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.
To use the Combine Tool, enter the name of a player and click go. The charts display that player’s scores in the 6 main NFL Combine drills: 40-yard dash, Broad Jump, Vertical Jump, Shuttle Run, 3 Cone Run, and Bench Press. The Yellow distribution curves show the distribution of other players’ scores from the selected player’s position. The numbers above each chart are the given player’s score for the drill and the percentile that that score falls in relative to other players at that position. The data comes from www.pro-football-reference.com and nflcombineresults.com. Our sample consists of all NFL Combine Invitees from 2005 – 2018. Look up some current and former players, or some players that will be coming off the board in this year’s draft!
Continue reading NFL Combine Tool
In sports, people love to categorize players by their playing style. For example, in hockey, people distinguish defensemen as offensive or defensive, or the rare all-around defensemen. In this week’s installment of My Model Monday, I look to create mathematical groupings of NHL defensemen using 2017-2018 NHL data.
In this week’s My Model Monday, I compare our 2018 NHL Season Simulation results to the current NHL futures odds to see where our model is differing from betting markets. Continue reading My Model Monday: NHL Playoff Futures
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
- Now that the NBA season is halfway completed, I trained a model to predict the major individual award winners – MVP, Defensive Player of the Year, and Rookie of the Year, using statistics from the first half of the season. The modeled probabilities do not reflect who we at Model 284 think should win the award; instead, they indicate the probability of a given player winning the award based on the statistics from the players who the voters have chosen in the past. Continue reading My Model Monday: Predicting NBA Awards
Ever since the first Olympic games were recorded in 760 B.C., sports have had an important social component in society. Whether its Baseball in Japan, Soccer in England, or Hockey in Canada, it’s almost impossible to live in a major sports market without being somehow affected by the local sports. While these celebrations of competition and the strive for excellence have brought neighbors together for centuries, there is another tradition that bonds us in a way that is much stronger: the tradition of complaining about our teams. It doesn’t matter if your team is a dominant force, or a bottom-feeding afterthought; we will always find something to complain about. But is it all warranted? Surely there are teams whose suffering has afforded their fans the right to complain more than all the others. In this week’s My Model Monday, I attempt to quantify the factors that lead to this allowance of complaining for each NFL team, and rank each fan base on their comparative level of what I like to call “complain-ability.”
Continue reading My Model Monday: NFL Team Complain-Ability
In my first ever My Model Monday, I wanted to get back to my roots: ice hockey and St. Olaf College. For those who don’t know, I used to play ice hockey (sometimes) and did so at St. Olaf College; therefore, I figured it would be fun to bring some analysis to a sport and level that is rarely covered: Division III Men’s Ice Hockey.
Continue reading My Model Monday: DIII Men’s MIAC Hockey Rankings
In this week’s installment of My Model Monday, I take a shot at quantifying home-field advantage for NFL teams and unpacking exactly what goes into that advantage.
Continue reading My Model Monday: NFL Home-Field Advantage
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.