walker IV
Leading up to the 2018 NBA Draft on June 21st, we will be using our NBA Draft Models (PNSP Model, Role Probability Model, and Similarity Scores) to investigate this year’s top prospects. These Prospect Profiles look at which stats affect NBA projections, present unique data points from a player’s stats, and give relevant comparisons to current NBA players. You can find all of our Prospect Profiles here or through the header menu above (NBA –> NBA Draft –> Prospect Profiles). In today’s article, we look at Lonnie Walker IV.
Model 284 Podcast: NBA Draft Models Big Board
With the NBA Draft less than a week, Sam and Marc go through ESPN / DX Express’s top 10 NBA Draft Prospects and what our Models are saying about them. Also, Sam and Marc give their top 10 ranked prospects combining all aspects of our Draft Models and other considerations not directly captured by the Models.
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Prospect Profile: Deandre Ayton
Leading up to the 2018 NBA Draft on June 21st, we will be using our NBA Draft Models (PNSP Model, Role Probability Model, and Similarity Scores) to investigate this year’s top prospects. These Prospect Profiles look at which stats affect NBA projections, present unique data points from a player’s stats, and give relevant comparisons to current NBA players. You can find all of our Prospect Profiles here or through the header menu above (NBA –> NBA Draft –> Prospect Profiles). In today’s article, we look at Deandre Ayton.
Prospect Profile: Wendell Carter Jr.
Leading up to the 2018 NBA Draft on June 21st, we will be using our NBA Draft Models (PNSP Model, Role Probability Model, and Similarity Scores) to investigate this year’s top prospects. These Prospect Profiles look at which stats affect NBA projections, present unique data points from a player’s stats, and give relevant comparisons to current NBA players. You can find all of our Prospect Profiles here or through the header menu above (NBA –> NBA Draft –> Prospect Profiles). In today’s article, we look at Wendell Carter Jr.
2018 Similarity Score Tool
Select any 2018 NBA Draft prospect from the drop-down menu below to view their top 10 most similar college basketball players. Similarity Scores provide insight into how a player will translate to the NBA based on how their historical comparisons have performed in the NBA. This model considers a player’s college production, physical measurements, and age/experience to generate their most similar historical players. For more background on the calculation of the similarity scores, see this article. This model is one of three pieces that we use to evaluate the NBA potential of college players, with the other two being PNSP and NBA Role Probability Model.
Continue reading 2018 Similarity Score Tool
2018 NBA Role Probability Model
The NBA Role Probability Model predicts the likelihood that a given college basketball player becomes an All-Star, starter, bench player, or does not make it in the NBA. The model considers individual box score statistics, team-level statistics (e.g. strength of schedule), physical measurements, high school scouting rank, position, and age/experience to predict the probability of a player landing each NBA role. For more detail on how this model is formulated, see this article. The Role Probability model is one of three pieces that we use to evaluate the NBA potential of college and international players, with the other two being PNSP and Similarity Scores. In the table below, you can find the model’s predicted probabilities for each 2018 prospect landing in a given role in the NBA.
Continue reading 2018 NBA Role Probability Model