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
When I heard I was scheduled for the second-ever installment of My Model Monday, I felt a heavy responsibility on my shoulders. I’m very excited about the opportunity to bring regular shorter-form analysis to Model 284, but I also knew that in week two, this series would still be finding its footing. I needed to choose my topic with care. I needed a subject that was interesting, important, and relevant. Something worthy of the short but solid history of analysis we have here at Model 284.
So I chose to write about Engelb Vielma.
Below is a table of our NBA Lineup spacing metric applied to all NBA Lineups that played more than 50 minutes together in the 2016-2017 season. Our NBA Lineup Spacing metric seeks to quantify a lineup’s ability to generate and score from efficient shots (i.e. at the rim and from the three point line). For complete methodology behind the calculation, see here.
First and foremost, I regret to inform you that this analysis is NOT done with player tracking data.
Secondly, I want to say this lineup metric is called spacing, but it is not really a measure of spacing; it is more a measure of how capable a lineup is of producing efficient shots. So, why call it spacing? Firstly, because spacing is catchy and trendy, but also because we believe that when the average fan thinks or hears the word spacing, they are generally thinking about maximizing the optimal shots in basketball: three-pointers and shots at the rim.
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.
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.
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.