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.
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
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
Peak NBA Statline Projection (PNSP) is a model used to project NBA success for college and International basketball players. PNSP considers each player’s individual and team statistics, physical measurements, high school scouting ranking, and age/experience. The PNSP model returns a single rating value from 0 to 100, with higher values indicating a “better” NBA prospect. We provide a detailed article outlining how PNSP is formulated here, and PNSP rankings from previous years can be found here. Below are a few highlights from PNSP’s ratings for the 2018 NBA Draft Class.
On this episode of the Model 284 Podcast, Sam and Marc are joined by fellow Model 284er Jack Werner to give some background on our NBA Draft models, discuss draft theory and positional value, and look a bit at the 2018 draft class.
All-encompassing stats are great for many things. WAR has become the standard for comparing baseball players because, more than any other stat, it encapsulates all of a player’s contributions in one number. On the offensive side, stats like OPS and wOBA provide a single stat that basically amount to offensive production per plate appearance. Stats like these are useful both in that they assign values to the different ways in which players provide value, and because universal measures allow easy comparisons between players. Want to know whether Giancarlo Stanton or Joey Votto was a more productive hitter last year? No number will give a perfect answer, but wOBA will get you close.
The first weekend of March Madness 2018 is in the books, and it was wild. When all was said and done, the same number of 11-seeds made the Sweet 16 as 1-seeds. Half of teams seeded 1-3 were knocked out in these first two rounds. And of course, the tournament saw a 1-seed exit in the first round for the first time ever (at least in the men’s tournament). It’s hard to imagine anyone’s bracket coming out of a weekend like this looking good. How bad was it? I dug into Model 284’s own March Madness contest to find out.
Imagine you’re the analytics director for the Cleveland Cavaliers. After the Cavs added a bunch of new players at the trade deadline, coach Tyronn Lue might come to you for advice on how best to fit them into lineups. Luckily, your team of analysts has already designed a Lineup Evaluator Tool to rate and score any five-player lineup. But that doesn’t quite get you where you want to go. You need to take in the players on the Cleveland roster and spit out a ranking of lineups. You need to get a feel for the best player and the best two-man, three-man, or four-man groupings to fit into your game plans.
You need Model 284’s Lineup Optimizer Tool.
It seems like every four years when the Winter Olympics come around, curling has a moment. This year’s Pyeongchang games are no different. Curling gets a ton of love online, for reasons both ironic (its shuffleboard-on-ice aesthetic and inherent meme-ability) and non-ironic (its simple-enough rules and interesting strategy). Sure it’s a little goofy, but if you’ve found yourself getting into the sport within the past week or so, you’re not alone. Continue reading My Model Monday: Curling Win Probability Model