In most sports and at most skill levels, if you are unpredictable in your movements and actions you will have a better chance at being successful; you’ll have a better chance of beating your defender if he doesn’t know what you’re going to do. Granted, at the end of the day, high-performance level always wins out, but one can give themselves a better chance of winning a battle by being unpredictable or diverse.
Category Archives: Methodologies
NBA Lineup Evaluator: Diversity (2016-2017)
Below is a table of our NBA Lineup Diversity metric applied to all NBA Lineups that played more than 50 minutes together in the 2016-2017 season. Our NBA Lineup Diversity metric attempts to measure the diversity of play types a lineup will run. For complete Methodology, see here.
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Components Methodology: NBA Lineup Evaluator
This article details the methodology and calculations of the components found on our NBA Lineup Evaluator. Each component represents a different skill or ability an NBA lineup could have. We can use these to asses strengths and weaknesses of NBA lineups that have yet to play together, or that haven’t played enough minutes to accurately evaluate their performance. Data is trained from NBA Lineups from 2015-2017 that played at least 50 minutes together. All data comes from either NBA.com or Basketball-Reference.com.
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A Poisson Maximum Likelihood Model for prediction and evaluation of MLB teams and pitchers
This article was originally published in 2016 at jackoliverwerner.wordpress.com.
Introduction
Sports are all about matchups: two teams battling it out on the court, rink, or field until one side emerges the winner. Head-to-head matchups are an especially prominent component of baseball. The game is an overarching matchup of one team against another, but it contains smaller competitions: a pitcher and a catcher trying to outscheme an opposing hitter, a second baseman playing cat-and-mouse to hold a runner close to the bag, or a starting pitcher with a scissors trying to get out of wearing throwback jerseys.
Pitcher Similarity Score Methodology
Why Pitcher Similarity Scores?
Similarity scores, at their best, are a fun and useful way to understand patterns in athletes. We’ve dabbled in similarity scores here at Model 284 before, and now we’re applying it to Major League Baseball pitchers. Our motivation for this project is twofold.
NFL Season Simulation Methodology
This article gives an in-depth dive into our prediction and ranking system for NFL teams. Tune into our win projections, rankings, and Elo ratings throughout the 2017 NFL Season – our numbers going into Week 1 can be found here.
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Model 284 NFL Elo Ratings Methodology
This article dives into the concept of Elo ratings in general and then covers the details of our own methodology for creating Elo ratings for NFL teams. Be sure to tune into our weekly Elo ratings and ranking system for the 2017 NFL season, which we will be publishing weekly. Our numbers going into Week 1 can be found here.
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NHL Playoff Model Methodology
The NHL Playoff Model uses team-level and individual player statistics to predict the probability that a given team will win a series (rather than predicting each game individually). Theoretically, one would think predicting a winner of a series would be easier than predicting the probability a team wins a single game, but many things can happen throughout the course of a series, most significantly, injuries that make predicting the series winner difficult.
Similarity Score Methodology
Introduction
“Buddy Hield is the next Stephen Curry”
“Brandon Ingram is a poor man’s Kevin Durant”
“Andrew Wiggins’ upside is Carmelo Anthony, but his floor is James Posey”
So often when we talk about NBA players, we do it through comparisons to other players, and with good reason—comparisons are a good way to quickly convey lots of information about a player. For example, if I tell you that a player had a Box Plus/Minus of 7.8 last season, you might get a vague idea of how good he is. If I then tell you that player had 19.5 points per game, you might have a slightly better idea, but it’s still far from the full picture. But if I claim that this player is the next Chris Paul, it immediately brings to mind an idea of not only how good he is, but also his strengths, weaknesses, and overall playing style. Maybe your mind also queues up a mental highlight reel of Chris Paul-like plays, for good measure. Comparisons quickly give a complete picture of a player which would otherwise require taking the time to slowly digest each number in his stat line.
Also, they’re lots of fun!
We’ve created our own set of similarity scores to make comparisons using math for the purpose of prospecting players coming out of college. Our goal is to produce a useful complement to our PNSP model. Where our PNSP model answers the question, “How valuable will this player be?”, our similarity scores aim to answer the question, “Who will this player be like?”
For a glimpse of some player comparisons, you can check out Similarity Scores for 2016 NBA Draftees, here.
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A Simple Way to Measure Pitch Mixing
On Sunday, the NFL ended its season with the Super Bowl. As soon as tomorrow, pitchers and catchers will report to Florida or Arizona to begin spring training. And in Minneapolis as I type this, it’s a balmy 42 degrees. I have no doubt that we haven’t seen the last of this Minnesota winter, but nonetheless, I’ll take it as a sign that it’s time to turn our attention to baseball. So let’s talk about pitchers.